diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index d55a266c70..93e4f16129 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,3 +1,4 @@ * @MaxHalford @smastelini river/facto @gbolmier river/stats @AdilZouitine +river/cluster @hoanganhngo610 @Dennis1989 diff --git a/.github/actions/install-env/action.yml b/.github/actions/install-env/action.yml new file mode 100644 index 0000000000..700444c61e --- /dev/null +++ b/.github/actions/install-env/action.yml @@ -0,0 +1,53 @@ +name: Install env + +inputs: + python-version: + description: "Python version to use" + required: true + build-root: + default: "true" + options: + - true + - false + +runs: + using: "composite" + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Set up Python + id: set-up-python + uses: actions/setup-python@v5 + with: + python-version: ${{ inputs.python-version }} + + - name: Load cached Poetry installation + uses: actions/cache@v4 + with: + path: ~/.local # the path depends on the OS + key: poetry-2 # increment to reset cache + + - name: Install poetry + uses: snok/install-poetry@v1 + with: + virtualenvs-create: true + virtualenvs-in-project: true + installer-parallel: true + + - name: Load cached virtual env + id: cached-poetry-dependencies + uses: actions/cache@v4 + with: + path: .venv + key: venv-${{ runner.os }}-${{ steps.set-up-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} + + - name: Install dependencies + shell: bash + if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + run: poetry install --no-interaction --no-ansi --no-root + + - name: Build + shell: bash + if: ${{ inputs.build-root == 'true' }} + run: poetry install --no-interaction --no-ansi diff --git a/.github/actions/retrieve-env/action.yml b/.github/actions/retrieve-env/action.yml deleted file mode 100644 index 84c5b3b8d8..0000000000 --- a/.github/actions/retrieve-env/action.yml +++ /dev/null @@ -1,27 +0,0 @@ -name: Create Environment -description: Retrieves Python - -inputs: - python: - description: 'Python version' - -runs: - using: "composite" - - - steps: - - name: Retrieve the Python environment - uses: actions/cache/restore@v3 - id: retrieve-venv - with: - path: ~/.venv - key: ${{ github.run_id }}-venv-${{ runner.os }}-${{ inputs.python }} - restore-keys: | - ${{ runner.os }}-${{ inputs.python }}-venv - - - name: Retrieve River - uses: actions/cache/restore@v3 - id: retrieve-river - with: - path: ${{ github.workspace }} - key: river-build-${{ github.run_id }}-${{ runner.os }}-${{ inputs.python }} diff --git a/.github/workflows/build-river.yml b/.github/workflows/build-river.yml deleted file mode 100644 index 6bb11456d2..0000000000 --- a/.github/workflows/build-river.yml +++ /dev/null @@ -1,71 +0,0 @@ -name: build-river - -on: - workflow_call: - inputs: - python: - type: string - os: - type: string - -jobs: - build-river: - runs-on: ${{ inputs.os }} - - # Instead of using two matrices in the calling Workflow, we can use conditionals here - if: (inputs.python == '3.11') || github.event_name == 'push' - - steps: - - uses: actions/checkout@v3 - - - name: set up rust - if: runner.os != 'Linux' - uses: actions-rs/toolchain@v1 - with: - profile: minimal - toolchain: nightly - override: true - - - run: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show - if: runner.os == 'Linux' - - - name: Set up Python ${{ inputs.python }} - uses: actions/setup-python@v4 - with: - python-version: ${{ inputs.python }} - - - name: Cache the Python environment - uses: actions/cache@v3 - id: cache-venv - with: - path: ~/.venv - key: ${{ runner.os }}-${{ inputs.python }}-venv-${{ hashFiles('**/setup.py') }} - restore-keys: | - ${{ github.run_id }}-venv-${{ runner.os }}-${{ inputs.python }} - ${{ runner.os }}-${{ inputs.python }}-venv- - - - name: Install Python dependencies - if: ${{ steps.cache-venv.outputs.cache-hit != 'true' }} - run: | - python -m pip install --upgrade pip - python -m venv ~/.venv - source ~/.venv/bin/activate - pip install wheel - pip install scikit-learn sqlalchemy - pip install pytest-xdist[psutil] - pip install numpydoc jupyter - - - name: Build River - run: | - source ~/.venv/bin/activate - pip install -e ".[dev,docs]" - - # We should delete the git project from the build cache to avoid conflicts - - name: Delete the Git project - run: rm -r .git - - - uses: actions/cache/save@v3 - id: cache-river - with: - path: ${{ github.workspace }} - key: river-build-${{ github.run_id }}-${{ runner.os }}-${{ inputs.python }} diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml deleted file mode 100644 index 8482c33c48..0000000000 --- a/.github/workflows/ci.yml +++ /dev/null @@ -1,47 +0,0 @@ -name: ci - -on: - push: - branches: - - main - - pull_request: - branches: - - "*" - -jobs: - build-river: - strategy: - fail-fast: false - matrix: - python: [3.9, "3.10", "3.11"] - os: [ubuntu-latest, macos-latest] - - uses: ./.github/workflows/build-river.yml - with: - python: ${{ matrix.python }} - os: ${{ matrix.os }} - - unit-tests: - needs: build-river - strategy: - fail-fast: false - matrix: - python: [3.9, "3.10", "3.11"] - os: [ubuntu-latest, macos-latest] - - uses: ./.github/workflows/unit-tests.yml - with: - python: ${{ matrix.python }} - os: ${{ matrix.os }} - - dev-docs: - if: github.event_name == 'push' - needs: unit-tests # The workflow will actually update docs, so it's best to wait for unit tests too. - uses: ./.github/workflows/docs.yml - secrets: inherit - - branch-docs: - if: github.event_name == 'pull_request' - needs: build-river # The workflow will only build docs, so no need to wait for unit tests. - uses: ./.github/workflows/docs.yml diff --git a/.github/workflows/code-quality.yml b/.github/workflows/code-quality.yml index c349ae1e99..39b544cebd 100644 --- a/.github/workflows/code-quality.yml +++ b/.github/workflows/code-quality.yml @@ -12,29 +12,13 @@ jobs: ubuntu: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v4 + - name: Build River + uses: ./.github/actions/install-env with: - python-version: 3.11 - - - run: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show - if: matrix.os == 'ubuntu-latest' - - - name: Cache Python dependencies - uses: actions/cache@v2 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('**/setup.py') }} - restore-keys: | - ${{ runner.os }}-pip- - - - name: Install Python dependencies - run: | - python -m pip install --upgrade pip - pip install wheel - pip install -e ".[dev]" + python-version: "3.12" + build-root: false - name: Run pre-commit on all files - run: pre-commit run --all-files + run: poetry run pre-commit run --all-files diff --git a/.github/workflows/delete-caches.yml b/.github/workflows/delete-caches.yml index 46bd3637a2..e4bedb060b 100644 --- a/.github/workflows/delete-caches.yml +++ b/.github/workflows/delete-caches.yml @@ -1,13 +1,13 @@ name: Clear all Github Actions caches on: + workflow_dispatch: schedule: - cron: "0 0 * * 0" - workflow_dispatch: jobs: my-job: name: Delete all caches - runs-on: ubuntu-20.04 + runs-on: ubuntu-latest steps: - name: Clear caches diff --git a/.github/workflows/docs.yml b/.github/workflows/dev-docs.yml similarity index 66% rename from .github/workflows/docs.yml rename to .github/workflows/dev-docs.yml index 376c0d2429..84194ffe32 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/dev-docs.yml @@ -1,7 +1,9 @@ -name: docs +name: dev-docs on: - workflow_call: + push: + branches: + - main jobs: docs: @@ -10,25 +12,29 @@ jobs: steps: - uses: actions/checkout@v3 - - name: Retrieve the environment and the River build - uses: ./.github/actions/retrieve-env + - name: Build River + uses: ./.github/actions/install-env with: - python: "3.11" + python-version: "3.12" + build-root: false - - name: Install Ubuntu dependencies + - name: Install extra Ubuntu dependencies run: sudo apt-get install graphviz pandoc + - name: Install extra Python dependencies + run: | + poetry install --with docs + - name: Build docs run: | - source ~/.venv/bin/activate + source $VENV make doc - name: Deploy docs - if: github.event_name == 'push' env: GH_TOKEN: ${{ secrets.GitHubToken }} run: | - source ~/.venv/bin/activate + source $VENV git config user.name github-actions git config user.email github-actions@github.com git config pull.rebase false diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 8539ba7082..c3316ba4f1 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -11,77 +11,88 @@ jobs: name: Build wheels on ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: + # https://github.com/actions/runner-images/tree/main matrix: - os: [ubuntu-latest, windows-latest, macos-latest] - arch: [main, alt] - include: - - os: ubuntu-latest - platform: linux - - os: windows-latest - ls: dir - - os: macos-latest - arch: alt - alt_arch_name: arm64 - exclude: - - os: windows-latest - arch: alt - - os: macos-latest - arch: alt - - os: ubuntu-latest - arch: alt + os: + [ + ubuntu-20.04, + ubuntu-22.04, + windows-2019, + windows-2022, + macos-12, + macos-13, + macos-14, + ] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - - name: set up rust - if: matrix.os != 'ubuntu-latest' + - name: Set up rust + if: matrix.os != 'ubuntu-20.04' && matrix.os != 'ubuntu-22.04' uses: actions-rs/toolchain@v1 with: profile: minimal toolchain: nightly override: true - - run: rustup target add aarch64-apple-darwin - if: matrix.os == 'macos-latest' + - run: rustup target add aarch64-apple-darwin && rustup target add x86_64-apple-darwin + if: matrix.os == 'macos-12' || matrix.os == 'macos-13' || matrix.os == 'macos-14' - run: rustup toolchain install stable-i686-pc-windows-msvc - if: matrix.os == 'windows-latest' + if: matrix.os == 'windows-2019' || matrix.os == 'windows-2022' - run: rustup target add i686-pc-windows-msvc - if: matrix.os == 'windows-latest' + if: matrix.os == 'windows-2019' || matrix.os == 'windows-2022' - name: Set up QEMU - if: matrix.os == 'ubuntu-latest' - uses: docker/setup-qemu-action@v1 + if: matrix.os == 'ubuntu-20.04' || matrix.os == 'ubuntu-22.04' + uses: docker/setup-qemu-action@v3 with: platforms: all - name: Build wheels - uses: pypa/cibuildwheel@v2.12.3 + uses: pypa/cibuildwheel@v2.19.2 + timeout-minutes: 720 env: - CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-*" - CIBW_BEFORE_BUILD: > - pip install setuptools-rust cython && - rustup default nightly && - rustup show - # rust doesn't seem to be available for musl linux on i686 + CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*" + CIBW_ARCHS_LINUX: "x86_64 i686 aarch64" + # CIBW_ARCHS_MACOS: "x86_64 arm64" + CIBW_ARCHS_MACOS: "universal2" + # We don't build ARM64 wheels yet because there's a Rust issue + CIBW_ARCHS_WINDOWS: "AMD64 x86" + # Rust nighlty doesn't seem to be available for musl linux on i686 CIBW_SKIP: "*-musllinux_i686" - # we build for "alt_arch_name" if it exists, else 'auto - CIBW_ARCHS: ${{ matrix.alt_arch_name || 'auto' }} + + # arm64 and universal2 wheels are tagged with x86_64 because there's an issue with Poetry + # More information here: https://cibuildwheel.readthedocs.io/en/stable/faq/#how-to-cross-compile (CTRL + F "poetry") + # https://github.com/pypa/cibuildwheel/issues/1415 + CIBW_REPAIR_WHEEL_COMMAND_MACOS: > + ls {dest_dir} && + delocate-wheel --require-archs {delocate_archs} -w {dest_dir} -v {wheel} && + for file in {dest_dir}/*.whl ; do mv $file ${file//x86_64/universal2} ; done CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014" - CIBW_MUSLLINUX_X86_64_IMAGE: "musllinux_1_1" + CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" CIBW_MANYLINUX_AARCH64_IMAGE: "manylinux2014" + CIBW_MANYLINUX_PYPY_X86_64_IMAGE: "manylinux2014" + CIBW_MANYLINUX_PYPY_I686_IMAGE: "manylinux2014" + CIBW_MANYLINUX_PYPY_AARCH64_IMAGE: "manylinux2014" + + CIBW_MUSLLINUX_X86_64_IMAGE: "musllinux_1_1" + CIBW_MUSLLINUX_I686_IMAGE: "musllinux_1_1" CIBW_MUSLLINUX_AARCH64_IMAGE: "musllinux_1_1" + CIBW_ENVIRONMENT: 'PATH="$HOME/.cargo/bin:$PATH"' # Fix the following error: error: cargo rustc --lib --message-format=json-render-diagnostics --manifest-path Cargo.toml --release -v --features pyo3/extension-module -- --crate-type cdylibfailed with code -9 # You need to set a second environment variable CARGO_NET_GIT_FETCH_WITH_CLI="true" for linux environments # Solutio found here: https://github.com/rust-lang/cargo/issues/10583 CIBW_ENVIRONMENT_LINUX: 'PATH="$HOME/.cargo/bin:$PATH" CARGO_NET_GIT_FETCH_WITH_CLI="true"' - CIBW_MANYLINUX_I686_IMAGE: "manylinux2014" CIBW_ENVIRONMENT_WINDOWS: 'PATH="$UserProfile\.cargo\bin;$PATH"' + + CIBW_BEFORE_BUILD: > + rustup default nightly && + rustup show CIBW_BEFORE_BUILD_LINUX: > - pip install cython numpy setuptools wheel setuptools-rust && curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show @@ -93,12 +104,17 @@ jobs: name: Build source distribution runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 + + - name: Build River + uses: ./.github/actions/install-env + with: + python-version: "3.12" - - name: Build sdist - run: pipx run build --sdist + - name: Build dist + run: poetry build - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: path: dist/*.tar.gz @@ -106,12 +122,12 @@ jobs: needs: [build_wheels, build_sdist] runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v2 + - uses: actions/download-artifact@v4.1.7 with: name: artifact path: dist - - uses: pypa/gh-action-pypi-publish@v1.4.2 + - uses: pypa/gh-action-pypi-publish@v1.8.10 with: - user: ${{ secrets.pypi_user }} - password: ${{ secrets.pypi_password }} + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 156c3baaf0..76a09d0450 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -10,55 +10,49 @@ jobs: ubuntu: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - - name: Install Ubuntu dependencies - run: sudo apt-get install graphviz pandoc - - - name: Set up Python - uses: actions/setup-python@v4 + - name: Build River + uses: ./.github/actions/install-env with: - python-version: "3.10" + python-version: "3.12" - - run: curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain=nightly --profile=minimal -y && rustup show - if: matrix.os == 'ubuntu-latest' + - name: Install extra Ubuntu dependencies + run: sudo apt-get install graphviz pandoc - - name: Install Python dependencies + - name: Install extra Python dependencies run: | - python -m pip install --upgrade pip - pip install wheel - pip install -e ".[compat,dev,docs]" - pip install rich - python -m spacy download en_core_web_sm + poetry install --with docs --with compat + poetry run python -m spacy download en_core_web_sm - name: Use Rich in notebooks run: | - ipython profile create + poetry run ipython profile create echo "%load_ext rich" > ~/.ipython/profile_default/startup/00_rich.ipy - name: Execute notebooks run: | - pip install numpy --upgrade + source $VENV make execute-notebooks - name: Build docs - run: make doc + run: | + source $VENV + make doc - name: Deploy docs env: GH_TOKEN: ${{ secrets.GitHubToken }} run: | + source $VENV git config user.name github-actions git config user.email github-actions@github.com git config pull.rebase false - git add --all git commit -m "Execute notebooks" - git fetch git checkout gh-pages git pull - git checkout main RIVER_VERSION=$(python -c "import river; print(river.__version__)") - mike deploy ${RIVER_VERSION} latest --update-aliases --push --force --remote https://${GH_TOKEN}@github.com/online-ml/river.git + mike deploy ${RIVER_VERSION} latest --update-aliases --push --remote https://${GH_TOKEN}@github.com/online-ml/river.git diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 542b2ac8d1..0d7c00e016 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -1,28 +1,29 @@ name: unit-tests on: - workflow_call: - inputs: - python: - type: string - os: - type: string + pull_request: + branches: + - "*" + push: + branches: + - main jobs: - test: - runs-on: ${{ inputs.os }} + run: + strategy: + matrix: + os: [ubuntu-latest] + python-version: ["3.12", "3.11", "3.10"] - # Instead of using two matrices in the calling Workflow, we can use conditionals here - # if: (inputs.os == 'ubuntu-latest' && inputs.python == '3.11') || github.event_name == 'push' - if: (inputs.python == '3.11') || github.event_name == 'push' + runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v3 - - name: Retrieve the environment and the River build - uses: ./.github/actions/retrieve-env + - name: Build River + uses: ./.github/actions/install-env with: - python: ${{ inputs.python }} + python-version: "3.12" - name: Cache River datasets uses: actions/cache@v3 @@ -38,17 +39,9 @@ jobs: - name: Download datasets run: | - source ~/.venv/bin/activate - python -c "from river import datasets; datasets.CreditCard().download(); datasets.Elec2().download(); datasets.SMSSpam().download()" + poetry run python -c "from river import datasets; datasets.CreditCard().download(); datasets.Elec2().download(); datasets.SMSSpam().download()" + poetry run python -c "from river import bandit; bandit.datasets.NewsArticles().download()" - - name: pytest [Branch] - if: github.event_name == 'pull_request' + - name: pytest run: | - source ~/.venv/bin/activate - pytest --durations=10 -n logical # Run pytest on all logical CPU cores - - - name: pytest [Main] - if: github.event_name == 'push' - run: | - source ~/.venv/bin/activate - pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores + poetry run pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores diff --git a/.gitignore b/.gitignore index 312295e81d..7940257d13 100644 --- a/.gitignore +++ b/.gitignore @@ -7,8 +7,6 @@ docs/recipes/*_files/ docs/examples/*.md docs/examples/*/*.md docs/examples/*_files/ -docs/benchmarks/*/*.md -docs/benchmarks/*/*.csv docs/api/ site docs/linkified @@ -130,3 +128,6 @@ benchmarks/.asv # Cargo file Cargo.lock + +# WASM +/*.html diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index eed258f2bd..bc049ead6b 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,30 +1,28 @@ files: river repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.2.0 + rev: v4.4.0 hooks: - id: check-json - id: check-yaml - - id: end-of-file-fixer - - id: trailing-whitespace - - id: mixed-line-ending - - repo: local + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.5.7 hooks: - - id: black - name: black - language: python - types: [python] - entry: black - + # Run the linter. - id: ruff - name: ruff - language: python - types: [python] - entry: ruff + types_or: [python, pyi, jupyter] + args: [--fix] + # Run the formatter. + - id: ruff-format + types_or: [python, pyi, jupyter] + - repo: https://github.com/pre-commit/mirrors-mypy + rev: "v1.1.1" + hooks: - id: mypy - name: mypy - language: python - types: [python] - entry: mypy --implicit-optional + args: + - "--config-file=pyproject.toml" + - "--python-version=3.11" + - "--implicit-optional" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 310318ea09..8de672f6c5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -18,34 +18,29 @@ The typical workflow for contributing to River is: ## Local setup -We encourage you to use a virtual environment. You'll want to activate it every time you want to work on River. +Start by cloning the repository: ```sh -python -m venv .venv -source .venv/bin/activate +git clone https://github.com/online-ml/river ``` -You can also create a virtual environment via `conda`: +Next, you'll need a Python environment. A nice way to manage your Python versions is to use pyenv, which can installed [here](https://github.com/pyenv/pyenv-installer). Once you have pyenv, you can install the latest Python version River supports: ```sh -conda create -n river -y python=3.9 -conda activate river +pyenv install -v $(cat .python-version) ``` -Yet another option is to use `pyenv`: +You need a `Rust` compiler you can install it by following this [link](https://www.rust-lang.org/fr/tools/install). You'll also need [Poetry](https://python-poetry.org/): ```sh -pyenv virtualenv 3.10 river310 -pyenv activate river310 +curl -sSL https://install.python-poetry.org | python3 - ``` -You need a `Rust` compiler you can install it by following this [link](https://www.rust-lang.org/fr/tools/install) - -Then, navigate to your cloned fork and install River and the required dependencies in [development mode](https://stackoverflow.com/questions/19048732/python-setup-py-develop-vs-install): - +Now you're set to install River and activate the virtual environment: ```sh -pip install -e ".[dev]" +poetry install +poetry shell ``` Finally, install the [pre-commit](https://pre-commit.com/) push hooks. This will run some code quality checks every time you push to GitHub. @@ -66,12 +61,15 @@ You're now ready to make some changes. We strongly recommend that you to check o - Create and open a Jupyter notebook at the root of the directory. - Add the following in the code cell: + ```py %load_ext autoreload %autoreload 2 ``` + - The previous code will automatically reimport River for you whenever you make changes. - For instance, if a change is made to `linear_model.LinearRegression`, then rerunning the following code doesn't require rebooting the notebook: + ```py from river import linear_model @@ -98,7 +96,7 @@ If you're adding a class or a function, then you'll need to add a docstring. We To build the documentation, you need to install some extra dependencies: ```sh -pip install -e ".[docs]" +poetry install --with docs pip install git+https://github.com/MaxHalford/yamp ``` @@ -108,40 +106,10 @@ From the root of the repository, you can then run the `make livedoc` command to All classes and function are automatically picked up and added to the documentation. The only thing you have to do is to add an entry to the relevant file in the [`docs/releases` directory](docs/releases). -## Building Cython extensions - -```sh -make build-cython -``` - -## Building Rust extensions - -Debug settings: - -```sh -make develop -``` - -Release settings: - -```sh -make build-rust -``` - -After building the project by modifying the rust part of the codebase (changing the project architecture, renaming it, etc.), it happens that by importing `river,` the python process is killed. If this happens, we invite you to remove the following things and start a new build: - -```sh -# remove all .so output from rust ie river/stats/_rust_stats.cpython* -rm -rf target -rm -rf river.egg-info -rm Cargo.lock -rm -rf build -``` - ## Build Cython and Rust extensions ```sh -make build_all +poetry install ``` ## Testing @@ -184,14 +152,18 @@ make execute-notebooks 2. Run `make execute-notebooks` just to be safe 3. Run the [benchmarks](benchmarks) 4. Bump the version in `river/__version__.py` -5. Tag and date the `docs/releases/unreleased.md` file -6. Commit and push -7. Wait for CI to [run the unit tests](https://github.com/online-ml/river/actions/workflows/ci.yml) -8. Push the tag: +5. Bump the version in `pyproject.toml` +6. Tag and date the `docs/releases/unreleased.md` file +7. Commit and push +8. Wait for CI to [run the unit tests](https://github.com/online-ml/river/actions/workflows/ci.yml) +9. Push the tag: ```sh RIVER_VERSION=$(python -c "import river; print(river.__version__)") echo $RIVER_VERSION +``` + +```sh git tag $RIVER_VERSION git push origin $RIVER_VERSION ``` @@ -208,3 +180,5 @@ END brew update && brew install gh gh release create $RIVER_VERSION --notes $RELEASE_NOTES ``` + +11. Pyodide needs to be told there is a new release. This can done by updating [`packages/river`](https://github.com/online-ml/pyodide/tree/main/packages/river) in [online-ml/pyodide](https://github.com/online-ml/pyodide) diff --git a/Cargo.toml b/Cargo.toml index eee905b262..4c53bc7e9b 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -11,7 +11,7 @@ path = "rust_src/lib.rs" crate-type = ["cdylib"] [dependencies] -pyo3 = { version = "0.16.5", features = ["extension-module"] } +pyo3 = { version = "0.18.3", features = ["extension-module"] } watermill = "0.1.1" bincode = "1.3.3" serde = { version = "1.0", features = ["derive"] } diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index ac3faa3ca1..0000000000 --- a/MANIFEST.in +++ /dev/null @@ -1,8 +0,0 @@ -global-include *.pyx -global-include *.pxd -include river/datasets/*.csv -include river/datasets/*.gz -include river/datasets/*.zip -include river/stream/*.zip -include Cargo.toml -recursive-include rust_src * diff --git a/Makefile b/Makefile index 748db226f2..b5eb969486 100644 --- a/Makefile +++ b/Makefile @@ -25,15 +25,3 @@ livedoc: doc rebase: git fetch && git rebase origin/main - -develop: - python ./setup.py develop - -build-cython: - python setup.py build_ext --inplace --force - -build-rust: - python setup.py build_rust --inplace --release - -build: - python setup.py build_rust --inplace --release build_ext --inplace --force diff --git a/README.md b/README.md index 80e94208a3..c1ffd72905 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,15 @@

- river_logo + river_logo

- - CI Pipeline + + unit-tests + + + + code-quality @@ -15,10 +19,6 @@ discord - - - roadmap - pypi @@ -91,11 +91,11 @@ Now let's run the model on the dataset in a streaming fashion. We sequentially i >>> for x, y in dataset: ... y_pred = model.predict_one(x) # make a prediction -... metric = metric.update(y, y_pred) # update the metric -... model = model.learn_one(x, y) # make the model learn +... metric.update(y, y_pred) # update the metric +... model.learn_one(x, y) # make the model learn >>> metric -Accuracy: 89.20% +Accuracy: 89.28% ``` @@ -109,19 +109,16 @@ River is intended to work with **Python 3.8 and above**. Installation can be don pip install river ``` -There are [wheels available](https://pypi.org/project/river/#files) for Linux, MacOS, and Windows, which means that you most probably won't have to build River from source. +There are [wheels available](https://pypi.org/project/river/#files) for Linux, MacOS, and Windows. This means you most probably won't have to build River from source. You can install the latest development version from GitHub as so: ```sh pip install git+https://github.com/online-ml/river --upgrade +pip install git+ssh://git@github.com/online-ml/river.git --upgrade # using SSH ``` -Or, through SSH: - -```sh -pip install git+ssh://git@github.com/online-ml/river.git --upgrade -``` +This method requires having Cython and Rust installed on your machine. ## 🔮 Features diff --git a/benchmarks/README.md b/benchmarks/README.md index 99d3ff644f..cbde02dcdf 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -1,17 +1,43 @@ # Benchmarks ## Installation + +The recommended way to run the benchmarks is to create a dedicated environment for river and its contenders. + +An easy way to achieve that is through [Anaconda](https://docs.conda.io/projects/miniconda/en/latest/). Here is an example of creating an environment for the benchmarks: + +```sh +conda create --name river-benchmark python=3.10 +``` + +The next step is to clone river if you have not done that already: + +```sh +git clone https://github.com/online-ml/river +cd river +``` + +From the river folder you can run the following command to install the needed dependencies: + ```sh pip install ".[benchmarks]" ``` ## Usage -The `run.py` executes the benchmarks and creates the necessary .csv files for rendering the plots. + +The `run.py` script executes the benchmarks and creates the necessary .csv files for rendering the plots. + ```sh cd benchmarks python run.py ``` + The `render.py` renders the plots from the .csv files and moves them to the `docs/benchmarks` folder. + ```sh python render.py ``` + +## Notes: VolpalWabbit + +Installing Volpal Wabbit (VW) can be tricky sometimes. That is especially true when using apple silicon. If cannot make the pip install guidelines from VW work a workaround is the following. When using anaconda, you can install the recommended dependencies utilized for building VW with conda. You can get more info [here](https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Building#conda) about such dependencies. After that, `pip install volpalwabbit` should work just fine. diff --git a/benchmarks/binary_classification.csv b/benchmarks/binary_classification.csv index e6fb618dbf..4b38617bef 100644 --- a/benchmarks/binary_classification.csv +++ b/benchmarks/binary_classification.csv @@ -1,3401 +1,3637 @@ step,track,model,dataset,Accuracy,F1,Memory in Mb,Time in s -106,Binary classification,Logistic regression,Bananas,0.49056603773584906,0.325,0.004187583923339844,0.013756 -212,Binary classification,Logistic regression,Bananas,0.5141509433962265,0.3757575757575758,0.004187583923339844,0.038575 -318,Binary classification,Logistic regression,Bananas,0.5188679245283019,0.41379310344827586,0.004240989685058594,0.073067 -424,Binary classification,Logistic regression,Bananas,0.5165094339622641,0.39528023598820067,0.004240989685058594,0.11707399999999998 -530,Binary classification,Logistic regression,Bananas,0.5320754716981132,0.35751295336787564,0.004240989685058594,0.17098599999999997 -636,Binary 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classification,Logistic regression,Phishing,0.8133333333333334,0.8157894736842105,0.005324363708496094,0.027624000000000003 -100,Binary classification,Logistic regression,Phishing,0.82,0.8163265306122449,0.005324363708496094,0.045128 -125,Binary classification,Logistic regression,Phishing,0.808,0.8032786885245902,0.005324363708496094,0.065471 -150,Binary classification,Logistic regression,Phishing,0.8133333333333334,0.8157894736842104,0.005324363708496094,0.088498 -175,Binary classification,Logistic regression,Phishing,0.8228571428571428,0.8143712574850299,0.005324363708496094,0.11416399999999999 -200,Binary classification,Logistic regression,Phishing,0.82,0.8105263157894737,0.005324363708496094,0.143232 -225,Binary classification,Logistic regression,Phishing,0.8177777777777778,0.8038277511961723,0.005324363708496094,0.175843 -250,Binary classification,Logistic regression,Phishing,0.824,0.811965811965812,0.005324363708496094,0.212135 -275,Binary classification,Logistic regression,Phishing,0.8254545454545454,0.8125,0.005564689636230469,0.25199099999999997 -300,Binary classification,Logistic regression,Phishing,0.8366666666666667,0.8205128205128205,0.005564689636230469,0.295434 -325,Binary classification,Logistic regression,Phishing,0.8430769230769231,0.8222996515679442,0.005564689636230469,0.34243999999999997 -350,Binary classification,Logistic regression,Phishing,0.8542857142857143,0.8316831683168316,0.005564689636230469,0.39299199999999995 -375,Binary classification,Logistic regression,Phishing,0.8506666666666667,0.825,0.005564689636230469,0.44723899999999994 -400,Binary classification,Logistic regression,Phishing,0.8525,0.8249258160237388,0.005564689636230469,0.5051399999999999 -425,Binary classification,Logistic regression,Phishing,0.8588235294117647,0.8285714285714286,0.005564689636230469,0.5668209999999999 -450,Binary classification,Logistic regression,Phishing,0.8622222222222222,0.8306010928961749,0.005564689636230469,0.6322009999999999 -475,Binary classification,Logistic regression,Phishing,0.8589473684210527,0.8277634961439589,0.005564689636230469,0.7013439999999999 -500,Binary classification,Logistic regression,Phishing,0.86,0.8325358851674641,0.005564689636230469,0.7743169999999998 -525,Binary classification,Logistic regression,Phishing,0.8590476190476191,0.827906976744186,0.005564689636230469,0.8510559999999998 -550,Binary classification,Logistic regression,Phishing,0.86,0.8300220750551875,0.005564689636230469,0.9315169999999998 -575,Binary classification,Logistic regression,Phishing,0.8626086956521739,0.8329809725158562,0.005564689636230469,1.015688 -600,Binary classification,Logistic regression,Phishing,0.8666666666666667,0.8353909465020577,0.005564689636230469,1.103615 -625,Binary classification,Logistic regression,Phishing,0.8688,0.8346774193548386,0.005564689636230469,1.195378 -650,Binary classification,Logistic regression,Phishing,0.8723076923076923,0.8413001912045889,0.005564689636230469,1.291006 -675,Binary classification,Logistic regression,Phishing,0.8725925925925926,0.8447653429602888,0.005564689636230469,1.390501 -700,Binary classification,Logistic regression,Phishing,0.8771428571428571,0.8485915492957746,0.005564689636230469,1.494099 -725,Binary classification,Logistic regression,Phishing,0.8786206896551724,0.8533333333333334,0.005564689636230469,1.601701 -750,Binary classification,Logistic regression,Phishing,0.88,0.8557692307692307,0.005564689636230469,1.713159 -775,Binary classification,Logistic regression,Phishing,0.8812903225806452,0.8566978193146417,0.005564689636230469,1.8285060000000002 -800,Binary classification,Logistic regression,Phishing,0.88125,0.8584202682563338,0.005564689636230469,1.9476680000000002 -825,Binary classification,Logistic regression,Phishing,0.8812121212121212,0.8595988538681948,0.005564689636230469,2.0707090000000004 -850,Binary classification,Logistic regression,Phishing,0.8823529411764706,0.8603351955307262,0.005564689636230469,2.1975890000000002 -875,Binary classification,Logistic regression,Phishing,0.8857142857142857,0.8637602179836512,0.005564689636230469,2.3283080000000003 -900,Binary classification,Logistic regression,Phishing,0.8855555555555555,0.8632138114209827,0.005564689636230469,2.462826 -925,Binary classification,Logistic regression,Phishing,0.8875675675675676,0.867007672634271,0.005564689636230469,2.6011680000000004 -950,Binary classification,Logistic regression,Phishing,0.8863157894736842,0.8669950738916257,0.005564689636230469,2.7434260000000004 -975,Binary classification,Logistic regression,Phishing,0.8871794871794871,0.8677884615384616,0.005564689636230469,2.889565 -1000,Binary classification,Logistic regression,Phishing,0.888,0.8688524590163934,0.005564689636230469,3.039628 -1025,Binary classification,Logistic regression,Phishing,0.8878048780487805,0.8691695108077361,0.005564689636230469,3.19355 -1050,Binary classification,Logistic regression,Phishing,0.8895238095238095,0.8716814159292035,0.005564689636230469,3.351259 -1075,Binary classification,Logistic regression,Phishing,0.8883720930232558,0.8715203426124196,0.005564689636230469,3.5126910000000002 -1100,Binary classification,Logistic regression,Phishing,0.89,0.8735632183908045,0.005564689636230469,3.677881 -1125,Binary classification,Logistic regression,Phishing,0.8906666666666667,0.8753799392097265,0.005564689636230469,3.846795 -1150,Binary classification,Logistic regression,Phishing,0.8904347826086957,0.8750000000000001,0.005564689636230469,4.0194600000000005 -1175,Binary classification,Logistic regression,Phishing,0.8893617021276595,0.8735408560311284,0.005564689636230469,4.195879000000001 -1200,Binary classification,Logistic regression,Phishing,0.89,0.8740458015267174,0.005564689636230469,4.376099000000001 -1225,Binary classification,Logistic regression,Phishing,0.8906122448979592,0.874766355140187,0.005564689636230469,4.560123000000001 -1250,Binary classification,Logistic regression,Phishing,0.888,0.8722627737226277,0.005564689636230469,4.747978000000001 -1903,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,0.201733 -3806,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,0.541161 -5709,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,0.9894339999999999 -7612,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,1.548242 -9515,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,2.214419 -11418,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,2.987891 -13321,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,3.868043 -15224,Binary classification,Logistic regression,SMTP,0.9996715712033631,0.7058823529411764,0.004383087158203125,4.854806 -17127,Binary classification,Logistic regression,SMTP,0.9997080632918783,0.761904761904762,0.004383087158203125,5.948104 -19030,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.761904761904762,0.004383087158203125,7.14776 -20933,Binary classification,Logistic regression,SMTP,0.999761142693355,0.761904761904762,0.004383087158203125,8.453778 -22836,Binary classification,Logistic regression,SMTP,0.9997810474689087,0.761904761904762,0.004383087158203125,9.865972 -24739,Binary classification,Logistic regression,SMTP,0.9997978899713004,0.761904761904762,0.004383087158203125,11.384388 -26642,Binary classification,Logistic regression,SMTP,0.9997747916823061,0.7272727272727273,0.004383087158203125,13.008481999999999 -28545,Binary classification,Logistic regression,SMTP,0.9997898055701524,0.7272727272727273,0.004383087158203125,14.738380999999999 -30448,Binary classification,Logistic regression,SMTP,0.9998029427220179,0.7272727272727273,0.004383087158203125,16.574147999999997 -32351,Binary classification,Logistic regression,SMTP,0.999814534326605,0.7272727272727273,0.004383087158203125,18.521969999999996 -34254,Binary classification,Logistic regression,SMTP,0.999824837975127,0.7272727272727273,0.004383087158203125,20.589920999999997 -36157,Binary classification,Logistic regression,SMTP,0.9998340570290677,0.7272727272727273,0.004383087158203125,22.774089999999998 -38060,Binary classification,Logistic regression,SMTP,0.9998423541776142,0.7272727272727273,0.004383087158203125,25.073776 -39963,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.7272727272727273,0.004383087158203125,27.489114999999998 -41866,Binary classification,Logistic regression,SMTP,0.999856685616013,0.7272727272727273,0.004383087158203125,30.019976 -43769,Binary classification,Logistic regression,SMTP,0.9998629166761863,0.7272727272727273,0.004383087158203125,32.666409 -45672,Binary classification,Logistic regression,SMTP,0.9998686284813453,0.7272727272727273,0.004383087158203125,35.428179 -47575,Binary classification,Logistic regression,SMTP,0.9998738833420915,0.7272727272727273,0.004383087158203125,38.305364 -49478,Binary classification,Logistic regression,SMTP,0.9998787339827803,0.7272727272727273,0.004383087158203125,41.297895 -51381,Binary classification,Logistic regression,SMTP,0.9998443004223351,0.6666666666666666,0.004383087158203125,44.405542 -53284,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.6666666666666666,0.004383087158203125,47.6267 -55187,Binary classification,Logistic regression,SMTP,0.999855038324243,0.6666666666666666,0.004383087158203125,50.959272 -57090,Binary classification,Logistic regression,SMTP,0.9997022245577158,0.48484848484848486,0.004383087158203125,54.404277 -58993,Binary classification,Logistic regression,SMTP,0.9997118302171444,0.48484848484848486,0.004383087158203125,57.960817 -60896,Binary classification,Logistic regression,SMTP,0.9997208355228586,0.48484848484848486,0.004383087158203125,61.629022 -62799,Binary classification,Logistic regression,SMTP,0.999697447411583,0.45714285714285713,0.004383087158203125,65.409349 -64702,Binary classification,Logistic regression,SMTP,0.9997063460171247,0.45714285714285713,0.004383087158203125,69.30026500000001 -66605,Binary classification,Logistic regression,SMTP,0.9997147361309211,0.45714285714285713,0.004383087158203125,73.30473800000001 -68508,Binary classification,Logistic regression,SMTP,0.9996934664564723,0.4324324324324324,0.004383087158203125,77.42011300000001 -70411,Binary classification,Logistic regression,SMTP,0.9997017511468379,0.4324324324324324,0.004383087158203125,81.64355900000001 -72314,Binary classification,Logistic regression,SMTP,0.9997095998008685,0.4324324324324324,0.004383087158203125,85.97794200000001 -74217,Binary classification,Logistic regression,SMTP,0.9997170459598205,0.4324324324324324,0.004383087158203125,90.42391200000002 -76120,Binary classification,Logistic regression,SMTP,0.999724119810825,0.4324324324324324,0.004383087158203125,94.97981200000001 -78023,Binary classification,Logistic regression,SMTP,0.9997308485959269,0.4324324324324324,0.004383087158203125,99.646713 -79926,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.4324324324324324,0.004383087158203125,104.42510200000001 -81829,Binary classification,Logistic regression,SMTP,0.9997433672658838,0.4324324324324324,0.004383087158203125,109.315273 -83732,Binary classification,Logistic regression,SMTP,0.9997491998280228,0.4324324324324324,0.004383087158203125,114.31710000000001 -85635,Binary classification,Logistic regression,SMTP,0.9997547731651778,0.4324324324324324,0.004383087158203125,119.42729800000001 -87538,Binary classification,Logistic regression,SMTP,0.9997601041833261,0.4324324324324324,0.004383087158203125,124.64502 -89441,Binary classification,Logistic regression,SMTP,0.9997540277948592,0.4210526315789474,0.004383087158203125,129.97108 -91344,Binary classification,Logistic regression,SMTP,0.9997591522157996,0.4210526315789474,0.004383087158203125,135.407078 -93247,Binary classification,Logistic regression,SMTP,0.9997640674767017,0.4210526315789474,0.004383087158203125,140.95343100000002 -95150,Binary classification,Logistic regression,SMTP,0.9997687861271676,0.4210526315789474,0.004383087158203125,146.60779900000003 -106,Binary classification,ALMA,Bananas,0.5377358490566038,0.5242718446601942,0.0028944015502929688,0.022931 -212,Binary classification,ALMA,Bananas,0.5330188679245284,0.5217391304347825,0.0028944015502929688,0.057388 -318,Binary classification,ALMA,Bananas,0.5188679245283019,0.5173501577287066,0.0029211044311523438,0.098467 -424,Binary classification,ALMA,Bananas,0.5330188679245284,0.5330188679245282,0.0029211044311523438,0.14786 -530,Binary classification,ALMA,Bananas,0.5207547169811321,0.5115384615384615,0.0029211044311523438,0.20447099999999999 -636,Binary classification,ALMA,Bananas,0.5377358490566038,0.5303514376996804,0.0029211044311523438,0.268 -742,Binary classification,ALMA,Bananas,0.522911051212938,0.512396694214876,0.0029211044311523438,0.339749 -848,Binary classification,ALMA,Bananas,0.5235849056603774,0.5061124694376529,0.0029211044311523438,0.42003100000000004 -954,Binary classification,ALMA,Bananas,0.5157232704402516,0.5,0.0029211044311523438,0.5084810000000001 -1060,Binary classification,ALMA,Bananas,0.5160377358490567,0.4975514201762978,0.0029211044311523438,0.605006 -1166,Binary classification,ALMA,Bananas,0.5154373927958834,0.49598572702943805,0.0029211044311523438,0.7097530000000001 -1272,Binary classification,ALMA,Bananas,0.5165094339622641,0.4979591836734694,0.0029211044311523438,0.823412 -1378,Binary classification,ALMA,Bananas,0.5195936139332366,0.4977238239757208,0.0029211044311523438,0.94616 -1484,Binary classification,ALMA,Bananas,0.5195417789757413,0.4968242766407903,0.0029211044311523438,1.077952 -1590,Binary classification,ALMA,Bananas,0.5226415094339623,0.4983476536682089,0.0029211044311523438,1.218949 -1696,Binary classification,ALMA,Bananas,0.5194575471698113,0.49473031618102914,0.0029211044311523438,1.368908 -1802,Binary classification,ALMA,Bananas,0.5205327413984462,0.4965034965034965,0.0029211044311523438,1.527947 -1908,Binary classification,ALMA,Bananas,0.5193920335429769,0.4964305326743548,0.0029211044311523438,1.696168 -2014,Binary classification,ALMA,Bananas,0.519364448857994,0.4989648033126293,0.0029211044311523438,1.873583 -2120,Binary classification,ALMA,Bananas,0.5174528301886793,0.4997555012224939,0.0029211044311523438,2.060039 -2226,Binary classification,ALMA,Bananas,0.5197663971248877,0.5002337540906966,0.0029211044311523438,2.255465 -2332,Binary classification,ALMA,Bananas,0.5175814751286449,0.4975435462259938,0.0029211044311523438,2.460073 -2438,Binary classification,ALMA,Bananas,0.5176374077112387,0.4957118353344769,0.0029211044311523438,2.67358 -2544,Binary classification,ALMA,Bananas,0.5196540880503144,0.5008169934640523,0.0029211044311523438,2.8963289999999997 -2650,Binary classification,ALMA,Bananas,0.520377358490566,0.5037094884810621,0.0029211044311523438,3.127217 -2756,Binary classification,ALMA,Bananas,0.521044992743106,0.5041322314049587,0.0029211044311523438,3.366825 -2862,Binary classification,ALMA,Bananas,0.5213137665967854,0.5032632342277013,0.0029211044311523438,3.615642 -2968,Binary classification,ALMA,Bananas,0.5175202156334232,0.49859943977591037,0.0029211044311523438,3.87375 -3074,Binary classification,ALMA,Bananas,0.5152895250487963,0.49696151249155973,0.0029211044311523438,4.141236999999999 -3180,Binary classification,ALMA,Bananas,0.5132075471698113,0.4931237721021611,0.0029211044311523438,4.4182619999999995 -3286,Binary classification,ALMA,Bananas,0.5130858186244674,0.4927076727964489,0.0029211044311523438,4.703974 -3392,Binary classification,ALMA,Bananas,0.5103183962264151,0.49095923996322405,0.0029211044311523438,4.999187999999999 -3498,Binary classification,ALMA,Bananas,0.5091480846197828,0.48914013686402846,0.0029211044311523438,5.303419999999999 -3604,Binary classification,ALMA,Bananas,0.5097114317425083,0.4876775877065816,0.0029211044311523438,5.616715999999999 -3710,Binary classification,ALMA,Bananas,0.5118598382749326,0.49086308687095864,0.0029211044311523438,5.938947999999999 -3816,Binary classification,ALMA,Bananas,0.510482180293501,0.4893384363039912,0.0029211044311523438,6.270639999999999 -3922,Binary classification,ALMA,Bananas,0.50790413054564,0.48588172615876407,0.0029211044311523438,6.611250999999999 -4028,Binary classification,ALMA,Bananas,0.506454816285998,0.48443983402489627,0.0029211044311523438,6.9609049999999995 -4134,Binary classification,ALMA,Bananas,0.5050798258345428,0.48281092012133464,0.0029211044311523438,7.319711 -4240,Binary classification,ALMA,Bananas,0.5068396226415094,0.48484848484848486,0.0029211044311523438,7.6869439999999996 -4346,Binary classification,ALMA,Bananas,0.5080533824206167,0.4858104858104858,0.0029211044311523438,8.062541999999999 -4452,Binary classification,ALMA,Bananas,0.5080862533692723,0.4847058823529412,0.0029211044311523438,8.446741 -4558,Binary classification,ALMA,Bananas,0.5063624396665204,0.48370812299219823,0.0029211044311523438,8.840034999999999 -4664,Binary classification,ALMA,Bananas,0.5051457975986278,0.4829749103942652,0.0029211044311523438,9.242386999999999 -4770,Binary classification,ALMA,Bananas,0.5048218029350104,0.48201754385964907,0.0029211044311523438,9.653844999999999 -4876,Binary classification,ALMA,Bananas,0.5036915504511895,0.4802405498281787,0.0029211044311523438,10.074017999999999 -4982,Binary classification,ALMA,Bananas,0.5038137294259334,0.4811083123425693,0.0029211044311523438,10.503025 -5088,Binary classification,ALMA,Bananas,0.5029481132075472,0.47995064774830354,0.0029211044311523438,10.941391 -5194,Binary classification,ALMA,Bananas,0.5040431266846361,0.4810636583400483,0.0029211044311523438,11.388345 -5300,Binary classification,ALMA,Bananas,0.5064150943396226,0.4825949367088608,0.0029211044311523438,11.844057 -906,Binary classification,ALMA,Elec2,0.9072847682119205,0.9056179775280899,0.0043582916259765625,0.085634 -1812,Binary classification,ALMA,Elec2,0.9166666666666666,0.8967874231032126,0.0043582916259765625,0.273724 -2718,Binary classification,ALMA,Elec2,0.9175864606328182,0.898458748866727,0.0043582916259765625,0.563875 -3624,Binary classification,ALMA,Elec2,0.9268763796909493,0.9098945936756205,0.0043582916259765625,0.954455 -4530,Binary classification,ALMA,Elec2,0.9271523178807947,0.9076664801343034,0.0043582916259765625,1.4444780000000002 -5436,Binary classification,ALMA,Elec2,0.9269683590875644,0.9074376311494521,0.0043582916259765625,2.035788 -6342,Binary classification,ALMA,Elec2,0.9274676758120467,0.9089108910891088,0.0043582916259765625,2.724631 -7248,Binary classification,ALMA,Elec2,0.9253587196467992,0.9064499394777797,0.0043582916259765625,3.51195 -8154,Binary classification,ALMA,Elec2,0.9250674515575178,0.9098687121994394,0.0043582916259765625,4.397951 -9060,Binary classification,ALMA,Elec2,0.9264900662251656,0.9133714880332986,0.0043582916259765625,5.380316 -9966,Binary classification,ALMA,Elec2,0.9292594822396147,0.9181279758448496,0.0043582916259765625,6.461929 -10872,Binary classification,ALMA,Elec2,0.9312913907284768,0.9216077237905342,0.0043582916259765625,7.640261 -11778,Binary classification,ALMA,Elec2,0.9313126167430803,0.9217525872908405,0.0043582916259765625,8.918386 -12684,Binary classification,ALMA,Elec2,0.9289656259854935,0.9190694332165634,0.0043582916259765625,10.293447 -13590,Binary classification,ALMA,Elec2,0.9297277409860192,0.9208978712830284,0.0043582916259765625,11.76578 -14496,Binary classification,ALMA,Elec2,0.9304635761589404,0.9221381121581956,0.0043582916259765625,13.33625 -15402,Binary classification,ALMA,Elec2,0.9307882093234645,0.9222011385199241,0.0043582916259765625,15.00584 -16308,Binary classification,ALMA,Elec2,0.9292985038018151,0.9202572792032644,0.0043582916259765625,16.773224 -17214,Binary classification,ALMA,Elec2,0.9279075171372139,0.9175579618680662,0.0043582916259765625,18.640940999999998 -18120,Binary classification,ALMA,Elec2,0.9265452538631347,0.915945689927376,0.0043582916259765625,20.621169 -19026,Binary classification,ALMA,Elec2,0.9265216020182908,0.9150771473696999,0.0043582916259765625,22.710023999999997 -19932,Binary classification,ALMA,Elec2,0.9262492474413004,0.915526950925181,0.0043582916259765625,24.907693 -20838,Binary classification,ALMA,Elec2,0.9231692100969383,0.9122114382848056,0.0043582916259765625,27.214713 -21744,Binary classification,ALMA,Elec2,0.9224613686534217,0.910137511992325,0.0043582916259765625,29.631112 -22650,Binary classification,ALMA,Elec2,0.9216777041942605,0.9086978898610396,0.0043582916259765625,32.155455 -23556,Binary classification,ALMA,Elec2,0.9186194600101885,0.9050096625538873,0.0043582916259765625,34.791879 -24462,Binary classification,ALMA,Elec2,0.9172594227781866,0.9028324531925108,0.0043582916259765625,37.534909 -25368,Binary classification,ALMA,Elec2,0.9144591611479028,0.8997134670487106,0.0043582916259765625,40.38939 -26274,Binary classification,ALMA,Elec2,0.9142117682880414,0.899213020926489,0.0043582916259765625,43.350156 -27180,Binary classification,ALMA,Elec2,0.9137969094922738,0.8990477831875565,0.0043582916259765625,46.420679 -28086,Binary classification,ALMA,Elec2,0.9109876806950082,0.8954587271054613,0.0043582916259765625,49.601356 -28992,Binary classification,ALMA,Elec2,0.9101131346578366,0.8940478126524638,0.0043582916259765625,52.888972 -29898,Binary classification,ALMA,Elec2,0.9094588266773698,0.8931264558411306,0.0043582916259765625,56.282618 -30804,Binary classification,ALMA,Elec2,0.9082911310219453,0.8912666948924213,0.0043582916259765625,59.78184 -31710,Binary classification,ALMA,Elec2,0.9061810154525386,0.8888307611823175,0.0043582916259765625,63.389233000000004 -32616,Binary classification,ALMA,Elec2,0.9052612214863871,0.8878891227051738,0.0043582916259765625,67.10073700000001 -33522,Binary classification,ALMA,Elec2,0.9050176003818388,0.887665819926616,0.0043582916259765625,70.91761400000001 -34428,Binary classification,ALMA,Elec2,0.9050482165679098,0.8877519486316657,0.0043582916259765625,74.83635300000002 -35334,Binary classification,ALMA,Elec2,0.9045112356370635,0.8865729846029718,0.0043582916259765625,78.86287600000001 -36240,Binary classification,ALMA,Elec2,0.9047737306843268,0.8860115606936415,0.0043582916259765625,82.99223100000002 -37146,Binary classification,ALMA,Elec2,0.9044850051149518,0.8853857087479002,0.0043582916259765625,87.22379500000002 -38052,Binary classification,ALMA,Elec2,0.9042363082098182,0.884573962622743,0.0043582916259765625,91.56013600000003 -38958,Binary classification,ALMA,Elec2,0.9043842086349402,0.8849918182098861,0.0043582916259765625,96.00151600000002 -39864,Binary classification,ALMA,Elec2,0.904901665663255,0.8863302449701658,0.0043582916259765625,100.54490000000003 -40770,Binary classification,ALMA,Elec2,0.9054942359578121,0.8878344153008646,0.0043582916259765625,105.19249400000002 -41676,Binary classification,ALMA,Elec2,0.9060850369517228,0.8891971464160343,0.0043582916259765625,109.94207100000003 -42582,Binary classification,ALMA,Elec2,0.9063688882626462,0.8897796699195533,0.0043582916259765625,114.79624900000003 -43488,Binary classification,ALMA,Elec2,0.906686902133922,0.8902234485743656,0.0043582916259765625,119.75466700000003 -44394,Binary classification,ALMA,Elec2,0.9062485921520926,0.8894437656059077,0.0043582916259765625,124.81683700000002 -45300,Binary classification,ALMA,Elec2,0.906401766004415,0.8897555902236091,0.0043582916259765625,129.97946500000003 -25,Binary classification,ALMA,Phishing,0.56,0.5217391304347826,0.004366874694824219,0.006962 -50,Binary classification,ALMA,Phishing,0.7,0.6341463414634146,0.004366874694824219,0.020485 -75,Binary classification,ALMA,Phishing,0.72,0.6956521739130435,0.004366874694824219,0.040554 -100,Binary classification,ALMA,Phishing,0.73,0.7157894736842104,0.004366874694824219,0.063947 -125,Binary classification,ALMA,Phishing,0.728,0.7166666666666666,0.004366874694824219,0.08978900000000001 -150,Binary classification,ALMA,Phishing,0.72,0.7272727272727273,0.004366874694824219,0.11811500000000001 -175,Binary classification,ALMA,Phishing,0.7371428571428571,0.7261904761904763,0.004366874694824219,0.148737 -200,Binary classification,ALMA,Phishing,0.74,0.7291666666666666,0.004366874694824219,0.181823 -225,Binary classification,ALMA,Phishing,0.7288888888888889,0.7081339712918661,0.004366874694824219,0.21743300000000002 -250,Binary classification,ALMA,Phishing,0.728,0.7094017094017095,0.004366874694824219,0.25563600000000003 -275,Binary classification,ALMA,Phishing,0.7381818181818182,0.7187499999999999,0.004580497741699219,0.29612400000000005 -300,Binary classification,ALMA,Phishing,0.74,0.717391304347826,0.004580497741699219,0.33938700000000005 -325,Binary classification,ALMA,Phishing,0.7507692307692307,0.7216494845360825,0.004580497741699219,0.3849 -350,Binary classification,ALMA,Phishing,0.7571428571428571,0.7266881028938907,0.004580497741699219,0.43290900000000004 -375,Binary classification,ALMA,Phishing,0.76,0.7272727272727273,0.004580497741699219,0.48366600000000004 -400,Binary classification,ALMA,Phishing,0.7625,0.7293447293447294,0.004580497741699219,0.537154 -425,Binary classification,ALMA,Phishing,0.7623529411764706,0.7232876712328767,0.004580497741699219,0.593599 -450,Binary classification,ALMA,Phishing,0.7644444444444445,0.7239583333333334,0.004580497741699219,0.65326 -475,Binary classification,ALMA,Phishing,0.7684210526315789,0.7303921568627451,0.004580497741699219,0.7162679999999999 -500,Binary classification,ALMA,Phishing,0.77,0.735632183908046,0.004580497741699219,0.7828129999999999 -525,Binary classification,ALMA,Phishing,0.7733333333333333,0.7349665924276169,0.004580497741699219,0.8526399999999998 -550,Binary classification,ALMA,Phishing,0.7727272727272727,0.7368421052631579,0.004580497741699219,0.9258199999999999 -575,Binary classification,ALMA,Phishing,0.7756521739130435,0.7404426559356138,0.004580497741699219,1.00229 -600,Binary classification,ALMA,Phishing,0.7816666666666666,0.7426326129666011,0.004580497741699219,1.08201 -625,Binary classification,ALMA,Phishing,0.776,0.7338403041825096,0.004580497741699219,1.165182 -650,Binary classification,ALMA,Phishing,0.7830769230769231,0.7450271247739602,0.004580497741699219,1.2515479999999999 -675,Binary classification,ALMA,Phishing,0.7851851851851852,0.7521367521367521,0.004580497741699219,1.34123 -700,Binary classification,ALMA,Phishing,0.7914285714285715,0.7566666666666668,0.004580497741699219,1.434087 -725,Binary classification,ALMA,Phishing,0.7931034482758621,0.7626582278481012,0.004580497741699219,1.5303769999999999 -750,Binary classification,ALMA,Phishing,0.7933333333333333,0.7640791476407914,0.004580497741699219,1.6300339999999998 -775,Binary classification,ALMA,Phishing,0.7935483870967742,0.7633136094674556,0.004580497741699219,1.7333069999999997 -800,Binary classification,ALMA,Phishing,0.79625,0.7687943262411348,0.004580497741699219,1.8400829999999997 -825,Binary classification,ALMA,Phishing,0.7951515151515152,0.7688098495212038,0.004580497741699219,1.9503149999999998 -850,Binary classification,ALMA,Phishing,0.7988235294117647,0.7723035952063916,0.004580497741699219,2.063938 -875,Binary classification,ALMA,Phishing,0.8034285714285714,0.7760416666666667,0.004580497741699219,2.180811 -900,Binary classification,ALMA,Phishing,0.8022222222222222,0.7752525252525253,0.004580497741699219,2.30125 -925,Binary classification,ALMA,Phishing,0.8064864864864865,0.781973203410475,0.004580497741699219,2.424984 -950,Binary classification,ALMA,Phishing,0.8084210526315789,0.7863849765258215,0.004580497741699219,2.552135 -975,Binary classification,ALMA,Phishing,0.8112820512820513,0.7894736842105262,0.004580497741699219,2.6827029999999996 -1000,Binary classification,ALMA,Phishing,0.812,0.7906458797327395,0.004580497741699219,2.8167349999999995 -1025,Binary classification,ALMA,Phishing,0.8156097560975609,0.7956756756756757,0.004580497741699219,2.9540499999999996 -1050,Binary classification,ALMA,Phishing,0.8171428571428572,0.7983193277310925,0.004580497741699219,3.0947329999999997 -1075,Binary classification,ALMA,Phishing,0.8167441860465117,0.7995930824008138,0.004580497741699219,3.2387509999999997 -1100,Binary classification,ALMA,Phishing,0.82,0.8035714285714286,0.004580497741699219,3.3859479999999995 -1125,Binary classification,ALMA,Phishing,0.8222222222222222,0.8073217726396917,0.004580497741699219,3.5363979999999997 -1150,Binary classification,ALMA,Phishing,0.8234782608695652,0.8083097261567517,0.004580497741699219,3.6900359999999996 -1175,Binary classification,ALMA,Phishing,0.8221276595744681,0.8070175438596491,0.004580497741699219,3.847107 -1200,Binary classification,ALMA,Phishing,0.8241666666666667,0.8087035358114234,0.004580497741699219,4.0074749999999995 -1225,Binary classification,ALMA,Phishing,0.8253061224489796,0.8099467140319715,0.004580497741699219,4.171155 -1250,Binary classification,ALMA,Phishing,0.8264,0.8117953165654813,0.004580497741699219,4.338235999999999 -1903,Binary classification,ALMA,SMTP,0.720966894377299,0.0,0.003093719482421875,0.171046 -3806,Binary classification,ALMA,SMTP,0.7769311613242249,0.0,0.003093719482421875,0.510929 -5709,Binary classification,ALMA,SMTP,0.7509196006305833,0.0,0.003093719482421875,1.019887 -7612,Binary classification,ALMA,SMTP,0.7900683131897005,0.0,0.003093719482421875,1.685465 -9515,Binary classification,ALMA,SMTP,0.7826589595375723,0.0,0.003093719482421875,2.527149 -11418,Binary classification,ALMA,SMTP,0.7699246803293046,0.0,0.003093719482421875,3.545039 -13321,Binary classification,ALMA,SMTP,0.7722393213722694,0.0,0.003093719482421875,4.73657 -15224,Binary classification,ALMA,SMTP,0.7791644771413557,0.004146919431279621,0.003093719482421875,6.098207 -17127,Binary classification,ALMA,SMTP,0.783207800548841,0.004824443848834093,0.003093719482421875,7.63166 -19030,Binary classification,ALMA,SMTP,0.7891224382553862,0.004465393202679235,0.003093719482421875,9.334478 -20933,Binary classification,ALMA,SMTP,0.7832131084889887,0.003950834064969272,0.003093719482421875,11.212632000000001 -22836,Binary classification,ALMA,SMTP,0.7821422315641969,0.0036050470658922497,0.003093719482421875,13.266531 -24739,Binary classification,ALMA,SMTP,0.7877440478596548,0.0034162080091098878,0.003093719482421875,15.48839 -26642,Binary classification,ALMA,SMTP,0.78188574431349,0.003429943405933802,0.003093719482421875,17.885128 -28545,Binary classification,ALMA,SMTP,0.7857418111753371,0.003259452411994785,0.003093719482421875,20.449795 -30448,Binary classification,ALMA,SMTP,0.7871452968996322,0.0030764497769573914,0.003093719482421875,23.184561000000002 -32351,Binary classification,ALMA,SMTP,0.7866835646502427,0.0028897558156335793,0.003093719482421875,26.090132000000004 -34254,Binary classification,ALMA,SMTP,0.7860979739592456,0.0027221995372260785,0.003093719482421875,29.168944000000003 -36157,Binary classification,ALMA,SMTP,0.7771939043615345,0.0024764735017335313,0.003093719482421875,32.428274 -38060,Binary classification,ALMA,SMTP,0.7831581713084603,0.00241750271969056,0.003093719482421875,35.852824 -39963,Binary classification,ALMA,SMTP,0.779496033831294,0.002264492753623189,0.003093719482421875,39.452307 -41866,Binary classification,ALMA,SMTP,0.7831175655663307,0.0021978021978021974,0.003093719482421875,43.218835 -43769,Binary classification,ALMA,SMTP,0.7791130708949257,0.002064409578860446,0.003093719482421875,47.160889 -45672,Binary classification,ALMA,SMTP,0.7808066211245402,0.001993819160602133,0.003093719482421875,51.272051 -47575,Binary classification,ALMA,SMTP,0.7799684708355229,0.001906941266209001,0.003093719482421875,55.555831999999995 -49478,Binary classification,ALMA,SMTP,0.7778810784591131,0.00181653042688465,0.003093719482421875,60.013819999999996 -51381,Binary classification,ALMA,SMTP,0.7807944570950351,0.0021263400372109505,0.003093719482421875,64.63894599999999 -53284,Binary classification,ALMA,SMTP,0.7777193904361535,0.0020222446916076846,0.003093719482421875,69.43803199999999 -55187,Binary classification,ALMA,SMTP,0.7785891604906953,0.0019603038470963,0.003093719482421875,74.40731799999999 -57090,Binary classification,ALMA,SMTP,0.7758801891749869,0.002650245537454205,0.003093719482421875,79.551965 -58993,Binary classification,ALMA,SMTP,0.774159646059702,0.002545481769858501,0.003093719482421875,84.828086 -60896,Binary classification,ALMA,SMTP,0.7746157383079348,0.002471109819027546,0.003093719482421875,90.195459 -62799,Binary classification,ALMA,SMTP,0.7704899759550311,0.0023534297778085413,0.003093719482421875,95.659582 -64702,Binary classification,ALMA,SMTP,0.771274458285679,0.0022921863412660956,0.003093719482421875,101.21574 -66605,Binary classification,ALMA,SMTP,0.7721942797087306,0.002235812454790557,0.003093719482421875,106.861577 -68508,Binary classification,ALMA,SMTP,0.7705085537455479,0.0024111675126903555,0.003093719482421875,112.596175 -70411,Binary classification,ALMA,SMTP,0.7685872945988553,0.0023267205486162137,0.003093719482421875,118.42066 -72314,Binary classification,ALMA,SMTP,0.7687999557485411,0.002267709017127171,0.003093719482421875,124.335619 -74217,Binary classification,ALMA,SMTP,0.7657140547313958,0.0021806496040399402,0.003093719482421875,130.34374499999998 -76120,Binary classification,ALMA,SMTP,0.7665002627430373,0.0021333932180552435,0.003093719482421875,136.44014199999998 -78023,Binary classification,ALMA,SMTP,0.7657101111210797,0.002074462277541216,0.003093719482421875,142.623664 -79926,Binary classification,ALMA,SMTP,0.7636313590070816,0.002007395668251453,0.003093719482421875,148.896051 -81829,Binary classification,ALMA,SMTP,0.7647777682728617,0.001970341180130665,0.003093719482421875,155.253168 -83732,Binary classification,ALMA,SMTP,0.7652868676252806,0.0019298156518206286,0.003093719482421875,161.69571599999998 -85635,Binary classification,ALMA,SMTP,0.7642552694575816,0.0018787699001285476,0.003093719482421875,168.224788 -87538,Binary classification,ALMA,SMTP,0.7644680024674998,0.0018396591789310612,0.003093719482421875,174.840187 -89441,Binary classification,ALMA,SMTP,0.7635312664214399,0.0018876828692779614,0.003093719482421875,181.54098299999998 -91344,Binary classification,ALMA,SMTP,0.7650091960063058,0.0018600325505696352,0.003093719482421875,188.32426499999997 -93247,Binary classification,ALMA,SMTP,0.7647859984771628,0.0018204159650480134,0.003093719482421875,195.19191599999996 -95150,Binary classification,ALMA,SMTP,0.7649710982658959,0.0017854751595768425,0.003093719482421875,202.14365499999997 -106,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.4731182795698925,0.0054683685302734375,0.075077 -212,Binary classification,sklearn SGDClassifier,Bananas,0.5424528301886793,0.46994535519125685,0.0054683685302734375,0.21882000000000001 -318,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.4878048780487805,0.0054950714111328125,0.428712 -424,Binary classification,sklearn SGDClassifier,Bananas,0.5212264150943396,0.4671916010498687,0.0054950714111328125,0.704487 -530,Binary classification,sklearn SGDClassifier,Bananas,0.5283018867924528,0.42660550458715596,0.0054950714111328125,1.047006 -636,Binary classification,sklearn SGDClassifier,Bananas,0.5251572327044025,0.38866396761133604,0.0054950714111328125,1.456112 -742,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.36363636363636365,0.0054950714111328125,1.9325 -848,Binary classification,sklearn SGDClassifier,Bananas,0.5412735849056604,0.33955857385398974,0.0054950714111328125,2.475946 -954,Binary classification,sklearn SGDClassifier,Bananas,0.5450733752620545,0.31545741324921134,0.0054950714111328125,3.086975 -1060,Binary classification,sklearn SGDClassifier,Bananas,0.5528301886792453,0.29673590504451036,0.0054950714111328125,3.7645649999999997 -1166,Binary classification,sklearn SGDClassifier,Bananas,0.5531732418524872,0.2793914246196404,0.0054950714111328125,4.50753 -1272,Binary classification,sklearn SGDClassifier,Bananas,0.5550314465408805,0.27621483375959077,0.0054950714111328125,5.315441 -1378,Binary classification,sklearn SGDClassifier,Bananas,0.5573294629898403,0.26150121065375304,0.0054950714111328125,6.1886849999999995 -1484,Binary classification,sklearn SGDClassifier,Bananas,0.5579514824797843,0.2477064220183486,0.0054950714111328125,7.126671999999999 -1590,Binary classification,sklearn SGDClassifier,Bananas,0.5584905660377358,0.23529411764705882,0.0054950714111328125,8.129404999999998 -1696,Binary classification,sklearn SGDClassifier,Bananas,0.5601415094339622,0.22614107883817425,0.0054950714111328125,9.196330999999999 -1802,Binary classification,sklearn SGDClassifier,Bananas,0.5571587125416204,0.21611001964636542,0.0054950714111328125,10.327506 -1908,Binary classification,sklearn SGDClassifier,Bananas,0.5550314465408805,0.21169916434540387,0.0054950714111328125,11.523138 -2014,Binary classification,sklearn SGDClassifier,Bananas,0.5501489572989077,0.20105820105820105,0.0054950714111328125,12.782968 -2120,Binary classification,sklearn SGDClassifier,Bananas,0.5471698113207547,0.19463087248322145,0.0054950714111328125,14.106907 -2226,Binary classification,sklearn SGDClassifier,Bananas,0.550763701707098,0.2038216560509554,0.0054950714111328125,15.494886 -2332,Binary classification,sklearn SGDClassifier,Bananas,0.5497427101200686,0.21052631578947367,0.0054950714111328125,16.946832999999998 -2438,Binary classification,sklearn SGDClassifier,Bananas,0.5484003281378179,0.21188260558339295,0.0054950714111328125,18.462691 -2544,Binary classification,sklearn SGDClassifier,Bananas,0.5487421383647799,0.22641509433962262,0.0054950714111328125,20.042348 -2650,Binary classification,sklearn SGDClassifier,Bananas,0.5464150943396227,0.23243933588761176,0.0054950714111328125,21.68579 -2756,Binary classification,sklearn SGDClassifier,Bananas,0.5399129172714079,0.23058252427184467,0.0054950714111328125,23.392549000000002 -2862,Binary classification,sklearn SGDClassifier,Bananas,0.539832285115304,0.23117338003502627,0.0054950714111328125,25.163525000000003 -2968,Binary classification,sklearn SGDClassifier,Bananas,0.5414420485175202,0.23063877897117013,0.0054950714111328125,26.998099000000003 -3074,Binary classification,sklearn SGDClassifier,Bananas,0.5400130123617437,0.22732240437158474,0.0054950714111328125,28.897093000000005 -3180,Binary classification,sklearn SGDClassifier,Bananas,0.5433962264150943,0.22848034006376194,0.0054950714111328125,30.860975000000003 -3286,Binary classification,sklearn SGDClassifier,Bananas,0.5447352404138771,0.22487046632124352,0.0054950714111328125,32.894016 -3392,Binary classification,sklearn SGDClassifier,Bananas,0.5448113207547169,0.2248995983935743,0.0054950714111328125,34.991593 -3498,Binary classification,sklearn SGDClassifier,Bananas,0.5465980560320183,0.22025565388397245,0.0054950714111328125,37.153313000000004 -3604,Binary classification,sklearn SGDClassifier,Bananas,0.5491120976692564,0.21686746987951808,0.0054950714111328125,39.379211000000005 -3710,Binary classification,sklearn SGDClassifier,Bananas,0.5482479784366577,0.212406015037594,0.0054950714111328125,41.66962600000001 -3816,Binary classification,sklearn SGDClassifier,Bananas,0.5476939203354297,0.20752984389348023,0.0054950714111328125,44.024291000000005 -3922,Binary classification,sklearn SGDClassifier,Bananas,0.5486996430392657,0.20342034203420342,0.0054950714111328125,46.443344 -4028,Binary classification,sklearn SGDClassifier,Bananas,0.5491559086395233,0.19929453262786598,0.0054950714111328125,48.926930000000006 -4134,Binary classification,sklearn SGDClassifier,Bananas,0.5493468795355588,0.19524838012958964,0.0054950714111328125,51.474790000000006 -4240,Binary classification,sklearn SGDClassifier,Bananas,0.5485849056603773,0.19103972950126796,0.0054950714111328125,54.086862 -4346,Binary classification,sklearn SGDClassifier,Bananas,0.5487804878048781,0.18866363260239966,0.0054950714111328125,56.763353 -4452,Binary classification,sklearn SGDClassifier,Bananas,0.5509883198562444,0.1936264622831787,0.0054950714111328125,59.503949000000006 -4558,Binary classification,sklearn SGDClassifier,Bananas,0.5484861781483107,0.19357366771159876,0.0054950714111328125,62.30891200000001 -4664,Binary classification,sklearn SGDClassifier,Bananas,0.5493138936535163,0.19770992366412216,0.0054950714111328125,65.176854 -4770,Binary classification,sklearn SGDClassifier,Bananas,0.550314465408805,0.1999254009697874,0.0054950714111328125,68.08610900000001 -4876,Binary classification,sklearn SGDClassifier,Bananas,0.550656275635767,0.20007301935012778,0.0054950714111328125,71.03653600000001 -4982,Binary classification,sklearn SGDClassifier,Bananas,0.5505820955439582,0.20630981921304498,0.0054950714111328125,74.02805500000001 -5088,Binary classification,sklearn SGDClassifier,Bananas,0.5481525157232704,0.20422291450328833,0.0054950714111328125,77.060803 -5194,Binary classification,sklearn SGDClassifier,Bananas,0.5463996919522526,0.20243737305348675,0.0054950714111328125,80.134657 -5300,Binary classification,sklearn SGDClassifier,Bananas,0.5466037735849056,0.20509427720807144,0.0054950714111328125,83.249657 -906,Binary classification,sklearn SGDClassifier,Elec2,0.7991169977924945,0.7853773584905659,0.006672859191894531,0.581912 -1812,Binary classification,sklearn SGDClassifier,Elec2,0.8134657836644592,0.7492581602373888,0.006672859191894531,1.751174 -2718,Binary classification,sklearn SGDClassifier,Elec2,0.8002207505518764,0.7256189994946943,0.006672859191894531,3.503934 -3624,Binary classification,sklearn SGDClassifier,Elec2,0.8187086092715232,0.7581891792418107,0.006672859191894531,5.845535 -4530,Binary classification,sklearn SGDClassifier,Elec2,0.8275938189845474,0.7584287039901021,0.006672859191894531,8.772576 -5436,Binary classification,sklearn SGDClassifier,Elec2,0.8210080941869021,0.7495495495495494,0.006672859191894531,12.203207 -6342,Binary classification,sklearn SGDClassifier,Elec2,0.8221381267738883,0.7573149741824441,0.006672859191894531,16.003328 -7248,Binary classification,sklearn SGDClassifier,Elec2,0.8251931567328918,0.7596281540504647,0.006672859191894531,20.176524 -8154,Binary classification,sklearn SGDClassifier,Elec2,0.8302673534461614,0.7805960684844642,0.006672859191894531,24.718011 -9060,Binary classification,sklearn SGDClassifier,Elec2,0.8363134657836645,0.7957018873123021,0.006672859191894531,29.627709 -9966,Binary classification,sklearn SGDClassifier,Elec2,0.8370459562512542,0.8009803921568629,0.006672859191894531,34.907269 -10872,Binary classification,sklearn SGDClassifier,Elec2,0.8392200147167035,0.8078276165347406,0.006672859191894531,40.555268999999996 -11778,Binary classification,sklearn SGDClassifier,Elec2,0.8422482594668025,0.8113705583756344,0.006672859191894531,46.57328199999999 -12684,Binary classification,sklearn SGDClassifier,Elec2,0.8409019236833807,0.8104096204434422,0.006672859191894531,52.95826299999999 -13590,Binary classification,sklearn SGDClassifier,Elec2,0.8427520235467255,0.8153779697624189,0.006672859191894531,59.70527599999999 -14496,Binary classification,sklearn SGDClassifier,Elec2,0.8438189845474614,0.8177427145387216,0.006672859191894531,66.808032 -15402,Binary classification,sklearn SGDClassifier,Elec2,0.845214907154915,0.8184310738766185,0.006672859191894531,74.254711 -16308,Binary classification,sklearn SGDClassifier,Elec2,0.8397105714986509,0.8107990735379271,0.006672859191894531,82.035365 -17214,Binary classification,sklearn SGDClassifier,Elec2,0.8384454513767864,0.8052930056710774,0.006672859191894531,90.148338 -18120,Binary classification,sklearn SGDClassifier,Elec2,0.840728476821192,0.8082392026578072,0.006672859191894531,98.57508899999999 -19026,Binary classification,sklearn SGDClassifier,Elec2,0.843950383685483,0.8100083189351762,0.006672859191894531,107.31510699999998 -19932,Binary classification,sklearn SGDClassifier,Elec2,0.8412101143889223,0.8075402858011553,0.006672859191894531,116.36858499999998 -20838,Binary classification,sklearn SGDClassifier,Elec2,0.8373164411171897,0.8027923211169286,0.006672859191894531,125.73531899999998 -21744,Binary classification,sklearn SGDClassifier,Elec2,0.8382082413539367,0.8007250481477285,0.006672859191894531,135.41517199999998 -22650,Binary classification,sklearn SGDClassifier,Elec2,0.8376158940397351,0.7981560750740864,0.006672859191894531,145.412659 -23556,Binary classification,sklearn SGDClassifier,Elec2,0.8337154015961963,0.7923888270525256,0.006672859191894531,155.725043 -24462,Binary classification,sklearn SGDClassifier,Elec2,0.8312893467418854,0.7886732551589942,0.006672859191894531,166.35011 -25368,Binary classification,sklearn SGDClassifier,Elec2,0.8278145695364238,0.7841897233201582,0.006672859191894531,177.288636 -26274,Binary classification,sklearn SGDClassifier,Elec2,0.8282332343761893,0.7844073950222137,0.006672859191894531,188.539768 -27180,Binary classification,sklearn SGDClassifier,Elec2,0.828513612950699,0.7853557448768134,0.006672859191894531,200.10320000000002 -28086,Binary classification,sklearn SGDClassifier,Elec2,0.8253222245958841,0.7808451710890736,0.006672859191894531,211.980006 -28992,Binary classification,sklearn SGDClassifier,Elec2,0.8246067880794702,0.7784410265347915,0.006672859191894531,224.170025 -29898,Binary classification,sklearn SGDClassifier,Elec2,0.822830958592548,0.7765638840848695,0.006672859191894531,236.67211600000002 -30804,Binary classification,sklearn SGDClassifier,Elec2,0.8227178288533956,0.775479998355466,0.006672859191894531,249.48686600000002 -31710,Binary classification,sklearn SGDClassifier,Elec2,0.8179754020813623,0.768545994065282,0.006672859191894531,262.615336 -32616,Binary classification,sklearn SGDClassifier,Elec2,0.8155506499877361,0.7653116954045408,0.006672859191894531,276.05616200000003 -33522,Binary classification,sklearn SGDClassifier,Elec2,0.813614939442754,0.7635840774935674,0.006672859191894531,289.80868200000003 -34428,Binary classification,sklearn SGDClassifier,Elec2,0.8107935401417451,0.7596842027595366,0.006672859191894531,303.873891 -35334,Binary classification,sklearn SGDClassifier,Elec2,0.8109752646176487,0.758173720989174,0.006672859191894531,318.256576 -36240,Binary classification,sklearn SGDClassifier,Elec2,0.8122792494481236,0.7584590804189597,0.006672859191894531,332.952142 -37146,Binary classification,sklearn SGDClassifier,Elec2,0.8118774565229095,0.7566852367688023,0.006672859191894531,347.96241499999996 -38052,Binary classification,sklearn SGDClassifier,Elec2,0.811783874697782,0.7561623314721503,0.006672859191894531,363.28641 -38958,Binary classification,sklearn SGDClassifier,Elec2,0.8127983982750655,0.7582617919055984,0.006672859191894531,378.92485999999997 -39864,Binary classification,sklearn SGDClassifier,Elec2,0.8135661248244029,0.7615350060963869,0.006672859191894531,394.87776699999995 -40770,Binary classification,sklearn SGDClassifier,Elec2,0.8153544272749571,0.7661821344266367,0.006672859191894531,411.1448869999999 -41676,Binary classification,sklearn SGDClassifier,Elec2,0.8169450043190325,0.7701762313601446,0.006672859191894531,427.72309199999995 -42582,Binary classification,sklearn SGDClassifier,Elec2,0.8179747311070406,0.7719824669784955,0.006672859191894531,444.61337399999996 -43488,Binary classification,sklearn SGDClassifier,Elec2,0.8185476453274466,0.7730057820096079,0.006672859191894531,461.81686899999994 -44394,Binary classification,sklearn SGDClassifier,Elec2,0.8179258458350227,0.77092248830948,0.006672859191894531,479.33440799999994 -45300,Binary classification,sklearn SGDClassifier,Elec2,0.8190507726269316,0.7728544905367584,0.006672859191894531,497.16619699999995 -25,Binary classification,sklearn SGDClassifier,Phishing,0.64,0.6666666666666666,0.0066585540771484375,0.023761 -50,Binary classification,sklearn SGDClassifier,Phishing,0.78,0.7659574468085107,0.0066585540771484375,0.064622 -75,Binary classification,sklearn SGDClassifier,Phishing,0.8133333333333334,0.8108108108108109,0.0066585540771484375,0.122385 -100,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8125,0.0066585540771484375,0.197128 -125,Binary classification,sklearn SGDClassifier,Phishing,0.808,0.8,0.0066585540771484375,0.288843 -150,Binary classification,sklearn SGDClassifier,Phishing,0.8133333333333334,0.8133333333333335,0.0066585540771484375,0.397551 -175,Binary classification,sklearn SGDClassifier,Phishing,0.8228571428571428,0.812121212121212,0.0066585540771484375,0.523492 -200,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8085106382978724,0.0066585540771484375,0.6664519999999999 -225,Binary classification,sklearn SGDClassifier,Phishing,0.8177777777777778,0.8019323671497586,0.0066585540771484375,0.8264199999999999 -250,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8068669527896996,0.0066585540771484375,1.0034779999999999 -275,Binary classification,sklearn SGDClassifier,Phishing,0.8218181818181818,0.8078431372549019,0.0068721771240234375,1.197523 -300,Binary classification,sklearn SGDClassifier,Phishing,0.8333333333333334,0.8161764705882353,0.0068721771240234375,1.4085949999999998 -325,Binary classification,sklearn SGDClassifier,Phishing,0.8430769230769231,0.8222996515679442,0.0068721771240234375,1.6370319999999998 -350,Binary classification,sklearn SGDClassifier,Phishing,0.8485714285714285,0.8262295081967213,0.0068721771240234375,1.8824899999999998 -375,Binary classification,sklearn SGDClassifier,Phishing,0.848,0.8246153846153846,0.0068721771240234375,2.144992 -400,Binary classification,sklearn SGDClassifier,Phishing,0.85,0.8245614035087719,0.0068721771240234375,2.424587 -425,Binary classification,sklearn SGDClassifier,Phishing,0.8541176470588235,0.8258426966292134,0.0068721771240234375,2.721266 -450,Binary classification,sklearn SGDClassifier,Phishing,0.8577777777777778,0.8279569892473118,0.0068721771240234375,3.035051 -475,Binary classification,sklearn SGDClassifier,Phishing,0.8568421052631578,0.8282828282828283,0.0068721771240234375,3.366223 -500,Binary classification,sklearn SGDClassifier,Phishing,0.856,0.8309859154929577,0.0068721771240234375,3.714529 -525,Binary classification,sklearn SGDClassifier,Phishing,0.8552380952380952,0.8264840182648402,0.0068721771240234375,4.0798760000000005 -550,Binary classification,sklearn SGDClassifier,Phishing,0.86,0.8336933045356371,0.0068721771240234375,4.4622850000000005 -575,Binary classification,sklearn SGDClassifier,Phishing,0.8608695652173913,0.8347107438016529,0.0068721771240234375,4.8618310000000005 -600,Binary classification,sklearn SGDClassifier,Phishing,0.865,0.8370221327967807,0.0068721771240234375,5.278527 -625,Binary classification,sklearn SGDClassifier,Phishing,0.8656,0.8346456692913387,0.0068721771240234375,5.712664 -650,Binary classification,sklearn SGDClassifier,Phishing,0.8692307692307693,0.8417132216014899,0.0068721771240234375,6.163824 -675,Binary classification,sklearn SGDClassifier,Phishing,0.8711111111111111,0.8471001757469244,0.0068721771240234375,6.632034 -700,Binary classification,sklearn SGDClassifier,Phishing,0.8757142857142857,0.8507718696397941,0.0068721771240234375,7.117376 -725,Binary classification,sklearn SGDClassifier,Phishing,0.8772413793103448,0.8552845528455284,0.0068721771240234375,7.619913 -750,Binary classification,sklearn SGDClassifier,Phishing,0.8786666666666667,0.8575899843505477,0.0068721771240234375,8.139518 -775,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8584474885844748,0.0068721771240234375,8.676567 -800,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8600583090379008,0.0068721771240234375,9.230727 -825,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8611500701262274,0.0068721771240234375,9.801975 -850,Binary classification,sklearn SGDClassifier,Phishing,0.8811764705882353,0.8618331053351573,0.0068721771240234375,10.390364 -875,Binary classification,sklearn SGDClassifier,Phishing,0.8845714285714286,0.8651535380507342,0.0068721771240234375,10.99587 -900,Binary classification,sklearn SGDClassifier,Phishing,0.8833333333333333,0.8634590377113134,0.0068721771240234375,11.618539 -925,Binary classification,sklearn SGDClassifier,Phishing,0.8854054054054054,0.8671679197994987,0.0068721771240234375,12.258516 -950,Binary classification,sklearn SGDClassifier,Phishing,0.8852631578947369,0.8685162846803377,0.0068721771240234375,12.91558 -975,Binary classification,sklearn SGDClassifier,Phishing,0.8861538461538462,0.8692579505300353,0.0068721771240234375,13.589863000000001 -1000,Binary classification,sklearn SGDClassifier,Phishing,0.887,0.8702640642939151,0.0068721771240234375,14.281225000000001 -1025,Binary classification,sklearn SGDClassifier,Phishing,0.8868292682926829,0.8705357142857143,0.0068721771240234375,14.989779 -1050,Binary classification,sklearn SGDClassifier,Phishing,0.8885714285714286,0.8729641693811075,0.0068721771240234375,15.714934 -1075,Binary classification,sklearn SGDClassifier,Phishing,0.8874418604651163,0.8727655099894849,0.0068721771240234375,16.456921 -1100,Binary classification,sklearn SGDClassifier,Phishing,0.889090909090909,0.8747433264887063,0.0068721771240234375,17.215539 -1125,Binary classification,sklearn SGDClassifier,Phishing,0.8906666666666667,0.8776119402985074,0.0068721771240234375,17.990769 -1150,Binary classification,sklearn SGDClassifier,Phishing,0.8904347826086957,0.8771929824561404,0.0068721771240234375,18.782632 -1175,Binary classification,sklearn SGDClassifier,Phishing,0.8893617021276595,0.875717017208413,0.0068721771240234375,19.591204 -1200,Binary classification,sklearn SGDClassifier,Phishing,0.89,0.8761726078799249,0.0068721771240234375,20.416515 -1225,Binary classification,sklearn SGDClassifier,Phishing,0.8906122448979592,0.8768382352941176,0.0068721771240234375,21.258769 -1250,Binary classification,sklearn SGDClassifier,Phishing,0.8888,0.8753363228699551,0.0068721771240234375,22.117832 -1903,Binary classification,sklearn SGDClassifier,SMTP,0.998949027850762,0.0,0.005644798278808594,1.261861 -3806,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.0,0.005644798278808594,3.784872 -5709,Binary classification,sklearn SGDClassifier,SMTP,0.999649675950254,0.0,0.005644798278808594,7.562933 -7612,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.0,0.005644798278808594,12.592241000000001 -9515,Binary classification,sklearn SGDClassifier,SMTP,0.9997898055701524,0.0,0.005644798278808594,18.508988000000002 -11418,Binary classification,sklearn SGDClassifier,SMTP,0.999824837975127,0.0,0.005644798278808594,25.182225000000003 -13321,Binary classification,sklearn SGDClassifier,SMTP,0.9998498611215374,0.0,0.005644798278808594,32.571041 -15224,Binary classification,sklearn SGDClassifier,SMTP,0.9995401996847083,0.631578947368421,0.005644798278808594,40.604957 -17127,Binary classification,sklearn SGDClassifier,SMTP,0.9995912886086297,0.6956521739130435,0.005644798278808594,49.282818 -19030,Binary classification,sklearn SGDClassifier,SMTP,0.9996321597477666,0.6956521739130435,0.005644798278808594,58.610725 -20933,Binary classification,sklearn SGDClassifier,SMTP,0.999665599770697,0.6956521739130435,0.005644798278808594,68.59192900000001 -22836,Binary classification,sklearn SGDClassifier,SMTP,0.9996934664564723,0.6956521739130435,0.005644798278808594,79.219372 -24739,Binary classification,sklearn SGDClassifier,SMTP,0.9997170459598205,0.6956521739130435,0.005644798278808594,90.49290300000001 -26642,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.6666666666666666,0.005644798278808594,102.41066000000001 -28545,Binary classification,sklearn SGDClassifier,SMTP,0.9997197407602032,0.6666666666666666,0.005644798278808594,114.97610900000001 -30448,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.6666666666666666,0.005644798278808594,128.185954 -32351,Binary classification,sklearn SGDClassifier,SMTP,0.9997527124354734,0.6666666666666666,0.005644798278808594,142.03946100000002 -34254,Binary classification,sklearn SGDClassifier,SMTP,0.9997664506335027,0.6666666666666666,0.005644798278808594,156.53684400000003 -36157,Binary classification,sklearn SGDClassifier,SMTP,0.9997787427054236,0.6666666666666666,0.005644798278808594,171.68202700000003 -38060,Binary classification,sklearn SGDClassifier,SMTP,0.9997898055701524,0.6666666666666666,0.005644798278808594,187.47483000000003 -39963,Binary classification,sklearn SGDClassifier,SMTP,0.9997998148287166,0.6666666666666666,0.005644798278808594,203.91174800000002 -41866,Binary classification,sklearn SGDClassifier,SMTP,0.999808914154684,0.6666666666666666,0.005644798278808594,220.99296 -43769,Binary classification,sklearn SGDClassifier,SMTP,0.9998172222349151,0.6666666666666666,0.005644798278808594,238.718792 -45672,Binary classification,sklearn SGDClassifier,SMTP,0.999824837975127,0.6666666666666666,0.005644798278808594,257.088535 -47575,Binary classification,sklearn SGDClassifier,SMTP,0.9998318444561219,0.6666666666666666,0.005644798278808594,276.10186899999997 -49478,Binary classification,sklearn SGDClassifier,SMTP,0.9998383119770403,0.6666666666666666,0.005644798278808594,295.75838 -51381,Binary classification,sklearn SGDClassifier,SMTP,0.9998053755279189,0.6153846153846154,0.005644798278808594,316.06348299999996 -53284,Binary classification,sklearn SGDClassifier,SMTP,0.9998123264019217,0.6153846153846154,0.005644798278808594,337.01301299999994 -55187,Binary classification,sklearn SGDClassifier,SMTP,0.9998187979053038,0.6153846153846154,0.005644798278808594,358.6068609999999 -57090,Binary classification,sklearn SGDClassifier,SMTP,0.9996671921527412,0.45714285714285713,0.005644798278808594,380.84445099999994 -58993,Binary classification,sklearn SGDClassifier,SMTP,0.9996779278897496,0.45714285714285713,0.005644798278808594,403.72630799999996 -60896,Binary classification,sklearn SGDClassifier,SMTP,0.999687992643195,0.45714285714285713,0.005644798278808594,427.25217999999995 -62799,Binary classification,sklearn SGDClassifier,SMTP,0.999665599770697,0.43243243243243246,0.005644798278808594,451.4221749999999 -64702,Binary classification,sklearn SGDClassifier,SMTP,0.9996754350715589,0.43243243243243246,0.005644798278808594,476.23565699999995 -66605,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.43243243243243246,0.005644798278808594,501.6931359999999 -68508,Binary classification,sklearn SGDClassifier,SMTP,0.9996642727856601,0.4102564102564103,0.005644798278808594,527.7941669999999 -70411,Binary classification,sklearn SGDClassifier,SMTP,0.9996733464941557,0.4102564102564103,0.005644798278808594,554.5388179999999 -72314,Binary classification,sklearn SGDClassifier,SMTP,0.9996819426390464,0.4102564102564103,0.005644798278808594,581.9269989999999 -74217,Binary classification,sklearn SGDClassifier,SMTP,0.999690097955994,0.4102564102564103,0.005644798278808594,609.9586109999999 -76120,Binary classification,sklearn SGDClassifier,SMTP,0.9996978455070941,0.4102564102564103,0.005644798278808594,638.633216 -78023,Binary classification,sklearn SGDClassifier,SMTP,0.9997052151288722,0.4102564102564103,0.005644798278808594,667.951428 -79926,Binary classification,sklearn SGDClassifier,SMTP,0.99971223381628,0.4102564102564103,0.005644798278808594,697.9132639999999 -81829,Binary classification,sklearn SGDClassifier,SMTP,0.9997189260531107,0.4102564102564103,0.005644798278808594,728.5228309999999 -83732,Binary classification,sklearn SGDClassifier,SMTP,0.9997253140973582,0.4102564102564103,0.005644798278808594,759.7753099999999 -85635,Binary classification,sklearn SGDClassifier,SMTP,0.9997314182285281,0.4102564102564103,0.005644798278808594,791.6709739999999 -87538,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.4102564102564103,0.005644798278808594,824.2098649999999 -89441,Binary classification,sklearn SGDClassifier,SMTP,0.9997316666853009,0.4,0.005644798278808594,857.3917109999999 -91344,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.4,0.005644798278808594,891.2164919999999 -93247,Binary classification,sklearn SGDClassifier,SMTP,0.9997426190654928,0.4,0.005644798278808594,925.6838509999999 -95150,Binary classification,sklearn SGDClassifier,SMTP,0.9997477666841829,0.4,0.005644798278808594,960.7942089999999 -106,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5,0.0,0.0006465911865234375,0.01481 -212,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5283018867924528,0.0,0.0006465911865234375,0.041137 -318,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5314465408805031,0.0,0.0006465911865234375,0.078177 -424,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5400943396226415,0.0,0.0006465911865234375,0.125909 -530,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5547169811320755,0.0,0.0006465911865234375,0.184447 -636,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5550314465408805,0.0,0.0006465911865234375,0.253805 -742,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5660377358490566,0.0,0.0006465911865234375,0.333961 -848,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5636792452830188,0.0,0.0006465911865234375,0.425093 -954,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5649895178197065,0.0,0.0006465911865234375,0.527744 -1060,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5707547169811321,0.0,0.0006465911865234375,0.641554 -1166,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5686106346483705,0.0,0.0006465911865234375,0.7661 -1272,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5644654088050315,0.0,0.0006465911865234375,0.90156 -1378,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5682148040638607,0.0,0.0006465911865234375,1.047827 -1484,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5680592991913747,0.0,0.0006465911865234375,1.2046620000000001 -1590,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5679245283018868,0.0,0.0006465911865234375,1.3725140000000002 -1696,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5683962264150944,0.0,0.0006465911865234375,1.5511630000000003 -1802,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5643729189789123,0.0,0.0006465911865234375,1.7405230000000003 -1908,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.560272536687631,0.0,0.0006465911865234375,1.9407960000000002 -2014,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5551142005958292,0.0,0.0006465911865234375,2.1520810000000004 -2120,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,2.3743700000000003 -2226,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5512129380053908,0.0,0.0006465911865234375,2.6075880000000002 -2332,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5506003430531733,0.0,0.0006465911865234375,2.8515750000000004 -2438,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.551681706316653,0.0,0.0006465911865234375,3.1063150000000004 -2544,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487421383647799,0.0,0.0006465911865234375,3.3719430000000004 -2650,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5467924528301886,0.0,0.0006465911865234375,3.648299 -2756,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5471698113207547,0.0,0.0006465911865234375,3.9354750000000003 -2862,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5489168413696716,0.0,0.0006465911865234375,4.233696 -2968,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505390835579514,0.0,0.0006465911865234375,4.542693 -3074,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487963565387117,0.0,0.0006465911865234375,4.86295 -3180,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,5.194204 -3286,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5517346317711503,0.0,0.0006465911865234375,5.536035 -3392,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5498231132075472,0.0,0.0006465911865234375,5.888475 -3498,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514579759862779,0.0,0.0006465911865234375,6.251513999999999 -3604,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5535516093229744,0.0,0.0006465911865234375,6.625144999999999 -3710,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5522911051212938,0.0,0.0006465911865234375,7.009503999999999 -3816,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5516247379454927,0.0,0.0006465911865234375,7.4043329999999985 -3922,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5525242223355431,0.0,0.0006465911865234375,7.8097699999999985 -4028,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5528798411122146,0.0,0.0006465911865234375,8.225969999999998 -4134,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5529753265602322,0.0,0.0006465911865234375,8.653192999999998 -4240,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5523584905660377,0.0,0.0006465911865234375,9.090957999999999 -4346,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5526921306948919,0.0,0.0006465911865234375,9.539357999999998 -4452,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5530098831985625,0.0,0.0006465911865234375,9.998287999999999 -4558,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5508995173321632,0.0,0.0006465911865234375,10.467735 -4664,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5497427101200686,0.0,0.0006465911865234375,10.947871999999998 -4770,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505241090146751,0.0,0.0006465911865234375,11.438429999999999 -4876,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5518867924528302,0.0,0.0006465911865234375,11.939414999999999 -4982,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509835407466881,0.0,0.0006465911865234375,12.450955999999998 -5088,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5511006289308176,0.0,0.0006465911865234375,12.972935999999997 -5194,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514054678475163,0.0,0.0006465911865234375,13.505583999999997 -5300,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5513207547169812,0.0,0.0006465911865234375,14.048808999999997 -906,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6799116997792495,0.5482866043613708,0.0006465911865234375,0.144107 -1812,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7190949227373068,0.4904904904904904,0.0006465911865234375,0.426551 -2718,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.43243243243243246,0.0006465911865234375,0.845137 -3624,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7047461368653422,0.4478844169246646,0.0006465911865234375,1.400054 -4530,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7024282560706402,0.4118673647469459,0.0006465911865234375,2.0918919999999996 -5436,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7041942604856513,0.4165457184325108,0.0006465911865234375,2.9195709999999995 -6342,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.40485829959514175,0.0006465911865234375,3.8823039999999995 -7248,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695364238410596,0.3953997809419496,0.0006465911865234375,4.980137999999999 -8154,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6873926907039489,0.4084474355999072,0.0006465911865234375,6.213938999999999 -9060,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6864238410596026,0.42408270829110073,0.0006465911865234375,7.583110999999999 -9966,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.687537627934979,0.4433321415802646,0.0006465911865234375,9.088204999999999 -10872,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6938925680647535,0.4717460317460317,0.0006465911865234375,10.727540999999999 -11778,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6932416369502462,0.47155185022670765,0.0006465911865234375,12.500958999999998 -12684,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944970040996531,0.47557179591284343,0.0006465911865234375,14.408948999999998 -13590,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6942604856512141,0.48429936701005344,0.0006465911865234375,16.456863 -14496,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6935016556291391,0.48606130711393875,0.0006465911865234375,18.638716 -15402,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6929619529931178,0.48095708484249805,0.0006465911865234375,20.95394 -16308,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6904586705911209,0.47130289065772935,0.0006465911865234375,23.402753999999998 -17214,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6921691646334379,0.4645852278468223,0.0006465911865234375,25.9851 -18120,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.694205298013245,0.46859115757168884,0.0006465911865234375,28.701738 -19026,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6967307894460212,0.467515688445921,0.0006465911865234375,31.552559 -19932,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6958157736303432,0.4737435986459509,0.0006465911865234375,34.538332 -20838,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.4696604963891426,0.0006465911865234375,37.65893 -21744,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6968359087564385,0.4670978172999191,0.0006465911865234375,40.913762 -22650,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6977041942604857,0.4643667370726746,0.0006465911865234375,44.302397 -23556,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952368823229751,0.4573285962657797,0.0006465911865234375,47.824759 -24462,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6978170223203336,0.4597281099254495,0.0006465911865234375,51.480483 -25368,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6976505834121728,0.46122506322000567,0.0006465911865234375,55.26987 -26274,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6983329527289336,0.4614757439869547,0.0006465911865234375,59.192657 -27180,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6959896983075791,0.4576304561864129,0.0006465911865234375,63.254197999999995 -28086,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695649077832372,0.4559572301425662,0.0006465911865234375,67.449702 -28992,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952262693156733,0.4515207945375543,0.0006465911865234375,71.778902 -29898,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6939260151180681,0.4465678863017841,0.0006465911865234375,76.241703 -30804,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6941630957018569,0.44330201500915917,0.0006465911865234375,80.838056 -31710,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6917376222011984,0.4368266405484819,0.0006465911865234375,85.568011 -32616,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6893549178317391,0.4316805025802109,0.0006465911865234375,90.431842 -33522,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.688353916830738,0.42909448603748845,0.0006465911865234375,95.42948200000001 -34428,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6863599395840595,0.4245363461948412,0.0006465911865234375,100.561249 -35334,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6869304352748061,0.4212013394725827,0.0006465911865234375,105.826946 -36240,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6911147902869758,0.4267718148299877,0.0006465911865234375,111.170297 -37146,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6919722177354224,0.42698317307692313,0.0006465911865234375,116.582555 -38052,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944181646168401,0.43117111828588206,0.0006465911865234375,122.063741 -38958,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6937727809435803,0.43082061068702293,0.0006465911865234375,127.61367 -39864,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6930814770218744,0.4344288818009522,0.0006465911865234375,133.232984 -40770,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6924208977189109,0.4391771019677997,0.0006465911865234375,138.92121699999998 -41676,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.44722270288977334,0.0006465911865234375,144.67862699999998 -42582,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6956225635244939,0.45507672903090185,0.0006465911865234375,150.505407 -43488,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6962150478292862,0.4576097220511558,0.0006465911865234375,156.401784 -44394,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6963103122043519,0.45575649927337314,0.0006465911865234375,162.367508 -45300,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.697439293598234,0.4596278189560006,0.0006465911865234375,168.40291499999998 -25,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.52,0.33333333333333337,0.0006465911865234375,0.006954 -50,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.56,0.21428571428571427,0.0006465911865234375,0.018084 -75,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.5866666666666667,0.3404255319148936,0.0006465911865234375,0.032476 -100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6,0.375,0.0006465911865234375,0.049891 -125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.64,0.4705882352941176,0.0006465911865234375,0.070336 -150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.62,0.44660194174757284,0.0006465911865234375,0.09389399999999999 -175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6342857142857142,0.41818181818181815,0.0006465911865234375,0.120402 -200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.63,0.4126984126984127,0.0006465911865234375,0.149914 -225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6488888888888888,0.4316546762589928,0.0006465911865234375,0.18237399999999998 -250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.648,0.4358974358974359,0.0006465911865234375,0.218053 -275,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6618181818181819,0.4561403508771929,0.0006465911865234375,0.257041 -300,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6733333333333333,0.46153846153846156,0.0006465911865234375,0.29910800000000004 -325,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.683076923076923,0.46632124352331616,0.0006465911865234375,0.34419000000000005 -350,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6942857142857143,0.47804878048780486,0.0006465911865234375,0.39236000000000004 -375,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7013333333333334,0.4909090909090909,0.0006465911865234375,0.44361000000000006 -400,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.705,0.4913793103448276,0.0006465911865234375,0.49797800000000003 -425,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7105882352941176,0.4896265560165975,0.0006465911865234375,0.555381 -450,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7222222222222222,0.5098039215686275,0.0006465911865234375,0.615936 -475,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7157894736842105,0.5054945054945055,0.0006465911865234375,0.679555 -500,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.718,0.5252525252525252,0.0006465911865234375,0.74642 -525,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7257142857142858,0.5294117647058824,0.0006465911865234375,0.816531 -550,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7218181818181818,0.5233644859813085,0.0006465911865234375,0.889759 -575,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7217391304347827,0.5209580838323353,0.0006465911865234375,0.9661489999999999 -600,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7283333333333334,0.5275362318840581,0.0006465911865234375,1.0457699999999999 -625,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7376,0.5340909090909091,0.0006465911865234375,1.1284889999999999 -650,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7369230769230769,0.5415549597855228,0.0006465911865234375,1.2144389999999998 -675,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7333333333333333,0.5477386934673367,0.0006465911865234375,1.3035119999999998 -700,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74,0.5560975609756097,0.0006465911865234375,1.3960879999999998 -725,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.743448275862069,0.5753424657534246,0.0006465911865234375,1.4918939999999998 -750,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7453333333333333,0.5820568927789934,0.0006465911865234375,1.5909119999999999 -775,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7470967741935484,0.5847457627118644,0.0006465911865234375,1.6930509999999999 -800,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74625,0.5915492957746479,0.0006465911865234375,1.7983589999999998 -825,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7490909090909091,0.602687140115163,0.0006465911865234375,1.9068189999999998 -850,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7541176470588236,0.6122448979591837,0.0006465911865234375,2.0184699999999998 -875,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7554285714285714,0.6123188405797102,0.0006465911865234375,2.13332 -900,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7566666666666667,0.6123893805309735,0.0006465911865234375,2.251249 -925,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.76,0.6237288135593221,0.0006465911865234375,2.372436 -950,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7589473684210526,0.6288492706645057,0.0006465911865234375,2.496743 -975,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7610256410256411,0.631911532385466,0.0006465911865234375,2.624394 -1000,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.761,0.6328725038402457,0.0006465911865234375,2.755204 -1025,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7609756097560976,0.635958395245171,0.0006465911865234375,2.889431 -1050,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7638095238095238,0.6436781609195402,0.0006465911865234375,3.0268070000000002 -1075,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7665116279069767,0.651872399445215,0.0006465911865234375,3.1673590000000003 -1100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.77,0.6594885598923284,0.0006465911865234375,3.311033 -1125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.768,0.6597131681877444,0.0006465911865234375,3.457802 -1150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7695652173913043,0.6615581098339719,0.0006465911865234375,3.607738 -1175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7702127659574468,0.6633416458852868,0.0006465911865234375,3.760779 -1200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7741666666666667,0.6691086691086692,0.0006465911865234375,3.917058 -1225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7771428571428571,0.6746126340882003,0.0006465911865234375,4.076379 -1250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7736,0.6697782963827306,0.0006465911865234375,4.239016 -1903,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,0.209651 -3806,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,0.63219 -5709,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,1.262199 -7612,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,2.0972720000000002 -9515,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,3.138863 -11418,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,4.386558 -13321,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,5.8399909999999995 -15224,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9992774566473989,0.0,0.0006465911865234375,7.498149999999999 -17127,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999299351900508,0.14285714285714288,0.0006465911865234375,9.361125999999999 -19030,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9993694167104572,0.14285714285714288,0.0006465911865234375,11.452409999999999 -20933,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9994267424640519,0.14285714285714288,0.0006465911865234375,13.765639999999998 -22836,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999474513925381,0.14285714285714288,0.0006465911865234375,16.300417999999997 -24739,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999514935931121,0.14285714285714288,0.0006465911865234375,19.056179999999998 -26642,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995120486449967,0.13333333333333333,0.0006465911865234375,22.032825999999996 -28545,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995445787353302,0.13333333333333333,0.0006465911865234375,25.228374999999996 -30448,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995730425643721,0.13333333333333333,0.0006465911865234375,28.637877999999997 -32351,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,32.260672 -34254,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996204822794418,0.13333333333333333,0.0006465911865234375,36.096896 -36157,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996404568963133,0.13333333333333333,0.0006465911865234375,40.148391000000004 -38060,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996584340514977,0.13333333333333333,0.0006465911865234375,44.38058100000001 -39963,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996746990966644,0.13333333333333333,0.0006465911865234375,48.731359000000005 -41866,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996894855013615,0.13333333333333333,0.0006465911865234375,53.205186000000005 -43769,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999702986131737,0.13333333333333333,0.0006465911865234375,57.777449000000004 -45672,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997153617095814,0.13333333333333333,0.0006465911865234375,62.448002 -47575,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997267472411981,0.13333333333333333,0.0006465911865234375,67.217049 -49478,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997372569626904,0.13333333333333333,0.0006465911865234375,72.084119 -51381,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997080632918783,0.11764705882352941,0.0006465911865234375,77.049615 -53284,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997184896028827,0.11764705882352941,0.0006465911865234375,82.113263 -55187,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997281968579557,0.11764705882352941,0.0006465911865234375,87.275593 -57090,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995796111403048,0.14285714285714285,0.0006465911865234375,92.536371 -58993,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995931720712626,0.14285714285714285,0.0006465911865234375,97.895723 -60896,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996058854440357,0.14285714285714285,0.0006465911865234375,103.35374 -62799,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999585980668482,0.13333333333333333,0.0006465911865234375,108.910478 -64702,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,114.565649 -66605,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996096389159973,0.13333333333333333,0.0006465911865234375,120.31946599999999 -68508,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995912886086297,0.125,0.0006465911865234375,126.17180599999999 -70411,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996023348624504,0.125,0.0006465911865234375,132.122918 -72314,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996127997344912,0.125,0.0006465911865234375,138.18042 -74217,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996227279464274,0.125,0.0006465911865234375,144.337343 -76120,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996321597477666,0.125,0.0006465911865234375,150.592744 -78023,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996411314612358,0.125,0.0006465911865234375,156.94675700000002 -79926,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999649675950254,0.125,0.0006465911865234375,163.39908200000002 -81829,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996578230211783,0.125,0.0006465911865234375,169.949778 -83732,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999665599770697,0.125,0.0006465911865234375,176.598846 -85635,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996730308869037,0.125,0.0006465911865234375,183.346496 -87538,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996801389111014,0.125,0.0006465911865234375,190.192536 -89441,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996757639114053,0.1212121212121212,0.0006465911865234375,197.137274 -91344,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996825188299177,0.1212121212121212,0.0006465911865234375,204.18062799999998 -93247,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996889980374704,0.1212121212121212,0.0006465911865234375,211.32290999999998 -95150,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999695218076721,0.1212121212121212,0.0006465911865234375,218.56361699999997 -106,Binary classification,Naive Bayes,Bananas,0.5333333333333333,0.46153846153846156,0.014024734497070312,0.027532 -212,Binary classification,Naive Bayes,Bananas,0.5592417061611374,0.5026737967914437,0.014024734497070312,0.072437 -318,Binary classification,Naive Bayes,Bananas,0.555205047318612,0.5154639175257733,0.014024734497070312,0.134216 -424,Binary classification,Naive Bayes,Bananas,0.5626477541371159,0.5066666666666667,0.014024734497070312,0.212262 -530,Binary classification,Naive Bayes,Bananas,0.5689981096408318,0.48181818181818187,0.014024734497070312,0.306946 -636,Binary classification,Naive Bayes,Bananas,0.5716535433070866,0.4645669291338582,0.014024734497070312,0.418348 -742,Binary classification,Naive Bayes,Bananas,0.5870445344129555,0.4555160142348755,0.014024734497070312,0.54645 -848,Binary classification,Naive Bayes,Bananas,0.5962219598583235,0.4554140127388535,0.014024734497070312,0.690987 -954,Binary classification,Naive Bayes,Bananas,0.6002098635886673,0.4454148471615721,0.014024734497070312,0.852195 -1060,Binary classification,Naive Bayes,Bananas,0.6090651558073654,0.44054054054054054,0.014024734497070312,1.029786 -1166,Binary classification,Naive Bayes,Bananas,0.6068669527896996,0.42606516290726815,0.014024734497070312,1.2235980000000002 -1272,Binary classification,Naive Bayes,Bananas,0.6136900078678206,0.433679354094579,0.014024734497070312,1.4337380000000002 -1378,Binary classification,Naive Bayes,Bananas,0.6143790849673203,0.419672131147541,0.014024734497070312,1.660063 -1484,Binary classification,Naive Bayes,Bananas,0.6142953472690492,0.4127310061601643,0.014024734497070312,1.902843 -1590,Binary classification,Naive Bayes,Bananas,0.6135934550031467,0.40618955512572535,0.014024734497070312,2.161936 -1696,Binary classification,Naive Bayes,Bananas,0.6141592920353982,0.4010989010989011,0.014024734497070312,2.4372089999999997 -1802,Binary classification,Naive Bayes,Bananas,0.614658523042754,0.40378006872852235,0.014024734497070312,2.7285959999999996 -1908,Binary classification,Naive Bayes,Bananas,0.6151022548505506,0.4080645161290322,0.014024734497070312,3.0363329999999995 -2014,Binary classification,Naive Bayes,Bananas,0.6100347739692003,0.40485216072782415,0.014024734497070312,3.3602269999999996 -2120,Binary classification,Naive Bayes,Bananas,0.608305804624823,0.4071428571428571,0.014024734497070312,3.7002569999999997 -2226,Binary classification,Naive Bayes,Bananas,0.6089887640449438,0.4089673913043478,0.014024734497070312,4.056792 -2332,Binary classification,Naive Bayes,Bananas,0.6096096096096096,0.4098573281452659,0.014024734497070312,4.429501999999999 -2438,Binary classification,Naive Bayes,Bananas,0.6101764464505539,0.40846824408468246,0.014024734497070312,4.818382999999999 -2544,Binary classification,Naive Bayes,Bananas,0.6114825009830909,0.41538461538461535,0.014024734497070312,5.223696999999999 -2650,Binary classification,Naive Bayes,Bananas,0.6100415251038127,0.41273450824332003,0.014024734497070312,5.6451509999999985 -2756,Binary classification,Naive Bayes,Bananas,0.6076225045372051,0.4070213933077345,0.014024734497070312,6.082884999999998 -2862,Binary classification,Naive Bayes,Bananas,0.6085284865431667,0.4092827004219409,0.014024734497070312,6.537074999999998 -2968,Binary classification,Naive Bayes,Bananas,0.6083586113919784,0.4065372829417773,0.014024734497070312,7.007774999999998 -3074,Binary classification,Naive Bayes,Bananas,0.60624796615685,0.40628066732090284,0.014024734497070312,7.494666999999998 -3180,Binary classification,Naive Bayes,Bananas,0.6071091538219566,0.4077761972498815,0.014024734497070312,7.997970999999998 -3286,Binary classification,Naive Bayes,Bananas,0.6063926940639269,0.4049700874367234,0.014024734497070312,8.517511999999998 -3392,Binary classification,Naive Bayes,Bananas,0.6048363314656443,0.40602836879432624,0.014024734497070312,9.053436999999999 -3498,Binary classification,Naive Bayes,Bananas,0.6065198741778668,0.40535868625756266,0.014024734497070312,9.605597 -3604,Binary classification,Naive Bayes,Bananas,0.6086594504579517,0.40905280804694044,0.014024734497070312,10.173998 -3710,Binary classification,Naive Bayes,Bananas,0.6085198166621731,0.4078303425774878,0.014024734497070312,10.759293 -3816,Binary classification,Naive Bayes,Bananas,0.6070773263433814,0.40492258832870187,0.014024734497070312,11.360927 -3922,Binary classification,Naive Bayes,Bananas,0.6067329762815609,0.4027885360185902,0.014024734497070312,11.978992 -4028,Binary classification,Naive Bayes,Bananas,0.6088899925502855,0.405436013590034,0.014024734497070312,12.613729 -4134,Binary classification,Naive Bayes,Bananas,0.6106944108395839,0.40780272359219727,0.014024734497070312,13.264737 -4240,Binary classification,Naive Bayes,Bananas,0.611936777541873,0.41186986056489094,0.014024734497070312,13.931975 -4346,Binary classification,Naive Bayes,Bananas,0.6131185270425776,0.4128536500174642,0.014024734497070312,14.615508 -4452,Binary classification,Naive Bayes,Bananas,0.6137946528869916,0.413510747185261,0.014024734497070312,15.315549 -4558,Binary classification,Naive Bayes,Bananas,0.6122448979591837,0.4115884115884116,0.014024734497070312,16.031772 -4664,Binary classification,Naive Bayes,Bananas,0.6126956894702981,0.41249186727391024,0.014024734497070312,16.764316 -4770,Binary classification,Naive Bayes,Bananas,0.6143845669951772,0.41302266198531756,0.014024734497070312,17.512985 -4876,Binary classification,Naive Bayes,Bananas,0.6153846153846154,0.4131455399061033,0.014024734497070312,18.277781 -4982,Binary classification,Naive Bayes,Bananas,0.6163420999799237,0.41684467500762895,0.014024734497070312,19.058798 -5088,Binary classification,Naive Bayes,Bananas,0.6150973068606251,0.41412327947336924,0.014024734497070312,19.855913 -5194,Binary classification,Naive Bayes,Bananas,0.6146735990756788,0.4133685136323659,0.014024734497070312,20.669466 -5300,Binary classification,Naive Bayes,Bananas,0.6152104170598226,0.4139120436907157,0.014024734497070312,21.499334 -906,Binary classification,Naive Bayes,Elec2,0.8187845303867404,0.8284518828451883,0.05103778839111328,0.17926 -1812,Binary classification,Naive Bayes,Elec2,0.8023191606847045,0.7475317348377998,0.05103778839111328,0.525852 -2718,Binary classification,Naive Bayes,Elec2,0.784688995215311,0.706177800100452,0.05103778839111328,1.0396 -3624,Binary classification,Naive Bayes,Elec2,0.8032017664918576,0.7356321839080461,0.05103778839111328,1.7208420000000002 -4530,Binary classification,Naive Bayes,Elec2,0.7979686465003312,0.7073872721458268,0.05103778839111328,2.5693340000000005 -5436,Binary classification,Naive Bayes,Elec2,0.7937442502299908,0.6972724817715366,0.05103778839111328,3.5852350000000004 -6342,Binary classification,Naive Bayes,Elec2,0.7982967986122063,0.7065840789171829,0.05103778839111328,4.768595 -7248,Binary classification,Naive Bayes,Elec2,0.790396025941769,0.6875128574367414,0.05103778839111328,6.119432000000001 -8154,Binary classification,Naive Bayes,Elec2,0.7841285416411137,0.6888260254596887,0.05103778839111328,7.6376610000000005 -9060,Binary classification,Naive Bayes,Elec2,0.7897118887294403,0.7086710506193606,0.05103778839111328,9.323211 -9966,Binary classification,Naive Bayes,Elec2,0.793176116407426,0.7240594457089301,0.05103778839111328,11.17581 -10872,Binary classification,Naive Bayes,Elec2,0.7960629196946003,0.7361656551231703,0.05103778839111328,13.198889000000001 -11778,Binary classification,Naive Bayes,Elec2,0.792137216608644,0.7295027624309391,0.05103778839111328,15.388480000000001 -12684,Binary classification,Naive Bayes,Elec2,0.7820704880548766,0.7260111022997621,0.05103778839111328,17.743988 -13590,Binary classification,Naive Bayes,Elec2,0.7858562072264331,0.7383564107174968,0.05103778839111328,20.265390000000004 -14496,Binary classification,Naive Bayes,Elec2,0.7866850638151086,0.7435727317963178,0.05103778839111328,22.952880000000004 -15402,Binary classification,Naive Bayes,Elec2,0.785728199467567,0.738593155893536,0.05103778839111328,25.806911000000003 -16308,Binary classification,Naive Bayes,Elec2,0.7806463481940271,0.7274666666666666,0.05103778839111328,28.828021000000003 -17214,Binary classification,Naive Bayes,Elec2,0.7788880497298554,0.7181158346911569,0.05103778839111328,32.016234000000004 -18120,Binary classification,Naive Bayes,Elec2,0.7728903361112645,0.7138983522213725,0.05103778839111328,35.376132000000005 -19026,Binary classification,Naive Bayes,Elec2,0.7701445466491459,0.7094931242941608,0.05103778839111328,38.860646 -19932,Binary classification,Naive Bayes,Elec2,0.7628317696051378,0.702236220472441,0.05103778839111328,42.445598000000004 -20838,Binary classification,Naive Bayes,Elec2,0.7537553390603254,0.6903626817934946,0.05103778839111328,46.130991 -21744,Binary classification,Naive Bayes,Elec2,0.7508163546888654,0.6836389115964032,0.05103778839111328,49.916905 -22650,Binary classification,Naive Bayes,Elec2,0.7509823833281822,0.6798001589644601,0.05103778839111328,53.803318 -23556,Binary classification,Naive Bayes,Elec2,0.7457015495648482,0.668217569513681,0.05103778839111328,57.790136999999994 -24462,Binary classification,Naive Bayes,Elec2,0.7466170638976329,0.665839982747466,0.05103778839111328,61.87940199999999 -25368,Binary classification,Naive Bayes,Elec2,0.7447865336854969,0.6611180904522613,0.05103778839111328,66.06947 -26274,Binary classification,Naive Bayes,Elec2,0.7448711605069843,0.6581322996888865,0.05103778839111328,70.360011 -27180,Binary classification,Naive Bayes,Elec2,0.741123661650539,0.650402464473815,0.05103778839111328,74.751133 -28086,Binary classification,Naive Bayes,Elec2,0.7390065871461634,0.6440019426906265,0.05103778839111328,79.242684 -28992,Binary classification,Naive Bayes,Elec2,0.7358145631402849,0.6343280019097637,0.05103778839111328,83.83464099999999 -29898,Binary classification,Naive Bayes,Elec2,0.7320466936481921,0.6243023964732918,0.05103778839111328,88.52693 -30804,Binary classification,Naive Bayes,Elec2,0.7297990455475116,0.6158319870759289,0.05103778839111328,93.31947199999999 -31710,Binary classification,Naive Bayes,Elec2,0.7256930209088902,0.6059617649723658,0.05103778839111328,98.21253099999998 -32616,Binary classification,Naive Bayes,Elec2,0.7215391690939752,0.596427301813011,0.05103778839111328,103.20618199999998 -33522,Binary classification,Naive Bayes,Elec2,0.7176695205990274,0.5867248908296943,0.05103778839111328,108.30076099999998 -34428,Binary classification,Naive Bayes,Elec2,0.7142359194818021,0.5779493779493778,0.05103778839111328,113.49672599999998 -35334,Binary classification,Naive Bayes,Elec2,0.7138369229898395,0.5724554949469323,0.05103778839111328,118.79330799999998 -36240,Binary classification,Naive Bayes,Elec2,0.7174866856149452,0.5752924583091347,0.05103778839111328,124.19030599999998 -37146,Binary classification,Naive Bayes,Elec2,0.7169740207295733,0.5716148486206756,0.05103778839111328,129.68778099999997 -38052,Binary classification,Naive Bayes,Elec2,0.7183516858952459,0.573859795618116,0.05103778839111328,135.28579999999997 -38958,Binary classification,Naive Bayes,Elec2,0.7206407064198989,0.5799529121154812,0.05103778839111328,140.98460499999996 -39864,Binary classification,Naive Bayes,Elec2,0.7217720693374808,0.5866964784795975,0.05103778839111328,146.78445599999995 -40770,Binary classification,Naive Bayes,Elec2,0.7228776766660944,0.5923065819861432,0.05103778839111328,152.68634499999996 -41676,Binary classification,Naive Bayes,Elec2,0.724127174565087,0.5973170817134251,0.05103778839111328,158.68998499999995 -42582,Binary classification,Naive Bayes,Elec2,0.7260280406754186,0.6013259517462921,0.05103778839111328,164.79474299999995 -43488,Binary classification,Naive Bayes,Elec2,0.7277117299422816,0.6045222270465248,0.05103778839111328,170.99996699999994 -44394,Binary classification,Naive Bayes,Elec2,0.7273894532921857,0.6015933631814591,0.05103778839111328,177.30586899999994 -45300,Binary classification,Naive Bayes,Elec2,0.7287136581381487,0.6038234630387828,0.05103778839111328,183.71242299999994 -25,Binary classification,Naive Bayes,Phishing,0.5833333333333334,0.7058823529411764,0.05722999572753906,0.01864 -50,Binary classification,Naive Bayes,Phishing,0.7346938775510204,0.7636363636363637,0.05722999572753906,0.044672 -75,Binary classification,Naive Bayes,Phishing,0.7837837837837838,0.8048780487804877,0.05722999572753906,0.0769 -100,Binary classification,Naive Bayes,Phishing,0.8080808080808081,0.819047619047619,0.05722999572753906,0.11524799999999999 -125,Binary classification,Naive Bayes,Phishing,0.8145161290322581,0.8217054263565893,0.05722999572753906,0.15967399999999998 -150,Binary classification,Naive Bayes,Phishing,0.8187919463087249,0.830188679245283,0.05722999572753906,0.21009599999999998 -175,Binary classification,Naive Bayes,Phishing,0.8333333333333334,0.8323699421965318,0.05722999572753906,0.266544 -200,Binary classification,Naive Bayes,Phishing,0.8341708542713567,0.83248730964467,0.05722999572753906,0.329068 -225,Binary classification,Naive Bayes,Phishing,0.8303571428571429,0.8240740740740741,0.05722999572753906,0.397573 -250,Binary classification,Naive Bayes,Phishing,0.8313253012048193,0.825,0.05722999572753906,0.472283 -275,Binary classification,Naive Bayes,Phishing,0.8321167883211679,0.8244274809160306,0.05722999572753906,0.553059 -300,Binary classification,Naive Bayes,Phishing,0.8394648829431438,0.8285714285714285,0.05722999572753906,0.639906 -325,Binary classification,Naive Bayes,Phishing,0.845679012345679,0.8299319727891157,0.05722999572753906,0.732828 -350,Binary classification,Naive Bayes,Phishing,0.8510028653295129,0.8322580645161292,0.05722999572753906,0.831756 -375,Binary classification,Naive Bayes,Phishing,0.8529411764705882,0.8318042813455658,0.05722999572753906,0.937046 -400,Binary classification,Naive Bayes,Phishing,0.8546365914786967,0.8313953488372093,0.05722999572753906,1.048453 -425,Binary classification,Naive Bayes,Phishing,0.8561320754716981,0.8291316526610645,0.05722999572753906,1.1659410000000001 -450,Binary classification,Naive Bayes,Phishing,0.8596881959910914,0.8310991957104559,0.05722999572753906,1.289511 -475,Binary classification,Naive Bayes,Phishing,0.8565400843881856,0.8291457286432161,0.05722999572753906,1.419108 -500,Binary classification,Naive Bayes,Phishing,0.8577154308617234,0.8337236533957845,0.05722999572753906,1.554885 -525,Binary classification,Naive Bayes,Phishing,0.8587786259541985,0.8310502283105022,0.05722999572753906,1.6967670000000001 -550,Binary classification,Naive Bayes,Phishing,0.8579234972677595,0.8311688311688311,0.05722999572753906,1.8446930000000001 -575,Binary classification,Naive Bayes,Phishing,0.8606271777003485,0.8340248962655602,0.05722999572753906,1.998693 -600,Binary classification,Naive Bayes,Phishing,0.8647746243739566,0.8363636363636363,0.05722999572753906,2.1587810000000003 -625,Binary classification,Naive Bayes,Phishing,0.8669871794871795,0.8356435643564357,0.05722999572753906,2.3249540000000004 -650,Binary classification,Naive Bayes,Phishing,0.8705701078582434,0.8426966292134833,0.05722999572753906,2.4971910000000004 -675,Binary classification,Naive Bayes,Phishing,0.870919881305638,0.8465608465608465,0.05722999572753906,2.675498 -700,Binary classification,Naive Bayes,Phishing,0.8755364806866953,0.8502581755593803,0.05722999572753906,2.85988 -725,Binary classification,Naive Bayes,Phishing,0.8784530386740331,0.8562091503267973,0.05722999572753906,3.050473 -750,Binary classification,Naive Bayes,Phishing,0.8798397863818425,0.8584905660377359,0.05722999572753906,3.247084 -775,Binary classification,Naive Bayes,Phishing,0.8798449612403101,0.8580152671755725,0.05722999572753906,3.4497720000000003 -800,Binary classification,Naive Bayes,Phishing,0.8798498122653317,0.8596491228070174,0.05722999572753906,3.6587890000000005 -825,Binary classification,Naive Bayes,Phishing,0.8798543689320388,0.860759493670886,0.05722999572753906,3.8738800000000007 -850,Binary classification,Naive Bayes,Phishing,0.8798586572438163,0.8602739726027396,0.05722999572753906,4.094979 -875,Binary classification,Naive Bayes,Phishing,0.8832951945080092,0.8636363636363635,0.05722999572753906,4.322174 -900,Binary classification,Naive Bayes,Phishing,0.8809788654060067,0.8608582574772432,0.05722999572753906,4.555426000000001 -925,Binary classification,Naive Bayes,Phishing,0.8820346320346321,0.8635794743429286,0.05722999572753906,4.7947690000000005 -950,Binary classification,Naive Bayes,Phishing,0.8819810326659642,0.8650602409638554,0.05722999572753906,5.040106000000001 -975,Binary classification,Naive Bayes,Phishing,0.8829568788501027,0.8661971830985915,0.05722999572753906,5.291615 -1000,Binary classification,Naive Bayes,Phishing,0.8808808808808809,0.8643101482326111,0.05722999572753906,5.549186000000001 -1025,Binary classification,Naive Bayes,Phishing,0.880859375,0.8647450110864746,0.05722999572753906,5.812868000000001 -1050,Binary classification,Naive Bayes,Phishing,0.882745471877979,0.8673139158576052,0.05722999572753906,6.082483000000001 -1075,Binary classification,Naive Bayes,Phishing,0.8817504655493482,0.8672936259143157,0.05722999572753906,6.358049000000001 -1100,Binary classification,Naive Bayes,Phishing,0.8835304822565969,0.8693877551020409,0.05722999572753906,6.639582000000001 -1125,Binary classification,Naive Bayes,Phishing,0.8861209964412812,0.8735177865612648,0.05722999572753906,6.927116000000001 -1150,Binary classification,Naive Bayes,Phishing,0.8859878154917319,0.8731848983543079,0.05722999572753906,7.220572000000001 -1175,Binary classification,Naive Bayes,Phishing,0.8850085178875639,0.8717948717948718,0.05722999572753906,7.520004000000001 -1200,Binary classification,Naive Bayes,Phishing,0.8865721434528774,0.8731343283582089,0.05722999572753906,7.825445000000001 -1225,Binary classification,Naive Bayes,Phishing,0.886437908496732,0.8728270814272644,0.05722999572753906,8.137127000000001 -1250,Binary classification,Naive Bayes,Phishing,0.8847077662129704,0.8714285714285714,0.05722999572753906,8.454914 -1903,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,0.257042 -3806,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,0.764431 -5709,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,1.522309 -7612,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,2.530537 -9515,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,3.789363 -11418,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,5.298413 -13321,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,7.057599 -15224,Binary classification,Naive Bayes,SMTP,0.9997372397030808,0.7777777777777778,0.020140647888183594,9.078054999999999 -17127,Binary classification,Naive Bayes,SMTP,0.9997664369963798,0.8181818181818181,0.020140647888183594,11.376218 -19030,Binary classification,Naive Bayes,SMTP,0.9997897945241474,0.8181818181818181,0.020140647888183594,13.952029 -20933,Binary classification,Naive Bayes,SMTP,0.9998089050257978,0.8181818181818181,0.020140647888183594,16.806125 -22836,Binary classification,Naive Bayes,SMTP,0.9998248303043573,0.8181818181818181,0.020140647888183594,19.939484 -24739,Binary classification,Naive Bayes,SMTP,0.9998383054410219,0.8181818181818181,0.020140647888183594,23.347474 -26642,Binary classification,Naive Bayes,SMTP,0.9998498554859052,0.8333333333333333,0.020140647888183594,26.939926 -28545,Binary classification,Naive Bayes,SMTP,0.999859865470852,0.8333333333333333,0.020140647888183594,30.696722 -30448,Binary classification,Naive Bayes,SMTP,0.9998686241665845,0.8333333333333333,0.020140647888183594,34.617556 -32351,Binary classification,Naive Bayes,SMTP,0.9998763523956723,0.8333333333333333,0.020140647888183594,38.704559 -34254,Binary classification,Naive Bayes,SMTP,0.9998832219075702,0.8333333333333333,0.020140647888183594,42.956191000000004 -36157,Binary classification,Naive Bayes,SMTP,0.9998893682929527,0.8333333333333333,0.020140647888183594,47.37231800000001 -38060,Binary classification,Naive Bayes,SMTP,0.9998949000236474,0.8333333333333333,0.020140647888183594,51.95229900000001 -39963,Binary classification,Naive Bayes,SMTP,0.9998999049096642,0.8333333333333333,0.020140647888183594,56.69619700000001 -41866,Binary classification,Naive Bayes,SMTP,0.999904454795175,0.8333333333333333,0.020140647888183594,61.60400300000001 -43769,Binary classification,Naive Bayes,SMTP,0.9999086090294279,0.8333333333333333,0.020140647888183594,66.676032 -45672,Binary classification,Naive Bayes,SMTP,0.9999124170699131,0.8333333333333333,0.020140647888183594,71.913656 -47575,Binary classification,Naive Bayes,SMTP,0.9999159204607558,0.8333333333333333,0.020140647888183594,77.315763 -49478,Binary classification,Naive Bayes,SMTP,0.9999191543545486,0.8333333333333333,0.020140647888183594,82.88181300000001 -51381,Binary classification,Naive Bayes,SMTP,0.9999026858699883,0.8275862068965517,0.020140647888183594,88.61249600000001 -53284,Binary classification,Naive Bayes,SMTP,0.9999061614398589,0.8275862068965517,0.020140647888183594,94.50806600000001 -55187,Binary classification,Naive Bayes,SMTP,0.9998912767730946,0.7999999999999999,0.020140647888183594,100.56946700000002 -57090,Binary classification,Naive Bayes,SMTP,0.9993869221741492,0.4444444444444444,0.020140647888183594,106.79522700000001 -58993,Binary classification,Naive Bayes,SMTP,0.9988473013289938,0.29166666666666663,0.020140647888183594,113.18503100000001 -60896,Binary classification,Naive Bayes,SMTP,0.9986369981115034,0.2522522522522523,0.020140647888183594,119.73505000000002 -62799,Binary classification,Naive Bayes,SMTP,0.9979139463040224,0.1761006289308176,0.020140647888183594,126.44640100000001 -64702,Binary classification,Naive Bayes,SMTP,0.9979443903494536,0.17391304347826086,0.020140647888183594,133.31736600000002 -66605,Binary classification,Naive Bayes,SMTP,0.9977478830100295,0.15730337078651685,0.020140647888183594,140.34740000000002 -68508,Binary classification,Naive Bayes,SMTP,0.9967302611411972,0.12500000000000003,0.020140647888183594,147.535714 -70411,Binary classification,Naive Bayes,SMTP,0.9964777730436017,0.1142857142857143,0.020140647888183594,154.879523 -72314,Binary classification,Naive Bayes,SMTP,0.9964045192427364,0.10958904109589042,0.020140647888183594,162.37251700000002 -74217,Binary classification,Naive Bayes,SMTP,0.9958230031260106,0.0935672514619883,0.020140647888183594,170.01463900000002 -76120,Binary classification,Naive Bayes,SMTP,0.9956515456062218,0.08815426997245178,0.020140647888183594,177.804854 -78023,Binary classification,Naive Bayes,SMTP,0.9951936633257287,0.07862407862407862,0.020140647888183594,185.743198 -79926,Binary classification,Naive Bayes,SMTP,0.9946700031279324,0.06986899563318777,0.020140647888183594,193.82952600000002 -81829,Binary classification,Naive Bayes,SMTP,0.9945862052109302,0.06736842105263158,0.020140647888183594,202.05744900000002 -83732,Binary classification,Naive Bayes,SMTP,0.9945539883675102,0.06557377049180328,0.020140647888183594,210.44477400000002 -85635,Binary classification,Naive Bayes,SMTP,0.9939860335847911,0.05850091407678244,0.020140647888183594,218.97044600000004 -87538,Binary classification,Naive Bayes,SMTP,0.9938540274398254,0.05614035087719298,0.020140647888183594,227.63481900000005 -89441,Binary classification,Naive Bayes,SMTP,0.9938618067978533,0.05507745266781411,0.020140647888183594,236.43819800000006 -91344,Binary classification,Naive Bayes,SMTP,0.9939677917300723,0.0548885077186964,0.020140647888183594,245.38462600000005 -93247,Binary classification,Naive Bayes,SMTP,0.993543958990198,0.050473186119873815,0.020140647888183594,254.47095700000006 -95150,Binary classification,Naive Bayes,SMTP,0.993483904192372,0.049079754601226995,0.020140647888183594,263.6963870000001 -106,Binary classification,Hoeffding Tree,Bananas,0.49523809523809526,0.208955223880597,0.01929473876953125,0.019877 -212,Binary classification,Hoeffding Tree,Bananas,0.5213270142180095,0.3129251700680272,0.019317626953125,0.052088999999999996 -318,Binary classification,Hoeffding Tree,Bananas,0.5299684542586751,0.40637450199203184,0.019317626953125,0.095483 -424,Binary classification,Hoeffding Tree,Bananas,0.5437352245862884,0.42388059701492536,0.019317626953125,0.150343 -530,Binary classification,Hoeffding Tree,Bananas,0.553875236294896,0.4099999999999999,0.019317626953125,0.216684 -636,Binary classification,Hoeffding Tree,Bananas,0.5590551181102362,0.4017094017094017,0.019317626953125,0.295132 -742,Binary classification,Hoeffding Tree,Bananas,0.5762483130904184,0.3984674329501916,0.019317626953125,0.38501799999999997 -848,Binary classification,Hoeffding Tree,Bananas,0.5867768595041323,0.40476190476190477,0.019317626953125,0.486959 -954,Binary classification,Hoeffding Tree,Bananas,0.5918153200419727,0.3987635239567234,0.019317626953125,0.601178 -1060,Binary classification,Hoeffding Tree,Bananas,0.6015108593012276,0.39714285714285713,0.019317626953125,0.727681 -1166,Binary classification,Hoeffding Tree,Bananas,0.6,0.38522427440633245,0.019317626953125,0.865767 -1272,Binary classification,Hoeffding Tree,Bananas,0.6073957513768686,0.3966142684401451,0.019317626953125,1.0159 -1378,Binary classification,Hoeffding Tree,Bananas,0.6085693536673928,0.384,0.019317626953125,1.177562 -1484,Binary classification,Hoeffding Tree,Bananas,0.6089008766014835,0.3790149892933619,0.019317626953125,1.351249 -1590,Binary classification,Hoeffding Tree,Bananas,0.6085588420390182,0.37424547283702214,0.019317626953125,1.5365639999999998 -1696,Binary classification,Hoeffding Tree,Bananas,0.6094395280235988,0.37072243346007605,0.019317626953125,1.73385 -1802,Binary classification,Hoeffding Tree,Bananas,0.6102165463631316,0.37544483985765126,0.019317626953125,1.943126 -1908,Binary classification,Hoeffding Tree,Bananas,0.610907184058731,0.3816666666666667,0.019317626953125,2.16423 -2014,Binary classification,Hoeffding Tree,Bananas,0.6060606060606061,0.3799843627834245,0.019317626953125,2.39723 -2120,Binary classification,Hoeffding Tree,Bananas,0.6045304388862671,0.38382352941176473,0.019317626953125,2.6418049999999997 -2226,Binary classification,Hoeffding Tree,Bananas,0.6053932584269663,0.38687150837988826,0.019317626953125,2.8985119999999998 -2332,Binary classification,Hoeffding Tree,Bananas,0.6061776061776062,0.38881491344873503,0.019317626953125,3.1667389999999997 -2438,Binary classification,Hoeffding Tree,Bananas,0.606893721789085,0.388250319284802,0.019317626953125,3.4470539999999996 -2544,Binary classification,Hoeffding Tree,Bananas,0.608336610302792,0.39636363636363636,0.019317626953125,3.7390399999999997 -2650,Binary classification,Hoeffding Tree,Bananas,0.6070215175537939,0.3944153577661431,0.019317626953125,4.043011 -2756,Binary classification,Hoeffding Tree,Bananas,0.6047186932849364,0.3892316320807628,0.019317626953125,4.3585199999999995 -2862,Binary classification,Hoeffding Tree,Bananas,0.6057322614470465,0.3922413793103448,0.019317626953125,4.686463999999999 -2968,Binary classification,Hoeffding Tree,Bananas,0.6056622851365016,0.3899895724713243,0.019317626953125,5.0259599999999995 -3074,Binary classification,Hoeffding Tree,Bananas,0.6036446469248291,0.3903903903903904,0.019317626953125,5.377489 -3180,Binary classification,Hoeffding Tree,Bananas,0.6045926391947153,0.3924601256645723,0.019317626953125,5.740578999999999 -3286,Binary classification,Hoeffding Tree,Bananas,0.6039573820395738,0.39006094702297234,0.019317626953125,6.115373999999999 -3392,Binary classification,Hoeffding Tree,Bananas,0.6024771453848422,0.39169675090252704,0.019317626953125,6.501596999999999 -3498,Binary classification,Hoeffding Tree,Bananas,0.6030883614526737,0.39335664335664333,0.035147666931152344,6.903521 -3604,Binary classification,Hoeffding Tree,Bananas,0.6069941715237303,0.40353833192923344,0.035147666931152344,7.317575 -3710,Binary classification,Hoeffding Tree,Bananas,0.6079805877595039,0.40798045602605865,0.035147666931152344,7.744409999999999 -3816,Binary classification,Hoeffding Tree,Bananas,0.6107470511140236,0.4146629877808436,0.035147666931152344,8.183644999999999 -3922,Binary classification,Hoeffding Tree,Bananas,0.6123437898495282,0.4180704441041348,0.04465007781982422,8.636627999999998 -4028,Binary classification,Hoeffding Tree,Bananas,0.6143531164638689,0.4246017043349389,0.05081462860107422,9.102371999999999 -4134,Binary classification,Hoeffding Tree,Bananas,0.617227195741592,0.43216080402010054,0.05081462860107422,9.580540999999998 -4240,Binary classification,Hoeffding Tree,Bananas,0.6218447747110167,0.4439819632327437,0.05081462860107422,10.071727 -4346,Binary classification,Hoeffding Tree,Bananas,0.6239355581127733,0.45130960376091334,0.05081462860107422,10.575598999999999 -4452,Binary classification,Hoeffding Tree,Bananas,0.6259267580319029,0.45676998368678623,0.05081462860107422,11.092444999999998 -4558,Binary classification,Hoeffding Tree,Bananas,0.6276058810621022,0.46382306477093216,0.05081462860107422,11.621768999999999 -4664,Binary classification,Hoeffding Tree,Bananas,0.6283508470941453,0.4695439240893787,0.05081462860107422,12.163799 -4770,Binary classification,Hoeffding Tree,Bananas,0.6288530090165653,0.47164179104477605,0.06022167205810547,12.720220999999999 -4876,Binary classification,Hoeffding Tree,Bananas,0.6311794871794871,0.47580174927113705,0.06026744842529297,13.289238 -4982,Binary classification,Hoeffding Tree,Bananas,0.6336077092953222,0.484026010743568,0.06026744842529297,13.87141 -5088,Binary classification,Hoeffding Tree,Bananas,0.6361313151169649,0.49050371593724196,0.06026744842529297,14.466033 -5194,Binary classification,Hoeffding Tree,Bananas,0.6383593298671288,0.495703544575725,0.06026744842529297,15.073179 -5300,Binary classification,Hoeffding Tree,Bananas,0.6421966408756369,0.5034049240440022,0.06026744842529297,15.693544 -906,Binary classification,Hoeffding Tree,Elec2,0.8530386740331491,0.8513966480446927,0.17850685119628906,0.136823 -1812,Binary classification,Hoeffding Tree,Elec2,0.8663721700717836,0.8393094289508632,0.21218299865722656,0.37234100000000003 -2718,Binary classification,Hoeffding Tree,Elec2,0.8369525211630475,0.810926163038839,0.2367534637451172,0.715196 -3624,Binary classification,Hoeffding Tree,Elec2,0.8459839911675407,0.8219527760051053,0.2367534637451172,1.157087 -4530,Binary classification,Hoeffding Tree,Elec2,0.8511812762199161,0.8165487207403377,0.23670005798339844,1.692418 -5436,Binary classification,Hoeffding Tree,Elec2,0.8404783808647655,0.8027303754266211,0.23670005798339844,2.325246 -6342,Binary classification,Hoeffding Tree,Elec2,0.8334647531935025,0.7973128598848368,0.23670005798339844,3.058544 -7248,Binary classification,Hoeffding Tree,Elec2,0.8330343590451221,0.7918100481761873,0.23670005798339844,3.886934 -8154,Binary classification,Hoeffding Tree,Elec2,0.8344167790997179,0.8018203170874927,0.23670005798339844,4.816345 -9060,Binary classification,Hoeffding Tree,Elec2,0.8403797328623468,0.8133711925658234,0.30373573303222656,5.843331 -9966,Binary classification,Hoeffding Tree,Elec2,0.8398394380331159,0.8174748398902103,0.3038501739501953,6.967896 -10872,Binary classification,Hoeffding Tree,Elec2,0.840493054916751,0.8203108808290155,0.3038501739501953,8.190139 -11778,Binary classification,Hoeffding Tree,Elec2,0.8404517279442982,0.8186818488854579,0.38779258728027344,9.510034000000001 -12684,Binary classification,Hoeffding Tree,Elec2,0.8397066939998423,0.8187572434697333,0.38779258728027344,10.932755 -13590,Binary classification,Hoeffding Tree,Elec2,0.8422253293104717,0.8231023102310231,0.38779258728027344,12.458737 -14496,Binary classification,Hoeffding Tree,Elec2,0.8440841669541221,0.8261270964763809,0.3890361785888672,14.085847999999999 -15402,Binary classification,Hoeffding Tree,Elec2,0.8445555483410169,0.8248207229620957,0.38906288146972656,15.806373999999998 -16308,Binary classification,Hoeffding Tree,Elec2,0.8382289814190225,0.8146430578976953,0.4148235321044922,17.623171 -17214,Binary classification,Hoeffding Tree,Elec2,0.8344855632370882,0.8052764677739047,0.4148235321044922,19.541045999999998 -18120,Binary classification,Hoeffding Tree,Elec2,0.8333793255698438,0.8031044153133764,0.4155101776123047,21.569664999999997 -19026,Binary classification,Hoeffding Tree,Elec2,0.8341655716162943,0.8009086893418313,0.41680335998535156,23.708108999999997 -19932,Binary classification,Hoeffding Tree,Elec2,0.8308163162912047,0.7980112615310889,0.5072460174560547,25.964372999999995 -20838,Binary classification,Hoeffding Tree,Elec2,0.8293900273551855,0.7961699443839229,0.5655117034912109,28.344830999999996 -21744,Binary classification,Hoeffding Tree,Elec2,0.8298302902083429,0.7941012799109627,0.6239414215087891,30.844750999999995 -22650,Binary classification,Hoeffding Tree,Elec2,0.8288224645679722,0.7904437597967678,0.6005496978759766,33.457668999999996 -23556,Binary classification,Hoeffding Tree,Elec2,0.8244109530885162,0.7830920914621354,0.6264286041259766,36.193138999999995 -24462,Binary classification,Hoeffding Tree,Elec2,0.8225747107640734,0.7806973218797373,0.6265659332275391,39.050647999999995 -25368,Binary classification,Hoeffding Tree,Elec2,0.8180707218039185,0.7764375333042677,0.6265659332275391,42.027952 -26274,Binary classification,Hoeffding Tree,Elec2,0.8183306055646481,0.7761572011443043,0.6265659332275391,45.127396 -27180,Binary classification,Hoeffding Tree,Elec2,0.8178005077449502,0.7755416553349651,0.6265659332275391,48.341336999999996 -28086,Binary classification,Hoeffding Tree,Elec2,0.8154174826419797,0.771830985915493,0.6266345977783203,51.670154999999994 -28992,Binary classification,Hoeffding Tree,Elec2,0.81342485598979,0.7672447179310641,0.6266345977783203,55.116457 -29898,Binary classification,Hoeffding Tree,Elec2,0.8088771448640332,0.763747622591582,0.6848773956298828,58.686243999999995 -30804,Binary classification,Hoeffding Tree,Elec2,0.8062526377300913,0.7606673083092718,0.6848773956298828,62.38209 -31710,Binary classification,Hoeffding Tree,Elec2,0.8052603361821565,0.759212322090076,0.7534084320068359,66.209893 -32616,Binary classification,Hoeffding Tree,Elec2,0.8025755020695998,0.7564951026736755,0.7792224884033203,70.16157899999999 -33522,Binary classification,Hoeffding Tree,Elec2,0.8007517675487008,0.7563742476746306,0.8375339508056641,74.24016799999998 -34428,Binary classification,Hoeffding Tree,Elec2,0.7980945188369594,0.7537464130088213,0.8621463775634766,78.43750399999999 -35334,Binary classification,Hoeffding Tree,Elec2,0.797186765912886,0.7523842432619212,0.9281406402587891,82.758889 -36240,Binary classification,Hoeffding Tree,Elec2,0.7972350230414746,0.7516392888528357,0.9552211761474609,87.200985 -37146,Binary classification,Hoeffding Tree,Elec2,0.7969847893390766,0.7511960143851661,1.0704402923583984,91.76110200000001 -38052,Binary classification,Hoeffding Tree,Elec2,0.7938293343144727,0.748388338304628,1.0715465545654297,96.434966 -38958,Binary classification,Hoeffding Tree,Elec2,0.7918730908437508,0.7483706784184718,1.0715465545654297,101.21983300000001 -39864,Binary classification,Hoeffding Tree,Elec2,0.7913854953214761,0.7506596306068601,1.1051769256591797,106.11888400000001 -40770,Binary classification,Hoeffding Tree,Elec2,0.7935686428413746,0.7554199360650974,1.1051769256591797,111.1286 -41676,Binary classification,Hoeffding Tree,Elec2,0.7953209358128375,0.7590667721161451,1.129629135131836,116.244938 -42582,Binary classification,Hoeffding Tree,Elec2,0.7966933608886593,0.7607572198424761,1.129629135131836,121.471609 -43488,Binary classification,Hoeffding Tree,Elec2,0.7973647296893325,0.7615800865800865,1.129629135131836,126.806064 -44394,Binary classification,Hoeffding Tree,Elec2,0.7960714527065078,0.7581933278132429,1.129629135131836,132.253655 -45300,Binary classification,Hoeffding Tree,Elec2,0.7969933111106206,0.7591535278403435,1.1878719329833984,137.81786 -25,Binary classification,Hoeffding Tree,Phishing,0.5833333333333334,0.6428571428571429,0.0693511962890625,0.016417 -50,Binary classification,Hoeffding Tree,Phishing,0.7346938775510204,0.7346938775510203,0.0693511962890625,0.037579 -75,Binary classification,Hoeffding Tree,Phishing,0.7837837837837838,0.7894736842105262,0.0693511962890625,0.062555 -100,Binary classification,Hoeffding Tree,Phishing,0.8080808080808081,0.8080808080808081,0.0693511962890625,0.093468 -125,Binary classification,Hoeffding Tree,Phishing,0.8145161290322581,0.8130081300813008,0.0693511962890625,0.128067 -150,Binary classification,Hoeffding Tree,Phishing,0.8187919463087249,0.8235294117647058,0.0693511962890625,0.166217 -175,Binary classification,Hoeffding Tree,Phishing,0.8333333333333334,0.8263473053892215,0.0693511962890625,0.207895 -200,Binary classification,Hoeffding Tree,Phishing,0.8341708542713567,0.8272251308900525,0.06937408447265625,0.254237 -225,Binary classification,Hoeffding Tree,Phishing,0.8303571428571429,0.8190476190476189,0.06937408447265625,0.304152 -250,Binary classification,Hoeffding Tree,Phishing,0.8313253012048193,0.8205128205128206,0.06937408447265625,0.35768099999999997 -275,Binary classification,Hoeffding Tree,Phishing,0.8321167883211679,0.8203125000000001,0.06937408447265625,0.41473299999999996 -300,Binary classification,Hoeffding Tree,Phishing,0.8394648829431438,0.8248175182481753,0.06937408447265625,0.47548999999999997 -325,Binary classification,Hoeffding Tree,Phishing,0.845679012345679,0.8263888888888888,0.06937408447265625,0.539997 -350,Binary classification,Hoeffding Tree,Phishing,0.8510028653295129,0.8289473684210527,0.06937408447265625,0.608045 -375,Binary classification,Hoeffding Tree,Phishing,0.8529411764705882,0.8286604361370716,0.06937408447265625,0.679831 -400,Binary classification,Hoeffding Tree,Phishing,0.8546365914786967,0.8284023668639053,0.06937408447265625,0.75623 -425,Binary classification,Hoeffding Tree,Phishing,0.8561320754716981,0.8262108262108262,0.06937408447265625,0.836328 -450,Binary classification,Hoeffding Tree,Phishing,0.8596881959910914,0.8283378746594006,0.06937408447265625,0.920157 -475,Binary classification,Hoeffding Tree,Phishing,0.8565400843881856,0.826530612244898,0.06937408447265625,1.007627 -500,Binary classification,Hoeffding Tree,Phishing,0.8577154308617234,0.8313539192399049,0.06937408447265625,1.0988580000000001 -525,Binary classification,Hoeffding Tree,Phishing,0.8587786259541985,0.8287037037037036,0.06937408447265625,1.193786 -550,Binary classification,Hoeffding Tree,Phishing,0.8579234972677595,0.8289473684210527,0.06937408447265625,1.292398 -575,Binary classification,Hoeffding Tree,Phishing,0.8606271777003485,0.8319327731092437,0.06937408447265625,1.395047 -600,Binary classification,Hoeffding Tree,Phishing,0.8647746243739566,0.834355828220859,0.06937408447265625,1.502529 -625,Binary classification,Hoeffding Tree,Phishing,0.8669871794871795,0.8336673346693387,0.06937408447265625,1.61388 -650,Binary classification,Hoeffding Tree,Phishing,0.8705701078582434,0.8409090909090909,0.06937408447265625,1.728993 -675,Binary classification,Hoeffding Tree,Phishing,0.870919881305638,0.8449197860962566,0.06937408447265625,1.847898 -700,Binary classification,Hoeffding Tree,Phishing,0.8755364806866953,0.8486956521739131,0.06937408447265625,1.970613 -725,Binary classification,Hoeffding Tree,Phishing,0.8784530386740331,0.8547854785478548,0.06937408447265625,2.097195 -750,Binary classification,Hoeffding Tree,Phishing,0.8798397863818425,0.8571428571428571,0.06937408447265625,2.2275650000000002 -775,Binary classification,Hoeffding Tree,Phishing,0.8798449612403101,0.8567026194144837,0.06937408447265625,2.3617150000000002 -800,Binary classification,Hoeffding Tree,Phishing,0.8798498122653317,0.8584070796460177,0.0058956146240234375,2.500804 -825,Binary classification,Hoeffding Tree,Phishing,0.8786407766990292,0.8575498575498576,0.13469600677490234,2.646368 -850,Binary classification,Hoeffding Tree,Phishing,0.8798586572438163,0.8579387186629527,0.1347188949584961,2.795782 -875,Binary classification,Hoeffding Tree,Phishing,0.8810068649885584,0.8583106267029972,0.1347188949584961,2.94896 -900,Binary classification,Hoeffding Tree,Phishing,0.882091212458287,0.8590425531914893,0.13474178314208984,3.105941 -925,Binary classification,Hoeffding Tree,Phishing,0.8831168831168831,0.8611825192802056,0.13474178314208984,3.266898 -950,Binary classification,Hoeffding Tree,Phishing,0.880927291886196,0.8599752168525404,0.13474178314208984,3.4318049999999998 -975,Binary classification,Hoeffding Tree,Phishing,0.8819301848049281,0.8609431680773881,0.13474178314208984,3.6005749999999996 -1000,Binary classification,Hoeffding Tree,Phishing,0.8828828828828829,0.8621908127208481,0.13474178314208984,3.773271 -1025,Binary classification,Hoeffding Tree,Phishing,0.8818359375,0.8613974799541809,0.13474178314208984,3.949737 -1050,Binary classification,Hoeffding Tree,Phishing,0.8836987607244995,0.8641425389755011,0.13474178314208984,4.130098 -1075,Binary classification,Hoeffding Tree,Phishing,0.8845437616387337,0.8658008658008659,0.13474178314208984,4.314272 -1100,Binary classification,Hoeffding Tree,Phishing,0.8844404003639672,0.8656084656084656,0.13474178314208984,4.502263 -1125,Binary classification,Hoeffding Tree,Phishing,0.8816725978647687,0.8630278063851698,0.13474178314208984,4.694139 -1150,Binary classification,Hoeffding Tree,Phishing,0.8807658833768495,0.8614762386248735,0.13474178314208984,4.889705 -1175,Binary classification,Hoeffding Tree,Phishing,0.879045996592845,0.8594059405940594,0.13474178314208984,5.0886830000000005 -1200,Binary classification,Hoeffding Tree,Phishing,0.8807339449541285,0.8610301263362489,0.13474178314208984,5.292268000000001 -1225,Binary classification,Hoeffding Tree,Phishing,0.880718954248366,0.8609523809523809,0.13474178314208984,5.499479000000001 -1250,Binary classification,Hoeffding Tree,Phishing,0.8799039231385108,0.8605947955390334,0.13474178314208984,5.711295000000001 -1903,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,0.17714 -3806,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,0.53242 -5709,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,1.05881 -7612,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,1.7555610000000001 -9515,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,2.619724 -11418,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,3.6522500000000004 -13321,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.017212867736816406,4.852976 -15224,Binary classification,Hoeffding Tree,SMTP,0.9995401694803915,0.5882352941176471,0.0401153564453125,6.232766 -17127,Binary classification,Hoeffding Tree,SMTP,0.9992409202382343,0.48000000000000004,0.0401153564453125,7.809704 -19030,Binary classification,Hoeffding Tree,SMTP,0.9993168322034789,0.48000000000000004,0.0401153564453125,9.583916 -20933,Binary classification,Hoeffding Tree,SMTP,0.999378941333843,0.48000000000000004,0.0401153564453125,11.564329 -22836,Binary classification,Hoeffding Tree,SMTP,0.9994306984891613,0.48000000000000004,0.0401153564453125,13.76196 -24739,Binary classification,Hoeffding Tree,SMTP,0.9994744926833212,0.48000000000000004,0.0401153564453125,16.175204 -26642,Binary classification,Hoeffding Tree,SMTP,0.9995120303291919,0.5185185185185186,0.04013824462890625,18.806955000000002 -28545,Binary classification,Hoeffding Tree,SMTP,0.999544562780269,0.5185185185185186,0.04013824462890625,21.653103 -30448,Binary classification,Hoeffding Tree,SMTP,0.9995730285413998,0.5185185185185186,0.04013824462890625,24.713929 -32351,Binary classification,Hoeffding Tree,SMTP,0.999598145285935,0.5185185185185186,0.04013824462890625,27.986042 -34254,Binary classification,Hoeffding Tree,SMTP,0.999620471199603,0.5185185185185186,0.04013824462890625,31.466943 -36157,Binary classification,Hoeffding Tree,SMTP,0.9996404469520964,0.5185185185185186,0.04013824462890625,35.15479 -38060,Binary classification,Hoeffding Tree,SMTP,0.9996584250768543,0.5185185185185186,0.032138824462890625,39.051373 -39963,Binary classification,Hoeffding Tree,SMTP,0.9996746909564086,0.5185185185185186,0.032138824462890625,43.155234 -41866,Binary classification,Hoeffding Tree,SMTP,0.9996894780843186,0.5185185185185186,0.032138824462890625,47.461241 -43769,Binary classification,Hoeffding Tree,SMTP,0.9997029793456407,0.5185185185185186,0.032138824462890625,51.972515 -45672,Binary classification,Hoeffding Tree,SMTP,0.9997153554772175,0.5185185185185186,0.032138824462890625,56.689378000000005 -47575,Binary classification,Hoeffding Tree,SMTP,0.9996847017278345,0.4827586206896552,0.044315338134765625,61.613517 -49478,Binary classification,Hoeffding Tree,SMTP,0.9996766174181944,0.4666666666666667,0.044315338134765625,66.740207 -51381,Binary classification,Hoeffding Tree,SMTP,0.9996691319579603,0.5142857142857142,0.053562164306640625,72.066529 -53284,Binary classification,Hoeffding Tree,SMTP,0.9996809488955202,0.5142857142857142,0.053562164306640625,77.596723 -55187,Binary classification,Hoeffding Tree,SMTP,0.9996738303192839,0.5,0.053562164306640625,83.324048 -57090,Binary classification,Hoeffding Tree,SMTP,0.9995270542486293,0.4489795918367347,0.08054351806640625,89.177762 -58993,Binary classification,Hoeffding Tree,SMTP,0.9995423108218063,0.4489795918367347,0.08054351806640625,95.152342 -60896,Binary classification,Hoeffding Tree,SMTP,0.9995566138435011,0.4489795918367347,0.08054351806640625,101.246537 -62799,Binary classification,Hoeffding Tree,SMTP,0.9995222777795472,0.4230769230769231,0.08979034423828125,107.45837900000001 -64702,Binary classification,Hoeffding Tree,SMTP,0.9995363286502527,0.4230769230769231,0.08979034423828125,113.786816 -66605,Binary classification,Hoeffding Tree,SMTP,0.9995495766020059,0.4230769230769231,0.08979034423828125,120.231179 -68508,Binary classification,Hoeffding Tree,SMTP,0.9995620885456961,0.4642857142857143,0.08979034423828125,126.79213 -70411,Binary classification,Hoeffding Tree,SMTP,0.9995739241585002,0.4642857142857143,0.08979034423828125,133.468631 -72314,Binary classification,Hoeffding Tree,SMTP,0.9995851368357004,0.4642857142857143,0.08979034423828125,140.25926299999998 -74217,Binary classification,Hoeffding Tree,SMTP,0.9995823003126011,0.456140350877193,0.08979034423828125,147.163887 -76120,Binary classification,Hoeffding Tree,SMTP,0.9995927429419724,0.456140350877193,0.08979034423828125,154.18114 -78023,Binary classification,Hoeffding Tree,SMTP,0.9996026761682603,0.456140350877193,0.08979034423828125,161.310049 -79926,Binary classification,Hoeffding Tree,SMTP,0.9996121363778543,0.456140350877193,0.08979034423828125,168.551426 -81829,Binary classification,Hoeffding Tree,SMTP,0.9996211565723224,0.456140350877193,0.08979034423828125,175.901071 -83732,Binary classification,Hoeffding Tree,SMTP,0.9996178237450885,0.4482758620689655,0.08979034423828125,183.360737 -85635,Binary classification,Hoeffding Tree,SMTP,0.9996146390452391,0.44067796610169496,0.09405899047851562,190.930796 -87538,Binary classification,Hoeffding Tree,SMTP,0.9996230165530005,0.44067796610169496,0.09405899047851562,198.60706 -89441,Binary classification,Hoeffding Tree,SMTP,0.9996198568872987,0.43333333333333335,0.10326004028320312,206.389291 -91344,Binary classification,Hoeffding Tree,SMTP,0.999616828875776,0.4262295081967213,0.10326004028320312,214.276883 -93247,Binary classification,Hoeffding Tree,SMTP,0.9996139244578855,0.41935483870967744,0.10326004028320312,222.269881 -95150,Binary classification,Hoeffding Tree,SMTP,0.9996216460498797,0.41935483870967744,0.10326004028320312,230.368202 -106,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5714285714285714,0.628099173553719,0.02575397491455078,0.030279 -212,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5592417061611374,0.5903083700440529,0.02583789825439453,0.079572 -318,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5615141955835962,0.5947521865889213,0.02589893341064453,0.147698 -424,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5555555555555556,0.5822222222222222,0.02589893341064453,0.234734 -530,Binary classification,Hoeffding Adaptive Tree,Bananas,0.555765595463138,0.5506692160611854,0.02589893341064453,0.340374 -636,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5543307086614173,0.5291181364392679,0.02595996856689453,0.46589800000000003 -742,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5708502024291497,0.5167173252279634,0.02595996856689453,0.6107940000000001 -848,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5761511216056671,0.510231923601637,0.02595996856689453,0.774445 -954,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5844700944386149,0.505,0.02595996856689453,0.95753 -1060,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5920679886685553,0.49532710280373826,0.02595996856689453,1.15978 -1166,Binary classification,Hoeffding Adaptive Tree,Bananas,0.590557939914163,0.478688524590164,0.02595996856689453,1.380797 -1272,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971675845790716,0.48073022312373226,0.02595996856689453,1.6202560000000001 -1378,Binary classification,Hoeffding Adaptive Tree,Bananas,0.599128540305011,0.4661508704061895,0.02602100372314453,1.8811630000000001 -1484,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5994605529332434,0.458029197080292,0.02602100372314453,2.166507 -1590,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5997482693517936,0.4517241379310345,0.02602100372314453,2.464497 -1696,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6011799410029498,0.4459016393442623,0.02602100372314453,2.7711840000000003 -1802,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6018878400888396,0.44547563805104406,0.02602100372314453,3.0861490000000003 -1908,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6030414263240692,0.44704163623082543,0.02602100372314453,3.4094810000000004 -2014,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5986090412319921,0.44352617079889806,0.02602100372314453,3.7411250000000003 -2120,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5960358659745163,0.4427083333333333,0.02602100372314453,4.081019 -2226,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5968539325842697,0.4425108763206961,0.02602100372314453,4.429484 -2332,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5975975975975976,0.44233055885850175,0.02602100372314453,4.786285 -2438,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5982765695527288,0.4396107613050944,0.02602100372314453,5.150978 -2544,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5973259929217459,0.4398249452954048,0.030361175537109375,5.5260560000000005 -2650,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5956964892412231,0.44363636363636366,0.0572662353515625,5.913093000000001 -2756,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5985480943738657,0.44975124378109455,0.0575103759765625,6.312855000000001 -2862,Binary classification,Hoeffding Adaptive Tree,Bananas,0.600139811254806,0.4536771728748806,0.0577545166015625,6.725712000000001 -2968,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5979103471520054,0.45250114731528224,0.057861328125,7.151638 -3074,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971363488447771,0.4497777777777778,0.0579833984375,7.588719 -3180,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6008178672538534,0.44993498049414826,0.05804443359375,8.037238 -3286,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6024353120243531,0.4470787468247248,0.05816650390625,8.49667 -3392,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6012975523444412,0.444991789819376,0.0582275390625,8.967725 -3498,Binary classification,Hoeffding Adaptive Tree,Bananas,0.603946239633972,0.44310414153598715,0.05828857421875,9.449361 -3604,Binary classification,Hoeffding Adaptive Tree,Bananas,0.607826810990841,0.4452296819787986,0.05828857421875,9.942275 -3710,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6071717444055001,0.441976254308694,0.05828857421875,10.446028 -3816,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6062909567496724,0.43787425149700593,0.058349609375,10.961132 -3922,Binary classification,Hoeffding Adaptive Tree,Bananas,0.606988013261923,0.4353242946134115,0.05841064453125,11.487205 -4028,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6088899925502855,0.4360902255639098,0.05841064453125,12.024685999999999 -4134,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6082748608758771,0.4341139461726669,0.0584716796875,12.573182999999998 -4240,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6105213493748526,0.4370951244459598,0.0584716796875,13.132692999999998 -4346,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6119677790563867,0.43724966622162886,0.05853271484375,13.703477999999999 -4452,Binary classification,Hoeffding Adaptive Tree,Bananas,0.614243990114581,0.4387054593004249,0.05853271484375,14.285281 -4558,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6126837831906956,0.4355612408058842,0.05853271484375,14.878356 -4664,Binary classification,Hoeffding Adaptive Tree,Bananas,0.613339052112374,0.4360337816703159,0.05853271484375,15.482258 -4770,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6148039421262319,0.4352905010759299,0.06467437744140625,16.097208 -4876,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6157948717948718,0.4332829046898639,0.06467437744140625,16.723433999999997 -4982,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6167436257779563,0.43470535978679303,0.06467437744140625,17.360799999999998 -5088,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6158836249262827,0.4313154831199068,0.06473541259765625,18.009093999999997 -5194,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6160215674947044,0.42963386727688785,0.06479644775390625,18.668470999999997 -5300,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6165314210228345,0.42824985931344967,0.062404632568359375,19.339783999999998 -906,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8386740331491712,0.8370535714285713,0.1590566635131836,0.369424 -1812,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8823854224185533,0.857334226389819,0.29515743255615234,1.082516 -2718,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8715495031284505,0.8438478747203579,0.13015270233154297,2.245646 -3624,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8755175269113994,0.8480970023576963,0.25374507904052734,3.6837349999999995 -4530,Binary classification,Hoeffding Adaptive Tree,Elec2,0.873923603444469,0.8402797202797203,0.37697887420654297,5.436553 -5436,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8680772769089237,0.8326721120186699,0.43615245819091797,7.563805 -6342,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8667402617883615,0.8319076984284862,0.2912740707397461,10.057711000000001 -7248,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8665654753691182,0.8309144955411786,0.31587886810302734,12.896512000000001 -8154,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8588249724027965,0.8328249818445898,0.31593990325927734,16.103426000000002 -9060,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8594767634396733,0.8384722750919934,0.31581783294677734,19.644725 -9966,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8557952834922228,0.8381938970836617,0.31569576263427734,23.527363 -10872,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8595345414405299,0.8445801526717558,0.3772764205932617,27.740459 -11778,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8540375307803345,0.8368605865046976,0.4951925277709961,32.366043000000005 -12684,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8543719940077269,0.8371970030850595,0.19228267669677734,37.423083000000005 -13590,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8561336375009199,0.8404472374112462,0.19668865203857422,42.800608000000004 -14496,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8560193170058641,0.8406018483158941,0.19699382781982422,48.503182 -15402,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8546198298811766,0.8374119526541282,0.19674968719482422,54.449200000000005 -16308,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8514748267615134,0.8324339283243393,0.1668386459350586,60.610803000000004 -17214,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8503456689711265,0.8286094477711246,0.17186641693115234,66.962846 -18120,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8505436282355539,0.8284555935639174,0.20143413543701172,73.50726399999999 -19026,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8529829172141918,0.8293992070753278,0.2609548568725586,80.25162699999998 -19932,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8476744769454618,0.8245289561900357,0.3200864791870117,87.27384199999999 -20838,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8440274511685943,0.8206203775251132,0.3286733627319336,94.62519899999998 -21744,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8459734167318217,0.8206693440428381,0.32692623138427734,102.22022699999998 -22650,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8452470307739856,0.8179693586081537,0.44559764862060547,110.06165699999998 -23556,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8426236467841223,0.813952321204517,0.3226041793823242,118.13610299999998 -24462,Binary classification,Hoeffding Adaptive Tree,Elec2,0.83966313723887,0.8094081057439986,0.32233715057373047,126.42298599999998 -25368,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8335632908897387,0.804101707498144,0.32264232635498047,134.92385099999998 -26274,Binary classification,Hoeffding Adaptive Tree,Elec2,0.833859856126061,0.8041634887164072,0.3227415084838867,143.62308599999997 -27180,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8353508223260606,0.8067872717067484,0.33442211151123047,152.52188499999997 -28086,Binary classification,Hoeffding Adaptive Tree,Elec2,0.832366031689514,0.8022679546409073,0.4427366256713867,161.69157399999997 -28992,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8298092511469076,0.7967539957159334,0.44315624237060547,171.13838399999997 -29898,Binary classification,Hoeffding Adaptive Tree,Elec2,0.828912599926414,0.7954572719638501,0.44604015350341797,180.84818699999997 -30804,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8286855176443852,0.7940682926829268,0.4524259567260742,190.78517699999998 -31710,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8270522564571573,0.7921309984080055,0.31789684295654297,200.97628499999996 -32616,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8257550206959988,0.7908123826701513,0.5092306137084961,211.41806699999995 -33522,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8259001819754781,0.7917201998572448,0.45040416717529297,222.10332899999995 -34428,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8258634211520028,0.7914564998086757,0.33066463470458984,233.03381899999994 -35334,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8252625024764385,0.7899285471248724,0.49124813079833984,244.20613599999993 -36240,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8261541433262507,0.7894103489771359,0.5119619369506836,255.69628199999994 -37146,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8248754879526181,0.7869936802121876,0.4187917709350586,267.45490699999993 -38052,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8248403458516202,0.7864398090294465,0.4483175277709961,279.43877899999995 -38958,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8243447904099391,0.7868689070919114,0.5077886581420898,291.70049499999993 -39864,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8249504553094348,0.7887758808572466,0.1471853256225586,304.13865899999996 -40770,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8262159974490422,0.7919908399635948,0.22976970672607422,316.729427 -41676,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8275224955008998,0.794816168074903,0.23425960540771484,329.475528 -42582,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8281393109602875,0.7956550876801073,0.3305959701538086,342.401664 -43488,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8284774760273185,0.7958619557185473,0.3348875045776367,355.508994 -44394,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8276530083571735,0.7938902508014333,0.28391170501708984,368.88313999999997 -45300,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8286717146073864,0.795391632174211,0.3993253707885742,382.50372699999997 -25,Binary classification,Hoeffding Adaptive Tree,Phishing,0.5833333333333334,0.6428571428571429,0.07568836212158203,0.0191 -50,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7346938775510204,0.7346938775510203,0.07574939727783203,0.048719 -75,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7837837837837838,0.7894736842105262,0.07574939727783203,0.086072 -100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8080808080808081,0.8080808080808081,0.07581043243408203,0.130387 -125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8225806451612904,0.819672131147541,0.07581043243408203,0.181491 -150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.825503355704698,0.8289473684210527,0.07583332061767578,0.241937 -175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8333333333333334,0.8242424242424242,0.07589435577392578,0.309852 -200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8291457286432161,0.8191489361702128,0.07589435577392578,0.385335 -225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8303571428571429,0.8155339805825242,0.07589435577392578,0.468101 -250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8313253012048193,0.817391304347826,0.07589435577392578,0.558214 -275,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8321167883211679,0.8174603174603176,0.07589435577392578,0.656226 -300,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8361204013377926,0.8178438661710038,0.07589435577392578,0.765253 -325,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8425925925925926,0.8197879858657244,0.07595539093017578,0.883707 -350,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8481375358166189,0.822742474916388,0.07595539093017578,1.011557 -375,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8502673796791443,0.8227848101265823,0.07595539093017578,1.148821 -400,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8521303258145363,0.8228228228228228,0.07595539093017578,1.2953860000000001 -425,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8537735849056604,0.8208092485549133,0.07595539093017578,1.4534280000000002 -450,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8574610244988864,0.8232044198895027,0.07595539093017578,1.6208710000000002 -475,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8565400843881856,0.8238341968911918,0.07595539093017578,1.7976930000000002 -500,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8557114228456913,0.8260869565217391,0.07595539093017578,1.9839770000000003 -525,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8568702290076335,0.823529411764706,0.07595539093017578,2.179609 -550,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8561020036429873,0.8240534521158129,0.07595539093017578,2.3846100000000003 -575,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8554006968641115,0.8230277185501066,0.11611557006835938,2.6084530000000004 -600,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8547579298831386,0.8176100628930818,0.14400863647460938,2.84161 -625,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8573717948717948,0.8172484599589321,0.14424514770507812,3.082865 -650,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8597842835130971,0.8233009708737864,0.14441299438476562,3.332264 -675,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8590504451038575,0.8263254113345521,0.14447402954101562,3.5897799999999997 -700,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8640915593705293,0.8306595365418894,0.14453506469726562,3.8554809999999997 -725,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8646408839779005,0.8344594594594595,0.14459609985351562,4.1295 -750,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8664886515353805,0.8371335504885993,0.14465713500976562,4.411739 -775,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8643410852713178,0.8330683624801273,0.14468002319335938,4.702108 -800,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8635794743429287,0.8340943683409437,0.14468002319335938,5.000653 -825,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8628640776699029,0.8345534407027819,0.14468002319335938,5.309277 -850,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8645465253239105,0.8364153627311521,0.14474105834960938,5.626329 -875,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8672768878718535,0.838888888888889,0.14474105834960938,5.951525 -900,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8665183537263627,0.8378378378378378,0.14480209350585938,6.284981 -925,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8668831168831169,0.8400520156046815,0.14480209350585938,6.628637 -950,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8661749209694415,0.8410513141426783,0.14486312866210938,6.980485000000001 -975,Binary classification,Hoeffding Adaptive Tree,Phishing,0.86652977412731,0.8414634146341464,0.14486312866210938,7.342308000000001 -1000,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8638638638638638,0.8392434988179669,0.14486312866210938,7.713462000000001 -1025,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8623046875,0.8377445339470656,0.14486312866210938,8.093853000000001 -1050,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8636796949475691,0.8402234636871508,0.14486312866210938,8.485573 -1075,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8649906890130353,0.8429035752979415,0.14486312866210938,8.889089 -1100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8671519563239308,0.8456659619450316,0.14486312866210938,9.305059 -1125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8701067615658363,0.8507157464212679,0.14486312866210938,9.730762 -1150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8720626631853786,0.852852852852853,0.14492416381835938,10.16685 -1175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8713798977853492,0.8521057786483839,0.14492416381835938,10.613004 -1200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.872393661384487,0.8530259365994236,0.14492416381835938,11.069492 -1225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8733660130718954,0.8541862652869238,0.14498519897460938,11.536355 -1250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8742994395516414,0.8560953253895509,0.14498519897460938,12.014925 -1903,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.023916244506835938,0.25062 -3806,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.023977279663085938,0.745512 -5709,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024038314819335938,1.4971299999999998 -7612,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024038314819335938,2.521262 -9515,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024038314819335938,3.817259 -11418,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024099349975585938,5.360746 -13321,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.024099349975585938,7.021896 -15224,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996058595546213,0.625,0.05017566680908203,8.821636 -17127,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9991825294873292,0.46153846153846156,0.045350074768066406,10.797386 -19030,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992642808345158,0.46153846153846156,0.045716285705566406,12.952086 -20933,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9993311675902924,0.46153846153846156,0.045838356018066406,15.282579 -22836,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9993869060652507,0.46153846153846156,0.045594215393066406,17.788716 -24739,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994340690435767,0.46153846153846156,0.045838356018066406,20.470769 -26642,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744942006681,0.5,0.058136940002441406,23.327571 -28545,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995095291479821,0.5,0.058197975158691406,26.358995999999998 -30448,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995401845830459,0.5,0.058197975158691406,29.565096999999998 -32351,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995672333848532,0.5,0.058320045471191406,32.946387 -34254,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995912766764955,0.5,0.058381080627441406,36.502912 -36157,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996127890253347,0.5,0.058442115783691406,40.234574 -38060,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996321500827662,0.5,0.058442115783691406,44.142312000000004 -39963,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996496671838246,0.5,0.058442115783691406,48.227315000000004 -41866,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996655917831124,0.5,0.058442115783691406,52.488216 -43769,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996801316029976,0.5,0.058503150939941406,56.92422 -45672,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996934597446958,0.5,0.058625221252441406,61.535205 -47575,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996636818430235,0.4666666666666667,0.06784915924072266,66.321748 -49478,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996564060068315,0.45161290322580644,0.06784915924072266,71.282934 -51381,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999649669131958,0.5,0.0679788589477539,76.42026 -53284,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996621811834919,0.5,0.0679788589477539,81.732337 -55187,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996738303192839,0.5,0.0679788589477539,87.220308 -57090,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995095377393193,0.41666666666666663,0.1018075942993164,92.923409 -58993,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994914564686738,0.4000000000000001,0.1022958755493164,98.85935500000001 -60896,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995073487150012,0.4000000000000001,0.1024179458618164,105.024921 -62799,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994745055575018,0.3773584905660377,0.1118478775024414,111.43003800000001 -64702,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999489961515278,0.3773584905660377,0.1119699478149414,118.06567900000002 -66605,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995045342622064,0.3773584905660377,0.1120920181274414,124.93220100000002 -68508,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995182974002657,0.42105263157894735,0.1121530532836914,132.02968600000003 -70411,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995313165743502,0.42105263157894735,0.1122140884399414,139.35861700000004 -72314,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995436505192704,0.42105263157894735,0.1123361587524414,146.91762900000003 -74217,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995418777622076,0.41379310344827586,0.1123971939086914,154.70829700000004 -76120,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999553330968615,0.41379310344827586,0.1123971939086914,162.73183200000005 -78023,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99953859167927,0.39999999999999997,0.11986637115478516,170.98992200000006 -79926,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999549577729121,0.39999999999999997,0.12023258209228516,179.48569900000007 -81829,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995600527936648,0.39999999999999997,0.12035465240478516,188.21505800000006 -83732,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995581087052585,0.3934426229508197,0.12047672271728516,197.18003100000004 -85635,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995679286264801,0.3934426229508197,0.12047672271728516,206.38234800000004 -87538,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995773215897278,0.3934426229508197,0.12053775787353516,215.81852000000003 -89441,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995751341681575,0.38709677419354843,0.12986087799072266,225.49133900000004 -91344,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995620901437439,0.37500000000000006,0.12986087799072266,235.39794000000003 -93247,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995388542135856,0.3582089552238806,0.13713932037353516,245.54396400000005 -95150,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995480772262452,0.3582089552238806,0.13726139068603516,255.92452900000004 -106,Binary classification,Adaptive Random Forest,Bananas,0.638095238095238,0.5777777777777778,0.6364564895629883,0.262298 -212,Binary classification,Adaptive Random Forest,Bananas,0.7488151658767772,0.7103825136612022,1.0992326736450195,0.748759 -318,Binary classification,Adaptive Random Forest,Bananas,0.7917981072555205,0.7659574468085106,1.488083839416504,1.463509 -424,Binary classification,Adaptive Random Forest,Bananas,0.8274231678486997,0.8042895442359249,1.8509149551391602,2.4097 -530,Binary classification,Adaptive Random Forest,Bananas,0.831758034026465,0.802660753880266,2.4203081130981445,3.615781 -636,Binary classification,Adaptive Random Forest,Bananas,0.8456692913385827,0.8191881918819188,2.83583927154541,5.093309 -742,Binary classification,Adaptive Random Forest,Bananas,0.8569500674763832,0.8284789644012946,3.2995615005493164,6.854744 -848,Binary classification,Adaptive Random Forest,Bananas,0.859504132231405,0.8326300984528833,3.4132471084594727,8.933942 -954,Binary classification,Adaptive Random Forest,Bananas,0.863588667366212,0.8370927318295739,3.8167009353637695,11.329061 -1060,Binary classification,Adaptive Random Forest,Bananas,0.8715769593956563,0.8454545454545456,4.3293962478637695,14.065921 -1166,Binary classification,Adaptive Random Forest,Bananas,0.8738197424892704,0.8489208633093526,4.776837348937988,17.153408 -1272,Binary classification,Adaptive Random Forest,Bananas,0.8749016522423289,0.8512628624883068,5.179757118225098,20.492268 -1378,Binary classification,Adaptive Random Forest,Bananas,0.8765432098765432,0.8516579406631763,5.6712846755981445,24.00032 -1484,Binary classification,Adaptive Random Forest,Bananas,0.8813216453135536,0.8580645161290322,6.118577003479004,27.676707 -1590,Binary classification,Adaptive Random Forest,Bananas,0.8779106356198867,0.8554396423248881,6.625298500061035,31.525227 -1696,Binary classification,Adaptive Random Forest,Bananas,0.879646017699115,0.8573426573426574,5.8858842849731445,35.585591 -1802,Binary classification,Adaptive Random Forest,Bananas,0.8800666296501943,0.8590078328981724,6.279637336730957,39.950222000000004 -1908,Binary classification,Adaptive Random Forest,Bananas,0.8778185631882538,0.8578401464307503,6.042496681213379,44.519385 -2014,Binary classification,Adaptive Random Forest,Bananas,0.877297565822156,0.8584527220630374,6.480931282043457,49.302272 -2120,Binary classification,Adaptive Random Forest,Bananas,0.8801321378008494,0.8631465517241379,6.779278755187988,54.294834 -2226,Binary classification,Adaptive Random Forest,Bananas,0.8791011235955056,0.8621219887237315,7.120572090148926,59.4987 -2332,Binary classification,Adaptive Random Forest,Bananas,0.8781638781638782,0.8611925708699902,7.709580421447754,64.935467 -2438,Binary classification,Adaptive Random Forest,Bananas,0.8789495281083299,0.8618266978922717,8.127808570861816,70.60280800000001 -2544,Binary classification,Adaptive Random Forest,Bananas,0.8788832088084939,0.8624999999999999,8.531121253967285,76.48792700000001 -2650,Binary classification,Adaptive Random Forest,Bananas,0.8803322008305021,0.8645877829987185,8.842900276184082,82.60938300000001 -2756,Binary classification,Adaptive Random Forest,Bananas,0.8827586206896552,0.8671328671328672,9.237275123596191,88.95420100000001 -2862,Binary classification,Adaptive Random Forest,Bananas,0.88325760223698,0.8674603174603175,9.480591773986816,95.534715 -2968,Binary classification,Adaptive Random Forest,Bananas,0.8843950117964273,0.8681276432141485,9.87939167022705,102.339589 -3074,Binary classification,Adaptive Random Forest,Bananas,0.8848031239830785,0.8690828402366864,10.33882999420166,109.37876800000001 -3180,Binary classification,Adaptive Random Forest,Bananas,0.8867568417741428,0.8707824838478105,10.641068458557129,116.664225 -3286,Binary classification,Adaptive Random Forest,Bananas,0.8861491628614916,0.8697771587743733,10.188206672668457,124.173867 -3392,Binary classification,Adaptive Random Forest,Bananas,0.8867590681214981,0.8712273641851107,10.46657657623291,131.921991 -3498,Binary classification,Adaptive Random Forest,Bananas,0.8881898770374607,0.8723473718576559,9.652070045471191,139.895805 -3604,Binary classification,Adaptive Random Forest,Bananas,0.8889814043852345,0.8726925525143221,8.887116432189941,148.067873 -3710,Binary classification,Adaptive Random Forest,Bananas,0.8889188460501483,0.873152709359606,9.264924049377441,156.441655 -3816,Binary classification,Adaptive Random Forest,Bananas,0.89043250327654,0.875,9.5267915725708,165.011312 -3922,Binary classification,Adaptive Random Forest,Bananas,0.8888038765621015,0.8728862973760932,9.897248268127441,173.777209 -4028,Binary classification,Adaptive Random Forest,Bananas,0.8872609883287808,0.8710227272727272,10.25338077545166,182.74826 -4134,Binary classification,Adaptive Random Forest,Bananas,0.8877328816840068,0.8716104039845047,10.579400062561035,191.919884 -4240,Binary classification,Adaptive Random Forest,Bananas,0.8886529841943854,0.8727762803234501,10.81624698638916,201.289592 -4346,Binary classification,Adaptive Random Forest,Bananas,0.8895281933256617,0.8738833420914347,10.986077308654785,210.860402 -4452,Binary classification,Adaptive Random Forest,Bananas,0.8890137047854415,0.8732032854209446,11.276833534240723,220.647955 -4558,Binary classification,Adaptive Random Forest,Bananas,0.8887425938117183,0.8734082397003745,11.626667976379395,230.650874 -4664,Binary classification,Adaptive Random Forest,Bananas,0.8889127171348917,0.8740272373540856,12.038758277893066,240.866021 -4770,Binary classification,Adaptive Random Forest,Bananas,0.887188089746278,0.871843735111958,12.429780006408691,251.436375 -4876,Binary classification,Adaptive Random Forest,Bananas,0.8875897435897436,0.8720224194301728,12.7014741897583,262.227068 -4982,Binary classification,Adaptive Random Forest,Bananas,0.8883758281469585,0.8732907930720145,12.836377143859863,273.239924 -5088,Binary classification,Adaptive Random Forest,Bananas,0.8875565166109691,0.8722644037516749,13.209172248840332,284.48340199999996 -5194,Binary classification,Adaptive Random Forest,Bananas,0.8875409204698633,0.8722100656455142,13.583405494689941,295.95770799999997 -5300,Binary classification,Adaptive Random Forest,Bananas,0.886959803736554,0.8715419257988419,13.845444679260254,307.673049 -906,Binary classification,Adaptive Random Forest,Elec2,0.8674033149171271,0.8669623059866962,3.0924072265625,1.44234 -1812,Binary classification,Adaptive Random Forest,Elec2,0.895085588072888,0.8724832214765101,4.682834625244141,4.396686 -2718,Binary classification,Adaptive Random Forest,Elec2,0.8844313581155686,0.8575317604355717,7.755058288574219,9.151923 -3624,Binary classification,Adaptive Random Forest,Elec2,0.8923544024289263,0.8677069199457259,8.665351867675781,15.539978999999999 -4530,Binary classification,Adaptive Random Forest,Elec2,0.8940163391477147,0.8633257403189066,11.046634674072266,23.437977 -5436,Binary classification,Adaptive Random Forest,Elec2,0.8868445262189513,0.8541617263457434,15.550697326660156,33.146859 -6342,Binary classification,Adaptive Random Forest,Elec2,0.8856647216527361,0.8543884314119301,17.033367156982422,44.650345 -7248,Binary classification,Adaptive Random Forest,Elec2,0.8840899682627295,0.8505869797225187,21.311607360839844,57.88122 -8154,Binary classification,Adaptive Random Forest,Elec2,0.8850729792714338,0.8592881814086198,20.813556671142578,72.837822 -9060,Binary classification,Adaptive Random Forest,Elec2,0.8881775030356551,0.866657891272871,22.198707580566406,89.470796 -9966,Binary classification,Adaptive Random Forest,Elec2,0.8867034621174109,0.8678450193140582,25.867393493652344,108.078283 -10872,Binary classification,Adaptive Random Forest,Elec2,0.8887866801582192,0.8723202027669237,24.029823303222656,128.509921 -11778,Binary classification,Adaptive Random Forest,Elec2,0.8868981913899975,0.8695141065830722,23.736316680908203,150.696171 -12684,Binary classification,Adaptive Random Forest,Elec2,0.8834660569265946,0.8662201303403331,17.480976104736328,174.61452699999998 -13590,Binary classification,Adaptive Random Forest,Elec2,0.8837294870851424,0.8684648684648686,17.212547302246094,200.15146099999998 -14496,Binary classification,Adaptive Random Forest,Elec2,0.8826491893756467,0.8678013522965726,16.54094696044922,227.26288399999999 -15402,Binary classification,Adaptive Random Forest,Elec2,0.8836439192260243,0.8679439941046426,17.876373291015625,255.95351 -16308,Binary classification,Adaptive Random Forest,Elec2,0.8814006254982523,0.8648119670068503,12.619304656982422,286.224579 -17214,Binary classification,Adaptive Random Forest,Elec2,0.8818334979376053,0.8629380053908356,9.807907104492188,317.969431 -18120,Binary classification,Adaptive Random Forest,Elec2,0.8821126993763453,0.8632872503840245,8.873615264892578,351.00359 -19026,Binary classification,Adaptive Random Forest,Elec2,0.883784494086728,0.8636279528773206,11.29254150390625,385.315707 -19932,Binary classification,Adaptive Random Forest,Elec2,0.8832973759470172,0.8638810861423221,13.30521011352539,421.18981299999996 -20838,Binary classification,Adaptive Random Forest,Elec2,0.8826126601718097,0.8630919064144185,13.75485610961914,458.867106 -21744,Binary classification,Adaptive Random Forest,Elec2,0.8820769902957274,0.8605005440696408,10.461296081542969,498.08269199999995 -22650,Binary classification,Adaptive Random Forest,Elec2,0.8810985032451764,0.8581959875730609,10.731792449951172,538.811106 -23556,Binary classification,Adaptive Random Forest,Elec2,0.880025472298875,0.8563732465948364,8.147632598876953,581.039793 -24462,Binary classification,Adaptive Random Forest,Elec2,0.8793998610032296,0.8547799547110366,10.927783966064453,624.8074220000001 -25368,Binary classification,Adaptive Random Forest,Elec2,0.8777545630149407,0.8528029619784497,11.403583526611328,670.1639380000001 -26274,Binary classification,Adaptive Random Forest,Elec2,0.8783922658242302,0.8533663775299463,14.942352294921875,717.1095470000001 -27180,Binary classification,Adaptive Random Forest,Elec2,0.8798704882446006,0.855843525100446,14.17806625366211,765.6544720000002 -28086,Binary classification,Adaptive Random Forest,Elec2,0.8780843866832829,0.8531732418524872,12.432361602783203,815.7617920000001 -28992,Binary classification,Adaptive Random Forest,Elec2,0.8780656065675555,0.852406997620141,10.199352264404297,867.3023770000001 -29898,Binary classification,Adaptive Random Forest,Elec2,0.8784158945713617,0.8527684393859614,13.818798065185547,920.296853 -30804,Binary classification,Adaptive Random Forest,Elec2,0.8787455767295393,0.8524356998933271,16.783336639404297,975.029218 -31710,Binary classification,Adaptive Random Forest,Elec2,0.8776057270806396,0.8507250278856878,18.14492416381836,1031.673342 -32616,Binary classification,Adaptive Random Forest,Elec2,0.8769277939598344,0.8501567866208751,18.455127716064453,1090.2745810000001 -33522,Binary classification,Adaptive Random Forest,Elec2,0.8766743235583664,0.8503366881471291,21.331356048583984,1150.683688 -34428,Binary classification,Adaptive Random Forest,Elec2,0.875940395619717,0.8493102353314751,20.511539459228516,1212.996992 -35334,Binary classification,Adaptive Random Forest,Elec2,0.8751591996150907,0.8476285882068464,17.70761489868164,1277.0202020000002 -36240,Binary classification,Adaptive Random Forest,Elec2,0.8746930102927785,0.8461772975170219,18.968151092529297,1342.6432340000001 -37146,Binary classification,Adaptive Random Forest,Elec2,0.8737649750975905,0.8444930852651478,21.176280975341797,1410.0019350000002 -38052,Binary classification,Adaptive Random Forest,Elec2,0.874063756537279,0.8444256866437246,13.57645034790039,1479.1416360000003 -38958,Binary classification,Adaptive Random Forest,Elec2,0.8743999794645378,0.8453001991842929,13.581947326660156,1549.8962470000004 -39864,Binary classification,Adaptive Random Forest,Elec2,0.8743446303589795,0.8465990873732889,12.123741149902344,1622.0853150000003 -40770,Binary classification,Adaptive Random Forest,Elec2,0.8747823100885477,0.8484848484848485,12.675861358642578,1695.6369460000003 -41676,Binary classification,Adaptive Random Forest,Elec2,0.8752969406118776,0.850217598063233,15.801628112792969,1770.5477020000003 -42582,Binary classification,Adaptive Random Forest,Elec2,0.87569573283859,0.8509895554742266,16.52715301513672,1847.0334210000003 -43488,Binary classification,Adaptive Random Forest,Elec2,0.87568698691563,0.8510087090728695,18.189510345458984,1925.1978090000002 -44394,Binary classification,Adaptive Random Forest,Elec2,0.8757236501250197,0.8505242623750305,18.102394104003906,2005.1269950000003 -45300,Binary classification,Adaptive Random Forest,Elec2,0.8766197929314112,0.8519587847323391,20.355426788330078,2086.7182620000003 -25,Binary classification,Adaptive Random Forest,Phishing,0.625,0.7096774193548387,0.42359256744384766,0.126705 -50,Binary classification,Adaptive Random Forest,Phishing,0.7346938775510204,0.7450980392156864,0.6303834915161133,0.333958 -75,Binary classification,Adaptive Random Forest,Phishing,0.7837837837837838,0.7999999999999999,0.8403291702270508,0.61983 -100,Binary classification,Adaptive Random Forest,Phishing,0.797979797979798,0.8039215686274509,0.9226388931274414,0.981624 -125,Binary classification,Adaptive Random Forest,Phishing,0.7903225806451613,0.7968749999999999,1.0709314346313477,1.421284 -150,Binary classification,Adaptive Random Forest,Phishing,0.8120805369127517,0.8227848101265823,1.1753358840942383,1.9403350000000001 -175,Binary classification,Adaptive Random Forest,Phishing,0.8390804597701149,0.8372093023255814,1.2494592666625977,2.543018 -200,Binary classification,Adaptive Random Forest,Phishing,0.8442211055276382,0.8426395939086295,1.3681573867797852,3.2301029999999997 -225,Binary classification,Adaptive Random Forest,Phishing,0.8526785714285714,0.8465116279069769,1.4882898330688477,4.000846999999999 -250,Binary classification,Adaptive Random Forest,Phishing,0.8433734939759037,0.8354430379746836,1.6624422073364258,4.857951999999999 -275,Binary classification,Adaptive Random Forest,Phishing,0.843065693430657,0.833976833976834,1.7254152297973633,5.796970999999999 -300,Binary classification,Adaptive Random Forest,Phishing,0.8494983277591973,0.8375451263537907,1.8179521560668945,6.820338 -325,Binary classification,Adaptive Random Forest,Phishing,0.8580246913580247,0.8424657534246577,1.875351905822754,7.930464 -350,Binary classification,Adaptive Random Forest,Phishing,0.8595988538681948,0.8414239482200646,2.064530372619629,9.128264999999999 -375,Binary classification,Adaptive Random Forest,Phishing,0.8582887700534759,0.8379204892966361,2.210324287414551,10.415761 -400,Binary classification,Adaptive Random Forest,Phishing,0.8646616541353384,0.8439306358381503,2.3119516372680664,11.792425 -425,Binary classification,Adaptive Random Forest,Phishing,0.8679245283018868,0.8435754189944134,2.393784523010254,13.261163 -450,Binary classification,Adaptive Random Forest,Phishing,0.8752783964365256,0.8502673796791443,2.504483222961426,14.820437 -475,Binary classification,Adaptive Random Forest,Phishing,0.8776371308016878,0.8550000000000001,2.601761817932129,16.462295 -500,Binary classification,Adaptive Random Forest,Phishing,0.8797595190380761,0.8598130841121494,2.5846261978149414,18.198054000000003 -525,Binary classification,Adaptive Random Forest,Phishing,0.8816793893129771,0.859090909090909,2.742630958557129,20.024147000000003 -550,Binary classification,Adaptive Random Forest,Phishing,0.8779599271402551,0.855291576673866,2.8854761123657227,21.959689000000004 -575,Binary classification,Adaptive Random Forest,Phishing,0.8763066202090593,0.8530020703933747,3.0752573013305664,24.005933000000006 -600,Binary classification,Adaptive Random Forest,Phishing,0.8797996661101837,0.8548387096774194,3.1360864639282227,26.161920000000006 -625,Binary classification,Adaptive Random Forest,Phishing,0.8830128205128205,0.8554455445544554,3.285130500793457,28.421390000000006 -650,Binary classification,Adaptive Random Forest,Phishing,0.8859784283513097,0.8609022556390977,3.3397645950317383,30.794885000000008 -675,Binary classification,Adaptive Random Forest,Phishing,0.8887240356083086,0.8672566371681416,3.5764551162719727,33.284549000000005 -700,Binary classification,Adaptive Random Forest,Phishing,0.8927038626609443,0.8704663212435233,3.464848518371582,35.882938 -725,Binary classification,Adaptive Random Forest,Phishing,0.893646408839779,0.8735632183908045,3.70070743560791,38.601152000000006 -750,Binary classification,Adaptive Random Forest,Phishing,0.8958611481975968,0.8765822784810127,3.883671760559082,41.43692500000001 -775,Binary classification,Adaptive Random Forest,Phishing,0.896640826873385,0.8769230769230768,4.02083683013916,44.39908700000001 -800,Binary classification,Adaptive Random Forest,Phishing,0.8948685857321652,0.8761061946902655,4.127713203430176,47.483821000000006 -825,Binary classification,Adaptive Random Forest,Phishing,0.8944174757281553,0.8765957446808511,4.256714820861816,50.69677800000001 -850,Binary classification,Adaptive Random Forest,Phishing,0.8963486454652533,0.8784530386740332,4.299836158752441,54.03081400000001 -875,Binary classification,Adaptive Random Forest,Phishing,0.8993135011441648,0.8814016172506738,4.410748481750488,57.488212000000004 -900,Binary classification,Adaptive Random Forest,Phishing,0.8987764182424917,0.8804204993429698,4.566498756408691,61.07896100000001 -925,Binary classification,Adaptive Random Forest,Phishing,0.9015151515151515,0.8846641318124209,4.655289649963379,64.810524 -950,Binary classification,Adaptive Random Forest,Phishing,0.9030558482613277,0.8878048780487805,4.339470863342285,68.683627 -975,Binary classification,Adaptive Random Forest,Phishing,0.9034907597535934,0.8880952380952382,4.029709815979004,72.689378 -1000,Binary classification,Adaptive Random Forest,Phishing,0.9029029029029029,0.8876013904982619,3.6762208938598633,76.821914 -1025,Binary classification,Adaptive Random Forest,Phishing,0.9013671875,0.8861330326944759,3.8425302505493164,81.06784200000001 -1050,Binary classification,Adaptive Random Forest,Phishing,0.9027645376549094,0.8881578947368421,3.957364082336426,85.43608100000002 -1075,Binary classification,Adaptive Random Forest,Phishing,0.9031657355679702,0.8893617021276596,4.039715766906738,89.92310000000002 -1100,Binary classification,Adaptive Random Forest,Phishing,0.9044585987261147,0.8911917098445594,4.059922218322754,94.51467400000001 -1125,Binary classification,Adaptive Random Forest,Phishing,0.9065836298932385,0.8946840521564694,4.122437477111816,99.180878 -1150,Binary classification,Adaptive Random Forest,Phishing,0.9077458659704091,0.8958742632612966,4.3834123611450195,103.92086 -1175,Binary classification,Adaptive Random Forest,Phishing,0.9063032367972743,0.8940269749518305,3.936264991760254,108.73406700000001 -1200,Binary classification,Adaptive Random Forest,Phishing,0.9074228523769808,0.8949858088930936,3.502232551574707,113.61840500000001 -1225,Binary classification,Adaptive Random Forest,Phishing,0.9084967320261438,0.8961038961038962,3.7125635147094727,118.566747 -1250,Binary classification,Adaptive Random Forest,Phishing,0.9087269815852682,0.8969258589511755,3.826443672180176,123.577759 -1903,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17264461517333984,2.131651 -3806,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17386531829833984,5.506803 -5709,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17508602142333984,10.14235 -7612,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17508602142333984,15.888209 -9515,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17508602142333984,22.677279 -11418,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17630672454833984,30.407676 -13321,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17630672454833984,39.088803 -15224,Binary classification,Adaptive Random Forest,SMTP,0.9998686198515404,0.9,0.3923320770263672,48.816854 -17127,Binary classification,Adaptive Random Forest,SMTP,0.9998832184981898,0.9166666666666666,0.4169597625732422,59.770396 -19030,Binary classification,Adaptive Random Forest,SMTP,0.9998948972620737,0.9166666666666666,0.4169597625732422,71.94360999999999 -20933,Binary classification,Adaptive Random Forest,SMTP,0.999904452512899,0.9166666666666666,0.4181804656982422,85.33762899999999 -22836,Binary classification,Adaptive Random Forest,SMTP,0.9999124151521787,0.9166666666666666,0.4181804656982422,99.94903 -24739,Binary classification,Adaptive Random Forest,SMTP,0.9999191527205109,0.9166666666666666,0.4181804656982422,115.77779899999999 -26642,Binary classification,Adaptive Random Forest,SMTP,0.9999249277429526,0.923076923076923,0.4445209503173828,132.832942 -28545,Binary classification,Adaptive Random Forest,SMTP,0.999929932735426,0.923076923076923,0.44460105895996094,151.111146 -30448,Binary classification,Adaptive Random Forest,SMTP,0.9999343120832923,0.923076923076923,0.44460105895996094,170.611749 -32351,Binary classification,Adaptive Random Forest,SMTP,0.9999381761978362,0.923076923076923,0.44460105895996094,191.33527700000002 -34254,Binary classification,Adaptive Random Forest,SMTP,0.9999416109537851,0.923076923076923,0.4561443328857422,213.28333600000002 -36157,Binary classification,Adaptive Random Forest,SMTP,0.9999446841464764,0.923076923076923,0.4642963409423828,236.45182400000002 -38060,Binary classification,Adaptive Random Forest,SMTP,0.9999474500118237,0.923076923076923,0.4643535614013672,260.85276600000003 -39963,Binary classification,Adaptive Random Forest,SMTP,0.9999499524548321,0.923076923076923,0.4643535614013672,286.481847 -41866,Binary classification,Adaptive Random Forest,SMTP,0.9999522273975875,0.923076923076923,0.4655742645263672,313.33932500000003 -43769,Binary classification,Adaptive Random Forest,SMTP,0.9999543045147139,0.923076923076923,0.4655742645263672,341.422365 -45672,Binary classification,Adaptive Random Forest,SMTP,0.9999562085349566,0.923076923076923,0.4655742645263672,370.73064 -47575,Binary classification,Adaptive Random Forest,SMTP,0.999957960230378,0.923076923076923,0.48300743103027344,401.265116 -49478,Binary classification,Adaptive Random Forest,SMTP,0.9999595771772742,0.923076923076923,0.49118995666503906,433.02892199999997 -51381,Binary classification,Adaptive Random Forest,SMTP,0.9999221486959906,0.8666666666666666,0.5249767303466797,466.02567999999997 -53284,Binary classification,Adaptive Random Forest,SMTP,0.9999249291518871,0.8666666666666666,0.5249767303466797,500.253815 -55187,Binary classification,Adaptive Random Forest,SMTP,0.9999275178487298,0.8666666666666666,0.5554332733154297,535.714615 -57090,Binary classification,Adaptive Random Forest,SMTP,0.9997547688696596,0.6818181818181819,0.8414325714111328,572.520543 -58993,Binary classification,Adaptive Random Forest,SMTP,0.999762679685381,0.6818181818181819,0.8605022430419922,610.6533019999999 -60896,Binary classification,Adaptive Random Forest,SMTP,0.9997700960670006,0.6818181818181819,0.8891468048095703,650.109621 -62799,Binary classification,Adaptive Random Forest,SMTP,0.9997452148157585,0.6521739130434783,1.0364971160888672,690.9092899999999 -64702,Binary classification,Adaptive Random Forest,SMTP,0.9997527086134681,0.6521739130434783,1.061361312866211,733.0436639999999 -66605,Binary classification,Adaptive Random Forest,SMTP,0.9997597741877364,0.6521739130434783,1.0615253448486328,776.5083099999999 -68508,Binary classification,Adaptive Random Forest,SMTP,0.9997664472243712,0.68,1.1361942291259766,821.3091039999999 -70411,Binary classification,Adaptive Random Forest,SMTP,0.9997727595512002,0.68,1.1363086700439453,867.450083 -72314,Binary classification,Adaptive Random Forest,SMTP,0.9997787396457068,0.68,1.1362667083740234,914.92252 -74217,Binary classification,Adaptive Random Forest,SMTP,0.9997844130645683,0.68,1.1445026397705078,963.73181 -76120,Binary classification,Adaptive Random Forest,SMTP,0.99978980280876,0.68,1.144460678100586,1013.878015 -78023,Binary classification,Adaptive Random Forest,SMTP,0.9997949296352311,0.68,1.1445598602294922,1065.363214 -79926,Binary classification,Adaptive Random Forest,SMTP,0.9997998123240538,0.68,1.154123306274414,1118.183887 -81829,Binary classification,Adaptive Random Forest,SMTP,0.9998044679082955,0.68,1.1541500091552734,1172.3398009999999 -83732,Binary classification,Adaptive Random Forest,SMTP,0.9998089118725442,0.68,1.1554203033447266,1227.8271129999998 -85635,Binary classification,Adaptive Random Forest,SMTP,0.9998131583249644,0.68,1.1554012298583984,1284.6488259999999 -87538,Binary classification,Adaptive Random Forest,SMTP,0.9998172201469093,0.68,1.1554012298583984,1342.8019989999998 -89441,Binary classification,Adaptive Random Forest,SMTP,0.999798747763864,0.6538461538461539,1.2423763275146484,1402.2973039999997 -91344,Binary classification,Adaptive Random Forest,SMTP,0.9998029405646848,0.6538461538461539,1.2579975128173828,1463.1308069999998 -93247,Binary classification,Adaptive Random Forest,SMTP,0.9998069622289428,0.6538461538461539,1.3168392181396484,1525.3166049999998 -95150,Binary classification,Adaptive Random Forest,SMTP,0.9998108230249398,0.6538461538461539,1.3265628814697266,1588.8623109999999 -106,Binary classification,Streaming Random Patches,Bananas,0.5238095238095238,0.41860465116279066,0.2278289794921875,0.437833 -212,Binary classification,Streaming Random Patches,Bananas,0.5308056872037915,0.4530386740331491,0.5808591842651367,1.338953 -318,Binary classification,Streaming Random Patches,Bananas,0.6025236593059937,0.5467625899280575,1.0978193283081055,2.727335 -424,Binary classification,Streaming Random Patches,Bananas,0.6690307328605201,0.6236559139784946,1.4776067733764648,4.6028839999999995 -530,Binary classification,Streaming Random Patches,Bananas,0.7069943289224953,0.6547884187082404,1.862696647644043,7.038838 -636,Binary classification,Streaming Random Patches,Bananas,0.7401574803149606,0.6983546617915906,2.7163190841674805,10.152007000000001 -742,Binary classification,Streaming Random Patches,Bananas,0.7624831309041835,0.7188498402555912,3.166998863220215,14.047877000000002 -848,Binary classification,Streaming Random Patches,Bananas,0.7792207792207793,0.7399165507649514,3.2281599044799805,18.677316 -954,Binary classification,Streaming Random Patches,Bananas,0.7911857292759706,0.754017305315204,2.101862907409668,23.96263 -1060,Binary classification,Streaming Random Patches,Bananas,0.8035882908404155,0.7657657657657657,1.8145971298217773,29.783676 -1166,Binary classification,Streaming Random Patches,Bananas,0.8077253218884121,0.7723577235772358,2.202631950378418,35.938969 -1272,Binary classification,Streaming Random Patches,Bananas,0.8127458693941778,0.7800369685767098,2.1398725509643555,42.34268 -1378,Binary classification,Streaming Random Patches,Bananas,0.8191721132897604,0.7855297157622738,2.4873228073120117,48.992986 -1484,Binary classification,Streaming Random Patches,Bananas,0.8240053944706676,0.7916999201915402,2.964076042175293,55.892559000000006 -1590,Binary classification,Streaming Random Patches,Bananas,0.8244178728760226,0.7934863064396744,3.3597822189331055,63.05187300000001 -1696,Binary classification,Streaming Random Patches,Bananas,0.8283185840707965,0.797776233495483,3.6371755599975586,70.47404700000001 -1802,Binary classification,Streaming Random Patches,Bananas,0.8306496390893948,0.802588996763754,4.0314741134643555,78.15954100000002 -1908,Binary classification,Streaming Random Patches,Bananas,0.8311484006292607,0.8048484848484848,4.422553062438965,86.11403800000002 -2014,Binary classification,Streaming Random Patches,Bananas,0.8310978638847492,0.806378132118451,4.790541648864746,94.35073800000002 -2120,Binary classification,Streaming Random Patches,Bananas,0.8343558282208589,0.8119978575254418,4.553057670593262,102.86313600000003 -2226,Binary classification,Streaming Random Patches,Bananas,0.8350561797752809,0.8126595201633486,4.935397148132324,111.64595000000003 -2332,Binary classification,Streaming Random Patches,Bananas,0.8361218361218361,0.8140214216163584,5.2692365646362305,120.70485400000003 -2438,Binary classification,Streaming Random Patches,Bananas,0.8379154698399671,0.8156789547363509,5.532515525817871,130.039733 -2544,Binary classification,Streaming Random Patches,Bananas,0.8391663389697208,0.8178173719376391,5.79874324798584,139.65537500000002 -2650,Binary classification,Streaming Random Patches,Bananas,0.840317100792752,0.81976991904559,5.978323936462402,149.55427200000003 -2756,Binary classification,Streaming Random Patches,Bananas,0.8442831215970962,0.8242523555919706,6.180487632751465,159.73621300000002 -2862,Binary classification,Streaming Random Patches,Bananas,0.846906675987417,0.826603325415677,6.367312431335449,170.195916 -2968,Binary classification,Streaming Random Patches,Bananas,0.8490057296932929,0.828352490421456,6.73636531829834,180.938789 -3074,Binary classification,Streaming Random Patches,Bananas,0.8503091441588024,0.8303834808259588,6.987029075622559,191.964907 -3180,Binary classification,Streaming Random Patches,Bananas,0.8530984586347908,0.832796276405299,7.20963191986084,203.271668 -3286,Binary classification,Streaming Random Patches,Bananas,0.8535768645357686,0.8329281000347343,7.467520713806152,214.85883900000002 -3392,Binary classification,Streaming Random Patches,Bananas,0.8554998525508699,0.8360107095046854,7.765227317810059,226.73409900000001 -3498,Binary classification,Streaming Random Patches,Bananas,0.8573062625107235,0.8374063212772891,8.026595115661621,238.898839 -3604,Binary classification,Streaming Random Patches,Bananas,0.8592839300582847,0.8388941849380362,8.217352867126465,251.354959 -3710,Binary classification,Streaming Random Patches,Bananas,0.8603397142086816,0.8405172413793103,8.39356517791748,264.100164 -3816,Binary classification,Streaming Random Patches,Bananas,0.8629095674967234,0.843553694286569,8.53998851776123,277.140513 -3922,Binary classification,Streaming Random Patches,Bananas,0.8630451415455241,0.8433945756780402,8.81647777557373,290.481842 -4028,Binary classification,Streaming Random Patches,Bananas,0.8629252545319096,0.8431818181818181,9.181269645690918,304.12213299999996 -4134,Binary classification,Streaming Random Patches,Bananas,0.8637793370433099,0.8442600276625173,9.408194541931152,318.06952099999995 -4240,Binary classification,Streaming Random Patches,Bananas,0.8655343241330502,0.8464439655172414,9.550837516784668,332.31391899999994 -4346,Binary classification,Streaming Random Patches,Bananas,0.8672036823935558,0.8484370895718414,9.808123588562012,346.87205299999994 -4452,Binary classification,Streaming Random Patches,Bananas,0.8669961806335655,0.8480492813141683,10.045561790466309,361.75512599999996 -4558,Binary classification,Streaming Random Patches,Bananas,0.8670177748518763,0.848424212106053,10.332926750183105,376.95892399999997 -4664,Binary classification,Streaming Random Patches,Bananas,0.8674672957323611,0.8494152046783626,10.668997764587402,392.48464199999995 -4770,Binary classification,Streaming Random Patches,Bananas,0.866429020759069,0.8480076354092102,11.001662254333496,408.34403199999997 -4876,Binary classification,Streaming Random Patches,Bananas,0.8666666666666667,0.8477751756440282,11.155909538269043,424.538143 -4982,Binary classification,Streaming Random Patches,Bananas,0.8678980124472997,0.8495656149977137,11.33142375946045,441.055719 -5088,Binary classification,Streaming Random Patches,Bananas,0.868291724002359,0.8499776085982983,11.580191612243652,457.901482 -5194,Binary classification,Streaming Random Patches,Bananas,0.868476795686501,0.8501864443957009,11.828421592712402,475.084789 -5300,Binary classification,Streaming Random Patches,Bananas,0.8690318928099642,0.850816852966466,12.135478019714355,492.613208 -906,Binary classification,Streaming Random Patches,Elec2,0.8839779005524862,0.8810872027180068,5.370833396911621,5.916109 -1812,Binary classification,Streaming Random Patches,Elec2,0.9033683048039757,0.8805460750853241,9.031210899353027,15.079536999999998 -2718,Binary classification,Streaming Random Patches,Elec2,0.9006256900993743,0.8771610555050046,13.98334789276123,27.621333 -3624,Binary classification,Streaming Random Patches,Elec2,0.9014628760695557,0.8784473953013278,18.162775993347168,43.541312 -4530,Binary classification,Streaming Random Patches,Elec2,0.9008611172444249,0.87226173541963,21.492114067077637,63.022082 -5436,Binary classification,Streaming Random Patches,Elec2,0.8943882244710212,0.863398381722989,25.749701499938965,86.108323 -6342,Binary classification,Streaming Random Patches,Elec2,0.8941807285917048,0.86403242147923,30.034838676452637,112.908333 -7248,Binary classification,Streaming Random Patches,Elec2,0.8907133986477163,0.8586723768736617,31.268176078796387,143.442151 -8154,Binary classification,Streaming Random Patches,Elec2,0.8912056911566295,0.8662343537927913,34.28826427459717,177.844322 -9060,Binary classification,Streaming Random Patches,Elec2,0.8912683519152225,0.8698639186154049,37.821166038513184,216.19197300000002 -9966,Binary classification,Streaming Random Patches,Elec2,0.8896136477671851,0.8707706766917294,41.62779140472412,258.820887 -10872,Binary classification,Streaming Random Patches,Elec2,0.8902584858798639,0.8737966783031843,45.202799797058105,305.617005 -11778,Binary classification,Streaming Random Patches,Elec2,0.8899549970281057,0.873015873015873,44.638304710388184,356.880829 -12684,Binary classification,Streaming Random Patches,Elec2,0.8842545139162659,0.8672934369915024,49.475626945495605,412.597623 -13590,Binary classification,Streaming Random Patches,Elec2,0.884538965339613,0.8694566935685165,52.02482509613037,472.74852699999997 -14496,Binary classification,Streaming Random Patches,Elec2,0.8854777509486029,0.871138022046266,51.378371238708496,537.275724 -15402,Binary classification,Streaming Random Patches,Elec2,0.8870203233556263,0.8725461470846764,45.887526512145996,605.997132 -16308,Binary classification,Streaming Random Patches,Elec2,0.8855092904887472,0.8701398066355985,50.451903343200684,678.971724 -17214,Binary classification,Streaming Random Patches,Elec2,0.8856097135885668,0.8680206448153361,42.41952419281006,756.021088 -18120,Binary classification,Streaming Random Patches,Elec2,0.8854241404050996,0.8677032882997705,44.88027477264404,837.367748 -19026,Binary classification,Streaming Random Patches,Elec2,0.8873587385019711,0.8687128591557923,30.162745475769043,922.595636 -19932,Binary classification,Streaming Random Patches,Elec2,0.887612262304952,0.8700243704305443,11.138346672058105,1011.395865 -20838,Binary classification,Streaming Random Patches,Elec2,0.8870758746460623,0.8696182190945864,16.41995906829834,1103.9159909999998 -21744,Binary classification,Streaming Random Patches,Elec2,0.8869981143356482,0.8677539157112869,20.066325187683105,1200.0114859999999 -22650,Binary classification,Streaming Random Patches,Elec2,0.8854254050951477,0.8647944563121971,23.948283195495605,1299.755313 -23556,Binary classification,Streaming Random Patches,Elec2,0.8836765018042878,0.8620481321115698,27.3106107711792,1403.457577 -24462,Binary classification,Streaming Random Patches,Elec2,0.8826294918441601,0.859903381642512,30.048666954040527,1511.3882449999999 -25368,Binary classification,Streaming Random Patches,Elec2,0.8818149564394686,0.8590105342362679,27.528754234313965,1623.579427 -26274,Binary classification,Streaming Random Patches,Elec2,0.882883568682678,0.8599899895345134,32.44980525970459,1739.735045 -27180,Binary classification,Streaming Random Patches,Elec2,0.8840649030501491,0.8618891080429543,36.050021171569824,1859.9378299999998 -28086,Binary classification,Streaming Random Patches,Elec2,0.8826063735089905,0.8595287801968386,42.42660045623779,1984.3543829999999 -28992,Binary classification,Streaming Random Patches,Elec2,0.8825842502845711,0.8587200132813148,47.772982597351074,2113.0065689999997 -29898,Binary classification,Streaming Random Patches,Elec2,0.8824296752182493,0.8583175460518361,47.37248516082764,2246.2362179999996 -30804,Binary classification,Streaming Random Patches,Elec2,0.8822517287277213,0.8574348492590699,51.80053234100342,2383.7768769999993 -31710,Binary classification,Streaming Random Patches,Elec2,0.8812324576618625,0.8558965332517028,57.629515647888184,2525.7469279999996 -32616,Binary classification,Streaming Random Patches,Elec2,0.8799018856354438,0.8544785823085782,57.82863521575928,2672.1227849999996 -33522,Binary classification,Streaming Random Patches,Elec2,0.8803436651651204,0.8553604269589989,49.21776485443115,2822.5668949999995 -34428,Binary classification,Streaming Random Patches,Elec2,0.8800360182414965,0.8549248278769145,40.49333477020264,2976.7213359999996 -35334,Binary classification,Streaming Random Patches,Elec2,0.8794045226841762,0.8535789148139239,46.44182109832764,3134.3897929999994 -36240,Binary classification,Streaming Random Patches,Elec2,0.8791081431606832,0.8524468694217102,49.9929723739624,3295.6545219999994 -37146,Binary classification,Streaming Random Patches,Elec2,0.8779378112801185,0.8505800158186132,54.79160213470459,3460.6177849999995 -38052,Binary classification,Streaming Random Patches,Elec2,0.8777693096107855,0.8499532212794787,58.49489498138428,3629.3978659999993 -38958,Binary classification,Streaming Random Patches,Elec2,0.8784814025720666,0.8511414376454312,60.34530162811279,3802.3246269999995 -39864,Binary classification,Streaming Random Patches,Elec2,0.8790106113438527,0.8529528339278637,65.39763927459717,3979.2983159999994 -40770,Binary classification,Streaming Random Patches,Elec2,0.8795408275895902,0.8548286972715718,71.3544225692749,4160.658093999999 -41676,Binary classification,Streaming Random Patches,Elec2,0.8803359328134374,0.8566995201287319,58.179503440856934,4346.269614 -42582,Binary classification,Streaming Random Patches,Elec2,0.8808858411028393,0.8576000898422146,62.87830638885498,4535.872427 -43488,Binary classification,Streaming Random Patches,Elec2,0.8812518683744567,0.8580850829943938,52.03429698944092,4729.486494000001 -44394,Binary classification,Streaming Random Patches,Elec2,0.8814452729033857,0.8579065309538595,55.38854122161865,4926.889749000001 -45300,Binary classification,Streaming Random Patches,Elec2,0.8822490562705578,0.8591125198098257,58.343642234802246,5128.273015000001 -25,Binary classification,Streaming Random Patches,Phishing,0.75,0.7692307692307692,0.6668167114257812,0.240714 -50,Binary classification,Streaming Random Patches,Phishing,0.7959183673469388,0.7826086956521738,1.0995216369628906,0.6628620000000001 -75,Binary classification,Streaming Random Patches,Phishing,0.8378378378378378,0.8378378378378377,1.2478713989257812,1.252732 -100,Binary classification,Streaming Random Patches,Phishing,0.8686868686868687,0.8686868686868686,1.3291473388671875,2.007362 -125,Binary classification,Streaming Random Patches,Phishing,0.8629032258064516,0.8640000000000001,1.6638565063476562,2.948271 -150,Binary classification,Streaming Random Patches,Phishing,0.8657718120805369,0.8701298701298702,1.6782913208007812,4.062992 -175,Binary classification,Streaming Random Patches,Phishing,0.8850574712643678,0.8809523809523809,1.7604293823242188,5.350090000000001 -200,Binary classification,Streaming Random Patches,Phishing,0.8844221105527639,0.8795811518324608,1.9450531005859375,6.8260000000000005 -225,Binary classification,Streaming Random Patches,Phishing,0.8883928571428571,0.8803827751196173,2.0522689819335938,8.475057 -250,Binary classification,Streaming Random Patches,Phishing,0.8795180722891566,0.8695652173913043,2.2534027099609375,10.305544 -275,Binary classification,Streaming Random Patches,Phishing,0.8795620437956204,0.8685258964143425,2.2874794006347656,12.343079 -300,Binary classification,Streaming Random Patches,Phishing,0.8795986622073578,0.8666666666666666,2.5460891723632812,14.59325 -325,Binary classification,Streaming Random Patches,Phishing,0.8796296296296297,0.8641114982578397,2.7360763549804688,17.040637 -350,Binary classification,Streaming Random Patches,Phishing,0.8739255014326648,0.8562091503267973,2.8272323608398438,19.684707 -375,Binary classification,Streaming Random Patches,Phishing,0.8743315508021391,0.8553846153846153,3.0366439819335938,22.544767999999998 -400,Binary classification,Streaming Random Patches,Phishing,0.8721804511278195,0.8513119533527697,3.1284713745117188,25.604048 -425,Binary classification,Streaming Random Patches,Phishing,0.875,0.8515406162464987,3.107044219970703,28.861271 -450,Binary classification,Streaming Random Patches,Phishing,0.8752783964365256,0.851063829787234,3.134662628173828,32.309061 -475,Binary classification,Streaming Random Patches,Phishing,0.8776371308016878,0.8557213930348259,3.1429481506347656,35.963049 -500,Binary classification,Streaming Random Patches,Phishing,0.8817635270541082,0.8624708624708626,3.273334503173828,39.811295 -525,Binary classification,Streaming Random Patches,Phishing,0.8854961832061069,0.8642533936651584,3.4039268493652344,43.854378000000004 -550,Binary classification,Streaming Random Patches,Phishing,0.8834244080145719,0.8626609442060086,3.5256080627441406,48.092981 -575,Binary classification,Streaming Random Patches,Phishing,0.8832752613240418,0.8618556701030927,3.730621337890625,52.525854 -600,Binary classification,Streaming Random Patches,Phishing,0.8864774624373957,0.8634538152610441,3.6573638916015625,57.153422000000006 -625,Binary classification,Streaming Random Patches,Phishing,0.8862179487179487,0.8605108055009822,3.691375732421875,61.980237 -650,Binary classification,Streaming Random Patches,Phishing,0.889060092449923,0.8656716417910448,3.879222869873047,67.019405 -675,Binary classification,Streaming Random Patches,Phishing,0.8887240356083086,0.8681898066783831,4.001224517822266,72.2172 -700,Binary classification,Streaming Random Patches,Phishing,0.8927038626609443,0.8713550600343053,4.033683776855469,77.519148 -725,Binary classification,Streaming Random Patches,Phishing,0.893646408839779,0.8743882544861339,4.112117767333984,82.931309 -750,Binary classification,Streaming Random Patches,Phishing,0.8958611481975968,0.8773584905660378,4.362846374511719,88.455864 -775,Binary classification,Streaming Random Patches,Phishing,0.8953488372093024,0.8763358778625954,4.623798370361328,94.093371 -800,Binary classification,Streaming Random Patches,Phishing,0.8936170212765957,0.8755490483162518,4.795074462890625,99.843891 -825,Binary classification,Streaming Random Patches,Phishing,0.8932038834951457,0.876056338028169,5.260898590087891,105.71761 -850,Binary classification,Streaming Random Patches,Phishing,0.8939929328621908,0.8767123287671234,5.305454254150391,111.70711899999999 -875,Binary classification,Streaming Random Patches,Phishing,0.8958810068649885,0.8781793842034805,5.302814483642578,117.814416 -900,Binary classification,Streaming Random Patches,Phishing,0.8976640711902113,0.8795811518324608,5.489250183105469,124.034882 -925,Binary classification,Streaming Random Patches,Phishing,0.9004329004329005,0.8838383838383839,5.587982177734375,130.365318 -950,Binary classification,Streaming Random Patches,Phishing,0.9020021074815595,0.8869987849331712,5.680080413818359,136.807919 -975,Binary classification,Streaming Random Patches,Phishing,0.9045174537987679,0.889679715302491,5.6697998046875,143.36438 -1000,Binary classification,Streaming Random Patches,Phishing,0.9049049049049049,0.8901734104046244,5.689472198486328,150.035681 -1025,Binary classification,Streaming Random Patches,Phishing,0.9052734375,0.8911335578002244,5.899868011474609,156.818681 -1050,Binary classification,Streaming Random Patches,Phishing,0.9065776930409915,0.8930131004366813,6.014961242675781,163.714224 -1075,Binary classification,Streaming Random Patches,Phishing,0.9068901303538175,0.8940677966101694,6.185920715332031,170.723809 -1100,Binary classification,Streaming Random Patches,Phishing,0.908098271155596,0.8957688338493291,6.174674987792969,177.84336199999998 -1125,Binary classification,Streaming Random Patches,Phishing,0.9092526690391459,0.8979999999999999,6.282234191894531,185.077801 -1150,Binary classification,Streaming Random Patches,Phishing,0.9103568320278503,0.8991185112634672,6.438121795654297,192.421784 -1175,Binary classification,Streaming Random Patches,Phishing,0.9080068143100511,0.8963531669865643,6.65753173828125,199.88529400000002 -1200,Binary classification,Streaming Random Patches,Phishing,0.9090909090909091,0.8972667295004714,6.8576507568359375,207.46223300000003 -1225,Binary classification,Streaming Random Patches,Phishing,0.9101307189542484,0.898336414048059,6.963230133056641,215.149468 -1250,Binary classification,Streaming Random Patches,Phishing,0.911128903122498,0.8999098286744815,7.0904388427734375,222.947789 -1903,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.16929054260253906,5.29779 -3806,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17051124572753906,13.064902 -5709,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17173194885253906,23.298966999999998 -7612,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17173194885253906,35.999469999999995 -9515,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17173194885253906,51.163714999999996 -11418,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17295265197753906,68.795189 -13321,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.17295265197753906,88.896271 -15224,Binary classification,Streaming Random Patches,SMTP,0.9998029297773107,0.8421052631578948,0.45761585235595703,111.604078 -17127,Binary classification,Streaming Random Patches,SMTP,0.9998248277472849,0.8695652173913044,0.4529237747192383,137.211141 -19030,Binary classification,Streaming Random Patches,SMTP,0.9998423458931105,0.8695652173913044,0.4529237747192383,165.706974 -20933,Binary classification,Streaming Random Patches,SMTP,0.9998566787693484,0.8695652173913044,0.4541444778442383,197.110825 -22836,Binary classification,Streaming Random Patches,SMTP,0.999868622728268,0.8695652173913044,0.4541444778442383,231.41631 -24739,Binary classification,Streaming Random Patches,SMTP,0.9998787290807665,0.8695652173913044,0.4541444778442383,268.619505 -26642,Binary classification,Streaming Random Patches,SMTP,0.9998498554859052,0.8333333333333333,0.4768953323364258,308.72625800000003 -28545,Binary classification,Streaming Random Patches,SMTP,0.999859865470852,0.8333333333333333,0.4768953323364258,351.754387 -30448,Binary classification,Streaming Random Patches,SMTP,0.9998686241665845,0.8333333333333333,0.4768953323364258,397.678816 -32351,Binary classification,Streaming Random Patches,SMTP,0.9998763523956723,0.8333333333333333,0.48476123809814453,446.50390899999996 -34254,Binary classification,Streaming Random Patches,SMTP,0.9998832219075702,0.8333333333333333,0.48471546173095703,498.23545599999994 -36157,Binary classification,Streaming Random Patches,SMTP,0.9998893682929527,0.8333333333333333,0.48471546173095703,552.8655449999999 -38060,Binary classification,Streaming Random Patches,SMTP,0.9998949000236474,0.8333333333333333,0.4847383499145508,610.4097629999999 -39963,Binary classification,Streaming Random Patches,SMTP,0.9998999049096642,0.8333333333333333,0.4847383499145508,670.8597949999998 -41866,Binary classification,Streaming Random Patches,SMTP,0.999904454795175,0.8333333333333333,0.4859590530395508,734.2226509999998 -43769,Binary classification,Streaming Random Patches,SMTP,0.9999086090294279,0.8333333333333333,0.4859590530395508,800.4598269999998 -45672,Binary classification,Streaming Random Patches,SMTP,0.9999124170699131,0.8333333333333333,0.4859590530395508,869.5777599999998 -47575,Binary classification,Streaming Random Patches,SMTP,0.9998738806911338,0.7692307692307692,0.5104570388793945,941.5744449999997 -49478,Binary classification,Streaming Random Patches,SMTP,0.9998787315318228,0.7692307692307692,0.5104570388793945,1016.4635609999998 -51381,Binary classification,Streaming Random Patches,SMTP,0.999883223043986,0.7999999999999999,0.593510627746582,1094.2664539999998 -53284,Binary classification,Streaming Random Patches,SMTP,0.9998873937278306,0.7999999999999999,0.593510627746582,1174.983157 -55187,Binary classification,Streaming Random Patches,SMTP,0.9998912767730946,0.7999999999999999,0.6139116287231445,1258.5948749999998 -57090,Binary classification,Streaming Random Patches,SMTP,0.9997722853789697,0.6829268292682927,1.0072031021118164,1345.2451959999999 -58993,Binary classification,Streaming Random Patches,SMTP,0.9997796311364253,0.6829268292682927,1.0209360122680664,1434.9545749999997 -60896,Binary classification,Streaming Random Patches,SMTP,0.9997865177765005,0.6829268292682927,1.0209360122680664,1527.6493129999997 -62799,Binary classification,Streaming Random Patches,SMTP,0.9997611388897736,0.6511627906976744,1.1647844314575195,1623.3843759999997 -64702,Binary classification,Streaming Random Patches,SMTP,0.9997681643251264,0.6511627906976744,1.185868263244629,1722.1888749999998 -66605,Binary classification,Streaming Random Patches,SMTP,0.999774788301003,0.6511627906976744,1.195298194885254,1824.0406229999999 -68508,Binary classification,Streaming Random Patches,SMTP,0.9997810442728481,0.6808510638297872,1.2139558792114258,1928.948048 -70411,Binary classification,Streaming Random Patches,SMTP,0.9997869620792501,0.6808510638297872,1.2139787673950195,2036.917723 -72314,Binary classification,Streaming Random Patches,SMTP,0.9997925684178501,0.6808510638297872,1.2139787673950195,2147.927521 -74217,Binary classification,Streaming Random Patches,SMTP,0.9997978872480328,0.6808510638297872,1.2303056716918945,2261.972689 -76120,Binary classification,Streaming Random Patches,SMTP,0.9998029401332125,0.6808510638297872,1.2302827835083008,2379.0496510000003 -78023,Binary classification,Streaming Random Patches,SMTP,0.9998077465330292,0.6808510638297872,1.2303743362426758,2499.17284 -79926,Binary classification,Streaming Random Patches,SMTP,0.9998123240538004,0.6808510638297872,1.2315950393676758,2622.406245 -81829,Binary classification,Streaming Random Patches,SMTP,0.999816688664027,0.6808510638297872,1.2398500442504883,2748.681939 -83732,Binary classification,Streaming Random Patches,SMTP,0.9998208548805102,0.6808510638297872,1.2410707473754883,2878.007463 -85635,Binary classification,Streaming Random Patches,SMTP,0.9998248359296541,0.6808510638297872,1.2410707473754883,3010.373781 -87538,Binary classification,Streaming Random Patches,SMTP,0.9998286438877275,0.6808510638297872,1.2410707473754883,3145.7757699999997 -89441,Binary classification,Streaming Random Patches,SMTP,0.9998211091234347,0.6666666666666667,1.338292121887207,3284.247438 -91344,Binary classification,Streaming Random Patches,SMTP,0.9998248360574976,0.6666666666666667,1.3284997940063477,3425.773292 -93247,Binary classification,Streaming Random Patches,SMTP,0.9998284108701714,0.6666666666666667,1.4100160598754883,3570.387533 -95150,Binary classification,Streaming Random Patches,SMTP,0.9998318426888354,0.6666666666666667,1.4101762771606445,3718.075789 -106,Binary classification,k-Nearest Neighbors,Bananas,0.7619047619047619,0.736842105263158,0.041853904724121094,0.035102 -212,Binary classification,k-Nearest Neighbors,Bananas,0.8199052132701422,0.7978723404255319,0.041350364685058594,0.119282 -318,Binary classification,k-Nearest Neighbors,Bananas,0.8391167192429022,0.8197879858657242,0.04188060760498047,0.25200100000000003 -424,Binary classification,k-Nearest Neighbors,Bananas,0.8581560283687943,0.8412698412698413,0.04188060760498047,0.433555 -530,Binary classification,k-Nearest Neighbors,Bananas,0.8525519848771267,0.8266666666666667,0.04137706756591797,0.663618 -636,Binary classification,k-Nearest Neighbors,Bananas,0.8488188976377953,0.8222222222222222,0.04188060760498047,0.942176 -742,Binary classification,k-Nearest Neighbors,Bananas,0.8461538461538461,0.8155339805825242,0.04188060760498047,1.268723 -848,Binary classification,k-Nearest Neighbors,Bananas,0.8488783943329398,0.8217270194986072,0.04137706756591797,1.64013 -954,Binary classification,k-Nearest Neighbors,Bananas,0.8541448058761805,0.8268991282689911,0.04188060760498047,2.060089 -1060,Binary classification,k-Nearest Neighbors,Bananas,0.8583569405099151,0.8299319727891157,0.04188060760498047,2.5284690000000003 -1166,Binary classification,k-Nearest Neighbors,Bananas,0.8549356223175966,0.8263103802672147,0.04137706756591797,3.0446800000000005 -1272,Binary classification,k-Nearest Neighbors,Bananas,0.8575924468922108,0.8309990662931841,0.04188060760498047,3.6088570000000004 -1378,Binary classification,k-Nearest Neighbors,Bananas,0.8576615831517792,0.8298611111111109,0.04188060760498047,4.221075000000001 -1484,Binary classification,k-Nearest Neighbors,Bananas,0.8604180714767363,0.833467417538214,0.04137706756591797,4.881183000000001 -1590,Binary classification,k-Nearest Neighbors,Bananas,0.8590308370044053,0.8318318318318318,0.04188060760498047,5.5893250000000005 -1696,Binary classification,k-Nearest Neighbors,Bananas,0.8613569321533924,0.8341566690190544,0.04137706756591797,6.345487 -1802,Binary classification,k-Nearest Neighbors,Bananas,0.8617434758467518,0.836076366030283,0.04137706756591797,7.149425000000001 -1908,Binary classification,k-Nearest Neighbors,Bananas,0.8573675930781332,0.8329238329238329,0.04188060760498047,8.001348 -2014,Binary classification,k-Nearest Neighbors,Bananas,0.8544461003477397,0.8317059161401493,0.04137706756591797,8.901356 -2120,Binary classification,k-Nearest Neighbors,Bananas,0.8551203397829165,0.8343227199136536,0.04137706756591797,9.849324 -2226,Binary classification,k-Nearest Neighbors,Bananas,0.8512359550561798,0.8301693175987686,0.04188060760498047,10.845353999999999 -2332,Binary classification,k-Nearest Neighbors,Bananas,0.8511368511368511,0.8304836345872008,0.04137706756591797,11.889458999999999 -2438,Binary classification,k-Nearest Neighbors,Bananas,0.8518670496512105,0.8312295465170642,0.04137706756591797,12.981528999999998 -2544,Binary classification,k-Nearest Neighbors,Bananas,0.8509634290208415,0.8307280035730238,0.04188060760498047,14.121683999999998 -2650,Binary classification,k-Nearest Neighbors,Bananas,0.8501321253303133,0.8307036247334755,0.04137706756591797,15.309940999999998 -2756,Binary classification,k-Nearest Neighbors,Bananas,0.8500907441016334,0.8310838445807771,0.04137706756591797,16.545870999999998 -2862,Binary classification,k-Nearest Neighbors,Bananas,0.8504019573575673,0.8310970797158642,0.04188060760498047,17.828975 -2968,Binary classification,k-Nearest Neighbors,Bananas,0.8513650151668352,0.8317436093094239,0.04137706756591797,19.159858 -3074,Binary classification,k-Nearest Neighbors,Bananas,0.8490074845427921,0.8294117647058825,0.04188060760498047,20.53851 -3180,Binary classification,k-Nearest Neighbors,Bananas,0.8499528153507392,0.8298251872993221,0.04188060760498047,21.964264999999997 -3286,Binary classification,k-Nearest Neighbors,Bananas,0.8502283105022831,0.8295218295218295,0.04137706756591797,23.437226 -3392,Binary classification,k-Nearest Neighbors,Bananas,0.8493069890887643,0.8294961628294961,0.04188060760498047,24.957963 -3498,Binary classification,k-Nearest Neighbors,Bananas,0.8507291964541035,0.830299089726918,0.04188060760498047,26.526512 -3604,Binary classification,k-Nearest Neighbors,Bananas,0.852345267832362,0.8313253012048194,0.04137706756591797,28.143271 -3710,Binary classification,k-Nearest Neighbors,Bananas,0.8522512806686439,0.8315918869084205,0.04188060760498047,29.808070999999998 -3816,Binary classification,k-Nearest Neighbors,Bananas,0.8521625163826999,0.8315412186379928,0.04188060760498047,31.520677 -3922,Binary classification,k-Nearest Neighbors,Bananas,0.8520785513899516,0.8309037900874635,0.04137706756591797,33.281115 -4028,Binary classification,k-Nearest Neighbors,Bananas,0.8505090638192203,0.8290743895513913,0.04188060760498047,35.088929 -4134,Binary classification,k-Nearest Neighbors,Bananas,0.8499879022501815,0.8286346047540077,0.04188060760498047,36.944224 -4240,Binary classification,k-Nearest Neighbors,Bananas,0.851380042462845,0.8306451612903226,0.04137706756591797,38.847373 -4346,Binary classification,k-Nearest Neighbors,Bananas,0.8515535097813579,0.8308418568056648,0.04188060760498047,40.797646 -4452,Binary classification,k-Nearest Neighbors,Bananas,0.8508200404403505,0.8296562339661364,0.04188060760498047,42.797836000000004 -4558,Binary classification,k-Nearest Neighbors,Bananas,0.8507790212859337,0.8302546180728907,0.04137706756591797,44.846666000000006 -4664,Binary classification,k-Nearest Neighbors,Bananas,0.8496675959682608,0.8294818778885916,0.04188060760498047,46.942725 -4770,Binary classification,k-Nearest Neighbors,Bananas,0.848605577689243,0.8280133396855646,0.04188060760498047,49.086211000000006 -4876,Binary classification,k-Nearest Neighbors,Bananas,0.848,0.8265855370933771,0.04137706756591797,51.27634200000001 -4982,Binary classification,k-Nearest Neighbors,Bananas,0.8490262999397711,0.8281535648994516,0.04188060760498047,53.513903000000006 -5088,Binary classification,k-Nearest Neighbors,Bananas,0.84863377236092,0.8275089605734768,0.04137706756591797,55.79899700000001 -5194,Binary classification,k-Nearest Neighbors,Bananas,0.8488349701521278,0.8278131169116035,0.04137706756591797,58.13196400000001 -5300,Binary classification,k-Nearest Neighbors,Bananas,0.8484619739573505,0.8274231678486997,0.04188060760498047,60.51276400000001 -906,Binary classification,k-Nearest Neighbors,Elec2,0.9049723756906077,0.903153153153153,0.06160545349121094,0.342387 -1812,Binary classification,k-Nearest Neighbors,Elec2,0.9260077305356157,0.9075862068965518,0.06160545349121094,1.0313949999999998 -2718,Binary classification,k-Nearest Neighbors,Elec2,0.9120353330879647,0.8907178783721992,0.06160545349121094,2.0623139999999998 -3624,Binary classification,k-Nearest Neighbors,Elec2,0.9149875793541264,0.8948087431693988,0.06210899353027344,3.4346819999999996 -4530,Binary classification,k-Nearest Neighbors,Elec2,0.9116802826230956,0.8866855524079319,0.06210899353027344,5.146577 -5436,Binary classification,k-Nearest Neighbors,Elec2,0.9098436062557498,0.8844884488448844,0.06210899353027344,7.219511 -6342,Binary classification,k-Nearest Neighbors,Elec2,0.9090048888187983,0.8844382134988984,0.06160545349121094,9.664227 -7248,Binary classification,k-Nearest Neighbors,Elec2,0.9064440458120602,0.881509961551905,0.06160545349121094,12.478866 -8154,Binary classification,k-Nearest Neighbors,Elec2,0.9053109284925794,0.8852556480380499,0.06160545349121094,15.66161 -9060,Binary classification,k-Nearest Neighbors,Elec2,0.9076056959929352,0.8903732809430256,0.06210899353027344,19.203613999999998 -9966,Binary classification,k-Nearest Neighbors,Elec2,0.9092824887104867,0.8943431510051426,0.06210899353027344,23.103247999999997 -10872,Binary classification,k-Nearest Neighbors,Elec2,0.9103118388372735,0.8971193415637859,0.06210899353027344,27.356534999999997 -11778,Binary classification,k-Nearest Neighbors,Elec2,0.9094845886049079,0.896323672437269,0.06210899353027344,31.965635999999996 -12684,Binary classification,k-Nearest Neighbors,Elec2,0.9086178348971063,0.8953120765965135,0.06160545349121094,36.933350999999995 -13590,Binary classification,k-Nearest Neighbors,Elec2,0.9089704908381779,0.8970796239287795,0.06160545349121094,42.24853099999999 -14496,Binary classification,k-Nearest Neighbors,Elec2,0.9087271472921697,0.8973861785464982,0.06160545349121094,47.913715999999994 -15402,Binary classification,k-Nearest Neighbors,Elec2,0.9094863969872086,0.8977556109725686,0.06210899353027344,53.917573999999995 -16308,Binary classification,k-Nearest Neighbors,Elec2,0.9067271723799595,0.8941323867195656,0.06210899353027344,60.251802 -17214,Binary classification,k-Nearest Neighbors,Elec2,0.9044907918433742,0.8901656867985035,0.06210899353027344,66.911875 -18120,Binary classification,k-Nearest Neighbors,Elec2,0.9041889729013742,0.8895674300254454,0.06844902038574219,73.89174799999999 -19026,Binary classification,k-Nearest Neighbors,Elec2,0.9049145860709593,0.8893239522789844,0.06844902038574219,81.18615299999999 -19932,Binary classification,k-Nearest Neighbors,Elec2,0.9037679995986152,0.8889403590040533,0.06844902038574219,88.788198 -20838,Binary classification,k-Nearest Neighbors,Elec2,0.9006094927292796,0.8855990719770204,0.06895256042480469,96.68869799999999 -21744,Binary classification,k-Nearest Neighbors,Elec2,0.8996918548498367,0.8828112406641234,0.06895256042480469,104.88540299999998 -22650,Binary classification,k-Nearest Neighbors,Elec2,0.8992891518389333,0.8815987542174929,0.06895256042480469,113.37844899999999 -23556,Binary classification,k-Nearest Neighbors,Elec2,0.8977711738484399,0.8795999999999999,0.06844902038574219,122.16799199999998 -24462,Binary classification,k-Nearest Neighbors,Elec2,0.8973059155390213,0.8783652914971914,0.06844902038574219,131.253708 -25368,Binary classification,k-Nearest Neighbors,Elec2,0.8955729885284031,0.8765782975352934,0.06844902038574219,140.635731 -26274,Binary classification,k-Nearest Neighbors,Elec2,0.8962052297034979,0.8771012663932579,0.06895256042480469,150.314023 -27180,Binary classification,k-Nearest Neighbors,Elec2,0.896133043894183,0.8774792760730873,0.06895256042480469,160.288049 -28086,Binary classification,k-Nearest Neighbors,Elec2,0.8954602100765533,0.8762330326279403,0.06895256042480469,170.561257 -28992,Binary classification,k-Nearest Neighbors,Elec2,0.8944500017246731,0.874518166160912,0.06895256042480469,181.13060800000002 -29898,Binary classification,k-Nearest Neighbors,Elec2,0.8934006756530756,0.8730025901574019,0.06844902038574219,191.99638700000003 -30804,Binary classification,k-Nearest Neighbors,Elec2,0.8926403272408532,0.8713780094123137,0.06844902038574219,203.15792800000003 -31710,Binary classification,k-Nearest Neighbors,Elec2,0.8906304203853795,0.8690233401314299,0.06844902038574219,214.61555800000002 -32616,Binary classification,k-Nearest Neighbors,Elec2,0.8895293576575195,0.8679010082493126,0.06895256042480469,226.36836300000002 -33522,Binary classification,k-Nearest Neighbors,Elec2,0.8885773097461293,0.8667926816220265,0.06895256042480469,238.416278 -34428,Binary classification,k-Nearest Neighbors,Elec2,0.8875010892613355,0.8655721772933948,0.06895256042480469,250.75965100000002 -35334,Binary classification,k-Nearest Neighbors,Elec2,0.88653666544024,0.8635976999761832,0.06844902038574219,263.39898200000005 -36240,Binary classification,k-Nearest Neighbors,Elec2,0.8864483015535749,0.8623423543973505,0.06844902038574219,276.33438000000007 -37146,Binary classification,k-Nearest Neighbors,Elec2,0.8854219948849105,0.8608149650075216,0.06844902038574219,289.5663660000001 -38052,Binary classification,k-Nearest Neighbors,Elec2,0.885574623531576,0.8604308244646749,0.06895256042480469,303.09499200000005 -38958,Binary classification,k-Nearest Neighbors,Elec2,0.8853864517288292,0.8605515475186608,0.06895256042480469,316.91924300000005 -39864,Binary classification,k-Nearest Neighbors,Elec2,0.8853071770815042,0.8614545454545455,0.06895256042480469,331.03949900000003 -40770,Binary classification,k-Nearest Neighbors,Elec2,0.8846672717015379,0.8618034328709147,0.06895256042480469,345.45792700000004 -41676,Binary classification,k-Nearest Neighbors,Elec2,0.8847030593881223,0.862796607749636,0.06844902038574219,360.17251400000004 -42582,Binary classification,k-Nearest Neighbors,Elec2,0.8848547474225593,0.8633767102293309,0.06844902038574219,375.183354 -43488,Binary classification,k-Nearest Neighbors,Elec2,0.8845632027962379,0.86316305947773,0.06844902038574219,390.490282 -44394,Binary classification,k-Nearest Neighbors,Elec2,0.8843511364404298,0.8626023657870793,0.06895256042480469,406.09386 -45300,Binary classification,k-Nearest Neighbors,Elec2,0.8844345349786971,0.8629042817860416,0.06895256042480469,421.994249 -25,Binary classification,k-Nearest Neighbors,Phishing,0.6666666666666666,0.7499999999999999,0.021185874938964844,0.008448 -50,Binary classification,k-Nearest Neighbors,Phishing,0.7959183673469388,0.8076923076923077,0.037901878356933594,0.023055 -75,Binary classification,k-Nearest Neighbors,Phishing,0.8513513513513513,0.8641975308641976,0.054114341735839844,0.045431 -100,Binary classification,k-Nearest Neighbors,Phishing,0.8484848484848485,0.854368932038835,0.07080364227294922,0.07803199999999999 -125,Binary classification,k-Nearest Neighbors,Phishing,0.8548387096774194,0.859375,0.07121944427490234,0.12217799999999998 -150,Binary classification,k-Nearest Neighbors,Phishing,0.8590604026845637,0.8679245283018867,0.07071590423583984,0.17806999999999998 -175,Binary classification,k-Nearest Neighbors,Phishing,0.8735632183908046,0.8735632183908046,0.07121944427490234,0.24575799999999998 -200,Binary classification,k-Nearest Neighbors,Phishing,0.8693467336683417,0.8686868686868686,0.07071590423583984,0.325372 -225,Binary classification,k-Nearest Neighbors,Phishing,0.8616071428571429,0.8571428571428571,0.07121944427490234,0.416902 -250,Binary classification,k-Nearest Neighbors,Phishing,0.8473895582329317,0.8416666666666667,0.07121944427490234,0.520874 -275,Binary classification,k-Nearest Neighbors,Phishing,0.8467153284671532,0.8384615384615385,0.07092952728271484,0.637346 -300,Binary classification,k-Nearest Neighbors,Phishing,0.8494983277591973,0.8375451263537907,0.07143306732177734,0.76611 -325,Binary classification,k-Nearest Neighbors,Phishing,0.8518518518518519,0.8356164383561644,0.07092952728271484,0.9070609999999999 -350,Binary classification,k-Nearest Neighbors,Phishing,0.8538681948424068,0.8349514563106796,0.07092952728271484,1.0603289999999999 -375,Binary classification,k-Nearest Neighbors,Phishing,0.8502673796791443,0.8271604938271604,0.07143306732177734,1.2260179999999998 -400,Binary classification,k-Nearest Neighbors,Phishing,0.849624060150376,0.8224852071005918,0.07092952728271484,1.4039159999999997 -425,Binary classification,k-Nearest Neighbors,Phishing,0.8514150943396226,0.8194842406876792,0.07143306732177734,1.5942239999999996 -450,Binary classification,k-Nearest Neighbors,Phishing,0.8552338530066815,0.8219178082191781,0.07143306732177734,1.7968749999999996 -475,Binary classification,k-Nearest Neighbors,Phishing,0.8481012658227848,0.8134715025906737,0.07092952728271484,2.0118709999999997 -500,Binary classification,k-Nearest Neighbors,Phishing,0.8476953907815631,0.8164251207729469,0.07143306732177734,2.2391799999999997 -525,Binary classification,k-Nearest Neighbors,Phishing,0.8492366412213741,0.8141176470588235,0.07092952728271484,2.479037 -550,Binary classification,k-Nearest Neighbors,Phishing,0.8506375227686703,0.8177777777777777,0.07143306732177734,2.7311829999999997 -575,Binary classification,k-Nearest Neighbors,Phishing,0.8519163763066202,0.8187633262260127,0.07143306732177734,2.9956419999999997 -600,Binary classification,k-Nearest Neighbors,Phishing,0.8514190317195326,0.8149688149688149,0.07092952728271484,3.2723329999999997 -625,Binary classification,k-Nearest Neighbors,Phishing,0.8509615384615384,0.8105906313645621,0.07143306732177734,3.5612229999999996 -650,Binary classification,k-Nearest Neighbors,Phishing,0.8567026194144838,0.8208092485549132,0.07092952728271484,3.8623549999999995 -675,Binary classification,k-Nearest Neighbors,Phishing,0.8590504451038575,0.8275862068965517,0.07143306732177734,4.17585 -700,Binary classification,k-Nearest Neighbors,Phishing,0.8640915593705293,0.831858407079646,0.07143306732177734,4.5015719999999995 -725,Binary classification,k-Nearest Neighbors,Phishing,0.8646408839779005,0.8355704697986577,0.07092952728271484,4.839746999999999 -750,Binary classification,k-Nearest Neighbors,Phishing,0.8624833110814419,0.8341384863123994,0.07143306732177734,5.190338999999999 -775,Binary classification,k-Nearest Neighbors,Phishing,0.8591731266149871,0.8294209702660407,0.07092952728271484,5.552793999999999 -800,Binary classification,k-Nearest Neighbors,Phishing,0.8573216520650814,0.8288288288288288,0.07092952728271484,5.927242999999999 -825,Binary classification,k-Nearest Neighbors,Phishing,0.8567961165048543,0.8299711815561961,0.07143306732177734,6.313648999999999 -850,Binary classification,k-Nearest Neighbors,Phishing,0.8598351001177856,0.8330995792426368,0.07092952728271484,6.712033999999999 -875,Binary classification,k-Nearest Neighbors,Phishing,0.8592677345537757,0.8312757201646092,0.07143306732177734,7.122354999999999 -900,Binary classification,k-Nearest Neighbors,Phishing,0.8587319243604005,0.8304405874499332,0.07092952728271484,7.544645999999999 -925,Binary classification,k-Nearest Neighbors,Phishing,0.8593073593073594,0.8324742268041236,0.07092952728271484,7.978914999999999 -950,Binary classification,k-Nearest Neighbors,Phishing,0.8577449947312961,0.8327137546468402,0.07143306732177734,8.425289 -975,Binary classification,k-Nearest Neighbors,Phishing,0.8613963039014374,0.8367593712212819,0.07092952728271484,8.883884 -1000,Binary classification,k-Nearest Neighbors,Phishing,0.8628628628628628,0.8386336866902238,0.07143306732177734,9.354461 -1025,Binary classification,k-Nearest Neighbors,Phishing,0.8623046875,0.8384879725085911,0.07143306732177734,9.837197 -1050,Binary classification,k-Nearest Neighbors,Phishing,0.86558627264061,0.843159065628476,0.07092952728271484,10.331855 -1075,Binary classification,k-Nearest Neighbors,Phishing,0.8640595903165735,0.8426724137931035,0.07143306732177734,10.838539999999998 -1100,Binary classification,k-Nearest Neighbors,Phishing,0.8653321201091901,0.8445378151260504,0.07092952728271484,11.357143999999998 -1125,Binary classification,k-Nearest Neighbors,Phishing,0.8674377224199288,0.8484231943031536,0.07143306732177734,11.887570999999998 -1150,Binary classification,k-Nearest Neighbors,Phishing,0.8685813751087903,0.8494516450648055,0.07143306732177734,12.429893999999997 -1175,Binary classification,k-Nearest Neighbors,Phishing,0.8679727427597955,0.8484848484848486,0.07092952728271484,12.984101999999996 -1200,Binary classification,k-Nearest Neighbors,Phishing,0.8673894912427023,0.8478468899521532,0.07143306732177734,13.550580999999996 -1225,Binary classification,k-Nearest Neighbors,Phishing,0.8676470588235294,0.848030018761726,0.07092952728271484,14.128897999999996 -1250,Binary classification,k-Nearest Neighbors,Phishing,0.8670936749399519,0.847985347985348,0.07143306732177734,14.719141999999996 -1903,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0443115234375,0.843623 -3806,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0438079833984375,2.5515749999999997 -5709,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0438079833984375,5.191815 -7612,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0443115234375,8.760857 -9515,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0443115234375,12.963128 -11418,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0438079833984375,17.55601 -13321,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,0.0438079833984375,22.538869000000002 -15224,Binary classification,k-Nearest Neighbors,SMTP,0.9999343099257703,0.9523809523809523,0.044338226318359375,27.911978 -17127,Binary classification,k-Nearest Neighbors,SMTP,0.9999416092490949,0.9600000000000001,0.044338226318359375,33.678549000000004 -19030,Binary classification,k-Nearest Neighbors,SMTP,0.9999474486310368,0.9600000000000001,0.043834686279296875,39.834148000000006 -20933,Binary classification,k-Nearest Neighbors,SMTP,0.9999522262564494,0.9600000000000001,0.043834686279296875,46.380227000000005 -22836,Binary classification,k-Nearest Neighbors,SMTP,0.9999562075760894,0.9600000000000001,0.044338226318359375,53.31643100000001 -24739,Binary classification,k-Nearest Neighbors,SMTP,0.9999595763602555,0.9600000000000001,0.044338226318359375,60.64312500000001 -26642,Binary classification,k-Nearest Neighbors,SMTP,0.9999249277429526,0.923076923076923,0.043834686279296875,68.360031 -28545,Binary classification,k-Nearest Neighbors,SMTP,0.999929932735426,0.923076923076923,0.044338226318359375,76.46777800000001 -30448,Binary classification,k-Nearest Neighbors,SMTP,0.9999343120832923,0.923076923076923,0.044338226318359375,84.96788600000001 -32351,Binary classification,k-Nearest Neighbors,SMTP,0.9999381761978362,0.923076923076923,0.043834686279296875,93.86216100000001 -34254,Binary classification,k-Nearest Neighbors,SMTP,0.9999416109537851,0.923076923076923,0.043834686279296875,103.14740500000002 -36157,Binary classification,k-Nearest Neighbors,SMTP,0.9999446841464764,0.923076923076923,0.044338226318359375,112.82379200000003 -38060,Binary classification,k-Nearest Neighbors,SMTP,0.9999474500118237,0.923076923076923,0.044338226318359375,122.89276800000002 -39963,Binary classification,k-Nearest Neighbors,SMTP,0.9999499524548321,0.923076923076923,0.043834686279296875,133.353338 -41866,Binary classification,k-Nearest Neighbors,SMTP,0.9999522273975875,0.923076923076923,0.043834686279296875,144.204522 -43769,Binary classification,k-Nearest Neighbors,SMTP,0.9999543045147139,0.923076923076923,0.044338226318359375,155.44662499999998 -45672,Binary classification,k-Nearest Neighbors,SMTP,0.9999562085349566,0.923076923076923,0.044338226318359375,167.07809799999998 -47575,Binary classification,k-Nearest Neighbors,SMTP,0.999957960230378,0.923076923076923,0.043834686279296875,179.099709 -49478,Binary classification,k-Nearest Neighbors,SMTP,0.9999595771772742,0.923076923076923,0.043834686279296875,191.51085999999998 -51381,Binary classification,k-Nearest Neighbors,SMTP,0.999941611521993,0.896551724137931,0.044338226318359375,204.31316499999997 -53284,Binary classification,k-Nearest Neighbors,SMTP,0.9999436968639153,0.896551724137931,0.044338226318359375,217.50674499999997 -55187,Binary classification,k-Nearest Neighbors,SMTP,0.9999456383865473,0.896551724137931,0.043834686279296875,231.09231699999998 -57090,Binary classification,k-Nearest Neighbors,SMTP,0.9998423514162098,0.8085106382978724,0.044338226318359375,245.069369 -58993,Binary classification,k-Nearest Neighbors,SMTP,0.9998474369406021,0.8085106382978724,0.044338226318359375,259.43782 -60896,Binary classification,k-Nearest Neighbors,SMTP,0.9998522046145004,0.8085106382978724,0.043834686279296875,274.196817 -62799,Binary classification,k-Nearest Neighbors,SMTP,0.999824835185834,0.7755102040816326,0.043834686279296875,289.350286 -64702,Binary classification,k-Nearest Neighbors,SMTP,0.9998299871717593,0.7755102040816326,0.044338226318359375,304.894594 -66605,Binary classification,k-Nearest Neighbors,SMTP,0.9998348447540688,0.7755102040816326,0.044338226318359375,320.829102 -68508,Binary classification,k-Nearest Neighbors,SMTP,0.9998248354182784,0.7692307692307693,0.043834686279296875,337.153755 -70411,Binary classification,k-Nearest Neighbors,SMTP,0.9998295696634001,0.7692307692307693,0.043834686279296875,353.86875 -72314,Binary classification,k-Nearest Neighbors,SMTP,0.9998340547342801,0.7692307692307693,0.044338226318359375,370.973732 -74217,Binary classification,k-Nearest Neighbors,SMTP,0.9998383097984263,0.7692307692307693,0.044338226318359375,388.469718 -76120,Binary classification,k-Nearest Neighbors,SMTP,0.99984235210657,0.7692307692307693,0.043834686279296875,406.356452 -78023,Binary classification,k-Nearest Neighbors,SMTP,0.9998461972264233,0.7692307692307693,0.043834686279296875,424.633943 -79926,Binary classification,k-Nearest Neighbors,SMTP,0.9998498592430404,0.7692307692307693,0.044338226318359375,443.301766 -81829,Binary classification,k-Nearest Neighbors,SMTP,0.9998533509312216,0.7692307692307693,0.044338226318359375,462.36095 -83732,Binary classification,k-Nearest Neighbors,SMTP,0.9998566839044082,0.7692307692307693,0.043834686279296875,481.813199 -85635,Binary classification,k-Nearest Neighbors,SMTP,0.9998598687437232,0.7692307692307693,0.044338226318359375,501.656136 -87538,Binary classification,k-Nearest Neighbors,SMTP,0.999862915110182,0.7692307692307693,0.044338226318359375,521.891566 -89441,Binary classification,k-Nearest Neighbors,SMTP,0.9998434704830054,0.7407407407407408,0.044338226318359375,542.516877 -91344,Binary classification,k-Nearest Neighbors,SMTP,0.9998467315503103,0.7407407407407408,0.043834686279296875,563.531742 -93247,Binary classification,k-Nearest Neighbors,SMTP,0.9998498595113999,0.7407407407407408,0.044338226318359375,584.936913 -95150,Binary classification,k-Nearest Neighbors,SMTP,0.999852862352731,0.7407407407407408,0.044338226318359375,606.731321 -106,Binary classification,ADWIN Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.19251441955566406,0.128533 -212,Binary classification,ADWIN Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.19330787658691406,0.37692000000000003 -318,Binary classification,ADWIN Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.1939868927001953,0.746299 -424,Binary classification,ADWIN Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.1940326690673828,1.2403140000000001 -530,Binary classification,ADWIN Bagging,Bananas,0.55765595463138,0.45581395348837206,0.19405555725097656,1.8468680000000002 -636,Binary classification,ADWIN Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.19471168518066406,2.5750140000000004 -742,Binary classification,ADWIN Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.19475746154785156,3.4160590000000006 -848,Binary classification,ADWIN Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.1946887969970703,4.380047 -954,Binary classification,ADWIN Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.19466590881347656,5.464953 -1060,Binary classification,ADWIN Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.19466590881347656,6.672680000000001 -1166,Binary classification,ADWIN Bagging,Bananas,0.590557939914163,0.4015056461731493,0.1946430206298828,7.997066 -1272,Binary classification,ADWIN Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.1946430206298828,9.441186 -1378,Binary classification,ADWIN Bagging,Bananas,0.599128540305011,0.3973799126637554,0.1952533721923828,11.005176 -1484,Binary classification,ADWIN Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.1952075958251953,12.688563 -1590,Binary classification,ADWIN Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.19518470764160156,14.491668 -1696,Binary classification,ADWIN Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.19518470764160156,16.418884000000002 -1802,Binary classification,ADWIN Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.1952075958251953,18.478568000000003 -1908,Binary classification,ADWIN Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.1952075958251953,20.667158000000004 -2014,Binary classification,ADWIN Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.1952075958251953,22.989297000000004 -2120,Binary classification,ADWIN Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.1952075958251953,25.441149000000003 -2226,Binary classification,ADWIN Bagging,Bananas,0.597752808988764,0.40133779264214053,0.1951618194580078,28.026090000000003 -2332,Binary classification,ADWIN Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.1951618194580078,30.739590000000003 -2438,Binary classification,ADWIN Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.19518470764160156,33.582607 -2544,Binary classification,ADWIN Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.19518470764160156,36.555510000000005 -2650,Binary classification,ADWIN Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.1958179473876953,39.657922000000006 -2756,Binary classification,ADWIN Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.1958179473876953,42.88800400000001 -2862,Binary classification,ADWIN Bagging,Bananas,0.599790283117791,0.4051948051948052,0.1958179473876953,46.243761000000006 -2968,Binary classification,ADWIN Bagging,Bananas,0.599932591843613,0.40261701056869653,0.19584083557128906,49.729156 -3074,Binary classification,ADWIN Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.19584083557128906,53.340997 -3180,Binary classification,ADWIN Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.19584083557128906,57.083209000000004 -3286,Binary classification,ADWIN Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.1913156509399414,60.947023 -3392,Binary classification,ADWIN Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.25013065338134766,64.942815 -3498,Binary classification,ADWIN Bagging,Bananas,0.597369173577352,0.40237691001697795,0.2948274612426758,69.00059 -3604,Binary classification,ADWIN Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3147249221801758,73.119483 -3710,Binary classification,ADWIN Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.3621034622192383,77.300669 -3816,Binary classification,ADWIN Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.3922090530395508,81.542932 -3922,Binary classification,ADWIN Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.42818164825439453,85.85207799999999 -4028,Binary classification,ADWIN Bagging,Bananas,0.608393344921778,0.4195804195804196,0.4565858840942383,90.22588999999999 -4134,Binary classification,ADWIN Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.4708681106567383,94.66819799999999 -4240,Binary classification,ADWIN Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.47220706939697266,99.17538499999999 -4346,Binary classification,ADWIN Bagging,Bananas,0.6186421173762946,0.4384954252795662,0.4545450210571289,103.74793499999998 -4452,Binary classification,ADWIN Bagging,Bananas,0.6212087171422153,0.44209133024487096,0.45485782623291016,108.38424899999998 -4558,Binary classification,ADWIN Bagging,Bananas,0.6214614878209348,0.44372782973234437,0.4440469741821289,113.08568299999999 -4664,Binary classification,ADWIN Bagging,Bananas,0.6219172206733863,0.44542308902170497,0.4133005142211914,117.851137 -4770,Binary classification,ADWIN Bagging,Bananas,0.6227720696162717,0.4449244060475162,0.4420938491821289,122.680723 -4876,Binary classification,ADWIN Bagging,Bananas,0.6235897435897436,0.4444444444444444,0.40938663482666016,127.574861 -4982,Binary classification,ADWIN Bagging,Bananas,0.6251756675366392,0.44910002950722927,0.40953922271728516,132.5326 -5088,Binary classification,ADWIN Bagging,Bananas,0.624139964615687,0.44675925925925924,0.40966129302978516,137.553125 -5194,Binary classification,ADWIN Bagging,Bananas,0.6248796456768727,0.44690516751845544,0.41005802154541016,142.637397 -5300,Binary classification,ADWIN Bagging,Bananas,0.6259671636157765,0.44821826280623617,0.4162149429321289,147.78569 -906,Binary classification,ADWIN Bagging,Elec2,0.8651933701657458,0.8685344827586208,1.6044349670410156,1.944742 -1812,Binary classification,ADWIN Bagging,Elec2,0.8890115958034235,0.8678500986193294,1.9405479431152344,5.818992 -2718,Binary classification,ADWIN Bagging,Elec2,0.8778064041221936,0.8540017590149517,1.8252983093261719,10.85362 -3624,Binary classification,ADWIN Bagging,Elec2,0.8857300579630141,0.8630952380952381,1.6178092956542969,16.937274 -4530,Binary classification,ADWIN Bagging,Elec2,0.8887171561051005,0.8605423353624794,2.356822967529297,24.006141 -5436,Binary classification,ADWIN Bagging,Elec2,0.884820607175713,0.8560257589696412,2.6076393127441406,32.019509 -6342,Binary classification,ADWIN Bagging,Elec2,0.8821952373442674,0.8529238038984053,2.2810935974121094,40.994451 -7248,Binary classification,ADWIN Bagging,Elec2,0.8780184904098247,0.8452380952380951,2.1880455017089844,50.877339 -8154,Binary classification,ADWIN Bagging,Elec2,0.8797988470501655,0.8548148148148149,2.1096839904785156,61.695664 -9060,Binary classification,ADWIN Bagging,Elec2,0.8818854178165361,0.8612191958495461,2.1360397338867188,73.45561000000001 -9966,Binary classification,ADWIN Bagging,Elec2,0.8771700953336679,0.8589861751152074,1.9618644714355469,86.17474100000001 -10872,Binary classification,ADWIN Bagging,Elec2,0.8781160886762948,0.8612710710920323,1.9593772888183594,99.82607500000002 -11778,Binary classification,ADWIN Bagging,Elec2,0.8748407913730152,0.8565868846079004,2.148365020751953,114.35339900000002 -12684,Binary classification,ADWIN Bagging,Elec2,0.8732161160608689,0.8548736462093863,1.7726364135742188,129.76244800000003 -13590,Binary classification,ADWIN Bagging,Elec2,0.8749724041504158,0.8586404858973291,1.6236610412597656,146.05238800000004 -14496,Binary classification,ADWIN Bagging,Elec2,0.8746464298033805,0.858917617827471,1.9961967468261719,163.22720400000003 -15402,Binary classification,ADWIN Bagging,Elec2,0.8757223556911888,0.859347442680776,1.8728065490722656,181.33590900000002 -16308,Binary classification,ADWIN Bagging,Elec2,0.8744097626786043,0.8568432825387949,2.1095123291015625,200.414984 -17214,Binary classification,ADWIN Bagging,Elec2,0.8735839191308894,0.8532703978422117,2.2479400634765625,220.47164 -18120,Binary classification,ADWIN Bagging,Elec2,0.8715712787681439,0.8510338646693554,2.008777618408203,241.502757 -19026,Binary classification,ADWIN Bagging,Elec2,0.872904073587385,0.8509615384615385,0.9681053161621094,263.516635 -19932,Binary classification,ADWIN Bagging,Elec2,0.8660378305152777,0.8433282478582326,0.8690452575683594,286.595649 -20838,Binary classification,ADWIN Bagging,Elec2,0.8576090608052983,0.8330050092868801,0.6502227783203125,310.73945299999997 -21744,Binary classification,ADWIN Bagging,Elec2,0.8574713700961228,0.8301452452726775,0.7763557434082031,335.928343 -22650,Binary classification,ADWIN Bagging,Elec2,0.8562850456973817,0.8269077373039085,1.1911430358886719,362.15469099999996 -23556,Binary classification,ADWIN Bagging,Elec2,0.8503502441095309,0.8183645076518782,1.1728401184082031,389.48303799999996 -24462,Binary classification,ADWIN Bagging,Elec2,0.8480438248640694,0.8142707240293808,1.1973991394042969,417.87456299999997 -25368,Binary classification,ADWIN Bagging,Elec2,0.8443647258248906,0.8102105566772425,1.3723030090332031,447.33757799999995 -26274,Binary classification,ADWIN Bagging,Elec2,0.8447074943858714,0.8100558659217878,1.3146781921386719,477.8580079999999 -27180,Binary classification,ADWIN Bagging,Elec2,0.8453953419919791,0.8118395128067348,1.0832901000976562,509.40859299999994 -28086,Binary classification,ADWIN Bagging,Elec2,0.8421221292504896,0.8068142209829209,1.0510520935058594,541.9996819999999 -28992,Binary classification,ADWIN Bagging,Elec2,0.839570901314201,0.8022113544546035,1.0500526428222656,575.6711059999999 -29898,Binary classification,ADWIN Bagging,Elec2,0.8373749874569355,0.7989247311827957,1.0177803039550781,610.3966439999999 -30804,Binary classification,ADWIN Bagging,Elec2,0.8364445021588807,0.7961149332254148,1.2030601501464844,646.1707399999999 -31710,Binary classification,ADWIN Bagging,Elec2,0.8328234885994512,0.7904660263251513,1.1833610534667969,683.0083409999999 -32616,Binary classification,ADWIN Bagging,Elec2,0.8277479687260463,0.7829714903809009,0.9862403869628906,720.8979669999999 -33522,Binary classification,ADWIN Bagging,Elec2,0.8274216163002297,0.7829675483023824,0.9778022766113281,759.8188009999999 -34428,Binary classification,ADWIN Bagging,Elec2,0.8249339181456415,0.7797229633419832,1.1589393615722656,799.8066919999999 -35334,Binary classification,ADWIN Bagging,Elec2,0.8250643873998811,0.7787838660033642,1.4676322937011719,840.8572599999999 -36240,Binary classification,ADWIN Bagging,Elec2,0.8262093324870995,0.7784422711602055,1.2339591979980469,882.9239929999999 -37146,Binary classification,ADWIN Bagging,Elec2,0.8240409207161126,0.7737625475943233,1.2780303955078125,926.0049009999999 -38052,Binary classification,ADWIN Bagging,Elec2,0.8238679666763029,0.7722731906218145,1.326324462890625,970.0561169999999 -38958,Binary classification,ADWIN Bagging,Elec2,0.8231383320070847,0.7715365740433715,1.1276435852050781,1015.1628579999999 -39864,Binary classification,ADWIN Bagging,Elec2,0.8224920352206306,0.7721975404030647,0.9921760559082031,1061.3434909999999 -40770,Binary classification,ADWIN Bagging,Elec2,0.8226348451028969,0.774362654850688,0.6841468811035156,1108.5828599999998 -41676,Binary classification,ADWIN Bagging,Elec2,0.8231073785242952,0.7766331353775299,0.6752357482910156,1156.8826949999998 -42582,Binary classification,ADWIN Bagging,Elec2,0.8236537422794205,0.7780569266692283,0.8912887573242188,1206.1759319999999 -43488,Binary classification,ADWIN Bagging,Elec2,0.8242693218663049,0.7790690951141949,0.8946151733398438,1256.4285579999998 -44394,Binary classification,ADWIN Bagging,Elec2,0.8235082107539476,0.7769013924086677,0.9767723083496094,1307.6797829999998 -45300,Binary classification,ADWIN Bagging,Elec2,0.823285282235811,0.7772366773340753,0.7331352233886719,1359.9622719999998 -25,Binary classification,ADWIN Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.6845798492431641,0.140915 -50,Binary classification,ADWIN Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.6852588653564453,0.368444 -75,Binary classification,ADWIN Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.6852130889892578,0.67832 -100,Binary classification,ADWIN Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.6858234405517578,1.071639 -125,Binary classification,ADWIN Bagging,Phishing,0.8548387096774194,0.85,0.6858234405517578,1.5472890000000001 -150,Binary classification,ADWIN Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.6858234405517578,2.1056880000000002 -175,Binary classification,ADWIN Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.6864566802978516,2.749261 -200,Binary classification,ADWIN Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.6865940093994141,3.4872330000000002 -225,Binary classification,ADWIN Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7248620986938477,4.314044 -250,Binary classification,ADWIN Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.7525568008422852,5.225865 -275,Binary classification,ADWIN Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7526025772094727,6.222143 -300,Binary classification,ADWIN Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7526025772094727,7.300394 -325,Binary classification,ADWIN Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7532129287719727,8.460934 -350,Binary classification,ADWIN Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7532358169555664,9.705145 -375,Binary classification,ADWIN Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.7908792495727539,11.033679 -400,Binary classification,ADWIN Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.8290948867797852,12.456399999999999 -425,Binary classification,ADWIN Bagging,Phishing,0.8679245283018868,0.839080459770115,0.8842554092407227,13.968094999999998 -450,Binary classification,ADWIN Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.8843240737915039,15.559030999999997 -475,Binary classification,ADWIN Bagging,Phishing,0.869198312236287,0.8402061855670103,0.8843927383422852,17.224721999999996 -500,Binary classification,ADWIN Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.8844156265258789,18.966558999999997 -525,Binary classification,ADWIN Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.8844156265258789,20.785335999999997 -550,Binary classification,ADWIN Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.8844614028930664,22.682567 -575,Binary classification,ADWIN Bagging,Phishing,0.867595818815331,0.8382978723404255,0.8844614028930664,24.656325 -600,Binary classification,ADWIN Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.8844614028930664,26.722161999999997 -625,Binary classification,ADWIN Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.9222650527954102,28.872863 -650,Binary classification,ADWIN Bagging,Phishing,0.8767334360554699,0.846153846153846,0.9229669570922852,31.097205 -675,Binary classification,ADWIN Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.9505243301391602,33.398984999999996 -700,Binary classification,ADWIN Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.8879518508911133,35.778321 -725,Binary classification,ADWIN Bagging,Phishing,0.8825966850828729,0.8576214405360134,0.9880342483520508,38.239126 -750,Binary classification,ADWIN Bagging,Phishing,0.8865153538050734,0.8631239935587761,1.0254030227661133,40.785592 -775,Binary classification,ADWIN Bagging,Phishing,0.8875968992248062,0.863849765258216,1.0804262161254883,43.415615 -800,Binary classification,ADWIN Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.1831789016723633,46.140861 -825,Binary classification,ADWIN Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.1837968826293945,48.939828 -850,Binary classification,ADWIN Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.1943635940551758,51.816843 -875,Binary classification,ADWIN Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.2218294143676758,54.767134 -900,Binary classification,ADWIN Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.2768526077270508,57.795454 -925,Binary classification,ADWIN Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.2769441604614258,60.902165 -950,Binary classification,ADWIN Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.2769899368286133,64.084493 -975,Binary classification,ADWIN Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.2770357131958008,67.34321899999999 -1000,Binary classification,ADWIN Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.277012825012207,70.663215 -1025,Binary classification,ADWIN Bagging,Phishing,0.8876953125,0.8700564971751412,1.2770586013793945,74.022235 -1050,Binary classification,ADWIN Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.2770357131958008,77.421815 -1075,Binary classification,ADWIN Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.2770357131958008,80.860683 -1100,Binary classification,ADWIN Bagging,Phishing,0.89171974522293,0.8761706555671176,1.2770357131958008,84.339469 -1125,Binary classification,ADWIN Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.2770357131958008,87.855325 -1150,Binary classification,ADWIN Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.2770357131958008,91.40832099999999 -1175,Binary classification,ADWIN Bagging,Phishing,0.8926746166950597,0.877906976744186,1.2770357131958008,95.00115199999999 -1200,Binary classification,ADWIN Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.2872819900512695,98.634357 -1225,Binary classification,ADWIN Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3422365188598633,102.306286 -1250,Binary classification,ADWIN Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3423280715942383,106.015532 -1903,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17387676239013672,1.523397 -3806,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17448711395263672,4.675035 -5709,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17502880096435547,8.984261 -7612,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17505168914794922,14.031006 -9515,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17505168914794922,19.82114 -11418,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17566204071044922,26.34929 -13321,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.17568492889404297,33.615204 -15224,Binary classification,ADWIN Bagging,SMTP,0.9993430992577021,0.16666666666666669,0.4191761016845703,41.690008999999996 -17127,Binary classification,ADWIN Bagging,SMTP,0.9992993109891393,0.14285714285714288,0.4156208038330078,50.773073999999994 -19030,Binary classification,ADWIN Bagging,SMTP,0.9993693835724421,0.14285714285714288,0.3918170928955078,60.837182999999996 -20933,Binary classification,ADWIN Bagging,SMTP,0.9994267150773934,0.14285714285714288,0.39249610900878906,71.877708 -22836,Binary classification,ADWIN Bagging,SMTP,0.9994744909130721,0.14285714285714288,0.3925189971923828,83.900187 -24739,Binary classification,ADWIN Bagging,SMTP,0.9995149163230658,0.14285714285714288,0.4046955108642578,96.90004 -26642,Binary classification,ADWIN Bagging,SMTP,0.9995495664577155,0.25,0.42109107971191406,110.87602600000001 -28545,Binary classification,ADWIN Bagging,SMTP,0.999579596412556,0.25,0.42104530334472656,125.82831600000002 -30448,Binary classification,ADWIN Bagging,SMTP,0.9996058724997536,0.25,0.4210681915283203,141.756728 -32351,Binary classification,ADWIN Bagging,SMTP,0.999629057187017,0.25,0.43326759338378906,158.664658 -34254,Binary classification,ADWIN Bagging,SMTP,0.9996496657227104,0.25,0.4455127716064453,176.542575 -36157,Binary classification,ADWIN Bagging,SMTP,0.9996681048788583,0.25,0.44553565979003906,195.393511 -38060,Binary classification,ADWIN Bagging,SMTP,0.9996847000709425,0.25,0.4455127716064453,215.224785 -39963,Binary classification,ADWIN Bagging,SMTP,0.9996997147289926,0.25,0.4455127716064453,236.028977 -41866,Binary classification,ADWIN Bagging,SMTP,0.9997133643855249,0.25,0.4461231231689453,257.804564 -43769,Binary classification,ADWIN Bagging,SMTP,0.9997258270882837,0.25,0.44610023498535156,280.547182 -45672,Binary classification,ADWIN Bagging,SMTP,0.9997372512097392,0.25,0.44610023498535156,304.26359 -47575,Binary classification,ADWIN Bagging,SMTP,0.9997477613822676,0.25,0.4643878936767578,328.95641 -49478,Binary classification,ADWIN Bagging,SMTP,0.9997574630636458,0.25,0.4765186309814453,354.62865 -51381,Binary classification,ADWIN Bagging,SMTP,0.9997469832619696,0.3157894736842105,0.5477771759033203,381.28529299999997 -53284,Binary classification,ADWIN Bagging,SMTP,0.999756019743633,0.3157894736842105,0.5477771759033203,408.929269 -55187,Binary classification,ADWIN Bagging,SMTP,0.9997644330083717,0.3157894736842105,0.5598621368408203,437.55238499999996 -57090,Binary classification,ADWIN Bagging,SMTP,0.9996321533044895,0.3225806451612903,0.9146518707275391,467.22095799999994 -58993,Binary classification,ADWIN Bagging,SMTP,0.9996440195280716,0.3225806451612903,0.9280223846435547,497.92269099999993 -60896,Binary classification,ADWIN Bagging,SMTP,0.9996551441005008,0.3225806451612903,0.9402217864990234,529.6450219999999 -62799,Binary classification,ADWIN Bagging,SMTP,0.9996337462976528,0.303030303030303,1.022481918334961,562.409421 -64702,Binary classification,ADWIN Bagging,SMTP,0.9996445186318604,0.303030303030303,1.0225505828857422,596.2119009999999 -66605,Binary classification,ADWIN Bagging,SMTP,0.9996546753948712,0.303030303030303,1.0225963592529297,631.0619619999999 -68508,Binary classification,ADWIN Bagging,SMTP,0.9996642678850336,0.3783783783783784,1.036111831665039,666.9514569999999 -70411,Binary classification,ADWIN Bagging,SMTP,0.9996733418548501,0.3783783783783784,1.0532817840576172,703.8889889999999 -72314,Binary classification,ADWIN Bagging,SMTP,0.9996819382407036,0.3783783783783784,1.053213119506836,741.862274 -74217,Binary classification,ADWIN Bagging,SMTP,0.9996900937803169,0.3783783783783784,1.0532588958740234,780.872663 -76120,Binary classification,ADWIN Bagging,SMTP,0.9996978415375924,0.3783783783783784,1.0533504486083984,820.917591 -78023,Binary classification,ADWIN Bagging,SMTP,0.9997052113506447,0.3783783783783784,1.0533275604248047,862.00148 -79926,Binary classification,ADWIN Bagging,SMTP,0.9997122302158273,0.3783783783783784,1.0532817840576172,904.126635 -81829,Binary classification,ADWIN Bagging,SMTP,0.9997189226181747,0.3783783783783784,1.0533275604248047,947.288015 -83732,Binary classification,ADWIN Bagging,SMTP,0.9997253108167823,0.3783783783783784,1.0539379119873047,991.4768349999999 -85635,Binary classification,ADWIN Bagging,SMTP,0.9997314150921363,0.3783783783783784,1.0626277923583984,1036.7058969999998 -87538,Binary classification,ADWIN Bagging,SMTP,0.9997372539611822,0.3783783783783784,1.0626049041748047,1082.9660009999998 -89441,Binary classification,ADWIN Bagging,SMTP,0.9997316636851521,0.3684210526315789,1.0989017486572266,1130.2730419999998 -91344,Binary classification,ADWIN Bagging,SMTP,0.9997372540862464,0.3684210526315789,1.0989704132080078,1178.6145779999997 -93247,Binary classification,ADWIN Bagging,SMTP,0.9997426163052571,0.3684210526315789,1.0987415313720703,1227.9921859999997 -95150,Binary classification,ADWIN Bagging,SMTP,0.9997477640332532,0.3684210526315789,1.0987186431884766,1278.4102429999998 -106,Binary classification,AdaBoost,Bananas,0.5523809523809524,0.5252525252525252,0.17705154418945312,0.137989 -212,Binary classification,AdaBoost,Bananas,0.5829383886255924,0.5555555555555555,0.17725753784179688,0.392857 -318,Binary classification,AdaBoost,Bananas,0.6025236593059937,0.5827814569536425,0.17730331420898438,0.757641 -424,Binary classification,AdaBoost,Bananas,0.6099290780141844,0.5758354755784061,0.17730331420898438,1.237112 -530,Binary classification,AdaBoost,Bananas,0.5841209829867675,0.5089285714285714,0.17725753784179688,1.823779 -636,Binary classification,AdaBoost,Bananas,0.5748031496062992,0.4981412639405205,0.17730331420898438,2.523282 -742,Binary classification,AdaBoost,Bananas,0.582995951417004,0.48925619834710743,0.17723464965820312,3.326693 -848,Binary classification,AdaBoost,Bananas,0.5749704840613932,0.4812680115273775,0.17718887329101562,4.242125 -954,Binary classification,AdaBoost,Bananas,0.5760755508919203,0.482051282051282,0.17718887329101562,5.269066 -1060,Binary classification,AdaBoost,Bananas,0.5873465533522191,0.48284023668639053,0.17718887329101562,6.408509 -1166,Binary classification,AdaBoost,Bananas,0.5931330472103005,0.49250535331905776,0.17723464965820312,7.6600589999999995 -1272,Binary classification,AdaBoost,Bananas,0.5979543666404405,0.5034013605442177,0.17723464965820312,9.024008 -1378,Binary classification,AdaBoost,Bananas,0.6005809731299927,0.4990892531876139,0.17723464965820312,10.499633 -1484,Binary classification,AdaBoost,Bananas,0.6089008766014835,0.5117845117845117,0.17723464965820312,12.08531 -1590,Binary classification,AdaBoost,Bananas,0.6091881686595343,0.5121759622937941,0.17723464965820312,13.781775 -1696,Binary classification,AdaBoost,Bananas,0.6135693215339233,0.5194424064563462,0.17723464965820312,15.591861999999999 -1802,Binary classification,AdaBoost,Bananas,0.6185452526374237,0.5354969574036511,0.17723464965820312,17.520521 -1908,Binary classification,AdaBoost,Bananas,0.6208704771893025,0.5467084639498432,0.17725753784179688,19.574793999999997 -2014,Binary classification,AdaBoost,Bananas,0.620963735717834,0.5561372891215823,0.17728042602539062,21.756324999999997 -2120,Binary classification,AdaBoost,Bananas,0.6252949504483247,0.56941431670282,0.17728042602539062,24.064420999999996 -2226,Binary classification,AdaBoost,Bananas,0.6242696629213483,0.5721596724667348,0.17730331420898438,26.498976999999996 -2332,Binary classification,AdaBoost,Bananas,0.6229086229086229,0.5763855421686748,0.17730331420898438,29.055622999999997 -2438,Binary classification,AdaBoost,Bananas,0.62330734509643,0.5796703296703297,0.17730331420898438,31.733449999999998 -2544,Binary classification,AdaBoost,Bananas,0.6244593000393236,0.5860424794104898,0.17730331420898438,34.533392 -2650,Binary classification,AdaBoost,Bananas,0.6266515666289165,0.591828312009905,0.17734909057617188,37.454912 -2756,Binary classification,AdaBoost,Bananas,0.6250453720508167,0.5921831819976313,0.17734909057617188,40.501518 -2862,Binary classification,AdaBoost,Bananas,0.6249563089828731,0.5927893738140417,0.17734909057617188,43.671228 -2968,Binary classification,AdaBoost,Bananas,0.6248736097067745,0.5924569754668619,0.17734909057617188,46.964135 -3074,Binary classification,AdaBoost,Bananas,0.6260982753010088,0.5958494548012664,0.17734909057617188,50.377784999999996 -3180,Binary classification,AdaBoost,Bananas,0.62378106322743,0.5934738273283481,0.1645193099975586,53.913639999999994 -3286,Binary classification,AdaBoost,Bananas,0.6246575342465753,0.5937397034596376,0.20851802825927734,57.57642799999999 -3392,Binary classification,AdaBoost,Bananas,0.6234149218519611,0.5931825422108953,0.24359798431396484,61.313441999999995 -3498,Binary classification,AdaBoost,Bananas,0.6211038032599371,0.5894019212891229,0.27881526947021484,65.12271799999999 -3604,Binary classification,AdaBoost,Bananas,0.6194837635303914,0.5866747060596926,0.3256673812866211,69.00323599999999 -3710,Binary classification,AdaBoost,Bananas,0.6238878403882449,0.5915080527086384,0.3337392807006836,72.95431899999998 -3816,Binary classification,AdaBoost,Bananas,0.6277850589777195,0.5970488081725313,0.3397665023803711,76.97715599999998 -3922,Binary classification,AdaBoost,Bananas,0.6322366743177761,0.6009961261759823,0.3677358627319336,81.06970999999999 -4028,Binary classification,AdaBoost,Bananas,0.6354606406754407,0.6034575904916262,0.38204097747802734,85.23008099999998 -4134,Binary classification,AdaBoost,Bananas,0.6399709654004355,0.6073878627968339,0.39020442962646484,89.45861099999999 -4240,Binary classification,AdaBoost,Bananas,0.644963434772352,0.6130110568269478,0.3902273178100586,93.75413599999999 -4346,Binary classification,AdaBoost,Bananas,0.6508630609896433,0.6185567010309279,0.39025020599365234,98.11858299999999 -4452,Binary classification,AdaBoost,Bananas,0.6535609975286453,0.620384047267356,0.39020442962646484,102.55151499999998 -4558,Binary classification,AdaBoost,Bananas,0.6570111915734036,0.6243691420331651,0.39038753509521484,107.05130299999998 -4664,Binary classification,AdaBoost,Bananas,0.6607334334119666,0.6288127639605818,0.39043331146240234,111.61866799999997 -4770,Binary classification,AdaBoost,Bananas,0.6630320821975257,0.6303197607545433,0.4466238021850586,116.25797499999997 -4876,Binary classification,AdaBoost,Bananas,0.6670769230769231,0.6330544879041374,0.4547872543334961,120.96504799999997 -4982,Binary classification,AdaBoost,Bananas,0.6707488456133307,0.6378091872791519,0.4610433578491211,125.74414799999997 -5088,Binary classification,AdaBoost,Bananas,0.6734814232356988,0.6407094959982694,0.4671621322631836,130.59500899999998 -5194,Binary classification,AdaBoost,Bananas,0.674369343346813,0.6412051771695311,0.46713924407958984,135.51973499999997 -5300,Binary classification,AdaBoost,Bananas,0.6778637478769579,0.64504054897068,0.46845149993896484,140.51779399999998 -906,Binary classification,AdaBoost,Elec2,0.9337016574585635,0.933184855233853,1.4600162506103516,2.214325 -1812,Binary classification,AdaBoost,Elec2,0.9469906129210381,0.9351351351351351,2.0955753326416016,6.240333 -2718,Binary classification,AdaBoost,Elec2,0.9370629370629371,0.9227990970654628,2.479440689086914,11.722802 -3624,Binary classification,AdaBoost,Elec2,0.9343085840463704,0.9197031039136303,2.829832077026367,18.401477 -4530,Binary classification,AdaBoost,Elec2,0.9322146169132258,0.9140778057654632,3.4876842498779297,26.240317 -5436,Binary classification,AdaBoost,Elec2,0.9262189512419503,0.9061988304093569,3.730012893676758,35.261976000000004 -6342,Binary classification,AdaBoost,Elec2,0.9252483835357199,0.9056904098686828,4.267904281616211,45.380796000000004 -7248,Binary classification,AdaBoost,Elec2,0.922174692976404,0.9017079121645174,4.513330459594727,56.606028 -8154,Binary classification,AdaBoost,Elec2,0.9222372132957194,0.9063515509601181,4.528413772583008,68.87909400000001 -9060,Binary classification,AdaBoost,Elec2,0.9222872281708798,0.9083810515356586,4.871156692504883,82.16100100000001 -9966,Binary classification,AdaBoost,Elec2,0.9173105870546914,0.9042528468510341,4.923883438110352,96.59034200000002 -10872,Binary classification,AdaBoost,Elec2,0.9180388188759084,0.9064370471490076,5.256982803344727,112.03179200000002 -11778,Binary classification,AdaBoost,Elec2,0.913645240723444,0.9013100436681224,5.652528762817383,128.76319300000003 -12684,Binary classification,AdaBoost,Elec2,0.9114562800599227,0.8990743237170845,6.184247970581055,146.63877800000003 -13590,Binary classification,AdaBoost,Elec2,0.9116932813304879,0.9004810084591143,6.168107986450195,165.54320300000003 -14496,Binary classification,AdaBoost,Elec2,0.9084511900655399,0.8974893781382773,6.370748519897461,185.57117500000004 -15402,Binary classification,AdaBoost,Elec2,0.9089669501980391,0.8977090325404932,6.689512252807617,206.60419600000003 -16308,Binary classification,AdaBoost,Elec2,0.907033789170295,0.8952315134761576,7.013689041137695,228.76060700000002 -17214,Binary classification,AdaBoost,Elec2,0.9056527043513624,0.8922362309223624,7.149255752563477,252.05960700000003 -18120,Binary classification,AdaBoost,Elec2,0.9003256250344942,0.8855368234250223,7.591207504272461,276.72553400000004 -19026,Binary classification,AdaBoost,Elec2,0.901287779237845,0.8854738382729601,7.899053573608398,302.410993 -19932,Binary classification,AdaBoost,Elec2,0.9013596909337214,0.8864633864633865,8.218050003051758,329.267044 -20838,Binary classification,AdaBoost,Elec2,0.8999376109804674,0.8850669753596825,8.369176864624023,357.490064 -21744,Binary classification,AdaBoost,Elec2,0.8988640022076071,0.8821227552934869,8.549039840698242,386.978862 -22650,Binary classification,AdaBoost,Elec2,0.8967283323767054,0.8787517495205017,8.670183181762695,417.760723 -23556,Binary classification,AdaBoost,Elec2,0.8951814901294842,0.8767287433221829,8.96574592590332,449.897837 -24462,Binary classification,AdaBoost,Elec2,0.8929724868157475,0.8734287371881647,9.238008499145508,483.328506 -25368,Binary classification,AdaBoost,Elec2,0.8902905349469784,0.8701535016096672,9.398244857788086,518.131339 -26274,Binary classification,AdaBoost,Elec2,0.8890115327522552,0.8681497558328811,9.500497817993164,554.2859920000001 -27180,Binary classification,AdaBoost,Elec2,0.8889215938776261,0.8685734186583084,9.676286697387695,591.703834 -28086,Binary classification,AdaBoost,Elec2,0.886629873598006,0.8655291832080412,9.930196762084961,630.490308 -28992,Binary classification,AdaBoost,Elec2,0.8864130247318133,0.8647916238965305,10.322111129760742,670.5577900000001 -29898,Binary classification,AdaBoost,Elec2,0.885774492423989,0.8637977106848004,10.777273178100586,711.8606440000001 -30804,Binary classification,AdaBoost,Elec2,0.885498165763075,0.8626931911083429,10.977670669555664,754.3481340000001 -31710,Binary classification,AdaBoost,Elec2,0.8835661799489104,0.8603736479842674,11.286626815795898,798.241067 -32616,Binary classification,AdaBoost,Elec2,0.882906638049977,0.8599097611973149,11.60590934753418,843.3988 -33522,Binary classification,AdaBoost,Elec2,0.8826109006294561,0.8598995976786413,11.809194564819336,889.816371 -34428,Binary classification,AdaBoost,Elec2,0.8817788363784239,0.8588765603328711,11.96577262878418,937.652835 -35334,Binary classification,AdaBoost,Elec2,0.8808762346814593,0.8571816361847239,12.22038459777832,986.872877 -36240,Binary classification,AdaBoost,Elec2,0.8797980076712933,0.8550029958058717,12.516416549682617,1037.336075 -37146,Binary classification,AdaBoost,Elec2,0.8785839278503164,0.8529891127192124,12.637868881225586,1089.1696239999999 -38052,Binary classification,AdaBoost,Elec2,0.8770071745814827,0.8506891271056662,13.111181259155273,1142.33469 -38958,Binary classification,AdaBoost,Elec2,0.8756064378673922,0.8493440278554995,13.190984725952148,1196.840346 -39864,Binary classification,AdaBoost,Elec2,0.8754233248877406,0.8501327860936744,13.482259750366211,1252.532055 -40770,Binary classification,AdaBoost,Elec2,0.8749049522921828,0.8505713448578962,13.769769668579102,1309.427191 -41676,Binary classification,AdaBoost,Elec2,0.8752729454109178,0.8519510111079466,13.892538070678711,1367.440063 -42582,Binary classification,AdaBoost,Elec2,0.8754139170052371,0.8523148019264498,14.423887252807617,1426.636647 -43488,Binary classification,AdaBoost,Elec2,0.8745142226412491,0.8513605534824177,14.559076309204102,1487.084751 -44394,Binary classification,AdaBoost,Elec2,0.8743270335413241,0.8507211088218767,14.714178085327148,1548.853591 -45300,Binary classification,AdaBoost,Elec2,0.8751186560409722,0.851922623877706,14.867197036743164,1611.802241 -25,Binary classification,AdaBoost,Phishing,0.6666666666666666,0.7142857142857143,0.6709518432617188,0.096996 -50,Binary classification,AdaBoost,Phishing,0.7551020408163265,0.7391304347826088,0.671112060546875,0.227732 -75,Binary classification,AdaBoost,Phishing,0.7972972972972973,0.7945205479452055,0.6711349487304688,0.390548 -100,Binary classification,AdaBoost,Phishing,0.8080808080808081,0.7999999999999999,0.6711578369140625,0.5919840000000001 -125,Binary classification,AdaBoost,Phishing,0.8064516129032258,0.8000000000000002,0.6711845397949219,0.8358070000000001 -150,Binary classification,AdaBoost,Phishing,0.8187919463087249,0.8211920529801323,0.6711845397949219,1.1185070000000001 -175,Binary classification,AdaBoost,Phishing,0.8390804597701149,0.8313253012048192,0.6711845397949219,1.4342450000000002 -200,Binary classification,AdaBoost,Phishing,0.8341708542713567,0.8253968253968254,0.7092657089233398,1.8037020000000001 -225,Binary classification,AdaBoost,Phishing,0.8303571428571429,0.8173076923076923,0.7094945907592773,2.2161530000000003 -250,Binary classification,AdaBoost,Phishing,0.8273092369477911,0.8154506437768241,0.7095174789428711,2.6652750000000003 -275,Binary classification,AdaBoost,Phishing,0.8321167883211679,0.8188976377952757,0.7095861434936523,3.1578190000000004 -300,Binary classification,AdaBoost,Phishing,0.8394648829431438,0.823529411764706,0.7096090316772461,3.688734 -325,Binary classification,AdaBoost,Phishing,0.845679012345679,0.8263888888888888,0.7096090316772461,4.255895000000001 -350,Binary classification,AdaBoost,Phishing,0.8510028653295129,0.8289473684210527,0.7096090316772461,4.8554520000000005 -375,Binary classification,AdaBoost,Phishing,0.8502673796791443,0.8260869565217391,0.7095823287963867,5.4957590000000005 -400,Binary classification,AdaBoost,Phishing,0.849624060150376,0.8235294117647061,0.7096090316772461,6.1750370000000006 -425,Binary classification,AdaBoost,Phishing,0.8561320754716981,0.8271954674220963,0.7096090316772461,6.8921660000000005 -450,Binary classification,AdaBoost,Phishing,0.8530066815144766,0.8225806451612903,0.7096090316772461,7.648219 -475,Binary classification,AdaBoost,Phishing,0.8523206751054853,0.8241206030150755,0.7096090316772461,8.452871 -500,Binary classification,AdaBoost,Phishing,0.8557114228456913,0.8317757009345793,0.7096090316772461,9.291999 -525,Binary classification,AdaBoost,Phishing,0.8530534351145038,0.8253968253968255,0.7096090316772461,10.171949000000001 -550,Binary classification,AdaBoost,Phishing,0.8579234972677595,0.832618025751073,0.7096090316772461,11.089958000000001 -575,Binary classification,AdaBoost,Phishing,0.8588850174216028,0.8336755646817249,0.7096090316772461,12.050434000000001 -600,Binary classification,AdaBoost,Phishing,0.8631051752921536,0.8360000000000001,0.7096090316772461,13.044778 -625,Binary classification,AdaBoost,Phishing,0.8621794871794872,0.83203125,0.7096090316772461,14.08632 -650,Binary classification,AdaBoost,Phishing,0.8659476117103235,0.8391866913123845,0.7096319198608398,15.163796000000001 -675,Binary classification,AdaBoost,Phishing,0.8679525222551929,0.8446771378708552,0.7096319198608398,16.281047 -700,Binary classification,AdaBoost,Phishing,0.8726752503576538,0.848381601362862,0.7096319198608398,17.426965000000003 -725,Binary classification,AdaBoost,Phishing,0.8756906077348067,0.8543689320388349,0.7096319198608398,18.607993000000004 -750,Binary classification,AdaBoost,Phishing,0.87716955941255,0.8566978193146417,0.7096319198608398,19.829159000000004 -775,Binary classification,AdaBoost,Phishing,0.8785529715762274,0.8575757575757577,0.7096319198608398,21.096679000000005 -800,Binary classification,AdaBoost,Phishing,0.8785982478097623,0.8592162554426704,0.7489309310913086,22.415380000000006 -825,Binary classification,AdaBoost,Phishing,0.8798543689320388,0.8619246861924686,0.7852392196655273,23.779028000000007 -850,Binary classification,AdaBoost,Phishing,0.8798586572438163,0.8614130434782608,0.7852849960327148,25.182311000000006 -875,Binary classification,AdaBoost,Phishing,0.8787185354691075,0.8594164456233422,0.7853536605834961,26.619257000000005 -900,Binary classification,AdaBoost,Phishing,0.8787541713014461,0.8589909443725743,0.7853765487670898,28.105865000000005 -925,Binary classification,AdaBoost,Phishing,0.8809523809523809,0.8628428927680798,0.7853765487670898,29.627597000000005 -950,Binary classification,AdaBoost,Phishing,0.8798735511064278,0.8629807692307693,0.7853765487670898,31.194797000000005 -975,Binary classification,AdaBoost,Phishing,0.8819301848049281,0.8651817116060961,0.7853765487670898,32.795573000000005 -1000,Binary classification,AdaBoost,Phishing,0.8828828828828829,0.8662857142857143,0.7870550155639648,34.450223 -1025,Binary classification,AdaBoost,Phishing,0.8828125,0.8666666666666666,0.8609609603881836,36.14349 -1050,Binary classification,AdaBoost,Phishing,0.8846520495710201,0.8691891891891892,0.8610067367553711,37.870798 -1075,Binary classification,AdaBoost,Phishing,0.8836126629422719,0.8691099476439791,0.8625936508178711,39.648817 -1100,Binary classification,AdaBoost,Phishing,0.8844404003639672,0.8702757916241062,0.8627080917358398,41.461037000000005 -1125,Binary classification,AdaBoost,Phishing,0.8861209964412812,0.8732673267326733,0.8989248275756836,43.309543000000005 -1150,Binary classification,AdaBoost,Phishing,0.8842471714534378,0.8707482993197277,0.8989477157592773,45.19996400000001 -1175,Binary classification,AdaBoost,Phishing,0.8816013628620102,0.8677450047573739,0.8990621566772461,47.14264000000001 -1200,Binary classification,AdaBoost,Phishing,0.8798999165971643,0.8654205607476635,0.8990621566772461,49.13049000000001 -1225,Binary classification,AdaBoost,Phishing,0.880718954248366,0.8660550458715598,0.8990850448608398,51.16391500000001 -1250,Binary classification,AdaBoost,Phishing,0.8783026421136909,0.8635547576301617,0.8991079330444336,53.25500000000001 -1903,Binary classification,AdaBoost,SMTP,1.0,0.0,0.15647220611572266,0.853094 -3806,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1565408706665039,2.059998 -5709,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1564950942993164,3.616244 -7612,Binary classification,AdaBoost,SMTP,1.0,0.0,0.15651798248291016,5.5300910000000005 -9515,Binary classification,AdaBoost,SMTP,1.0,0.0,0.15651798248291016,7.803705000000001 -11418,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1564950942993164,10.437897000000001 -13321,Binary classification,AdaBoost,SMTP,1.0,0.0,0.15656375885009766,13.429757000000002 -15224,Binary classification,AdaBoost,SMTP,0.9996715496288511,0.761904761904762,0.3805198669433594,17.782309 -17127,Binary classification,AdaBoost,SMTP,0.9997080462454747,0.8,0.3727455139160156,22.84111 -19030,Binary classification,AdaBoost,SMTP,0.9997372431551842,0.8,0.3727455139160156,28.521633 -20933,Binary classification,AdaBoost,SMTP,0.9997611312822473,0.8,0.3727455139160156,34.826752 -22836,Binary classification,AdaBoost,SMTP,0.9997810378804467,0.8,0.3727455139160156,41.750952 -24739,Binary classification,AdaBoost,SMTP,0.9997978818012774,0.8,0.3649024963378906,49.282837 -26642,Binary classification,AdaBoost,SMTP,0.9998123193573815,0.8148148148148148,0.4159393310546875,57.542832000000004 -28545,Binary classification,AdaBoost,SMTP,0.9998248318385651,0.8148148148148148,0.4159393310546875,66.418254 -30448,Binary classification,AdaBoost,SMTP,0.9998357802082307,0.8148148148148148,0.4159393310546875,75.902957 -32351,Binary classification,AdaBoost,SMTP,0.9998454404945905,0.8148148148148148,0.425140380859375,86.000011 -34254,Binary classification,AdaBoost,SMTP,0.9998540273844627,0.8148148148148148,0.4250946044921875,96.707721 -36157,Binary classification,AdaBoost,SMTP,0.999861710366191,0.8148148148148148,0.4250030517578125,108.022851 -38060,Binary classification,AdaBoost,SMTP,0.9998686250295594,0.8148148148148148,0.417205810546875,119.949225 -39963,Binary classification,AdaBoost,SMTP,0.9998748811370802,0.8148148148148148,0.417205810546875,132.490409 -41866,Binary classification,AdaBoost,SMTP,0.9998805684939687,0.8148148148148148,0.417205810546875,145.64785799999999 -43769,Binary classification,AdaBoost,SMTP,0.9998857612867849,0.8148148148148148,0.41722869873046875,159.41857399999998 -45672,Binary classification,AdaBoost,SMTP,0.9998905213373913,0.8148148148148148,0.41722869873046875,173.80738499999998 -47575,Binary classification,AdaBoost,SMTP,0.9998738806911338,0.7857142857142857,0.46401214599609375,189.004291 -49478,Binary classification,AdaBoost,SMTP,0.9998787315318228,0.7857142857142857,0.4561920166015625,204.850148 -51381,Binary classification,AdaBoost,SMTP,0.9998637602179836,0.787878787878788,0.5220794677734375,221.427526 -53284,Binary classification,AdaBoost,SMTP,0.9998686260158024,0.787878787878788,0.5220794677734375,238.605883 -55187,Binary classification,AdaBoost,SMTP,0.9998550356974595,0.7647058823529411,0.56781005859375,256.499342 -57090,Binary classification,AdaBoost,SMTP,0.9995445707579393,0.5666666666666667,1.0016555786132812,277.003919 -58993,Binary classification,AdaBoost,SMTP,0.9995592622728505,0.5666666666666667,1.056976318359375,298.475709 -60896,Binary classification,AdaBoost,SMTP,0.999573035553001,0.5666666666666667,1.0613327026367188,320.766705 -62799,Binary classification,AdaBoost,SMTP,0.9995382018535622,0.5538461538461538,1.28204345703125,344.04407 -64702,Binary classification,AdaBoost,SMTP,0.9995517843619109,0.5538461538461538,1.2822036743164062,368.01597899999996 -66605,Binary classification,AdaBoost,SMTP,0.9995495766020059,0.5454545454545455,1.2944259643554688,392.68268599999993 -68508,Binary classification,AdaBoost,SMTP,0.9995620885456961,0.5714285714285714,1.2944488525390625,418.04215199999993 -70411,Binary classification,AdaBoost,SMTP,0.9995739241585002,0.5714285714285714,1.2945404052734375,444.07242899999994 -72314,Binary classification,AdaBoost,SMTP,0.999571308063557,0.5633802816901408,1.2945404052734375,470.78104299999995 -74217,Binary classification,AdaBoost,SMTP,0.9995823003126011,0.5633802816901408,1.294586181640625,498.25360399999994 -76120,Binary classification,AdaBoost,SMTP,0.9995927429419724,0.5633802816901408,1.2867202758789062,526.392846 -78023,Binary classification,AdaBoost,SMTP,0.9995898592704622,0.5555555555555556,1.2990570068359375,555.302544 -79926,Binary classification,AdaBoost,SMTP,0.9995996246481076,0.5555555555555556,1.2990341186523438,584.881396 -81829,Binary classification,AdaBoost,SMTP,0.9996089358165909,0.5555555555555556,1.2991256713867188,615.117937 -83732,Binary classification,AdaBoost,SMTP,0.9996178237450885,0.5555555555555556,1.2991943359375,646.0958009999999 -85635,Binary classification,AdaBoost,SMTP,0.9996263166499287,0.5555555555555556,1.2991714477539062,677.7414719999999 -87538,Binary classification,AdaBoost,SMTP,0.9996344402938186,0.5555555555555556,1.2991714477539062,710.050307 -89441,Binary classification,AdaBoost,SMTP,0.9996086762075134,0.5333333333333333,1.4483489990234375,743.2331419999999 -91344,Binary classification,AdaBoost,SMTP,0.999616828875776,0.5333333333333333,1.4483261108398438,777.273881 -93247,Binary classification,AdaBoost,SMTP,0.9996139244578855,0.5263157894736841,1.4622306823730469,812.316683 -95150,Binary classification,AdaBoost,SMTP,0.9996216460498797,0.5263157894736841,1.4664268493652344,848.066126 -106,Binary classification,Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.2364511489868164,0.160512 -212,Binary classification,Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.2372446060180664,0.465512 -318,Binary classification,Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.23786258697509766,0.9126559999999999 -424,Binary classification,Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.23796939849853516,1.508959 -530,Binary classification,Bagging,Bananas,0.55765595463138,0.45581395348837206,0.2379922866821289,2.237684 -636,Binary classification,Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.2384653091430664,3.113606 -742,Binary classification,Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.2386941909790039,4.124442 -848,Binary classification,Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.23862552642822266,5.284904 -954,Binary classification,Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.2386026382446289,6.590759 -1060,Binary classification,Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.2383584976196289,8.045558 -1166,Binary classification,Bagging,Bananas,0.590557939914163,0.4015056461731493,0.23845767974853516,9.640388999999999 -1272,Binary classification,Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.23876285552978516,11.379489999999999 -1378,Binary classification,Bagging,Bananas,0.599128540305011,0.3973799126637554,0.23906803131103516,13.268759999999999 -1484,Binary classification,Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.23902225494384766,15.320884999999999 -1590,Binary classification,Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.2389993667602539,17.530882 -1696,Binary classification,Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.2390604019165039,19.899158 -1802,Binary classification,Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.23908329010009766,22.423363 -1908,Binary classification,Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.23908329010009766,25.102832999999997 -2014,Binary classification,Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.23908329010009766,27.945050999999996 -2120,Binary classification,Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.23908329010009766,30.943526999999996 -2226,Binary classification,Bagging,Bananas,0.597752808988764,0.40133779264214053,0.23903751373291016,34.101409999999994 -2332,Binary classification,Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.23909854888916016,37.41135799999999 -2438,Binary classification,Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.2391214370727539,40.87347599999999 -2544,Binary classification,Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.2394876480102539,44.48936199999999 -2650,Binary classification,Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.23969364166259766,48.25778699999999 -2756,Binary classification,Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.23969364166259766,52.17665199999999 -2862,Binary classification,Bagging,Bananas,0.599790283117791,0.4051948051948052,0.23969364166259766,56.248780999999994 -2968,Binary classification,Bagging,Bananas,0.599932591843613,0.40261701056869653,0.2397165298461914,60.47524299999999 -3074,Binary classification,Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.2397165298461914,64.856117 -3180,Binary classification,Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.2397165298461914,69.39380299999999 -3286,Binary classification,Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.23760986328125,74.079456 -3392,Binary classification,Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.31256866455078125,78.938547 -3498,Binary classification,Bagging,Bananas,0.597369173577352,0.40237691001697795,0.3672904968261719,83.975893 -3604,Binary classification,Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3972740173339844,89.206915 -3710,Binary classification,Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.4523735046386719,94.64485099999999 -3816,Binary classification,Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.4865531921386719,100.28768 -3922,Binary classification,Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.5345191955566406,106.16068299999999 -4028,Binary classification,Bagging,Bananas,0.608393344921778,0.4195804195804196,0.5653800964355469,112.191076 -4134,Binary classification,Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.5808448791503906,118.35405 -4240,Binary classification,Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.5852127075195312,124.637983 -4346,Binary classification,Bagging,Bananas,0.6193325661680092,0.438560760353021,0.6001129150390625,131.04469 -4452,Binary classification,Bagging,Bananas,0.6218827229835991,0.4421610871726881,0.6065597534179688,137.573932 -4558,Binary classification,Bagging,Bananas,0.6219003730524468,0.44293566117038474,0.6456527709960938,144.226405 -4664,Binary classification,Bagging,Bananas,0.623203945957538,0.4455664247396655,0.6509552001953125,151.005134 -4770,Binary classification,Bagging,Bananas,0.6250786328370728,0.446096654275093,0.7009811401367188,157.911799 -4876,Binary classification,Bagging,Bananas,0.6266666666666667,0.44680851063829785,0.7141494750976562,164.949721 -4982,Binary classification,Bagging,Bananas,0.629592451314997,0.4530091906314853,0.73150634765625,172.116039 -5088,Binary classification,Bagging,Bananas,0.6298407705917043,0.4527753560011624,0.7153549194335938,179.401331 -5194,Binary classification,Bagging,Bananas,0.6321971885230118,0.456459874786568,0.7158050537109375,186.806087 -5300,Binary classification,Bagging,Bananas,0.6340819022457067,0.4594368553108447,0.7224349975585938,194.332292 -906,Binary classification,Bagging,Elec2,0.8629834254143647,0.8663793103448276,1.7905378341674805,2.379335 -1812,Binary classification,Bagging,Elec2,0.8884594146880177,0.8674540682414698,2.5719175338745117,6.1243490000000005 -2718,Binary classification,Bagging,Elec2,0.8759661391240339,0.8536691272253583,2.0127248764038086,11.743707 -3624,Binary classification,Bagging,Elec2,0.8843499861992824,0.8625778943916038,2.5987844467163086,18.955423 -4530,Binary classification,Bagging,Elec2,0.8869507617575624,0.8584070796460177,3.065375328063965,27.785826999999998 -5436,Binary classification,Bagging,Elec2,0.8833486660533578,0.8529684601113172,2.4308347702026367,38.240294 -6342,Binary classification,Bagging,Elec2,0.882983756505283,0.8526023043305523,2.7551145553588867,50.337362 -7248,Binary classification,Bagging,Elec2,0.8842279563957499,0.8536032106089687,2.720797538757324,63.955505 -8154,Binary classification,Bagging,Elec2,0.8842143996075065,0.8612172890326375,2.593207359313965,79.221878 -9060,Binary classification,Bagging,Elec2,0.8847554917761342,0.8655678599021375,2.3350706100463867,95.984043 -9966,Binary classification,Bagging,Elec2,0.88509784244857,0.8683454064619983,2.6046972274780273,114.346975 -10872,Binary classification,Bagging,Elec2,0.8848312022812989,0.8691745036572621,2.73319149017334,134.274448 -11778,Binary classification,Bagging,Elec2,0.88222807166511,0.8655877507510417,2.9825429916381836,155.860777 -12684,Binary classification,Bagging,Elec2,0.878498777891666,0.8616572403267798,2.5146703720092773,179.01547000000002 -13590,Binary classification,Bagging,Elec2,0.8796820958127898,0.8645738424583782,2.628962516784668,203.671512 -14496,Binary classification,Bagging,Elec2,0.8797516384960331,0.8652701553683234,2.4732847213745117,229.977136 -15402,Binary classification,Bagging,Elec2,0.8800077917018375,0.8645558487247141,2.1737966537475586,257.791658 -16308,Binary classification,Bagging,Elec2,0.8778438707303612,0.861262014208107,2.5011510848999023,287.33973499999996 -17214,Binary classification,Bagging,Elec2,0.8773601347818509,0.8583506676508086,2.443587303161621,318.628337 -18120,Binary classification,Bagging,Elec2,0.8774214912522766,0.8581827469510249,2.8467397689819336,351.823445 -19026,Binary classification,Bagging,Elec2,0.8781603153745072,0.8573362875430822,2.840205192565918,386.938658 -19932,Binary classification,Bagging,Elec2,0.8755707189804827,0.8552923328276345,2.7734594345092773,424.419923 -20838,Binary classification,Bagging,Elec2,0.8726784086000864,0.8517959890508909,3.382327079772949,464.42461 -21744,Binary classification,Bagging,Elec2,0.8735225129926873,0.8505921981962403,2.277277946472168,506.25035599999995 -22650,Binary classification,Bagging,Elec2,0.8713850501125877,0.8461741564133707,2.6045217514038086,549.67693 -23556,Binary classification,Bagging,Elec2,0.8655062619401401,0.8378212347701444,2.0687971115112305,595.047504 -24462,Binary classification,Bagging,Elec2,0.8629246555741793,0.8334740501614105,1.965815544128418,642.194018 -25368,Binary classification,Bagging,Elec2,0.8587929199353491,0.8286944045911048,1.5182180404663086,691.397121 -26274,Binary classification,Bagging,Elec2,0.8580672172953222,0.8274010645683869,1.035365104675293,742.026028 -27180,Binary classification,Bagging,Elec2,0.8578682070716361,0.8276446705037256,1.274672508239746,793.972348 -28086,Binary classification,Bagging,Elec2,0.8549403596225743,0.8230542043085476,1.6423864364624023,847.62915 -28992,Binary classification,Bagging,Elec2,0.8532648063192025,0.8196846388606307,2.145480155944824,903.499383 -29898,Binary classification,Bagging,Elec2,0.8507208081078369,0.8162391402808086,1.650496482849121,961.52369 -30804,Binary classification,Bagging,Elec2,0.8489108203746388,0.812746439204957,1.6065664291381836,1021.4058 -31710,Binary classification,Bagging,Elec2,0.8464789176574474,0.8088731841382018,1.891444206237793,1083.110109 -32616,Binary classification,Bagging,Elec2,0.8443660892227502,0.8061855670103092,1.5838193893432617,1146.603723 -33522,Binary classification,Bagging,Elec2,0.8444258822827482,0.8069876753395758,2.1811208724975586,1211.933286 -34428,Binary classification,Bagging,Elec2,0.8422168646701716,0.8034590057167668,2.362921714782715,1279.642459 -35334,Binary classification,Bagging,Elec2,0.8416777516769026,0.8019121813031161,2.295699119567871,1349.6385269999998 -36240,Binary classification,Bagging,Elec2,0.8424625403570739,0.8013915463558879,2.210890769958496,1421.6820649999997 -37146,Binary classification,Bagging,Elec2,0.8423206353479606,0.8005720317341414,1.5720243453979492,1496.1115339999997 -38052,Binary classification,Bagging,Elec2,0.8416335970145331,0.7984750183934186,1.657557487487793,1572.0838559999997 -38958,Binary classification,Bagging,Elec2,0.8402854429242498,0.7969321148825065,1.542536735534668,1649.6649279999997 -39864,Binary classification,Bagging,Elec2,0.8404786393397387,0.7989503303929938,1.814925193786621,1728.6718509999996 -40770,Binary classification,Bagging,Elec2,0.8419141995143369,0.8025852298832972,1.6994237899780273,1809.1572959999996 -41676,Binary classification,Bagging,Elec2,0.8428554289142172,0.8053036834438267,2.179030418395996,1890.9828949999996 -42582,Binary classification,Bagging,Elec2,0.8435217585308001,0.8066061010652193,2.626959800720215,1974.3648729999995 -43488,Binary classification,Bagging,Elec2,0.8437004162163405,0.8069418013463233,2.851019859313965,2059.4074959999994 -44394,Binary classification,Bagging,Elec2,0.8424751650034915,0.804298547561078,3.6125402450561523,2146.5727129999996 -45300,Binary classification,Bagging,Elec2,0.8419391156537672,0.8040932472365109,3.2033262252807617,2236.8483089999995 -25,Binary classification,Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.7285165786743164,0.081182 -50,Binary classification,Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.7291955947875977,0.246708 -75,Binary classification,Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.7295160293579102,0.48988200000000004 -100,Binary classification,Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.7297601699829102,0.81137 -125,Binary classification,Bagging,Phishing,0.8548387096774194,0.85,0.7297601699829102,1.210797 -150,Binary classification,Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.7300043106079102,1.687212 -175,Binary classification,Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.7303934097290039,2.243398 -200,Binary classification,Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.7305307388305664,2.888269 -225,Binary classification,Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.77154541015625,3.6171050000000005 -250,Binary classification,Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.7995452880859375,4.427161000000001 -275,Binary classification,Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.799774169921875,5.319298000000001 -300,Binary classification,Bagging,Phishing,0.8528428093645485,0.837037037037037,0.799957275390625,6.288776 -325,Binary classification,Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.800323486328125,7.336677 -350,Binary classification,Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.8004684448242188,8.465301 -375,Binary classification,Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.8407363891601562,9.675054 -400,Binary classification,Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.8817596435546875,10.973937 -425,Binary classification,Bagging,Phishing,0.8679245283018868,0.839080459770115,0.937408447265625,12.361792999999999 -450,Binary classification,Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.9376602172851562,13.826239 -475,Binary classification,Bagging,Phishing,0.869198312236287,0.8402061855670103,0.9379119873046875,15.36192 -500,Binary classification,Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.9381179809570312,16.969597999999998 -525,Binary classification,Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.9381790161132812,18.650675999999997 -550,Binary classification,Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.9382858276367188,20.413451999999996 -575,Binary classification,Bagging,Phishing,0.867595818815331,0.8382978723404255,0.9383468627929688,22.261479999999995 -600,Binary classification,Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.9384689331054688,24.204291999999995 -625,Binary classification,Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.9792633056640625,26.239623999999996 -650,Binary classification,Bagging,Phishing,0.8767334360554699,0.846153846153846,0.9797210693359375,28.350958999999996 -675,Binary classification,Bagging,Phishing,0.8753709198813057,0.8478260869565216,1.0075225830078125,30.546932999999996 -700,Binary classification,Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.9475822448730469,32.825613999999995 -725,Binary classification,Bagging,Phishing,0.8825966850828729,0.8576214405360134,1.0479621887207031,35.192032999999995 -750,Binary classification,Bagging,Phishing,0.8865153538050734,0.8631239935587761,1.0882606506347656,37.652956999999994 -775,Binary classification,Bagging,Phishing,0.8875968992248062,0.863849765258216,1.1435890197753906,40.206813999999994 -800,Binary classification,Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.2515907287597656,42.859536999999996 -825,Binary classification,Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.2530021667480469,45.593545 -850,Binary classification,Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.2665596008300781,48.414666 -875,Binary classification,Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.2945747375488281,51.318084999999996 -900,Binary classification,Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.3499031066894531,54.308453 -925,Binary classification,Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.3501167297363281,57.385236 -950,Binary classification,Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.3506507873535156,60.544707 -975,Binary classification,Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.3507575988769531,63.787028 -1000,Binary classification,Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.3509178161621094,67.116503 -1025,Binary classification,Bagging,Phishing,0.8876953125,0.8700564971751412,1.3512077331542969,70.53228 -1050,Binary classification,Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.3513069152832031,74.046134 -1075,Binary classification,Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.3514289855957031,77.61240799999999 -1100,Binary classification,Bagging,Phishing,0.89171974522293,0.8761706555671176,1.3517951965332031,81.22848299999998 -1125,Binary classification,Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.3518562316894531,84.88876699999999 -1150,Binary classification,Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.3518562316894531,88.60766799999999 -1175,Binary classification,Bagging,Phishing,0.8926746166950597,0.877906976744186,1.3519172668457031,92.36705899999998 -1200,Binary classification,Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.3650932312011719,96.17394699999998 -1225,Binary classification,Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.4202919006347656,100.02749999999999 -1250,Binary classification,Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.4205055236816406,103.925929 -1903,Binary classification,Bagging,SMTP,1.0,0.0,0.21781349182128906,1.823771 -3806,Binary classification,Bagging,SMTP,1.0,0.0,0.21842384338378906,5.582317 -5709,Binary classification,Bagging,SMTP,1.0,0.0,0.2189655303955078,10.437190999999999 -7612,Binary classification,Bagging,SMTP,1.0,0.0,0.21898841857910156,16.255529 -9515,Binary classification,Bagging,SMTP,1.0,0.0,0.21898841857910156,22.995497 -11418,Binary classification,Bagging,SMTP,1.0,0.0,0.21959877014160156,30.599323 -13321,Binary classification,Bagging,SMTP,1.0,0.0,0.2196216583251953,39.036327 -15224,Binary classification,Bagging,SMTP,0.9993430992577021,0.16666666666666669,0.5055475234985352,48.440921 -17127,Binary classification,Bagging,SMTP,0.9992993109891393,0.14285714285714288,0.5140314102172852,59.356571 -19030,Binary classification,Bagging,SMTP,0.9993693835724421,0.14285714285714288,0.5010766983032227,71.78490500000001 -20933,Binary classification,Bagging,SMTP,0.9994267150773934,0.14285714285714288,0.5033426284790039,85.737205 -22836,Binary classification,Bagging,SMTP,0.9994744909130721,0.14285714285714288,0.5038537979125977,101.22132500000001 -24739,Binary classification,Bagging,SMTP,0.9995149163230658,0.14285714285714288,0.5166254043579102,118.224146 -26642,Binary classification,Bagging,SMTP,0.9995495664577155,0.25,0.5370950698852539,136.728077 -28545,Binary classification,Bagging,SMTP,0.999579596412556,0.25,0.5375986099243164,156.75763500000002 -30448,Binary classification,Bagging,SMTP,0.9996058724997536,0.25,0.5378046035766602,178.30216700000003 -32351,Binary classification,Bagging,SMTP,0.999629057187017,0.25,0.5502328872680664,201.36650900000004 -34254,Binary classification,Bagging,SMTP,0.9996496657227104,0.25,0.5628290176391602,225.93287100000003 -36157,Binary classification,Bagging,SMTP,0.9996681048788583,0.25,0.5643777847290039,252.01061500000003 -38060,Binary classification,Bagging,SMTP,0.9996847000709425,0.25,0.5652093887329102,279.629747 -39963,Binary classification,Bagging,SMTP,0.9996997147289926,0.25,0.5653314590454102,308.76820100000003 -41866,Binary classification,Bagging,SMTP,0.9997133643855249,0.25,0.5653314590454102,339.41859300000004 -43769,Binary classification,Bagging,SMTP,0.9997258270882837,0.25,0.5653085708618164,371.57202200000006 -45672,Binary classification,Bagging,SMTP,0.9997372512097392,0.25,0.5653696060180664,405.2428510000001 -47575,Binary classification,Bagging,SMTP,0.9997477613822676,0.25,0.5838403701782227,440.4367680000001 -49478,Binary classification,Bagging,SMTP,0.9997574630636458,0.25,0.5960397720336914,477.1431370000001 -51381,Binary classification,Bagging,SMTP,0.9997469832619696,0.3157894736842105,0.6703958511352539,515.395289 -53284,Binary classification,Bagging,SMTP,0.999756019743633,0.3157894736842105,0.6706399917602539,555.1871910000001 -55187,Binary classification,Bagging,SMTP,0.9997644330083717,0.3157894736842105,0.6832361221313477,596.5168480000001 -57090,Binary classification,Bagging,SMTP,0.9996321533044895,0.3225806451612903,1.1210947036743164,639.6973030000001 -58993,Binary classification,Bagging,SMTP,0.9996440195280716,0.3225806451612903,1.145817756652832,684.8963280000002 -60896,Binary classification,Bagging,SMTP,0.9996551441005008,0.3225806451612903,1.1608476638793945,732.0726040000002 -62799,Binary classification,Bagging,SMTP,0.9996337462976528,0.303030303030303,1.2456941604614258,781.2786070000002 -64702,Binary classification,Bagging,SMTP,0.9996445186318604,0.303030303030303,1.246922492980957,832.4886640000002 -66605,Binary classification,Bagging,SMTP,0.9996546753948712,0.303030303030303,1.2487382888793945,885.7141210000002 -68508,Binary classification,Bagging,SMTP,0.9996642678850336,0.3783783783783784,1.266160011291504,940.9452050000002 -70411,Binary classification,Bagging,SMTP,0.9996733418548501,0.3783783783783784,1.296757698059082,998.2257750000002 -72314,Binary classification,Bagging,SMTP,0.9996819382407036,0.3783783783783784,1.2987031936645508,1057.5572380000003 -74217,Binary classification,Bagging,SMTP,0.9996900937803169,0.3783783783783784,1.2991762161254883,1118.9337870000004 -76120,Binary classification,Bagging,SMTP,0.9996978415375924,0.3783783783783784,1.3004274368286133,1182.3536270000004 -78023,Binary classification,Bagging,SMTP,0.9997052113506447,0.3783783783783784,1.3022356033325195,1247.8338970000004 -79926,Binary classification,Bagging,SMTP,0.9997122302158273,0.3783783783783784,1.302800178527832,1315.3680150000005 -81829,Binary classification,Bagging,SMTP,0.9997189226181747,0.3783783783783784,1.3035783767700195,1384.9414330000004 -83732,Binary classification,Bagging,SMTP,0.9997253108167823,0.3783783783783784,1.3040666580200195,1456.5304290000004 -85635,Binary classification,Bagging,SMTP,0.9997314150921363,0.3783783783783784,1.3194093704223633,1530.1954770000004 -87538,Binary classification,Bagging,SMTP,0.9997372539611822,0.3783783783783784,1.3199357986450195,1605.9064890000004 -89441,Binary classification,Bagging,SMTP,0.9997316636851521,0.3684210526315789,1.3624582290649414,1683.7024010000005 -91344,Binary classification,Bagging,SMTP,0.9997372540862464,0.3684210526315789,1.3635034561157227,1763.5593190000004 -93247,Binary classification,Bagging,SMTP,0.9997426163052571,0.3684210526315789,1.3640680313110352,1845.4940930000005 -95150,Binary classification,Bagging,SMTP,0.9997477640332532,0.3684210526315789,1.3650827407836914,1929.4936330000005 -106,Binary classification,Leveraging Bagging,Bananas,0.5142857142857142,0.45161290322580644,0.19296932220458984,0.378114 -212,Binary classification,Leveraging Bagging,Bananas,0.5402843601895735,0.4756756756756757,0.19357967376708984,1.064058 -318,Binary classification,Leveraging Bagging,Bananas,0.5394321766561514,0.4930555555555555,0.1942129135131836,1.944827 -424,Binary classification,Leveraging Bagging,Bananas,0.5531914893617021,0.4932975871313673,0.19419002532958984,3.02079 -530,Binary classification,Leveraging Bagging,Bananas,0.5614366729678639,0.4703196347031963,0.19419002532958984,4.293626 -636,Binary classification,Leveraging Bagging,Bananas,0.5763779527559055,0.4836852207293666,0.4277210235595703,5.781232999999999 -742,Binary classification,Leveraging Bagging,Bananas,0.5991902834008097,0.4940374787052811,0.5387935638427734,7.493556 -848,Binary classification,Leveraging Bagging,Bananas,0.6210153482880756,0.5201793721973094,0.6348705291748047,9.456389999999999 -954,Binary classification,Leveraging Bagging,Bananas,0.6411332633788038,0.5464190981432361,0.7018413543701172,11.663967 -1060,Binary classification,Leveraging Bagging,Bananas,0.6515580736543909,0.555956678700361,0.7448062896728516,14.117771 -1166,Binary classification,Leveraging Bagging,Bananas,0.6626609442060086,0.5732899022801302,0.8341083526611328,16.817314 -1272,Binary classification,Leveraging Bagging,Bananas,0.6766325727773407,0.5958702064896755,0.8756198883056641,19.759644 -1378,Binary classification,Leveraging Bagging,Bananas,0.6877269426289034,0.6062271062271062,0.9611110687255859,22.944053 -1484,Binary classification,Leveraging Bagging,Bananas,0.6999325691166555,0.6238377007607777,1.0045452117919922,26.370908 -1590,Binary classification,Leveraging Bagging,Bananas,0.7073631214600378,0.6375681995323461,1.1097278594970703,30.041027 -1696,Binary classification,Leveraging Bagging,Bananas,0.7162241887905605,0.6496722505462491,1.175821304321289,33.956331 -1802,Binary classification,Leveraging Bagging,Bananas,0.7262631871182677,0.6662153012863914,1.262613296508789,38.117335 -1908,Binary classification,Leveraging Bagging,Bananas,0.7315154693235448,0.6767676767676768,1.3344478607177734,42.524713 -2014,Binary classification,Leveraging Bagging,Bananas,0.7386984600099354,0.6894923258559622,1.391103744506836,47.18564 -2120,Binary classification,Leveraging Bagging,Bananas,0.7451628126474752,0.7013274336283186,1.475076675415039,52.095729 -2226,Binary classification,Leveraging Bagging,Bananas,0.7501123595505618,0.7073684210526315,1.496999740600586,57.242367 -2332,Binary classification,Leveraging Bagging,Bananas,0.7550407550407551,0.7143571785892947,1.516103744506836,62.636095000000005 -2438,Binary classification,Leveraging Bagging,Bananas,0.7595404185473943,0.7196172248803827,1.5630512237548828,68.252973 -2544,Binary classification,Leveraging Bagging,Bananas,0.7620920173023987,0.725124943207633,1.6238231658935547,74.089247 -2650,Binary classification,Leveraging Bagging,Bananas,0.7674594186485466,0.7326388888888887,1.6640300750732422,80.135514 -2756,Binary classification,Leveraging Bagging,Bananas,0.7727767695099819,0.7391666666666666,1.7581462860107422,86.389582 -2862,Binary classification,Leveraging Bagging,Bananas,0.777001048584411,0.7435691318327974,1.8173961639404297,92.85523400000001 -2968,Binary classification,Leveraging Bagging,Bananas,0.7809234917425009,0.7474747474747475,1.919931411743164,99.52929700000001 -3074,Binary classification,Leveraging Bagging,Bananas,0.784249918646274,0.7521495327102804,2.0252437591552734,106.42743700000001 -3180,Binary classification,Leveraging Bagging,Bananas,0.7889273356401384,0.7567959405581733,2.059762954711914,113.53746300000002 -3286,Binary classification,Leveraging Bagging,Bananas,0.7920852359208523,0.7600983491394451,2.1231555938720703,120.86059200000001 -3392,Binary classification,Leveraging Bagging,Bananas,0.7935712179298142,0.7631935047361299,2.208223342895508,128.39806900000002 -3498,Binary classification,Leveraging Bagging,Bananas,0.7963969116385473,0.7653263019116677,2.2967967987060547,136.14712200000002 -3604,Binary classification,Leveraging Bagging,Bananas,0.7993338884263114,0.7677481529071635,2.342061996459961,144.10985700000003 -3710,Binary classification,Leveraging Bagging,Bananas,0.8015637638177406,0.7710018668326073,2.4194507598876953,152.28979100000004 -3816,Binary classification,Leveraging Bagging,Bananas,0.8049803407601572,0.7753623188405797,2.452432632446289,160.68369700000002 -3922,Binary classification,Leveraging Bagging,Bananas,0.8066819688854884,0.7769276044732195,2.484903335571289,169.29472 -4028,Binary classification,Leveraging Bagging,Bananas,0.8080456915818227,0.7781922525107604,2.555143356323242,178.12388700000002 -4134,Binary classification,Leveraging Bagging,Bananas,0.8103072828453908,0.7810055865921788,2.665616989135742,187.16988400000002 -4240,Binary classification,Leveraging Bagging,Bananas,0.8131634819532909,0.7845484221980414,2.698610305786133,196.43184000000002 -4346,Binary classification,Leveraging Bagging,Bananas,0.8158803222094362,0.7877984084880637,2.7711353302001953,205.91274200000004 -4452,Binary classification,Leveraging Bagging,Bananas,0.8173444169849472,0.78932365897901,2.792703628540039,215.61058800000004 -4558,Binary classification,Leveraging Bagging,Bananas,0.8183015141540487,0.7909090909090909,2.8151988983154297,225.53181000000004 -4664,Binary classification,Leveraging Bagging,Bananas,0.8205018228608192,0.7940959409594096,2.871114730834961,235.67375100000004 -4770,Binary classification,Leveraging Bagging,Bananas,0.8209268190396309,0.7941176470588236,2.9113216400146484,246.03360500000005 -4876,Binary classification,Leveraging Bagging,Bananas,0.822974358974359,0.7958362905133666,3.014688491821289,256.6143220000001 -4982,Binary classification,Leveraging Bagging,Bananas,0.825135514956836,0.7989845372720977,3.062814712524414,267.41563500000007 -5088,Binary classification,Leveraging Bagging,Bananas,0.825437389424022,0.7994579945799458,3.1556224822998047,278.43394300000006 -5194,Binary classification,Leveraging Bagging,Bananas,0.8266897746967071,0.800796812749004,3.260141372680664,289.67931000000004 -5300,Binary classification,Leveraging Bagging,Bananas,0.8282694848084544,0.8026030368763557,3.3130626678466797,301.15239700000006 -906,Binary classification,Leveraging Bagging,Elec2,0.8883977900552487,0.8861330326944759,2.6054086685180664,3.368733 -1812,Binary classification,Leveraging Bagging,Elec2,0.9127553837658752,0.8939597315436243,3.465878486633301,9.891931 -2718,Binary classification,Leveraging Bagging,Elec2,0.9013617960986382,0.8816254416961131,3.735013008117676,19.447972 -3624,Binary classification,Leveraging Bagging,Elec2,0.9100193210046923,0.8914780292942742,3.9933290481567383,31.888989000000002 -4530,Binary classification,Leveraging Bagging,Elec2,0.9125634797968647,0.890728476821192,3.547215461730957,47.051529 -5436,Binary classification,Leveraging Bagging,Elec2,0.9100275988960441,0.8870408870408871,4.102688789367676,65.23951100000001 -6342,Binary classification,Leveraging Bagging,Elec2,0.9101088156442202,0.8873517786561265,4.165738105773926,86.445256 -7248,Binary classification,Leveraging Bagging,Elec2,0.9079619152752864,0.8839798225778397,3.951657295227051,110.70864399999999 -8154,Binary classification,Leveraging Bagging,Elec2,0.9089905556236968,0.8907216494845361,4.5953264236450195,137.95385399999998 -9060,Binary classification,Leveraging Bagging,Elec2,0.9097030577326416,0.8939040207522698,4.363041877746582,168.15601999999998 -9966,Binary classification,Leveraging Bagging,Elec2,0.9079779227295535,0.893631829254147,4.951889991760254,201.32075999999998 -10872,Binary classification,Leveraging Bagging,Elec2,0.9092079845460399,0.896399706098457,4.796502113342285,237.348168 -11778,Binary classification,Leveraging Bagging,Elec2,0.9081260083213042,0.8950533462657614,5.402684211730957,276.331972 -12684,Binary classification,Leveraging Bagging,Elec2,0.9066466924229283,0.8936972526485905,4.1159868240356445,318.342965 -13590,Binary classification,Leveraging Bagging,Elec2,0.9079402457870336,0.896363184491757,5.441300392150879,363.45851799999997 -14496,Binary classification,Leveraging Bagging,Elec2,0.9079682649189376,0.8968131188118812,6.5885820388793945,411.598932 -15402,Binary classification,Leveraging Bagging,Elec2,0.908252710862931,0.8965062623599208,5.050324440002441,462.698224 -16308,Binary classification,Leveraging Bagging,Elec2,0.9068498190960937,0.8945797765285585,6.200932502746582,516.871701 -17214,Binary classification,Leveraging Bagging,Elec2,0.9064079474815546,0.8923775803326874,6.6633195877075195,574.184961 -18120,Binary classification,Leveraging Bagging,Elec2,0.9061758375186268,0.8919399949148233,6.39217472076416,634.677368 -19026,Binary classification,Leveraging Bagging,Elec2,0.9064388961892247,0.8910515362957523,7.244908332824707,698.371295 -19932,Binary classification,Leveraging Bagging,Elec2,0.9069790778184738,0.8925092764378478,8.84801197052002,765.4898820000001 -20838,Binary classification,Leveraging Bagging,Elec2,0.9057445889523444,0.8911670176216338,6.796334266662598,836.163928 -21744,Binary classification,Leveraging Bagging,Elec2,0.9054408315319873,0.8892837910608508,8.134293556213379,910.125477 -22650,Binary classification,Leveraging Bagging,Elec2,0.9049847675394057,0.8878816296759404,6.271161079406738,987.478572 -23556,Binary classification,Leveraging Bagging,Elec2,0.903077902780726,0.8852244733799206,4.8961381912231445,1068.279683 -24462,Binary classification,Leveraging Bagging,Elec2,0.9014349372470463,0.8823845065612956,4.712262153625488,1152.538044 -25368,Binary classification,Leveraging Bagging,Elec2,0.8988055347498719,0.8794439487155403,4.656708717346191,1240.2539379999998 -26274,Binary classification,Leveraging Bagging,Elec2,0.8987934381304,0.8792625891113836,4.200020790100098,1331.2764919999997 -27180,Binary classification,Leveraging Bagging,Elec2,0.8987453548695684,0.8798777826276736,3.921463966369629,1425.5964879999997 -28086,Binary classification,Leveraging Bagging,Elec2,0.8965283959408937,0.8766762858597862,3.647244453430176,1523.2409069999997 -28992,Binary classification,Leveraging Bagging,Elec2,0.8964851160705046,0.8761606074361409,3.9709863662719727,1624.2218739999996 -29898,Binary classification,Leveraging Bagging,Elec2,0.8961434257617821,0.8756059452746283,3.793328285217285,1728.6222849999997 -30804,Binary classification,Leveraging Bagging,Elec2,0.8959516930169139,0.8747704450435666,4.122292518615723,1836.3575379999997 -31710,Binary classification,Leveraging Bagging,Elec2,0.8945725188432306,0.8729911477527449,3.9709787368774414,1947.5418809999996 -32616,Binary classification,Leveraging Bagging,Elec2,0.8943124329296336,0.8729872139725119,4.574315071105957,2061.9964669999995 -33522,Binary classification,Leveraging Bagging,Elec2,0.8941559022702187,0.8730862784375448,5.331856727600098,2179.8050099999996 -34428,Binary classification,Leveraging Bagging,Elec2,0.8936299997095303,0.8722973915469383,5.328598976135254,2300.9518229999994 -35334,Binary classification,Leveraging Bagging,Elec2,0.893612203888716,0.8717196191516227,5.664120674133301,2425.5167449999994 -36240,Binary classification,Leveraging Bagging,Elec2,0.8932641629184028,0.8702709954386908,4.706181526184082,2553.3715769999994 -37146,Binary classification,Leveraging Bagging,Elec2,0.893067707632252,0.8697875688434304,5.890534400939941,2684.5254249999994 -38052,Binary classification,Leveraging Bagging,Elec2,0.8926178024230638,0.8686004630820685,5.6988115310668945,2819.0438189999995 -38958,Binary classification,Leveraging Bagging,Elec2,0.8924198475241933,0.8686165710523841,5.71596622467041,2956.9620109999996 -39864,Binary classification,Leveraging Bagging,Elec2,0.8929583824599252,0.8702763505913111,6.792008399963379,3098.2064909999995 -40770,Binary classification,Leveraging Bagging,Elec2,0.8931541121930879,0.8715801886792452,8.543190956115723,3242.7027479999997 -41676,Binary classification,Leveraging Bagging,Elec2,0.8934373125374925,0.8727689442773243,7.9951677322387695,3390.4303709999995 -42582,Binary classification,Leveraging Bagging,Elec2,0.8937554308259552,0.8733411725180581,7.843605995178223,3541.2605409999996 -43488,Binary classification,Leveraging Bagging,Elec2,0.8933474371651298,0.8727572016460905,8.17009449005127,3695.3817859999995 -44394,Binary classification,Leveraging Bagging,Elec2,0.8920550537246863,0.8708216519301273,5.159916877746582,3852.7783049999994 -45300,Binary classification,Leveraging Bagging,Elec2,0.8923817302810216,0.8714568226763348,4.8946428298950195,4013.2815599999994 -25,Binary classification,Leveraging Bagging,Phishing,0.75,0.75,0.6839132308959961,0.224986 -50,Binary classification,Leveraging Bagging,Phishing,0.8163265306122449,0.8,0.6847753524780273,0.690283 -75,Binary classification,Leveraging Bagging,Phishing,0.8378378378378378,0.8333333333333334,0.6847753524780273,1.396176 -100,Binary classification,Leveraging Bagging,Phishing,0.8484848484848485,0.8421052631578947,0.6699657440185547,2.3378490000000003 -125,Binary classification,Leveraging Bagging,Phishing,0.8467741935483871,0.8403361344537815,0.9459667205810547,3.5085690000000005 -150,Binary classification,Leveraging Bagging,Phishing,0.8456375838926175,0.8456375838926175,0.9459896087646484,4.905419 -175,Binary classification,Leveraging Bagging,Phishing,0.867816091954023,0.8588957055214724,1.1184329986572266,6.555308 -200,Binary classification,Leveraging Bagging,Phishing,0.8693467336683417,0.8617021276595744,1.313650131225586,8.437938 -225,Binary classification,Leveraging Bagging,Phishing,0.8660714285714286,0.8557692307692308,1.3411617279052734,10.542565 -250,Binary classification,Leveraging Bagging,Phishing,0.8554216867469879,0.8434782608695653,1.3412303924560547,12.865947 -275,Binary classification,Leveraging Bagging,Phishing,0.8576642335766423,0.844621513944223,1.278768539428711,15.438039 -300,Binary classification,Leveraging Bagging,Phishing,0.862876254180602,0.8464419475655431,1.497152328491211,18.241575 -325,Binary classification,Leveraging Bagging,Phishing,0.8703703703703703,0.851063829787234,1.5338878631591797,21.265574 -350,Binary classification,Leveraging Bagging,Phishing,0.8710601719197708,0.8494983277591974,1.6005840301513672,24.507018000000002 -375,Binary classification,Leveraging Bagging,Phishing,0.8716577540106952,0.8481012658227849,1.8806438446044922,27.994389 -400,Binary classification,Leveraging Bagging,Phishing,0.8696741854636592,0.8433734939759037,2.144338607788086,31.712731 -425,Binary classification,Leveraging Bagging,Phishing,0.8702830188679245,0.8405797101449276,2.182210922241211,35.657971 -450,Binary classification,Leveraging Bagging,Phishing,0.8752783964365256,0.845303867403315,2.182027816772461,39.813046 -475,Binary classification,Leveraging Bagging,Phishing,0.8776371308016878,0.8505154639175259,2.2095394134521484,44.205113 -500,Binary classification,Leveraging Bagging,Phishing,0.875751503006012,0.8502415458937198,2.2298946380615234,48.820324 -525,Binary classification,Leveraging Bagging,Phishing,0.8778625954198473,0.8497652582159624,2.294797897338867,53.667583 -550,Binary classification,Leveraging Bagging,Phishing,0.8743169398907104,0.8463251670378619,2.4045467376708984,58.748532 -575,Binary classification,Leveraging Bagging,Phishing,0.8763066202090593,0.8479657387580299,2.4595699310302734,64.055891 -600,Binary classification,Leveraging Bagging,Phishing,0.8764607679465777,0.8451882845188285,2.4595699310302734,69.582297 -625,Binary classification,Leveraging Bagging,Phishing,0.8782051282051282,0.8442622950819672,2.459615707397461,75.26312 -650,Binary classification,Leveraging Bagging,Phishing,0.8813559322033898,0.850485436893204,2.390268325805664,81.075862 -675,Binary classification,Leveraging Bagging,Phishing,0.8798219584569733,0.8513761467889909,2.665342330932617,87.004325 -700,Binary classification,Leveraging Bagging,Phishing,0.8841201716738197,0.8550983899821109,2.685148239135742,93.043775 -725,Binary classification,Leveraging Bagging,Phishing,0.8825966850828729,0.8556876061120544,2.988882064819336,99.203547 -750,Binary classification,Leveraging Bagging,Phishing,0.8838451268357811,0.8576104746317513,3.061452865600586,105.474091 -775,Binary classification,Leveraging Bagging,Phishing,0.8850129198966409,0.8585055643879173,3.171110153198242,111.86121 -800,Binary classification,Leveraging Bagging,Phishing,0.8848560700876095,0.8601823708206686,3.2535533905029297,118.359722 -825,Binary classification,Leveraging Bagging,Phishing,0.8822815533980582,0.8583941605839417,3.308439254760742,124.969526 -850,Binary classification,Leveraging Bagging,Phishing,0.8845700824499411,0.8607954545454546,3.3186397552490234,131.697623 -875,Binary classification,Leveraging Bagging,Phishing,0.88558352402746,0.8611111111111112,3.2835521697998047,138.541741 -900,Binary classification,Leveraging Bagging,Phishing,0.8843159065628476,0.859078590785908,3.3748836517333984,145.497394 -925,Binary classification,Leveraging Bagging,Phishing,0.8852813852813853,0.8612565445026178,3.4775447845458984,152.56592600000002 -950,Binary classification,Leveraging Bagging,Phishing,0.8861959957850368,0.864321608040201,3.5079097747802734,159.751347 -975,Binary classification,Leveraging Bagging,Phishing,0.8880903490759754,0.8665850673194614,3.5628185272216797,167.044698 -1000,Binary classification,Leveraging Bagging,Phishing,0.8888888888888888,0.867699642431466,3.645017623901367,174.44997500000002 -1025,Binary classification,Leveraging Bagging,Phishing,0.888671875,0.8680555555555557,3.708791732788086,181.96372800000003 -1050,Binary classification,Leveraging Bagging,Phishing,0.8903717826501429,0.8706411698537682,3.783597946166992,189.59074800000002 -1075,Binary classification,Leveraging Bagging,Phishing,0.8910614525139665,0.8724100327153763,3.893186569213867,197.32835100000003 -1100,Binary classification,Leveraging Bagging,Phishing,0.8926296633303002,0.8744680851063831,3.893209457397461,205.17488800000004 -1125,Binary classification,Leveraging Bagging,Phishing,0.891459074733096,0.8742268041237113,3.893209457397461,213.13117300000005 -1150,Binary classification,Leveraging Bagging,Phishing,0.8929503916449086,0.8758829465186679,3.8932552337646484,221.19416900000004 -1175,Binary classification,Leveraging Bagging,Phishing,0.8918228279386712,0.874381800197824,3.8934383392333984,229.37121900000005 -1200,Binary classification,Leveraging Bagging,Phishing,0.8932443703085905,0.8757281553398059,3.8674678802490234,237.66201400000006 -1225,Binary classification,Leveraging Bagging,Phishing,0.8946078431372549,0.8772597526165558,3.903593063354492,246.05749800000007 -1250,Binary classification,Leveraging Bagging,Phishing,0.8951160928742994,0.8783658310120707,3.932668685913086,254.56059300000007 -1903,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1741485595703125,4.196349 -3806,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1747589111328125,11.133977999999999 -5709,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1753692626953125,20.515058 -7612,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1753692626953125,32.334394 -9515,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1753692626953125,46.585565 -11418,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1759796142578125,63.272850000000005 -13321,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.1759796142578125,82.384207 -15224,Binary classification,Leveraging Bagging,SMTP,0.9996058595546213,0.625,0.48906993865966797,104.078316 -17127,Binary classification,Leveraging Bagging,SMTP,0.9996496554945696,0.7000000000000001,0.4890470504760742,128.646042 -19030,Binary classification,Leveraging Bagging,SMTP,0.999684691786221,0.7000000000000001,0.4890470504760742,156.063076 -20933,Binary classification,Leveraging Bagging,SMTP,0.9997133575386967,0.7000000000000001,0.4896574020385742,186.34026699999998 -22836,Binary classification,Leveraging Bagging,SMTP,0.999737245456536,0.7000000000000001,0.4896574020385742,219.48144299999998 -24739,Binary classification,Leveraging Bagging,SMTP,0.9997574581615328,0.7000000000000001,0.4896574020385742,255.483814 -26642,Binary classification,Leveraging Bagging,SMTP,0.9997747832288578,0.7272727272727273,0.4897031784057617,294.351753 -28545,Binary classification,Leveraging Bagging,SMTP,0.999789798206278,0.7272727272727273,0.4897031784057617,336.077392 -30448,Binary classification,Leveraging Bagging,SMTP,0.9998029362498768,0.7272727272727273,0.4897031784057617,380.664356 -32351,Binary classification,Leveraging Bagging,SMTP,0.9998145285935085,0.7272727272727273,0.4897031784057617,428.135176 -34254,Binary classification,Leveraging Bagging,SMTP,0.9998248328613553,0.7272727272727273,0.4897031784057617,478.442458 -36157,Binary classification,Leveraging Bagging,SMTP,0.9998340524394291,0.7272727272727273,0.4984426498413086,531.6199799999999 -38060,Binary classification,Leveraging Bagging,SMTP,0.9998423500354713,0.7272727272727273,0.5112142562866211,587.6570539999999 -39963,Binary classification,Leveraging Bagging,SMTP,0.9998498573644963,0.7272727272727273,0.5112142562866211,646.546353 -41866,Binary classification,Leveraging Bagging,SMTP,0.9998566821927625,0.7272727272727273,0.5118017196655273,708.303189 -43769,Binary classification,Leveraging Bagging,SMTP,0.9998629135441418,0.7272727272727273,0.5118017196655273,772.911335 -45672,Binary classification,Leveraging Bagging,SMTP,0.9998686256048697,0.7272727272727273,0.5118017196655273,840.389935 -47575,Binary classification,Leveraging Bagging,SMTP,0.9998528608063227,0.6956521739130435,0.6032171249389648,910.729159 -49478,Binary classification,Leveraging Bagging,SMTP,0.9998585201204601,0.6956521739130435,0.6032171249389648,983.9244679999999 -51381,Binary classification,Leveraging Bagging,SMTP,0.9998248345659789,0.6666666666666666,0.6488561630249023,1059.968701 -53284,Binary classification,Leveraging Bagging,SMTP,0.9998310905917459,0.6666666666666666,0.6488561630249023,1138.856016 -55187,Binary classification,Leveraging Bagging,SMTP,0.9998369151596419,0.6666666666666666,0.6487874984741211,1220.600051 -57090,Binary classification,Leveraging Bagging,SMTP,0.9997197358510396,0.5789473684210525,0.915858268737793,1305.292865 -58993,Binary classification,Leveraging Bagging,SMTP,0.9997118253322484,0.5641025641025641,0.9158124923706055,1392.9564999999998 -60896,Binary classification,Leveraging Bagging,SMTP,0.9997208309385007,0.5641025641025641,0.9158353805541992,1483.5784209999997 -62799,Binary classification,Leveraging Bagging,SMTP,0.9996974425937132,0.5365853658536585,1.0681943893432617,1577.1896599999998 -64702,Binary classification,Leveraging Bagging,SMTP,0.9997063414784934,0.5365853658536585,1.0681486129760742,1673.7731769999998 -66605,Binary classification,Leveraging Bagging,SMTP,0.999714731847937,0.5365853658536585,1.0681257247924805,1773.3049339999998 -68508,Binary classification,Leveraging Bagging,SMTP,0.9997226560789408,0.5777777777777777,1.0767507553100586,1875.8071019999998 -70411,Binary classification,Leveraging Bagging,SMTP,0.9997301519670502,0.5777777777777777,1.0766592025756836,1981.2768389999997 -72314,Binary classification,Leveraging Bagging,SMTP,0.9997372533292769,0.5777777777777777,1.0766363143920898,2089.690205 -74217,Binary classification,Leveraging Bagging,SMTP,0.9997439905141748,0.5777777777777777,1.183516502380371,2201.0645729999997 -76120,Binary classification,Leveraging Bagging,SMTP,0.9997503908354025,0.5777777777777777,1.1835393905639648,2315.3792299999996 -78023,Binary classification,Leveraging Bagging,SMTP,0.999756478941837,0.5777777777777777,1.183516502380371,2432.6354309999997 -79926,Binary classification,Leveraging Bagging,SMTP,0.9997622771348139,0.5777777777777777,1.1834936141967773,2552.8498939999995 -81829,Binary classification,Leveraging Bagging,SMTP,0.9997678056411008,0.5777777777777777,1.183516502380371,2676.0109319999997 -83732,Binary classification,Leveraging Bagging,SMTP,0.9997730828486463,0.5777777777777777,1.184126853942871,2802.1051509999998 -85635,Binary classification,Leveraging Bagging,SMTP,0.9997781255108952,0.5777777777777777,1.1841497421264648,2931.1451239999997 -87538,Binary classification,Leveraging Bagging,SMTP,0.9997829489244549,0.5777777777777777,1.1841497421264648,3063.115766 -89441,Binary classification,Leveraging Bagging,SMTP,0.999765205724508,0.5531914893617021,1.323287010192871,3198.03692 -91344,Binary classification,Leveraging Bagging,SMTP,0.9997700973254655,0.5531914893617021,1.3272314071655273,3335.9142659999998 -93247,Binary classification,Leveraging Bagging,SMTP,0.9997747892671,0.5531914893617021,1.3272314071655273,3476.7248499999996 -95150,Binary classification,Leveraging Bagging,SMTP,0.9997792935290964,0.5531914893617021,1.327254295349121,3620.3557379999997 -106,Binary classification,Stacking,Bananas,0.6,0.5434782608695652,0.7756900787353516,0.335809 -212,Binary classification,Stacking,Bananas,0.7251184834123223,0.6881720430107526,1.166391372680664,1.0610979999999999 -318,Binary classification,Stacking,Bananas,0.7539432176656151,0.7253521126760563,1.6065692901611328,2.189762 -424,Binary classification,Stacking,Bananas,0.7825059101654847,0.7566137566137565,2.022294044494629,3.739465 -530,Binary classification,Stacking,Bananas,0.7958412098298677,0.7631578947368421,2.4158077239990234,5.557825 -636,Binary classification,Stacking,Bananas,0.7937007874015748,0.7622504537205083,2.785458564758301,7.636877 -742,Binary classification,Stacking,Bananas,0.8029689608636977,0.7675159235668789,3.1855859756469727,9.989946 -848,Binary classification,Stacking,Bananas,0.8110979929161747,0.7790055248618785,3.6375198364257812,12.613619 -954,Binary classification,Stacking,Bananas,0.8163693599160545,0.7836835599505564,3.940545082092285,15.512474000000001 -1060,Binary classification,Stacking,Bananas,0.8243626062322946,0.7905405405405406,4.2790374755859375,18.684266 -1166,Binary classification,Stacking,Bananas,0.8240343347639485,0.790602655771195,4.743890762329102,22.147393 -1272,Binary classification,Stacking,Bananas,0.8253343823760818,0.7936802973977696,5.073901176452637,25.9097 -1378,Binary classification,Stacking,Bananas,0.8271604938271605,0.7941176470588235,5.443793296813965,29.969434 -1484,Binary classification,Stacking,Bananas,0.8293998651382333,0.7967871485943775,5.808208465576172,34.339833999999996 -1590,Binary classification,Stacking,Bananas,0.8313404657016992,0.7994011976047903,6.233636856079102,39.030108 -1696,Binary classification,Stacking,Bananas,0.8348082595870207,0.8030942334739802,6.672585487365723,44.033544 -1802,Binary classification,Stacking,Bananas,0.8384230982787341,0.8094302554027505,7.1109819412231445,49.366899 -1908,Binary classification,Stacking,Bananas,0.8363922391190352,0.8095238095238096,7.617318153381348,55.036325 -2014,Binary classification,Stacking,Bananas,0.8335817188276204,0.8080229226361031,8.179092407226562,61.04992 -2120,Binary classification,Stacking,Bananas,0.8357715903728173,0.8127018299246501,8.54446792602539,67.397947 -2226,Binary classification,Stacking,Bananas,0.8368539325842697,0.8143222506393862,9.00230598449707,74.092607 -2332,Binary classification,Stacking,Bananas,0.8365508365508365,0.8142369575816674,9.503581047058105,81.1143 -2438,Binary classification,Stacking,Bananas,0.8379154698399671,0.8160223567768979,9.875147819519043,88.456785 -2544,Binary classification,Stacking,Bananas,0.8375933936295714,0.816525988449578,10.285362243652344,96.129153 -2650,Binary classification,Stacking,Bananas,0.8369195922989807,0.8161702127659575,10.684521675109863,104.109815 -2756,Binary classification,Stacking,Bananas,0.8381125226860254,0.8176614881439086,11.088122367858887,112.408624 -2862,Binary classification,Stacking,Bananas,0.8392170569730864,0.8184688239936858,11.425690650939941,121.03154500000001 -2968,Binary classification,Stacking,Bananas,0.840242669362993,0.8188073394495413,11.867729187011719,129.980691 -3074,Binary classification,Stacking,Bananas,0.8395704523267166,0.8186833394630378,12.388784408569336,139.271806 -3180,Binary classification,Stacking,Bananas,0.8417741428122051,0.8205494113449874,12.724870681762695,148.909629 -3286,Binary classification,Stacking,Bananas,0.8423135464231355,0.8207612456747405,13.150970458984375,158.896732 -3392,Binary classification,Stacking,Bananas,0.8428192273665586,0.8221554888221555,13.55066967010498,169.223096 -3498,Binary classification,Stacking,Bananas,0.8441521303974836,0.8228794280142996,13.954619407653809,179.903449 -3604,Binary classification,Stacking,Bananas,0.8454066056064391,0.8234548335974643,14.297491073608398,190.93427 -3710,Binary classification,Stacking,Bananas,0.845510919385279,0.8241791960724149,14.779010772705078,202.32720799999998 -3816,Binary classification,Stacking,Bananas,0.8453473132372215,0.8241954707985697,15.263079643249512,214.07821499999997 -3922,Binary classification,Stacking,Bananas,0.8454475899005356,0.8239395700174318,15.65628433227539,226.20807199999996 -4028,Binary classification,Stacking,Bananas,0.8435559970201142,0.8216308040770102,16.25907039642334,238.71700499999997 -4134,Binary classification,Stacking,Bananas,0.8439390273409146,0.8220689655172413,16.753341674804688,251.59892599999998 -4240,Binary classification,Stacking,Bananas,0.8461901391837697,0.8249194414607949,17.15353012084961,264.97802099999996 -4346,Binary classification,Stacking,Bananas,0.8474108170310702,0.8263033796175006,17.548202514648438,278.74497199999996 -4452,Binary classification,Stacking,Bananas,0.8467760053920468,0.8253968253968255,17.966373443603516,292.92157399999996 -4558,Binary classification,Stacking,Bananas,0.847267939433838,0.8265204386839482,18.466463088989258,307.49799799999994 -4664,Binary classification,Stacking,Bananas,0.8475230538280077,0.8273014330823415,18.886920928955078,322.48837999999995 -4770,Binary classification,Stacking,Bananas,0.8483958901237156,0.828143570240076,19.283724784851074,337.9195859999999 -4876,Binary classification,Stacking,Bananas,0.8488205128205129,0.8279243520896566,19.64915180206299,353.77839399999993 -4982,Binary classification,Stacking,Bananas,0.8496285886368199,0.8291904218928163,19.982958793640137,370.05846699999995 -5088,Binary classification,Stacking,Bananas,0.850206408492235,0.829682610639249,20.404964447021484,386.77508599999993 -5194,Binary classification,Stacking,Bananas,0.8501829385711535,0.8296847635726795,20.84154510498047,403.93132599999996 -5300,Binary classification,Stacking,Bananas,0.8503491224759389,0.8299378082779326,21.28391456604004,421.53966099999997 -906,Binary classification,Stacking,Elec2,0.9082872928176795,0.905788876276958,2.982789993286133,3.516385 -1812,Binary classification,Stacking,Elec2,0.9276642738818333,0.9100892244337679,5.035035133361816,9.67052 -2718,Binary classification,Stacking,Elec2,0.9193963930806036,0.9004092769440655,7.4160261154174805,18.410774 -3624,Binary classification,Stacking,Elec2,0.9213359094672923,0.9030941856511392,8.590222358703613,29.484026 -4530,Binary classification,Stacking,Elec2,0.919629057187017,0.896825396825397,10.053829193115234,42.932107 -5436,Binary classification,Stacking,Elec2,0.9164673413063478,0.8929245283018867,11.804065704345703,58.899071000000006 -6342,Binary classification,Stacking,Elec2,0.9165746727645482,0.8945585010962727,14.095972061157227,77.41200400000001 -7248,Binary classification,Stacking,Elec2,0.9136194287291293,0.8907885554780182,16.2540225982666,98.532638 -8154,Binary classification,Stacking,Elec2,0.9135287624187416,0.8957254843957995,18.652454376220703,122.39659 -9060,Binary classification,Stacking,Elec2,0.9157743680317916,0.9006122183144457,20.98922061920166,148.829731 -9966,Binary classification,Stacking,Elec2,0.9175112895132965,0.9043741275011633,21.67603302001953,177.92592100000002 -10872,Binary classification,Stacking,Elec2,0.9189586974519364,0.9074093536521283,23.620224952697754,209.60222600000003 -11778,Binary classification,Stacking,Elec2,0.9187399167869577,0.907222491517208,26.53412914276123,243.98565400000004 -12684,Binary classification,Stacking,Elec2,0.9176062445793582,0.9060842994517839,30.32783317565918,281.19943900000004 -13590,Binary classification,Stacking,Elec2,0.9178747516373538,0.9076464746772592,30.98683452606201,321.28235000000006 -14496,Binary classification,Stacking,Elec2,0.9177647464642981,0.9080388828884431,33.476722717285156,364.2111060000001 -15402,Binary classification,Stacking,Elec2,0.9181221998571522,0.9079091506609217,36.0946741104126,409.94528800000006 -16308,Binary classification,Stacking,Elec2,0.9156803826577543,0.90483770503149,39.825303077697754,458.76832800000005 -17214,Binary classification,Stacking,Elec2,0.9136118050310812,0.9010184383944618,31.60810947418213,510.75424300000003 -18120,Binary classification,Stacking,Elec2,0.9136265798333242,0.9008803597441257,29.540003776550293,565.7272700000001 -19026,Binary classification,Stacking,Elec2,0.9144809461235217,0.9008350094471872,30.251863479614258,623.5764490000001 -19932,Binary classification,Stacking,Elec2,0.9137022728413025,0.9008188213585515,34.67849349975586,684.5888420000001 -20838,Binary classification,Stacking,Elec2,0.9112156260498152,0.8982286280118825,25.089420318603516,748.7394940000001 -21744,Binary classification,Stacking,Elec2,0.9108678655199375,0.8963414634146342,28.645745277404785,815.8674180000002 -22650,Binary classification,Stacking,Elec2,0.910062254404168,0.8947232415111892,32.00656318664551,886.1270340000001 -23556,Binary classification,Stacking,Elec2,0.9082148163871789,0.8923306772908367,29.995322227478027,959.6051970000001 -24462,Binary classification,Stacking,Elec2,0.9068312824496136,0.8901633813677768,31.142220497131348,1036.2892310000002 -25368,Binary classification,Stacking,Elec2,0.9048764142389719,0.8880745860197596,17.65717887878418,1115.9640360000003 -26274,Binary classification,Stacking,Elec2,0.9052639591976553,0.8883004981375936,19.38848114013672,1198.3281000000004 -27180,Binary classification,Stacking,Elec2,0.905331321976526,0.8887639963685098,20.18451499938965,1283.4271300000005 -28086,Binary classification,Stacking,Elec2,0.9040769093822325,0.886768661735037,24.225767135620117,1371.4128720000006 -28992,Binary classification,Stacking,Elec2,0.9030388741333517,0.8849883392659875,26.150443077087402,1462.6499510000006 -29898,Binary classification,Stacking,Elec2,0.9022644412482858,0.8837708830548926,31.19912052154541,1557.0334400000006 -30804,Binary classification,Stacking,Elec2,0.9016004934584294,0.8822684016313848,35.062607765197754,1654.6421970000006 -31710,Binary classification,Stacking,Elec2,0.9002491406225361,0.8805739097602416,34.509202003479004,1755.5826140000006 -32616,Binary classification,Stacking,Elec2,0.8993407941131382,0.8797127468581688,39.23386001586914,1859.7925240000006 -33522,Binary classification,Stacking,Elec2,0.8986307091077235,0.879040296169728,43.53414344787598,1967.3677710000006 -34428,Binary classification,Stacking,Elec2,0.8976384814244633,0.8779440288168467,46.41888236999512,2078.5063070000006 -35334,Binary classification,Stacking,Elec2,0.8964990235756941,0.8759119134063996,47.29628944396973,2193.0138890000007 -36240,Binary classification,Stacking,Elec2,0.8963271613455117,0.874812568724801,50.92376518249512,2310.7523430000006 -37146,Binary classification,Stacking,Elec2,0.8956252523892853,0.8736433855881107,52.943902015686035,2431.8466670000007 -38052,Binary classification,Stacking,Elec2,0.8955612204672676,0.8730756946662408,54.53785705566406,2556.3609540000007 -38958,Binary classification,Stacking,Elec2,0.895320481556588,0.8731176104542625,58.273138999938965,2684.2112230000007 -39864,Binary classification,Stacking,Elec2,0.8957178335800116,0.8745056603773586,61.72060203552246,2815.6197070000007 -40770,Binary classification,Stacking,Elec2,0.8957541269101523,0.8755198875285573,62.47746276855469,2950.3941420000006 -41676,Binary classification,Stacking,Elec2,0.8962447510497901,0.8769003017707682,58.149333000183105,3088.5413130000006 -42582,Binary classification,Stacking,Elec2,0.8967614663817195,0.8777598576274956,60.56365966796875,3230.0396030000006 -43488,Binary classification,Stacking,Elec2,0.896612780831053,0.8775932480261367,59.691514015197754,3375.0306610000007 -44394,Binary classification,Stacking,Elec2,0.8962223774018426,0.8767423816785724,64.22966861724854,3523.5486860000005 -45300,Binary classification,Stacking,Elec2,0.8967968387823131,0.8776210046857412,42.88050365447998,3675.4418880000007 -25,Binary classification,Stacking,Phishing,0.6666666666666666,0.7142857142857143,0.5762147903442383,0.119361 -50,Binary classification,Stacking,Phishing,0.7551020408163265,0.7391304347826088,0.6475057601928711,0.366938 -75,Binary classification,Stacking,Phishing,0.7837837837837838,0.7777777777777778,0.9396762847900391,0.745521 -100,Binary classification,Stacking,Phishing,0.8080808080808081,0.7999999999999999,1.1059551239013672,1.266772 -125,Binary classification,Stacking,Phishing,0.8145161290322581,0.8067226890756303,1.2883186340332031,1.944005 -150,Binary classification,Stacking,Phishing,0.8187919463087249,0.8187919463087249,1.3393936157226562,2.776317 -175,Binary classification,Stacking,Phishing,0.8390804597701149,0.8292682926829268,1.354720115661621,3.764387 -200,Binary classification,Stacking,Phishing,0.8391959798994975,0.8297872340425532,1.492502212524414,4.90761 -225,Binary classification,Stacking,Phishing,0.84375,0.8309178743961353,1.6093759536743164,6.207708 -250,Binary classification,Stacking,Phishing,0.8353413654618473,0.8225108225108225,1.7083539962768555,7.674896 -275,Binary classification,Stacking,Phishing,0.8394160583941606,0.8253968253968254,1.7150201797485352,9.301078 -300,Binary classification,Stacking,Phishing,0.842809364548495,0.825278810408922,1.782989501953125,11.084076 -325,Binary classification,Stacking,Phishing,0.8518518518518519,0.8309859154929577,1.8577651977539062,13.027607 -350,Binary classification,Stacking,Phishing,0.8567335243553008,0.8344370860927152,1.8672637939453125,15.126795999999999 -375,Binary classification,Stacking,Phishing,0.8529411764705882,0.8286604361370716,2.0530452728271484,17.394419 -400,Binary classification,Stacking,Phishing,0.8546365914786967,0.8284023668639053,2.076310157775879,19.823605 -425,Binary classification,Stacking,Phishing,0.8584905660377359,0.8295454545454545,2.0878963470458984,22.419713 -450,Binary classification,Stacking,Phishing,0.8596881959910914,0.8292682926829269,2.0654611587524414,25.175931000000002 -475,Binary classification,Stacking,Phishing,0.8649789029535865,0.8383838383838383,2.202821731567383,28.096353 -500,Binary classification,Stacking,Phishing,0.8677354709418837,0.8443396226415094,2.377251625061035,31.184831 -525,Binary classification,Stacking,Phishing,0.8702290076335878,0.8447488584474886,2.37432861328125,34.435066 -550,Binary classification,Stacking,Phishing,0.8706739526411658,0.8466522678185745,2.44970703125,37.85251 -575,Binary classification,Stacking,Phishing,0.872822299651568,0.8488612836438924,2.5103416442871094,41.431292 -600,Binary classification,Stacking,Phishing,0.8764607679465777,0.8508064516129032,2.478057861328125,45.177182 -625,Binary classification,Stacking,Phishing,0.875,0.8464566929133858,2.529691696166992,49.087450000000004 -650,Binary classification,Stacking,Phishing,0.8782742681047766,0.8528864059590316,2.6017770767211914,53.16255700000001 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classification,Stacking,Phishing,0.8901601830663616,0.8713136729222519,3.264657974243164,96.04768600000001 -900,Binary classification,Stacking,Phishing,0.8887652947719689,0.8694516971279374,3.3888587951660156,101.33402500000001 -925,Binary classification,Stacking,Phishing,0.8906926406926406,0.8729559748427673,3.3625974655151367,106.71510400000001 -950,Binary classification,Stacking,Phishing,0.8914646996838778,0.875453446191052,3.5129919052124023,112.20148900000001 -975,Binary classification,Stacking,Phishing,0.893223819301848,0.8773584905660378,3.552186965942383,117.78607000000001 -1000,Binary classification,Stacking,Phishing,0.8928928928928929,0.8771526980482205,3.6711978912353516,123.468781 -1025,Binary classification,Stacking,Phishing,0.892578125,0.8772321428571428,3.734159469604492,129.25173700000002 -1050,Binary classification,Stacking,Phishing,0.894184938036225,0.8794788273615636,3.775693893432617,135.127992 -1075,Binary classification,Stacking,Phishing,0.8929236499068901,0.879074658254469,3.8186750411987305,141.101233 -1100,Binary classification,Stacking,Phishing,0.8944494995450409,0.8809034907597535,3.859647750854492,147.172282 -1125,Binary classification,Stacking,Phishing,0.8959074733096085,0.8835820895522387,3.8923940658569336,153.346878 -1150,Binary classification,Stacking,Phishing,0.896431679721497,0.8839024390243903,4.0131940841674805,159.622141 -1175,Binary classification,Stacking,Phishing,0.8952299829642248,0.8822966507177035,4.097073554992676,165.998758 -1200,Binary classification,Stacking,Phishing,0.896580483736447,0.8834586466165414,4.154815673828125,172.479929 -1225,Binary classification,Stacking,Phishing,0.8978758169934641,0.8847926267281105,4.238761901855469,179.059934 -1250,Binary classification,Stacking,Phishing,0.899119295436349,0.8866906474820143,4.319509506225586,185.738258 -1903,Binary classification,Stacking,SMTP,1.0,0.0,0.24710845947265625,2.767854 -3806,Binary classification,Stacking,SMTP,1.0,0.0,0.24883270263671875,8.302014 -5709,Binary classification,Stacking,SMTP,1.0,0.0,0.25005340576171875,16.382949 -7612,Binary classification,Stacking,SMTP,1.0,0.0,0.24954986572265625,26.809829999999998 -9515,Binary classification,Stacking,SMTP,1.0,0.0,0.24954986572265625,39.580928 -11418,Binary classification,Stacking,SMTP,1.0,0.0,0.25127410888671875,54.705622 -13321,Binary classification,Stacking,SMTP,1.0,0.0,0.25127410888671875,72.18382 -15224,Binary classification,Stacking,SMTP,0.9996058595546213,0.625,0.6267004013061523,92.15184599999999 -17127,Binary classification,Stacking,SMTP,0.9996496554945696,0.7000000000000001,0.6147451400756836,114.903201 -19030,Binary classification,Stacking,SMTP,0.999684691786221,0.7000000000000001,0.6187658309936523,140.42757699999999 -20933,Binary classification,Stacking,SMTP,0.9997133575386967,0.7000000000000001,0.6200857162475586,168.711887 -22836,Binary classification,Stacking,SMTP,0.999737245456536,0.7000000000000001,0.6196355819702148,199.762171 -24739,Binary classification,Stacking,SMTP,0.9997574581615328,0.7000000000000001,0.619715690612793,233.587883 -26642,Binary classification,Stacking,SMTP,0.9997372471003341,0.6666666666666666,0.656519889831543,270.202649 -28545,Binary classification,Stacking,SMTP,0.999754764573991,0.6666666666666666,0.6566305160522461,309.590328 -30448,Binary classification,Stacking,SMTP,0.999770092291523,0.6666666666666666,0.6724729537963867,351.755182 -32351,Binary classification,Stacking,SMTP,0.9997836166924265,0.6666666666666666,0.6681547164916992,396.69714999999997 -34254,Binary classification,Stacking,SMTP,0.9997956383382477,0.6666666666666666,0.669642448425293,444.42250599999994 -36157,Binary classification,Stacking,SMTP,0.9998063945126673,0.6666666666666666,0.6692113876342773,494.92236899999995 -38060,Binary classification,Stacking,SMTP,0.9998160750413831,0.6666666666666666,0.6613035202026367,548.2094599999999 -39963,Binary classification,Stacking,SMTP,0.9998248335919123,0.6666666666666666,0.6618070602416992,604.285405 -41866,Binary classification,Stacking,SMTP,0.9998327958915562,0.6666666666666666,0.6630735397338867,663.137659 -43769,Binary classification,Stacking,SMTP,0.9998400658014989,0.6666666666666666,0.6625699996948242,724.773781 -45672,Binary classification,Stacking,SMTP,0.9998467298723479,0.6666666666666666,0.6625699996948242,789.184255 -47575,Binary classification,Stacking,SMTP,0.9998528608063227,0.6666666666666666,0.7120122909545898,856.394593 -49478,Binary classification,Stacking,SMTP,0.9998585201204601,0.6666666666666666,0.7120351791381836,926.380828 -51381,Binary classification,Stacking,SMTP,0.9998442973919813,0.6666666666666666,0.741633415222168,999.155992 -53284,Binary classification,Stacking,SMTP,0.9998498583037742,0.6666666666666666,0.7417364120483398,1074.696379 -55187,Binary classification,Stacking,SMTP,0.9998550356974595,0.6666666666666666,0.7564992904663086,1153.0240469999999 -57090,Binary classification,Stacking,SMTP,0.9997022193417295,0.5142857142857143,1.0212621688842773,1234.302477 -58993,Binary classification,Stacking,SMTP,0.9997118253322484,0.5142857142857143,1.0188016891479492,1318.4794789999999 -60896,Binary classification,Stacking,SMTP,0.9997208309385007,0.5142857142857143,1.0275907516479492,1405.549891 -62799,Binary classification,Stacking,SMTP,0.9996974425937132,0.48648648648648646,1.2421979904174805,1495.555588 -64702,Binary classification,Stacking,SMTP,0.9997063414784934,0.48648648648648646,1.258589744567871,1588.500286 -66605,Binary classification,Stacking,SMTP,0.999714731847937,0.48648648648648646,1.266993522644043,1684.370287 -68508,Binary classification,Stacking,SMTP,0.9997226560789408,0.5365853658536586,1.3167448043823242,1783.171155 -70411,Binary classification,Stacking,SMTP,0.9997301519670502,0.5365853658536586,1.3264188766479492,1884.908958 -72314,Binary classification,Stacking,SMTP,0.9997372533292769,0.5365853658536586,1.3260221481323242,1989.570925 -74217,Binary classification,Stacking,SMTP,0.9997439905141748,0.5365853658536586,1.342616081237793,2097.157749 -76120,Binary classification,Stacking,SMTP,0.9997503908354025,0.5365853658536586,1.3624944686889648,2207.674598 -78023,Binary classification,Stacking,SMTP,0.999756478941837,0.5365853658536586,1.357996940612793,2321.128083 -79926,Binary classification,Stacking,SMTP,0.9997622771348139,0.5365853658536586,1.3575201034545898,2437.510157 -81829,Binary classification,Stacking,SMTP,0.9997678056411008,0.5365853658536586,1.3575468063354492,2556.8217050000003 -83732,Binary classification,Stacking,SMTP,0.9997730828486463,0.5365853658536586,1.3463621139526367,2679.067711 -85635,Binary classification,Stacking,SMTP,0.9997781255108952,0.5365853658536586,1.3506765365600586,2804.239644 -87538,Binary classification,Stacking,SMTP,0.9997829489244549,0.5365853658536586,1.350123405456543,2932.339248 -89441,Binary classification,Stacking,SMTP,0.9997763864042933,0.5238095238095238,1.4778623580932617,3063.373685 -91344,Binary classification,Stacking,SMTP,0.9997810450718719,0.5238095238095238,1.489375114440918,3197.337821 -93247,Binary classification,Stacking,SMTP,0.9997855135877142,0.5238095238095238,1.5083913803100586,3334.235023 -95150,Binary classification,Stacking,SMTP,0.9997898033610443,0.5238095238095238,1.5167646408081055,3474.068956 -106,Binary classification,Voting,Bananas,0.7142857142857143,0.6590909090909091,0.08138561248779297,0.066609 -212,Binary classification,Voting,Bananas,0.7819905213270142,0.7444444444444445,0.08191204071044922,0.219203 -318,Binary classification,Voting,Bananas,0.7949526813880127,0.7583643122676579,0.08151531219482422,0.458077 -424,Binary classification,Voting,Bananas,0.806146572104019,0.7696629213483147,0.08151531219482422,0.7833680000000001 -530,Binary classification,Voting,Bananas,0.7977315689981096,0.7446300715990454,0.08201885223388672,1.194727 -636,Binary classification,Voting,Bananas,0.7984251968503937,0.7460317460317459,0.08151531219482422,1.6920620000000002 -742,Binary classification,Voting,Bananas,0.805668016194332,0.75,0.08151531219482422,2.2749610000000002 -848,Binary classification,Voting,Bananas,0.8110979929161747,0.7597597597597598,0.08201885223388672,2.9435640000000003 -954,Binary classification,Voting,Bananas,0.8174186778593914,0.7661290322580646,0.08151531219482422,3.697699 -1060,Binary classification,Voting,Bananas,0.8253068932955618,0.774114774114774,0.08151531219482422,4.5372580000000005 -1166,Binary classification,Voting,Bananas,0.8266094420600858,0.7770419426048565,0.08201885223388672,5.437299 -1272,Binary classification,Voting,Bananas,0.8284815106215578,0.7811244979919679,0.08151531219482422,6.385956 -1378,Binary classification,Voting,Bananas,0.8264342774146696,0.7764265668849394,0.08201885223388672,7.383082 -1484,Binary classification,Voting,Bananas,0.8287255563047876,0.7795138888888888,0.08201885223388672,8.428656 -1590,Binary classification,Voting,Bananas,0.830081812460667,0.7822580645161291,0.08151531219482422,9.522527 -1696,Binary classification,Voting,Bananas,0.8348082595870207,0.7881996974281392,0.08201885223388672,10.665041 -1802,Binary classification,Voting,Bananas,0.8367573570238757,0.7929577464788733,0.08201885223388672,11.856044 -1908,Binary classification,Voting,Bananas,0.8342947037231253,0.7931937172774869,0.08151531219482422,13.095519000000001 -2014,Binary classification,Voting,Bananas,0.8301043219076006,0.7901840490797546,0.08201885223388672,14.384085 -2120,Binary classification,Voting,Bananas,0.8310523831996225,0.7935409457900807,0.08201885223388672,15.721188000000001 -2226,Binary classification,Voting,Bananas,0.8301123595505618,0.792535675082327,0.08151531219482422,17.109526000000002 -2332,Binary classification,Voting,Bananas,0.8301158301158301,0.792887029288703,0.08201885223388672,18.546805000000003 -2438,Binary classification,Voting,Bananas,0.8297086581862946,0.7921882824236354,0.08201885223388672,20.032509 -2544,Binary classification,Voting,Bananas,0.8297286669288242,0.7933174224343675,0.08151531219482422,21.566602 -2650,Binary classification,Voting,Bananas,0.8289920724801813,0.7932450935645824,0.08201885223388672,23.149486 -2756,Binary classification,Voting,Bananas,0.8294010889292196,0.7942206654991244,0.08201885223388672,24.780488 -2862,Binary classification,Voting,Bananas,0.8304788535477106,0.7949260042283298,0.08151531219482422,26.460054999999997 -2968,Binary classification,Voting,Bananas,0.8308055274688237,0.7944307944307943,0.08201885223388672,28.187981999999998 -3074,Binary classification,Voting,Bananas,0.8285063455906281,0.7924379677038204,0.08201885223388672,29.964311 -3180,Binary classification,Voting,Bananas,0.8307643913180245,0.7941851568477429,0.08151531219482422,31.788843999999997 -3286,Binary classification,Voting,Bananas,0.8310502283105022,0.7939101373932418,0.08201885223388672,33.661829 -3392,Binary classification,Voting,Bananas,0.8307283987024476,0.7948534667619728,0.08201885223388672,35.582957 -3498,Binary classification,Voting,Bananas,0.8301401201029454,0.7933194154488518,0.09734535217285156,37.553729 -3604,Binary classification,Voting,Bananas,0.8320843741326672,0.7952622673434856,0.09784889221191406,39.573418999999994 -3710,Binary classification,Voting,Bananas,0.8306821245618765,0.7939632545931758,0.09734535217285156,41.642475999999995 -3816,Binary classification,Voting,Bananas,0.8311926605504587,0.794904458598726,0.09734535217285156,43.760552999999994 -3922,Binary classification,Voting,Bananas,0.831165519000255,0.7945375543140905,0.10735130310058594,45.928838999999996 -4028,Binary classification,Voting,Bananas,0.8301465110504097,0.7932285368802902,0.11301231384277344,48.146786 -4134,Binary classification,Voting,Bananas,0.8296636825550447,0.7929411764705883,0.11301231384277344,50.413841 -4240,Binary classification,Voting,Bananas,0.8310922387355508,0.7955454026270702,0.11351585388183594,52.731009 -4346,Binary classification,Voting,Bananas,0.8317606444188723,0.7965488449763429,0.11301231384277344,55.097904 -4452,Binary classification,Voting,Bananas,0.8312738710402157,0.7955349850258643,0.11301231384277344,57.514886 -4558,Binary classification,Voting,Bananas,0.8294930875576036,0.7937350676931244,0.11351585388183594,59.981716999999996 -4664,Binary classification,Voting,Bananas,0.8286510829937809,0.7933798810447376,0.11301231384277344,62.498267999999996 -4770,Binary classification,Voting,Bananas,0.8280561962675613,0.7922998986828774,0.12292289733886719,65.06543599999999 -4876,Binary classification,Voting,Bananas,0.8274871794871795,0.7908480477493159,0.12296867370605469,67.68219599999999 -4982,Binary classification,Voting,Bananas,0.8285484842401124,0.7928190198932558,0.12246513366699219,70.34849499999999 -5088,Binary classification,Voting,Bananas,0.8287792412030667,0.7930624851508671,0.12296867370605469,73.06406199999998 -5194,Binary classification,Voting,Bananas,0.8297708453687657,0.7943229409027454,0.12296867370605469,75.82921599999997 -5300,Binary classification,Voting,Bananas,0.8301566333270428,0.7949886104783599,0.12246513366699219,78.64422599999997 -906,Binary classification,Voting,Elec2,0.8806629834254144,0.8820960698689956,0.29908084869384766,0.533765 -1812,Binary classification,Voting,Elec2,0.8901159580342353,0.8654496281271129,0.33275699615478516,1.6121 -2718,Binary classification,Voting,Elec2,0.8884799411115201,0.8614540466392318,0.3573274612426758,3.262924 -3624,Binary classification,Voting,Elec2,0.8948385316036434,0.8697435897435898,0.3568239212036133,5.47711 -4530,Binary classification,Voting,Elec2,0.8922499448001766,0.8587145338737695,0.35677051544189453,8.248427 -5436,Binary classification,Voting,Elec2,0.8833486660533578,0.846116504854369,0.35677051544189453,11.585141 -6342,Binary classification,Voting,Elec2,0.8836145718340955,0.8482730263157895,0.35727405548095703,15.487845 -7248,Binary classification,Voting,Elec2,0.8802263005381538,0.8418367346938775,0.35727405548095703,19.950209 -8154,Binary classification,Voting,Elec2,0.8828652029927634,0.8526461965746027,0.35727405548095703,24.978187000000002 -9060,Binary classification,Voting,Elec2,0.8872944033557788,0.8620456695041211,0.42380619049072266,30.568141 -9966,Binary classification,Voting,Elec2,0.8887104867034621,0.8668187822745287,0.4239206314086914,36.726803000000004 -10872,Binary classification,Voting,Elec2,0.8916383037439058,0.8724003466204506,0.4239206314086914,43.450649000000006 -11778,Binary classification,Voting,Elec2,0.8908890209730831,0.871229582122457,0.508366584777832,50.737672 -12684,Binary classification,Voting,Elec2,0.8903256327367343,0.8711917770163904,0.508366584777832,58.591770000000004 -13590,Binary classification,Voting,Elec2,0.8921186253587461,0.8751702997275205,0.508366584777832,67.02087300000001 -14496,Binary classification,Voting,Elec2,0.8925146602276647,0.8765842839036756,0.5096101760864258,76.01746500000002 -15402,Binary classification,Voting,Elec2,0.8924095837932602,0.875628612174435,0.5091333389282227,85.58361400000001 -16308,Binary classification,Voting,Elec2,0.8862451707855522,0.8668437298112124,0.5348939895629883,95.71704600000001 -17214,Binary classification,Voting,Elec2,0.882646836693197,0.8594880356149137,0.5348939895629883,106.42052500000001 -18120,Binary classification,Voting,Elec2,0.882664606214471,0.8596143687268886,0.5429277420043945,117.69460800000002 -19026,Binary classification,Voting,Elec2,0.8833114323258869,0.858634742740703,0.5442209243774414,129.542011 -19932,Binary classification,Voting,Elec2,0.8807385479905675,0.8562095457020145,0.6346635818481445,141.970262 -20838,Binary classification,Voting,Elec2,0.8784853865719633,0.8535739070090216,0.6924257278442383,154.986156 -21744,Binary classification,Voting,Elec2,0.8788115715402658,0.8517414055027289,0.7508554458618164,168.584532 -22650,Binary classification,Voting,Elec2,0.8773014261115281,0.8483988871310894,0.7274637222290039,182.766068 -23556,Binary classification,Voting,Elec2,0.8728507747824241,0.8417102690132656,0.7538461685180664,197.53556899999998 -24462,Binary classification,Voting,Elec2,0.8720820898573239,0.8400224960376297,0.7539834976196289,212.89370699999998 -25368,Binary classification,Voting,Elec2,0.8691607206212796,0.8364944085915562,0.7539834976196289,228.84110799999996 -26274,Binary classification,Voting,Elec2,0.8692954744414417,0.8360233024543979,0.7539834976196289,245.37829699999998 -27180,Binary classification,Voting,Elec2,0.8690901063320946,0.8360217531569729,0.7534799575805664,262.49924799999997 -28086,Binary classification,Voting,Elec2,0.8664055545664946,0.8316280739544067,0.7535486221313477,280.20757899999995 -28992,Binary classification,Voting,Elec2,0.8639232865372012,0.826859776168532,0.7540521621704102,298.510007 -29898,Binary classification,Voting,Elec2,0.86353145800582,0.8261017816042963,0.8122949600219727,317.403419 -30804,Binary classification,Voting,Elec2,0.8633899295523163,0.8248272416951128,0.8122949600219727,336.895126 -31710,Binary classification,Voting,Elec2,0.8610804503453279,0.8213199204964914,0.8808259963989258,356.985514 -32616,Binary classification,Voting,Elec2,0.8589299402115591,0.8183648493940232,0.9061365127563477,377.673556 -33522,Binary classification,Voting,Elec2,0.8585961039348469,0.8181399631675875,0.9644479751586914,398.957872 -34428,Binary classification,Voting,Elec2,0.8569436779272083,0.8155499794015206,0.9890604019165039,420.833858 -35334,Binary classification,Voting,Elec2,0.8570741233407863,0.8144610184436769,1.055558204650879,443.30417900000003 -36240,Binary classification,Voting,Elec2,0.8577223433317697,0.8140239503679124,1.0826387405395508,466.36460700000003 -37146,Binary classification,Voting,Elec2,0.8573966886525778,0.812978851110405,1.1978578567504883,490.019084 -38052,Binary classification,Voting,Elec2,0.8572967858926178,0.8125388386384037,1.198460578918457,514.261258 -38958,Binary classification,Voting,Elec2,0.8578432630849399,0.8141610738255034,1.198460578918457,539.085152 -39864,Binary classification,Voting,Elec2,0.8585906730552141,0.816998344317112,1.232090950012207,564.495213 -40770,Binary classification,Voting,Elec2,0.8591822217861611,0.8196298972635019,1.2325944900512695,590.495221 -41676,Binary classification,Voting,Elec2,0.859868026394721,0.8219620754832023,1.2570466995239258,617.0763880000001 -42582,Binary classification,Voting,Elec2,0.8602663159625185,0.82283230109576,1.2570466995239258,644.2465100000001 -43488,Binary classification,Voting,Elec2,0.8601651068135305,0.8228301721877459,1.2570466995239258,671.9994270000001 -44394,Binary classification,Voting,Elec2,0.8584912035681301,0.8195968066165068,1.2565431594848633,700.3413830000001 -45300,Binary classification,Voting,Elec2,0.8588710567562198,0.8202547305086175,1.3147859573364258,729.273758 -25,Binary classification,Voting,Phishing,0.5833333333333334,0.7058823529411764,0.15483379364013672,0.027523 -50,Binary classification,Voting,Phishing,0.7346938775510204,0.7636363636363637,0.17104625701904297,0.082708 -75,Binary classification,Voting,Phishing,0.7837837837837838,0.8048780487804877,0.18776226043701172,0.17051 -100,Binary classification,Voting,Phishing,0.8080808080808081,0.819047619047619,0.2039480209350586,0.296263 -125,Binary classification,Voting,Phishing,0.8145161290322581,0.8217054263565893,0.20436382293701172,0.463136 -150,Binary classification,Voting,Phishing,0.8187919463087249,0.830188679245283,0.20486736297607422,0.670871 -175,Binary classification,Voting,Phishing,0.8390804597701149,0.8390804597701148,0.20436382293701172,0.919601 -200,Binary classification,Voting,Phishing,0.8391959798994975,0.8383838383838383,0.2048635482788086,1.21153 -225,Binary classification,Voting,Phishing,0.8348214285714286,0.8294930875576038,0.20489025115966797,1.544633 -250,Binary classification,Voting,Phishing,0.8353413654618473,0.8298755186721991,0.20438671112060547,1.91889 -275,Binary classification,Voting,Phishing,0.8357664233576643,0.8288973384030419,0.20537090301513672,2.334116 -300,Binary classification,Voting,Phishing,0.8394648829431438,0.8285714285714285,0.20486736297607422,2.7905379999999997 -325,Binary classification,Voting,Phishing,0.8487654320987654,0.8338983050847458,0.20537090301513672,3.2879229999999997 -350,Binary classification,Voting,Phishing,0.8538681948424068,0.8360128617363344,0.20537090301513672,3.8262739999999997 -375,Binary classification,Voting,Phishing,0.8529411764705882,0.8318042813455658,0.20486736297607422,4.4058399999999995 -400,Binary classification,Voting,Phishing,0.8546365914786967,0.8313953488372093,0.20537090301513672,5.0283679999999995 -425,Binary classification,Voting,Phishing,0.8561320754716981,0.8291316526610645,0.20486736297607422,5.69196 -450,Binary classification,Voting,Phishing,0.8596881959910914,0.8310991957104559,0.20537090301513672,6.39676 -475,Binary classification,Voting,Phishing,0.8586497890295358,0.8312342569269521,0.20537090301513672,7.142548 -500,Binary classification,Voting,Phishing,0.8597194388777555,0.835680751173709,0.20486736297607422,7.929462 -525,Binary classification,Voting,Phishing,0.8606870229007634,0.8337129840546698,0.20537090301513672,8.757545 -550,Binary classification,Voting,Phishing,0.8615664845173042,0.8362068965517241,0.20486736297607422,9.626280000000001 -575,Binary classification,Voting,Phishing,0.8641114982578397,0.8388429752066116,0.20486736297607422,10.535573000000001 -600,Binary classification,Voting,Phishing,0.8664440734557596,0.8387096774193549,0.20537090301513672,11.487542000000001 -625,Binary classification,Voting,Phishing,0.8669871794871795,0.8362919132149902,0.20486736297607422,12.480171000000002 -650,Binary classification,Voting,Phishing,0.8705701078582434,0.8432835820895523,0.20537090301513672,13.513483000000003 -675,Binary classification,Voting,Phishing,0.8724035608308606,0.8485915492957745,0.20486736297607422,14.588001000000002 -700,Binary classification,Voting,Phishing,0.876967095851216,0.8522336769759451,0.20486736297607422,15.703553000000003 -725,Binary classification,Voting,Phishing,0.8784530386740331,0.8562091503267973,0.20537090301513672,16.863107000000003 -750,Binary classification,Voting,Phishing,0.8785046728971962,0.8571428571428572,0.20486736297607422,18.064417000000002 -775,Binary classification,Voting,Phishing,0.8785529715762274,0.8567073170731707,0.20537090301513672,19.306597000000004 -800,Binary classification,Voting,Phishing,0.8785982478097623,0.8583941605839417,0.1428241729736328,20.591420000000003 -825,Binary classification,Voting,Phishing,0.8786407766990292,0.8595505617977528,0.2701892852783203,21.920167000000003 -850,Binary classification,Voting,Phishing,0.8798586572438163,0.8602739726027396,0.27071571350097656,23.289893000000003 -875,Binary classification,Voting,Phishing,0.8832951945080092,0.8636363636363635,0.27021217346191406,24.700235000000003 -900,Binary classification,Voting,Phishing,0.882091212458287,0.8619791666666667,0.2707386016845703,26.151721000000002 -925,Binary classification,Voting,Phishing,0.8841991341991342,0.8657465495608533,0.2707386016845703,27.644342 -950,Binary classification,Voting,Phishing,0.8840885142255005,0.8671497584541062,0.2702350616455078,29.178064000000003 -975,Binary classification,Voting,Phishing,0.8860369609856262,0.8692579505300354,0.2707386016845703,30.753129 -1000,Binary classification,Voting,Phishing,0.8868868868868869,0.870264064293915,0.2702350616455078,32.369334 -1025,Binary classification,Voting,Phishing,0.88671875,0.8705357142857143,0.2707386016845703,34.02633 -1050,Binary classification,Voting,Phishing,0.888465204957102,0.8729641693811074,0.2707386016845703,35.724225000000004 -1075,Binary classification,Voting,Phishing,0.8873370577281192,0.8727655099894847,0.2702350616455078,37.462729 -1100,Binary classification,Voting,Phishing,0.8889899909008189,0.8747433264887065,0.2707386016845703,39.24172900000001 -1125,Binary classification,Voting,Phishing,0.8905693950177936,0.8776119402985074,0.2702350616455078,41.061817000000005 -1150,Binary classification,Voting,Phishing,0.8912097476066144,0.8780487804878049,0.2702350616455078,42.922291 -1175,Binary classification,Voting,Phishing,0.8901192504258943,0.8765550239234451,0.2707386016845703,44.822872000000004 -1200,Binary classification,Voting,Phishing,0.8915763135946623,0.8778195488721804,0.2702350616455078,46.765498 -1225,Binary classification,Voting,Phishing,0.8913398692810458,0.8774193548387096,0.2707386016845703,48.748369000000004 -1250,Binary classification,Voting,Phishing,0.8903122497998399,0.8769092542677449,0.2707386016845703,50.77338400000001 -1903,Binary classification,Voting,SMTP,1.0,0.0,0.07773971557617188,1.484811 -3806,Binary classification,Voting,SMTP,1.0,0.0,0.07824325561523438,4.457186 -5709,Binary classification,Voting,SMTP,1.0,0.0,0.07824325561523438,8.746386000000001 -7612,Binary classification,Voting,SMTP,1.0,0.0,0.07773971557617188,13.887875000000001 -9515,Binary classification,Voting,SMTP,1.0,0.0,0.07773971557617188,19.865436000000003 -11418,Binary classification,Voting,SMTP,1.0,0.0,0.07824325561523438,26.640571 -13321,Binary classification,Voting,SMTP,1.0,0.0,0.07824325561523438,34.167751 -15224,Binary classification,Voting,SMTP,0.9998029297773107,0.8421052631578948,0.11086559295654297,42.434428000000004 -17127,Binary classification,Voting,SMTP,0.9998248277472849,0.8695652173913044,0.11086559295654297,51.484422 -19030,Binary classification,Voting,SMTP,0.9998423458931105,0.8695652173913044,0.11136913299560547,61.31819 -20933,Binary classification,Voting,SMTP,0.9998566787693484,0.8695652173913044,0.11136913299560547,71.93436700000001 -22836,Binary classification,Voting,SMTP,0.999868622728268,0.8695652173913044,0.11086559295654297,83.32890400000001 -24739,Binary classification,Voting,SMTP,0.9998787290807665,0.8695652173913044,0.11086559295654297,95.504963 -26642,Binary classification,Voting,SMTP,0.9998873916144289,0.88,0.11139202117919922,108.464893 -28545,Binary classification,Voting,SMTP,0.999894899103139,0.88,0.11139202117919922,122.206969 -30448,Binary classification,Voting,SMTP,0.9999014681249384,0.88,0.11088848114013672,136.730455 -32351,Binary classification,Voting,SMTP,0.9999072642967543,0.88,0.11139202117919922,152.048808 -34254,Binary classification,Voting,SMTP,0.9999124164306776,0.88,0.11139202117919922,168.14980500000001 -36157,Binary classification,Voting,SMTP,0.9999170262197146,0.88,0.11088848114013672,185.035032 -38060,Binary classification,Voting,SMTP,0.9999211750177356,0.88,0.1028890609741211,202.707805 -39963,Binary classification,Voting,SMTP,0.9999249286822481,0.88,0.1033926010131836,221.16629700000001 -41866,Binary classification,Voting,SMTP,0.9999283410963812,0.88,0.1033926010131836,240.410098 -43769,Binary classification,Voting,SMTP,0.999931456772071,0.88,0.1028890609741211,260.439908 -45672,Binary classification,Voting,SMTP,0.9999343128024348,0.88,0.1028890609741211,281.255775 -47575,Binary classification,Voting,SMTP,0.9999369403455669,0.88,0.1155691146850586,302.85620600000004 -49478,Binary classification,Voting,SMTP,0.9999393657659115,0.88,0.1155691146850586,325.23865100000006 -51381,Binary classification,Voting,SMTP,0.9999221486959906,0.8666666666666666,0.1243124008178711,348.4004960000001 -53284,Binary classification,Voting,SMTP,0.9999249291518871,0.8666666666666666,0.1243124008178711,372.3442280000001 -55187,Binary classification,Voting,SMTP,0.9999275178487298,0.8666666666666666,0.1248159408569336,397.0676850000001 -57090,Binary classification,Voting,SMTP,0.9998073183975897,0.7317073170731707,0.15177059173583984,422.5782570000001 -58993,Binary classification,Voting,SMTP,0.9998135340385137,0.7317073170731707,0.15126705169677734,448.87363900000014 -60896,Binary classification,Voting,SMTP,0.9998193611955004,0.7317073170731707,0.15177059173583984,475.95484300000015 -62799,Binary classification,Voting,SMTP,0.9997929870378037,0.6976744186046512,0.16101741790771484,503.8239590000002 -64702,Binary classification,Voting,SMTP,0.9997990757484428,0.6976744186046512,0.16051387786865234,532.4818360000002 -66605,Binary classification,Voting,SMTP,0.9998048165275358,0.6976744186046512,0.16051387786865234,561.9301430000002 -68508,Binary classification,Voting,SMTP,0.9998102383698017,0.7234042553191489,0.16101741790771484,592.1790100000002 -70411,Binary classification,Voting,SMTP,0.9998153671353501,0.7234042553191489,0.16101741790771484,623.2264690000002 -72314,Binary classification,Voting,SMTP,0.9998202259621368,0.7234042553191489,0.16051387786865234,655.0760590000002 -74217,Binary classification,Voting,SMTP,0.9998113614314973,0.7083333333333333,0.16051387786865234,687.7245370000002 -76120,Binary classification,Voting,SMTP,0.999816077457665,0.7083333333333333,0.16101741790771484,721.1718600000002 -78023,Binary classification,Voting,SMTP,0.9998205634308271,0.7083333333333333,0.16101741790771484,755.4176530000002 -79926,Binary classification,Voting,SMTP,0.9998248357835471,0.7083333333333333,0.16051387786865234,790.4648560000002 -81829,Binary classification,Voting,SMTP,0.9998289094197585,0.7083333333333333,0.16051387786865234,826.3125100000002 -83732,Binary classification,Voting,SMTP,0.9998327978884762,0.7083333333333333,0.16101741790771484,862.9590210000002 -85635,Binary classification,Voting,SMTP,0.9998365135343439,0.7083333333333333,0.1653127670288086,900.4018990000002 -87538,Binary classification,Voting,SMTP,0.9998400676285456,0.7083333333333333,0.1648092269897461,938.6351690000001 -89441,Binary classification,Voting,SMTP,0.9997875670840787,0.6415094339622641,0.1745138168334961,977.6602510000001 -91344,Binary classification,Voting,SMTP,0.9997810450718719,0.6296296296296297,0.1745138168334961,1017.4767610000001 -93247,Binary classification,Voting,SMTP,0.9996782703815713,0.53125,0.1740102767944336,1058.0853630000001 -95150,Binary classification,Voting,SMTP,0.9996847050415664,0.53125,0.1740102767944336,1099.4855200000002 -106,Binary classification,[baseline] Last Class,Bananas,0.5333333333333333,0.5242718446601942,0.0005102157592773438,0.003183 -212,Binary classification,[baseline] Last Class,Bananas,0.5876777251184834,0.5538461538461539,0.0005102157592773438,0.008041 -318,Binary classification,[baseline] Last Class,Bananas,0.5457413249211357,0.5102040816326531,0.0005102157592773438,0.014648999999999999 -424,Binary classification,[baseline] Last Class,Bananas,0.5460992907801419,0.5025906735751295,0.0005102157592773438,0.022962999999999997 -530,Binary classification,[baseline] Last Class,Bananas,0.5671077504725898,0.5096359743040686,0.0005102157592773438,0.032934 -636,Binary classification,[baseline] Last Class,Bananas,0.5464566929133858,0.4875444839857651,0.0005102157592773438,0.044653 -742,Binary classification,[baseline] Last Class,Bananas,0.5573549257759784,0.4875,0.0005102157592773438,0.058023 -848,Binary classification,[baseline] Last Class,Bananas,0.5501770956316411,0.4816326530612245,0.0005102157592773438,0.073029 -954,Binary classification,[baseline] Last Class,Bananas,0.5487932843651626,0.4794188861985472,0.0005102157592773438,0.08975 -1060,Binary classification,[baseline] Last Class,Bananas,0.5448536355051936,0.46799116997792495,0.0005102157592773438,0.108101 -1166,Binary classification,[baseline] Last Class,Bananas,0.534763948497854,0.4590818363273453,0.0005102157592773438,0.128117 -1272,Binary classification,[baseline] Last Class,Bananas,0.5287175452399685,0.456935630099728,0.0005102157592773438,0.149836 -1378,Binary classification,[baseline] Last Class,Bananas,0.5286855482933914,0.45232067510548524,0.0005102157592773438,0.173181 -1484,Binary classification,[baseline] Last Class,Bananas,0.5252865812542145,0.44913928012519555,0.0005102157592773438,0.19815 -1590,Binary classification,[baseline] Last Class,Bananas,0.5204531151667715,0.4437956204379563,0.0005102157592773438,0.224847 -1696,Binary classification,[baseline] Last Class,Bananas,0.5227138643067847,0.4455106237148732,0.0005102157592773438,0.253189 -1802,Binary classification,[baseline] Last Class,Bananas,0.524153248195447,0.4523961661341854,0.0005102157592773438,0.283166 -1908,Binary classification,[baseline] Last Class,Bananas,0.5233350812794966,0.456664674237896,0.0005102157592773438,0.31489199999999995 -2014,Binary classification,[baseline] Last Class,Bananas,0.5171385991058122,0.4563758389261745,0.0005102157592773438,0.34829999999999994 -2120,Binary classification,[baseline] Last Class,Bananas,0.5143935818782445,0.45813586097946285,0.0005102157592773438,0.38336699999999996 -2226,Binary classification,[baseline] Last Class,Bananas,0.5114606741573033,0.45459106874059213,0.0005102157592773438,0.42015499999999995 -2332,Binary classification,[baseline] Last Class,Bananas,0.510939510939511,0.45506692160611856,0.0005102157592773438,0.45858199999999993 -2438,Binary classification,[baseline] Last Class,Bananas,0.5104636848584325,0.4530032095369097,0.0005102157592773438,0.49864699999999995 -2544,Binary classification,[baseline] Last Class,Bananas,0.5084545812033032,0.45462478184991273,0.0005102157592773438,0.5404629999999999 -2650,Binary classification,[baseline] Last Class,Bananas,0.5096262740656852,0.458072590738423,0.0005102157592773438,0.5839479999999999 -2756,Binary classification,[baseline] Last Class,Bananas,0.5092558983666061,0.45746388443017655,0.0005102157592773438,0.6291049999999999 -2862,Binary classification,[baseline] Last Class,Bananas,0.5103110800419434,0.4563445867287544,0.0005102157592773438,0.6760149999999999 -2968,Binary classification,[baseline] Last Class,Bananas,0.5133131108864173,0.457957957957958,0.0005102157592773438,0.7245799999999999 -3074,Binary classification,[baseline] Last Class,Bananas,0.5099251545720794,0.4563176895306859,0.0005102157592773438,0.7747889999999998 -3180,Binary classification,[baseline] Last Class,Bananas,0.5102233406731677,0.45387583304103823,0.0005102157592773438,0.8267639999999998 -3286,Binary classification,[baseline] Last Class,Bananas,0.5095890410958904,0.45222713362801764,0.0005102157592773438,0.8803849999999999 -3392,Binary classification,[baseline] Last Class,Bananas,0.5107637864936597,0.4558871761233191,0.0005102157592773438,0.9357289999999998 -3498,Binary classification,[baseline] Last Class,Bananas,0.5124392336288247,0.45579316948611553,0.0005102157592773438,0.9927189999999998 -3604,Binary classification,[baseline] Last Class,Bananas,0.5134610047182903,0.45440398381574854,0.0005102157592773438,1.0513469999999998 -3710,Binary classification,[baseline] Last Class,Bananas,0.5122674575357239,0.4546276756104914,0.0005102157592773438,1.1117049999999997 -3816,Binary classification,[baseline] Last Class,Bananas,0.510615989515072,0.4536142815335089,0.0005102157592773438,1.1737269999999997 -3922,Binary classification,[baseline] Last Class,Bananas,0.5090538128028564,0.45078459343794575,0.0005102157592773438,1.2373829999999997 -4028,Binary classification,[baseline] Last Class,Bananas,0.5108020859200397,0.45247359644246804,0.0005102157592773438,1.3028159999999998 -4134,Binary classification,[baseline] Last Class,Bananas,0.5102830873457537,0.4517876489707476,0.0005102157592773438,1.3698969999999997 -4240,Binary classification,[baseline] Last Class,Bananas,0.5102618542108988,0.4525316455696203,0.0005102157592773438,1.4386049999999997 -4346,Binary classification,[baseline] Last Class,Bananas,0.5074798619102416,0.4490216271884655,0.0005102157592773438,1.5090079999999997 -4452,Binary classification,[baseline] Last Class,Bananas,0.5099977533138621,0.45132075471698113,0.0005102157592773438,1.5810569999999997 -4558,Binary classification,[baseline] Last Class,Bananas,0.5099846390168971,0.45390070921985815,0.0005102157592773438,1.6547379999999996 -4664,Binary classification,[baseline] Last Class,Bananas,0.5099721209521767,0.4553039332538737,0.0005102157592773438,1.7301709999999997 -4770,Binary classification,[baseline] Last Class,Bananas,0.5110085971901867,0.4556489262371615,0.0005102157592773438,1.8072459999999997 -4876,Binary classification,[baseline] Last Class,Bananas,0.5109743589743589,0.4539624370132845,0.0005102157592773438,1.8859549999999996 -4982,Binary classification,[baseline] Last Class,Bananas,0.5099377635013049,0.45379279480868207,0.0005102157592773438,1.9663889999999995 -5088,Binary classification,[baseline] Last Class,Bananas,0.5099272655789266,0.45364891518737677,0.0005102157592773438,2.0484569999999995 -5194,Binary classification,[baseline] Last Class,Bananas,0.5097246293086848,0.4531786941580756,0.0005102157592773438,2.1321769999999995 -5300,Binary classification,[baseline] Last Class,Bananas,0.5095301000188714,0.4529572721532309,0.0005102157592773438,2.2176369999999994 -906,Binary classification,[baseline] Last Class,Elec2,0.8530386740331491,0.8500563697857948,0.0005102157592773438,0.021647 -1812,Binary classification,[baseline] Last Class,Elec2,0.8619547211485368,0.8287671232876712,0.0005102157592773438,0.064308 -2718,Binary classification,[baseline] Last Class,Elec2,0.8450496871549503,0.80958842152872,0.0005102157592773438,0.12793700000000002 -3624,Binary classification,[baseline] Last Class,Elec2,0.8418437758763456,0.8056968463886063,0.0005102157592773438,0.21264700000000003 -4530,Binary classification,[baseline] Last Class,Elec2,0.8388165157871494,0.7960893854748604,0.0005102157592773438,0.31851300000000005 -5436,Binary classification,[baseline] Last Class,Elec2,0.8413983440662374,0.7995348837209302,0.0005102157592773438,0.445933 -6342,Binary classification,[baseline] Last Class,Elec2,0.8370919413341744,0.7958094485076103,0.0005102157592773438,0.594503 -7248,Binary classification,[baseline] Last Class,Elec2,0.8359321098385539,0.7948231233822259,0.0005102157592773438,0.763985 -8154,Binary classification,[baseline] Last Class,Elec2,0.8352753587636453,0.8021799970540581,0.0005102157592773438,0.954384 -9060,Binary classification,[baseline] Last Class,Elec2,0.8358538470029805,0.8069081937410726,0.0005102157592773438,1.166026 -9966,Binary classification,[baseline] Last Class,Elec2,0.8372303060712494,0.8118765947575969,0.0005102157592773438,1.399073 -10872,Binary classification,[baseline] Last Class,Elec2,0.8368135406126391,0.8140461215932915,0.0005102157592773438,1.6530930000000001 -11778,Binary classification,[baseline] Last Class,Elec2,0.8374798335739153,0.8150724637681159,0.0005102157592773438,1.9281220000000001 -12684,Binary classification,[baseline] Last Class,Elec2,0.8384451628163684,0.8161177420802298,0.0005102157592773438,2.224333 -13590,Binary classification,[baseline] Last Class,Elec2,0.842004562513798,0.8223417459660736,0.0005102157592773438,2.541433 -14496,Binary classification,[baseline] Last Class,Elec2,0.8448430493273542,0.8264794383149447,0.0005102157592773438,2.879854 -15402,Binary classification,[baseline] Last Class,Elec2,0.8460489578598792,0.8270983738058776,0.0005102157592773438,3.239289 -16308,Binary classification,[baseline] Last Class,Elec2,0.844851904090268,0.8251313243019076,0.0005102157592773438,3.6196129999999997 -17214,Binary classification,[baseline] Last Class,Elec2,0.8443618195549875,0.8222177981286084,0.0005102157592773438,4.020822 -18120,Binary classification,[baseline] Last Class,Elec2,0.8450797505381091,0.8227792158595871,0.0005102157592773438,4.443085 -19026,Binary classification,[baseline] Last Class,Elec2,0.8462023653088042,0.8224083515416363,0.0005102157592773438,4.886686 -19932,Binary classification,[baseline] Last Class,Elec2,0.847523957653906,0.8255753888538139,0.0005102157592773438,5.35136 -20838,Binary classification,[baseline] Last Class,Elec2,0.84661899505687,0.8249917862227577,0.0005102157592773438,5.836996 -21744,Binary classification,[baseline] Last Class,Elec2,0.8452835395299637,0.8209495422610177,0.0005102157592773438,6.343734 -22650,Binary classification,[baseline] Last Class,Elec2,0.8444081416398075,0.8188733552631579,0.0005102157592773438,6.871264 -23556,Binary classification,[baseline] Last Class,Elec2,0.8451284228401613,0.8194595664654062,0.0005102157592773438,7.419847 -24462,Binary classification,[baseline] Last Class,Elec2,0.8464903315481788,0.8198781599270878,0.0005102157592773438,7.989367 -25368,Binary classification,[baseline] Last Class,Elec2,0.8462963692986951,0.8199492034172247,0.0005102157592773438,8.579944 -26274,Binary classification,[baseline] Last Class,Elec2,0.8477524454763445,0.8213168944876262,0.0005102157592773438,9.191269 -27180,Binary classification,[baseline] Last Class,Elec2,0.8495529636851982,0.8240457851026293,0.0005102157592773438,9.823483 -28086,Binary classification,[baseline] Last Class,Elec2,0.8509880719245149,0.825107610012955,0.0005102157592773438,10.476909 -28992,Binary classification,[baseline] Last Class,Elec2,0.8521265220240765,0.8258237516759436,0.0005102157592773438,11.151511999999999 -29898,Binary classification,[baseline] Last Class,Elec2,0.8531959728400843,0.8268160833366216,0.0005102157592773438,11.847221999999999 -30804,Binary classification,[baseline] Last Class,Elec2,0.8537480115573158,0.8267107743201139,0.0005102157592773438,12.564065 -31710,Binary classification,[baseline] Last Class,Elec2,0.8530385694913116,0.8259895444361464,0.0005102157592773438,13.301931999999999 -32616,Binary classification,[baseline] Last Class,Elec2,0.8536869538555879,0.8269760696156635,0.0005102157592773438,14.060873999999998 -33522,Binary classification,[baseline] Last Class,Elec2,0.8541511291429253,0.8276032300151628,0.0005102157592773438,14.840822999999999 -34428,Binary classification,[baseline] Last Class,Elec2,0.8549684840386905,0.8286724084685859,0.0005102157592773438,15.641784999999999 -35334,Binary classification,[baseline] Last Class,Elec2,0.8555175048821215,0.8284321962695346,0.0005102157592773438,16.463894999999997 -36240,Binary classification,[baseline] Last Class,Elec2,0.8545213720025387,0.8259146744155329,0.0005102157592773438,17.307088999999998 -37146,Binary classification,[baseline] Last Class,Elec2,0.854354556467896,0.8252696854208386,0.0005102157592773438,18.171425 -38052,Binary classification,[baseline] Last Class,Elec2,0.8545636119944285,0.8247736052181622,0.0005102157592773438,19.056746999999998 -38958,Binary classification,[baseline] Last Class,Elec2,0.8548142824139435,0.8254213223038459,0.0005102157592773438,19.962946 -39864,Binary classification,[baseline] Last Class,Elec2,0.8546521837292728,0.8262981172802495,0.0005102157592773438,20.890172 -40770,Binary classification,[baseline] Last Class,Elec2,0.8540067207927592,0.8267652366261132,0.0005102157592773438,21.838151 -41676,Binary classification,[baseline] Last Class,Elec2,0.8537012597480504,0.8274320002264302,0.0005102157592773438,22.807005 -42582,Binary classification,[baseline] Last Class,Elec2,0.8536201592259458,0.8277177368086459,0.0005102157592773438,23.796527 -43488,Binary classification,[baseline] Last Class,Elec2,0.853473451836181,0.8276626818845675,0.0005102157592773438,24.806653 -44394,Binary classification,[baseline] Last Class,Elec2,0.8533777847858897,0.8271686890948196,0.0005102157592773438,25.837451 -45300,Binary classification,[baseline] Last Class,Elec2,0.8533521711296055,0.8273155007928462,0.0005102157592773438,26.888684 -25,Binary classification,[baseline] Last Class,Phishing,0.625,0.64,0.0005102157592773438,0.002343 -50,Binary classification,[baseline] Last Class,Phishing,0.6530612244897959,0.6222222222222223,0.0005102157592773438,0.006017 -75,Binary classification,[baseline] Last Class,Phishing,0.5675675675675675,0.5555555555555556,0.0005102157592773438,0.010981999999999999 -100,Binary classification,[baseline] Last Class,Phishing,0.5555555555555556,0.5416666666666666,0.0005102157592773438,0.017228 -125,Binary classification,[baseline] Last Class,Phishing,0.5241935483870968,0.5123966942148761,0.0005102157592773438,0.024779000000000002 -150,Binary classification,[baseline] Last Class,Phishing,0.5234899328859061,0.5298013245033113,0.0005102157592773438,0.033621 -175,Binary classification,[baseline] Last Class,Phishing,0.5229885057471264,0.496969696969697,0.0005102157592773438,0.043754 -200,Binary classification,[baseline] Last Class,Phishing,0.507537688442211,0.47872340425531923,0.0005102157592773438,0.055183 -225,Binary classification,[baseline] Last Class,Phishing,0.5,0.45098039215686275,0.0005102157592773438,0.06790500000000001 -250,Binary classification,[baseline] Last Class,Phishing,0.5180722891566265,0.4782608695652174,0.0005102157592773438,0.082 -275,Binary classification,[baseline] Last Class,Phishing,0.5218978102189781,0.4738955823293172,0.0005102157592773438,0.097411 -300,Binary classification,[baseline] Last Class,Phishing,0.5217391304347826,0.460377358490566,0.0005102157592773438,0.114126 -325,Binary classification,[baseline] Last Class,Phishing,0.5216049382716049,0.44839857651245546,0.0005102157592773438,0.132158 -350,Binary classification,[baseline] Last Class,Phishing,0.5329512893982808,0.4511784511784511,0.0005102157592773438,0.151512 -375,Binary classification,[baseline] Last Class,Phishing,0.5267379679144385,0.4380952380952381,0.0005102157592773438,0.172161 -400,Binary classification,[baseline] Last Class,Phishing,0.5263157894736842,0.43243243243243246,0.0005102157592773438,0.194113 -425,Binary classification,[baseline] Last Class,Phishing,0.5424528301886793,0.436046511627907,0.0005102157592773438,0.217363 -450,Binary classification,[baseline] Last Class,Phishing,0.5367483296213809,0.4222222222222222,0.0005102157592773438,0.241916 -475,Binary classification,[baseline] Last Class,Phishing,0.5358649789029536,0.43298969072164945,0.0005102157592773438,0.267769 -500,Binary classification,[baseline] Last Class,Phishing,0.5370741482965932,0.44604316546762596,0.0005102157592773438,0.29501499999999997 -525,Binary classification,[baseline] Last Class,Phishing,0.5400763358778626,0.43822843822843827,0.0005102157592773438,0.323563 -550,Binary classification,[baseline] Last Class,Phishing,0.5391621129326047,0.44150110375275936,0.0005102157592773438,0.35340099999999997 -575,Binary classification,[baseline] Last Class,Phishing,0.5418118466898955,0.4416135881104034,0.0005102157592773438,0.384549 -600,Binary classification,[baseline] Last Class,Phishing,0.5509181969949917,0.443064182194617,0.0005102157592773438,0.41699899999999995 -625,Binary classification,[baseline] Last Class,Phishing,0.5560897435897436,0.43584521384928715,0.0005102157592773438,0.45074299999999995 -650,Binary classification,[baseline] Last Class,Phishing,0.551617873651772,0.4393063583815029,0.0005102157592773438,0.48578699999999997 -675,Binary classification,[baseline] Last Class,Phishing,0.5459940652818991,0.44363636363636366,0.0005102157592773438,0.522164 -700,Binary classification,[baseline] Last Class,Phishing,0.5464949928469242,0.4389380530973452,0.0005102157592773438,0.55984 -725,Binary classification,[baseline] Last Class,Phishing,0.5441988950276243,0.44630872483221484,0.0005102157592773438,0.598852 -750,Binary classification,[baseline] Last Class,Phishing,0.5367156208277704,0.44122383252818037,0.0005102157592773438,0.639119 -775,Binary classification,[baseline] Last Class,Phishing,0.5310077519379846,0.43369734789391573,0.0005102157592773438,0.680612 -800,Binary classification,[baseline] Last Class,Phishing,0.5294117647058824,0.4388059701492537,0.0005102157592773438,0.723331 -825,Binary classification,[baseline] Last Class,Phishing,0.5266990291262136,0.43965517241379315,0.0005102157592773438,0.7672859999999999 -850,Binary classification,[baseline] Last Class,Phishing,0.5241460541813898,0.4341736694677871,0.0005102157592773438,0.812452 -875,Binary classification,[baseline] Last Class,Phishing,0.522883295194508,0.4311050477489768,0.0005102157592773438,0.858842 -900,Binary classification,[baseline] Last Class,Phishing,0.5272525027808677,0.4340878828229028,0.0005102157592773438,0.906455 -925,Binary classification,[baseline] Last Class,Phishing,0.5227272727272727,0.43388960205391536,0.0005102157592773438,0.955289 -950,Binary classification,[baseline] Last Class,Phishing,0.5205479452054794,0.43896424167694204,0.0005102157592773438,1.005349 -975,Binary classification,[baseline] Last Class,Phishing,0.5174537987679672,0.43373493975903615,0.0005102157592773438,1.056718 -1000,Binary classification,[baseline] Last Class,Phishing,0.5185185185185185,0.4361078546307151,0.0005102157592773438,1.109312 -1025,Binary classification,[baseline] Last Class,Phishing,0.517578125,0.43863636363636366,0.0005102157592773438,1.1631 -1050,Binary classification,[baseline] Last Class,Phishing,0.5138226882745471,0.4370860927152318,0.0005102157592773438,1.2181790000000001 -1075,Binary classification,[baseline] Last Class,Phishing,0.5111731843575419,0.43729903536977494,0.0005102157592773438,1.2745680000000001 -1100,Binary classification,[baseline] Last Class,Phishing,0.5122838944494995,0.4393305439330544,0.0005102157592773438,1.3322450000000001 -1125,Binary classification,[baseline] Last Class,Phishing,0.5124555160142349,0.44534412955465585,0.0005102157592773438,1.3912120000000001 -1150,Binary classification,[baseline] Last Class,Phishing,0.5143603133159269,0.44642857142857145,0.0005102157592773438,1.451489 -1175,Binary classification,[baseline] Last Class,Phishing,0.5187393526405452,0.4509232264334305,0.0005102157592773438,1.513052 -1200,Binary classification,[baseline] Last Class,Phishing,0.5187656380316931,0.448901623686724,0.0005102157592773438,1.5759610000000002 -1225,Binary classification,[baseline] Last Class,Phishing,0.5171568627450981,0.4471468662301216,0.0005102157592773438,1.6401780000000001 -1250,Binary classification,[baseline] Last Class,Phishing,0.5156124899919936,0.4474885844748858,0.0005102157592773438,1.705799 -1903,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.070354 -3806,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.210001 -5709,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.417744 -7612,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.694871 -9515,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.039777 -11418,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.454499 -13321,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.9382489999999999 -15224,Binary classification,[baseline] Last Class,SMTP,0.9985548183669447,0.0,0.0005102157592773438,2.491975 -17127,Binary classification,[baseline] Last Class,SMTP,0.9984818404764685,0.0,0.0005102157592773438,3.11529 -19030,Binary classification,[baseline] Last Class,SMTP,0.9986336644069578,0.0,0.0005102157592773438,3.8077389999999998 -20933,Binary classification,[baseline] Last Class,SMTP,0.9987578826676858,0.0,0.0005102157592773438,4.570752 -22836,Binary classification,[baseline] Last Class,SMTP,0.9988613969783228,0.0,0.0005102157592773438,5.4033109999999995 -24739,Binary classification,[baseline] Last Class,SMTP,0.9989489853666425,0.0,0.0005102157592773438,6.304936 -26642,Binary classification,[baseline] Last Class,SMTP,0.9989489884013363,0.0,0.0005102157592773438,7.275472 -28545,Binary classification,[baseline] Last Class,SMTP,0.9990190582959642,0.0,0.0005102157592773438,8.315878999999999 -30448,Binary classification,[baseline] Last Class,SMTP,0.9990803691660919,0.0,0.0005102157592773438,9.425080999999999 -32351,Binary classification,[baseline] Last Class,SMTP,0.9991344667697063,0.0,0.0005102157592773438,10.602661 -34254,Binary classification,[baseline] Last Class,SMTP,0.999182553352991,0.0,0.0005102157592773438,11.849262 -36157,Binary classification,[baseline] Last Class,SMTP,0.9992255780506694,0.0,0.0005102157592773438,13.164257 -38060,Binary classification,[baseline] Last Class,SMTP,0.9992643001655325,0.0,0.0005102157592773438,14.54421 -39963,Binary classification,[baseline] Last Class,SMTP,0.9992993343676493,0.0,0.0005102157592773438,15.991924 -41866,Binary classification,[baseline] Last Class,SMTP,0.9993311835662247,0.0,0.0005102157592773438,17.508838 -43769,Binary classification,[baseline] Last Class,SMTP,0.9993602632059952,0.0,0.0005102157592773438,19.096664 -45672,Binary classification,[baseline] Last Class,SMTP,0.9993869194893915,0.0,0.0005102157592773438,20.752142 -47575,Binary classification,[baseline] Last Class,SMTP,0.9994114432252911,0.0,0.0005102157592773438,22.476952 -49478,Binary classification,[baseline] Last Class,SMTP,0.99943408048184,0.0,0.0005102157592773438,24.271157000000002 -51381,Binary classification,[baseline] Last Class,SMTP,0.9994161152199299,0.0625,0.0005102157592773438,26.132842000000004 -53284,Binary classification,[baseline] Last Class,SMTP,0.9994369686391532,0.0625,0.0005102157592773438,28.063918000000005 -55187,Binary classification,[baseline] Last Class,SMTP,0.9994563838654731,0.0625,0.0005102157592773438,30.065042000000005 -57090,Binary classification,[baseline] Last Class,SMTP,0.9994394717020793,0.36,0.0005102157592773438,32.13415200000001 -58993,Binary classification,[baseline] Last Class,SMTP,0.9994575535665853,0.36,0.0005102157592773438,34.27360200000001 -60896,Binary classification,[baseline] Last Class,SMTP,0.9994745052960013,0.36,0.0005102157592773438,36.48320000000001 -62799,Binary classification,[baseline] Last Class,SMTP,0.9994585814834868,0.37037037037037035,0.0005102157592773438,38.76173800000001 -64702,Binary classification,[baseline] Last Class,SMTP,0.9994745058036197,0.37037037037037035,0.0005102157592773438,41.10958900000001 -66605,Binary classification,[baseline] Last Class,SMTP,0.99948952014894,0.37037037037037035,0.0005102157592773438,43.52762700000001 -68508,Binary classification,[baseline] Last Class,SMTP,0.9994745062548352,0.3793103448275862,0.0005102157592773438,46.01474500000001 -70411,Binary classification,[baseline] Last Class,SMTP,0.9994887089902003,0.3793103448275862,0.0005102157592773438,48.570884000000014 -72314,Binary classification,[baseline] Last Class,SMTP,0.9995021642028404,0.3793103448275862,0.0005102157592773438,51.19747000000002 -74217,Binary classification,[baseline] Last Class,SMTP,0.9995149293952786,0.3793103448275862,0.0005102157592773438,53.894680000000015 -76120,Binary classification,[baseline] Last Class,SMTP,0.99952705631971,0.3793103448275862,0.0005102157592773438,56.660361000000016 -78023,Binary classification,[baseline] Last Class,SMTP,0.99953859167927,0.3793103448275862,0.0005102157592773438,59.49662100000002 -79926,Binary classification,[baseline] Last Class,SMTP,0.999549577729121,0.3793103448275862,0.0005102157592773438,62.40230700000002 -81829,Binary classification,[baseline] Last Class,SMTP,0.9995600527936648,0.3793103448275862,0.0005102157592773438,65.37601400000003 -83732,Binary classification,[baseline] Last Class,SMTP,0.9995700517132244,0.3793103448275862,0.0005102157592773438,68.41937500000003 -85635,Binary classification,[baseline] Last Class,SMTP,0.9995796062311698,0.3793103448275862,0.0005102157592773438,71.53305000000003 -87538,Binary classification,[baseline] Last Class,SMTP,0.999588745330546,0.3793103448275862,0.0005102157592773438,74.71604400000002 -89441,Binary classification,[baseline] Last Class,SMTP,0.9995751341681575,0.36666666666666664,0.0005102157592773438,77.96763000000003 -91344,Binary classification,[baseline] Last Class,SMTP,0.9995839856365567,0.36666666666666664,0.0005102157592773438,81.28881300000003 -93247,Binary classification,[baseline] Last Class,SMTP,0.999592475816657,0.36666666666666664,0.0005102157592773438,84.68057600000003 -95150,Binary classification,[baseline] Last Class,SMTP,0.9996006263859841,0.36666666666666664,0.0005102157592773438,88.14010200000003 +106,Binary classification,Logistic regression,Bananas,0.49056603773584906,0.3414634146341463,0.004187583923339844,0.00989 +212,Binary classification,Logistic regression,Bananas,0.5141509433962265,0.3832335329341317,0.004187583923339844,0.123413 +318,Binary classification,Logistic regression,Bananas,0.5220125786163522,0.42424242424242425,0.004240989685058594,0.315017 +424,Binary classification,Logistic regression,Bananas,0.5165094339622641,0.40233236151603496,0.004240989685058594,0.5849610000000001 +530,Binary classification,Logistic regression,Bananas,0.5320754716981132,0.36410256410256414,0.004240989685058594,0.9372130000000001 +636,Binary classification,Logistic regression,Bananas,0.5377358490566038,0.32876712328767127,0.004240989685058594,1.342505 +742,Binary classification,Logistic regression,Bananas,0.5525606469002695,0.3054393305439331,0.004240989685058594,1.8950680000000002 +848,Binary classification,Logistic regression,Bananas,0.5530660377358491,0.28083491461100574,0.004240989685058594,2.518365 +954,Binary classification,Logistic regression,Bananas,0.5555555555555556,0.25874125874125875,0.004240989685058594,3.1930270000000003 +1060,Binary classification,Logistic regression,Bananas,0.5622641509433962,0.2418300653594771,0.004240989685058594,3.938137 +1166,Binary classification,Logistic regression,Bananas,0.5608919382504288,0.22424242424242424,0.004240989685058594,4.7351090000000005 +1272,Binary classification,Logistic regression,Bananas,0.5613207547169812,0.2206703910614525,0.004240989685058594,5.600857 +1378,Binary classification,Logistic regression,Bananas,0.5645863570391872,0.20844327176781002,0.004240989685058594,6.476079 +1484,Binary classification,Logistic regression,Bananas,0.5646900269541779,0.19651741293532338,0.004240989685058594,7.428853 +1590,Binary classification,Logistic regression,Bananas,0.5647798742138365,0.18588235294117644,0.004240989685058594,8.473991 +1696,Binary classification,Logistic regression,Bananas,0.5660377358490566,0.17857142857142858,0.004240989685058594,9.59319 +1802,Binary classification,Logistic regression,Bananas,0.562708102108768,0.17052631578947366,0.004240989685058594,10.745503 +1908,Binary classification,Logistic regression,Bananas,0.5587002096436059,0.16798418972332016,0.004240989685058594,11.962335 +2014,Binary classification,Logistic regression,Bananas,0.5516385302879842,0.16620498614958448,0.004240989685058594,13.252336 +2120,Binary classification,Logistic regression,Bananas,0.5495283018867925,0.1688424717145344,0.004240989685058594,14.603624 +2226,Binary classification,Logistic regression,Bananas,0.5485175202156334,0.18092909535452323,0.004240989685058594,15.981958 +2332,Binary classification,Logistic regression,Bananas,0.5484562607204116,0.19679633867276888,0.004240989685058594,17.395643 +2438,Binary classification,Logistic regression,Bananas,0.5471698113207547,0.19999999999999998,0.004240989685058594,18.850781 +2544,Binary classification,Logistic regression,Bananas,0.5479559748427673,0.21662125340599456,0.004240989685058594,20.422045 +2650,Binary classification,Logistic regression,Bananas,0.5452830188679245,0.2260757867694284,0.004240989685058594,22.049363 +2756,Binary classification,Logistic regression,Bananas,0.5395500725689405,0.22857142857142854,0.004240989685058594,23.763248 +2862,Binary classification,Logistic regression,Bananas,0.5391334730957372,0.230005837711617,0.004240989685058594,25.51638 +2968,Binary classification,Logistic regression,Bananas,0.5411051212938005,0.22613636363636364,0.004240989685058594,27.316788000000003 +3074,Binary classification,Logistic regression,Bananas,0.5403383214053351,0.22148760330578512,0.004240989685058594,29.124189 +3180,Binary classification,Logistic regression,Bananas,0.5437106918238994,0.22031166039763567,0.004240989685058594,31.016333000000003 +3286,Binary classification,Logistic regression,Bananas,0.5450395617772368,0.21604614577871,0.004240989685058594,32.984057 +3392,Binary classification,Logistic regression,Bananas,0.5439268867924528,0.21272264631043258,0.004240989685058594,35.003757 +3498,Binary classification,Logistic regression,Bananas,0.5457404230989137,0.20827105132037868,0.004240989685058594,37.068178 +3604,Binary classification,Logistic regression,Bananas,0.5480022197558269,0.2042012701514411,0.004240989685058594,39.232173 +3710,Binary classification,Logistic regression,Bananas,0.546900269541779,0.19914244878513576,0.004240989685058594,41.450117000000006 +3816,Binary classification,Logistic regression,Bananas,0.5463836477987422,0.19450907398790138,0.004240989685058594,43.72876300000001 +3922,Binary classification,Logistic regression,Bananas,0.5474247832738399,0.1906064751481988,0.004240989685058594,46.072390000000006 +4028,Binary classification,Logistic regression,Bananas,0.547914597815293,0.1866904868244752,0.004240989685058594,48.42327300000001 +4134,Binary classification,Logistic regression,Bananas,0.548137397194001,0.18285214348206474,0.004240989685058594,50.870554000000006 +4240,Binary classification,Logistic regression,Bananas,0.5474056603773585,0.17886178861788615,0.004240989685058594,53.39424700000001 +4346,Binary classification,Logistic regression,Bananas,0.5476300046019328,0.17671691792294805,0.004240989685058594,55.939767 +4452,Binary classification,Logistic regression,Bananas,0.5498652291105122,0.1820408163265306,0.004240989685058594,58.584779000000005 +4558,Binary classification,Logistic regression,Bananas,0.5467310223782361,0.1814580031695721,0.004240989685058594,61.26661800000001 +4664,Binary classification,Logistic regression,Bananas,0.5465265866209262,0.18809980806142035,0.004240989685058594,64.04445700000001 +4770,Binary classification,Logistic regression,Bananas,0.5467505241090147,0.19086826347305388,0.004240989685058594,66.91140200000001 +4876,Binary classification,Logistic regression,Bananas,0.5469647251845775,0.19113877700476017,0.004240989685058594,69.84398600000002 +4982,Binary classification,Logistic regression,Bananas,0.5469690887193898,0.19765375044436545,0.004240989685058594,72.84582100000002 +5088,Binary classification,Logistic regression,Bananas,0.5448113207547169,0.19583333333333333,0.004240989685058594,75.85667200000002 +5194,Binary classification,Logistic regression,Bananas,0.5429341547939931,0.19416157501697218,0.004240989685058594,78.94956300000001 +5300,Binary classification,Logistic regression,Bananas,0.5432075471698113,0.1970149253731343,0.004240989685058594,82.06889500000001 +906,Binary classification,Logistic regression,Elec2,0.7980132450331126,0.7834319526627219,0.0053730010986328125,0.687155 +1812,Binary classification,Logistic regression,Elec2,0.8134657836644592,0.7488855869242199,0.0053730010986328125,2.092465 +2718,Binary classification,Logistic regression,Elec2,0.8024282560706402,0.7300150829562596,0.0053730010986328125,4.064074 +3624,Binary classification,Logistic regression,Elec2,0.8192604856512141,0.7598093142647598,0.0053730010986328125,6.824807 +4530,Binary classification,Logistic regression,Elec2,0.8289183222958058,0.7613181398213735,0.0053730010986328125,10.234028 +5436,Binary classification,Logistic regression,Elec2,0.8226637233259749,0.7528205128205128,0.0053730010986328125,14.344314 +6342,Binary classification,Logistic regression,Elec2,0.8229265216020183,0.7589611504614724,0.0053730010986328125,19.167838 +7248,Binary classification,Logistic regression,Elec2,0.8261589403973509,0.7617246596066566,0.0053730010986328125,24.744494 +8154,Binary classification,Logistic regression,Elec2,0.8318616629874908,0.7833096254148886,0.0053730010986328125,31.081721 +9060,Binary classification,Logistic regression,Elec2,0.8375275938189846,0.7975797579757975,0.0053730010986328125,38.163875000000004 +9966,Binary classification,Logistic regression,Elec2,0.8377483443708609,0.802008081302804,0.0053730010986328125,45.915004 +10872,Binary classification,Logistic regression,Elec2,0.8400478292862399,0.8089220964729151,0.0053730010986328125,54.352834 +11778,Binary classification,Logistic regression,Elec2,0.8432671081677704,0.8127789046653143,0.0053730010986328125,63.489549000000004 +12684,Binary classification,Logistic regression,Elec2,0.8419268369599495,0.8117547648108159,0.0053730010986328125,73.399178 +13590,Binary classification,Logistic regression,Elec2,0.8437821927888153,0.8167141500474834,0.0053730010986328125,84.03825400000001 +14496,Binary classification,Logistic regression,Elec2,0.8447157836644592,0.8189204408334004,0.0053730010986328125,95.41495900000001 +15402,Binary classification,Logistic regression,Elec2,0.8464485131801065,0.8201110519510155,0.0053730010986328125,107.55183300000002 +16308,Binary classification,Logistic regression,Elec2,0.8411822418444935,0.812780106982796,0.0053730010986328125,120.38661500000002 +17214,Binary classification,Logistic regression,Elec2,0.8397234808876496,0.8069954529555788,0.0053730010986328125,133.99787400000002 +18120,Binary classification,Logistic regression,Elec2,0.8419426048565122,0.80987785448752,0.0053730010986328125,148.356557 +19026,Binary classification,Logistic regression,Elec2,0.8451066961000736,0.8115849370244869,0.0053730010986328125,163.518734 +19932,Binary classification,Logistic regression,Elec2,0.8428155729480232,0.8097637986520129,0.0053730010986328125,179.395561 +20838,Binary classification,Logistic regression,Elec2,0.8393799788847298,0.805689404934688,0.0053730010986328125,196.009478 +21744,Binary classification,Logistic regression,Elec2,0.8402777777777778,0.8036632935722765,0.0053730010986328125,213.342445 +22650,Binary classification,Logistic regression,Elec2,0.8394701986754967,0.8009198423127463,0.0053730010986328125,231.348647 +23556,Binary classification,Logistic regression,Elec2,0.8357106469689252,0.7954545454545454,0.0053730010986328125,250.064916 +24462,Binary classification,Logistic regression,Elec2,0.8330471752105306,0.791441119395363,0.0053730010986328125,269.489469 +25368,Binary classification,Logistic regression,Elec2,0.8298249763481551,0.7872875092387287,0.0053730010986328125,289.628629 +26274,Binary classification,Logistic regression,Elec2,0.8304407398949532,0.787745962170661,0.0053730010986328125,310.508458 +27180,Binary classification,Logistic regression,Elec2,0.8308682855040471,0.7889638709085066,0.0053730010986328125,332.12320800000003 +28086,Binary classification,Logistic regression,Elec2,0.8277077547532579,0.7843678980437593,0.0053730010986328125,354.49968 +28992,Binary classification,Logistic regression,Elec2,0.8270212472406181,0.7820039121930016,0.0053730010986328125,377.655941 +29898,Binary classification,Logistic regression,Elec2,0.8260418757107498,0.780872129766168,0.0053730010986328125,401.520333 +30804,Binary classification,Logistic regression,Elec2,0.8258992338657317,0.7797807251673304,0.0053730010986328125,426.09085 +31710,Binary classification,Logistic regression,Elec2,0.821286660359508,0.7731294287201249,0.0053730010986328125,451.387139 +32616,Binary classification,Logistic regression,Elec2,0.8188619082658818,0.7700093428838368,0.0053730010986328125,477.46355 +33522,Binary classification,Logistic regression,Elec2,0.8168963665652408,0.7682024169184289,0.0053730010986328125,504.189667 +34428,Binary classification,Logistic regression,Elec2,0.8143952596723597,0.7647795037915042,0.0053730010986328125,531.635688 +35334,Binary classification,Logistic regression,Elec2,0.8142016188373804,0.7627822944896115,0.0053730010986328125,559.807066 +36240,Binary classification,Logistic regression,Elec2,0.8154801324503311,0.7629984051036682,0.0053730010986328125,588.709839 +37146,Binary classification,Logistic regression,Elec2,0.815161794002046,0.7614481273017858,0.0053730010986328125,618.344034 +38052,Binary classification,Logistic regression,Elec2,0.8151476926311363,0.7609596955073744,0.0053730010986328125,648.6306599999999 +38958,Binary classification,Logistic regression,Elec2,0.8162379998973254,0.7631274195149389,0.0053730010986328125,679.6980239999999 +39864,Binary classification,Logistic regression,Elec2,0.8169275536825206,0.7661946562439931,0.0053730010986328125,711.4719719999999 +40770,Binary classification,Logistic regression,Elec2,0.8186656855531028,0.7707241432780277,0.0053730010986328125,743.966698 +41676,Binary classification,Logistic regression,Elec2,0.8201602840963624,0.7745390006918749,0.0053730010986328125,777.181242 +42582,Binary classification,Logistic regression,Elec2,0.8211920529801324,0.7763613934089174,0.0053730010986328125,811.063029 +43488,Binary classification,Logistic regression,Elec2,0.8216979396615158,0.7772863051470587,0.0053730010986328125,845.685827 +44394,Binary classification,Logistic regression,Elec2,0.8211695274136145,0.7754109027129481,0.0053730010986328125,881.122534 +45300,Binary classification,Logistic regression,Elec2,0.8221412803532009,0.7771292633675417,0.0053730010986328125,917.330239 +45312,Binary classification,Logistic regression,Elec2,0.8221442443502824,0.7770862722319033,0.0053730010986328125,953.539999 +25,Binary classification,Logistic regression,Phishing,0.6,0.6428571428571429,0.005324363708496094,0.005087 +50,Binary classification,Logistic regression,Phishing,0.76,0.7499999999999999,0.005324363708496094,0.014273000000000001 +75,Binary classification,Logistic regression,Phishing,0.8,0.8,0.005324363708496094,0.080154 +100,Binary classification,Logistic regression,Phishing,0.81,0.8041237113402061,0.005324363708496094,0.160529 +125,Binary classification,Logistic regression,Phishing,0.8,0.7933884297520661,0.005324363708496094,0.244823 +150,Binary classification,Logistic regression,Phishing,0.8066666666666666,0.8079470198675497,0.005324363708496094,0.373717 +175,Binary classification,Logistic regression,Phishing,0.8171428571428572,0.8072289156626506,0.005324363708496094,0.564558 +200,Binary classification,Logistic regression,Phishing,0.815,0.8042328042328043,0.005324363708496094,0.765703 +225,Binary classification,Logistic regression,Phishing,0.8133333333333334,0.7980769230769231,0.005324363708496094,0.969796 +250,Binary classification,Logistic regression,Phishing,0.82,0.8068669527896996,0.005324363708496094,1.176844 +275,Binary classification,Logistic regression,Phishing,0.8218181818181818,0.8078431372549019,0.005564689636230469,1.38745 +300,Binary classification,Logistic regression,Phishing,0.8333333333333334,0.8161764705882353,0.005564689636230469,1.6264800000000001 +325,Binary classification,Logistic regression,Phishing,0.84,0.8181818181818181,0.005564689636230469,1.9406150000000002 +350,Binary classification,Logistic regression,Phishing,0.8514285714285714,0.8278145695364238,0.005564689636230469,2.281543 +375,Binary classification,Logistic regression,Phishing,0.848,0.8213166144200628,0.005564689636230469,2.625835 +400,Binary classification,Logistic regression,Phishing,0.85,0.8214285714285715,0.005564689636230469,2.973623 +425,Binary classification,Logistic regression,Phishing,0.8564705882352941,0.825214899713467,0.005564689636230469,3.3575019999999998 +450,Binary classification,Logistic regression,Phishing,0.86,0.8273972602739726,0.005564689636230469,3.744344 +475,Binary classification,Logistic regression,Phishing,0.8568421052631578,0.8247422680412371,0.005564689636230469,4.182096 +500,Binary classification,Logistic regression,Phishing,0.858,0.8297362110311751,0.005564689636230469,4.631479 +525,Binary classification,Logistic regression,Phishing,0.8571428571428571,0.8251748251748252,0.005564689636230469,5.084116 +550,Binary classification,Logistic regression,Phishing,0.8581818181818182,0.827433628318584,0.005564689636230469,5.539997 +575,Binary classification,Logistic regression,Phishing,0.8608695652173913,0.8305084745762712,0.005564689636230469,6.065522 +600,Binary classification,Logistic regression,Phishing,0.865,0.8329896907216495,0.005564689636230469,6.5948839999999995 +625,Binary classification,Logistic regression,Phishing,0.8672,0.8323232323232322,0.005564689636230469,7.192367999999999 +650,Binary classification,Logistic regression,Phishing,0.8707692307692307,0.8390804597701149,0.005564689636230469,7.814115999999999 +675,Binary classification,Logistic regression,Phishing,0.8711111111111111,0.8426763110307414,0.005564689636230469,8.439065999999999 +700,Binary classification,Logistic regression,Phishing,0.8757142857142857,0.8465608465608465,0.005564689636230469,9.067184 +725,Binary classification,Logistic regression,Phishing,0.8772413793103448,0.8514190317195326,0.005564689636230469,9.744983999999999 +750,Binary classification,Logistic regression,Phishing,0.8786666666666667,0.8539325842696629,0.005564689636230469,10.426390999999999 +775,Binary classification,Logistic regression,Phishing,0.88,0.8549141965678626,0.005564689636230469,11.153806 +800,Binary classification,Logistic regression,Phishing,0.88,0.8567164179104476,0.005564689636230469,11.884597 +825,Binary classification,Logistic regression,Phishing,0.88,0.8579626972740315,0.005564689636230469,12.619003 +850,Binary classification,Logistic regression,Phishing,0.8811764705882353,0.8587412587412586,0.005564689636230469,13.411055999999999 +875,Binary classification,Logistic regression,Phishing,0.8845714285714286,0.8622100954979536,0.005564689636230469,14.234523999999999 +900,Binary classification,Logistic regression,Phishing,0.8844444444444445,0.8617021276595744,0.005564689636230469,15.105192999999998 +925,Binary classification,Logistic regression,Phishing,0.8864864864864865,0.8655569782330347,0.005564689636230469,15.990264999999997 +950,Binary classification,Logistic regression,Phishing,0.8852631578947369,0.8655980271270037,0.005564689636230469,16.878196999999997 +975,Binary classification,Logistic regression,Phishing,0.8861538461538462,0.8664259927797834,0.005564689636230469,17.769031 +1000,Binary classification,Logistic regression,Phishing,0.887,0.8675263774912075,0.005564689636230469,18.72316 +1025,Binary classification,Logistic regression,Phishing,0.8868292682926829,0.8678815489749431,0.005564689636230469,19.680949 +1050,Binary classification,Logistic regression,Phishing,0.8885714285714286,0.8704318936877077,0.005564689636230469,20.642059 +1075,Binary classification,Logistic regression,Phishing,0.8874418604651163,0.8703108252947481,0.005564689636230469,21.642509 +1100,Binary classification,Logistic regression,Phishing,0.889090909090909,0.8723849372384936,0.005564689636230469,22.64645 +1125,Binary classification,Logistic regression,Phishing,0.8897777777777778,0.8742393509127788,0.005564689636230469,23.715816 +1150,Binary classification,Logistic regression,Phishing,0.8895652173913043,0.8738828202581926,0.005564689636230469,24.78868 +1175,Binary classification,Logistic regression,Phishing,0.8885106382978724,0.872444011684518,0.005564689636230469,25.864657 +1200,Binary classification,Logistic regression,Phishing,0.8891666666666667,0.8729703915950333,0.005564689636230469,26.968066 +1225,Binary classification,Logistic regression,Phishing,0.889795918367347,0.8737137511693172,0.005564689636230469,28.075126 +1250,Binary classification,Logistic regression,Phishing,0.8872,0.8712328767123287,0.005564689636230469,29.206647 +1903,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,1.174944 +3806,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,3.465965 +5709,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,6.937403 +7612,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,11.610183 +9515,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,17.462392 +11418,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,24.519273000000002 +13321,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,32.784706 +15224,Binary classification,Logistic regression,SMTP,0.9996715712033631,0.7058823529411764,0.004383087158203125,42.234241 +17127,Binary classification,Logistic regression,SMTP,0.9997080632918783,0.761904761904762,0.004383087158203125,52.882453 +19030,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.761904761904762,0.004383087158203125,64.622668 +20933,Binary classification,Logistic regression,SMTP,0.999761142693355,0.761904761904762,0.004383087158203125,77.568109 +22836,Binary classification,Logistic regression,SMTP,0.9997810474689087,0.761904761904762,0.004383087158203125,91.771967 +24739,Binary classification,Logistic regression,SMTP,0.9997978899713004,0.761904761904762,0.004383087158203125,107.109486 +26642,Binary classification,Logistic regression,SMTP,0.9997747916823061,0.7272727272727273,0.004383087158203125,123.68183400000001 +28545,Binary classification,Logistic regression,SMTP,0.9997898055701524,0.7272727272727273,0.004383087158203125,141.369945 +30448,Binary classification,Logistic regression,SMTP,0.9998029427220179,0.7272727272727273,0.004383087158203125,160.23044 +32351,Binary classification,Logistic regression,SMTP,0.999814534326605,0.7272727272727273,0.004383087158203125,180.23963199999997 +34254,Binary classification,Logistic regression,SMTP,0.999824837975127,0.7272727272727273,0.004383087158203125,201.31894799999998 +36157,Binary classification,Logistic regression,SMTP,0.9998340570290677,0.7272727272727273,0.004383087158203125,223.51927299999997 +38060,Binary classification,Logistic regression,SMTP,0.9998423541776142,0.7272727272727273,0.004383087158203125,246.97671399999996 +39963,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.7272727272727273,0.004383087158203125,271.56812399999995 +41866,Binary classification,Logistic regression,SMTP,0.999856685616013,0.7272727272727273,0.004383087158203125,297.29584399999993 +43769,Binary classification,Logistic regression,SMTP,0.9998629166761863,0.7272727272727273,0.004383087158203125,324.2115329999999 +45672,Binary classification,Logistic regression,SMTP,0.9998686284813453,0.7272727272727273,0.004383087158203125,352.27523699999995 +47575,Binary classification,Logistic regression,SMTP,0.9998738833420915,0.7272727272727273,0.004383087158203125,381.59710399999994 +49478,Binary classification,Logistic regression,SMTP,0.9998787339827803,0.7272727272727273,0.004383087158203125,412.11662699999994 +51381,Binary classification,Logistic regression,SMTP,0.9998443004223351,0.6666666666666666,0.004383087158203125,443.86742899999996 +53284,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.6666666666666666,0.004383087158203125,476.83879799999994 +55187,Binary classification,Logistic regression,SMTP,0.999855038324243,0.6666666666666666,0.004383087158203125,510.9819989999999 +57090,Binary classification,Logistic regression,SMTP,0.9997022245577158,0.48484848484848486,0.004383087158203125,546.274013 +58993,Binary classification,Logistic regression,SMTP,0.9997118302171444,0.48484848484848486,0.004383087158203125,582.6678519999999 +60896,Binary classification,Logistic regression,SMTP,0.9997208355228586,0.48484848484848486,0.004383087158203125,620.2082039999999 +62799,Binary classification,Logistic regression,SMTP,0.999697447411583,0.45714285714285713,0.004383087158203125,658.8625569999999 +64702,Binary classification,Logistic regression,SMTP,0.9997063460171247,0.45714285714285713,0.004383087158203125,698.5852799999999 +66605,Binary classification,Logistic regression,SMTP,0.9997147361309211,0.45714285714285713,0.004383087158203125,739.3620329999999 +68508,Binary classification,Logistic regression,SMTP,0.9996934664564723,0.4324324324324324,0.004383087158203125,781.2563779999999 +70411,Binary classification,Logistic regression,SMTP,0.9997017511468379,0.4324324324324324,0.004383087158203125,824.198222 +72314,Binary classification,Logistic regression,SMTP,0.9997095998008685,0.4324324324324324,0.004383087158203125,868.202086 +74217,Binary classification,Logistic regression,SMTP,0.9997170459598205,0.4324324324324324,0.004383087158203125,913.268811 +76120,Binary classification,Logistic regression,SMTP,0.999724119810825,0.4324324324324324,0.004383087158203125,959.4161730000001 +78023,Binary classification,Logistic regression,SMTP,0.9997308485959269,0.4324324324324324,0.004383087158203125,1006.608919 +79926,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.4324324324324324,0.004383087158203125,1054.8516300000001 +81829,Binary classification,Logistic regression,SMTP,0.9997433672658838,0.4324324324324324,0.004383087158203125,1104.06085 +83732,Binary classification,Logistic regression,SMTP,0.9997491998280228,0.4324324324324324,0.004383087158203125,1154.258062 +85635,Binary classification,Logistic regression,SMTP,0.9997547731651778,0.4324324324324324,0.004383087158203125,1205.3715320000001 +87538,Binary classification,Logistic regression,SMTP,0.9997601041833261,0.4324324324324324,0.004383087158203125,1257.4462130000002 +89441,Binary classification,Logistic regression,SMTP,0.9997540277948592,0.4210526315789474,0.004383087158203125,1310.5048250000002 +91344,Binary classification,Logistic regression,SMTP,0.9997591522157996,0.4210526315789474,0.004383087158203125,1364.5437910000003 +93247,Binary classification,Logistic regression,SMTP,0.9997640674767017,0.4210526315789474,0.004383087158203125,1419.4942320000002 +95150,Binary classification,Logistic regression,SMTP,0.9997687861271676,0.4210526315789474,0.004383087158203125,1475.4318390000003 +95156,Binary classification,Logistic regression,SMTP,0.9997688007062088,0.4210526315789474,0.004383087158203125,1531.3705140000004 +106,Binary classification,Aggregated Mondrian Forest,Bananas,0.7047619047619048,0.6990291262135924,0.8133068084716797,0.833499 +212,Binary classification,Aggregated Mondrian Forest,Bananas,0.7867298578199052,0.7668393782383419,1.3378009796142578,2.8663 +318,Binary classification,Aggregated Mondrian Forest,Bananas,0.8233438485804416,0.806896551724138,1.855398178100586,6.250927 +424,Binary classification,Aggregated Mondrian Forest,Bananas,0.8392434988179669,0.8229166666666667,2.3226680755615234,11.143336 +530,Binary classification,Aggregated Mondrian Forest,Bananas,0.8412098298676749,0.8181818181818182,2.776212692260742,17.797124 +636,Binary classification,Aggregated Mondrian Forest,Bananas,0.8488188976377953,0.8267148014440434,3.173288345336914,26.396562 +742,Binary classification,Aggregated Mondrian Forest,Bananas,0.8596491228070176,0.8359621451104102,3.5500621795654297,36.969223 +848,Binary classification,Aggregated Mondrian Forest,Bananas,0.8677685950413223,0.8461538461538461,3.917997360229492,49.692848 +954,Binary classification,Aggregated Mondrian Forest,Bananas,0.8730325288562435,0.8515337423312884,4.238534927368164,64.631677 +1060,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772426817752597,0.8549107142857144,4.491437911987305,81.765253 +1166,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772532188841202,0.8557013118062564,4.809717178344727,101.295253 +1272,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772619984264359,0.8566176470588236,5.171953201293945,123.161687 +1378,Binary classification,Aggregated Mondrian Forest,Bananas,0.8779956427015251,0.8561643835616438,5.501619338989258,147.513883 +1484,Binary classification,Aggregated Mondrian Forest,Bananas,0.8813216453135536,0.860759493670886,5.80189323425293,174.53874199999998 +1590,Binary classification,Aggregated Mondrian Forest,Bananas,0.8785399622404028,0.8579838116261957,6.17225456237793,204.250002 +1696,Binary classification,Aggregated Mondrian Forest,Bananas,0.8790560471976401,0.8585231193926847,6.45002555847168,237.091398 +1802,Binary classification,Aggregated Mondrian Forest,Bananas,0.8806218767351471,0.8613797549967763,6.703157424926758,272.83416 +1908,Binary classification,Aggregated Mondrian Forest,Bananas,0.8783429470372313,0.8602409638554217,7.075212478637695,311.419605 +2014,Binary classification,Aggregated Mondrian Forest,Bananas,0.8777943368107303,0.8607021517553795,7.409914016723633,352.79492 +2120,Binary classification,Aggregated Mondrian Forest,Bananas,0.8791882963662104,0.8636847710330138,7.730207443237305,397.065386 +2226,Binary classification,Aggregated Mondrian Forest,Bananas,0.8782022471910113,0.8626457171819564,8.068941116333008,444.302777 +2332,Binary classification,Aggregated Mondrian Forest,Bananas,0.8777348777348777,0.8621190130624092,8.392999649047852,494.454577 +2438,Binary classification,Aggregated Mondrian Forest,Bananas,0.8781288469429627,0.8624363131079205,8.738908767700195,547.433225 +2544,Binary classification,Aggregated Mondrian Forest,Bananas,0.8784899724734565,0.8635761589403974,9.069158554077148,603.367304 +2650,Binary classification,Aggregated Mondrian Forest,Bananas,0.8799546998867497,0.8654822335025381,9.380228042602539,661.971994 +2756,Binary classification,Aggregated Mondrian Forest,Bananas,0.8820326678765881,0.8676171079429736,9.675683975219727,723.088894 +2862,Binary classification,Aggregated Mondrian Forest,Bananas,0.8836071303739951,0.86905230043256,10.005556106567383,786.780009 +2968,Binary classification,Aggregated Mondrian Forest,Bananas,0.8840579710144928,0.8691019786910198,10.283010482788086,853.0146269999999 +3074,Binary classification,Aggregated Mondrian Forest,Bananas,0.8831760494630654,0.8683535020168683,10.632661819458008,921.6671329999999 +3180,Binary classification,Aggregated Mondrian Forest,Bananas,0.8858131487889274,0.8707725169099323,10.90281867980957,992.810764 +3286,Binary classification,Aggregated Mondrian Forest,Bananas,0.8852359208523592,0.8696854476322157,11.200468063354492,1066.389204 +3392,Binary classification,Aggregated Mondrian Forest,Bananas,0.8849896785608965,0.87017310252996,11.512235641479492,1142.442462 +3498,Binary classification,Aggregated Mondrian Forest,Bananas,0.8864741206748642,0.8712293220888745,11.797895431518555,1221.036812 +3604,Binary classification,Aggregated Mondrian Forest,Bananas,0.8878712184290869,0.8721518987341771,12.102933883666992,1302.125963 +3710,Binary classification,Aggregated Mondrian Forest,Bananas,0.8878403882448099,0.8725490196078431,12.41331672668457,1385.838182 +3816,Binary classification,Aggregated Mondrian Forest,Bananas,0.889646133682831,0.8746650788925276,12.665735244750977,1472.135343 +3922,Binary classification,Aggregated Mondrian Forest,Bananas,0.8885488395817394,0.8730758059831543,13.002767562866211,1561.047711 +4028,Binary classification,Aggregated Mondrian Forest,Bananas,0.8872609883287808,0.8714609286523215,13.407987594604492,1652.580672 +4134,Binary classification,Aggregated Mondrian Forest,Bananas,0.8874909266876361,0.8717241379310345,13.751871109008789,1746.8148660000002 +4240,Binary classification,Aggregated Mondrian Forest,Bananas,0.8886529841943854,0.8731864588930682,13.96497917175293,1843.750561 +4346,Binary classification,Aggregated Mondrian Forest,Bananas,0.8895281933256617,0.8742138364779874,14.240518569946289,1943.4032140000002 +4452,Binary classification,Aggregated Mondrian Forest,Bananas,0.8890137047854415,0.8735926305015352,14.605810165405273,2045.776976 +4558,Binary classification,Aggregated Mondrian Forest,Bananas,0.8894009216589862,0.874439461883408,14.917993545532227,2150.6554650000003 +4664,Binary classification,Aggregated Mondrian Forest,Bananas,0.8893416255629423,0.8748180494905387,15.239774703979492,2258.064088 +4770,Binary classification,Aggregated Mondrian Forest,Bananas,0.8880268400083875,0.8729176582579724,15.676980972290039,2367.913167 +4876,Binary classification,Aggregated Mondrian Forest,Bananas,0.8888205128205128,0.8733644859813083,15.964864730834961,2480.267593 +4982,Binary classification,Aggregated Mondrian Forest,Bananas,0.889580405541056,0.8746010031919745,16.210702896118164,2595.134509 +5088,Binary classification,Aggregated Mondrian Forest,Bananas,0.8891291527422842,0.8740509155873157,16.543100357055664,2712.434229 +5194,Binary classification,Aggregated Mondrian Forest,Bananas,0.8894665896398999,0.8743982494529539,16.87101936340332,2832.294496 +5300,Binary classification,Aggregated Mondrian Forest,Bananas,0.889413096810719,0.8742489270386266,17.23769187927246,2954.746773 +906,Binary classification,Aggregated Mondrian Forest,Elec2,0.8662983425414365,0.8638920134983127,5.093213081359863,9.961559 +1812,Binary classification,Aggregated Mondrian Forest,Elec2,0.8895637769188294,0.863013698630137,9.274415016174316,34.997891 +2718,Binary classification,Aggregated Mondrian Forest,Elec2,0.8737578211262422,0.8433074463225217,14.81954288482666,77.180768 +3624,Binary classification,Aggregated Mondrian Forest,Elec2,0.8746894838531604,0.8451568894952252,20.35789203643799,135.799753 +4530,Binary classification,Aggregated Mondrian Forest,Elec2,0.869728416869066,0.8295782784517621,25.320820808410645,209.04868100000002 +5436,Binary classification,Aggregated Mondrian Forest,Elec2,0.8658693652253909,0.8254728273880776,30.942105293273926,297.509476 +6342,Binary classification,Aggregated Mondrian Forest,Elec2,0.8613783314934553,0.8220287507592631,36.922226905822754,401.254404 +7248,Binary classification,Aggregated Mondrian Forest,Elec2,0.8563543535255967,0.8144715736945286,42.8322229385376,518.853069 +8154,Binary classification,Aggregated Mondrian Forest,Elec2,0.8547773825585674,0.8211480362537765,49.13461780548096,650.61595 +9060,Binary classification,Aggregated Mondrian Forest,Elec2,0.8564963020200905,0.8276776246023331,54.274807929992676,797.031608 +9966,Binary classification,Aggregated Mondrian Forest,Elec2,0.8559959859508279,0.830478440637921,59.58850955963135,957.298151 +10872,Binary classification,Aggregated Mondrian Forest,Elec2,0.858522675006899,0.8360690684289065,64.43849277496338,1132.655012 +11778,Binary classification,Aggregated Mondrian Forest,Elec2,0.8588774730406725,0.8365138697619515,69.77676105499268,1321.3849659999998 +12684,Binary classification,Aggregated Mondrian Forest,Elec2,0.8572892848695104,0.8352148579752368,75.08023929595947,1522.7749099999999 +13590,Binary classification,Aggregated Mondrian Forest,Elec2,0.8577525940098609,0.8380665158750105,79.94311618804932,1737.8701859999999 +14496,Binary classification,Aggregated Mondrian Forest,Elec2,0.8584339427388755,0.8393863494051347,84.43613529205322,1968.1055499999998 +15402,Binary classification,Aggregated Mondrian Forest,Elec2,0.8584507499513019,0.8387335404645658,89.24470615386963,2211.4324739999997 +16308,Binary classification,Aggregated Mondrian Forest,Elec2,0.8561354019746121,0.8352296670880741,95.65516376495361,2468.910492 +17214,Binary classification,Aggregated Mondrian Forest,Elec2,0.8563295183872655,0.8333445649976414,100.85075855255127,2740.76049 +18120,Binary classification,Aggregated Mondrian Forest,Elec2,0.8570009382416248,0.834176,106.8406229019165,3026.823297 +19026,Binary classification,Aggregated Mondrian Forest,Elec2,0.858712220762155,0.8348082595870207,111.74584293365479,3325.548438 +19932,Binary classification,Aggregated Mondrian Forest,Elec2,0.8587125583262255,0.8363361618040218,117.02025699615479,3636.553219 +20838,Binary classification,Aggregated Mondrian Forest,Elec2,0.8564572635216202,0.8339348176114596,123.37252902984619,3960.554229 +21744,Binary classification,Aggregated Mondrian Forest,Elec2,0.8540219840868325,0.8286917098445596,130.42929553985596,4298.210438 +22650,Binary classification,Aggregated Mondrian Forest,Elec2,0.8531944015188309,0.8264160793526494,136.64212131500244,4650.500753 +23556,Binary classification,Aggregated Mondrian Forest,Elec2,0.8528550201655699,0.8255134917438581,142.6701021194458,5016.675492 +24462,Binary classification,Aggregated Mondrian Forest,Elec2,0.8532766444544376,0.8247130647130647,148.4442949295044,5397.142957 +25368,Binary classification,Aggregated Mondrian Forest,Elec2,0.8514605589939686,0.8225487425826504,154.72937488555908,5792.939295 +26274,Binary classification,Aggregated Mondrian Forest,Elec2,0.8521676245575306,0.8231490756761678,160.280930519104,6204.791143 +27180,Binary classification,Aggregated Mondrian Forest,Elec2,0.8530851024688179,0.8247069669432372,165.12001132965088,6630.671498000001 +28086,Binary classification,Aggregated Mondrian Forest,Elec2,0.8528752002848495,0.8239904583404327,171.1938066482544,7068.974646000001 +28992,Binary classification,Aggregated Mondrian Forest,Elec2,0.8532303128557138,0.8236415633937083,176.66365909576416,7519.88705 +29898,Binary classification,Aggregated Mondrian Forest,Elec2,0.8538649362812323,0.8241355713883187,181.78493976593018,7981.8746790000005 +30804,Binary classification,Aggregated Mondrian Forest,Elec2,0.8542349771126189,0.8238110186783865,187.08849048614502,8454.454599 +31710,Binary classification,Aggregated Mondrian Forest,Elec2,0.8525655176763695,0.8216125462662648,193.5201120376587,8938.242097 +32616,Binary classification,Aggregated Mondrian Forest,Elec2,0.852245899126169,0.821432541594101,199.6366205215454,9433.534304 +33522,Binary classification,Aggregated Mondrian Forest,Elec2,0.852003221860923,0.8214247147330909,205.81115818023682,9940.639789 +34428,Binary classification,Aggregated Mondrian Forest,Elec2,0.851715223516426,0.8209965286300361,212.10033893585205,10459.964952 +35334,Binary classification,Aggregated Mondrian Forest,Elec2,0.8513287861206238,0.8197137660019906,218.64550113677979,10993.026606 +36240,Binary classification,Aggregated Mondrian Forest,Elec2,0.8508788873865173,0.8179735920237133,225.19258975982666,11538.003928999999 +37146,Binary classification,Aggregated Mondrian Forest,Elec2,0.8496432898102032,0.8159741671883752,232.33557987213135,12096.169426999999 +38052,Binary classification,Aggregated Mondrian Forest,Elec2,0.8497279966360937,0.8155126798735239,238.56606006622314,12664.877691 +38958,Binary classification,Aggregated Mondrian Forest,Elec2,0.8494493929203994,0.8154906093686098,244.89648151397705,13243.508414 +39864,Binary classification,Aggregated Mondrian Forest,Elec2,0.8492336251661942,0.8164773421277635,251.12543201446533,13830.859859 +40770,Binary classification,Aggregated Mondrian Forest,Elec2,0.8486104638328141,0.8170174918470204,257.83575916290283,14427.278119 +41676,Binary classification,Aggregated Mondrian Forest,Elec2,0.8490941811637672,0.8186928820595613,264.1331262588501,15032.883602 +42582,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493929217256523,0.8194385787087872,270.1314744949341,15648.679676 +43488,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493802745648125,0.8194995590828924,276.04683017730713,16273.986894 +44394,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493681436262474,0.8189620164063134,282.1419038772583,16909.074578 +45300,Binary classification,Aggregated Mondrian Forest,Elec2,0.8499083864985982,0.8197651300267741,287.208477973938,17554.066457 +45312,Binary classification,Aggregated Mondrian Forest,Elec2,0.8499039968219637,0.8197312269727252,287.3145227432251,18206.640571 +25,Binary classification,Aggregated Mondrian Forest,Phishing,0.6666666666666666,0.6923076923076924,0.2663440704345703,0.180038 +50,Binary classification,Aggregated Mondrian Forest,Phishing,0.7755102040816326,0.7555555555555555,0.40291404724121094,0.591649 +75,Binary classification,Aggregated Mondrian Forest,Phishing,0.7972972972972973,0.7945205479452055,0.5196552276611328,1.2897159999999999 +100,Binary classification,Aggregated Mondrian Forest,Phishing,0.8181818181818182,0.8125,0.6383838653564453,2.331468 +125,Binary classification,Aggregated Mondrian Forest,Phishing,0.8225806451612904,0.819672131147541,0.7669887542724609,3.7241540000000004 +150,Binary classification,Aggregated Mondrian Forest,Phishing,0.8456375838926175,0.847682119205298,0.9175167083740234,5.520175 +175,Binary classification,Aggregated Mondrian Forest,Phishing,0.867816091954023,0.8606060606060606,1.0086803436279297,7.7498439999999995 +200,Binary classification,Aggregated Mondrian Forest,Phishing,0.864321608040201,0.8571428571428572,1.1245098114013672,10.53336 +225,Binary classification,Aggregated Mondrian Forest,Phishing,0.8660714285714286,0.8557692307692308,1.2114391326904297,13.795268 +250,Binary classification,Aggregated Mondrian Forest,Phishing,0.8554216867469879,0.8448275862068965,1.322244644165039,17.57486 +275,Binary classification,Aggregated Mondrian Forest,Phishing,0.8540145985401459,0.84251968503937,1.3987751007080078,21.876977 +300,Binary classification,Aggregated Mondrian Forest,Phishing,0.8561872909698997,0.8413284132841329,1.489828109741211,26.743447 +325,Binary classification,Aggregated Mondrian Forest,Phishing,0.8672839506172839,0.8501742160278746,1.5769939422607422,32.2729 +350,Binary classification,Aggregated Mondrian Forest,Phishing,0.8681948424068768,0.8486842105263156,1.638784408569336,38.477964 +375,Binary classification,Aggregated Mondrian Forest,Phishing,0.8689839572192514,0.8482972136222912,1.7178211212158203,45.357054 +400,Binary classification,Aggregated Mondrian Forest,Phishing,0.8671679197994987,0.8436578171091446,1.7941875457763672,52.888585 +425,Binary classification,Aggregated Mondrian Forest,Phishing,0.8702830188679245,0.8433048433048433,1.8353633880615234,61.095765 +450,Binary classification,Aggregated Mondrian Forest,Phishing,0.8730512249443207,0.8455284552845528,1.9096240997314453,70.024579 +475,Binary classification,Aggregated Mondrian Forest,Phishing,0.8755274261603375,0.8506329113924052,1.988790512084961,79.720297 +500,Binary classification,Aggregated Mondrian Forest,Phishing,0.875751503006012,0.8530805687203792,2.063833236694336,90.07863400000001 +525,Binary classification,Aggregated Mondrian Forest,Phishing,0.8778625954198473,0.8525345622119817,2.144712448120117,101.25810100000001 +550,Binary classification,Aggregated Mondrian Forest,Phishing,0.8779599271402551,0.8533916849015317,2.1996402740478516,113.25181900000001 +575,Binary classification,Aggregated Mondrian Forest,Phishing,0.8780487804878049,0.8535564853556484,2.2528209686279297,125.93584100000001 +600,Binary classification,Aggregated Mondrian Forest,Phishing,0.8797996661101837,0.8536585365853657,2.283121109008789,139.44840100000002 +625,Binary classification,Aggregated Mondrian Forest,Phishing,0.8814102564102564,0.852589641434263,2.343900680541992,153.77905700000002 +650,Binary classification,Aggregated Mondrian Forest,Phishing,0.884437596302003,0.8587570621468926,2.418844223022461,168.92061400000003 +675,Binary classification,Aggregated Mondrian Forest,Phishing,0.884272997032641,0.8617021276595745,2.468423843383789,184.94000100000002 +700,Binary classification,Aggregated Mondrian Forest,Phishing,0.8884120171673819,0.8650519031141869,2.478273391723633,201.76583000000002 +725,Binary classification,Aggregated Mondrian Forest,Phishing,0.8895027624309392,0.8684210526315789,2.5243663787841797,219.457713 +750,Binary classification,Aggregated Mondrian Forest,Phishing,0.8918558077436582,0.8716323296354993,2.5813236236572266,238.014124 +775,Binary classification,Aggregated Mondrian Forest,Phishing,0.8914728682170543,0.8707692307692307,2.6200389862060547,257.461391 +800,Binary classification,Aggregated Mondrian Forest,Phishing,0.8898623279098874,0.8702064896755163,2.657014846801758,277.779634 +825,Binary classification,Aggregated Mondrian Forest,Phishing,0.8907766990291263,0.872159090909091,2.706361770629883,298.980548 +850,Binary classification,Aggregated Mondrian Forest,Phishing,0.8928150765606596,0.8741355463347164,2.730466842651367,321.097396 +875,Binary classification,Aggregated Mondrian Forest,Phishing,0.8958810068649885,0.8771929824561403,2.7533512115478516,344.186724 +900,Binary classification,Aggregated Mondrian Forest,Phishing,0.8976640711902113,0.8786279683377309,2.807779312133789,368.101507 +925,Binary classification,Aggregated Mondrian Forest,Phishing,0.9004329004329005,0.8829516539440204,2.8523120880126953,392.98062400000003 +950,Binary classification,Aggregated Mondrian Forest,Phishing,0.9009483667017913,0.8850855745721271,2.913583755493164,418.83123200000006 +975,Binary classification,Aggregated Mondrian Forest,Phishing,0.9024640657084189,0.8867699642431467,2.943540573120117,445.63277700000003 +1000,Binary classification,Aggregated Mondrian Forest,Phishing,0.9009009009009009,0.8850174216027874,2.9903697967529297,473.39902800000004 +1025,Binary classification,Aggregated Mondrian Forest,Phishing,0.8994140625,0.8836158192090395,3.035707473754883,502.22467600000004 +1050,Binary classification,Aggregated Mondrian Forest,Phishing,0.9008579599618685,0.8857142857142858,3.069150924682617,532.049603 +1075,Binary classification,Aggregated Mondrian Forest,Phishing,0.9013035381750466,0.8869936034115138,3.114839553833008,562.838704 +1100,Binary classification,Aggregated Mondrian Forest,Phishing,0.9035486806187443,0.8898128898128899,3.132375717163086,594.67778 +1125,Binary classification,Aggregated Mondrian Forest,Phishing,0.905693950177936,0.8933601609657947,3.1889095306396484,627.518257 +1150,Binary classification,Aggregated Mondrian Forest,Phishing,0.9060052219321149,0.893491124260355,3.220029830932617,661.4048929999999 +1175,Binary classification,Aggregated Mondrian Forest,Phishing,0.9045996592844975,0.8916827852998066,3.270620346069336,696.4079739999999 +1200,Binary classification,Aggregated Mondrian Forest,Phishing,0.9040867389491243,0.8909952606635072,3.311410903930664,732.4743999999998 +1225,Binary classification,Aggregated Mondrian Forest,Phishing,0.9044117647058824,0.8911627906976743,3.344022750854492,769.4892029999999 +1250,Binary classification,Aggregated Mondrian Forest,Phishing,0.9047237790232185,0.8921124206708976,3.391061782836914,807.5726659999999 +1903,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,2.745403 +3806,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,8.183125 +5709,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,16.539666 +7612,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,27.755785000000003 +9515,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,41.777067 +11418,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,58.637769000000006 +13321,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,78.268206 +15224,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998686198515404,0.9090909090909091,0.09231853485107422,101.443914 +17127,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998832184981898,0.9230769230769231,0.09723186492919922,131.805417 +19030,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998948972620737,0.9230769230769231,0.09728145599365234,169.246217 +20933,Binary classification,Aggregated Mondrian Forest,SMTP,0.999904452512899,0.9230769230769231,0.09728145599365234,213.148727 +22836,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999124151521787,0.9230769230769231,0.09728145599365234,263.357684 +24739,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999191527205109,0.9230769230769231,0.09730815887451172,319.49775 +26642,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998873916144289,0.888888888888889,0.10914134979248047,381.401703 +28545,Binary classification,Aggregated Mondrian Forest,SMTP,0.999894899103139,0.888888888888889,0.10916423797607422,448.60874 +30448,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999014681249384,0.888888888888889,0.10916423797607422,520.91477 +32351,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999072642967543,0.888888888888889,0.10966777801513672,598.09858 +34254,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999124164306776,0.888888888888889,0.11131954193115234,680.064697 +36157,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999170262197146,0.888888888888889,0.11127376556396484,766.82968 +38060,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999211750177356,0.888888888888889,0.11127376556396484,858.2478070000001 +39963,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999249286822481,0.888888888888889,0.11127376556396484,954.233503 +41866,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999283410963812,0.888888888888889,0.11127376556396484,1054.7914 +43769,Binary classification,Aggregated Mondrian Forest,SMTP,0.999931456772071,0.888888888888889,0.11127376556396484,1159.6703400000001 +45672,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999343128024348,0.888888888888889,0.11127376556396484,1268.4432900000002 +47575,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999369403455669,0.888888888888889,0.1298818588256836,1381.2685860000001 +49478,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999393657659115,0.888888888888889,0.1299276351928711,1498.4984390000002 +51381,Binary classification,Aggregated Mondrian Forest,SMTP,0.999941611521993,0.9032258064516129,0.14348888397216797,1620.0599740000002 +53284,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999436968639153,0.9032258064516129,0.14348888397216797,1745.8256190000002 +55187,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999456383865473,0.9032258064516129,0.14403820037841797,1875.6542580000003 +57090,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998248349068998,0.7619047619047621,0.1476888656616211,2010.2723030000002 +58993,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998304854895579,0.7619047619047621,0.15108394622802734,2149.2802330000004 +60896,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998357829050004,0.7619047619047621,0.1510610580444336,2292.3763450000006 +62799,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998089111118188,0.7272727272727272,0.15114116668701172,2439.4913830000005 +64702,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998145314601011,0.7272727272727272,0.15341472625732422,2590.5360980000005 +66605,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998198306408024,0.7272727272727272,0.1576833724975586,2745.6113380000006 +68508,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998248354182784,0.75,0.1762075424194336,2905.2725530000007 +70411,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998295696634001,0.75,0.1762075424194336,3069.514522000001 +72314,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998340547342801,0.75,0.1762075424194336,3238.428366000001 +74217,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998383097984263,0.75,0.17613887786865234,3411.935267000001 +76120,Binary classification,Aggregated Mondrian Forest,SMTP,0.99984235210657,0.75,0.1760702133178711,3590.1136440000014 +78023,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998461972264233,0.75,0.1782979965209961,3773.0849660000013 +79926,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998498592430404,0.75,0.1782979965209961,3960.4867140000015 +81829,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998533509312216,0.75,0.1782979965209961,4152.338698000001 +83732,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998566839044082,0.75,0.17832088470458984,4348.642178000001 +85635,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998598687437232,0.75,0.17832088470458984,4549.410423000001 +87538,Binary classification,Aggregated Mondrian Forest,SMTP,0.999862915110182,0.75,0.17832088470458984,4754.622131000001 +89441,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998546511627907,0.7346938775510204,0.17834758758544922,4964.315109000001 +91344,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998576792967168,0.7346938775510204,0.19727230072021484,5178.776489000001 +93247,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998605838320143,0.7346938775510204,0.21181774139404297,5398.511461000001 +95150,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998633721846788,0.7346938775510204,0.21174907684326172,5623.669278000001 +95156,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998633807997478,0.7346938775510204,0.21174907684326172,5848.865968000001 +106,Binary classification,ALMA,Bananas,0.5377358490566038,0.5242718446601942,0.0028944015502929688,0.039715 +212,Binary classification,ALMA,Bananas,0.5330188679245284,0.5217391304347825,0.0028944015502929688,0.180531 +318,Binary classification,ALMA,Bananas,0.5188679245283019,0.5173501577287066,0.0029211044311523438,0.33386499999999997 +424,Binary classification,ALMA,Bananas,0.5330188679245284,0.5330188679245282,0.0029211044311523438,0.49377399999999994 +530,Binary classification,ALMA,Bananas,0.5207547169811321,0.5115384615384615,0.0029211044311523438,0.7446539999999999 +636,Binary classification,ALMA,Bananas,0.5377358490566038,0.5303514376996804,0.0029211044311523438,1.03169 +742,Binary classification,ALMA,Bananas,0.522911051212938,0.512396694214876,0.0029211044311523438,1.379859 +848,Binary classification,ALMA,Bananas,0.5235849056603774,0.5061124694376529,0.0029211044311523438,1.737155 +954,Binary classification,ALMA,Bananas,0.5157232704402516,0.5,0.0029211044311523438,2.173505 +1060,Binary classification,ALMA,Bananas,0.5160377358490567,0.4975514201762978,0.0029211044311523438,2.70321 +1166,Binary classification,ALMA,Bananas,0.5154373927958834,0.49598572702943805,0.0029211044311523438,3.270309 +1272,Binary classification,ALMA,Bananas,0.5165094339622641,0.4979591836734694,0.0029211044311523438,3.844268 +1378,Binary classification,ALMA,Bananas,0.5195936139332366,0.4977238239757208,0.0029211044311523438,4.501151 +1484,Binary classification,ALMA,Bananas,0.5195417789757413,0.4968242766407903,0.0029211044311523438,5.229491 +1590,Binary classification,ALMA,Bananas,0.5226415094339623,0.4983476536682089,0.0029211044311523438,6.030342 +1696,Binary classification,ALMA,Bananas,0.5194575471698113,0.49473031618102914,0.0029211044311523438,6.8837410000000006 +1802,Binary classification,ALMA,Bananas,0.5205327413984462,0.4965034965034965,0.0029211044311523438,7.813207 +1908,Binary classification,ALMA,Bananas,0.5193920335429769,0.4964305326743548,0.0029211044311523438,8.751116 +2014,Binary classification,ALMA,Bananas,0.519364448857994,0.4989648033126293,0.0029211044311523438,9.762632 +2120,Binary classification,ALMA,Bananas,0.5174528301886793,0.4997555012224939,0.0029211044311523438,10.806008 +2226,Binary classification,ALMA,Bananas,0.5197663971248877,0.5002337540906966,0.0029211044311523438,11.968014 +2332,Binary classification,ALMA,Bananas,0.5175814751286449,0.4975435462259938,0.0029211044311523438,13.16512 +2438,Binary classification,ALMA,Bananas,0.5176374077112387,0.4957118353344769,0.0029211044311523438,14.408045 +2544,Binary classification,ALMA,Bananas,0.5196540880503144,0.5008169934640523,0.0029211044311523438,15.661105 +2650,Binary classification,ALMA,Bananas,0.520377358490566,0.5037094884810621,0.0029211044311523438,17.014893999999998 +2756,Binary classification,ALMA,Bananas,0.521044992743106,0.5041322314049587,0.0029211044311523438,18.454389 +2862,Binary classification,ALMA,Bananas,0.5213137665967854,0.5032632342277013,0.0029211044311523438,19.942263 +2968,Binary classification,ALMA,Bananas,0.5175202156334232,0.49859943977591037,0.0029211044311523438,21.473074 +3074,Binary classification,ALMA,Bananas,0.5152895250487963,0.49696151249155973,0.0029211044311523438,23.106855 +3180,Binary classification,ALMA,Bananas,0.5132075471698113,0.4931237721021611,0.0029211044311523438,24.747764 +3286,Binary classification,ALMA,Bananas,0.5130858186244674,0.4927076727964489,0.0029211044311523438,26.464385 +3392,Binary classification,ALMA,Bananas,0.5103183962264151,0.49095923996322405,0.0029211044311523438,28.215584 +3498,Binary classification,ALMA,Bananas,0.5091480846197828,0.48914013686402846,0.0029211044311523438,30.068049 +3604,Binary classification,ALMA,Bananas,0.5097114317425083,0.4876775877065816,0.0029211044311523438,31.96012 +3710,Binary classification,ALMA,Bananas,0.5118598382749326,0.49086308687095864,0.0029211044311523438,33.864206 +3816,Binary classification,ALMA,Bananas,0.510482180293501,0.4893384363039912,0.0029211044311523438,35.803291 +3922,Binary classification,ALMA,Bananas,0.50790413054564,0.48588172615876407,0.0029211044311523438,37.844614 +4028,Binary classification,ALMA,Bananas,0.506454816285998,0.48443983402489627,0.0029211044311523438,39.968017 +4134,Binary classification,ALMA,Bananas,0.5050798258345428,0.48281092012133464,0.0029211044311523438,42.128298 +4240,Binary classification,ALMA,Bananas,0.5068396226415094,0.48484848484848486,0.0029211044311523438,44.30306 +4346,Binary classification,ALMA,Bananas,0.5080533824206167,0.4858104858104858,0.0029211044311523438,46.485881 +4452,Binary classification,ALMA,Bananas,0.5080862533692723,0.4847058823529412,0.0029211044311523438,48.746465 +4558,Binary classification,ALMA,Bananas,0.5063624396665204,0.48370812299219823,0.0029211044311523438,51.058035000000004 +4664,Binary classification,ALMA,Bananas,0.5051457975986278,0.4829749103942652,0.0029211044311523438,53.475871000000005 +4770,Binary classification,ALMA,Bananas,0.5048218029350104,0.48201754385964907,0.0029211044311523438,55.900409 +4876,Binary classification,ALMA,Bananas,0.5036915504511895,0.4802405498281787,0.0029211044311523438,58.409701000000005 +4982,Binary classification,ALMA,Bananas,0.5038137294259334,0.4811083123425693,0.0029211044311523438,60.970617000000004 +5088,Binary classification,ALMA,Bananas,0.5029481132075472,0.47995064774830354,0.0029211044311523438,63.61249900000001 +5194,Binary classification,ALMA,Bananas,0.5040431266846361,0.4810636583400483,0.0029211044311523438,66.28843800000001 +5300,Binary classification,ALMA,Bananas,0.5064150943396226,0.4825949367088608,0.0029211044311523438,68.97313600000001 +906,Binary classification,ALMA,Elec2,0.9072847682119205,0.9056179775280899,0.0043582916259765625,0.679052 +1812,Binary classification,ALMA,Elec2,0.9166666666666666,0.8967874231032126,0.0043582916259765625,1.978643 +2718,Binary classification,ALMA,Elec2,0.9175864606328182,0.898458748866727,0.0043582916259765625,3.929769 +3624,Binary classification,ALMA,Elec2,0.9268763796909493,0.9098945936756205,0.0043582916259765625,6.478699 +4530,Binary classification,ALMA,Elec2,0.9271523178807947,0.9076664801343034,0.0043582916259765625,9.702945 +5436,Binary classification,ALMA,Elec2,0.9269683590875644,0.9074376311494521,0.0043582916259765625,13.508006 +6342,Binary classification,ALMA,Elec2,0.9274676758120467,0.9089108910891088,0.0043582916259765625,17.915655 +7248,Binary classification,ALMA,Elec2,0.9254966887417219,0.9066390041493776,0.0043582916259765625,22.910275000000002 +8154,Binary classification,ALMA,Elec2,0.9251900907530046,0.9100294985250738,0.0043582916259765625,28.53571 +9060,Binary classification,ALMA,Elec2,0.9266004415011038,0.9135128105085185,0.0043582916259765625,34.833569000000004 +9966,Binary classification,ALMA,Elec2,0.9293598233995585,0.9182535996284256,0.0043582916259765625,41.779447000000005 +10872,Binary classification,ALMA,Elec2,0.931383370125092,0.9217208814270723,0.0043582916259765625,49.40882500000001 +11778,Binary classification,ALMA,Elec2,0.9313975208014943,0.9218568665377176,0.0043582916259765625,57.61408300000001 +12684,Binary classification,ALMA,Elec2,0.9290444654683065,0.9191665169750315,0.0043582916259765625,66.44160400000001 +13590,Binary classification,ALMA,Elec2,0.9298013245033112,0.9209872453205235,0.0043582916259765625,75.85703300000002 +14496,Binary classification,ALMA,Elec2,0.9305325607064018,0.9222213640225535,0.0043582916259765625,85.90938600000001 +15402,Binary classification,ALMA,Elec2,0.9308531359563693,0.922279792746114,0.0043582916259765625,96.62160000000002 +16308,Binary classification,ALMA,Elec2,0.9293598233995585,0.9203319502074688,0.0043582916259765625,107.96559300000001 +17214,Binary classification,ALMA,Elec2,0.9279656093877077,0.9176298658163943,0.0043582916259765625,119.98412300000001 +18120,Binary classification,ALMA,Elec2,0.9266004415011038,0.9160141449861076,0.0043582916259765625,132.63841200000002 +19026,Binary classification,ALMA,Elec2,0.9265741616734994,0.915143048047136,0.0043582916259765625,146.00166900000002 +19932,Binary classification,ALMA,Elec2,0.9262994180212724,0.9155892662184681,0.0043582916259765625,159.938555 +20838,Binary classification,ALMA,Elec2,0.9232171993473461,0.9122710823555213,0.0043582916259765625,174.54734100000002 +21744,Binary classification,ALMA,Elec2,0.9225073583517293,0.9101955977189149,0.0043582916259765625,189.73533200000003 +22650,Binary classification,ALMA,Elec2,0.9217218543046357,0.9087540528022233,0.0043582916259765625,205.61954500000002 +23556,Binary classification,ALMA,Elec2,0.9186619120393955,0.9050639183430779,0.0043582916259765625,222.16184900000002 +24462,Binary classification,ALMA,Elec2,0.9173003025100155,0.902885123133791,0.0043582916259765625,239.39901500000002 +25368,Binary classification,ALMA,Elec2,0.9144985808893094,0.8997643144322751,0.0043582916259765625,257.26822000000004 +26274,Binary classification,ALMA,Elec2,0.9142498287280201,0.8992622401073105,0.0043582916259765625,275.765482 +27180,Binary classification,ALMA,Elec2,0.9138337012509198,0.89909521757863,0.0043582916259765625,294.848784 +28086,Binary classification,ALMA,Elec2,0.9110232856227302,0.8955049132343716,0.0043582916259765625,314.617946 +28992,Binary classification,ALMA,Elec2,0.9101476269315674,0.8940927755417328,0.0043582916259765625,335.076303 +29898,Binary classification,ALMA,Elec2,0.9094922737306843,0.8931701539676272,0.0043582916259765625,356.063392 +30804,Binary classification,ALMA,Elec2,0.9083235943383976,0.8913093680240166,0.0043582916259765625,377.56682800000004 +31710,Binary classification,ALMA,Elec2,0.9062125512456638,0.8888722815933038,0.0043582916259765625,399.67833300000007 +32616,Binary classification,ALMA,Elec2,0.9052918812852587,0.8879294706671989,0.0043582916259765625,422.4669390000001 +33522,Binary classification,ALMA,Elec2,0.9050474315374978,0.8877050626212737,0.0043582916259765625,445.7904010000001 +34428,Binary classification,ALMA,Elec2,0.9050772626931567,0.8877901387172092,0.0043582916259765625,469.7481780000001 +35334,Binary classification,ALMA,Elec2,0.9045395369898681,0.8866104144955793,0.0043582916259765625,494.21097500000013 +36240,Binary classification,ALMA,Elec2,0.9048013245033113,0.8860483551327785,0.0043582916259765625,519.3392310000002 +37146,Binary classification,ALMA,Elec2,0.9045119259139611,0.8854217139903738,0.0043582916259765625,545.0643520000001 +38052,Binary classification,ALMA,Elec2,0.9042625880374224,0.8846092933388237,0.0043582916259765625,571.4397520000001 +38958,Binary classification,ALMA,Elec2,0.904409877303763,0.88502624266749,0.0043582916259765625,598.3711440000001 +39864,Binary classification,ALMA,Elec2,0.904926750953241,0.8863636363636365,0.0043582916259765625,626.0397580000001 +40770,Binary classification,ALMA,Elec2,0.9055187637969095,0.8878667908709827,0.0043582916259765625,654.3634580000002 +41676,Binary classification,ALMA,Elec2,0.9061090315769268,0.8892285916489738,0.0043582916259765625,683.2521370000002 +42582,Binary classification,ALMA,Elec2,0.9063923723639097,0.889810361032786,0.0043582916259765625,712.7682970000002 +43488,Binary classification,ALMA,Elec2,0.9067098969830758,0.8902534693104661,0.0043582916259765625,742.9022130000002 +44394,Binary classification,ALMA,Elec2,0.9062711177186106,0.8894732648019762,0.0043582916259765625,773.7300850000001 +45300,Binary classification,ALMA,Elec2,0.9064238410596026,0.8897844569823977,0.0043582916259765625,805.1132940000001 +45312,Binary classification,ALMA,Elec2,0.9064265536723164,0.8897670549084858,0.0043582916259765625,836.4982200000001 +25,Binary classification,ALMA,Phishing,0.56,0.5217391304347826,0.004366874694824219,0.003459 +50,Binary classification,ALMA,Phishing,0.7,0.6341463414634146,0.004366874694824219,0.050212 +75,Binary classification,ALMA,Phishing,0.7066666666666667,0.676470588235294,0.004366874694824219,0.100001 +100,Binary classification,ALMA,Phishing,0.72,0.702127659574468,0.004366874694824219,0.15312800000000001 +125,Binary classification,ALMA,Phishing,0.72,0.7058823529411765,0.004366874694824219,0.22806300000000002 +150,Binary classification,ALMA,Phishing,0.7133333333333334,0.7189542483660132,0.004366874694824219,0.334445 +175,Binary classification,ALMA,Phishing,0.7314285714285714,0.718562874251497,0.004366874694824219,0.511774 +200,Binary classification,ALMA,Phishing,0.735,0.7225130890052356,0.004366874694824219,0.692607 +225,Binary classification,ALMA,Phishing,0.7244444444444444,0.701923076923077,0.004366874694824219,0.876779 +250,Binary classification,ALMA,Phishing,0.724,0.7038626609442059,0.004366874694824219,1.156005 +275,Binary classification,ALMA,Phishing,0.7345454545454545,0.7137254901960783,0.004580497741699219,1.438242 +300,Binary classification,ALMA,Phishing,0.7366666666666667,0.7127272727272725,0.004580497741699219,1.723391 +325,Binary classification,ALMA,Phishing,0.7476923076923077,0.7172413793103447,0.004580497741699219,2.078902 +350,Binary classification,ALMA,Phishing,0.7542857142857143,0.7225806451612904,0.004580497741699219,2.4379969999999997 +375,Binary classification,ALMA,Phishing,0.7573333333333333,0.723404255319149,0.004580497741699219,2.8003189999999996 +400,Binary classification,ALMA,Phishing,0.76,0.7257142857142856,0.004580497741699219,3.1655669999999994 +425,Binary classification,ALMA,Phishing,0.76,0.7197802197802199,0.004580497741699219,3.585508999999999 +450,Binary classification,ALMA,Phishing,0.7622222222222222,0.7206266318537858,0.004580497741699219,4.009149999999999 +475,Binary classification,ALMA,Phishing,0.7663157894736842,0.7272727272727272,0.004580497741699219,4.435539999999999 +500,Binary classification,ALMA,Phishing,0.768,0.7327188940092165,0.004580497741699219,4.959426999999999 +525,Binary classification,ALMA,Phishing,0.7714285714285715,0.7321428571428573,0.004580497741699219,5.485955999999999 +550,Binary classification,ALMA,Phishing,0.7709090909090909,0.7341772151898734,0.004580497741699219,6.028689999999999 +575,Binary classification,ALMA,Phishing,0.7739130434782608,0.7379032258064516,0.004580497741699219,6.595545999999999 +600,Binary classification,ALMA,Phishing,0.78,0.7401574803149605,0.004580497741699219,7.165257999999999 +625,Binary classification,ALMA,Phishing,0.7744,0.7314285714285715,0.004580497741699219,7.741693999999999 +650,Binary classification,ALMA,Phishing,0.7815384615384615,0.7427536231884059,0.004580497741699219,8.363988999999998 +675,Binary classification,ALMA,Phishing,0.7837037037037037,0.75,0.004580497741699219,9.010548999999997 +700,Binary classification,ALMA,Phishing,0.79,0.7545909849749582,0.004580497741699219,9.660288999999997 +725,Binary classification,ALMA,Phishing,0.7917241379310345,0.7606973058637084,0.004580497741699219,10.349524999999996 +750,Binary classification,ALMA,Phishing,0.792,0.7621951219512195,0.004580497741699219,11.041959999999996 +775,Binary classification,ALMA,Phishing,0.792258064516129,0.7614814814814814,0.004580497741699219,11.737615999999996 +800,Binary classification,ALMA,Phishing,0.795,0.7670454545454546,0.004580497741699219,12.526707999999996 +825,Binary classification,ALMA,Phishing,0.793939393939394,0.7671232876712327,0.004580497741699219,13.319008999999996 +850,Binary classification,ALMA,Phishing,0.7976470588235294,0.7706666666666667,0.004580497741699219,14.117527999999997 +875,Binary classification,ALMA,Phishing,0.8022857142857143,0.7744458930899608,0.004580497741699219,14.959668999999996 +900,Binary classification,ALMA,Phishing,0.8011111111111111,0.7737041719342603,0.004580497741699219,15.804425999999996 +925,Binary classification,ALMA,Phishing,0.8054054054054054,0.7804878048780488,0.004580497741699219,16.651646999999997 +950,Binary classification,ALMA,Phishing,0.8073684210526316,0.7849588719153936,0.004580497741699219,17.502014999999997 +975,Binary classification,ALMA,Phishing,0.8102564102564103,0.7880870561282932,0.004580497741699219,18.418237999999995 +1000,Binary classification,ALMA,Phishing,0.811,0.7892976588628764,0.004580497741699219,19.337672999999995 +1025,Binary classification,ALMA,Phishing,0.8146341463414634,0.7943722943722944,0.004580497741699219,20.260238999999995 +1050,Binary classification,ALMA,Phishing,0.8161904761904762,0.7970557308096741,0.004580497741699219,21.242111999999995 +1075,Binary classification,ALMA,Phishing,0.815813953488372,0.7983706720977597,0.004580497741699219,22.243638999999995 +1100,Binary classification,ALMA,Phishing,0.8190909090909091,0.8023833167825224,0.004580497741699219,23.247954999999994 +1125,Binary classification,ALMA,Phishing,0.8213333333333334,0.8061716489874637,0.004580497741699219,24.273946999999993 +1150,Binary classification,ALMA,Phishing,0.8226086956521739,0.8071833648393195,0.004580497741699219,25.351544999999994 +1175,Binary classification,ALMA,Phishing,0.8212765957446808,0.8059149722735675,0.004580497741699219,26.431981999999994 +1200,Binary classification,ALMA,Phishing,0.8233333333333334,0.8076225045372051,0.004580497741699219,27.515220999999993 +1225,Binary classification,ALMA,Phishing,0.8244897959183674,0.8088888888888888,0.004580497741699219,28.636604999999992 +1250,Binary classification,ALMA,Phishing,0.8256,0.810763888888889,0.004580497741699219,29.761263999999994 +1903,Binary classification,ALMA,SMTP,0.720966894377299,0.0,0.003093719482421875,1.027868 +3806,Binary classification,ALMA,SMTP,0.7769311613242249,0.0,0.003093719482421875,3.106358 +5709,Binary classification,ALMA,SMTP,0.7509196006305833,0.0,0.003093719482421875,6.233245 +7612,Binary classification,ALMA,SMTP,0.7900683131897005,0.0,0.003093719482421875,10.302873 +9515,Binary classification,ALMA,SMTP,0.7826589595375723,0.0,0.003093719482421875,15.393504 +11418,Binary classification,ALMA,SMTP,0.7699246803293046,0.0,0.003093719482421875,21.578682 +13321,Binary classification,ALMA,SMTP,0.7722393213722694,0.0,0.003093719482421875,28.779608 +15224,Binary classification,ALMA,SMTP,0.7791644771413557,0.004146919431279621,0.003093719482421875,37.113003 +17127,Binary classification,ALMA,SMTP,0.783207800548841,0.004824443848834093,0.003093719482421875,46.3898 +19030,Binary classification,ALMA,SMTP,0.7891224382553862,0.004465393202679235,0.003093719482421875,56.715322 +20933,Binary classification,ALMA,SMTP,0.7832131084889887,0.003950834064969272,0.003093719482421875,68.217717 +22836,Binary classification,ALMA,SMTP,0.7821422315641969,0.0036050470658922497,0.003093719482421875,80.77427399999999 +24739,Binary classification,ALMA,SMTP,0.7877440478596548,0.0034162080091098878,0.003093719482421875,94.43257899999999 +26642,Binary classification,ALMA,SMTP,0.78188574431349,0.003429943405933802,0.003093719482421875,109.06303 +28545,Binary classification,ALMA,SMTP,0.7857418111753371,0.003259452411994785,0.003093719482421875,124.719605 +30448,Binary classification,ALMA,SMTP,0.7871452968996322,0.0030764497769573914,0.003093719482421875,141.369597 +32351,Binary classification,ALMA,SMTP,0.7866835646502427,0.0028897558156335793,0.003093719482421875,159.093537 +34254,Binary classification,ALMA,SMTP,0.7860979739592456,0.0027221995372260785,0.003093719482421875,177.829964 +36157,Binary classification,ALMA,SMTP,0.7771939043615345,0.0024764735017335313,0.003093719482421875,197.576045 +38060,Binary classification,ALMA,SMTP,0.7831581713084603,0.00241750271969056,0.003093719482421875,218.31617 +39963,Binary classification,ALMA,SMTP,0.779496033831294,0.002264492753623189,0.003093719482421875,240.067789 +41866,Binary classification,ALMA,SMTP,0.7831175655663307,0.0021978021978021974,0.003093719482421875,262.83473100000003 +43769,Binary classification,ALMA,SMTP,0.7791130708949257,0.002064409578860446,0.003093719482421875,286.65613700000006 +45672,Binary classification,ALMA,SMTP,0.7808066211245402,0.001993819160602133,0.003093719482421875,311.45044200000007 +47575,Binary classification,ALMA,SMTP,0.7799684708355229,0.001906941266209001,0.003093719482421875,337.28748800000005 +49478,Binary classification,ALMA,SMTP,0.7778810784591131,0.00181653042688465,0.003093719482421875,364.14950600000003 +51381,Binary classification,ALMA,SMTP,0.7807944570950351,0.0021263400372109505,0.003093719482421875,392.065506 +53284,Binary classification,ALMA,SMTP,0.7777193904361535,0.0020222446916076846,0.003093719482421875,421.04388200000005 +55187,Binary classification,ALMA,SMTP,0.7785891604906953,0.0019603038470963,0.003093719482421875,451.02283200000005 +57090,Binary classification,ALMA,SMTP,0.7758801891749869,0.002650245537454205,0.003093719482421875,482.06238400000007 +58993,Binary classification,ALMA,SMTP,0.774159646059702,0.002545481769858501,0.003093719482421875,514.138052 +60896,Binary classification,ALMA,SMTP,0.7746157383079348,0.002471109819027546,0.003093719482421875,547.227498 +62799,Binary classification,ALMA,SMTP,0.7704899759550311,0.0023534297778085413,0.003093719482421875,581.3499069999999 +64702,Binary classification,ALMA,SMTP,0.771274458285679,0.0022921863412660956,0.003093719482421875,616.580127 +66605,Binary classification,ALMA,SMTP,0.7721942797087306,0.002235812454790557,0.003093719482421875,652.800029 +68508,Binary classification,ALMA,SMTP,0.7705085537455479,0.0024111675126903555,0.003093719482421875,690.043858 +70411,Binary classification,ALMA,SMTP,0.7685872945988553,0.0023267205486162137,0.003093719482421875,728.276014 +72314,Binary classification,ALMA,SMTP,0.7687999557485411,0.002267709017127171,0.003093719482421875,767.526171 +74217,Binary classification,ALMA,SMTP,0.7657140547313958,0.0021806496040399402,0.003093719482421875,807.759105 +76120,Binary classification,ALMA,SMTP,0.7665002627430373,0.0021333932180552435,0.003093719482421875,848.897716 +78023,Binary classification,ALMA,SMTP,0.7657101111210797,0.002074462277541216,0.003093719482421875,890.953712 +79926,Binary classification,ALMA,SMTP,0.7636313590070816,0.002007395668251453,0.003093719482421875,933.942273 +81829,Binary classification,ALMA,SMTP,0.7647777682728617,0.001970341180130665,0.003093719482421875,977.888636 +83732,Binary classification,ALMA,SMTP,0.7652868676252806,0.0019298156518206286,0.003093719482421875,1022.739679 +85635,Binary classification,ALMA,SMTP,0.7642552694575816,0.0018787699001285476,0.003093719482421875,1068.5586680000001 +87538,Binary classification,ALMA,SMTP,0.7644680024674998,0.0018396591789310612,0.003093719482421875,1115.337297 +89441,Binary classification,ALMA,SMTP,0.7635312664214399,0.0018876828692779614,0.003093719482421875,1162.988807 +91344,Binary classification,ALMA,SMTP,0.7650091960063058,0.0018600325505696352,0.003093719482421875,1211.563326 +93247,Binary classification,ALMA,SMTP,0.7647859984771628,0.0018204159650480134,0.003093719482421875,1260.984755 +95150,Binary classification,ALMA,SMTP,0.7649710982658959,0.0017854751595768425,0.003093719482421875,1311.295723 +95156,Binary classification,ALMA,SMTP,0.7649859178611963,0.0017854751595768425,0.003093719482421875,1361.607838 +106,Binary classification,sklearn SGDClassifier,Bananas,0.5283018867924528,0.4680851063829788,0.005551338195800781,0.50714 +212,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.4673913043478261,0.005551338195800781,1.5449950000000001 +318,Binary classification,sklearn SGDClassifier,Bananas,0.5345911949685535,0.4861111111111111,0.005578041076660156,3.028568 +424,Binary classification,sklearn SGDClassifier,Bananas,0.5188679245283019,0.46596858638743455,0.005578041076660156,4.996646 +530,Binary classification,sklearn SGDClassifier,Bananas,0.5264150943396226,0.42562929061784893,0.005578041076660156,7.439068000000001 +636,Binary classification,sklearn SGDClassifier,Bananas,0.5235849056603774,0.3878787878787879,0.005578041076660156,10.329429000000001 +742,Binary classification,sklearn SGDClassifier,Bananas,0.5363881401617251,0.36296296296296293,0.005578041076660156,13.751562000000002 +848,Binary classification,sklearn SGDClassifier,Bananas,0.5400943396226415,0.33898305084745767,0.005578041076660156,17.710995 +954,Binary classification,sklearn SGDClassifier,Bananas,0.5440251572327044,0.31496062992125984,0.005578041076660156,22.189814 +1060,Binary classification,sklearn SGDClassifier,Bananas,0.5518867924528302,0.2962962962962963,0.005578041076660156,27.154881999999997 +1166,Binary classification,sklearn SGDClassifier,Bananas,0.5523156089193825,0.27900552486187846,0.005578041076660156,32.629664 +1272,Binary classification,sklearn SGDClassifier,Bananas,0.5542452830188679,0.27586206896551724,0.005578041076660156,38.64774 +1378,Binary classification,sklearn SGDClassifier,Bananas,0.5566037735849056,0.2611850060459492,0.005578041076660156,45.182197 +1484,Binary classification,sklearn SGDClassifier,Bananas,0.557277628032345,0.24742268041237112,0.005578041076660156,52.237503000000004 +1590,Binary classification,sklearn SGDClassifier,Bananas,0.5578616352201258,0.23503808487486397,0.005578041076660156,59.74126700000001 +1696,Binary classification,sklearn SGDClassifier,Bananas,0.5595518867924528,0.22590673575129533,0.005578041076660156,67.72058500000001 +1802,Binary classification,sklearn SGDClassifier,Bananas,0.5566037735849056,0.21589793915603536,0.005578041076660156,76.17010700000002 +1908,Binary classification,sklearn SGDClassifier,Bananas,0.5545073375262054,0.21150278293135436,0.005578041076660156,85.08376800000002 +2014,Binary classification,sklearn SGDClassifier,Bananas,0.5496524329692155,0.20088105726872246,0.005578041076660156,94.42289500000003 +2120,Binary classification,sklearn SGDClassifier,Bananas,0.5466981132075471,0.19446772841575857,0.005578041076660156,104.22299500000003 +2226,Binary classification,sklearn SGDClassifier,Bananas,0.550314465408805,0.2036595067621321,0.005578041076660156,114.52672400000003 +2332,Binary classification,sklearn SGDClassifier,Bananas,0.5493138936535163,0.21036814425244177,0.005578041076660156,125.29025300000004 +2438,Binary classification,sklearn SGDClassifier,Bananas,0.5479901558654635,0.21173104434907009,0.005578041076660156,136.49837800000003 +2544,Binary classification,sklearn SGDClassifier,Bananas,0.5483490566037735,0.22626262626262625,0.005578041076660156,148.22051100000004 +2650,Binary classification,sklearn SGDClassifier,Bananas,0.5460377358490566,0.2322910019144863,0.005578041076660156,160.35879600000004 +2756,Binary classification,sklearn SGDClassifier,Bananas,0.5395500725689405,0.23044269254093389,0.005578041076660156,173.00042100000005 +2862,Binary classification,sklearn SGDClassifier,Bananas,0.5394828791055206,0.2310385064177363,0.005578041076660156,186.18470300000004 +2968,Binary classification,sklearn SGDClassifier,Bananas,0.5411051212938005,0.23050847457627116,0.005578041076660156,199.85251500000004 +3074,Binary classification,sklearn SGDClassifier,Bananas,0.5396877033181522,0.227198252321136,0.005578041076660156,214.04388100000003 +3180,Binary classification,sklearn SGDClassifier,Bananas,0.5430817610062894,0.22835900159320233,0.005578041076660156,228.72034700000003 +3286,Binary classification,sklearn SGDClassifier,Bananas,0.5444309190505173,0.22475401346452614,0.005578041076660156,243.83191300000004 +3392,Binary classification,sklearn SGDClassifier,Bananas,0.5445165094339622,0.22478675363773204,0.005578041076660156,259.480064 +3498,Binary classification,sklearn SGDClassifier,Bananas,0.5463121783876501,0.22014742014742014,0.005578041076660156,275.54602900000003 +3604,Binary classification,sklearn SGDClassifier,Bananas,0.548834628190899,0.21676300578034682,0.005578041076660156,292.10262700000004 +3710,Binary classification,sklearn SGDClassifier,Bananas,0.547978436657682,0.21230624706434945,0.005578041076660156,309.14156900000006 +3816,Binary classification,sklearn SGDClassifier,Bananas,0.5474318658280922,0.20743460302891234,0.005578041076660156,326.6693460000001 +3922,Binary classification,sklearn SGDClassifier,Bananas,0.5484446710861806,0.20332883490778228,0.005578041076660156,344.5959390000001 +4028,Binary classification,sklearn SGDClassifier,Bananas,0.5489076464746773,0.1992066989863376,0.005578041076660156,363.0622570000001 +4134,Binary classification,sklearn SGDClassifier,Bananas,0.5491049830672472,0.19516407599309155,0.005578041076660156,382.0101650000001 +4240,Binary classification,sklearn SGDClassifier,Bananas,0.5483490566037735,0.19095901985635824,0.005578041076660156,401.4423870000001 +4346,Binary classification,sklearn SGDClassifier,Bananas,0.548550391164289,0.18858560794044665,0.005578041076660156,421.3360060000001 +4452,Binary classification,sklearn SGDClassifier,Bananas,0.550763701707098,0.1935483870967742,0.005578041076660156,441.6814400000001 +4558,Binary classification,sklearn SGDClassifier,Bananas,0.5482667836770513,0.19349784567175873,0.005578041076660156,462.3969810000001 +4664,Binary classification,sklearn SGDClassifier,Bananas,0.5490994854202401,0.19763449065242272,0.005578041076660156,483.6474930000001 +4770,Binary classification,sklearn SGDClassifier,Bananas,0.550104821802935,0.19985085756897839,0.005578041076660156,505.4107300000001 +4876,Binary classification,sklearn SGDClassifier,Bananas,0.5504511894995898,0.2,0.005578041076660156,527.6413840000001 +4982,Binary classification,sklearn SGDClassifier,Bananas,0.5503813729425934,0.2062367115520907,0.005578041076660156,550.3366250000001 +5088,Binary classification,sklearn SGDClassifier,Bananas,0.5479559748427673,0.20415224913494812,0.005578041076660156,573.5279610000001 +5194,Binary classification,sklearn SGDClassifier,Bananas,0.5462071621101271,0.20236886632825718,0.005578041076660156,597.2511250000001 +5300,Binary classification,sklearn SGDClassifier,Bananas,0.5464150943396227,0.205026455026455,0.005578041076660156,621.4261850000001 +906,Binary classification,sklearn SGDClassifier,Elec2,0.8002207505518764,0.7868080094228505,0.006801605224609375,4.395754 +1812,Binary classification,sklearn SGDClassifier,Elec2,0.8140176600441501,0.7501853224610822,0.006801605224609375,13.314942 +2718,Binary classification,sklearn SGDClassifier,Elec2,0.8005886681383371,0.7262626262626262,0.006801605224609375,26.594138 +3624,Binary classification,sklearn SGDClassifier,Elec2,0.8189845474613686,0.7586460632818247,0.006801605224609375,44.068779 +4530,Binary classification,sklearn SGDClassifier,Elec2,0.8278145695364238,0.7588126159554731,0.006801605224609375,65.924464 +5436,Binary classification,sklearn SGDClassifier,Elec2,0.8211920529801324,0.7498713329902212,0.006801605224609375,92.07692 +6342,Binary classification,sklearn SGDClassifier,Elec2,0.8222958057395143,0.7575822757582275,0.006801605224609375,122.546282 +7248,Binary classification,sklearn SGDClassifier,Elec2,0.8253311258278145,0.7598634294385433,0.006801605224609375,157.279906 +8154,Binary classification,sklearn SGDClassifier,Elec2,0.8303899926416483,0.780789348549691,0.006801605224609375,196.124663 +9060,Binary classification,sklearn SGDClassifier,Elec2,0.8364238410596027,0.7958677685950413,0.006801605224609375,238.938569 +9966,Binary classification,sklearn SGDClassifier,Elec2,0.8371462974111981,0.8011273128293102,0.006801605224609375,285.711115 +10872,Binary classification,sklearn SGDClassifier,Elec2,0.8393119941133186,0.8079586676926458,0.006801605224609375,336.134661 +11778,Binary classification,sklearn SGDClassifier,Elec2,0.8422482594668025,0.8114088509947219,0.006801605224609375,390.124895 +12684,Binary classification,sklearn SGDClassifier,Elec2,0.8409019236833807,0.810445237647943,0.006801605224609375,447.475874 +13590,Binary classification,sklearn SGDClassifier,Elec2,0.8427520235467255,0.8154098643862832,0.006801605224609375,507.886031 +14496,Binary classification,sklearn SGDClassifier,Elec2,0.8438189845474614,0.8177720540888602,0.006801605224609375,571.200551 +15402,Binary classification,sklearn SGDClassifier,Elec2,0.845214907154915,0.8184587267742918,0.006801605224609375,637.3575030000001 +16308,Binary classification,sklearn SGDClassifier,Elec2,0.8397105714986509,0.8108264582428716,0.006801605224609375,706.2352450000001 +17214,Binary classification,sklearn SGDClassifier,Elec2,0.8384454513767864,0.8053202660133008,0.006801605224609375,777.6642180000001 +18120,Binary classification,sklearn SGDClassifier,Elec2,0.840728476821192,0.8082646824342281,0.006801605224609375,851.7523750000001 +19026,Binary classification,sklearn SGDClassifier,Elec2,0.843950383685483,0.8100326316462987,0.006801605224609375,928.4036970000002 +19932,Binary classification,sklearn SGDClassifier,Elec2,0.8412101143889223,0.8075636894266431,0.006801605224609375,1007.5500400000002 +20838,Binary classification,sklearn SGDClassifier,Elec2,0.8373644303675977,0.8028848950154133,0.006801605224609375,1089.2694250000002 +21744,Binary classification,sklearn SGDClassifier,Elec2,0.8382542310522443,0.8008155405788072,0.006801605224609375,1173.5296240000002 +22650,Binary classification,sklearn SGDClassifier,Elec2,0.8376600441501104,0.7982441700960219,0.006801605224609375,1260.4177460000003 +23556,Binary classification,sklearn SGDClassifier,Elec2,0.8337578536254033,0.7924748277689453,0.006801605224609375,1349.7468710000003 +24462,Binary classification,sklearn SGDClassifier,Elec2,0.8313302264737144,0.7887569117345894,0.006801605224609375,1441.5955620000002 +25368,Binary classification,sklearn SGDClassifier,Elec2,0.8278539892778304,0.7842711060613546,0.006801605224609375,1535.8662290000002 +26274,Binary classification,sklearn SGDClassifier,Elec2,0.8282712948161681,0.784486052732136,0.006801605224609375,1632.5910050000002 +27180,Binary classification,sklearn SGDClassifier,Elec2,0.8285504047093452,0.785431439359057,0.006801605224609375,1731.7068200000003 +28086,Binary classification,sklearn SGDClassifier,Elec2,0.825357829523606,0.7809192013935414,0.006801605224609375,1833.2277320000003 +28992,Binary classification,sklearn SGDClassifier,Elec2,0.8246412803532008,0.7785135488368041,0.006801605224609375,1936.9708820000003 +29898,Binary classification,sklearn SGDClassifier,Elec2,0.8228644056458626,0.7766343315056937,0.006801605224609375,2042.9068730000004 +30804,Binary classification,sklearn SGDClassifier,Elec2,0.8227827554863005,0.775599128540305,0.006801605224609375,2150.889688 +31710,Binary classification,sklearn SGDClassifier,Elec2,0.8180384736676127,0.7686632988533396,0.006801605224609375,2260.9522580000003 +32616,Binary classification,sklearn SGDClassifier,Elec2,0.8156119695854795,0.765426320305796,0.006801605224609375,2373.012802 +33522,Binary classification,sklearn SGDClassifier,Elec2,0.8136746017540719,0.7636955205811137,0.006801605224609375,2487.1515040000004 +34428,Binary classification,sklearn SGDClassifier,Elec2,0.8108516323922389,0.7597934341571375,0.006801605224609375,2603.2748090000005 +35334,Binary classification,sklearn SGDClassifier,Elec2,0.811031867323258,0.7582811425261557,0.006801605224609375,2721.3856460000006 +36240,Binary classification,sklearn SGDClassifier,Elec2,0.8123344370860928,0.7585643792821898,0.006801605224609375,2841.498866000001 +37146,Binary classification,sklearn SGDClassifier,Elec2,0.8119312981209282,0.7567887480852249,0.006801605224609375,2963.634415000001 +38052,Binary classification,sklearn SGDClassifier,Elec2,0.8118364343529907,0.7562636165577343,0.006801605224609375,3087.755988000001 +38958,Binary classification,sklearn SGDClassifier,Elec2,0.8128497356127111,0.7583601232890332,0.006801605224609375,3213.7594090000007 +39864,Binary classification,sklearn SGDClassifier,Elec2,0.8136162954043749,0.7616297722168751,0.006801605224609375,3341.6530200000007 +40770,Binary classification,sklearn SGDClassifier,Elec2,0.8154034829531518,0.7662732919254659,0.006801605224609375,3471.3422760000008 +41676,Binary classification,sklearn SGDClassifier,Elec2,0.8169929935694404,0.7702641645832705,0.006801605224609375,3602.9648230000007 +42582,Binary classification,sklearn SGDClassifier,Elec2,0.8180216993095675,0.7720681236579698,0.006801605224609375,3737.0799900000006 +43488,Binary classification,sklearn SGDClassifier,Elec2,0.8185936350257542,0.7730894238789657,0.006801605224609375,3873.4157740000005 +44394,Binary classification,sklearn SGDClassifier,Elec2,0.8179708969680587,0.7710051290770494,0.006801605224609375,4011.6127160000005 +45300,Binary classification,sklearn SGDClassifier,Elec2,0.8190949227373069,0.7729350807680585,0.006801605224609375,4151.679177000001 +45312,Binary classification,sklearn SGDClassifier,Elec2,0.8190986935028248,0.7728922505749037,0.006801605224609375,4291.771713000001 +25,Binary classification,sklearn SGDClassifier,Phishing,0.68,0.6923076923076923,0.006802558898925781,0.149754 +50,Binary classification,sklearn SGDClassifier,Phishing,0.8,0.782608695652174,0.006802558898925781,0.457736 +75,Binary classification,sklearn SGDClassifier,Phishing,0.8266666666666667,0.8219178082191781,0.006802558898925781,0.892069 +100,Binary classification,sklearn SGDClassifier,Phishing,0.83,0.8210526315789473,0.006802558898925781,1.4190939999999999 +125,Binary classification,sklearn SGDClassifier,Phishing,0.816,0.8067226890756303,0.006802558898925781,2.091236 +150,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8187919463087249,0.006802558898925781,2.916232 +175,Binary classification,sklearn SGDClassifier,Phishing,0.8285714285714286,0.8170731707317075,0.006802558898925781,3.840025 +200,Binary classification,sklearn SGDClassifier,Phishing,0.825,0.8128342245989306,0.006802558898925781,4.9100459999999995 +225,Binary classification,sklearn SGDClassifier,Phishing,0.8222222222222222,0.8058252427184465,0.006802558898925781,6.121922 +250,Binary classification,sklearn SGDClassifier,Phishing,0.824,0.8103448275862069,0.006802558898925781,7.4794909999999994 +275,Binary classification,sklearn SGDClassifier,Phishing,0.8254545454545454,0.8110236220472441,0.007016181945800781,8.920382 +300,Binary classification,sklearn SGDClassifier,Phishing,0.8366666666666667,0.8191881918819188,0.007016181945800781,10.509974 +325,Binary classification,sklearn SGDClassifier,Phishing,0.8461538461538461,0.8251748251748252,0.007016181945800781,12.191811999999999 +350,Binary classification,sklearn SGDClassifier,Phishing,0.8514285714285714,0.8289473684210525,0.007016181945800781,13.999137 +375,Binary classification,sklearn SGDClassifier,Phishing,0.8506666666666667,0.8271604938271606,0.007016181945800781,15.959285 +400,Binary classification,sklearn SGDClassifier,Phishing,0.8525,0.8269794721407624,0.007016181945800781,18.058664 +425,Binary classification,sklearn SGDClassifier,Phishing,0.8564705882352941,0.828169014084507,0.007016181945800781,20.312993 +450,Binary classification,sklearn SGDClassifier,Phishing,0.86,0.8301886792452831,0.007016181945800781,22.675489 +475,Binary classification,sklearn SGDClassifier,Phishing,0.8589473684210527,0.830379746835443,0.007016181945800781,25.19503 +500,Binary classification,sklearn SGDClassifier,Phishing,0.858,0.8329411764705883,0.007016181945800781,27.784240999999998 +525,Binary classification,sklearn SGDClassifier,Phishing,0.8571428571428571,0.8283752860411898,0.007016181945800781,30.514065 +550,Binary classification,sklearn SGDClassifier,Phishing,0.8618181818181818,0.8354978354978354,0.007016181945800781,33.400870999999995 +575,Binary classification,sklearn SGDClassifier,Phishing,0.8626086956521739,0.8364389233954452,0.007016181945800781,36.397645999999995 +600,Binary classification,sklearn SGDClassifier,Phishing,0.8666666666666667,0.8387096774193549,0.007016181945800781,39.57675499999999 +625,Binary classification,sklearn SGDClassifier,Phishing,0.8672,0.8362919132149901,0.007016181945800781,42.828695999999994 +650,Binary classification,sklearn SGDClassifier,Phishing,0.8707692307692307,0.8432835820895522,0.007016181945800781,46.253181999999995 +675,Binary classification,sklearn SGDClassifier,Phishing,0.8725925925925926,0.8485915492957746,0.007016181945800781,49.816151 +700,Binary classification,sklearn SGDClassifier,Phishing,0.8771428571428571,0.8522336769759451,0.007016181945800781,53.516545 +725,Binary classification,sklearn SGDClassifier,Phishing,0.8786206896551724,0.8566775244299674,0.007016181945800781,57.358180000000004 +750,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8589341692789968,0.007016181945800781,61.281034000000005 +775,Binary classification,sklearn SGDClassifier,Phishing,0.8812903225806452,0.8597560975609757,0.007016181945800781,65.347537 +800,Binary classification,sklearn SGDClassifier,Phishing,0.88125,0.8613138686131386,0.007016181945800781,69.566336 +825,Binary classification,sklearn SGDClassifier,Phishing,0.8812121212121212,0.8623595505617978,0.007016181945800781,73.91498000000001 +850,Binary classification,sklearn SGDClassifier,Phishing,0.8823529411764706,0.8630136986301369,0.007016181945800781,78.39968800000001 +875,Binary classification,sklearn SGDClassifier,Phishing,0.8857142857142857,0.8663101604278075,0.007016181945800781,83.02084100000002 +900,Binary classification,sklearn SGDClassifier,Phishing,0.8844444444444445,0.8645833333333334,0.007016181945800781,87.71921500000002 +925,Binary classification,sklearn SGDClassifier,Phishing,0.8864864864864865,0.8682559598494354,0.007016181945800781,92.55798800000002 +950,Binary classification,sklearn SGDClassifier,Phishing,0.8863157894736842,0.8695652173913043,0.007016181945800781,97.51738800000003 +975,Binary classification,sklearn SGDClassifier,Phishing,0.8871794871794871,0.8702830188679245,0.007016181945800781,102.59954100000003 +1000,Binary classification,sklearn SGDClassifier,Phishing,0.888,0.871264367816092,0.007016181945800781,107.87282600000003 +1025,Binary classification,sklearn SGDClassifier,Phishing,0.8878048780487805,0.8715083798882682,0.007016181945800781,113.28564700000003 +1050,Binary classification,sklearn SGDClassifier,Phishing,0.8895238095238095,0.8739130434782609,0.007016181945800781,118.79277100000003 +1075,Binary classification,sklearn SGDClassifier,Phishing,0.8883720930232558,0.8736842105263158,0.007016181945800781,124.46348200000003 +1100,Binary classification,sklearn SGDClassifier,Phishing,0.89,0.8756423432682425,0.007016181945800781,130.26843700000003 +1125,Binary classification,sklearn SGDClassifier,Phishing,0.8915555555555555,0.8784860557768924,0.007016181945800781,136.21796400000002 +1150,Binary classification,sklearn SGDClassifier,Phishing,0.8913043478260869,0.878048780487805,0.007016181945800781,142.31432400000003 +1175,Binary classification,sklearn SGDClassifier,Phishing,0.8902127659574468,0.876555023923445,0.007016181945800781,148.52290000000002 +1200,Binary classification,sklearn SGDClassifier,Phishing,0.8908333333333334,0.8769953051643193,0.007016181945800781,154.887447 +1225,Binary classification,sklearn SGDClassifier,Phishing,0.8914285714285715,0.8776448942042319,0.007016181945800781,161.410896 +1250,Binary classification,sklearn SGDClassifier,Phishing,0.8896,0.8761220825852785,0.007016181945800781,167.984219 +1903,Binary classification,sklearn SGDClassifier,SMTP,0.9968470835522859,0.0,0.0057430267333984375,9.012274 +3806,Binary classification,sklearn SGDClassifier,SMTP,0.9984235417761429,0.0,0.0057430267333984375,26.992092 +5709,Binary classification,sklearn SGDClassifier,SMTP,0.998949027850762,0.0,0.0057430267333984375,53.749217 +7612,Binary classification,sklearn SGDClassifier,SMTP,0.9992117708880714,0.0,0.0057430267333984375,89.545782 +9515,Binary classification,sklearn SGDClassifier,SMTP,0.9993694167104572,0.0,0.0057430267333984375,133.365466 +11418,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.0,0.0057430267333984375,185.06742500000001 +13321,Binary classification,sklearn SGDClassifier,SMTP,0.9995495833646123,0.0,0.0057430267333984375,243.739666 +15224,Binary classification,sklearn SGDClassifier,SMTP,0.9992774566473989,0.5217391304347826,0.0057430267333984375,308.57406100000003 +17127,Binary classification,sklearn SGDClassifier,SMTP,0.9993577392421323,0.5925925925925927,0.0057430267333984375,378.971453 +19030,Binary classification,sklearn SGDClassifier,SMTP,0.999421965317919,0.5925925925925927,0.0057430267333984375,454.798992 +20933,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.5925925925925927,0.0057430267333984375,535.937031 +22836,Binary classification,sklearn SGDClassifier,SMTP,0.9995183044315993,0.5925925925925927,0.0057430267333984375,622.51799 +24739,Binary classification,sklearn SGDClassifier,SMTP,0.9995553579368608,0.5925925925925927,0.0057430267333984375,714.221122 +26642,Binary classification,sklearn SGDClassifier,SMTP,0.9995495833646123,0.5714285714285714,0.0057430267333984375,811.098386 +28545,Binary classification,sklearn SGDClassifier,SMTP,0.9995796111403048,0.5714285714285714,0.0057430267333984375,912.878884 +30448,Binary classification,sklearn SGDClassifier,SMTP,0.9996058854440357,0.5714285714285714,0.0057430267333984375,1019.269091 +32351,Binary classification,sklearn SGDClassifier,SMTP,0.9996290686532101,0.5714285714285714,0.0057430267333984375,1129.962426 +34254,Binary classification,sklearn SGDClassifier,SMTP,0.999649675950254,0.5714285714285714,0.0057430267333984375,1244.872652 +36157,Binary classification,sklearn SGDClassifier,SMTP,0.9996681140581354,0.5714285714285714,0.0057430267333984375,1363.9176400000001 +38060,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.5714285714285714,0.0057430267333984375,1487.072194 +39963,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.5714285714285714,0.0057430267333984375,1614.171257 +41866,Binary classification,sklearn SGDClassifier,SMTP,0.999713371232026,0.5714285714285714,0.0057430267333984375,1745.093316 +43769,Binary classification,sklearn SGDClassifier,SMTP,0.9997258333523726,0.5714285714285714,0.0057430267333984375,1880.714485 +45672,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.5714285714285714,0.0057430267333984375,2020.289668 +47575,Binary classification,sklearn SGDClassifier,SMTP,0.9997477666841829,0.5714285714285714,0.0057430267333984375,2163.67936 +49478,Binary classification,sklearn SGDClassifier,SMTP,0.9997574679655604,0.5714285714285714,0.0057430267333984375,2310.817497 +51381,Binary classification,sklearn SGDClassifier,SMTP,0.9997275257390864,0.5333333333333333,0.0057430267333984375,2461.472155 +53284,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.5333333333333333,0.0057430267333984375,2615.677493 +55187,Binary classification,sklearn SGDClassifier,SMTP,0.9997463170674253,0.5333333333333333,0.0057430267333984375,2773.449537 +57090,Binary classification,sklearn SGDClassifier,SMTP,0.999597127342792,0.41025641025641024,0.0057430267333984375,2934.793173 +58993,Binary classification,sklearn SGDClassifier,SMTP,0.9996101232349601,0.41025641025641024,0.0057430267333984375,3099.6490870000002 +60896,Binary classification,sklearn SGDClassifier,SMTP,0.9996223068838676,0.41025641025641024,0.0057430267333984375,3268.0999800000004 +62799,Binary classification,sklearn SGDClassifier,SMTP,0.9996019044889249,0.3902439024390244,0.0057430267333984375,3440.0722140000003 +64702,Binary classification,sklearn SGDClassifier,SMTP,0.9996136131804272,0.3902439024390244,0.0057430267333984375,3615.5032180000003 +66605,Binary classification,sklearn SGDClassifier,SMTP,0.9996246528038436,0.3902439024390244,0.0057430267333984375,3794.4378890000003 +68508,Binary classification,sklearn SGDClassifier,SMTP,0.9996058854440357,0.37209302325581395,0.0057430267333984375,3977.0945010000005 +70411,Binary classification,sklearn SGDClassifier,SMTP,0.9996165371887915,0.37209302325581395,0.0057430267333984375,4163.174128000001 +72314,Binary classification,sklearn SGDClassifier,SMTP,0.9996266283154023,0.37209302325581395,0.0057430267333984375,4352.606393000001 +74217,Binary classification,sklearn SGDClassifier,SMTP,0.9996362019483407,0.37209302325581395,0.0057430267333984375,4545.416162000001 +76120,Binary classification,sklearn SGDClassifier,SMTP,0.9996452968996321,0.37209302325581395,0.0057430267333984375,4741.562007000001 +78023,Binary classification,sklearn SGDClassifier,SMTP,0.999653948194763,0.37209302325581395,0.0057430267333984375,4941.079189000001 +79926,Binary classification,sklearn SGDClassifier,SMTP,0.9996621875234591,0.37209302325581395,0.0057430267333984375,5143.951781000001 +81829,Binary classification,sklearn SGDClassifier,SMTP,0.9996700436275648,0.37209302325581395,0.0057430267333984375,5350.231536 +83732,Binary classification,sklearn SGDClassifier,SMTP,0.9996775426360293,0.37209302325581395,0.0057430267333984375,5559.929647 +85635,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.37209302325581395,0.0057430267333984375,5773.003953 +87538,Binary classification,sklearn SGDClassifier,SMTP,0.9996915625214192,0.37209302325581395,0.0057430267333984375,5989.467769000001 +89441,Binary classification,sklearn SGDClassifier,SMTP,0.9996869444661844,0.36363636363636365,0.0057430267333984375,6209.264779000001 +91344,Binary classification,sklearn SGDClassifier,SMTP,0.9996934664564723,0.36363636363636365,0.0057430267333984375,6432.452666000001 +93247,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.36363636363636365,0.0057430267333984375,6658.918178000001 +95150,Binary classification,sklearn SGDClassifier,SMTP,0.9997057277982133,0.36363636363636365,0.0057430267333984375,6888.546542000001 +95156,Binary classification,sklearn SGDClassifier,SMTP,0.9997057463533566,0.36363636363636365,0.0057430267333984375,7118.179378000001 +106,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5,0.0,0.0006465911865234375,0.16057 +212,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5283018867924528,0.0,0.0006465911865234375,0.37729 +318,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5314465408805031,0.0,0.0006465911865234375,0.7064710000000001 +424,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5400943396226415,0.0,0.0006465911865234375,1.0774430000000002 +530,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5547169811320755,0.0,0.0006465911865234375,1.4923790000000001 +636,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5550314465408805,0.0,0.0006465911865234375,1.9966470000000003 +742,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5660377358490566,0.0,0.0006465911865234375,2.539797 +848,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5636792452830188,0.0,0.0006465911865234375,3.1757850000000003 +954,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5649895178197065,0.0,0.0006465911865234375,3.8551140000000004 +1060,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5707547169811321,0.0,0.0006465911865234375,4.635951 +1166,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5686106346483705,0.0,0.0006465911865234375,5.458947 +1272,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5644654088050315,0.0,0.0006465911865234375,6.34328 +1378,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5682148040638607,0.0,0.0006465911865234375,7.308669 +1484,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5680592991913747,0.0,0.0006465911865234375,8.359952 +1590,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5679245283018868,0.0,0.0006465911865234375,9.451883 +1696,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5683962264150944,0.0,0.0006465911865234375,10.590847 +1802,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5643729189789123,0.0,0.0006465911865234375,11.83715 +1908,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.560272536687631,0.0,0.0006465911865234375,13.126961999999999 +2014,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5551142005958292,0.0,0.0006465911865234375,14.497203999999998 +2120,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,15.938437999999998 +2226,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5512129380053908,0.0,0.0006465911865234375,17.424999 +2332,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5506003430531733,0.0,0.0006465911865234375,19.022886 +2438,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.551681706316653,0.0,0.0006465911865234375,20.666828 +2544,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487421383647799,0.0,0.0006465911865234375,22.355415999999998 +2650,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5467924528301886,0.0,0.0006465911865234375,24.051772 +2756,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5471698113207547,0.0,0.0006465911865234375,25.858309 +2862,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5489168413696716,0.0,0.0006465911865234375,27.751458999999997 +2968,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505390835579514,0.0,0.0006465911865234375,29.665551999999998 +3074,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487963565387117,0.0,0.0006465911865234375,31.686176 +3180,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,33.740652 +3286,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5517346317711503,0.0,0.0006465911865234375,35.89104 +3392,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5498231132075472,0.0,0.0006465911865234375,38.079414 +3498,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514579759862779,0.0,0.0006465911865234375,40.353903 +3604,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5535516093229744,0.0,0.0006465911865234375,42.668922 +3710,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5522911051212938,0.0,0.0006465911865234375,45.086801 +3816,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5516247379454927,0.0,0.0006465911865234375,47.540759 +3922,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5525242223355431,0.0,0.0006465911865234375,50.094246 +4028,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5528798411122146,0.0,0.0006465911865234375,52.697210999999996 +4134,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5529753265602322,0.0,0.0006465911865234375,55.369586999999996 +4240,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5523584905660377,0.0,0.0006465911865234375,58.109435999999995 +4346,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5526921306948919,0.0,0.0006465911865234375,60.894093 +4452,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5530098831985625,0.0,0.0006465911865234375,63.717346 +4558,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5508995173321632,0.0,0.0006465911865234375,66.643891 +4664,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5497427101200686,0.0,0.0006465911865234375,69.6601 +4770,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505241090146751,0.0,0.0006465911865234375,72.725555 +4876,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5518867924528302,0.0,0.0006465911865234375,75.798736 +4982,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509835407466881,0.0,0.0006465911865234375,78.970205 +5088,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5511006289308176,0.0,0.0006465911865234375,82.150165 +5194,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514054678475163,0.0,0.0006465911865234375,85.416127 +5300,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5513207547169812,0.0,0.0006465911865234375,88.72481 +906,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6799116997792495,0.5482866043613708,0.0006465911865234375,0.820242 +1812,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7190949227373068,0.4904904904904904,0.0006465911865234375,2.329863 +2718,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.43243243243243246,0.0006465911865234375,4.585071 +3624,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7047461368653422,0.4478844169246646,0.0006465911865234375,7.424633 +4530,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7024282560706402,0.4118673647469459,0.0006465911865234375,10.992865 +5436,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7041942604856513,0.4165457184325108,0.0006465911865234375,15.263433 +6342,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.40485829959514175,0.0006465911865234375,20.287067999999998 +7248,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695364238410596,0.3953997809419496,0.0006465911865234375,26.004013999999998 +8154,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6873926907039489,0.4084474355999072,0.0006465911865234375,32.433811999999996 +9060,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6864238410596026,0.42408270829110073,0.0006465911865234375,39.59982599999999 +9966,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.687537627934979,0.4433321415802646,0.0006465911865234375,47.447314999999996 +10872,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6938925680647535,0.4717460317460317,0.0006465911865234375,55.964904999999995 +11778,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6932416369502462,0.47155185022670765,0.0006465911865234375,65.217817 +12684,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944970040996531,0.47557179591284343,0.0006465911865234375,75.258293 +13590,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6942604856512141,0.48429936701005344,0.0006465911865234375,85.993354 +14496,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6935016556291391,0.48606130711393875,0.0006465911865234375,97.389046 +15402,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6929619529931178,0.48095708484249805,0.0006465911865234375,109.49806 +16308,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6904586705911209,0.47130289065772935,0.0006465911865234375,122.273049 +17214,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6921691646334379,0.4645852278468223,0.0006465911865234375,135.723897 +18120,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.694205298013245,0.46859115757168884,0.0006465911865234375,150.008391 +19026,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6967307894460212,0.467515688445921,0.0006465911865234375,164.94785199999998 +19932,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6958157736303432,0.4737435986459509,0.0006465911865234375,180.578389 +20838,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.4696604963891426,0.0006465911865234375,196.972492 +21744,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6968359087564385,0.4670978172999191,0.0006465911865234375,214.03759399999998 +22650,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6977041942604857,0.4643667370726746,0.0006465911865234375,231.758696 +23556,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952368823229751,0.4573285962657797,0.0006465911865234375,250.24763199999998 +24462,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6978170223203336,0.4597281099254495,0.0006465911865234375,269.425119 +25368,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6976505834121728,0.46122506322000567,0.0006465911865234375,289.286378 +26274,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6983329527289336,0.4614757439869547,0.0006465911865234375,309.833587 +27180,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6959896983075791,0.4576304561864129,0.0006465911865234375,331.075152 +28086,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695649077832372,0.4559572301425662,0.0006465911865234375,353.085242 +28992,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952262693156733,0.4515207945375543,0.0006465911865234375,375.713328 +29898,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6939260151180681,0.4465678863017841,0.0006465911865234375,399.011341 +30804,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6941630957018569,0.44330201500915917,0.0006465911865234375,423.007885 +31710,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6917376222011984,0.4368266405484819,0.0006465911865234375,447.82068599999997 +32616,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6893549178317391,0.4316805025802109,0.0006465911865234375,473.25918699999994 +33522,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.688353916830738,0.42909448603748845,0.0006465911865234375,499.4094079999999 +34428,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6863599395840595,0.4245363461948412,0.0006465911865234375,526.1781749999999 +35334,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6869304352748061,0.4212013394725827,0.0006465911865234375,553.6489789999999 +36240,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6911147902869758,0.4267718148299877,0.0006465911865234375,581.8684239999999 +37146,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6919722177354224,0.42698317307692313,0.0006465911865234375,610.7708879999999 +38052,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944181646168401,0.43117111828588206,0.0006465911865234375,640.2659799999999 +38958,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6937727809435803,0.43082061068702293,0.0006465911865234375,670.4759659999999 +39864,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6930814770218744,0.4344288818009522,0.0006465911865234375,701.3646249999998 +40770,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6924208977189109,0.4391771019677997,0.0006465911865234375,733.0001779999998 +41676,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.44722270288977334,0.0006465911865234375,765.3741459999998 +42582,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6956225635244939,0.45507672903090185,0.0006465911865234375,798.4329459999998 +43488,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6962150478292862,0.4576097220511558,0.0006465911865234375,832.1587539999998 +44394,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6963103122043519,0.45575649927337314,0.0006465911865234375,866.6068249999998 +45300,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.697439293598234,0.4596278189560006,0.0006465911865234375,901.8081399999999 +45312,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6974752824858758,0.45959157927935035,0.0006465911865234375,937.0113409999999 +25,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.52,0.33333333333333337,0.0006465911865234375,0.00395 +50,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.56,0.21428571428571427,0.0006465911865234375,0.07842199999999999 +75,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.5866666666666667,0.3404255319148936,0.0006465911865234375,0.15624899999999997 +100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6,0.375,0.0006465911865234375,0.23731799999999997 +125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.64,0.4705882352941176,0.0006465911865234375,0.41813199999999995 +150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.62,0.44660194174757284,0.0006465911865234375,0.6021909999999999 +175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6342857142857142,0.41818181818181815,0.0006465911865234375,0.7890869999999999 +200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.63,0.4126984126984127,0.0006465911865234375,1.0120959999999999 +225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6488888888888888,0.4316546762589928,0.0006465911865234375,1.2378889999999998 +250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.648,0.4358974358974359,0.0006465911865234375,1.4692909999999997 +275,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6618181818181819,0.4561403508771929,0.0006465911865234375,1.7531039999999996 +300,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6733333333333333,0.46153846153846156,0.0006465911865234375,2.0408739999999996 +325,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.683076923076923,0.46632124352331616,0.0006465911865234375,2.33165 +350,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6942857142857143,0.47804878048780486,0.0006465911865234375,2.715045 +375,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7013333333333334,0.4909090909090909,0.0006465911865234375,3.1015189999999997 +400,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.705,0.4913793103448276,0.0006465911865234375,3.4910129999999997 +425,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7105882352941176,0.4896265560165975,0.0006465911865234375,3.9231499999999997 +450,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7222222222222222,0.5098039215686275,0.0006465911865234375,4.358171 +475,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7157894736842105,0.5054945054945055,0.0006465911865234375,4.7960329999999995 +500,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.718,0.5252525252525252,0.0006465911865234375,5.248043999999999 +525,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7257142857142858,0.5294117647058824,0.0006465911865234375,5.702586999999999 +550,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7218181818181818,0.5233644859813085,0.0006465911865234375,6.1599829999999995 +575,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7217391304347827,0.5209580838323353,0.0006465911865234375,6.620335 +600,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7283333333333334,0.5275362318840581,0.0006465911865234375,7.1135079999999995 +625,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7376,0.5340909090909091,0.0006465911865234375,7.613357 +650,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7369230769230769,0.5415549597855228,0.0006465911865234375,8.116107 +675,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7333333333333333,0.5477386934673367,0.0006465911865234375,8.713363 +700,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74,0.5560975609756097,0.0006465911865234375,9.313647 +725,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.743448275862069,0.5753424657534246,0.0006465911865234375,9.917033 +750,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7453333333333333,0.5820568927789934,0.0006465911865234375,10.613639 +775,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7470967741935484,0.5847457627118644,0.0006465911865234375,11.313362999999999 +800,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74625,0.5915492957746479,0.0006465911865234375,12.015877 +825,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7490909090909091,0.602687140115163,0.0006465911865234375,12.766805 +850,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7541176470588236,0.6122448979591837,0.0006465911865234375,13.520246 +875,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7554285714285714,0.6123188405797102,0.0006465911865234375,14.2887 +900,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7566666666666667,0.6123893805309735,0.0006465911865234375,15.059985000000001 +925,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.76,0.6237288135593221,0.0006465911865234375,15.877357000000002 +950,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7589473684210526,0.6288492706645057,0.0006465911865234375,16.697524 +975,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7610256410256411,0.631911532385466,0.0006465911865234375,17.562951 +1000,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.761,0.6328725038402457,0.0006465911865234375,18.43148 +1025,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7609756097560976,0.635958395245171,0.0006465911865234375,19.325245 +1050,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7638095238095238,0.6436781609195402,0.0006465911865234375,20.222005 +1075,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7665116279069767,0.651872399445215,0.0006465911865234375,21.121639 +1100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.77,0.6594885598923284,0.0006465911865234375,22.071389 +1125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.768,0.6597131681877444,0.0006465911865234375,23.024294 +1150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7695652173913043,0.6615581098339719,0.0006465911865234375,23.980116000000002 +1175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7702127659574468,0.6633416458852868,0.0006465911865234375,24.939110000000003 +1200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7741666666666667,0.6691086691086692,0.0006465911865234375,25.901192 +1225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7771428571428571,0.6746126340882003,0.0006465911865234375,26.865956 +1250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7736,0.6697782963827306,0.0006465911865234375,27.833412 +1903,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,1.287853 +3806,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,3.7805989999999996 +5709,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,7.576109 +7612,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,12.534125 +9515,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,18.771881 +11418,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,26.322128 +13321,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,35.214625 +15224,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9992774566473989,0.0,0.0006465911865234375,45.441497999999996 +17127,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999299351900508,0.14285714285714288,0.0006465911865234375,56.927386 +19030,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9993694167104572,0.14285714285714288,0.0006465911865234375,69.74153799999999 +20933,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9994267424640519,0.14285714285714288,0.0006465911865234375,83.765543 +22836,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999474513925381,0.14285714285714288,0.0006465911865234375,99.06549199999999 +24739,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999514935931121,0.14285714285714288,0.0006465911865234375,115.581943 +26642,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995120486449967,0.13333333333333333,0.0006465911865234375,133.361343 +28545,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995445787353302,0.13333333333333333,0.0006465911865234375,152.36548100000002 +30448,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995730425643721,0.13333333333333333,0.0006465911865234375,172.57866 +32351,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,193.96982500000001 +34254,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996204822794418,0.13333333333333333,0.0006465911865234375,216.600052 +36157,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996404568963133,0.13333333333333333,0.0006465911865234375,240.511883 +38060,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996584340514977,0.13333333333333333,0.0006465911865234375,265.709607 +39963,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996746990966644,0.13333333333333333,0.0006465911865234375,292.142637 +41866,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996894855013615,0.13333333333333333,0.0006465911865234375,319.87834699999996 +43769,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999702986131737,0.13333333333333333,0.0006465911865234375,348.79444399999994 +45672,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997153617095814,0.13333333333333333,0.0006465911865234375,378.9697039999999 +47575,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997267472411981,0.13333333333333333,0.0006465911865234375,410.4053809999999 +49478,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997372569626904,0.13333333333333333,0.0006465911865234375,443.05761399999994 +51381,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997080632918783,0.11764705882352941,0.0006465911865234375,477.02532299999996 +53284,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997184896028827,0.11764705882352941,0.0006465911865234375,512.247669 +55187,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997281968579557,0.11764705882352941,0.0006465911865234375,548.612896 +57090,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995796111403048,0.14285714285714285,0.0006465911865234375,586.233337 +58993,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995931720712626,0.14285714285714285,0.0006465911865234375,625.057298 +60896,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996058854440357,0.14285714285714285,0.0006465911865234375,665.0820319999999 +62799,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999585980668482,0.13333333333333333,0.0006465911865234375,706.1803269999999 +64702,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,748.3509649999999 +66605,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996096389159973,0.13333333333333333,0.0006465911865234375,791.6670189999999 +68508,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995912886086297,0.125,0.0006465911865234375,836.0877649999999 +70411,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996023348624504,0.125,0.0006465911865234375,881.7116259999999 +72314,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996127997344912,0.125,0.0006465911865234375,928.538605 +74217,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996227279464274,0.125,0.0006465911865234375,976.4092069999999 +76120,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996321597477666,0.125,0.0006465911865234375,1025.3623619999998 +78023,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996411314612358,0.125,0.0006465911865234375,1075.4142359999998 +79926,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999649675950254,0.125,0.0006465911865234375,1126.58116 +81829,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996578230211783,0.125,0.0006465911865234375,1178.778759 +83732,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999665599770697,0.125,0.0006465911865234375,1231.992839 +85635,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996730308869037,0.125,0.0006465911865234375,1286.303482 +87538,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996801389111014,0.125,0.0006465911865234375,1341.617815 +89441,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996757639114053,0.1212121212121212,0.0006465911865234375,1397.839199 +91344,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996825188299177,0.1212121212121212,0.0006465911865234375,1455.075933 +93247,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996889980374704,0.1212121212121212,0.0006465911865234375,1513.261041 +95150,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999695218076721,0.1212121212121212,0.0006465911865234375,1572.312523 +95156,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996952372945479,0.1212121212121212,0.0006465911865234375,1631.3703540000001 +106,Binary classification,Naive Bayes,Bananas,0.5333333333333333,0.46153846153846156,0.014024734497070312,0.089081 +212,Binary classification,Naive Bayes,Bananas,0.5592417061611374,0.5026737967914437,0.014024734497070312,0.244558 +318,Binary classification,Naive Bayes,Bananas,0.555205047318612,0.5154639175257733,0.014024734497070312,0.45393700000000003 +424,Binary classification,Naive Bayes,Bananas,0.5626477541371159,0.5066666666666667,0.014024734497070312,0.76271 +530,Binary classification,Naive Bayes,Bananas,0.5689981096408318,0.48181818181818187,0.014024734497070312,1.109688 +636,Binary classification,Naive Bayes,Bananas,0.5716535433070866,0.4645669291338582,0.014024734497070312,1.6198730000000001 +742,Binary classification,Naive Bayes,Bananas,0.5870445344129555,0.4555160142348755,0.014024734497070312,2.197835 +848,Binary classification,Naive Bayes,Bananas,0.5962219598583235,0.4554140127388535,0.014024734497070312,2.834188 +954,Binary classification,Naive Bayes,Bananas,0.6002098635886673,0.4454148471615721,0.014024734497070312,3.5547570000000004 +1060,Binary classification,Naive Bayes,Bananas,0.6090651558073654,0.44054054054054054,0.014024734497070312,4.339157 +1166,Binary classification,Naive Bayes,Bananas,0.6068669527896996,0.42606516290726815,0.014024734497070312,5.220598 +1272,Binary classification,Naive Bayes,Bananas,0.6136900078678206,0.433679354094579,0.014024734497070312,6.1398969999999995 +1378,Binary classification,Naive Bayes,Bananas,0.6143790849673203,0.419672131147541,0.014024734497070312,7.157157999999999 +1484,Binary classification,Naive Bayes,Bananas,0.6142953472690492,0.4127310061601643,0.014024734497070312,8.301379999999998 +1590,Binary classification,Naive Bayes,Bananas,0.6135934550031467,0.40618955512572535,0.014024734497070312,9.487101 +1696,Binary classification,Naive Bayes,Bananas,0.6141592920353982,0.4010989010989011,0.014024734497070312,10.730798 +1802,Binary classification,Naive Bayes,Bananas,0.614658523042754,0.40378006872852235,0.014024734497070312,12.063669 +1908,Binary classification,Naive Bayes,Bananas,0.6151022548505506,0.4080645161290322,0.014024734497070312,13.448557000000001 +2014,Binary classification,Naive Bayes,Bananas,0.6100347739692003,0.40485216072782415,0.014024734497070312,14.939018 +2120,Binary classification,Naive Bayes,Bananas,0.608305804624823,0.4071428571428571,0.014024734497070312,16.439687 +2226,Binary classification,Naive Bayes,Bananas,0.6089887640449438,0.4089673913043478,0.014024734497070312,18.077419 +2332,Binary classification,Naive Bayes,Bananas,0.6096096096096096,0.4098573281452659,0.014024734497070312,19.752885 +2438,Binary classification,Naive Bayes,Bananas,0.6101764464505539,0.40846824408468246,0.014024734497070312,21.52049 +2544,Binary classification,Naive Bayes,Bananas,0.6114825009830909,0.41538461538461535,0.014024734497070312,23.348549 +2650,Binary classification,Naive Bayes,Bananas,0.6100415251038127,0.41273450824332003,0.014024734497070312,25.279207 +2756,Binary classification,Naive Bayes,Bananas,0.6076225045372051,0.4070213933077345,0.014024734497070312,27.31295 +2862,Binary classification,Naive Bayes,Bananas,0.6085284865431667,0.4092827004219409,0.014024734497070312,29.37924 +2968,Binary classification,Naive Bayes,Bananas,0.6083586113919784,0.4065372829417773,0.014024734497070312,31.545651 +3074,Binary classification,Naive Bayes,Bananas,0.60624796615685,0.40628066732090284,0.014024734497070312,33.733230999999996 +3180,Binary classification,Naive Bayes,Bananas,0.6071091538219566,0.4077761972498815,0.014024734497070312,36.007059 +3286,Binary classification,Naive Bayes,Bananas,0.6063926940639269,0.4049700874367234,0.014024734497070312,38.312687 +3392,Binary classification,Naive Bayes,Bananas,0.6048363314656443,0.40602836879432624,0.014024734497070312,40.720333999999994 +3498,Binary classification,Naive Bayes,Bananas,0.6065198741778668,0.40535868625756266,0.014024734497070312,43.213055999999995 +3604,Binary classification,Naive Bayes,Bananas,0.6086594504579517,0.40905280804694044,0.014024734497070312,45.745915999999994 +3710,Binary classification,Naive Bayes,Bananas,0.6085198166621731,0.4078303425774878,0.014024734497070312,48.41046399999999 +3816,Binary classification,Naive Bayes,Bananas,0.6070773263433814,0.40492258832870187,0.014024734497070312,51.18183799999999 +3922,Binary classification,Naive Bayes,Bananas,0.6067329762815609,0.4027885360185902,0.014024734497070312,54.01538599999999 +4028,Binary classification,Naive Bayes,Bananas,0.6088899925502855,0.405436013590034,0.014024734497070312,56.94241999999999 +4134,Binary classification,Naive Bayes,Bananas,0.6106944108395839,0.40780272359219727,0.014024734497070312,59.88324999999999 +4240,Binary classification,Naive Bayes,Bananas,0.611936777541873,0.41186986056489094,0.014024734497070312,62.92792699999999 +4346,Binary classification,Naive Bayes,Bananas,0.6131185270425776,0.4128536500174642,0.014024734497070312,66.00451999999999 +4452,Binary classification,Naive Bayes,Bananas,0.6137946528869916,0.413510747185261,0.014024734497070312,69.16846599999998 +4558,Binary classification,Naive Bayes,Bananas,0.6122448979591837,0.4115884115884116,0.014024734497070312,72.34300699999999 +4664,Binary classification,Naive Bayes,Bananas,0.6126956894702981,0.41249186727391024,0.014024734497070312,75.66876099999999 +4770,Binary classification,Naive Bayes,Bananas,0.6143845669951772,0.41302266198531756,0.014024734497070312,79.08375899999999 +4876,Binary classification,Naive Bayes,Bananas,0.6153846153846154,0.4131455399061033,0.014024734497070312,82.54849799999998 +4982,Binary classification,Naive Bayes,Bananas,0.6163420999799237,0.41684467500762895,0.014024734497070312,86.09394899999998 +5088,Binary classification,Naive Bayes,Bananas,0.6150973068606251,0.41412327947336924,0.014024734497070312,89.70897599999998 +5194,Binary classification,Naive Bayes,Bananas,0.6146735990756788,0.4133685136323659,0.014024734497070312,93.40231399999998 +5300,Binary classification,Naive Bayes,Bananas,0.6152104170598226,0.4139120436907157,0.014024734497070312,97.15397799999998 +906,Binary classification,Naive Bayes,Elec2,0.8187845303867404,0.8284518828451883,0.05103778839111328,0.90253 +1812,Binary classification,Naive Bayes,Elec2,0.8023191606847045,0.7475317348377998,0.05103778839111328,2.6687279999999998 +2718,Binary classification,Naive Bayes,Elec2,0.784688995215311,0.706177800100452,0.05103778839111328,5.38565 +3624,Binary classification,Naive Bayes,Elec2,0.8032017664918576,0.7356321839080461,0.05103778839111328,8.965856 +4530,Binary classification,Naive Bayes,Elec2,0.7979686465003312,0.7073872721458268,0.05103778839111328,13.460125000000001 +5436,Binary classification,Naive Bayes,Elec2,0.7937442502299908,0.6972724817715366,0.05103778839111328,18.947959 +6342,Binary classification,Naive Bayes,Elec2,0.7982967986122063,0.7065840789171829,0.05103778839111328,25.368016 +7248,Binary classification,Naive Bayes,Elec2,0.790396025941769,0.6875128574367414,0.05103778839111328,32.74734 +8154,Binary classification,Naive Bayes,Elec2,0.7841285416411137,0.6888260254596887,0.05103778839111328,41.092102 +9060,Binary classification,Naive Bayes,Elec2,0.7897118887294403,0.7086710506193606,0.05103778839111328,50.330248999999995 +9966,Binary classification,Naive Bayes,Elec2,0.793176116407426,0.7240594457089301,0.05103778839111328,60.487047999999994 +10872,Binary classification,Naive Bayes,Elec2,0.7960629196946003,0.7361656551231703,0.05103778839111328,71.57583 +11778,Binary classification,Naive Bayes,Elec2,0.792137216608644,0.7295027624309391,0.05103778839111328,83.57396899999999 +12684,Binary classification,Naive Bayes,Elec2,0.7820704880548766,0.7260111022997621,0.05103778839111328,96.50585899999999 +13590,Binary classification,Naive Bayes,Elec2,0.7858562072264331,0.7383564107174968,0.05103778839111328,110.37856099999999 +14496,Binary classification,Naive Bayes,Elec2,0.7866850638151086,0.7435727317963178,0.05103778839111328,125.16282799999999 +15402,Binary classification,Naive Bayes,Elec2,0.785728199467567,0.738593155893536,0.05103778839111328,140.864825 +16308,Binary classification,Naive Bayes,Elec2,0.7806463481940271,0.7274666666666666,0.05103778839111328,157.49451299999998 +17214,Binary classification,Naive Bayes,Elec2,0.7788880497298554,0.7181158346911569,0.05103778839111328,175.04662 +18120,Binary classification,Naive Bayes,Elec2,0.7728903361112645,0.7138983522213725,0.05103778839111328,193.53438899999998 +19026,Binary classification,Naive Bayes,Elec2,0.7701445466491459,0.7094931242941608,0.05103778839111328,212.92156899999998 +19932,Binary classification,Naive Bayes,Elec2,0.7628317696051378,0.702236220472441,0.05103778839111328,233.287368 +20838,Binary classification,Naive Bayes,Elec2,0.7537553390603254,0.6903626817934946,0.05103778839111328,254.638983 +21744,Binary classification,Naive Bayes,Elec2,0.7508163546888654,0.6836389115964032,0.05103778839111328,276.932282 +22650,Binary classification,Naive Bayes,Elec2,0.7509823833281822,0.6798001589644601,0.05103778839111328,300.18967299999997 +23556,Binary classification,Naive Bayes,Elec2,0.7457015495648482,0.668217569513681,0.05103778839111328,324.33763999999996 +24462,Binary classification,Naive Bayes,Elec2,0.7466170638976329,0.665839982747466,0.05103778839111328,349.45202499999994 +25368,Binary classification,Naive Bayes,Elec2,0.7447865336854969,0.6611180904522613,0.05103778839111328,375.51598899999993 +26274,Binary classification,Naive Bayes,Elec2,0.7448711605069843,0.6581322996888865,0.05103778839111328,402.57675099999994 +27180,Binary classification,Naive Bayes,Elec2,0.741123661650539,0.650402464473815,0.05103778839111328,430.58128099999993 +28086,Binary classification,Naive Bayes,Elec2,0.7390065871461634,0.6440019426906265,0.05103778839111328,459.5402439999999 +28992,Binary classification,Naive Bayes,Elec2,0.7358145631402849,0.6343280019097637,0.05103778839111328,489.4018749999999 +29898,Binary classification,Naive Bayes,Elec2,0.7320466936481921,0.6243023964732918,0.05103778839111328,520.249024 +30804,Binary classification,Naive Bayes,Elec2,0.7297990455475116,0.6158319870759289,0.05103778839111328,552.052505 +31710,Binary classification,Naive Bayes,Elec2,0.7256930209088902,0.6059617649723658,0.05103778839111328,584.838155 +32616,Binary classification,Naive Bayes,Elec2,0.7215391690939752,0.596427301813011,0.05103778839111328,618.5052350000001 +33522,Binary classification,Naive Bayes,Elec2,0.7176695205990274,0.5867248908296943,0.05103778839111328,653.1230730000001 +34428,Binary classification,Naive Bayes,Elec2,0.7142359194818021,0.5779493779493778,0.05103778839111328,688.6949970000001 +35334,Binary classification,Naive Bayes,Elec2,0.7138369229898395,0.5724554949469323,0.05103778839111328,725.19325 +36240,Binary classification,Naive Bayes,Elec2,0.7174866856149452,0.5752924583091347,0.05103778839111328,762.649856 +37146,Binary classification,Naive Bayes,Elec2,0.7169740207295733,0.5716148486206756,0.05103778839111328,801.028112 +38052,Binary classification,Naive Bayes,Elec2,0.7183516858952459,0.573859795618116,0.05103778839111328,840.263393 +38958,Binary classification,Naive Bayes,Elec2,0.7206407064198989,0.5799529121154812,0.05103778839111328,880.2889349999999 +39864,Binary classification,Naive Bayes,Elec2,0.7217720693374808,0.5866964784795975,0.05103778839111328,921.221106 +40770,Binary classification,Naive Bayes,Elec2,0.7228776766660944,0.5923065819861432,0.05103778839111328,962.947955 +41676,Binary classification,Naive Bayes,Elec2,0.724127174565087,0.5973170817134251,0.05103778839111328,1005.542302 +42582,Binary classification,Naive Bayes,Elec2,0.7260280406754186,0.6013259517462921,0.05103778839111328,1049.006993 +43488,Binary classification,Naive Bayes,Elec2,0.7277117299422816,0.6045222270465248,0.05103778839111328,1093.33419 +44394,Binary classification,Naive Bayes,Elec2,0.7273894532921857,0.6015933631814591,0.05103778839111328,1138.520645 +45300,Binary classification,Naive Bayes,Elec2,0.7287136581381487,0.6038234630387828,0.05103778839111328,1184.586595 +45312,Binary classification,Naive Bayes,Elec2,0.7287413652314008,0.6037845330582509,0.05103778839111328,1230.65543 +25,Binary classification,Naive Bayes,Phishing,0.5833333333333334,0.7058823529411764,0.05722999572753906,0.005899 +50,Binary classification,Naive Bayes,Phishing,0.7346938775510204,0.7636363636363637,0.05722999572753906,0.034194 +75,Binary classification,Naive Bayes,Phishing,0.7837837837837838,0.8048780487804877,0.05722999572753906,0.070237 +100,Binary classification,Naive Bayes,Phishing,0.8080808080808081,0.819047619047619,0.05722999572753906,0.11917699999999999 +125,Binary classification,Naive Bayes,Phishing,0.8145161290322581,0.8217054263565893,0.05722999572753906,0.172458 +150,Binary classification,Naive Bayes,Phishing,0.8187919463087249,0.830188679245283,0.05722999572753906,0.294112 +175,Binary classification,Naive Bayes,Phishing,0.8333333333333334,0.8323699421965318,0.05722999572753906,0.432822 +200,Binary classification,Naive Bayes,Phishing,0.8341708542713567,0.83248730964467,0.05722999572753906,0.620751 +225,Binary classification,Naive Bayes,Phishing,0.8303571428571429,0.8240740740740741,0.05722999572753906,0.8126760000000001 +250,Binary classification,Naive Bayes,Phishing,0.8313253012048193,0.825,0.05722999572753906,1.097418 +275,Binary classification,Naive Bayes,Phishing,0.8321167883211679,0.8244274809160306,0.05722999572753906,1.3867479999999999 +300,Binary classification,Naive Bayes,Phishing,0.8394648829431438,0.8285714285714285,0.05722999572753906,1.7081089999999999 +325,Binary classification,Naive Bayes,Phishing,0.845679012345679,0.8299319727891157,0.05722999572753906,2.0343679999999997 +350,Binary classification,Naive Bayes,Phishing,0.8510028653295129,0.8322580645161292,0.05722999572753906,2.472974 +375,Binary classification,Naive Bayes,Phishing,0.8529411764705882,0.8318042813455658,0.05722999572753906,2.916035 +400,Binary classification,Naive Bayes,Phishing,0.8546365914786967,0.8313953488372093,0.05722999572753906,3.458949 +425,Binary classification,Naive Bayes,Phishing,0.8561320754716981,0.8291316526610645,0.05722999572753906,4.00711 +450,Binary classification,Naive Bayes,Phishing,0.8596881959910914,0.8310991957104559,0.05722999572753906,4.560221 +475,Binary classification,Naive Bayes,Phishing,0.8565400843881856,0.8291457286432161,0.05722999572753906,5.117465 +500,Binary classification,Naive Bayes,Phishing,0.8577154308617234,0.8337236533957845,0.05722999572753906,5.6788810000000005 +525,Binary classification,Naive Bayes,Phishing,0.8587786259541985,0.8310502283105022,0.05722999572753906,6.3168120000000005 +550,Binary classification,Naive Bayes,Phishing,0.8579234972677595,0.8311688311688311,0.05722999572753906,6.9590250000000005 +575,Binary classification,Naive Bayes,Phishing,0.8606271777003485,0.8340248962655602,0.05722999572753906,7.6702010000000005 +600,Binary classification,Naive Bayes,Phishing,0.8647746243739566,0.8363636363636363,0.05722999572753906,8.386169 +625,Binary classification,Naive Bayes,Phishing,0.8669871794871795,0.8356435643564357,0.05722999572753906,9.138945000000001 +650,Binary classification,Naive Bayes,Phishing,0.8705701078582434,0.8426966292134833,0.05722999572753906,9.901064000000002 +675,Binary classification,Naive Bayes,Phishing,0.870919881305638,0.8465608465608465,0.05722999572753906,10.713223000000001 +700,Binary classification,Naive Bayes,Phishing,0.8755364806866953,0.8502581755593803,0.05722999572753906,11.569231 +725,Binary classification,Naive Bayes,Phishing,0.8784530386740331,0.8562091503267973,0.05722999572753906,12.458796 +750,Binary classification,Naive Bayes,Phishing,0.8798397863818425,0.8584905660377359,0.05722999572753906,13.352328 +775,Binary classification,Naive Bayes,Phishing,0.8798449612403101,0.8580152671755725,0.05722999572753906,14.337352 +800,Binary classification,Naive Bayes,Phishing,0.8798498122653317,0.8596491228070174,0.05722999572753906,15.326948 +825,Binary classification,Naive Bayes,Phishing,0.8798543689320388,0.860759493670886,0.05722999572753906,16.325159 +850,Binary classification,Naive Bayes,Phishing,0.8798586572438163,0.8602739726027396,0.05722999572753906,17.375421 +875,Binary classification,Naive Bayes,Phishing,0.8832951945080092,0.8636363636363635,0.05722999572753906,18.429913 +900,Binary classification,Naive Bayes,Phishing,0.8809788654060067,0.8608582574772432,0.05722999572753906,19.528876999999998 +925,Binary classification,Naive Bayes,Phishing,0.8820346320346321,0.8635794743429286,0.05722999572753906,20.632713999999996 +950,Binary classification,Naive Bayes,Phishing,0.8819810326659642,0.8650602409638554,0.05722999572753906,21.817704999999997 +975,Binary classification,Naive Bayes,Phishing,0.8829568788501027,0.8661971830985915,0.05722999572753906,23.016962999999997 +1000,Binary classification,Naive Bayes,Phishing,0.8808808808808809,0.8643101482326111,0.05722999572753906,24.246232999999997 +1025,Binary classification,Naive Bayes,Phishing,0.880859375,0.8647450110864746,0.05722999572753906,25.480750999999998 +1050,Binary classification,Naive Bayes,Phishing,0.882745471877979,0.8673139158576052,0.05722999572753906,26.819710999999998 +1075,Binary classification,Naive Bayes,Phishing,0.8817504655493482,0.8672936259143157,0.05722999572753906,28.162913999999997 +1100,Binary classification,Naive Bayes,Phishing,0.8835304822565969,0.8693877551020409,0.05722999572753906,29.538843999999997 +1125,Binary classification,Naive Bayes,Phishing,0.8861209964412812,0.8735177865612648,0.05722999572753906,30.932978999999996 +1150,Binary classification,Naive Bayes,Phishing,0.8859878154917319,0.8731848983543079,0.05722999572753906,32.425236999999996 +1175,Binary classification,Naive Bayes,Phishing,0.8850085178875639,0.8717948717948718,0.05722999572753906,33.921729 +1200,Binary classification,Naive Bayes,Phishing,0.8865721434528774,0.8731343283582089,0.05722999572753906,35.452877 +1225,Binary classification,Naive Bayes,Phishing,0.886437908496732,0.8728270814272644,0.05722999572753906,36.988201000000004 +1250,Binary classification,Naive Bayes,Phishing,0.8847077662129704,0.8714285714285714,0.05722999572753906,38.528021 +1903,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,1.286863 +3806,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,3.863138 +5709,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,7.731956 +7612,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,13.024672 +9515,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,19.659339000000003 +11418,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,27.654251000000002 +13321,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,36.976608 +15224,Binary classification,Naive Bayes,SMTP,0.9997372397030808,0.7777777777777778,0.020140647888183594,47.719054 +17127,Binary classification,Naive Bayes,SMTP,0.9997664369963798,0.8181818181818181,0.020140647888183594,59.951688 +19030,Binary classification,Naive Bayes,SMTP,0.9997897945241474,0.8181818181818181,0.020140647888183594,73.68853899999999 +20933,Binary classification,Naive Bayes,SMTP,0.9998089050257978,0.8181818181818181,0.020140647888183594,88.86509 +22836,Binary classification,Naive Bayes,SMTP,0.9998248303043573,0.8181818181818181,0.020140647888183594,105.535247 +24739,Binary classification,Naive Bayes,SMTP,0.9998383054410219,0.8181818181818181,0.020140647888183594,123.661746 +26642,Binary classification,Naive Bayes,SMTP,0.9998498554859052,0.8333333333333333,0.020140647888183594,143.18039199999998 +28545,Binary classification,Naive Bayes,SMTP,0.999859865470852,0.8333333333333333,0.020140647888183594,164.12799199999998 +30448,Binary classification,Naive Bayes,SMTP,0.9998686241665845,0.8333333333333333,0.020140647888183594,186.62736299999997 +32351,Binary classification,Naive Bayes,SMTP,0.9998763523956723,0.8333333333333333,0.020140647888183594,210.51749199999998 +34254,Binary classification,Naive Bayes,SMTP,0.9998832219075702,0.8333333333333333,0.020140647888183594,235.83223599999997 +36157,Binary classification,Naive Bayes,SMTP,0.9998893682929527,0.8333333333333333,0.020140647888183594,262.657063 +38060,Binary classification,Naive Bayes,SMTP,0.9998949000236474,0.8333333333333333,0.020140647888183594,290.942762 +39963,Binary classification,Naive Bayes,SMTP,0.9998999049096642,0.8333333333333333,0.020140647888183594,320.716469 +41866,Binary classification,Naive Bayes,SMTP,0.999904454795175,0.8333333333333333,0.020140647888183594,352.027934 +43769,Binary classification,Naive Bayes,SMTP,0.9999086090294279,0.8333333333333333,0.020140647888183594,384.764181 +45672,Binary classification,Naive Bayes,SMTP,0.9999124170699131,0.8333333333333333,0.020140647888183594,419.010574 +47575,Binary classification,Naive Bayes,SMTP,0.9999159204607558,0.8333333333333333,0.020140647888183594,454.738206 +49478,Binary classification,Naive Bayes,SMTP,0.9999191543545486,0.8333333333333333,0.020140647888183594,491.833824 +51381,Binary classification,Naive Bayes,SMTP,0.9999026858699883,0.8275862068965517,0.020140647888183594,530.367488 +53284,Binary classification,Naive Bayes,SMTP,0.9999061614398589,0.8275862068965517,0.020140647888183594,570.267553 +55187,Binary classification,Naive Bayes,SMTP,0.9998912767730946,0.7999999999999999,0.020140647888183594,611.572363 +57090,Binary classification,Naive Bayes,SMTP,0.9993869221741492,0.4444444444444444,0.020140647888183594,654.167087 +58993,Binary classification,Naive Bayes,SMTP,0.9988473013289938,0.29166666666666663,0.020140647888183594,698.17064 +60896,Binary classification,Naive Bayes,SMTP,0.9986369981115034,0.2522522522522523,0.020140647888183594,743.490298 +62799,Binary classification,Naive Bayes,SMTP,0.9979139463040224,0.1761006289308176,0.020140647888183594,790.115891 +64702,Binary classification,Naive Bayes,SMTP,0.9979443903494536,0.17391304347826086,0.020140647888183594,838.025404 +66605,Binary classification,Naive Bayes,SMTP,0.9977478830100295,0.15730337078651685,0.020140647888183594,887.160342 +68508,Binary classification,Naive Bayes,SMTP,0.9967302611411972,0.12500000000000003,0.020140647888183594,937.511304 +70411,Binary classification,Naive Bayes,SMTP,0.9964777730436017,0.1142857142857143,0.020140647888183594,989.136144 +72314,Binary classification,Naive Bayes,SMTP,0.9964045192427364,0.10958904109589042,0.020140647888183594,1041.952402 +74217,Binary classification,Naive Bayes,SMTP,0.9958230031260106,0.0935672514619883,0.020140647888183594,1095.894331 +76120,Binary classification,Naive Bayes,SMTP,0.9956515456062218,0.08815426997245178,0.020140647888183594,1151.054816 +78023,Binary classification,Naive Bayes,SMTP,0.9951936633257287,0.07862407862407862,0.020140647888183594,1207.299045 +79926,Binary classification,Naive Bayes,SMTP,0.9946700031279324,0.06986899563318777,0.020140647888183594,1264.68116 +81829,Binary classification,Naive Bayes,SMTP,0.9945862052109302,0.06736842105263158,0.020140647888183594,1323.182614 +83732,Binary classification,Naive Bayes,SMTP,0.9945539883675102,0.06557377049180328,0.020140647888183594,1382.690018 +85635,Binary classification,Naive Bayes,SMTP,0.9939860335847911,0.05850091407678244,0.020140647888183594,1443.2513940000001 +87538,Binary classification,Naive Bayes,SMTP,0.9938540274398254,0.05614035087719298,0.020140647888183594,1504.7859910000002 +89441,Binary classification,Naive Bayes,SMTP,0.9938618067978533,0.05507745266781411,0.020140647888183594,1567.3680330000002 +91344,Binary classification,Naive Bayes,SMTP,0.9939677917300723,0.0548885077186964,0.020140647888183594,1630.9013820000002 +93247,Binary classification,Naive Bayes,SMTP,0.993543958990198,0.050473186119873815,0.020140647888183594,1695.4283070000001 +95150,Binary classification,Naive Bayes,SMTP,0.993483904192372,0.049079754601226995,0.020140647888183594,1760.949483 +95156,Binary classification,Naive Bayes,SMTP,0.9934843150648941,0.049079754601226995,0.020140647888183594,1826.472109 +106,Binary classification,Hoeffding Tree,Bananas,0.49523809523809526,0.208955223880597,0.019225120544433594,0.143993 +212,Binary classification,Hoeffding Tree,Bananas,0.5213270142180095,0.3129251700680272,0.019248008728027344,0.331364 +318,Binary classification,Hoeffding Tree,Bananas,0.5299684542586751,0.40637450199203184,0.019248008728027344,0.6339969999999999 +424,Binary classification,Hoeffding Tree,Bananas,0.5437352245862884,0.42388059701492536,0.019248008728027344,1.026482 +530,Binary classification,Hoeffding Tree,Bananas,0.553875236294896,0.4099999999999999,0.019248008728027344,1.502748 +636,Binary classification,Hoeffding Tree,Bananas,0.5590551181102362,0.4017094017094017,0.019248008728027344,2.038539 +742,Binary classification,Hoeffding Tree,Bananas,0.5762483130904184,0.3984674329501916,0.019248008728027344,2.585217 +848,Binary classification,Hoeffding Tree,Bananas,0.5867768595041323,0.40476190476190477,0.019248008728027344,3.2443470000000003 +954,Binary classification,Hoeffding Tree,Bananas,0.5918153200419727,0.3987635239567234,0.019248008728027344,4.029044000000001 +1060,Binary classification,Hoeffding Tree,Bananas,0.6015108593012276,0.39714285714285713,0.019248008728027344,4.857172 +1166,Binary classification,Hoeffding Tree,Bananas,0.6,0.38522427440633245,0.019248008728027344,5.757943 +1272,Binary classification,Hoeffding Tree,Bananas,0.6073957513768686,0.3966142684401451,0.019248008728027344,6.7312840000000005 +1378,Binary classification,Hoeffding Tree,Bananas,0.6085693536673928,0.384,0.019248008728027344,7.793338 +1484,Binary classification,Hoeffding Tree,Bananas,0.6089008766014835,0.3790149892933619,0.019248008728027344,8.86628 +1590,Binary classification,Hoeffding Tree,Bananas,0.6085588420390182,0.37424547283702214,0.019248008728027344,10.05345 +1696,Binary classification,Hoeffding Tree,Bananas,0.6094395280235988,0.37072243346007605,0.019248008728027344,11.283728 +1802,Binary classification,Hoeffding Tree,Bananas,0.6102165463631316,0.37544483985765126,0.019248008728027344,12.570083 +1908,Binary classification,Hoeffding Tree,Bananas,0.610907184058731,0.3816666666666667,0.019248008728027344,13.875162 +2014,Binary classification,Hoeffding Tree,Bananas,0.6060606060606061,0.3799843627834245,0.019248008728027344,15.24589 +2120,Binary classification,Hoeffding Tree,Bananas,0.6045304388862671,0.38382352941176473,0.019248008728027344,16.723857 +2226,Binary classification,Hoeffding Tree,Bananas,0.6053932584269663,0.38687150837988826,0.019248008728027344,18.213202 +2332,Binary classification,Hoeffding Tree,Bananas,0.6061776061776062,0.38881491344873503,0.019248008728027344,19.838043 +2438,Binary classification,Hoeffding Tree,Bananas,0.606893721789085,0.388250319284802,0.019248008728027344,21.528239 +2544,Binary classification,Hoeffding Tree,Bananas,0.608336610302792,0.39636363636363636,0.019248008728027344,23.295102 +2650,Binary classification,Hoeffding Tree,Bananas,0.6070215175537939,0.3944153577661431,0.019248008728027344,25.108421 +2756,Binary classification,Hoeffding Tree,Bananas,0.6047186932849364,0.3892316320807628,0.019248008728027344,26.99552 +2862,Binary classification,Hoeffding Tree,Bananas,0.6057322614470465,0.3922413793103448,0.019248008728027344,28.904989 +2968,Binary classification,Hoeffding Tree,Bananas,0.6056622851365016,0.3899895724713243,0.019248008728027344,30.87684 +3074,Binary classification,Hoeffding Tree,Bananas,0.6036446469248291,0.3903903903903904,0.019248008728027344,32.946938 +3180,Binary classification,Hoeffding Tree,Bananas,0.6045926391947153,0.3924601256645723,0.019248008728027344,35.112766 +3286,Binary classification,Hoeffding Tree,Bananas,0.6039573820395738,0.39006094702297234,0.019248008728027344,37.308874 +3392,Binary classification,Hoeffding Tree,Bananas,0.6024771453848422,0.39169675090252704,0.019248008728027344,39.588614 +3498,Binary classification,Hoeffding Tree,Bananas,0.6030883614526737,0.39335664335664333,0.03483390808105469,41.900558 +3604,Binary classification,Hoeffding Tree,Bananas,0.6069941715237303,0.40353833192923344,0.03483390808105469,44.304379999999995 +3710,Binary classification,Hoeffding Tree,Bananas,0.6079805877595039,0.40798045602605865,0.03483390808105469,46.776579 +3816,Binary classification,Hoeffding Tree,Bananas,0.6107470511140236,0.4146629877808436,0.03483390808105469,49.299973 +3922,Binary classification,Hoeffding Tree,Bananas,0.6123437898495282,0.4180704441041348,0.04409217834472656,51.9923 +4028,Binary classification,Hoeffding Tree,Bananas,0.6143531164638689,0.4246017043349389,0.05025672912597656,54.748055 +4134,Binary classification,Hoeffding Tree,Bananas,0.617227195741592,0.43216080402010054,0.05025672912597656,57.554127 +4240,Binary classification,Hoeffding Tree,Bananas,0.6218447747110167,0.4439819632327437,0.05025672912597656,60.441345 +4346,Binary classification,Hoeffding Tree,Bananas,0.6239355581127733,0.45130960376091334,0.05025672912597656,63.387968 +4452,Binary classification,Hoeffding Tree,Bananas,0.6259267580319029,0.45676998368678623,0.05025672912597656,66.368103 +4558,Binary classification,Hoeffding Tree,Bananas,0.6276058810621022,0.46382306477093216,0.05025672912597656,69.45485500000001 +4664,Binary classification,Hoeffding Tree,Bananas,0.6283508470941453,0.4695439240893787,0.05025672912597656,72.676145 +4770,Binary classification,Hoeffding Tree,Bananas,0.6288530090165653,0.47164179104477605,0.05941963195800781,75.94439200000001 +4876,Binary classification,Hoeffding Tree,Bananas,0.6311794871794871,0.47580174927113705,0.05946540832519531,79.31100500000001 +4982,Binary classification,Hoeffding Tree,Bananas,0.6336077092953222,0.484026010743568,0.05946540832519531,82.69585900000001 +5088,Binary classification,Hoeffding Tree,Bananas,0.6361313151169649,0.49050371593724196,0.05946540832519531,86.19871000000002 +5194,Binary classification,Hoeffding Tree,Bananas,0.6383593298671288,0.495703544575725,0.05946540832519531,89.82165300000003 +5300,Binary classification,Hoeffding Tree,Bananas,0.6421966408756369,0.5034049240440022,0.05946540832519531,93.53024900000003 +906,Binary classification,Hoeffding Tree,Elec2,0.8530386740331491,0.8513966480446927,0.1757516860961914,1.081486 +1812,Binary classification,Hoeffding Tree,Elec2,0.8663721700717836,0.8393094289508632,0.2084512710571289,3.2087309999999998 +2718,Binary classification,Hoeffding Tree,Elec2,0.8365844681634156,0.809278350515464,0.23302173614501953,6.394793 +3624,Binary classification,Hoeffding Tree,Elec2,0.8459839911675407,0.8210391276459269,0.23302173614501953,10.694791 +4530,Binary classification,Hoeffding Tree,Elec2,0.8511812762199161,0.8157463094587206,0.23296833038330078,16.02834 +5436,Binary classification,Hoeffding Tree,Elec2,0.8404783808647655,0.8020095912308747,0.23296833038330078,22.571918 +6342,Binary classification,Hoeffding Tree,Elec2,0.8334647531935025,0.7966884867154409,0.23296833038330078,30.238204 +7248,Binary classification,Hoeffding Tree,Elec2,0.8330343590451221,0.7912353347135956,0.23296833038330078,38.961308 +8154,Binary classification,Hoeffding Tree,Elec2,0.8344167790997179,0.8013537374926426,0.23296833038330078,48.732242 +9060,Binary classification,Hoeffding Tree,Elec2,0.8403797328623468,0.8129849974133472,0.2980508804321289,59.636444 +9966,Binary classification,Hoeffding Tree,Elec2,0.8398394380331159,0.8171402383134739,0.29816532135009766,71.55266999999999 +10872,Binary classification,Hoeffding Tree,Elec2,0.840493054916751,0.8200124558854057,0.29816532135009766,84.613385 +11778,Binary classification,Hoeffding Tree,Elec2,0.8404517279442982,0.8184014690248381,0.3811311721801758,98.771271 +12684,Binary classification,Hoeffding Tree,Elec2,0.8397066939998423,0.8184983483617534,0.3811311721801758,114.088656 +13590,Binary classification,Hoeffding Tree,Elec2,0.8422253293104717,0.8228684732319893,0.3811311721801758,130.484857 +14496,Binary classification,Hoeffding Tree,Elec2,0.8440841669541221,0.8259128023417038,0.38237476348876953,148.034702 +15402,Binary classification,Hoeffding Tree,Elec2,0.8445555483410169,0.8246153846153847,0.3824014663696289,166.630313 +16308,Binary classification,Hoeffding Tree,Elec2,0.8382903047770895,0.8146221441124781,0.40816211700439453,186.33286 +17214,Binary classification,Hoeffding Tree,Elec2,0.8345436588624876,0.8052516411378555,0.40816211700439453,207.14980100000002 +18120,Binary classification,Hoeffding Tree,Elec2,0.8332689442022186,0.8030253635000325,0.40884876251220703,229.10312700000003 +19026,Binary classification,Hoeffding Tree,Elec2,0.8340604467805519,0.8008327550312283,0.4101419448852539,252.19556200000002 +19932,Binary classification,Hoeffding Tree,Elec2,0.8288595655009784,0.7951228302000121,0.4740419387817383,276.349221 +20838,Binary classification,Hoeffding Tree,Elec2,0.8238230071507414,0.787570163763671,0.4986543655395508,301.796373 +21744,Binary classification,Hoeffding Tree,Elec2,0.8251391252357081,0.7858028169014086,0.49881458282470703,328.42960500000004 +22650,Binary classification,Hoeffding Tree,Elec2,0.8245838668373879,0.7828843106180666,0.4754457473754883,356.18046400000003 +23556,Binary classification,Hoeffding Tree,Elec2,0.81761834005519,0.7712703652433182,0.5000581741333008,385.033694 +24462,Binary classification,Hoeffding Tree,Elec2,0.8151342954090184,0.7656509121061359,0.5002222061157227,415.059878 +25368,Binary classification,Hoeffding Tree,Elec2,0.8133401663578665,0.7649540828989824,0.5574884414672852,446.38466700000004 +26274,Binary classification,Hoeffding Tree,Elec2,0.8142199215925094,0.7659329592864336,0.5574884414672852,478.875266 +27180,Binary classification,Hoeffding Tree,Elec2,0.8130909893667906,0.7650758416574177,0.5574884414672852,512.642228 +28086,Binary classification,Hoeffding Tree,Elec2,0.810646252447926,0.7611605137878379,0.5575571060180664,547.6069200000001 +28992,Binary classification,Hoeffding Tree,Elec2,0.8084233037839329,0.755846667838931,0.5575571060180664,583.7938770000001 +29898,Binary classification,Hoeffding Tree,Elec2,0.8039602635715958,0.7488322262695523,0.5575571060180664,621.109455 +30804,Binary classification,Hoeffding Tree,Elec2,0.8052787066194851,0.7498540328634582,0.6720895767211914,659.6567610000001 +31710,Binary classification,Hoeffding Tree,Elec2,0.802863540319783,0.7460285215130216,0.6720895767211914,699.5069490000001 +32616,Binary classification,Hoeffding Tree,Elec2,0.8010731258623333,0.7451889089623752,0.6838197708129883,740.492735 +33522,Binary classification,Hoeffding Tree,Elec2,0.8010500880045345,0.7469934367768125,0.7644319534301758,782.6537000000001 +34428,Binary classification,Hoeffding Tree,Elec2,0.799663055160194,0.7444893120438633,0.7656755447387695,825.979857 +35334,Binary classification,Hoeffding Tree,Elec2,0.7997056576005434,0.7438746335637508,0.796971321105957,870.4262610000001 +36240,Binary classification,Hoeffding Tree,Elec2,0.798283617097602,0.7418420680887131,0.8215837478637695,916.0524170000001 +37146,Binary classification,Hoeffding Tree,Elec2,0.7980347287656482,0.741577678263865,0.8528566360473633,962.7298460000001 +38052,Binary classification,Hoeffding Tree,Elec2,0.7942761031247536,0.7384913476314559,0.8296480178833008,1010.431184 +38958,Binary classification,Hoeffding Tree,Elec2,0.791975768154632,0.7385131646876614,0.8296480178833008,1059.228611 +39864,Binary classification,Hoeffding Tree,Elec2,0.7917617841105787,0.7414904549842734,0.8308916091918945,1109.092208 +40770,Binary classification,Hoeffding Tree,Elec2,0.7937158134857367,0.7465187775031646,0.8308916091918945,1159.9359670000001 +41676,Binary classification,Hoeffding Tree,Elec2,0.7945770845830834,0.749744219357479,0.8553438186645508,1211.819823 +42582,Binary classification,Hoeffding Tree,Elec2,0.7952373124163359,0.7509355271802782,0.8799333572387695,1264.739744 +43488,Binary classification,Hoeffding Tree,Elec2,0.7953871271874353,0.7515912897822445,0.881199836730957,1318.691004 +44394,Binary classification,Hoeffding Tree,Elec2,0.7949676750839096,0.7499038303016982,0.881199836730957,1373.745004 +45300,Binary classification,Hoeffding Tree,Elec2,0.7956246274752202,0.7508745492707605,0.9384660720825195,1429.8385990000002 +45312,Binary classification,Hoeffding Tree,Elec2,0.7956346141113637,0.7508341405661393,0.9384660720825195,1485.976427 +25,Binary classification,Hoeffding Tree,Phishing,0.5833333333333334,0.6428571428571429,0.06842708587646484,0.007366 +50,Binary classification,Hoeffding Tree,Phishing,0.7346938775510204,0.7346938775510203,0.06842708587646484,0.021904 +75,Binary classification,Hoeffding Tree,Phishing,0.7837837837837838,0.7894736842105262,0.06842708587646484,0.108104 +100,Binary classification,Hoeffding Tree,Phishing,0.8080808080808081,0.8080808080808081,0.06842708587646484,0.26246400000000003 +125,Binary classification,Hoeffding Tree,Phishing,0.8145161290322581,0.8130081300813008,0.06842708587646484,0.42699600000000004 +150,Binary classification,Hoeffding Tree,Phishing,0.8187919463087249,0.8235294117647058,0.06842708587646484,0.670297 +175,Binary classification,Hoeffding Tree,Phishing,0.8333333333333334,0.8263473053892215,0.06842708587646484,0.944361 +200,Binary classification,Hoeffding Tree,Phishing,0.8341708542713567,0.8272251308900525,0.0684499740600586,1.225091 +225,Binary classification,Hoeffding Tree,Phishing,0.8303571428571429,0.8190476190476189,0.0684499740600586,1.620606 +250,Binary classification,Hoeffding Tree,Phishing,0.8313253012048193,0.8205128205128206,0.0684499740600586,2.072395 +275,Binary classification,Hoeffding Tree,Phishing,0.8321167883211679,0.8203125000000001,0.0684499740600586,2.536963 +300,Binary classification,Hoeffding Tree,Phishing,0.8394648829431438,0.8248175182481753,0.0684499740600586,3.035956 +325,Binary classification,Hoeffding Tree,Phishing,0.845679012345679,0.8263888888888888,0.0684499740600586,3.5418380000000003 +350,Binary classification,Hoeffding Tree,Phishing,0.8510028653295129,0.8289473684210527,0.0684499740600586,4.130076000000001 +375,Binary classification,Hoeffding Tree,Phishing,0.8529411764705882,0.8286604361370716,0.0684499740600586,4.770647 +400,Binary classification,Hoeffding Tree,Phishing,0.8546365914786967,0.8284023668639053,0.0684499740600586,5.418701 +425,Binary classification,Hoeffding Tree,Phishing,0.8561320754716981,0.8262108262108262,0.0684499740600586,6.103302 +450,Binary classification,Hoeffding Tree,Phishing,0.8596881959910914,0.8283378746594006,0.0684499740600586,6.878701 +475,Binary classification,Hoeffding Tree,Phishing,0.8565400843881856,0.826530612244898,0.0684499740600586,7.659851000000001 +500,Binary classification,Hoeffding Tree,Phishing,0.8577154308617234,0.8313539192399049,0.0684499740600586,8.471725000000001 +525,Binary classification,Hoeffding Tree,Phishing,0.8587786259541985,0.8287037037037036,0.0684499740600586,9.290161000000001 +550,Binary classification,Hoeffding Tree,Phishing,0.8579234972677595,0.8289473684210527,0.0684499740600586,10.200081 +575,Binary classification,Hoeffding Tree,Phishing,0.8606271777003485,0.8319327731092437,0.0684499740600586,11.144835 +600,Binary classification,Hoeffding Tree,Phishing,0.8647746243739566,0.834355828220859,0.0684499740600586,12.149797 +625,Binary classification,Hoeffding Tree,Phishing,0.8669871794871795,0.8336673346693387,0.0684499740600586,13.191129 +650,Binary classification,Hoeffding Tree,Phishing,0.8705701078582434,0.8409090909090909,0.0684499740600586,14.297317 +675,Binary classification,Hoeffding Tree,Phishing,0.870919881305638,0.8449197860962566,0.0684499740600586,15.408972 +700,Binary classification,Hoeffding Tree,Phishing,0.8755364806866953,0.8486956521739131,0.0684499740600586,16.598807 +725,Binary classification,Hoeffding Tree,Phishing,0.8784530386740331,0.8547854785478548,0.0684499740600586,17.82593 +750,Binary classification,Hoeffding Tree,Phishing,0.8798397863818425,0.8571428571428571,0.0684499740600586,19.123649 +775,Binary classification,Hoeffding Tree,Phishing,0.8798449612403101,0.8567026194144837,0.0684499740600586,20.439518 +800,Binary classification,Hoeffding Tree,Phishing,0.8798498122653317,0.8584070796460177,0.006070137023925781,21.870793 +825,Binary classification,Hoeffding Tree,Phishing,0.8786407766990292,0.8575498575498576,0.1326732635498047,23.308925 +850,Binary classification,Hoeffding Tree,Phishing,0.8798586572438163,0.8579387186629527,0.13269615173339844,24.793982 +875,Binary classification,Hoeffding Tree,Phishing,0.8810068649885584,0.8583106267029972,0.13269615173339844,26.327422 +900,Binary classification,Hoeffding Tree,Phishing,0.882091212458287,0.8590425531914893,0.1327190399169922,27.867454 +925,Binary classification,Hoeffding Tree,Phishing,0.8831168831168831,0.8611825192802056,0.1327190399169922,29.49699 +950,Binary classification,Hoeffding Tree,Phishing,0.880927291886196,0.8599752168525404,0.1327190399169922,31.132602 +975,Binary classification,Hoeffding Tree,Phishing,0.8819301848049281,0.8609431680773881,0.1327190399169922,32.858381 +1000,Binary classification,Hoeffding Tree,Phishing,0.8828828828828829,0.8621908127208481,0.1327190399169922,34.600804000000004 +1025,Binary classification,Hoeffding Tree,Phishing,0.8818359375,0.8613974799541809,0.1327190399169922,36.37479200000001 +1050,Binary classification,Hoeffding Tree,Phishing,0.8836987607244995,0.8641425389755011,0.1327190399169922,38.236126000000006 +1075,Binary classification,Hoeffding Tree,Phishing,0.8845437616387337,0.8658008658008659,0.1327190399169922,40.114172 +1100,Binary classification,Hoeffding Tree,Phishing,0.8844404003639672,0.8656084656084656,0.1327190399169922,41.998405000000005 +1125,Binary classification,Hoeffding Tree,Phishing,0.8816725978647687,0.8630278063851698,0.1327190399169922,43.96255500000001 +1150,Binary classification,Hoeffding Tree,Phishing,0.8807658833768495,0.8614762386248735,0.1327190399169922,45.93298000000001 +1175,Binary classification,Hoeffding Tree,Phishing,0.879045996592845,0.8594059405940594,0.1327190399169922,47.92952100000001 +1200,Binary classification,Hoeffding Tree,Phishing,0.8807339449541285,0.8610301263362489,0.1327190399169922,50.02063300000001 +1225,Binary classification,Hoeffding Tree,Phishing,0.880718954248366,0.8609523809523809,0.1327190399169922,52.11693600000001 +1250,Binary classification,Hoeffding Tree,Phishing,0.8799039231385108,0.8605947955390334,0.1327190399169922,54.27575100000001 +1903,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,1.086226 +3806,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,3.167363 +5709,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,6.335451 +7612,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,10.587829 +9515,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,15.968691 +11418,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,22.515101 +13321,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,30.177580000000003 +15224,Binary classification,Hoeffding Tree,SMTP,0.9992774091834724,0.0,0.02622222900390625,38.907943 +17127,Binary classification,Hoeffding Tree,SMTP,0.9992409202382343,0.0,0.0170440673828125,48.927066 +19030,Binary classification,Hoeffding Tree,SMTP,0.9993168322034789,0.0,0.0170440673828125,60.172540000000005 +20933,Binary classification,Hoeffding Tree,SMTP,0.999378941333843,0.0,0.0170440673828125,72.66801500000001 +22836,Binary classification,Hoeffding Tree,SMTP,0.9994306984891613,0.0,0.0170440673828125,86.48422800000002 +24739,Binary classification,Hoeffding Tree,SMTP,0.9994744926833212,0.0,0.0170440673828125,101.51872100000001 +26642,Binary classification,Hoeffding Tree,SMTP,0.9994744942006681,0.0,0.0170440673828125,117.73184500000002 +28545,Binary classification,Hoeffding Tree,SMTP,0.9995095291479821,0.0,0.0170440673828125,135.114956 +30448,Binary classification,Hoeffding Tree,SMTP,0.9995401845830459,0.0,0.0170440673828125,153.67141 +32351,Binary classification,Hoeffding Tree,SMTP,0.9995672333848532,0.0,0.0170440673828125,173.49434000000002 +34254,Binary classification,Hoeffding Tree,SMTP,0.9995912766764955,0.0,0.0170440673828125,194.59387200000003 +36157,Binary classification,Hoeffding Tree,SMTP,0.9996127890253347,0.0,0.0170440673828125,216.90021000000004 +38060,Binary classification,Hoeffding Tree,SMTP,0.9996321500827662,0.0,0.0170440673828125,240.42103000000003 +39963,Binary classification,Hoeffding Tree,SMTP,0.9996496671838246,0.0,0.0170440673828125,265.208452 +41866,Binary classification,Hoeffding Tree,SMTP,0.9996655917831124,0.0,0.0170440673828125,291.209442 +43769,Binary classification,Hoeffding Tree,SMTP,0.9996801316029976,0.0,0.0170440673828125,318.43843100000004 +45672,Binary classification,Hoeffding Tree,SMTP,0.9996934597446958,0.0,0.0170440673828125,346.89099000000004 +47575,Binary classification,Hoeffding Tree,SMTP,0.9997057216126456,0.0,0.0170440673828125,376.52940800000005 +49478,Binary classification,Hoeffding Tree,SMTP,0.99971704024092,0.0,0.0170440673828125,407.49804200000005 +51381,Binary classification,Hoeffding Tree,SMTP,0.9996885947839627,0.0,0.0170440673828125,439.6062170000001 +53284,Binary classification,Hoeffding Tree,SMTP,0.9996997166075484,0.0,0.0170440673828125,472.95335100000005 +55187,Binary classification,Hoeffding Tree,SMTP,0.999710071394919,0.0,0.0170440673828125,507.47402400000004 +57090,Binary classification,Hoeffding Tree,SMTP,0.9995620872672494,0.0,0.0170440673828125,543.2100710000001 +58993,Binary classification,Hoeffding Tree,SMTP,0.9995762137238947,0.0,0.0170440673828125,580.098547 +60896,Binary classification,Hoeffding Tree,SMTP,0.999589457262501,0.0,0.0170440673828125,618.1800870000001 +62799,Binary classification,Hoeffding Tree,SMTP,0.9995700500015924,0.0,0.0170440673828125,657.2504170000001 +64702,Binary classification,Hoeffding Tree,SMTP,0.9995826957852274,0.0,0.0170440673828125,697.447209 +66605,Binary classification,Hoeffding Tree,SMTP,0.9995946189418053,0.0,0.0170440673828125,738.7766780000001 +68508,Binary classification,Hoeffding Tree,SMTP,0.9995766855941729,0.0,0.0170440673828125,781.207926 +70411,Binary classification,Hoeffding Tree,SMTP,0.9995881266865502,0.0,0.0170440673828125,824.7260210000001 +72314,Binary classification,Hoeffding Tree,SMTP,0.9995989656078437,0.0,0.0170440673828125,869.3947840000001 +74217,Binary classification,Hoeffding Tree,SMTP,0.99960924867953,0.0,0.0170440673828125,915.176721 +76120,Binary classification,Hoeffding Tree,SMTP,0.9996190175908775,0.0,0.0170440673828125,961.985028 +78023,Binary classification,Hoeffding Tree,SMTP,0.9996283099638563,0.0,0.0170440673828125,1009.890756 +79926,Binary classification,Hoeffding Tree,SMTP,0.9996371598373475,0.0,0.0170440673828125,1058.848202 +81829,Binary classification,Hoeffding Tree,SMTP,0.9996455980837855,0.0,0.0170440673828125,1108.7919539999998 +83732,Binary classification,Hoeffding Tree,SMTP,0.9996536527689864,0.0,0.0170440673828125,1159.7893379999998 +85635,Binary classification,Hoeffding Tree,SMTP,0.999661349463998,0.0,0.0170440673828125,1211.840415 +87538,Binary classification,Hoeffding Tree,SMTP,0.9996687115162731,0.0,0.0170440673828125,1264.8087919999998 +89441,Binary classification,Hoeffding Tree,SMTP,0.9996645796064401,0.0,0.0170440673828125,1318.8158339999998 +91344,Binary classification,Hoeffding Tree,SMTP,0.999671567607808,0.0,0.0170440673828125,1373.8298589999997 +93247,Binary classification,Hoeffding Tree,SMTP,0.9996782703815713,0.0,0.0170440673828125,1429.7685149999998 +95150,Binary classification,Hoeffding Tree,SMTP,0.9996847050415664,0.0,0.0170440673828125,1486.6476859999998 +95156,Binary classification,Hoeffding Tree,SMTP,0.9996847249224948,0.0,0.0170440673828125,1543.5553739999998 +106,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5714285714285714,0.628099173553719,0.025684356689453125,0.216494 +212,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5592417061611374,0.5903083700440529,0.025768280029296875,0.463954 +318,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5615141955835962,0.5947521865889213,0.025829315185546875,0.862573 +424,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5555555555555556,0.5822222222222222,0.025829315185546875,1.3898329999999999 +530,Binary classification,Hoeffding Adaptive Tree,Bananas,0.555765595463138,0.5506692160611854,0.025829315185546875,1.9641119999999999 +636,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5543307086614173,0.5291181364392679,0.025890350341796875,2.6939569999999997 +742,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5708502024291497,0.5167173252279634,0.025890350341796875,3.4801279999999997 +848,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5761511216056671,0.510231923601637,0.025890350341796875,4.453125999999999 +954,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5844700944386149,0.505,0.025890350341796875,5.580188 +1060,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5920679886685553,0.49532710280373826,0.025890350341796875,6.755147 +1166,Binary classification,Hoeffding Adaptive Tree,Bananas,0.590557939914163,0.478688524590164,0.025890350341796875,8.08575 +1272,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971675845790716,0.48073022312373226,0.025890350341796875,9.558451000000002 +1378,Binary classification,Hoeffding Adaptive Tree,Bananas,0.599128540305011,0.4661508704061895,0.025951385498046875,11.087295000000001 +1484,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5994605529332434,0.458029197080292,0.025951385498046875,12.740385000000002 +1590,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5997482693517936,0.4517241379310345,0.025951385498046875,14.490633000000003 +1696,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6011799410029498,0.4459016393442623,0.025951385498046875,16.383274000000004 +1802,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6018878400888396,0.44547563805104406,0.025951385498046875,18.381747000000004 +1908,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6030414263240692,0.44704163623082543,0.025951385498046875,20.420329000000002 +2014,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5986090412319921,0.44352617079889806,0.025951385498046875,22.615668000000003 +2120,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5960358659745163,0.4427083333333333,0.025951385498046875,24.891681000000002 +2226,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5968539325842697,0.4425108763206961,0.025951385498046875,27.295309000000003 +2332,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5975975975975976,0.44233055885850175,0.025951385498046875,29.792211 +2438,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5982765695527288,0.4396107613050944,0.025951385498046875,32.414577 +2544,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5973259929217459,0.4398249452954048,0.03029155731201172,35.17394 +2650,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5956964892412231,0.44363636363636366,0.056708335876464844,38.067898 +2756,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5985480943738657,0.44975124378109455,0.056952476501464844,41.158639 +2862,Binary classification,Hoeffding Adaptive Tree,Bananas,0.600139811254806,0.4536771728748806,0.057196617126464844,44.452629 +2968,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5979103471520054,0.45250114731528224,0.057303428649902344,47.888765 +3074,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971363488447771,0.4497777777777778,0.057425498962402344,51.476908 +3180,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6008178672538534,0.44993498049414826,0.057486534118652344,55.178717 +3286,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6024353120243531,0.4470787468247248,0.057608604431152344,59.035765 +3392,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6012975523444412,0.444991789819376,0.057669639587402344,63.084021 +3498,Binary classification,Hoeffding Adaptive Tree,Bananas,0.603946239633972,0.44310414153598715,0.057730674743652344,67.283017 +3604,Binary classification,Hoeffding Adaptive Tree,Bananas,0.607826810990841,0.4452296819787986,0.057730674743652344,71.628079 +3710,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6071717444055001,0.441976254308694,0.057730674743652344,76.17092 +3816,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6062909567496724,0.43787425149700593,0.057791709899902344,80.84267399999999 +3922,Binary classification,Hoeffding Adaptive Tree,Bananas,0.606988013261923,0.4353242946134115,0.057852745056152344,85.696272 +4028,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6088899925502855,0.4360902255639098,0.057852745056152344,90.65857299999999 +4134,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6082748608758771,0.4341139461726669,0.057913780212402344,95.89501499999999 +4240,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6105213493748526,0.4370951244459598,0.057913780212402344,101.21739399999998 +4346,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6119677790563867,0.43724966622162886,0.057974815368652344,106.75825799999998 +4452,Binary classification,Hoeffding Adaptive Tree,Bananas,0.614243990114581,0.4387054593004249,0.057974815368652344,112.41943399999998 +4558,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6126837831906956,0.4355612408058842,0.057974815368652344,118.26167899999999 +4664,Binary classification,Hoeffding Adaptive Tree,Bananas,0.613339052112374,0.4360337816703159,0.057974815368652344,124.26691199999999 +4770,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6148039421262319,0.4352905010759299,0.0641164779663086,130.389994 +4876,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6157948717948718,0.4332829046898639,0.0641164779663086,136.677955 +4982,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6167436257779563,0.43470535978679303,0.0641164779663086,143.134937 +5088,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6158836249262827,0.4313154831199068,0.0641775131225586,149.736273 +5194,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6160215674947044,0.42963386727688785,0.0642385482788086,156.525598 +5300,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6165314210228345,0.42824985931344967,0.06184673309326172,163.516222 +906,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8386740331491712,0.8370535714285713,0.15532493591308594,2.212895 +1812,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8823854224185533,0.857334226389819,0.2904033660888672,6.521798 +2718,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8715495031284505,0.8438478747203579,0.1283740997314453,13.845606 +3624,Binary classification,Hoeffding Adaptive Tree,Elec2,0.875241512558653,0.8472972972972973,0.2500133514404297,22.913432999999998 +4530,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8737028041510267,0.8396860986547084,0.3712940216064453,34.075607999999995 +5436,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8656853725850966,0.8300744878957169,0.4407672882080078,47.709683999999996 +6342,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8646901119697209,0.8296943231441047,0.26204872131347656,63.50523799999999 +7248,Binary classification,Hoeffding Adaptive Tree,Elec2,0.864771629639851,0.8289703315881326,0.2866535186767578,81.25362299999999 +8154,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8572304673126456,0.8312064965197217,0.28668785095214844,101.144105 +9060,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8580417264598742,0.8370501773948302,0.2865924835205078,123.033084 +9966,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8544907175112895,0.8369320737741789,0.3109416961669922,147.021637 +10872,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8583386992916935,0.8434322895485971,0.37159156799316406,172.950216 +11778,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8529336842999066,0.8357982555934774,0.46280479431152344,201.490822 +12684,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8533469999211543,0.8362099330750264,0.1895275115966797,232.642586 +13590,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8551769813820002,0.8395303326810176,0.19393348693847656,265.763258 +14496,Binary classification,Hoeffding Adaptive Tree,Elec2,0.855122456019317,0.8397435897435896,0.1697406768798828,300.834382 +15402,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8537757288487761,0.8365984617617181,0.1694965362548828,338.213883 +16308,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8506776231066413,0.8316626339440029,0.16408348083496094,377.804328 +17214,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8495904258409341,0.8278704873346187,0.1691112518310547,419.462025 +18120,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8498261493459904,0.827752104830031,0.19867897033691406,463.148728 +19026,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8522996057818659,0.8287211995611362,0.25722312927246094,508.959572 +19932,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8470222266820531,0.8238895627563102,0.31537818908691406,557.675148 +20838,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8434035609732687,0.8200121352529097,0.32396507263183594,609.8923100000001 +21744,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8450535804626776,0.8196563353139554,0.3222179412841797,664.4541280000001 +22650,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8452911828336792,0.8184455958549223,0.4409503936767578,721.910691 +23556,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8424962852897474,0.8143700590413289,0.44409751892089844,782.4177400000001 +24462,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8403990024937655,0.8111089607122121,0.5018138885498047,845.9758730000001 +25368,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8367169945204399,0.8072053621299572,0.5608501434326172,913.0025360000001 +26274,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8375137974346287,0.8080226649278229,0.31543540954589844,983.5552650000001 +27180,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8378527539644579,0.8096081565645656,0.31577491760253906,1056.640488 +28086,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8349652839594089,0.8051456678017405,0.18702125549316406,1131.521461 +28992,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8328791693973991,0.8007566722868775,0.2230243682861328,1208.700577 +29898,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8303843194969395,0.7968267959453503,0.10404396057128906,1288.2399990000001 +30804,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8300490211992338,0.7958984755740965,0.22612571716308594,1369.046902 +31710,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8277460657857391,0.7934815486993346,0.37830543518066406,1451.951614 +32616,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8227502682814656,0.7867025790502896,0.1292285919189453,1536.962592 +33522,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8198442767220548,0.7850354180756772,0.1289234161376953,1623.2849270000002 +34428,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8166264850262875,0.7809127190699289,0.19405555725097656,1710.9311020000002 +35334,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8170265757224124,0.7804530172852923,0.34708213806152344,1800.2335220000002 +36240,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8175446342338365,0.7795559111822364,0.4078502655029297,1890.9592170000003 +37146,Binary classification,Hoeffding Adaptive Tree,Elec2,0.816610580158837,0.7771817349208426,0.4166545867919922,1983.2562510000002 +38052,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8155107618722242,0.7753456221198157,0.1291065216064453,2077.01254 +38958,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8146674538593834,0.7751479289940829,0.20058250427246094,2172.116472 +39864,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8157188370167825,0.7778516995282448,0.16962623596191406,2268.811937 +40770,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8171650028207706,0.7812151452891106,0.2508678436279297,2366.696648 +41676,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8187402519496101,0.7846022241231821,0.2554492950439453,2465.8461540000003 +42582,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8196848359597003,0.7859015113490603,0.3113727569580078,2566.2285070000003 +43488,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8203141168625107,0.7864093592827466,0.2909717559814453,2667.8448940000003 +44394,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8199265649989863,0.7853959731543624,0.43657493591308594,2771.03508 +45300,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8212543323252169,0.7873854475750336,0.43532752990722656,2875.842657 +45312,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8212575312837942,0.7873440987265327,0.43532752990722656,2980.68937 +25,Binary classification,Hoeffding Adaptive Tree,Phishing,0.5833333333333334,0.6428571428571429,0.07476425170898438,0.008848 +50,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7346938775510204,0.7346938775510203,0.07482528686523438,0.123836 +75,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7837837837837838,0.7894736842105262,0.07482528686523438,0.332733 +100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8080808080808081,0.8080808080808081,0.07488632202148438,0.575353 +125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8225806451612904,0.819672131147541,0.07488632202148438,0.909775 +150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.825503355704698,0.8289473684210527,0.07490921020507812,1.284417 +175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8333333333333334,0.8242424242424242,0.07497024536132812,1.765942 +200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8291457286432161,0.8191489361702128,0.07497024536132812,2.25515 +225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8303571428571429,0.8155339805825242,0.07497024536132812,2.805996 +250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8313253012048193,0.817391304347826,0.07497024536132812,3.45663 +275,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8321167883211679,0.8174603174603176,0.07497024536132812,4.129749 +300,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8361204013377926,0.8178438661710038,0.07497024536132812,4.871246 +325,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8425925925925926,0.8197879858657244,0.07503128051757812,5.6743250000000005 +350,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8481375358166189,0.822742474916388,0.07503128051757812,6.528728000000001 +375,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8502673796791443,0.8227848101265823,0.07503128051757812,7.4517880000000005 +400,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8521303258145363,0.8228228228228228,0.07503128051757812,8.382915 +425,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8537735849056604,0.8208092485549133,0.07503128051757812,9.397859 +450,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8574610244988864,0.8232044198895027,0.07503128051757812,10.451359 +475,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8565400843881856,0.8238341968911918,0.07503128051757812,11.619284 +500,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8557114228456913,0.8260869565217391,0.07503128051757812,12.854081 +525,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8568702290076335,0.823529411764706,0.07503128051757812,14.155744 +550,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8561020036429873,0.8240534521158129,0.07503128051757812,15.508673 +575,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8554006968641115,0.8230277185501066,0.11409282684326172,16.956902 +600,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8547579298831386,0.8176100628930818,0.14198589324951172,18.498414999999998 +625,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8573717948717948,0.8172484599589321,0.14222240447998047,20.046891 +650,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8597842835130971,0.8233009708737864,0.14239025115966797,21.659211 +675,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8590504451038575,0.8263254113345521,0.14245128631591797,23.289464 +700,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8640915593705293,0.8306595365418894,0.14251232147216797,25.025232 +725,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8646408839779005,0.8344594594594595,0.14257335662841797,26.77059 +750,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8664886515353805,0.8371335504885993,0.14263439178466797,28.602532 +775,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8643410852713178,0.8330683624801273,0.14265727996826172,30.506622 +800,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8635794743429287,0.8340943683409437,0.14265727996826172,32.425334 +825,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8628640776699029,0.8345534407027819,0.14265727996826172,34.352118 +850,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8645465253239105,0.8364153627311521,0.14271831512451172,36.371837 +875,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8672768878718535,0.838888888888889,0.14271831512451172,38.48177 +900,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8665183537263627,0.8378378378378378,0.14277935028076172,40.637242 +925,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8668831168831169,0.8400520156046815,0.14277935028076172,42.878637 +950,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8661749209694415,0.8410513141426783,0.14284038543701172,45.126805999999995 +975,Binary classification,Hoeffding Adaptive Tree,Phishing,0.86652977412731,0.8414634146341464,0.14284038543701172,47.480371 +1000,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8638638638638638,0.8392434988179669,0.14284038543701172,49.860963999999996 +1025,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8623046875,0.8377445339470656,0.14284038543701172,52.359728 +1050,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8636796949475691,0.8402234636871508,0.14284038543701172,54.917308999999996 +1075,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8649906890130353,0.8429035752979415,0.14284038543701172,57.530035 +1100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8671519563239308,0.8456659619450316,0.14284038543701172,60.237019 +1125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8701067615658363,0.8507157464212679,0.14284038543701172,62.977514 +1150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8720626631853786,0.852852852852853,0.14290142059326172,65.816825 +1175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8713798977853492,0.8521057786483839,0.14290142059326172,68.70086099999999 +1200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.872393661384487,0.8530259365994236,0.14290142059326172,71.69850399999999 +1225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8733660130718954,0.8541862652869238,0.14296245574951172,74.74610599999998 +1250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8742994395516414,0.8560953253895509,0.14296245574951172,77.86495099999998 +1903,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02372455596923828,1.538486 +3806,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02378559112548828,4.672632 +5709,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,9.280899 +7612,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,15.518128 +9515,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,23.359252 +11418,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02390766143798828,32.724381 +13321,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02390766143798828,43.566998 +15224,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992774091834724,0.0,0.03315448760986328,56.059492 +17127,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992409202382343,0.0,0.02393054962158203,70.315874 +19030,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9993168322034789,0.0,0.02393054962158203,86.215201 +20933,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999378941333843,0.0,0.02399158477783203,103.810178 +22836,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994306984891613,0.0,0.02399158477783203,123.100461 +24739,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744926833212,0.0,0.02399158477783203,144.232391 +26642,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744942006681,0.0,0.02399158477783203,167.059054 +28545,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995095291479821,0.0,0.02399158477783203,191.625058 +30448,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995401845830459,0.0,0.02399158477783203,217.971038 +32351,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995672333848532,0.0,0.02399158477783203,246.1385 +34254,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995912766764955,0.0,0.02399158477783203,276.028821 +36157,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996127890253347,0.0,0.02399158477783203,307.727155 +38060,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996321500827662,0.0,0.02399158477783203,341.14046099999996 +39963,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996496671838246,0.0,0.02399158477783203,376.24706999999995 +41866,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996655917831124,0.0,0.02405261993408203,412.98223499999995 +43769,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996801316029976,0.0,0.02405261993408203,451.37683599999997 +45672,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996934597446958,0.0,0.02405261993408203,491.34241199999997 +47575,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9997057216126456,0.0,0.02405261993408203,532.9409959999999 +49478,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99971704024092,0.0,0.02405261993408203,576.05932 +51381,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996885947839627,0.0,0.02405261993408203,620.875381 +53284,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996997166075484,0.0,0.02405261993408203,667.2134759999999 +55187,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999710071394919,0.0,0.02405261993408203,715.0678459999999 +57090,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995620872672494,0.0,0.02405261993408203,764.41064 +58993,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995762137238947,0.0,0.02405261993408203,815.214443 +60896,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999589457262501,0.0,0.02405261993408203,867.5634849999999 +62799,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995700500015924,0.0,0.02405261993408203,921.3075979999999 +64702,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995826957852274,0.0,0.02405261993408203,976.5012499999999 +66605,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995946189418053,0.0,0.02405261993408203,1033.066349 +68508,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995766855941729,0.0,0.02405261993408203,1090.838953 +70411,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995881266865502,0.0,0.02405261993408203,1149.858677 +72314,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995989656078437,0.0,0.02405261993408203,1210.0750699999999 +74217,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99960924867953,0.0,0.02405261993408203,1271.4412419999999 +76120,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996190175908775,0.0,0.02405261993408203,1333.994434 +78023,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996283099638563,0.0,0.02405261993408203,1397.762471 +79926,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996371598373475,0.0,0.02405261993408203,1462.67416 +81829,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996455980837855,0.0,0.02405261993408203,1528.680001 +83732,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996536527689864,0.0,0.02411365509033203,1595.853878 +85635,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999661349463998,0.0,0.02411365509033203,1664.0432529999998 +87538,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996687115162731,0.0,0.02411365509033203,1733.3243249999998 +89441,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996645796064401,0.0,0.02411365509033203,1803.716354 +91344,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999671567607808,0.0,0.02411365509033203,1875.203937 +93247,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996782703815713,0.0,0.02411365509033203,1947.740223 +95150,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996847050415664,0.0,0.02411365509033203,2021.343945 +95156,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996847249224948,0.0,0.02411365509033203,2094.94949 +106,Binary classification,Adaptive Random Forest,Bananas,0.638095238095238,0.5777777777777778,0.6023197174072266,1.25138 +212,Binary classification,Adaptive Random Forest,Bananas,0.7535545023696683,0.711111111111111,1.0872974395751953,3.920593 +318,Binary classification,Adaptive Random Forest,Bananas,0.7760252365930599,0.7380073800738007,1.471883773803711,8.005582 +424,Binary classification,Adaptive Random Forest,Bananas,0.8085106382978723,0.7768595041322315,1.8271961212158203,13.704046000000002 +530,Binary classification,Adaptive Random Forest,Bananas,0.8204158790170132,0.7845804988662132,2.2761096954345703,21.017212 +636,Binary classification,Adaptive Random Forest,Bananas,0.8362204724409449,0.8052434456928838,2.6539440155029297,30.07026 +742,Binary classification,Adaptive Random Forest,Bananas,0.8434547908232118,0.8110749185667754,3.0667247772216797,40.88011 +848,Binary classification,Adaptive Random Forest,Bananas,0.8512396694214877,0.8220338983050847,3.4897289276123047,53.580909000000005 +954,Binary classification,Adaptive Random Forest,Bananas,0.8583420776495279,0.8301886792452831,3.9347667694091797,68.252368 +1060,Binary classification,Adaptive Random Forest,Bananas,0.8659112370160529,0.8378995433789953,4.283300399780273,84.83204500000001 +1166,Binary classification,Adaptive Random Forest,Bananas,0.8695278969957082,0.8429752066115702,4.800313949584961,103.574646 +1272,Binary classification,Adaptive Random Forest,Bananas,0.8693941778127459,0.8442776735459662,5.391313552856445,124.497156 +1378,Binary classification,Adaptive Random Forest,Bananas,0.8714596949891068,0.8454148471615721,5.846994400024414,147.830368 +1484,Binary classification,Adaptive Random Forest,Bananas,0.8759271746459879,0.8518518518518519,6.193078994750977,173.44391299999998 +1590,Binary classification,Adaptive Random Forest,Bananas,0.8753933291378225,0.8520179372197308,6.296388626098633,201.55742899999998 +1696,Binary classification,Adaptive Random Forest,Bananas,0.8755162241887906,0.8523442967109867,6.211141586303711,232.060182 +1802,Binary classification,Adaptive Random Forest,Bananas,0.8767351471404775,0.8550913838120104,6.65928840637207,264.912691 +1908,Binary classification,Adaptive Random Forest,Bananas,0.8730991085474568,0.8522588522588523,6.686662673950195,300.275827 +2014,Binary classification,Adaptive Random Forest,Bananas,0.8708395429706905,0.8507462686567164,7.210599899291992,338.470819 +2120,Binary classification,Adaptive Random Forest,Bananas,0.8725814063237376,0.8540540540540541,7.48176383972168,378.991608 +2226,Binary classification,Adaptive Random Forest,Bananas,0.8723595505617977,0.8539094650205761,7.915548324584961,421.86917 +2332,Binary classification,Adaptive Random Forest,Bananas,0.8751608751608752,0.8574228319451249,8.423246383666992,467.302026 +2438,Binary classification,Adaptive Random Forest,Bananas,0.8740254411161263,0.8560712611345522,8.870996475219727,515.200386 +2544,Binary classification,Adaptive Random Forest,Bananas,0.874557609123083,0.857779759251003,9.376256942749023,565.505493 +2650,Binary classification,Adaptive Random Forest,Bananas,0.8761796904492262,0.8600682593856656,9.769472122192383,618.233402 +2756,Binary classification,Adaptive Random Forest,Bananas,0.8780399274047187,0.8621821164889254,10.359186172485352,673.368122 +2862,Binary classification,Adaptive Random Forest,Bananas,0.8797623208668298,0.8638163103721298,10.741575241088867,730.824809 +2968,Binary classification,Adaptive Random Forest,Bananas,0.8800134816312774,0.8637059724349159,11.09235954284668,790.5343869999999 +3074,Binary classification,Adaptive Random Forest,Bananas,0.8805727302310445,0.8648250460405157,11.562868118286133,852.458144 +3180,Binary classification,Adaptive Random Forest,Bananas,0.8826675055048757,0.8667381207574134,10.152639389038086,916.4431709999999 +3286,Binary classification,Adaptive Random Forest,Bananas,0.882496194824962,0.8663434903047091,10.670488357543945,982.3914759999999 +3392,Binary classification,Adaptive Random Forest,Bananas,0.8826304924800944,0.867244829886591,11.057397842407227,1050.1851049999998 +3498,Binary classification,Adaptive Random Forest,Bananas,0.8839004861309694,0.8680961663417803,10.334085464477539,1119.7824649999998 +3604,Binary classification,Adaptive Random Forest,Bananas,0.8850957535387177,0.8689043698543382,10.692270278930664,1191.1920959999998 +3710,Binary classification,Adaptive Random Forest,Bananas,0.8846050148287948,0.8687116564417178,11.112970352172852,1264.4750769999998 +3816,Binary classification,Adaptive Random Forest,Bananas,0.8859764089121888,0.870420017873101,11.59941291809082,1339.6069449999998 +3922,Binary classification,Adaptive Random Forest,Bananas,0.884723284876307,0.8687572590011614,12.03856086730957,1416.6409169999997 +4028,Binary classification,Adaptive Random Forest,Bananas,0.8840327787434815,0.867892503536068,12.434591293334961,1495.7064749999997 +4134,Binary classification,Adaptive Random Forest,Bananas,0.884587466731188,0.868558831634059,12.796384811401367,1576.7285749999996 +4240,Binary classification,Adaptive Random Forest,Bananas,0.8858221278603444,0.8701019860440149,13.120096206665039,1659.7498909999997 +4346,Binary classification,Adaptive Random Forest,Bananas,0.8872266973532796,0.8716605552645365,13.362188339233398,1744.8641249999996 +4452,Binary classification,Adaptive Random Forest,Bananas,0.8869916872612896,0.8713225888974162,13.906320571899414,1831.9815919999996 +4558,Binary classification,Adaptive Random Forest,Bananas,0.8872064955014264,0.8719481813652217,14.321008682250977,1921.0718599999996 +4664,Binary classification,Adaptive Random Forest,Bananas,0.8876259918507399,0.8728155339805825,14.677774429321289,2012.2265639999996 +4770,Binary classification,Adaptive Random Forest,Bananas,0.8867687146152233,0.8716119828815977,15.041936874389648,2105.5104569999994 +4876,Binary classification,Adaptive Random Forest,Bananas,0.886974358974359,0.8715318256003731,15.36302375793457,2200.9404059999993 +4982,Binary classification,Adaptive Random Forest,Bananas,0.8877735394499097,0.8726941471191073,14.241693496704102,2298.464636999999 +5088,Binary classification,Adaptive Random Forest,Bananas,0.886966778061726,0.8716804284757868,14.559698104858398,2397.961828999999 +5194,Binary classification,Adaptive Random Forest,Bananas,0.8869632197188523,0.8716939890710382,15.019205093383789,2499.520506999999 +5300,Binary classification,Adaptive Random Forest,Bananas,0.886959803736554,0.8717070036410367,15.355104446411133,2603.0162549999986 +906,Binary classification,Adaptive Random Forest,Elec2,0.8674033149171271,0.8669623059866962,3.022599220275879,14.706798 +1812,Binary classification,Adaptive Random Forest,Elec2,0.8956377691882937,0.8737474949899798,3.453568458557129,43.639849999999996 +2718,Binary classification,Adaptive Random Forest,Elec2,0.889216047110784,0.8638625056535504,5.134407997131348,89.85880599999999 +3624,Binary classification,Adaptive Random Forest,Elec2,0.8901462876069556,0.8665325285043594,5.045891761779785,149.36791399999998 +4530,Binary classification,Adaptive Random Forest,Elec2,0.8924707440936189,0.8628555336524922,6.377499580383301,220.195834 +5436,Binary classification,Adaptive Random Forest,Elec2,0.8870285188592456,0.8556652562294312,8.556572914123535,302.53910099999996 +6342,Binary classification,Adaptive Random Forest,Elec2,0.884245387162908,0.8540175019888624,10.355942726135254,396.16016899999994 +7248,Binary classification,Adaptive Random Forest,Elec2,0.8835380157306472,0.8516174402250353,10.061070442199707,501.0892999999999 +8154,Binary classification,Adaptive Random Forest,Elec2,0.8847050165583221,0.8605341246290802,12.516213417053223,615.7123809999999 +9060,Binary classification,Adaptive Random Forest,Elec2,0.8869632409758251,0.8668400520156047,14.3945894241333,740.068354 +9966,Binary classification,Adaptive Random Forest,Elec2,0.8839939789262419,0.8666974169741698,15.028592109680176,874.678214 +10872,Binary classification,Adaptive Random Forest,Elec2,0.886119032287738,0.8712830110210023,18.58602237701416,1018.7273250000001 +11778,Binary classification,Adaptive Random Forest,Elec2,0.8851150547677676,0.869464544138929,18.284192085266113,1172.329585 +12684,Binary classification,Adaptive Random Forest,Elec2,0.8825987542379563,0.8672550592850139,18.562626838684082,1335.5183499999998 +13590,Binary classification,Adaptive Random Forest,Elec2,0.8835087202884686,0.8699794661190965,22.36763858795166,1507.9316749999998 +14496,Binary classification,Adaptive Random Forest,Elec2,0.883270093135564,0.870085995085995,23.97218418121338,1690.157865 +15402,Binary classification,Adaptive Random Forest,Elec2,0.8826050256476852,0.8679713743245216,24.89116382598877,1881.8741799999998 +16308,Binary classification,Adaptive Random Forest,Elec2,0.8806034218433801,0.8649885583524027,9.630642890930176,2082.7138459999996 +17214,Binary classification,Adaptive Random Forest,Elec2,0.880787776680416,0.862742474916388,9.825531959533691,2290.8714669999995 +18120,Binary classification,Adaptive Random Forest,Elec2,0.881505601854407,0.8635178946030132,13.432568550109863,2506.1422499999994 +19026,Binary classification,Adaptive Random Forest,Elec2,0.8835742444152431,0.864335150364427,11.236374855041504,2728.1698299999994 +19932,Binary classification,Adaptive Random Forest,Elec2,0.8847022226682053,0.8666744024135531,10.915810585021973,2956.9763619999994 +20838,Binary classification,Adaptive Random Forest,Elec2,0.8845803138647598,0.866618601297765,6.771607398986816,3192.2184919999995 +21744,Binary classification,Adaptive Random Forest,Elec2,0.8842845973416732,0.8643665768194071,9.905537605285645,3433.7445519999997 +22650,Binary classification,Adaptive Random Forest,Elec2,0.8832178021104684,0.8619303648796786,11.609391212463379,3682.1047309999994 +23556,Binary classification,Adaptive Random Forest,Elec2,0.8819783485459562,0.8599637316139432,7.878331184387207,3937.9632889999993 +24462,Binary classification,Adaptive Random Forest,Elec2,0.8805854216916724,0.8573800107416631,10.653840065002441,4201.066475999999 +25368,Binary classification,Adaptive Random Forest,Elec2,0.8791343083533725,0.8556497175141242,11.591797828674316,4470.752341999999 +26274,Binary classification,Adaptive Random Forest,Elec2,0.8801431127012522,0.8566616596112704,13.86082935333252,4746.937765999999 +27180,Binary classification,Adaptive Random Forest,Elec2,0.881195040288458,0.8584082438061829,14.463074684143066,5029.888654999999 +28086,Binary classification,Adaptive Random Forest,Elec2,0.8798646964571836,0.8561807331628303,15.367924690246582,5319.9108369999985 +28992,Binary classification,Adaptive Random Forest,Elec2,0.8796523058880342,0.8551019560612982,16.377129554748535,5616.458601999999 +29898,Binary classification,Adaptive Random Forest,Elec2,0.879285547044854,0.8544934080554772,16.05477237701416,5919.470834999999 +30804,Binary classification,Adaptive Random Forest,Elec2,0.8791351491737818,0.8535347574648885,17.57622241973877,6228.255318999999 +31710,Binary classification,Adaptive Random Forest,Elec2,0.8775111167176511,0.851267519338286,17.546963691711426,6543.393541999999 +32616,Binary classification,Adaptive Random Forest,Elec2,0.8771117583933773,0.8510701545778836,17.15481662750244,6864.899302999999 +33522,Binary classification,Adaptive Random Forest,Elec2,0.8770621401509502,0.8512864927285193,13.577618598937988,7192.851994 +34428,Binary classification,Adaptive Random Forest,Elec2,0.8757080198681267,0.849643346568748,12.363858222961426,7526.9490559999995 +35334,Binary classification,Adaptive Random Forest,Elec2,0.8754139189992358,0.848624484181568,12.552750587463379,7866.609101999999 +36240,Binary classification,Adaptive Random Forest,Elec2,0.875134523579569,0.8474427699672971,12.88097858428955,8211.574848999999 +37146,Binary classification,Adaptive Random Forest,Elec2,0.8743034055727554,0.8459838363846282,15.634392738342285,8562.247513999999 +38052,Binary classification,Adaptive Random Forest,Elec2,0.8741163175737826,0.8451642099818981,17.75814151763916,8918.936239999999 +38958,Binary classification,Adaptive Random Forest,Elec2,0.8743743101368175,0.8458873913591133,18.55082416534424,9281.578228999999 +39864,Binary classification,Adaptive Random Forest,Elec2,0.8744951458746206,0.847381104908331,20.39273166656494,9650.394165999998 +40770,Binary classification,Adaptive Random Forest,Elec2,0.8750521229365449,0.8493434283686265,20.04684543609619,10025.208226999997 +41676,Binary classification,Adaptive Random Forest,Elec2,0.8757768446310737,0.8512911843276937,22.40410327911377,10405.883388999997 +42582,Binary classification,Adaptive Random Forest,Elec2,0.8760010333247223,0.8517603458925264,17.905674934387207,10792.424187999997 +43488,Binary classification,Adaptive Random Forest,Elec2,0.8758249591832041,0.8516320474777449,17.979458808898926,11185.171687999997 +44394,Binary classification,Adaptive Random Forest,Elec2,0.8758362804946725,0.8511557571829769,19.483532905578613,11584.749531999996 +45300,Binary classification,Adaptive Random Forest,Elec2,0.8765977173889048,0.8524053440354862,22.381768226623535,11990.855977999996 +45312,Binary classification,Adaptive Random Forest,Elec2,0.8766083291033082,0.8523906328378699,22.39494037628174,12397.578789999996 +25,Binary classification,Adaptive Random Forest,Phishing,0.625,0.7096774193548387,0.41788291931152344,0.504078 +50,Binary classification,Adaptive Random Forest,Phishing,0.7346938775510204,0.7450980392156864,0.6195468902587891,1.506996 +75,Binary classification,Adaptive Random Forest,Phishing,0.7837837837837838,0.7999999999999999,0.8261966705322266,2.945496 +100,Binary classification,Adaptive Random Forest,Phishing,0.797979797979798,0.8039215686274509,0.9074077606201172,4.810448 +125,Binary classification,Adaptive Random Forest,Phishing,0.7903225806451613,0.7968749999999999,1.0524044036865234,7.208508 +150,Binary classification,Adaptive Random Forest,Phishing,0.8120805369127517,0.8227848101265823,1.155344009399414,10.051324000000001 +175,Binary classification,Adaptive Random Forest,Phishing,0.8390804597701149,0.8372093023255814,1.2272701263427734,13.451327000000001 +200,Binary classification,Adaptive Random Forest,Phishing,0.8442211055276382,0.8426395939086295,1.3437442779541016,17.363237 +225,Binary classification,Adaptive Random Forest,Phishing,0.8526785714285714,0.8465116279069769,1.4417095184326172,21.777532 +250,Binary classification,Adaptive Random Forest,Phishing,0.8554216867469879,0.85,1.652822494506836,26.610844 +275,Binary classification,Adaptive Random Forest,Phishing,0.8540145985401459,0.8473282442748092,1.7137775421142578,32.103705 +300,Binary classification,Adaptive Random Forest,Phishing,0.8595317725752508,0.85,1.6836071014404297,38.177642 +325,Binary classification,Adaptive Random Forest,Phishing,0.8672839506172839,0.8542372881355932,1.8154468536376953,44.724647 +350,Binary classification,Adaptive Random Forest,Phishing,0.8681948424068768,0.8525641025641026,1.9355945587158203,51.783981999999995 +375,Binary classification,Adaptive Random Forest,Phishing,0.8663101604278075,0.8484848484848485,2.1126270294189453,59.453596999999995 +400,Binary classification,Adaptive Random Forest,Phishing,0.8696741854636592,0.8505747126436781,2.2513599395751953,67.701448 +425,Binary classification,Adaptive Random Forest,Phishing,0.8702830188679245,0.8467966573816157,2.4080867767333984,76.525873 +450,Binary classification,Adaptive Random Forest,Phishing,0.8775055679287305,0.8533333333333333,2.413846969604492,85.91210000000001 +475,Binary classification,Adaptive Random Forest,Phishing,0.879746835443038,0.85785536159601,2.540945053100586,95.91125100000001 +500,Binary classification,Adaptive Random Forest,Phishing,0.8817635270541082,0.8624708624708626,2.727457046508789,106.551347 +525,Binary classification,Adaptive Random Forest,Phishing,0.8835877862595419,0.8623024830699774,2.780088424682617,117.81370000000001 +550,Binary classification,Adaptive Random Forest,Phishing,0.8816029143897997,0.8602150537634409,2.8441905975341797,129.668358 +575,Binary classification,Adaptive Random Forest,Phishing,0.8832752613240418,0.8618556701030927,2.9667911529541016,142.149346 +600,Binary classification,Adaptive Random Forest,Phishing,0.8864774624373957,0.8634538152610441,2.9313793182373047,155.146824 +625,Binary classification,Adaptive Random Forest,Phishing,0.8878205128205128,0.8622047244094488,3.1180286407470703,168.863765 +650,Binary classification,Adaptive Random Forest,Phishing,0.8906009244992296,0.8672897196261682,3.1772937774658203,183.176428 +675,Binary classification,Adaptive Random Forest,Phishing,0.8931750741839762,0.8732394366197184,3.270914077758789,198.116157 +700,Binary classification,Adaptive Random Forest,Phishing,0.8969957081545065,0.8762886597938143,3.2819652557373047,213.702702 +725,Binary classification,Adaptive Random Forest,Phishing,0.8950276243093923,0.8762214983713356,3.465627670288086,229.894704 +750,Binary classification,Adaptive Random Forest,Phishing,0.897196261682243,0.8791208791208791,3.637697219848633,246.71919699999998 +775,Binary classification,Adaptive Random Forest,Phishing,0.8979328165374677,0.8793893129770992,3.6838626861572266,264.27439799999996 +800,Binary classification,Adaptive Random Forest,Phishing,0.8961201501877347,0.8784773060029282,3.7807750701904297,282.50010699999996 +825,Binary classification,Adaptive Random Forest,Phishing,0.8968446601941747,0.8801128349788435,3.913633346557617,301.46455399999996 +850,Binary classification,Adaptive Random Forest,Phishing,0.8987043580683156,0.8818681318681318,4.011789321899414,321.06457099999994 +875,Binary classification,Adaptive Random Forest,Phishing,0.9016018306636155,0.8847184986595175,4.15968132019043,341.44966299999993 +900,Binary classification,Adaptive Random Forest,Phishing,0.9010011123470523,0.8836601307189543,3.946676254272461,362.64137199999993 +925,Binary classification,Adaptive Random Forest,Phishing,0.9036796536796536,0.8877679697351829,4.049928665161133,384.6765929999999 +950,Binary classification,Adaptive Random Forest,Phishing,0.9030558482613277,0.8883495145631068,3.6841602325439453,407.5276099999999 +975,Binary classification,Adaptive Random Forest,Phishing,0.9045174537987679,0.8899408284023669,3.787748336791992,431.1052179999999 +1000,Binary classification,Adaptive Random Forest,Phishing,0.9049049049049049,0.8904267589388698,4.052656173706055,455.43358799999993 +1025,Binary classification,Adaptive Random Forest,Phishing,0.9033203125,0.888888888888889,4.062379837036133,480.5827999999999 +1050,Binary classification,Adaptive Random Forest,Phishing,0.9046711153479504,0.8908296943231442,4.190084457397461,506.4991779999999 +1075,Binary classification,Adaptive Random Forest,Phishing,0.9059590316573557,0.8928950159066809,4.285711288452148,533.2628339999999 +1100,Binary classification,Adaptive Random Forest,Phishing,0.9062784349408554,0.8934850051706308,4.370790481567383,560.8485399999998 +1125,Binary classification,Adaptive Random Forest,Phishing,0.9065836298932385,0.8948948948948948,3.9348621368408203,589.2233909999999 +1150,Binary classification,Adaptive Random Forest,Phishing,0.9077458659704091,0.896078431372549,4.19316291809082,618.4066789999998 +1175,Binary classification,Adaptive Random Forest,Phishing,0.9063032367972743,0.8942307692307692,4.349401473999023,648.4320089999999 +1200,Binary classification,Adaptive Random Forest,Phishing,0.9065888240200167,0.8943396226415095,4.34752082824707,679.2709969999999 +1225,Binary classification,Adaptive Random Forest,Phishing,0.9068627450980392,0.8944444444444444,4.031515121459961,710.9105619999998 +1250,Binary classification,Adaptive Random Forest,Phishing,0.9079263410728583,0.896115627822945,4.102910995483398,743.3769359999998 +1903,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17035293579101562,12.381202 +3806,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17157363891601562,37.073822 +5709,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,74.106824 +7612,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,122.30955 +9515,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,180.539768 +11418,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17401504516601562,247.17494200000002 +13321,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17401504516601562,321.611977 +15224,Binary classification,Adaptive Random Forest,SMTP,0.9992774091834724,0.0,0.23138427734375,404.612477 +17127,Binary classification,Adaptive Random Forest,SMTP,0.9992409202382343,0.0,0.17718124389648438,498.19936 +19030,Binary classification,Adaptive Random Forest,SMTP,0.9993168322034789,0.0,0.16917037963867188,601.949625 +20933,Binary classification,Adaptive Random Forest,SMTP,0.999378941333843,0.0,0.17043685913085938,714.510104 +22836,Binary classification,Adaptive Random Forest,SMTP,0.9994306984891613,0.0,0.17826461791992188,835.601872 +24739,Binary classification,Adaptive Random Forest,SMTP,0.9994744926833212,0.0,0.16260147094726562,964.8451829999999 +26642,Binary classification,Adaptive Random Forest,SMTP,0.9994744942006681,0.0,0.170440673828125,1103.1275799999999 +28545,Binary classification,Adaptive Random Forest,SMTP,0.9995095291479821,0.0,0.17826461791992188,1249.157139 +30448,Binary classification,Adaptive Random Forest,SMTP,0.9995401845830459,0.0,0.17816925048828125,1402.52966 +32351,Binary classification,Adaptive Random Forest,SMTP,0.9995672333848532,0.0,0.16262054443359375,1563.348194 +34254,Binary classification,Adaptive Random Forest,SMTP,0.9995912766764955,0.0,0.17043685913085938,1731.617157 +36157,Binary classification,Adaptive Random Forest,SMTP,0.9996127890253347,0.0,0.17043304443359375,1907.1505949999998 +38060,Binary classification,Adaptive Random Forest,SMTP,0.9996321500827662,0.0,0.1781158447265625,2090.2143509999996 +39963,Binary classification,Adaptive Random Forest,SMTP,0.9996496671838246,0.0,0.1704559326171875,2280.326256 +41866,Binary classification,Adaptive Random Forest,SMTP,0.9996655917831124,0.0,0.163818359375,2477.410916 +43769,Binary classification,Adaptive Random Forest,SMTP,0.9996801316029976,0.0,0.17158126831054688,2681.481315 +45672,Binary classification,Adaptive Random Forest,SMTP,0.9996934597446958,0.0,0.171630859375,2892.603814 +47575,Binary classification,Adaptive Random Forest,SMTP,0.9997057216126456,0.0,0.17947769165039062,3110.724932 +49478,Binary classification,Adaptive Random Forest,SMTP,0.99971704024092,0.0,0.32259368896484375,3336.815763 +51381,Binary classification,Adaptive Random Forest,SMTP,0.9996885947839627,0.0,0.3238716125488281,3571.848368 +53284,Binary classification,Adaptive Random Forest,SMTP,0.9996997166075484,0.0,0.2926750183105469,3815.616702 +55187,Binary classification,Adaptive Random Forest,SMTP,0.999710071394919,0.0,0.3244895935058594,4068.2038989999996 +57090,Binary classification,Adaptive Random Forest,SMTP,0.9995620872672494,0.0,0.2933502197265625,4329.847041999999 +58993,Binary classification,Adaptive Random Forest,SMTP,0.9995762137238947,0.0,0.27820587158203125,4599.769264 +60896,Binary classification,Adaptive Random Forest,SMTP,0.999589457262501,0.0,0.3094024658203125,4878.106527 +62799,Binary classification,Adaptive Random Forest,SMTP,0.9995700500015924,0.0,0.30953216552734375,5164.770925 +64702,Binary classification,Adaptive Random Forest,SMTP,0.9995826957852274,0.0,0.29401397705078125,5459.39874 +66605,Binary classification,Adaptive Random Forest,SMTP,0.9995946189418053,0.0,0.293975830078125,5761.313045999999 +68508,Binary classification,Adaptive Random Forest,SMTP,0.9995766855941729,0.0,0.32515716552734375,6070.487373999999 +70411,Binary classification,Adaptive Random Forest,SMTP,0.9995881266865502,0.0,0.3101234436035156,6386.879354 +72314,Binary classification,Adaptive Random Forest,SMTP,0.9995989656078437,0.0,0.3101387023925781,6710.520715 +74217,Binary classification,Adaptive Random Forest,SMTP,0.99960924867953,0.0,0.3101615905761719,7041.446151 +76120,Binary classification,Adaptive Random Forest,SMTP,0.9996190175908775,0.0,0.3101768493652344,7379.414547 +78023,Binary classification,Adaptive Random Forest,SMTP,0.9996283099638563,0.0,0.3100128173828125,7724.224697000001 +79926,Binary classification,Adaptive Random Forest,SMTP,0.9996371598373475,0.0,0.31014251708984375,8075.760258 +81829,Binary classification,Adaptive Random Forest,SMTP,0.9996455980837855,0.0,0.3100852966308594,8434.038373 +83732,Binary classification,Adaptive Random Forest,SMTP,0.9996536527689864,0.0,0.31072235107421875,8799.103777999999 +85635,Binary classification,Adaptive Random Forest,SMTP,0.999661349463998,0.0,0.310821533203125,9170.869249 +87538,Binary classification,Adaptive Random Forest,SMTP,0.9996687115162731,0.0,0.2950706481933594,9549.506615999999 +89441,Binary classification,Adaptive Random Forest,SMTP,0.9996645796064401,0.0,0.2951812744140625,9935.081315999998 +91344,Binary classification,Adaptive Random Forest,SMTP,0.999671567607808,0.0,0.2957954406738281,10327.647623999997 +93247,Binary classification,Adaptive Random Forest,SMTP,0.9996782703815713,0.0,0.3115119934082031,10728.119291999998 +95150,Binary classification,Adaptive Random Forest,SMTP,0.9996847050415664,0.0,0.3115577697753906,11135.737891999997 +95156,Binary classification,Adaptive Random Forest,SMTP,0.9996847249224948,0.0,0.32709503173828125,11543.384409999997 +106,Binary classification,Streaming Random Patches,Bananas,0.5428571428571428,0.4,0.2255392074584961,2.569769 +212,Binary classification,Streaming Random Patches,Bananas,0.5592417061611374,0.4685714285714286,0.6304416656494141,8.011061999999999 +318,Binary classification,Streaming Random Patches,Bananas,0.637223974763407,0.5724907063197027,0.9710559844970703,16.523663 +424,Binary classification,Streaming Random Patches,Bananas,0.6926713947990544,0.6448087431693988,1.2628002166748047,28.175313 +530,Binary classification,Streaming Random Patches,Bananas,0.7145557655954632,0.6621923937360179,1.5703105926513672,43.218913 +636,Binary classification,Streaming Random Patches,Bananas,0.7448818897637796,0.7000000000000001,1.467294692993164,61.573557 +742,Binary classification,Streaming Random Patches,Bananas,0.7624831309041835,0.7170418006430868,1.877767562866211,83.22394800000001 +848,Binary classification,Streaming Random Patches,Bananas,0.7827626918536009,0.7430167597765364,2.3253536224365234,108.413447 +954,Binary classification,Streaming Random Patches,Bananas,0.7964323189926548,0.7599009900990098,1.7426891326904297,137.183902 +1060,Binary classification,Streaming Random Patches,Bananas,0.8054768649669499,0.7674943566591422,1.7942829132080078,169.50541299999998 +1166,Binary classification,Streaming Random Patches,Bananas,0.8103004291845494,0.7747196738022425,1.8575687408447266,205.46256099999997 +1272,Binary classification,Streaming Random Patches,Bananas,0.8151062155782848,0.7822057460611677,1.917165756225586,244.58779499999997 +1378,Binary classification,Streaming Random Patches,Bananas,0.8191721132897604,0.7851596203623814,2.1873340606689453,286.663235 +1484,Binary classification,Streaming Random Patches,Bananas,0.8240053944706676,0.7916999201915402,2.2810306549072266,331.700902 +1590,Binary classification,Streaming Random Patches,Bananas,0.8231592196349906,0.7916975537435137,2.585817337036133,379.560946 +1696,Binary classification,Streaming Random Patches,Bananas,0.8271386430678466,0.7961029923451634,2.8855953216552734,430.279509 +1802,Binary classification,Streaming Random Patches,Bananas,0.8334258745141588,0.8046875,2.8240184783935547,483.724395 +1908,Binary classification,Streaming Random Patches,Bananas,0.8332459360251704,0.8060975609756097,3.138376235961914,539.732848 +2014,Binary classification,Streaming Random Patches,Bananas,0.8340784898161947,0.8084862385321101,3.5751514434814453,598.378334 +2120,Binary classification,Streaming Random Patches,Bananas,0.8367154318074563,0.813778256189451,3.890401840209961,659.469374 +2226,Binary classification,Streaming Random Patches,Bananas,0.8382022471910112,0.8157625383828044,4.414094924926758,723.240852 +2332,Binary classification,Streaming Random Patches,Bananas,0.8404118404118404,0.8185365853658537,4.828973770141602,789.4489060000001 +2438,Binary classification,Streaming Random Patches,Bananas,0.8432498974148543,0.8216619981325864,4.724649429321289,858.0992190000001 +2544,Binary classification,Streaming Random Patches,Bananas,0.8450648839952811,0.8247330960854093,4.20762825012207,929.1630060000001 +2650,Binary classification,Streaming Random Patches,Bananas,0.846734616836542,0.8270868824531515,4.517709732055664,1002.5522950000001 +2756,Binary classification,Streaming Random Patches,Bananas,0.8500907441016334,0.8306683066830667,4.757001876831055,1078.240465 +2862,Binary classification,Streaming Random Patches,Bananas,0.8521495980426425,0.8324752475247525,4.690572738647461,1156.1945970000002 +2968,Binary classification,Streaming Random Patches,Bananas,0.854061341422312,0.8339087073264287,4.873067855834961,1236.185934 +3074,Binary classification,Streaming Random Patches,Bananas,0.8538887081028311,0.8340110905730129,5.244169235229492,1318.3478 +3180,Binary classification,Streaming Random Patches,Bananas,0.8565586662472475,0.836441893830703,5.473237991333008,1402.707418 +3286,Binary classification,Streaming Random Patches,Bananas,0.8575342465753425,0.8371607515657619,5.716192245483398,1489.296647 +3392,Binary classification,Streaming Random Patches,Bananas,0.8593335299321734,0.8400938652363393,6.05610466003418,1578.3688909999998 +3498,Binary classification,Streaming Random Patches,Bananas,0.8615956534172148,0.8419333768778575,6.433168411254883,1669.799251 +3604,Binary classification,Streaming Random Patches,Bananas,0.8631695809048016,0.8430436166825852,6.670698165893555,1763.5519829999998 +3710,Binary classification,Streaming Random Patches,Bananas,0.8638447020760313,0.8444718201416692,7.050989151000977,1859.6268179999997 +3816,Binary classification,Streaming Random Patches,Bananas,0.8657929226736566,0.84688995215311,7.316404342651367,1958.0125029999997 +3922,Binary classification,Streaming Random Patches,Bananas,0.8653404743687835,0.846064139941691,7.61528205871582,2058.6755279999998 +4028,Binary classification,Streaming Random Patches,Bananas,0.8644151974174323,0.8449744463373083,7.967977523803711,2161.5792619999997 +4134,Binary classification,Streaming Random Patches,Bananas,0.8654730220179047,0.8462389380530975,7.394952774047852,2266.5958729999998 +4240,Binary classification,Streaming Random Patches,Bananas,0.8674215616890776,0.8486806677436726,7.571531295776367,2373.458397 +4346,Binary classification,Streaming Random Patches,Bananas,0.8688147295742232,0.8503937007874016,7.877435684204102,2482.305033 +4452,Binary classification,Streaming Random Patches,Bananas,0.8683441923163334,0.8496664956387892,8.180627822875977,2593.165336 +4558,Binary classification,Streaming Random Patches,Bananas,0.8689927583936801,0.8508618536097925,8.39448356628418,2705.945127 +4664,Binary classification,Streaming Random Patches,Bananas,0.8691829294445635,0.8516536964980544,8.710580825805664,2820.674173 +4770,Binary classification,Streaming Random Patches,Bananas,0.8689452715453974,0.8510131108462455,9.014997482299805,2937.453009 +4876,Binary classification,Streaming Random Patches,Bananas,0.8703589743589744,0.8523364485981308,9.167715072631836,3056.177406 +4982,Binary classification,Streaming Random Patches,Bananas,0.8713109817305762,0.8538864827900615,9.482858657836914,3176.936292 +5088,Binary classification,Streaming Random Patches,Bananas,0.8714369962649892,0.8539526574363555,9.87147331237793,3299.754349 +5194,Binary classification,Streaming Random Patches,Bananas,0.8717504332755632,0.8543944031482291,10.204122543334961,3424.594329 +5300,Binary classification,Streaming Random Patches,Bananas,0.8716739007359879,0.8542648949849978,10.538087844848633,3551.414099 +906,Binary classification,Streaming Random Patches,Elec2,0.8828729281767956,0.8811659192825113,5.258722305297852,37.408806 +1812,Binary classification,Streaming Random Patches,Elec2,0.9039204859193816,0.8804945054945055,8.443174362182617,104.985856 +2718,Binary classification,Streaming Random Patches,Elec2,0.8873757821126242,0.8602739726027397,12.445928573608398,198.51440200000002 +3624,Binary classification,Streaming Random Patches,Elec2,0.884902014904775,0.8576305906452714,16.533422470092773,314.268209 +4530,Binary classification,Streaming Random Patches,Elec2,0.8812099801280636,0.8452243958573072,19.266294479370117,451.77035 +5436,Binary classification,Streaming Random Patches,Elec2,0.8756209751609936,0.8372652864708715,24.12981605529785,609.66041 +6342,Binary classification,Streaming Random Patches,Elec2,0.8719444882510645,0.8340825500612996,28.348302841186523,788.9253299999999 +7248,Binary classification,Streaming Random Patches,Elec2,0.8691872498965089,0.8308351177730193,31.664392471313477,988.709629 +8154,Binary classification,Streaming Random Patches,Elec2,0.8690052741322213,0.8387681159420289,35.27585411071777,1209.125777 +9060,Binary classification,Streaming Random Patches,Elec2,0.869742797218236,0.844162704701532,38.39363670349121,1448.169528 +9966,Binary classification,Streaming Random Patches,Elec2,0.8681384846964375,0.8455934195064629,42.49019813537598,1706.33653 +10872,Binary classification,Streaming Random Patches,Elec2,0.8687333272008095,0.849265870920038,46.91076469421387,1983.228319 +11778,Binary classification,Streaming Random Patches,Elec2,0.8694064702386006,0.8495402073958129,41.518564224243164,2278.772125 +12684,Binary classification,Streaming Random Patches,Elec2,0.8672238429393676,0.8479320931912588,46.98099327087402,2591.81768 +13590,Binary classification,Streaming Random Patches,Elec2,0.8682758113179778,0.8513289036544851,50.757638931274414,2922.748705 +14496,Binary classification,Streaming Random Patches,Elec2,0.8687823387374957,0.8527863777089782,43.74130058288574,3269.777293 +15402,Binary classification,Streaming Random Patches,Elec2,0.8686448931887539,0.8518708354689903,49.06788444519043,3632.343799 +16308,Binary classification,Streaming Random Patches,Elec2,0.8649659655362728,0.8467427616926504,54.357858657836914,4011.129855 +17214,Binary classification,Streaming Random Patches,Elec2,0.865392435949573,0.8447987139125192,52.38222694396973,4407.893822 +18120,Binary classification,Streaming Random Patches,Elec2,0.8652795408135107,0.8448286822198208,59.36540412902832,4822.718697 +19026,Binary classification,Streaming Random Patches,Elec2,0.867700394218134,0.8459136822773186,57.10729789733887,5251.994155 +19932,Binary classification,Streaming Random Patches,Elec2,0.8692489087351363,0.84882236918436,51.34463310241699,5694.600867 +20838,Binary classification,Streaming Random Patches,Elec2,0.8691750251955656,0.8490085299656586,53.35045051574707,6149.875029 +21744,Binary classification,Streaming Random Patches,Elec2,0.8700271351699398,0.8478682170542635,59.89077186584473,6616.7506189999995 +22650,Binary classification,Streaming Random Patches,Elec2,0.8694865115457636,0.8459614382490881,65.61615180969238,7094.9355989999995 +23556,Binary classification,Streaming Random Patches,Elec2,0.868860114625345,0.8444690599667689,72.22853660583496,7585.744803 +24462,Binary classification,Streaming Random Patches,Elec2,0.8682392379706472,0.8428648042513772,80.47726249694824,8090.441156999999 +25368,Binary classification,Streaming Random Patches,Elec2,0.8672290771474751,0.8417888012025554,73.03231239318848,8608.413273 +26274,Binary classification,Streaming Random Patches,Elec2,0.8684581128915617,0.8430802760624773,79.16955757141113,9138.877569 +27180,Binary classification,Streaming Random Patches,Elec2,0.8698259685786821,0.8451234459814394,84.2670955657959,9681.232951 +28086,Binary classification,Streaming Random Patches,Elec2,0.8689335944454335,0.8434616202423985,93.53372383117676,10236.036265 +28992,Binary classification,Streaming Random Patches,Elec2,0.8688558518160808,0.8423322551215061,92.75358009338379,10801.143387 +29898,Binary classification,Streaming Random Patches,Elec2,0.8689166137070609,0.8421603769785332,90.79977607727051,11375.205452 +30804,Binary classification,Streaming Random Patches,Elec2,0.8684868356978216,0.8408063818917749,97.95510292053223,11957.401901000001 +31710,Binary classification,Streaming Random Patches,Elec2,0.8668832192752847,0.8384553561177235,105.25788688659668,12547.475367000001 +32616,Binary classification,Streaming Random Patches,Elec2,0.8664724819868159,0.8381943154374885,94.53887367248535,13145.300695000002 +33522,Binary classification,Streaming Random Patches,Elec2,0.8661436114674383,0.8380553650701988,102.01883506774902,13750.733881000002 +34428,Binary classification,Streaming Random Patches,Elec2,0.8648444534812793,0.8364441632394811,100.42782783508301,14363.910035000003 +35334,Binary classification,Streaming Random Patches,Elec2,0.8647723091727281,0.8357849876271651,107.87262153625488,14984.863075000003 +36240,Binary classification,Streaming Random Patches,Elec2,0.8642346643119292,0.83420946219167,112.83228874206543,15613.594311000003 +37146,Binary classification,Streaming Random Patches,Elec2,0.8632655808318751,0.8326689289361843,120.66686058044434,16250.422859000002 +38052,Binary classification,Streaming Random Patches,Elec2,0.8627631336889964,0.8316135689410551,126.15458106994629,16895.166705000003 +38958,Binary classification,Streaming Random Patches,Elec2,0.8634391765279668,0.8328620797989319,107.22049903869629,17548.303432000004 +39864,Binary classification,Streaming Random Patches,Elec2,0.8644356922459423,0.8353041570157259,103.5422191619873,18208.490072000004 +40770,Binary classification,Streaming Random Patches,Elec2,0.865436974171552,0.8378745788758201,97.78764533996582,18874.504490000003 +41676,Binary classification,Streaming Random Patches,Elec2,0.8666586682663467,0.8403940603728063,102.76390266418457,19545.753018000003 +42582,Binary classification,Streaming Random Patches,Elec2,0.8673821657546793,0.8415055151702265,107.51249122619629,20221.607860000004 +43488,Binary classification,Streaming Random Patches,Elec2,0.8677075907742544,0.841885392332005,102.9560489654541,20902.251232000002 +44394,Binary classification,Streaming Random Patches,Elec2,0.8679071024711104,0.8415905775568644,103.86480903625488,21587.715975000003 +45300,Binary classification,Streaming Random Patches,Elec2,0.8688933530541513,0.843053830501308,107.29028511047363,22278.011723000003 +45312,Binary classification,Streaming Random Patches,Elec2,0.8688839354682086,0.8430092751631743,107.32242012023926,22968.976341 +25,Binary classification,Streaming Random Patches,Phishing,0.8333333333333334,0.8333333333333334,0.7029104232788086,1.141902 +50,Binary classification,Streaming Random Patches,Phishing,0.8571428571428571,0.8372093023255814,0.9397382736206055,3.355867 +75,Binary classification,Streaming Random Patches,Phishing,0.8783783783783784,0.8695652173913043,0.9708013534545898,6.532426 +100,Binary classification,Streaming Random Patches,Phishing,0.8888888888888888,0.8817204301075269,1.056624412536621,10.815831 +125,Binary classification,Streaming Random Patches,Phishing,0.8790322580645161,0.8739495798319329,1.3782567977905273,16.293882 +150,Binary classification,Streaming Random Patches,Phishing,0.8791946308724832,0.8783783783783784,1.379134178161621,22.890072 +175,Binary classification,Streaming Random Patches,Phishing,0.896551724137931,0.888888888888889,1.4786596298217773,30.523139999999998 +200,Binary classification,Streaming Random Patches,Phishing,0.8944723618090452,0.8864864864864866,1.6607275009155273,39.247513 +225,Binary classification,Streaming Random Patches,Phishing,0.8973214285714286,0.8866995073891626,1.686568260192871,49.014512999999994 +250,Binary classification,Streaming Random Patches,Phishing,0.891566265060241,0.88,1.9668035507202148,59.910523 +275,Binary classification,Streaming Random Patches,Phishing,0.8905109489051095,0.8780487804878049,2.071291923522949,71.88595 +300,Binary classification,Streaming Random Patches,Phishing,0.8896321070234113,0.8754716981132077,2.2423620223999023,85.00905599999999 +325,Binary classification,Streaming Random Patches,Phishing,0.8888888888888888,0.8723404255319148,2.4750547409057617,99.14632499999999 +350,Binary classification,Streaming Random Patches,Phishing,0.8853868194842407,0.8666666666666667,2.5328550338745117,114.35437499999999 +375,Binary classification,Streaming Random Patches,Phishing,0.8850267379679144,0.8652037617554859,2.8150205612182617,130.79065599999998 +400,Binary classification,Streaming Random Patches,Phishing,0.8822055137844611,0.8613569321533923,2.795191764831543,148.41625799999997 +425,Binary classification,Streaming Random Patches,Phishing,0.8844339622641509,0.8611898016997167,2.962000846862793,167.06847699999997 +450,Binary classification,Streaming Random Patches,Phishing,0.888641425389755,0.8648648648648649,3.03415584564209,186.853211 +475,Binary classification,Streaming Random Patches,Phishing,0.890295358649789,0.8686868686868687,3.071761131286621,207.82899999999998 +500,Binary classification,Streaming Random Patches,Phishing,0.8917835671342685,0.8726415094339622,3.1551198959350586,229.951047 +525,Binary classification,Streaming Random Patches,Phishing,0.8950381679389313,0.8741418764302059,3.1928510665893555,253.214946 +550,Binary classification,Streaming Random Patches,Phishing,0.8943533697632058,0.8739130434782608,3.2878904342651367,277.566695 +575,Binary classification,Streaming Random Patches,Phishing,0.8937282229965157,0.8726513569937369,3.4417715072631836,303.140017 +600,Binary classification,Streaming Random Patches,Phishing,0.8964941569282137,0.8739837398373984,3.515273094177246,329.715755 +625,Binary classification,Streaming Random Patches,Phishing,0.8958333333333334,0.8707753479125249,3.5807180404663086,357.461609 +650,Binary classification,Streaming Random Patches,Phishing,0.8983050847457628,0.8754716981132076,3.695376396179199,386.398038 +675,Binary classification,Streaming Random Patches,Phishing,0.8961424332344213,0.8754448398576512,3.7550153732299805,416.46849299999997 +700,Binary classification,Streaming Random Patches,Phishing,0.899856938483548,0.8784722222222222,3.7909955978393555,447.643877 +725,Binary classification,Streaming Random Patches,Phishing,0.899171270718232,0.8797364085667215,3.9393529891967773,479.929216 +750,Binary classification,Streaming Random Patches,Phishing,0.9012016021361816,0.8825396825396825,3.942519187927246,513.493128 +775,Binary classification,Streaming Random Patches,Phishing,0.9018087855297158,0.8827160493827161,4.2751874923706055,548.2965389999999 +800,Binary classification,Streaming Random Patches,Phishing,0.899874843554443,0.8816568047337278,4.513812065124512,584.3583229999999 +825,Binary classification,Streaming Random Patches,Phishing,0.8992718446601942,0.8819345661450925,4.773520469665527,621.611368 +850,Binary classification,Streaming Random Patches,Phishing,0.901060070671378,0.8836565096952909,4.8153791427612305,660.1796979999999 +875,Binary classification,Streaming Random Patches,Phishing,0.902745995423341,0.884979702300406,4.980830192565918,699.8371419999999 +900,Binary classification,Streaming Random Patches,Phishing,0.9043381535038932,0.8862433862433862,5.134486198425293,740.7850809999999 +925,Binary classification,Streaming Random Patches,Phishing,0.9069264069264069,0.8903061224489796,5.209948539733887,782.8443949999998 +950,Binary classification,Streaming Random Patches,Phishing,0.9083245521601686,0.8932515337423312,5.338950157165527,826.1813729999999 +975,Binary classification,Streaming Random Patches,Phishing,0.9106776180698152,0.895808383233533,5.382990837097168,870.7373769999999 +1000,Binary classification,Streaming Random Patches,Phishing,0.9109109109109109,0.896149358226371,5.44773006439209,916.477949 +1025,Binary classification,Streaming Random Patches,Phishing,0.9111328125,0.896942242355606,5.5915327072143555,963.445117 +1050,Binary classification,Streaming Random Patches,Phishing,0.9113441372735939,0.8976897689768977,5.678961753845215,1011.481541 +1075,Binary classification,Streaming Random Patches,Phishing,0.9115456238361266,0.8986125933831376,5.788058280944824,1060.652982 +1100,Binary classification,Streaming Random Patches,Phishing,0.9117379435850773,0.8990634755463061,5.880267143249512,1110.965738 +1125,Binary classification,Streaming Random Patches,Phishing,0.9119217081850534,0.9003021148036253,6.120665550231934,1162.442095 +1150,Binary classification,Streaming Random Patches,Phishing,0.9129677980852916,0.9013806706114399,6.185591697692871,1215.0055750000001 +1175,Binary classification,Streaming Random Patches,Phishing,0.9114139693356048,0.8996138996138997,6.431841850280762,1268.8167280000002 +1200,Binary classification,Streaming Random Patches,Phishing,0.9124270225187656,0.9004739336492891,6.484606742858887,1323.7082160000002 +1225,Binary classification,Streaming Random Patches,Phishing,0.9133986928104575,0.9014869888475836,6.481654167175293,1379.6419000000003 +1250,Binary classification,Streaming Random Patches,Phishing,0.9135308246597278,0.9019963702359347,6.595587730407715,1436.6903440000003 +1903,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1670236587524414,31.246172 +3806,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1682443618774414,90.057064 +5709,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,168.92668600000002 +7612,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,266.339332 +9515,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,379.70068100000003 +11418,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1706857681274414,507.50093200000003 +13321,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1706857681274414,650.046105 +15224,Binary classification,Streaming Random Patches,SMTP,0.9992774091834724,0.0,0.2171335220336914,806.74928 +17127,Binary classification,Streaming Random Patches,SMTP,0.9992409202382343,0.0,0.1745767593383789,979.905317 +19030,Binary classification,Streaming Random Patches,SMTP,0.9993168322034789,0.0,0.1744394302368164,1169.37771 +20933,Binary classification,Streaming Random Patches,SMTP,0.999378941333843,0.0,0.17577457427978516,1374.513378 +22836,Binary classification,Streaming Random Patches,SMTP,0.9994306984891613,0.0,0.17572879791259766,1595.365052 +24739,Binary classification,Streaming Random Patches,SMTP,0.9994744926833212,0.0,0.1757516860961914,1830.5281120000002 +26642,Binary classification,Streaming Random Patches,SMTP,0.9994744942006681,0.0,0.17572879791259766,2079.072293 +28545,Binary classification,Streaming Random Patches,SMTP,0.9995095291479821,0.0,0.17572879791259766,2341.3308700000002 +30448,Binary classification,Streaming Random Patches,SMTP,0.9995401845830459,0.0,0.17563724517822266,2616.3107910000003 +32351,Binary classification,Streaming Random Patches,SMTP,0.9995672333848532,0.0,0.17577457427978516,2903.8369350000003 +34254,Binary classification,Streaming Random Patches,SMTP,0.9995912766764955,0.0,0.1756601333618164,3203.0985050000004 +36157,Binary classification,Streaming Random Patches,SMTP,0.9996127890253347,0.0,0.17568302154541016,3513.3936680000006 +38060,Binary classification,Streaming Random Patches,SMTP,0.9996321500827662,0.0,0.1757059097290039,3834.7595300000007 +39963,Binary classification,Streaming Random Patches,SMTP,0.9996496671838246,0.0,0.17577457427978516,4167.168481000001 +41866,Binary classification,Streaming Random Patches,SMTP,0.9996655917831124,0.0,0.1769266128540039,4510.027218000001 +43769,Binary classification,Streaming Random Patches,SMTP,0.9996801316029976,0.0,0.1769266128540039,4863.234695000001 +45672,Binary classification,Streaming Random Patches,SMTP,0.9996934597446958,0.0,0.1769266128540039,5226.731618000001 +47575,Binary classification,Streaming Random Patches,SMTP,0.9997057216126456,0.0,0.1770639419555664,5600.511358000001 +49478,Binary classification,Streaming Random Patches,SMTP,0.99971704024092,0.0,0.17690372467041016,5984.651066 +51381,Binary classification,Streaming Random Patches,SMTP,0.9996885947839627,0.0,0.16916751861572266,6379.477192 +53284,Binary classification,Streaming Random Patches,SMTP,0.9996997166075484,0.0,0.1770639419555664,6785.903036000001 +55187,Binary classification,Streaming Random Patches,SMTP,0.999710071394919,0.0,0.17704105377197266,7201.6518080000005 +57090,Binary classification,Streaming Random Patches,SMTP,0.9995620872672494,0.0,0.1769266128540039,7626.427031 +58993,Binary classification,Streaming Random Patches,SMTP,0.9995762137238947,0.0,0.1769266128540039,8059.133738 +60896,Binary classification,Streaming Random Patches,SMTP,0.999589457262501,0.0,0.1769723892211914,8499.283325 +62799,Binary classification,Streaming Random Patches,SMTP,0.9995700500015924,0.0,0.1769266128540039,8946.957028 +64702,Binary classification,Streaming Random Patches,SMTP,0.9995826957852274,0.0,0.17699527740478516,9401.959764000001 +66605,Binary classification,Streaming Random Patches,SMTP,0.9995946189418053,0.0,0.1769723892211914,9864.111715000001 +68508,Binary classification,Streaming Random Patches,SMTP,0.9995766855941729,0.0,0.1691446304321289,10332.853073 +70411,Binary classification,Streaming Random Patches,SMTP,0.9995881266865502,0.0,0.17690372467041016,10808.168746 +72314,Binary classification,Streaming Random Patches,SMTP,0.9995989656078437,0.0,0.16912174224853516,11290.14581 +74217,Binary classification,Streaming Random Patches,SMTP,0.99960924867953,0.0,0.1769723892211914,11778.656001 +76120,Binary classification,Streaming Random Patches,SMTP,0.9996190175908775,0.0,0.1769723892211914,12273.787996 +78023,Binary classification,Streaming Random Patches,SMTP,0.9996283099638563,0.0,0.17699527740478516,12775.472063 +79926,Binary classification,Streaming Random Patches,SMTP,0.9996371598373475,0.0,0.1769723892211914,13283.764207999999 +81829,Binary classification,Streaming Random Patches,SMTP,0.9996455980837855,0.0,0.1770181655883789,13798.661938 +83732,Binary classification,Streaming Random Patches,SMTP,0.9996536527689864,0.0,0.17826175689697266,14320.191281 +85635,Binary classification,Streaming Random Patches,SMTP,0.999661349463998,0.0,0.1703653335571289,14848.294436 +87538,Binary classification,Streaming Random Patches,SMTP,0.9996687115162731,0.0,0.17029666900634766,15383.005183 +89441,Binary classification,Streaming Random Patches,SMTP,0.9996645796064401,0.0,0.1781930923461914,15923.647685 +91344,Binary classification,Streaming Random Patches,SMTP,0.999671567607808,0.0,0.1781473159790039,16470.415157 +93247,Binary classification,Streaming Random Patches,SMTP,0.9996782703815713,0.0,0.17821598052978516,17023.687732 +95150,Binary classification,Streaming Random Patches,SMTP,0.9996847050415664,0.0,0.17821598052978516,17582.958979 +95156,Binary classification,Streaming Random Patches,SMTP,0.9996847249224948,0.0,0.17817020416259766,18142.251024999998 +106,Binary classification,k-Nearest Neighbors,Bananas,0.7238095238095238,0.6881720430107527,0.10328006744384766,0.213787 +212,Binary classification,k-Nearest Neighbors,Bananas,0.8056872037914692,0.7807486631016043,0.1952676773071289,0.888466 +318,Binary classification,k-Nearest Neighbors,Bananas,0.807570977917981,0.7859649122807018,0.28677845001220703,2.29757 +424,Binary classification,k-Nearest Neighbors,Bananas,0.8297872340425532,0.8115183246073298,0.3787660598754883,4.640547 +530,Binary classification,k-Nearest Neighbors,Bananas,0.831758034026465,0.8061002178649236,2.6361207962036133,29.527472000000003 +636,Binary classification,k-Nearest Neighbors,Bananas,0.8472440944881889,0.8245931283905967,3.060887336730957,56.29478 +742,Binary classification,k-Nearest Neighbors,Bananas,0.8529014844804319,0.8278041074249604,3.5180253982543945,85.033958 +848,Binary classification,k-Nearest Neighbors,Bananas,0.8559622195985832,0.8328767123287671,3.9749040603637695,116.00953899999999 +954,Binary classification,k-Nearest Neighbors,Bananas,0.8604407135362014,0.8372093023255813,4.4283952713012695,149.38786199999998 +1060,Binary classification,k-Nearest Neighbors,Bananas,0.8706326723323891,0.8476084538375974,4.5923662185668945,185.28018899999998 +1166,Binary classification,k-Nearest Neighbors,Bananas,0.871244635193133,0.8484848484848485,4.394963264465332,223.46140799999998 +1272,Binary classification,k-Nearest Neighbors,Bananas,0.8693941778127459,0.8477064220183486,4.242337226867676,263.59538599999996 +1378,Binary classification,k-Nearest Neighbors,Bananas,0.8714596949891068,0.8488471391972673,4.1376237869262695,305.59304199999997 +1484,Binary classification,k-Nearest Neighbors,Bananas,0.8759271746459879,0.8548895899053628,4.233838081359863,349.62285599999996 +1590,Binary classification,k-Nearest Neighbors,Bananas,0.8735053492762744,0.8527472527472527,4.485638618469238,396.123591 +1696,Binary classification,k-Nearest Neighbors,Bananas,0.8755162241887906,0.854982817869416,4.566784858703613,444.689218 +1802,Binary classification,k-Nearest Neighbors,Bananas,0.8778456413103831,0.858611825192802,4.580937385559082,495.109217 +1908,Binary classification,k-Nearest Neighbors,Bananas,0.8778185631882538,0.8598917618761276,4.5537919998168945,547.400313 +2014,Binary classification,k-Nearest Neighbors,Bananas,0.877297565822156,0.8605307735742519,4.4779558181762695,601.303554 +2120,Binary classification,k-Nearest Neighbors,Bananas,0.8787163756488909,0.8635156664896441,4.453892707824707,656.840699 +2226,Binary classification,k-Nearest Neighbors,Bananas,0.8782022471910113,0.8630621526023244,4.4562273025512695,714.0315049999999 +2332,Binary classification,k-Nearest Neighbors,Bananas,0.8777348777348777,0.862782859894078,4.439526557922363,772.745195 +2438,Binary classification,k-Nearest Neighbors,Bananas,0.8785391875256463,0.8635944700460828,4.450131416320801,833.024947 +2544,Binary classification,k-Nearest Neighbors,Bananas,0.8788832088084939,0.864793678665496,4.448788642883301,894.808032 +2650,Binary classification,k-Nearest Neighbors,Bananas,0.8784446961117403,0.8647058823529411,4.491581916809082,958.170481 +2756,Binary classification,k-Nearest Neighbors,Bananas,0.879491833030853,0.8659127625201939,4.482541084289551,1022.970177 +2862,Binary classification,k-Nearest Neighbors,Bananas,0.8808109052778749,0.867056530214425,4.4542436599731445,1089.131713 +2968,Binary classification,k-Nearest Neighbors,Bananas,0.8813616447590158,0.8673700075357951,4.489590644836426,1156.687087 +3074,Binary classification,k-Nearest Neighbors,Bananas,0.8805727302310445,0.8665939658306071,4.4426774978637695,1225.526022 +3180,Binary classification,k-Nearest Neighbors,Bananas,0.8820383768480654,0.8677248677248678,4.4409685134887695,1295.666774 +3286,Binary classification,k-Nearest Neighbors,Bananas,0.882496194824962,0.8678082191780822,4.441540718078613,1367.284144 +3392,Binary classification,k-Nearest Neighbors,Bananas,0.8832202890002949,0.8693931398416888,4.4570817947387695,1440.244405 +3498,Binary classification,k-Nearest Neighbors,Bananas,0.8850443237060337,0.8709055876685934,4.465977668762207,1514.504066 +3604,Binary classification,k-Nearest Neighbors,Bananas,0.8856508465167916,0.8710888610763454,4.4596757888793945,1590.040367 +3710,Binary classification,k-Nearest Neighbors,Bananas,0.8864923159881369,0.8724628900333233,4.477154731750488,1666.899992 +3816,Binary classification,k-Nearest Neighbors,Bananas,0.8875491480996068,0.8737120989108037,4.4705095291137695,1745.0344980000002 +3922,Binary classification,k-Nearest Neighbors,Bananas,0.8867635807192042,0.8724870763928776,4.4566545486450195,1824.4605280000003 +4028,Binary classification,k-Nearest Neighbors,Bananas,0.8852743978147505,0.8706606942889138,4.454602241516113,1905.1708120000003 +4134,Binary classification,k-Nearest Neighbors,Bananas,0.8857972417130414,0.8712493180578287,4.461682319641113,1987.1975480000003 +4240,Binary classification,k-Nearest Neighbors,Bananas,0.886765746638358,0.8724760892667376,4.4584245681762695,2070.530861 +4346,Binary classification,k-Nearest Neighbors,Bananas,0.8876869965477561,0.8735751295336789,4.494175910949707,2155.1880220000003 +4452,Binary classification,k-Nearest Neighbors,Bananas,0.8869916872612896,0.8725614390676463,4.517621040344238,2241.198533 +4558,Binary classification,k-Nearest Neighbors,Bananas,0.8869870528856704,0.8729336294103133,4.495129585266113,2328.452784 +4664,Binary classification,k-Nearest Neighbors,Bananas,0.886982629208664,0.873286847799952,4.453595161437988,2416.918556 +4770,Binary classification,k-Nearest Neighbors,Bananas,0.8857202767875865,0.8715531463587085,4.469174385070801,2506.628209 +4876,Binary classification,k-Nearest Neighbors,Bananas,0.8861538461538462,0.871616932685635,4.478787422180176,2597.661861 +4982,Binary classification,k-Nearest Neighbors,Bananas,0.8869704878538446,0.8728832693610296,4.4154863357543945,2689.8978660000002 +5088,Binary classification,k-Nearest Neighbors,Bananas,0.885983880479654,0.8716814159292035,4.439602851867676,2783.355406 +5194,Binary classification,k-Nearest Neighbors,Bananas,0.885422684382823,0.8711842390127733,4.5029191970825195,2878.2182510000002 +5300,Binary classification,k-Nearest Neighbors,Bananas,0.8850726552179656,0.8708377518557794,4.509961128234863,2974.330637 +906,Binary classification,k-Nearest Neighbors,Elec2,0.8784530386740331,0.8711943793911008,4.434150695800781,37.114054 +1812,Binary classification,k-Nearest Neighbors,Elec2,0.8801766979569299,0.8453314326443336,4.643096923828125,93.709907 +2718,Binary classification,k-Nearest Neighbors,Elec2,0.8568273831431726,0.8160756501182034,4.6672821044921875,164.56349699999998 +3624,Binary classification,k-Nearest Neighbors,Elec2,0.8746894838531604,0.8411476557032889,4.594398498535156,248.37817199999998 +4530,Binary classification,k-Nearest Neighbors,Elec2,0.8783395893133142,0.8399651466744118,4.710762023925781,344.82085099999995 +5436,Binary classification,k-Nearest Neighbors,Elec2,0.8745170193192272,0.8360576923076923,4.698677062988281,452.38148599999994 +6342,Binary classification,k-Nearest Neighbors,Elec2,0.8747831572307208,0.8384865744507731,4.6694183349609375,569.8523869999999 +7248,Binary classification,k-Nearest Neighbors,Elec2,0.8723609769559818,0.8348509194786646,4.666007995605469,697.1091419999999 +8154,Binary classification,k-Nearest Neighbors,Elec2,0.8718263215994113,0.8430695299594534,4.7265625,834.9178869999998 +9060,Binary classification,k-Nearest Neighbors,Elec2,0.8738271332376643,0.8493475682087781,4.708610534667969,981.8103579999998 +9966,Binary classification,k-Nearest Neighbors,Elec2,0.8720521826392373,0.8501234277653698,4.6251678466796875,1137.002296 +10872,Binary classification,k-Nearest Neighbors,Elec2,0.8740686229417717,0.8545628386274301,4.637184143066406,1300.798705 +11778,Binary classification,k-Nearest Neighbors,Elec2,0.8742464124989386,0.8546756942400157,4.6933135986328125,1473.412993 +12684,Binary classification,k-Nearest Neighbors,Elec2,0.872664196168099,0.8527937289217027,4.810676574707031,1655.5819629999999 +13590,Binary classification,k-Nearest Neighbors,Elec2,0.8748252262859666,0.8573824096587573,4.703468322753906,1846.5010799999998 +14496,Binary classification,k-Nearest Neighbors,Elec2,0.8750603656433252,0.85826093762229,4.7199859619140625,2046.3736259999998 +15402,Binary classification,k-Nearest Neighbors,Elec2,0.8755924939938965,0.8581371242410781,4.7149505615234375,2254.7741159999996 +16308,Binary classification,k-Nearest Neighbors,Elec2,0.872079475072055,0.8535112359550563,4.6830902099609375,2471.4717159999996 +17214,Binary classification,k-Nearest Neighbors,Elec2,0.8723058153721025,0.8517669274345832,4.657257080078125,2696.3467009999995 +18120,Binary classification,k-Nearest Neighbors,Elec2,0.87234394834152,0.8515118443859535,4.7351837158203125,2929.4346569999993 +19026,Binary classification,k-Nearest Neighbors,Elec2,0.8734822601839685,0.8509505232522138,4.8458709716796875,3171.2751989999992 +19932,Binary classification,k-Nearest Neighbors,Elec2,0.8722091214690683,0.8505193966782089,4.8552703857421875,3421.524358999999 +20838,Binary classification,k-Nearest Neighbors,Elec2,0.8678312616979411,0.8451765234989881,4.8942718505859375,3679.924087999999 +21744,Binary classification,k-Nearest Neighbors,Elec2,0.8677735363105368,0.8427672955974842,4.7196807861328125,3945.793201999999 +22650,Binary classification,k-Nearest Neighbors,Elec2,0.8669256920835356,0.840444679724722,4.8090057373046875,4218.904371999999 +23556,Binary classification,k-Nearest Neighbors,Elec2,0.8647845468053492,0.8373257061136934,4.794342041015625,4499.001777999999 +24462,Binary classification,k-Nearest Neighbors,Elec2,0.8644372674870201,0.8359715077166601,4.7589569091796875,4786.067247999999 +25368,Binary classification,k-Nearest Neighbors,Elec2,0.8619860448614342,0.8330710914032329,4.846771240234375,5079.937268999999 +26274,Binary classification,k-Nearest Neighbors,Elec2,0.8623301488219846,0.8333410127632125,4.699310302734375,5380.101847999999 +27180,Binary classification,k-Nearest Neighbors,Elec2,0.8632767945840538,0.8350350705851016,4.794769287109375,5686.6129089999995 +28086,Binary classification,k-Nearest Neighbors,Elec2,0.862061598718177,0.8333620096352374,4.6817474365234375,5999.306036 +28992,Binary classification,k-Nearest Neighbors,Elec2,0.8618191852643924,0.8323989624299222,4.8116455078125,6318.1198079999995 +29898,Binary classification,k-Nearest Neighbors,Elec2,0.8607218115529987,0.8308417289567761,4.769432067871094,6643.155825 +30804,Binary classification,k-Nearest Neighbors,Elec2,0.8599162419244879,0.8291562735083342,4.83782958984375,6975.21929 +31710,Binary classification,k-Nearest Neighbors,Elec2,0.8578006244283958,0.8263832736513803,4.7655487060546875,7313.109579 +32616,Binary classification,k-Nearest Neighbors,Elec2,0.8558332055802544,0.8246307623452186,4.726959228515625,7656.831982 +33522,Binary classification,k-Nearest Neighbors,Elec2,0.8543897855075923,0.8232354325861008,4.798057556152344,8006.181245 +34428,Binary classification,k-Nearest Neighbors,Elec2,0.8533128068086095,0.8218066337332393,4.773887634277344,8361.39601 +35334,Binary classification,k-Nearest Neighbors,Elec2,0.8518099227351201,0.8192737815822173,4.808341979980469,8722.632877 +36240,Binary classification,k-Nearest Neighbors,Elec2,0.8522310218273131,0.8186651315566692,4.722572326660156,9089.688296 +37146,Binary classification,k-Nearest Neighbors,Elec2,0.8505586216179836,0.8161859664227292,4.720252990722656,9462.293988 +38052,Binary classification,k-Nearest Neighbors,Elec2,0.8507792173661665,0.81590039556449,4.766929626464844,9840.72504 +38958,Binary classification,k-Nearest Neighbors,Elec2,0.8507841979618553,0.8163523204751524,4.769111633300781,10225.058019 +39864,Binary classification,k-Nearest Neighbors,Elec2,0.850889295838246,0.8178809976101478,4.736076354980469,10615.291715 +40770,Binary classification,k-Nearest Neighbors,Elec2,0.8509161372611543,0.8193329766363474,4.725471496582031,11011.540551999999 +41676,Binary classification,k-Nearest Neighbors,Elec2,0.8518536292741452,0.8217770336585647,4.700096130371094,11414.574327999999 +42582,Binary classification,k-Nearest Neighbors,Elec2,0.8529156196425636,0.8235028885444553,4.746559143066406,11823.240958999999 +43488,Binary classification,k-Nearest Neighbors,Elec2,0.8525536367190195,0.8231074817920989,4.826316833496094,12236.954316 +44394,Binary classification,k-Nearest Neighbors,Elec2,0.8525217939765278,0.8226754421602882,4.775764465332031,12655.735001 +45300,Binary classification,k-Nearest Neighbors,Elec2,0.853131415704541,0.8236541468974475,4.7673492431640625,13079.486139999999 +45312,Binary classification,k-Nearest Neighbors,Elec2,0.8531482421487057,0.8236416644579911,4.7660369873046875,13503.439196 +25,Binary classification,k-Nearest Neighbors,Phishing,0.5833333333333334,0.7058823529411764,0.041108131408691406,0.04635 +50,Binary classification,k-Nearest Neighbors,Phishing,0.7551020408163265,0.7777777777777778,0.0695962905883789,0.16308 +75,Binary classification,k-Nearest Neighbors,Phishing,0.7972972972972973,0.8235294117647058,0.09861469268798828,0.336872 +100,Binary classification,k-Nearest Neighbors,Phishing,0.797979797979798,0.8148148148148148,0.12712955474853516,0.6777850000000001 +125,Binary classification,k-Nearest Neighbors,Phishing,0.8064516129032258,0.8208955223880596,0.15564441680908203,1.226658 +150,Binary classification,k-Nearest Neighbors,Phishing,0.8187919463087249,0.834355828220859,0.1846628189086914,1.947513 +175,Binary classification,k-Nearest Neighbors,Phishing,0.8390804597701149,0.8426966292134832,0.21317768096923828,2.9471350000000003 +200,Binary classification,k-Nearest Neighbors,Phishing,0.8391959798994975,0.8415841584158417,0.24219608306884766,4.26255 +225,Binary classification,k-Nearest Neighbors,Phishing,0.8392857142857143,0.8363636363636364,0.27071094512939453,5.830504 +250,Binary classification,k-Nearest Neighbors,Phishing,0.8232931726907631,0.8225806451612903,0.2992258071899414,7.819846 +275,Binary classification,k-Nearest Neighbors,Phishing,0.8248175182481752,0.8208955223880596,0.3284578323364258,10.199689 +300,Binary classification,k-Nearest Neighbors,Phishing,0.8260869565217391,0.8181818181818181,0.35697269439697266,12.972731999999999 +325,Binary classification,k-Nearest Neighbors,Phishing,0.8364197530864198,0.8250825082508251,0.38599109649658203,16.225203999999998 +350,Binary classification,k-Nearest Neighbors,Phishing,0.8452722063037249,0.83125,0.4145059585571289,19.962148 +375,Binary classification,k-Nearest Neighbors,Phishing,0.839572192513369,0.8235294117647058,0.4430208206176758,24.257676 +400,Binary classification,k-Nearest Neighbors,Phishing,0.8421052631578947,0.8225352112676055,0.47203922271728516,29.175886 +425,Binary classification,k-Nearest Neighbors,Phishing,0.8443396226415094,0.819672131147541,0.500554084777832,34.714831 +450,Binary classification,k-Nearest Neighbors,Phishing,0.8463251670378619,0.8198433420365536,0.5295724868774414,40.875927999999995 +475,Binary classification,k-Nearest Neighbors,Phishing,0.8438818565400844,0.8177339901477833,0.5580873489379883,47.66810099999999 +500,Binary classification,k-Nearest Neighbors,Phishing,0.845691382765531,0.8229885057471266,2.6757898330688477,79.03492 +525,Binary classification,k-Nearest Neighbors,Phishing,0.8454198473282443,0.8187919463087249,2.7769289016723633,111.488727 +550,Binary classification,k-Nearest Neighbors,Phishing,0.848816029143898,0.8237791932059448,2.8829355239868164,145.039549 +575,Binary classification,k-Nearest Neighbors,Phishing,0.8519163763066202,0.8268839103869654,2.989964485168457,179.782132 +600,Binary classification,k-Nearest Neighbors,Phishing,0.8514190317195326,0.8230616302186878,3.098984718322754,215.642079 +625,Binary classification,k-Nearest Neighbors,Phishing,0.8525641025641025,0.8210116731517509,3.2059221267700195,252.521109 +650,Binary classification,k-Nearest Neighbors,Phishing,0.8582434514637904,0.8302583025830258,3.3169260025024414,290.529559 +675,Binary classification,k-Nearest Neighbors,Phishing,0.8620178041543026,0.8382608695652174,3.4291276931762695,329.62297 +700,Binary classification,k-Nearest Neighbors,Phishing,0.8669527896995708,0.8421052631578948,3.5458459854125977,369.665809 +725,Binary classification,k-Nearest Neighbors,Phishing,0.8674033149171271,0.8456591639871384,3.6591615676879883,410.97711300000003 +750,Binary classification,k-Nearest Neighbors,Phishing,0.8678237650200267,0.8465116279069768,3.769242286682129,453.55010200000004 +775,Binary classification,k-Nearest Neighbors,Phishing,0.8669250645994832,0.8446455505279035,3.881718635559082,497.265773 +800,Binary classification,k-Nearest Neighbors,Phishing,0.8648310387984981,0.8434782608695652,3.9942636489868164,542.189116 +825,Binary classification,k-Nearest Neighbors,Phishing,0.8628640776699029,0.8423988842398884,4.110844612121582,588.325742 +850,Binary classification,k-Nearest Neighbors,Phishing,0.8657243816254417,0.8451086956521738,4.225159645080566,635.696811 +875,Binary classification,k-Nearest Neighbors,Phishing,0.868421052631579,0.847277556440903,4.342709541320801,684.216091 +900,Binary classification,k-Nearest Neighbors,Phishing,0.8698553948832035,0.8482490272373541,4.455658912658691,733.952375 +925,Binary classification,k-Nearest Neighbors,Phishing,0.8712121212121212,0.8514357053682896,4.573666572570801,785.013527 +950,Binary classification,k-Nearest Neighbors,Phishing,0.8735511064278187,0.8561151079136691,4.697152137756348,837.27657 +975,Binary classification,k-Nearest Neighbors,Phishing,0.8757700205338809,0.8581477139507622,4.820996284484863,890.836474 +1000,Binary classification,k-Nearest Neighbors,Phishing,0.8758758758758759,0.858447488584475,4.93715763092041,945.663339 +1025,Binary classification,k-Nearest Neighbors,Phishing,0.8759765625,0.8590455049944505,4.905686378479004,1001.6659109999999 +1050,Binary classification,k-Nearest Neighbors,Phishing,0.8779790276453765,0.8617710583153347,4.881028175354004,1058.8496129999999 +1075,Binary classification,k-Nearest Neighbors,Phishing,0.8780260707635009,0.86282722513089,4.857575416564941,1117.1299479999998 +1100,Binary classification,k-Nearest Neighbors,Phishing,0.8789808917197452,0.8641470888661901,4.821175575256348,1176.4409139999998 +1125,Binary classification,k-Nearest Neighbors,Phishing,0.8798932384341637,0.8662041625371655,4.749619483947754,1236.7626339999997 +1150,Binary classification,k-Nearest Neighbors,Phishing,0.8807658833768495,0.8668610301263362,4.722535133361816,1298.0588499999997 +1175,Binary classification,k-Nearest Neighbors,Phishing,0.879045996592845,0.8645038167938931,4.706612586975098,1360.3228199999996 +1200,Binary classification,k-Nearest Neighbors,Phishing,0.8807339449541285,0.865979381443299,4.686341285705566,1423.5043029999997 +1225,Binary classification,k-Nearest Neighbors,Phishing,0.8815359477124183,0.8666053357865686,4.653275489807129,1487.6393369999996 +1250,Binary classification,k-Nearest Neighbors,Phishing,0.8815052041633307,0.867145421903052,4.596428871154785,1552.6498929999996 +1903,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.559709548950195,49.463009 +3806,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.594751358032227,126.299444 +5709,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.435243606567383,223.803561 +7612,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.493677139282227,340.763146 +9515,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.534708023071289,475.19475 +11418,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.455095291137695,625.35715 +13321,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.479013442993164,790.6914730000001 +15224,Binary classification,k-Nearest Neighbors,SMTP,0.9998686198515404,0.9,4.445444107055664,971.1285220000001 +17127,Binary classification,k-Nearest Neighbors,SMTP,0.9998832184981898,0.9166666666666666,4.544534683227539,1166.447296 +19030,Binary classification,k-Nearest Neighbors,SMTP,0.9998948972620737,0.9166666666666666,4.52708625793457,1376.022994 +20933,Binary classification,k-Nearest Neighbors,SMTP,0.999904452512899,0.9166666666666666,4.493997573852539,1599.513782 +22836,Binary classification,k-Nearest Neighbors,SMTP,0.9999124151521787,0.9166666666666666,4.490983963012695,1835.5325109999999 +24739,Binary classification,k-Nearest Neighbors,SMTP,0.9999191527205109,0.9166666666666666,4.531465530395508,2083.498651 +26642,Binary classification,k-Nearest Neighbors,SMTP,0.9998873916144289,0.88,4.54191780090332,2343.113276 +28545,Binary classification,k-Nearest Neighbors,SMTP,0.999894899103139,0.88,4.488824844360352,2613.833594 +30448,Binary classification,k-Nearest Neighbors,SMTP,0.9999014681249384,0.88,4.459695816040039,2894.8346570000003 +32351,Binary classification,k-Nearest Neighbors,SMTP,0.9999072642967543,0.88,4.475152969360352,3186.5040240000003 +34254,Binary classification,k-Nearest Neighbors,SMTP,0.9999124164306776,0.88,4.543954849243164,3487.9588300000005 +36157,Binary classification,k-Nearest Neighbors,SMTP,0.9999170262197146,0.88,4.482622146606445,3798.7831540000006 +38060,Binary classification,k-Nearest Neighbors,SMTP,0.9999211750177356,0.88,4.496248245239258,4119.269013000001 +39963,Binary classification,k-Nearest Neighbors,SMTP,0.9999249286822481,0.88,4.471353530883789,4448.958874000001 +41866,Binary classification,k-Nearest Neighbors,SMTP,0.9999283410963812,0.88,4.53770637512207,4788.142489000001 +43769,Binary classification,k-Nearest Neighbors,SMTP,0.999931456772071,0.88,4.51286506652832,5135.940338 +45672,Binary classification,k-Nearest Neighbors,SMTP,0.9999343128024348,0.88,4.49894905090332,5492.9415070000005 +47575,Binary classification,k-Nearest Neighbors,SMTP,0.9999369403455669,0.88,4.555765151977539,5859.118968000001 +49478,Binary classification,k-Nearest Neighbors,SMTP,0.9999393657659115,0.88,4.430139541625977,6234.600581000001 +51381,Binary classification,k-Nearest Neighbors,SMTP,0.9999221486959906,0.8571428571428571,4.466188430786133,6619.789592000001 +53284,Binary classification,k-Nearest Neighbors,SMTP,0.9999249291518871,0.8571428571428571,4.526651382446289,7013.956607000001 +55187,Binary classification,k-Nearest Neighbors,SMTP,0.9999275178487298,0.8571428571428571,4.485139846801758,7418.775951000001 +57090,Binary classification,k-Nearest Neighbors,SMTP,0.9997898018882797,0.7391304347826089,4.452577590942383,7831.9045620000015 +58993,Binary classification,k-Nearest Neighbors,SMTP,0.9997965825874695,0.7391304347826089,4.485406875610352,8252.946705000002 +60896,Binary classification,k-Nearest Neighbors,SMTP,0.9998029394860005,0.7391304347826089,4.502649307250977,8681.511861000003 +62799,Binary classification,k-Nearest Neighbors,SMTP,0.9997770629637887,0.7083333333333334,4.495584487915039,9116.499033000002 +64702,Binary classification,k-Nearest Neighbors,SMTP,0.9997836200367846,0.7083333333333334,4.500345230102539,9557.922914000002 +66605,Binary classification,k-Nearest Neighbors,SMTP,0.9997898024142694,0.7083333333333334,4.572656631469727,10005.892137000003 +68508,Binary classification,k-Nearest Neighbors,SMTP,0.9997664472243712,0.68,4.537866592407227,10460.064213000003 +70411,Binary classification,k-Nearest Neighbors,SMTP,0.9997727595512002,0.68,4.469621658325195,10920.643886000003 +72314,Binary classification,k-Nearest Neighbors,SMTP,0.9997787396457068,0.68,4.537904739379883,11387.046035000003 +74217,Binary classification,k-Nearest Neighbors,SMTP,0.9997844130645683,0.68,4.493074417114258,11859.048710000003 +76120,Binary classification,k-Nearest Neighbors,SMTP,0.99978980280876,0.68,4.520692825317383,12336.641580000003 +78023,Binary classification,k-Nearest Neighbors,SMTP,0.9997949296352311,0.68,4.566102981567383,12820.170836000003 +79926,Binary classification,k-Nearest Neighbors,SMTP,0.9997998123240538,0.68,4.500688552856445,13309.287446000002 +81829,Binary classification,k-Nearest Neighbors,SMTP,0.9998044679082955,0.68,4.506959915161133,13804.219559000003 +83732,Binary classification,k-Nearest Neighbors,SMTP,0.9998089118725442,0.68,4.503435134887695,14304.793113000003 +85635,Binary classification,k-Nearest Neighbors,SMTP,0.9998131583249644,0.68,4.498682022094727,14810.923265000003 +87538,Binary classification,k-Nearest Neighbors,SMTP,0.9998172201469093,0.68,4.491861343383789,15322.783751000003 +89441,Binary classification,k-Nearest Neighbors,SMTP,0.9998099284436494,0.6666666666666666,4.496858596801758,15840.351229000004 +91344,Binary classification,k-Nearest Neighbors,SMTP,0.9998138883110912,0.6666666666666666,4.480546951293945,16363.581709000004 +93247,Binary classification,k-Nearest Neighbors,SMTP,0.999817686549557,0.6666666666666666,4.533571243286133,16892.331870000005 +95150,Binary classification,k-Nearest Neighbors,SMTP,0.9998213328568876,0.6666666666666666,4.517786026000977,17426.665113000006 +95156,Binary classification,k-Nearest Neighbors,SMTP,0.9998213441227471,0.6666666666666666,4.518220901489258,17961.110841000005 +106,Binary classification,ADWIN Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.1797952651977539,0.693272 +212,Binary classification,ADWIN Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.1805887222290039,2.027128 +318,Binary classification,ADWIN Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.18126773834228516,4.089008 +424,Binary classification,ADWIN Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.18131351470947266,6.9179189999999995 +530,Binary classification,ADWIN Bagging,Bananas,0.55765595463138,0.45581395348837206,0.1813364028930664,10.429995 +636,Binary classification,ADWIN Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.1819925308227539,14.687229 +742,Binary classification,ADWIN Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.1820383071899414,19.647457 +848,Binary classification,ADWIN Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.18196964263916016,25.366910999999998 +954,Binary classification,ADWIN Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.1819467544555664,31.800241999999997 +1060,Binary classification,ADWIN Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.1819467544555664,39.029576 +1166,Binary classification,ADWIN Bagging,Bananas,0.590557939914163,0.4015056461731493,0.18192386627197266,46.984262 +1272,Binary classification,ADWIN Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.18192386627197266,55.672123 +1378,Binary classification,ADWIN Bagging,Bananas,0.599128540305011,0.3973799126637554,0.18253421783447266,65.02100899999999 +1484,Binary classification,ADWIN Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.18248844146728516,75.177128 +1590,Binary classification,ADWIN Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.1824655532836914,86.053176 +1696,Binary classification,ADWIN Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.1824655532836914,97.727606 +1802,Binary classification,ADWIN Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.18248844146728516,110.067211 +1908,Binary classification,ADWIN Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.18248844146728516,123.213825 +2014,Binary classification,ADWIN Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.18248844146728516,137.051202 +2120,Binary classification,ADWIN Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.18248844146728516,151.568436 +2226,Binary classification,ADWIN Bagging,Bananas,0.597752808988764,0.40133779264214053,0.18244266510009766,166.814875 +2332,Binary classification,ADWIN Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.18244266510009766,182.823981 +2438,Binary classification,ADWIN Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.1824655532836914,199.616425 +2544,Binary classification,ADWIN Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.1824655532836914,217.084375 +2650,Binary classification,ADWIN Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.18309879302978516,235.279245 +2756,Binary classification,ADWIN Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.18309879302978516,254.250965 +2862,Binary classification,ADWIN Bagging,Bananas,0.599790283117791,0.4051948051948052,0.18309879302978516,273.857236 +2968,Binary classification,ADWIN Bagging,Bananas,0.599932591843613,0.40261701056869653,0.1831216812133789,294.204326 +3074,Binary classification,ADWIN Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.1831216812133789,315.311898 +3180,Binary classification,ADWIN Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.1831216812133789,337.189575 +3286,Binary classification,ADWIN Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.17859649658203125,359.75124 +3392,Binary classification,ADWIN Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.2364349365234375,383.144231 +3498,Binary classification,ADWIN Bagging,Bananas,0.597369173577352,0.40237691001697795,0.2806434631347656,407.32428699999997 +3604,Binary classification,ADWIN Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3000526428222656,432.314941 +3710,Binary classification,ADWIN Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.3464546203613281,458.107983 +3816,Binary classification,ADWIN Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.3760719299316406,484.645149 +3922,Binary classification,ADWIN Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.4113121032714844,512.014837 +4028,Binary classification,ADWIN Bagging,Bananas,0.608393344921778,0.4195804195804196,0.4392280578613281,540.239956 +4134,Binary classification,ADWIN Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.4532661437988281,569.3761920000001 +4240,Binary classification,ADWIN Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.4546051025390625,599.333749 +4346,Binary classification,ADWIN Bagging,Bananas,0.6186421173762946,0.4384954252795662,0.4373931884765625,630.119 +4452,Binary classification,ADWIN Bagging,Bananas,0.6212087171422153,0.44209133024487096,0.43770599365234375,661.732786 +4558,Binary classification,ADWIN Bagging,Bananas,0.6214614878209348,0.44372782973234437,0.42758941650390625,694.0925080000001 +4664,Binary classification,ADWIN Bagging,Bananas,0.6219172206733863,0.44542308902170497,0.3975372314453125,727.2725200000001 +4770,Binary classification,ADWIN Bagging,Bananas,0.6227720696162717,0.4449244060475162,0.42584228515625,761.2761580000001 +4876,Binary classification,ADWIN Bagging,Bananas,0.6235897435897436,0.4444444444444444,0.393829345703125,796.1318860000001 +4982,Binary classification,ADWIN Bagging,Bananas,0.6251756675366392,0.44910002950722927,0.39398193359375,831.6856570000001 +5088,Binary classification,ADWIN Bagging,Bananas,0.624139964615687,0.44675925925925924,0.39410400390625,867.9313910000001 +5194,Binary classification,ADWIN Bagging,Bananas,0.6248796456768727,0.44690516751845544,0.394500732421875,904.9575060000001 +5300,Binary classification,ADWIN Bagging,Bananas,0.6259671636157765,0.44821826280623617,0.40065765380859375,942.730038 +906,Binary classification,ADWIN Bagging,Elec2,0.8651933701657458,0.8685344827586208,1.5650663375854492,11.845084 +1812,Binary classification,ADWIN Bagging,Elec2,0.8895637769188294,0.8684210526315789,1.8734617233276367,34.886970000000005 +2718,Binary classification,ADWIN Bagging,Elec2,0.8778064041221936,0.8547681539807523,1.7035398483276367,70.08374500000001 +3624,Binary classification,ADWIN Bagging,Elec2,0.8835219431410434,0.8607260726072606,1.6641263961791992,115.953507 +4530,Binary classification,ADWIN Bagging,Elec2,0.8878339589313314,0.8599007170435742,2.0698423385620117,171.720075 +5436,Binary classification,ADWIN Bagging,Elec2,0.886292548298068,0.8580615525953147,2.326838493347168,235.88433300000003 +6342,Binary classification,ADWIN Bagging,Elec2,0.8845607948273143,0.8556782334384859,1.8882322311401367,307.801578 +7248,Binary classification,ADWIN Bagging,Elec2,0.8835380157306472,0.8526021655606008,1.7675046920776367,386.842642 +8154,Binary classification,ADWIN Bagging,Elec2,0.8854409419845456,0.8617115783239561,1.8750486373901367,473.251889 +9060,Binary classification,ADWIN Bagging,Elec2,0.8863009162159179,0.8664765361680064,1.8668012619018555,566.6143930000001 +9966,Binary classification,ADWIN Bagging,Elec2,0.883492222779729,0.8657337805019083,1.624751091003418,666.8526760000001 +10872,Binary classification,ADWIN Bagging,Elec2,0.8851071658541072,0.8689539397754694,1.8364439010620117,773.0944300000001 +11778,Binary classification,ADWIN Bagging,Elec2,0.8819733378619343,0.8645224171539961,1.461909294128418,884.893217 +12684,Binary classification,ADWIN Bagging,Elec2,0.8788930063865016,0.8610709117221419,1.412806510925293,1002.145707 +13590,Binary classification,ADWIN Bagging,Elec2,0.880197218338362,0.863970588235294,1.521845817565918,1125.032122 +14496,Binary classification,ADWIN Bagging,Elec2,0.8799586064160055,0.8644437519476471,1.922499656677246,1253.0070850000002 +15402,Binary classification,ADWIN Bagging,Elec2,0.8805272384910071,0.8643667993513195,1.924330711364746,1386.9590420000002 +16308,Binary classification,ADWIN Bagging,Elec2,0.8794382780401054,0.8622670589883704,1.6739492416381836,1526.6043270000002 +17214,Binary classification,ADWIN Bagging,Elec2,0.8781153779120432,0.8583389601620527,2.050276756286621,1671.5850450000003 +18120,Binary classification,ADWIN Bagging,Elec2,0.8772559192008389,0.8570510348373829,2.0607213973999023,1821.6195730000002 +19026,Binary classification,ADWIN Bagging,Elec2,0.8782128777923784,0.8564702967230379,1.4914274215698242,1976.6826170000002 +19932,Binary classification,ADWIN Bagging,Elec2,0.8703025437760273,0.8482357776081723,0.7451009750366211,2137.4066430000003 +20838,Binary classification,ADWIN Bagging,Elec2,0.8626001823679033,0.8387314820030418,0.7786626815795898,2303.79277 +21744,Binary classification,ADWIN Bagging,Elec2,0.8638642321666743,0.8378259916721456,0.8927946090698242,2475.479733 +22650,Binary classification,ADWIN Bagging,Elec2,0.8620689655172413,0.8337590464027246,1.0149259567260742,2652.48421 +23556,Binary classification,ADWIN Bagging,Elec2,0.8552748885586924,0.8239789332369494,0.7698392868041992,2835.224884 +24462,Binary classification,ADWIN Bagging,Elec2,0.8513143371080496,0.8171902488062327,0.8274068832397461,3023.118045 +25368,Binary classification,ADWIN Bagging,Elec2,0.8471242165017543,0.8121670057153928,0.9264287948608398,3216.225907 +26274,Binary classification,ADWIN Bagging,Elec2,0.8469912077037263,0.8116213683223992,1.0318632125854492,3414.151818 +27180,Binary classification,ADWIN Bagging,Elec2,0.8476397218440708,0.8134600657687283,0.828364372253418,3617.194246 +28086,Binary classification,ADWIN Bagging,Elec2,0.8442585009791703,0.8083260297984224,0.9404935836791992,3825.52125 +28992,Binary classification,ADWIN Bagging,Elec2,0.8416405091235211,0.8035935828877006,0.932948112487793,4039.2676589999996 +29898,Binary classification,ADWIN Bagging,Elec2,0.8389804997156906,0.7997670742866649,0.8770322799682617,4258.3083369999995 +30804,Binary classification,ADWIN Bagging,Elec2,0.8374508976398403,0.7964054812344976,1.024897575378418,4482.681616999999 +31710,Binary classification,ADWIN Bagging,Elec2,0.8326973414488,0.7888725275599952,0.9494352340698242,4711.951208999999 +32616,Binary classification,ADWIN Bagging,Elec2,0.827318718381113,0.7808219178082191,0.861109733581543,4945.765051999999 +33522,Binary classification,ADWIN Bagging,Elec2,0.8271531278899794,0.7806964420893262,1.093031883239746,5184.141968999998 +34428,Binary classification,ADWIN Bagging,Elec2,0.8255439044935661,0.7779174678302028,1.133570671081543,5427.007607999998 +35334,Binary classification,ADWIN Bagging,Elec2,0.8253191067840263,0.7766196163590301,1.1872129440307617,5674.462564999998 +36240,Binary classification,ADWIN Bagging,Elec2,0.8264576837109192,0.7764070110569915,1.431788444519043,5926.318084999998 +37146,Binary classification,ADWIN Bagging,Elec2,0.8245255081437609,0.7721616331096196,1.357222557067871,6182.753490999998 +38052,Binary classification,ADWIN Bagging,Elec2,0.8241833328953247,0.7704974271012006,1.1449995040893555,6443.434381999998 +38958,Binary classification,ADWIN Bagging,Elec2,0.8233950252842878,0.7698534823041413,1.147334098815918,6708.424011999998 +39864,Binary classification,ADWIN Bagging,Elec2,0.8222160901086221,0.7699250073044834,0.7468709945678711,6977.570913999998 +40770,Binary classification,ADWIN Bagging,Elec2,0.8227329588658049,0.7725140860587366,0.9336042404174805,7251.011506999998 +41676,Binary classification,ADWIN Bagging,Elec2,0.8235872825434913,0.7755388654820785,1.097620964050293,7528.4976689999985 +42582,Binary classification,ADWIN Bagging,Elec2,0.8245696437378174,0.7776785714285713,1.5453977584838867,7809.843107999998 +43488,Binary classification,ADWIN Bagging,Elec2,0.8251431462276082,0.7787605469886529,0.8941831588745117,8094.859004999998 +44394,Binary classification,ADWIN Bagging,Elec2,0.8243867276372401,0.7766701042740918,0.744959831237793,8383.886548999999 +45300,Binary classification,ADWIN Bagging,Elec2,0.823770944170953,0.7766305716444221,0.5983161926269531,8676.996437 +45312,Binary classification,ADWIN Bagging,Elec2,0.8237734766392267,0.7765871128395959,0.5984382629394531,8970.151376 +25,Binary classification,ADWIN Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.6633157730102539,0.427424 +50,Binary classification,ADWIN Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.6639947891235352,1.324595 +75,Binary classification,ADWIN Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.6639490127563477,2.554164 +100,Binary classification,ADWIN Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.6645593643188477,4.28613 +125,Binary classification,ADWIN Bagging,Phishing,0.8548387096774194,0.85,0.6645593643188477,6.454494 +150,Binary classification,ADWIN Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.6645593643188477,8.992416 +175,Binary classification,ADWIN Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.6651926040649414,11.944220000000001 +200,Binary classification,ADWIN Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.6653299331665039,15.477702 +225,Binary classification,ADWIN Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7024993896484375,19.458772 +250,Binary classification,ADWIN Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.730194091796875,23.970287 +275,Binary classification,ADWIN Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7302398681640625,28.914006 +300,Binary classification,ADWIN Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7302398681640625,34.304573 +325,Binary classification,ADWIN Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7308502197265625,40.225778999999996 +350,Binary classification,ADWIN Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7308731079101562,46.580822 +375,Binary classification,ADWIN Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.7674179077148438,53.398646 +400,Binary classification,ADWIN Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.804534912109375,60.683253 +425,Binary classification,ADWIN Bagging,Phishing,0.8679245283018868,0.839080459770115,0.8596954345703125,68.459501 +450,Binary classification,ADWIN Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.8597640991210938,76.689807 +475,Binary classification,ADWIN Bagging,Phishing,0.869198312236287,0.8402061855670103,0.859832763671875,85.340536 +500,Binary classification,ADWIN Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.8598556518554688,94.431883 +525,Binary classification,ADWIN Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.8598556518554688,103.968058 +550,Binary classification,ADWIN Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.8599014282226562,113.947374 +575,Binary classification,ADWIN Bagging,Phishing,0.867595818815331,0.8382978723404255,0.8599014282226562,124.33795699999999 +600,Binary classification,ADWIN Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.8599014282226562,135.24069899999998 +625,Binary classification,ADWIN Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.8966064453125,146.56138699999997 +650,Binary classification,ADWIN Bagging,Phishing,0.8767334360554699,0.846153846153846,0.897308349609375,158.32837399999997 +675,Binary classification,ADWIN Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.92486572265625,170.52005599999995 +700,Binary classification,ADWIN Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.8633918762207031,183.10001399999996 +725,Binary classification,ADWIN Bagging,Phishing,0.8825966850828729,0.8576214405360134,0.9612770080566406,196.14419299999997 +750,Binary classification,ADWIN Bagging,Phishing,0.8865153538050734,0.8631239935587761,0.9975471496582031,209.59830599999998 +775,Binary classification,ADWIN Bagging,Phishing,0.8875968992248062,0.863849765258216,1.0525703430175781,223.56014999999996 +800,Binary classification,ADWIN Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.1531257629394531,237.94997899999996 +825,Binary classification,ADWIN Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.1537437438964844,252.78040299999995 +850,Binary classification,ADWIN Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.1632118225097656,267.983484 +875,Binary classification,ADWIN Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.1906776428222656,283.60495299999997 +900,Binary classification,ADWIN Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.2457008361816406,299.65263899999997 +925,Binary classification,ADWIN Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.2457923889160156,316.05876399999994 +950,Binary classification,ADWIN Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.2458381652832031,332.97263699999996 +975,Binary classification,ADWIN Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.2458839416503906,350.27117 +1000,Binary classification,ADWIN Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.2458610534667969,368.008026 +1025,Binary classification,ADWIN Bagging,Phishing,0.8876953125,0.8700564971751412,1.2459068298339844,386.172169 +1050,Binary classification,ADWIN Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.2458839416503906,404.74234 +1075,Binary classification,ADWIN Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.2458839416503906,423.71995200000003 +1100,Binary classification,ADWIN Bagging,Phishing,0.89171974522293,0.8761706555671176,1.2458839416503906,443.13918 +1125,Binary classification,ADWIN Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.2458839416503906,462.95518100000004 +1150,Binary classification,ADWIN Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.2458839416503906,483.090208 +1175,Binary classification,ADWIN Bagging,Phishing,0.8926746166950597,0.877906976744186,1.2458839416503906,503.74833900000004 +1200,Binary classification,ADWIN Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.2550315856933594,524.8299360000001 +1225,Binary classification,ADWIN Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3099861145019531,546.3067460000001 +1250,Binary classification,ADWIN Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3100776672363281,568.2182720000001 +1903,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.15993690490722656,9.565839 +3806,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16054725646972656,28.660555 +5709,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.1610889434814453,57.169533 +7612,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16111183166503906,95.02592100000001 +9515,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16111183166503906,141.315828 +11418,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16172218322753906,195.517174 +13321,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.1617450714111328,256.578558 +15224,Binary classification,ADWIN Bagging,SMTP,0.9992774091834724,0.0,0.2173633575439453,324.084886 +17127,Binary classification,ADWIN Bagging,SMTP,0.9992409202382343,0.0,0.16245460510253906,398.342181 +19030,Binary classification,ADWIN Bagging,SMTP,0.9993168322034789,0.0,0.16227149963378906,479.30544 +20933,Binary classification,ADWIN Bagging,SMTP,0.999378941333843,0.0,0.1629047393798828,566.848605 +22836,Binary classification,ADWIN Bagging,SMTP,0.9994306984891613,0.0,0.1629962921142578,660.867353 +24739,Binary classification,ADWIN Bagging,SMTP,0.9994744926833212,0.0,0.1631336212158203,761.190417 +26642,Binary classification,ADWIN Bagging,SMTP,0.9994744942006681,0.0,0.1628131866455078,867.1613560000001 +28545,Binary classification,ADWIN Bagging,SMTP,0.9995095291479821,0.0,0.1630420684814453,978.3809960000001 +30448,Binary classification,ADWIN Bagging,SMTP,0.9995401845830459,0.0,0.16292762756347656,1094.7710920000002 +32351,Binary classification,ADWIN Bagging,SMTP,0.9995672333848532,0.0,0.16306495666503906,1216.3368180000002 +34254,Binary classification,ADWIN Bagging,SMTP,0.9995912766764955,0.0,0.16297340393066406,1342.8439390000003 +36157,Binary classification,ADWIN Bagging,SMTP,0.9996127890253347,0.0,0.16297340393066406,1474.4175280000004 +38060,Binary classification,ADWIN Bagging,SMTP,0.9996321500827662,0.0,0.1629962921142578,1611.5765880000004 +39963,Binary classification,ADWIN Bagging,SMTP,0.9996496671838246,0.0,0.1628589630126953,1753.4950250000004 +41866,Binary classification,ADWIN Bagging,SMTP,0.9996655917831124,0.0,0.1635608673095703,1900.0594340000005 +43769,Binary classification,ADWIN Bagging,SMTP,0.9996801316029976,0.0,0.1636524200439453,2051.0940990000004 +45672,Binary classification,ADWIN Bagging,SMTP,0.9996934597446958,0.0,0.16362953186035156,2206.5822620000004 +47575,Binary classification,ADWIN Bagging,SMTP,0.9997057216126456,0.0,0.1635608673095703,2366.582792 +49478,Binary classification,ADWIN Bagging,SMTP,0.99971704024092,0.0,0.1516590118408203,2531.0740060000003 +51381,Binary classification,ADWIN Bagging,SMTP,0.9996885947839627,0.0,0.16358375549316406,2700.0024150000004 +53284,Binary classification,ADWIN Bagging,SMTP,0.9996997166075484,0.0,0.16358375549316406,2873.4051700000005 +55187,Binary classification,ADWIN Bagging,SMTP,0.999710071394919,0.0,0.16353797912597656,3051.2280980000005 +57090,Binary classification,ADWIN Bagging,SMTP,0.9995620872672494,0.0,0.1635608673095703,3233.6982610000005 +58993,Binary classification,ADWIN Bagging,SMTP,0.9995762137238947,0.0,0.1634693145751953,3420.4241430000006 +60896,Binary classification,ADWIN Bagging,SMTP,0.999589457262501,0.0,0.16340065002441406,3611.3532100000007 +62799,Binary classification,ADWIN Bagging,SMTP,0.9995700500015924,0.0,0.1635608673095703,3806.5756970000007 +64702,Binary classification,ADWIN Bagging,SMTP,0.9995826957852274,0.0,0.1636524200439453,4005.990633000001 +66605,Binary classification,ADWIN Bagging,SMTP,0.9995946189418053,0.0,0.1635608673095703,4209.692892000001 +68508,Binary classification,ADWIN Bagging,SMTP,0.9995766855941729,0.0,0.1636066436767578,4417.671552000001 +70411,Binary classification,ADWIN Bagging,SMTP,0.9995881266865502,0.0,0.16344642639160156,4629.924287000001 +72314,Binary classification,ADWIN Bagging,SMTP,0.9995989656078437,0.0,0.1636066436767578,4846.389066000001 +74217,Binary classification,ADWIN Bagging,SMTP,0.99960924867953,0.0,0.1636066436767578,5067.145737000001 +76120,Binary classification,ADWIN Bagging,SMTP,0.9996190175908775,0.0,0.1636524200439453,5292.119512000001 +78023,Binary classification,ADWIN Bagging,SMTP,0.9996283099638563,0.0,0.1636524200439453,5520.9977020000015 +79926,Binary classification,ADWIN Bagging,SMTP,0.9996371598373475,0.0,0.1633319854736328,5753.467598000001 +81829,Binary classification,ADWIN Bagging,SMTP,0.9996455980837855,0.0,0.1635608673095703,5989.673013000001 +83732,Binary classification,ADWIN Bagging,SMTP,0.9996536527689864,0.0,0.1642627716064453,6229.466851000001 +85635,Binary classification,ADWIN Bagging,SMTP,0.999661349463998,0.0,0.16428565979003906,6472.919364000001 +87538,Binary classification,ADWIN Bagging,SMTP,0.9996687115162731,0.0,0.16410255432128906,6719.889077000002 +89441,Binary classification,ADWIN Bagging,SMTP,0.9996645796064401,0.0,0.16414833068847656,6970.567347000002 +91344,Binary classification,ADWIN Bagging,SMTP,0.999671567607808,0.0,0.16405677795410156,7224.925889000002 +93247,Binary classification,ADWIN Bagging,SMTP,0.9996782703815713,0.0,0.1523609161376953,7483.091298000002 +95150,Binary classification,ADWIN Bagging,SMTP,0.9996847050415664,0.0,0.16419410705566406,7744.930013000002 +95156,Binary classification,ADWIN Bagging,SMTP,0.9996847249224948,0.0,0.1642169952392578,8006.777295000002 +106,Binary classification,AdaBoost,Bananas,0.5523809523809524,0.5252525252525252,0.16639232635498047,0.661448 +212,Binary classification,AdaBoost,Bananas,0.5829383886255924,0.5555555555555555,0.16659832000732422,2.064295 +318,Binary classification,AdaBoost,Bananas,0.6025236593059937,0.5827814569536425,0.16664409637451172,4.087538 +424,Binary classification,AdaBoost,Bananas,0.6099290780141844,0.5758354755784061,0.16664409637451172,6.767182 +530,Binary classification,AdaBoost,Bananas,0.5841209829867675,0.5089285714285714,0.16659832000732422,10.090322 +636,Binary classification,AdaBoost,Bananas,0.5748031496062992,0.4981412639405205,0.16664409637451172,14.036758 +742,Binary classification,AdaBoost,Bananas,0.582995951417004,0.48925619834710743,0.16657543182373047,18.750104 +848,Binary classification,AdaBoost,Bananas,0.5749704840613932,0.4812680115273775,0.16652965545654297,24.116907 +954,Binary classification,AdaBoost,Bananas,0.5760755508919203,0.482051282051282,0.16652965545654297,30.255333 +1060,Binary classification,AdaBoost,Bananas,0.5873465533522191,0.48284023668639053,0.16652965545654297,37.077733 +1166,Binary classification,AdaBoost,Bananas,0.5931330472103005,0.49250535331905776,0.16657543182373047,44.554975 +1272,Binary classification,AdaBoost,Bananas,0.5979543666404405,0.5034013605442177,0.16657543182373047,52.671883 +1378,Binary classification,AdaBoost,Bananas,0.6005809731299927,0.4990892531876139,0.16657543182373047,61.596702 +1484,Binary classification,AdaBoost,Bananas,0.6089008766014835,0.5117845117845117,0.16657543182373047,71.17423 +1590,Binary classification,AdaBoost,Bananas,0.6091881686595343,0.5121759622937941,0.16657543182373047,81.390284 +1696,Binary classification,AdaBoost,Bananas,0.6135693215339233,0.5194424064563462,0.16657543182373047,92.36515499999999 +1802,Binary classification,AdaBoost,Bananas,0.6185452526374237,0.5354969574036511,0.16657543182373047,104.03539999999998 +1908,Binary classification,AdaBoost,Bananas,0.6208704771893025,0.5467084639498432,0.16659832000732422,116.33010199999998 +2014,Binary classification,AdaBoost,Bananas,0.620963735717834,0.5561372891215823,0.16662120819091797,129.40978099999998 +2120,Binary classification,AdaBoost,Bananas,0.6252949504483247,0.56941431670282,0.16662120819091797,143.16628799999998 +2226,Binary classification,AdaBoost,Bananas,0.6242696629213483,0.5721596724667348,0.16664409637451172,157.57341499999998 +2332,Binary classification,AdaBoost,Bananas,0.6229086229086229,0.5763855421686748,0.16664409637451172,172.619666 +2438,Binary classification,AdaBoost,Bananas,0.62330734509643,0.5796703296703297,0.16664409637451172,188.342899 +2544,Binary classification,AdaBoost,Bananas,0.6244593000393236,0.5860424794104898,0.16664409637451172,204.77139699999998 +2650,Binary classification,AdaBoost,Bananas,0.6266515666289165,0.591828312009905,0.16668987274169922,221.90157299999998 +2756,Binary classification,AdaBoost,Bananas,0.6250453720508167,0.5921831819976313,0.16668987274169922,239.67413799999997 +2862,Binary classification,AdaBoost,Bananas,0.6249563089828731,0.5927893738140417,0.16668987274169922,258.142834 +2968,Binary classification,AdaBoost,Bananas,0.6248736097067745,0.5924569754668619,0.16668987274169922,277.299077 +3074,Binary classification,AdaBoost,Bananas,0.6260982753010088,0.5958494548012664,0.16668987274169922,297.096451 +3180,Binary classification,AdaBoost,Bananas,0.62378106322743,0.5934738273283481,0.15410423278808594,317.58276 +3286,Binary classification,AdaBoost,Bananas,0.6246575342465753,0.5937397034596376,0.1971263885498047,338.83455200000003 +3392,Binary classification,AdaBoost,Bananas,0.6234149218519611,0.5931825422108953,0.2317180633544922,360.725819 +3498,Binary classification,AdaBoost,Bananas,0.6211038032599371,0.5894019212891229,0.2662029266357422,383.343908 +3604,Binary classification,AdaBoost,Bananas,0.6194837635303914,0.5866747060596926,0.31232261657714844,406.679113 +3710,Binary classification,AdaBoost,Bananas,0.6238878403882449,0.5915080527086384,0.32015037536621094,430.74398699999995 +3816,Binary classification,AdaBoost,Bananas,0.6277850589777195,0.5970488081725313,0.32617759704589844,455.49479399999996 +3922,Binary classification,AdaBoost,Bananas,0.6322366743177761,0.6009961261759823,0.35390281677246094,480.941019 +4028,Binary classification,AdaBoost,Bananas,0.6354606406754407,0.6034575904916262,0.3679637908935547,507.154231 +4134,Binary classification,AdaBoost,Bananas,0.6399709654004355,0.6073878627968339,0.3758831024169922,534.089651 +4240,Binary classification,AdaBoost,Bananas,0.644963434772352,0.6130110568269478,0.37590599060058594,561.67777 +4346,Binary classification,AdaBoost,Bananas,0.6508630609896433,0.6185567010309279,0.3759288787841797,589.949243 +4452,Binary classification,AdaBoost,Bananas,0.6535609975286453,0.620384047267356,0.3758831024169922,618.987429 +4558,Binary classification,AdaBoost,Bananas,0.6570111915734036,0.6243691420331651,0.3760662078857422,648.691271 +4664,Binary classification,AdaBoost,Bananas,0.6607334334119666,0.6288127639605818,0.3761119842529297,679.16585 +4770,Binary classification,AdaBoost,Bananas,0.6630320821975257,0.6303197607545433,0.43157005310058594,710.3587739999999 +4876,Binary classification,AdaBoost,Bananas,0.6670769230769231,0.6330544879041374,0.43948936462402344,742.192598 +4982,Binary classification,AdaBoost,Bananas,0.6707488456133307,0.6378091872791519,0.44574546813964844,774.746403 +5088,Binary classification,AdaBoost,Bananas,0.6734814232356988,0.6407094959982694,0.45186424255371094,807.9427459999999 +5194,Binary classification,AdaBoost,Bananas,0.674369343346813,0.6412051771695311,0.4518413543701172,841.965614 +5300,Binary classification,AdaBoost,Bananas,0.6778637478769579,0.64504054897068,0.4531536102294922,876.7139659999999 +906,Binary classification,AdaBoost,Elec2,0.9337016574585635,0.933184855233853,1.423478126525879,13.145088 +1812,Binary classification,AdaBoost,Elec2,0.9491993373826615,0.9378378378378379,2.051041603088379,36.742593 +2718,Binary classification,AdaBoost,Elec2,0.9385351490614648,0.9243316719528772,2.3655481338500977,75.07794200000001 +3624,Binary classification,AdaBoost,Elec2,0.9359646701628485,0.9209809264305179,2.6522607803344727,124.64144900000001 +4530,Binary classification,AdaBoost,Elec2,0.9361890041951866,0.9185226952354102,3.339066505432129,184.39942100000002 +5436,Binary classification,AdaBoost,Elec2,0.9332106715731371,0.9144876325088339,3.582810401916504,253.628197 +6342,Binary classification,AdaBoost,Elec2,0.9309257214950323,0.9124350259896042,3.74349308013916,332.298993 +7248,Binary classification,AdaBoost,Elec2,0.9232785980405686,0.9024903542616626,3.9959611892700195,420.603134 +8154,Binary classification,AdaBoost,Elec2,0.9207653624432725,0.9042962962962964,4.062603950500488,517.241068 +9060,Binary classification,AdaBoost,Elec2,0.9214041284910034,0.9072191816523326,4.2443437576293945,621.271616 +9966,Binary classification,AdaBoost,Elec2,0.9173105870546914,0.9037158214536105,4.387467384338379,732.039898 +10872,Binary classification,AdaBoost,Elec2,0.916842976727072,0.9044195390145908,4.416756629943848,849.200167 +11778,Binary classification,AdaBoost,Elec2,0.9150887322747728,0.9024580569644947,4.712822914123535,973.870605 +12684,Binary classification,AdaBoost,Elec2,0.9128755026413309,0.9002077124537162,5.243111610412598,1105.403187 +13590,Binary classification,AdaBoost,Elec2,0.9123555817205092,0.900890405259216,5.419106483459473,1243.898383 +14496,Binary classification,AdaBoost,Elec2,0.9112107623318386,0.9002402914502752,5.619416236877441,1388.7625189999999 +15402,Binary classification,AdaBoost,Elec2,0.9125381468735796,0.9014414282578475,5.888123512268066,1539.2398159999998 +16308,Binary classification,AdaBoost,Elec2,0.9096093702091127,0.8977808599167822,6.072480201721191,1695.9498239999998 +17214,Binary classification,AdaBoost,Elec2,0.9093708243769244,0.8958611481975968,6.119706153869629,1858.6477019999998 +18120,Binary classification,AdaBoost,Elec2,0.9071140791434406,0.892972972972973,6.420571327209473,2027.8856049999997 +19026,Binary classification,AdaBoost,Elec2,0.907910643889619,0.8927784577723377,6.732544898986816,2202.4394989999996 +19932,Binary classification,AdaBoost,Elec2,0.9079323666649942,0.8936540133294696,6.836274147033691,2383.1617499999998 +20838,Binary classification,AdaBoost,Elec2,0.9073283102174018,0.8931673582295988,7.145352363586426,2570.030805 +21744,Binary classification,AdaBoost,Elec2,0.9069585613760751,0.8912424063222407,7.368103981018066,2762.4680289999997 +22650,Binary classification,AdaBoost,Elec2,0.9053379840169544,0.8884611382790553,7.513260841369629,2961.0147009999996 +23556,Binary classification,AdaBoost,Elec2,0.9031203566121843,0.885441767068273,7.7879228591918945,3165.9004179999997 +24462,Binary classification,AdaBoost,Elec2,0.9015984628592453,0.8830361047669955,7.954785346984863,3377.5787039999996 +25368,Binary classification,AdaBoost,Elec2,0.8990026412267907,0.8799775133514476,8.00295352935791,3596.2655339999997 +26274,Binary classification,AdaBoost,Elec2,0.8993263045712329,0.8800942925789926,8.124005317687988,3821.215918 +27180,Binary classification,AdaBoost,Elec2,0.8986717686449097,0.8798324461122262,8.133870124816895,4052.049016 +28086,Binary classification,AdaBoost,Elec2,0.8958874844222895,0.8761436801084379,8.60555362701416,4289.013778 +28992,Binary classification,AdaBoost,Elec2,0.8951398709944466,0.8747011787981206,8.944867134094238,4531.544758 +29898,Binary classification,AdaBoost,Elec2,0.8927986085560424,0.8719485396939551,9.235833168029785,4780.469894 +30804,Binary classification,AdaBoost,Elec2,0.8921533616855502,0.8705882352941176,9.317421913146973,5034.96976 +31710,Binary classification,AdaBoost,Elec2,0.8903465892964143,0.8684499262229957,9.565300941467285,5295.565511 +32616,Binary classification,AdaBoost,Elec2,0.8890387858347386,0.867226767435888,9.898663520812988,5561.886477999999 +33522,Binary classification,AdaBoost,Elec2,0.8882789892902956,0.8666547979348406,10.141366004943848,5833.648399 +34428,Binary classification,AdaBoost,Elec2,0.8878496528887211,0.8660444783679699,10.462204933166504,6110.893153 +35334,Binary classification,AdaBoost,Elec2,0.8864800611326522,0.8639185750636134,10.841256141662598,6393.894783 +36240,Binary classification,AdaBoost,Elec2,0.8857584370429648,0.8622387861040862,11.138581275939941,6682.129445 +37146,Binary classification,AdaBoost,Elec2,0.8846412706959214,0.8604643589827087,11.587563514709473,6975.5213029999995 +38052,Binary classification,AdaBoost,Elec2,0.883682426217445,0.8588377878420617,12.028901100158691,7273.546291 +38958,Binary classification,AdaBoost,Elec2,0.8819210924865878,0.8569117830036083,12.1774263381958,7576.403824 +39864,Binary classification,AdaBoost,Elec2,0.880741539773725,0.8567122792211707,12.330445289611816,7883.760394 +40770,Binary classification,AdaBoost,Elec2,0.880423851455763,0.8574603081781235,12.583298683166504,8195.49812 +41676,Binary classification,AdaBoost,Elec2,0.8811517696460708,0.8591977712710009,12.884881019592285,8511.394385 +42582,Binary classification,AdaBoost,Elec2,0.8815199267278834,0.8597403319525146,13.200516700744629,8831.520838 +43488,Binary classification,AdaBoost,Elec2,0.8809069377055212,0.8591399896646449,13.322876930236816,9156.403803000001 +44394,Binary classification,AdaBoost,Elec2,0.880476651724371,0.8583404527979496,13.499638557434082,9485.877502000001 +45300,Binary classification,AdaBoost,Elec2,0.8805713150400671,0.8587024655244463,13.542492866516113,9819.626928000001 +45312,Binary classification,AdaBoost,Elec2,0.8805808744013595,0.8586874200203704,13.542401313781738,10153.705154000001 +25,Binary classification,AdaBoost,Phishing,0.6666666666666666,0.7142857142857143,0.6517477035522461,0.344782 +50,Binary classification,AdaBoost,Phishing,0.7551020408163265,0.7391304347826088,0.6519079208374023,1.052047 +75,Binary classification,AdaBoost,Phishing,0.7972972972972973,0.7945205479452055,0.6519308090209961,2.04852 +100,Binary classification,AdaBoost,Phishing,0.8080808080808081,0.7999999999999999,0.6519536972045898,3.481699 +125,Binary classification,AdaBoost,Phishing,0.8064516129032258,0.8000000000000002,0.6519804000854492,5.345952 +150,Binary classification,AdaBoost,Phishing,0.8187919463087249,0.8211920529801323,0.6519804000854492,7.607799 +175,Binary classification,AdaBoost,Phishing,0.8390804597701149,0.8313253012048192,0.6519804000854492,10.226605 +200,Binary classification,AdaBoost,Phishing,0.8341708542713567,0.8253968253968254,0.6889629364013672,13.384675 +225,Binary classification,AdaBoost,Phishing,0.8303571428571429,0.8173076923076923,0.6891918182373047,16.995274 +250,Binary classification,AdaBoost,Phishing,0.8273092369477911,0.8154506437768241,0.6892147064208984,21.029961999999998 +275,Binary classification,AdaBoost,Phishing,0.8321167883211679,0.8188976377952757,0.6892833709716797,25.500590999999996 +300,Binary classification,AdaBoost,Phishing,0.8394648829431438,0.823529411764706,0.6893062591552734,30.459704999999996 +325,Binary classification,AdaBoost,Phishing,0.845679012345679,0.8263888888888888,0.6893062591552734,35.865097 +350,Binary classification,AdaBoost,Phishing,0.8510028653295129,0.8289473684210527,0.6893062591552734,41.618204999999996 +375,Binary classification,AdaBoost,Phishing,0.8502673796791443,0.8260869565217391,0.6892795562744141,47.877466 +400,Binary classification,AdaBoost,Phishing,0.849624060150376,0.8235294117647061,0.6893062591552734,54.483198 +425,Binary classification,AdaBoost,Phishing,0.8561320754716981,0.8271954674220963,0.6893062591552734,61.544972 +450,Binary classification,AdaBoost,Phishing,0.8530066815144766,0.8225806451612903,0.6893062591552734,69.002264 +475,Binary classification,AdaBoost,Phishing,0.8523206751054853,0.8241206030150755,0.6893062591552734,77.051638 +500,Binary classification,AdaBoost,Phishing,0.8557114228456913,0.8317757009345793,0.6893062591552734,85.50066 +525,Binary classification,AdaBoost,Phishing,0.8530534351145038,0.8253968253968255,0.6893062591552734,94.362743 +550,Binary classification,AdaBoost,Phishing,0.8579234972677595,0.832618025751073,0.6893062591552734,103.688841 +575,Binary classification,AdaBoost,Phishing,0.8588850174216028,0.8336755646817249,0.6893062591552734,113.60832099999999 +600,Binary classification,AdaBoost,Phishing,0.8631051752921536,0.8360000000000001,0.6893062591552734,123.90541499999999 +625,Binary classification,AdaBoost,Phishing,0.8621794871794872,0.83203125,0.6893062591552734,134.725616 +650,Binary classification,AdaBoost,Phishing,0.8659476117103235,0.8391866913123845,0.6893291473388672,146.008333 +675,Binary classification,AdaBoost,Phishing,0.8679525222551929,0.8446771378708552,0.6893291473388672,157.728693 +700,Binary classification,AdaBoost,Phishing,0.8726752503576538,0.848381601362862,0.6893291473388672,169.816185 +725,Binary classification,AdaBoost,Phishing,0.8756906077348067,0.8543689320388349,0.6893291473388672,182.22931499999999 +750,Binary classification,AdaBoost,Phishing,0.87716955941255,0.8566978193146417,0.6893291473388672,195.113196 +775,Binary classification,AdaBoost,Phishing,0.8785529715762274,0.8575757575757577,0.6893291473388672,208.50176599999998 +800,Binary classification,AdaBoost,Phishing,0.8785982478097623,0.8592162554426704,0.7275295257568359,222.49654599999997 +825,Binary classification,AdaBoost,Phishing,0.8798543689320388,0.8619246861924686,0.7627391815185547,236.94804099999996 +850,Binary classification,AdaBoost,Phishing,0.8798586572438163,0.8614130434782608,0.7627849578857422,251.81301299999996 +875,Binary classification,AdaBoost,Phishing,0.8787185354691075,0.8594164456233422,0.7628536224365234,267.105354 +900,Binary classification,AdaBoost,Phishing,0.8787541713014461,0.8589909443725743,0.7628765106201172,282.96574799999996 +925,Binary classification,AdaBoost,Phishing,0.8809523809523809,0.8628428927680798,0.7628765106201172,299.16754399999996 +950,Binary classification,AdaBoost,Phishing,0.8798735511064278,0.8629807692307693,0.7628765106201172,315.934336 +975,Binary classification,AdaBoost,Phishing,0.8819301848049281,0.8651817116060961,0.7628765106201172,333.021735 +1000,Binary classification,AdaBoost,Phishing,0.8828828828828829,0.8662857142857143,0.7645549774169922,350.749086 +1025,Binary classification,AdaBoost,Phishing,0.8828125,0.8666666666666666,0.8362636566162109,368.847305 +1050,Binary classification,AdaBoost,Phishing,0.8846520495710201,0.8691891891891892,0.8363094329833984,387.397742 +1075,Binary classification,AdaBoost,Phishing,0.8836126629422719,0.8691099476439791,0.8378963470458984,406.476047 +1100,Binary classification,AdaBoost,Phishing,0.8844404003639672,0.8702757916241062,0.8380107879638672,425.926155 +1125,Binary classification,AdaBoost,Phishing,0.8861209964412812,0.8732673267326733,0.8731288909912109,445.763528 +1150,Binary classification,AdaBoost,Phishing,0.8842471714534378,0.8707482993197277,0.8731517791748047,466.112685 +1175,Binary classification,AdaBoost,Phishing,0.8816013628620102,0.8677450047573739,0.8732662200927734,487.01292 +1200,Binary classification,AdaBoost,Phishing,0.8798999165971643,0.8654205607476635,0.8732662200927734,508.389566 +1225,Binary classification,AdaBoost,Phishing,0.880718954248366,0.8660550458715598,0.8732891082763672,530.180451 +1250,Binary classification,AdaBoost,Phishing,0.8783026421136909,0.8635547576301617,0.8733119964599609,552.608585 +1903,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14459228515625,4.671696 +3806,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14466094970703125,14.150102 +5709,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14461517333984375,28.360088 +7612,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1446380615234375,47.155736000000005 +9515,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1446380615234375,70.60316700000001 +11418,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14461517333984375,98.66041500000001 +13321,Binary classification,AdaBoost,SMTP,1.0,0.0,0.144683837890625,131.464682 +15224,Binary classification,AdaBoost,SMTP,0.9996715496288511,0.761904761904762,0.3174581527709961,185.699411 +17127,Binary classification,AdaBoost,SMTP,0.9997080462454747,0.8,0.3083944320678711,248.358611 +19030,Binary classification,AdaBoost,SMTP,0.9997372431551842,0.8,0.3005514144897461,315.58373 +20933,Binary classification,AdaBoost,SMTP,0.9997611312822473,0.8,0.29268550872802734,387.343573 +22836,Binary classification,AdaBoost,SMTP,0.9997810378804467,0.8,0.29268550872802734,463.52644699999996 +24739,Binary classification,AdaBoost,SMTP,0.9997978818012774,0.8,0.29268550872802734,544.0157879999999 +26642,Binary classification,AdaBoost,SMTP,0.9998123193573815,0.8148148148148148,0.35791683197021484,629.1407149999999 +28545,Binary classification,AdaBoost,SMTP,0.9998248318385651,0.8148148148148148,0.35791683197021484,718.5008859999999 +30448,Binary classification,AdaBoost,SMTP,0.9998357802082307,0.8148148148148148,0.35791683197021484,812.0251739999999 +32351,Binary classification,AdaBoost,SMTP,0.9998454404945905,0.8148148148148148,0.35796260833740234,909.6873759999999 +34254,Binary classification,AdaBoost,SMTP,0.9998540273844627,0.8148148148148148,0.3579854965209961,1011.4173349999999 +36157,Binary classification,AdaBoost,SMTP,0.999861710366191,0.8148148148148148,0.35800838470458984,1116.7962549999997 +38060,Binary classification,AdaBoost,SMTP,0.9998686250295594,0.8148148148148148,0.35800838470458984,1225.6397989999998 +39963,Binary classification,AdaBoost,SMTP,0.9998748811370802,0.8148148148148148,0.35800838470458984,1337.9609139999998 +41866,Binary classification,AdaBoost,SMTP,0.9998805684939687,0.8148148148148148,0.35800838470458984,1453.6581889999998 +43769,Binary classification,AdaBoost,SMTP,0.9998857612867849,0.8148148148148148,0.35800838470458984,1572.7472669999997 +45672,Binary classification,AdaBoost,SMTP,0.9998905213373913,0.8148148148148148,0.35800838470458984,1695.2891979999997 +47575,Binary classification,AdaBoost,SMTP,0.9998738806911338,0.7857142857142857,0.39035701751708984,1822.1171059999997 +49478,Binary classification,AdaBoost,SMTP,0.9998787315318228,0.7857142857142857,0.39284420013427734,1952.4393319999997 +51381,Binary classification,AdaBoost,SMTP,0.9998637602179836,0.787878787878788,0.48020076751708984,2088.5562569999997 +53284,Binary classification,AdaBoost,SMTP,0.9998686260158024,0.787878787878788,0.48024654388427734,2228.050331 +55187,Binary classification,AdaBoost,SMTP,0.9998550356974595,0.7647058823529411,0.5258626937866211,2371.062704 +57090,Binary classification,AdaBoost,SMTP,0.999281823118289,0.4383561643835616,0.8453359603881836,2524.551865 +58993,Binary classification,AdaBoost,SMTP,0.9993049905071875,0.4383561643835616,0.8887395858764648,2681.9732639999997 +60896,Binary classification,AdaBoost,SMTP,0.9993267099105017,0.4383561643835616,0.8967199325561523,2843.1145869999996 +62799,Binary classification,AdaBoost,SMTP,0.9993152648173509,0.4266666666666667,1.0689306259155273,3009.1891499999997 +64702,Binary classification,AdaBoost,SMTP,0.9993354043986955,0.4266666666666667,1.0783147811889648,3178.9568209999998 +66605,Binary classification,AdaBoost,SMTP,0.9993393790162753,0.42105263157894735,1.0915288925170898,3352.4236079999996 +68508,Binary classification,AdaBoost,SMTP,0.9993577298670209,0.45000000000000007,1.0735387802124023,3530.7159619999998 +70411,Binary classification,AdaBoost,SMTP,0.9993750887658003,0.45000000000000007,1.0788640975952148,3712.739099 +72314,Binary classification,AdaBoost,SMTP,0.9993915340256938,0.45000000000000007,1.0906057357788086,3898.148799 +74217,Binary classification,AdaBoost,SMTP,0.9994071359275628,0.45000000000000007,1.0906057357788086,4086.957815 +76120,Binary classification,AdaBoost,SMTP,0.9994219577240899,0.45000000000000007,1.090651512145996,4279.139143 +78023,Binary classification,AdaBoost,SMTP,0.9994232395990874,0.4444444444444444,1.1481237411499023,4474.636732 +79926,Binary classification,AdaBoost,SMTP,0.9994369721614013,0.4444444444444444,1.1493444442749023,4673.509203 +81829,Binary classification,AdaBoost,SMTP,0.999450065992081,0.4444444444444444,1.1612462997436523,4875.758715 +83732,Binary classification,AdaBoost,SMTP,0.9994625646415306,0.4444444444444444,1.161269187927246,5081.435613 +85635,Binary classification,AdaBoost,SMTP,0.9994745077889623,0.4444444444444444,1.1612234115600586,5290.523743 +87538,Binary classification,AdaBoost,SMTP,0.9994859316631824,0.4444444444444444,1.1584348678588867,5502.994331999999 +89441,Binary classification,AdaBoost,SMTP,0.9994633273703041,0.42857142857142855,1.2966947555541992,5719.6431489999995 +91344,Binary classification,AdaBoost,SMTP,0.9994745081724927,0.42857142857142855,1.3124494552612305,5939.715090999999 +93247,Binary classification,AdaBoost,SMTP,0.9994316110074427,0.40449438202247195,1.3362340927124023,6163.415711999999 +95150,Binary classification,AdaBoost,SMTP,0.9994429789067673,0.40449438202247195,1.3363256454467773,6390.433574999999 +95156,Binary classification,AdaBoost,SMTP,0.9994430140297409,0.40449438202247195,1.3363256454467773,6617.502892999999 +106,Binary classification,Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.22373199462890625,0.813651 +212,Binary classification,Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.22452545166015625,2.392298 +318,Binary classification,Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.2251434326171875,4.879886 +424,Binary classification,Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.225250244140625,8.257922 +530,Binary classification,Bagging,Bananas,0.55765595463138,0.45581395348837206,0.22527313232421875,12.416081000000002 +636,Binary classification,Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.22574615478515625,17.551695000000002 +742,Binary classification,Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.22597503662109375,23.418389 +848,Binary classification,Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.2259063720703125,30.181971 +954,Binary classification,Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.22588348388671875,37.806045 +1060,Binary classification,Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.22563934326171875,46.336236 +1166,Binary classification,Bagging,Bananas,0.590557939914163,0.4015056461731493,0.225738525390625,55.794626 +1272,Binary classification,Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.226043701171875,66.093431 +1378,Binary classification,Bagging,Bananas,0.599128540305011,0.3973799126637554,0.226348876953125,77.304266 +1484,Binary classification,Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.2263031005859375,89.41731899999999 +1590,Binary classification,Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.22628021240234375,102.38846199999999 +1696,Binary classification,Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.22634124755859375,116.26624899999999 +1802,Binary classification,Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.2263641357421875,130.90050499999998 +1908,Binary classification,Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.2263641357421875,146.406164 +2014,Binary classification,Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.2263641357421875,162.81699799999998 +2120,Binary classification,Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.2263641357421875,180.02605599999998 +2226,Binary classification,Bagging,Bananas,0.597752808988764,0.40133779264214053,0.226318359375,198.114263 +2332,Binary classification,Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.22637939453125,217.043548 +2438,Binary classification,Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.22640228271484375,236.778245 +2544,Binary classification,Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.22676849365234375,257.426783 +2650,Binary classification,Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.2269744873046875,278.871455 +2756,Binary classification,Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.2269744873046875,301.18545300000005 +2862,Binary classification,Bagging,Bananas,0.599790283117791,0.4051948051948052,0.2269744873046875,324.33878000000004 +2968,Binary classification,Bagging,Bananas,0.599932591843613,0.40261701056869653,0.22699737548828125,348.42370100000005 +3074,Binary classification,Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.22699737548828125,373.34600700000004 +3180,Binary classification,Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.22699737548828125,399.17600200000004 +3286,Binary classification,Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.22489070892333984,425.805579 +3392,Binary classification,Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.2988729476928711,453.430877 +3498,Binary classification,Bagging,Bananas,0.597369173577352,0.40237691001697795,0.3531064987182617,482.040674 +3604,Binary classification,Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3826017379760742,511.80088500000005 +3710,Binary classification,Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.4367246627807617,542.835536 +3816,Binary classification,Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.4704160690307617,575.071901 +3922,Binary classification,Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.5176496505737305,608.725741 +4028,Binary classification,Bagging,Bananas,0.608393344921778,0.4195804195804196,0.5480222702026367,643.745138 +4134,Binary classification,Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.5632429122924805,680.30634 +4240,Binary classification,Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.5676107406616211,718.216367 +4346,Binary classification,Bagging,Bananas,0.6193325661680092,0.438560760353021,0.5822668075561523,757.397991 +4452,Binary classification,Bagging,Bananas,0.6218827229835991,0.4421610871726881,0.5884695053100586,797.943115 +4558,Binary classification,Bagging,Bananas,0.6219003730524468,0.44293566117038474,0.6275625228881836,839.803567 +4664,Binary classification,Bagging,Bananas,0.623203945957538,0.4455664247396655,0.6328649520874023,883.024854 +4770,Binary classification,Bagging,Bananas,0.6250786328370728,0.446096654275093,0.6821584701538086,927.682473 +4876,Binary classification,Bagging,Bananas,0.6266666666666667,0.44680851063829785,0.6950826644897461,973.7206689999999 +4982,Binary classification,Bagging,Bananas,0.629592451314997,0.4530091906314853,0.7119512557983398,1021.0804549999999 +5088,Binary classification,Bagging,Bananas,0.6298407705917043,0.4527753560011624,0.6960439682006836,1069.7402539999998 +5194,Binary classification,Bagging,Bananas,0.6321971885230118,0.456459874786568,0.6964941024780273,1119.6924109999998 +5300,Binary classification,Bagging,Bananas,0.6340819022457067,0.4594368553108447,0.7031240463256836,1170.8531239999998 +906,Binary classification,Bagging,Elec2,0.8629834254143647,0.8663793103448276,1.7490100860595703,16.056337 +1812,Binary classification,Bagging,Elec2,0.8890115958034235,0.8680236375574525,2.496591567993164,50.652304 +2718,Binary classification,Bagging,Elec2,0.87523003312477,0.8521587440034889,1.8562908172607422,106.697102 +3624,Binary classification,Bagging,Elec2,0.8868341153739995,0.8653972422849641,2.5584278106689453,176.150463 +4530,Binary classification,Bagging,Elec2,0.8880547582247736,0.8593619972260749,3.1707210540771484,258.677392 +5436,Binary classification,Bagging,Elec2,0.8829806807727691,0.8518863530507685,2.113290786743164,353.604927 +6342,Binary classification,Bagging,Elec2,0.8814067181832519,0.8497802636835796,2.4726314544677734,459.993548 +7248,Binary classification,Bagging,Elec2,0.883262039464606,0.8516310066643283,2.354246139526367,576.649537 +8154,Binary classification,Bagging,Elec2,0.8828652029927634,0.8585394756332394,2.1453304290771484,702.348431 +9060,Binary classification,Bagging,Elec2,0.8839827795562424,0.8639129871811472,2.1982364654541016,836.637311 +9966,Binary classification,Bagging,Elec2,0.880983442047165,0.8635840809753854,2.4484920501708984,979.832141 +10872,Binary classification,Bagging,Elec2,0.881151687977187,0.8654446990210373,2.578580856323242,1131.438926 +11778,Binary classification,Bagging,Elec2,0.8799354674365288,0.8634344214796214,2.730459213256836,1291.261447 +12684,Binary classification,Bagging,Elec2,0.8768430182133564,0.8601361031518624,2.090116500854492,1459.3451100000002 +13590,Binary classification,Bagging,Elec2,0.8789462064905438,0.8639483913654784,1.8772754669189453,1635.065473 +14496,Binary classification,Bagging,Elec2,0.878854777509486,0.86444341516134,2.105062484741211,1818.351758 +15402,Binary classification,Bagging,Elec2,0.8775404194532822,0.86187197890728,2.440736770629883,2009.388651 +16308,Binary classification,Bagging,Elec2,0.8765560802109523,0.8599262403451395,2.627225875854492,2209.910977 +17214,Binary classification,Bagging,Elec2,0.8758496485214663,0.8567214213878646,2.5119991302490234,2419.733571 +18120,Binary classification,Bagging,Elec2,0.8760969148407749,0.8567600331780769,2.7164859771728516,2638.279319 +19026,Binary classification,Bagging,Elec2,0.8772141918528252,0.8562284588872477,3.019651412963867,2865.7383750000004 +19932,Binary classification,Bagging,Elec2,0.8739651798705534,0.8535106134826219,2.721925735473633,3103.6766270000003 +20838,Binary classification,Bagging,Elec2,0.8716225944233815,0.8503663925714606,2.4018421173095703,3351.3122810000004 +21744,Binary classification,Bagging,Elec2,0.872556684910086,0.8492300995701616,2.248655319213867,3607.2754260000006 +22650,Binary classification,Bagging,Elec2,0.870722769217184,0.845275840202917,2.6111698150634766,3871.072358000001 +23556,Binary classification,Bagging,Elec2,0.8645722776480578,0.8365611230658879,1.8957767486572266,4144.250301000001 +24462,Binary classification,Bagging,Elec2,0.8614120436613385,0.8315276811450154,1.5607776641845703,4424.237452000001 +25368,Binary classification,Bagging,Elec2,0.8560334292584855,0.8249113050148624,1.3715801239013672,4711.1951020000015 +26274,Binary classification,Bagging,Elec2,0.8558596277547292,0.824277295717136,1.6112499237060547,5004.571280000002 +27180,Binary classification,Bagging,Elec2,0.8564332756907906,0.8258035714285713,2.025979995727539,5304.465246000002 +28086,Binary classification,Bagging,Elec2,0.8535517179989318,0.8215385950449082,1.8488483428955078,5611.580754000001 +28992,Binary classification,Bagging,Elec2,0.8515746266082578,0.8178624338624338,2.0671520233154297,5927.3319900000015 +29898,Binary classification,Bagging,Elec2,0.849048399504967,0.8140885684860969,1.3224430084228516,6250.652578000001 +30804,Binary classification,Bagging,Elec2,0.8473849949680226,0.8106344410876132,1.549489974975586,6580.126329000001 +31710,Binary classification,Bagging,Elec2,0.8429783342268756,0.8039377830281552,1.5209712982177734,6916.182134000001 +32616,Binary classification,Bagging,Elec2,0.8411773723746743,0.8020785572367416,1.9952220916748047,7258.669481000001 +33522,Binary classification,Bagging,Elec2,0.8415023418155783,0.8033751526590429,1.8286800384521484,7608.320925000001 +34428,Binary classification,Bagging,Elec2,0.839689778371627,0.8006357692446627,2.2426509857177734,7966.087012000001 +35334,Binary classification,Bagging,Elec2,0.8395550901423598,0.7993487417265422,2.1107349395751953,8331.986249000001 +36240,Binary classification,Bagging,Elec2,0.8400618118601507,0.7984280447937677,1.8943347930908203,8703.460619000001 +37146,Binary classification,Bagging,Elec2,0.839278503163279,0.796356938190749,1.3389415740966797,9080.992051000001 +38052,Binary classification,Bagging,Elec2,0.8389267036345956,0.7946940006029546,1.6071338653564453,9463.837927 +38958,Binary classification,Bagging,Elec2,0.8382832353620658,0.7942655607079877,1.8687000274658203,9853.704286 +39864,Binary classification,Bagging,Elec2,0.8387477109098663,0.7967495098969203,1.466756820678711,10249.501094 +40770,Binary classification,Bagging,Elec2,0.8400009811376291,0.8001225677953119,2.0175647735595703,10651.197686 +41676,Binary classification,Bagging,Elec2,0.8407918416316736,0.8026413635146792,2.1117191314697266,11058.632461 +42582,Binary classification,Bagging,Elec2,0.8411732932528593,0.8035781708344224,2.033967971801758,11470.822586999999 +43488,Binary classification,Bagging,Elec2,0.8416538275806563,0.804308286915994,1.7070560455322266,11887.700533 +44394,Binary classification,Bagging,Elec2,0.8406280269411844,0.8019483246087955,2.2816905975341797,12309.209906 +45300,Binary classification,Bagging,Elec2,0.8404379787633282,0.802124397722295,2.2888126373291016,12736.77001 +45312,Binary classification,Bagging,Elec2,0.8404360971949416,0.8020804817957842,2.2889575958251953,13164.474026 +25,Binary classification,Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.7072525024414062,0.45657 +50,Binary classification,Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.7079315185546875,1.426682 +75,Binary classification,Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.708251953125,2.8732379999999997 +100,Binary classification,Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.70849609375,4.790442 +125,Binary classification,Bagging,Phishing,0.8548387096774194,0.85,0.70849609375,7.239611999999999 +150,Binary classification,Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.708740234375,10.202642999999998 +175,Binary classification,Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.7091293334960938,13.595279999999999 +200,Binary classification,Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.7092666625976562,17.527801 +225,Binary classification,Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7491827011108398,22.029145 +250,Binary classification,Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.7771825790405273,27.026806999999998 +275,Binary classification,Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7774114608764648,32.501577 +300,Binary classification,Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7775945663452148,38.42215899999999 +325,Binary classification,Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7779607772827148,44.92146699999999 +350,Binary classification,Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7781057357788086,51.897794999999995 +375,Binary classification,Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.8172750473022461,59.363139999999994 +400,Binary classification,Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.8571996688842773,67.416022 +425,Binary classification,Bagging,Phishing,0.8679245283018868,0.839080459770115,0.9128484725952148,76.017673 +450,Binary classification,Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.9131002426147461,85.092057 +475,Binary classification,Bagging,Phishing,0.869198312236287,0.8402061855670103,0.9133520126342773,94.603797 +500,Binary classification,Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.9135580062866211,104.609638 +525,Binary classification,Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.9136190414428711,115.080656 +550,Binary classification,Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.9137258529663086,126.050962 +575,Binary classification,Bagging,Phishing,0.867595818815331,0.8382978723404255,0.9137868881225586,137.397676 +600,Binary classification,Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.9139089584350586,149.31562 +625,Binary classification,Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.9536046981811523,161.695664 +650,Binary classification,Bagging,Phishing,0.8767334360554699,0.846153846153846,0.9540624618530273,174.565593 +675,Binary classification,Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.9818639755249023,187.898512 +700,Binary classification,Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.9230222702026367,201.73587500000002 +725,Binary classification,Bagging,Phishing,0.8825966850828729,0.8576214405360134,1.021204948425293,216.08538800000002 +750,Binary classification,Bagging,Phishing,0.8865153538050734,0.8631239935587761,1.0604047775268555,230.90612000000002 +775,Binary classification,Bagging,Phishing,0.8875968992248062,0.863849765258216,1.1157331466674805,246.307883 +800,Binary classification,Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.2215375900268555,262.241262 +825,Binary classification,Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.2229490280151367,278.553882 +850,Binary classification,Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.235407829284668,295.406724 +875,Binary classification,Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.263422966003418,312.749904 +900,Binary classification,Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.318751335144043,330.632511 +925,Binary classification,Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.318964958190918,348.945693 +950,Binary classification,Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.3194990158081055,367.70198800000003 +975,Binary classification,Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.319605827331543,386.96933700000005 +1000,Binary classification,Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.3197660446166992,406.75034200000005 +1025,Binary classification,Bagging,Phishing,0.8876953125,0.8700564971751412,1.3200559616088867,426.99481600000007 +1050,Binary classification,Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.320155143737793,447.8295280000001 +1075,Binary classification,Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.320277214050293,469.1965740000001 +1100,Binary classification,Bagging,Phishing,0.89171974522293,0.8761706555671176,1.320643424987793,491.1150510000001 +1125,Binary classification,Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.320704460144043,513.5552500000001 +1150,Binary classification,Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.320704460144043,536.4428780000001 +1175,Binary classification,Bagging,Phishing,0.8926746166950597,0.877906976744186,1.320765495300293,559.8515450000001 +1200,Binary classification,Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.3328428268432617,583.8125340000001 +1225,Binary classification,Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3880414962768555,608.2234330000001 +1250,Binary classification,Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3882551193237305,633.1359570000001 +1903,Binary classification,Bagging,SMTP,1.0,0.0,0.2038736343383789,10.878823 +3806,Binary classification,Bagging,SMTP,1.0,0.0,0.2044839859008789,32.501535000000004 +5709,Binary classification,Bagging,SMTP,1.0,0.0,0.20502567291259766,64.818606 +7612,Binary classification,Bagging,SMTP,1.0,0.0,0.2050485610961914,107.076722 +9515,Binary classification,Bagging,SMTP,1.0,0.0,0.2050485610961914,158.807432 +11418,Binary classification,Bagging,SMTP,1.0,0.0,0.2056589126586914,218.4459 +13321,Binary classification,Bagging,SMTP,1.0,0.0,0.20568180084228516,285.19327499999997 +15224,Binary classification,Bagging,SMTP,0.9992774091834724,0.0,0.26130008697509766,359.03964299999996 +17127,Binary classification,Bagging,SMTP,0.9992409202382343,0.0,0.2063913345336914,440.61769699999996 +19030,Binary classification,Bagging,SMTP,0.9993168322034789,0.0,0.2062082290649414,529.79805 +20933,Binary classification,Bagging,SMTP,0.999378941333843,0.0,0.20684146881103516,626.4074929999999 +22836,Binary classification,Bagging,SMTP,0.9994306984891613,0.0,0.20693302154541016,729.8228539999999 +24739,Binary classification,Bagging,SMTP,0.9994744926833212,0.0,0.20707035064697266,839.3276679999999 +26642,Binary classification,Bagging,SMTP,0.9994744942006681,0.0,0.20674991607666016,954.8656409999999 +28545,Binary classification,Bagging,SMTP,0.9995095291479821,0.0,0.20697879791259766,1076.4115539999998 +30448,Binary classification,Bagging,SMTP,0.9995401845830459,0.0,0.2068643569946289,1203.4939569999997 +32351,Binary classification,Bagging,SMTP,0.9995672333848532,0.0,0.2070016860961914,1337.1471509999997 +34254,Binary classification,Bagging,SMTP,0.9995912766764955,0.0,0.2069101333618164,1476.3596139999997 +36157,Binary classification,Bagging,SMTP,0.9996127890253347,0.0,0.2069101333618164,1621.0863639999998 +38060,Binary classification,Bagging,SMTP,0.9996321500827662,0.0,0.20693302154541016,1771.0710339999998 +39963,Binary classification,Bagging,SMTP,0.9996496671838246,0.0,0.20679569244384766,1926.3060879999998 +41866,Binary classification,Bagging,SMTP,0.9996655917831124,0.0,0.20749759674072266,2086.761849 +43769,Binary classification,Bagging,SMTP,0.9996801316029976,0.0,0.20697879791259766,2252.424258 +45672,Binary classification,Bagging,SMTP,0.9996934597446958,0.0,0.2072610855102539,2423.176177 +47575,Binary classification,Bagging,SMTP,0.9997057216126456,0.0,0.20725345611572266,2599.130792 +49478,Binary classification,Bagging,SMTP,0.99971704024092,0.0,0.19541263580322266,2780.435567 +51381,Binary classification,Bagging,SMTP,0.9996885947839627,0.0,0.2073373794555664,2966.651736 +53284,Binary classification,Bagging,SMTP,0.9996997166075484,0.0,0.2073373794555664,3157.874809 +55187,Binary classification,Bagging,SMTP,0.999710071394919,0.0,0.2073526382446289,3353.943675 +57090,Binary classification,Bagging,SMTP,0.9995620872672494,0.0,0.20707035064697266,3555.07983 +58993,Binary classification,Bagging,SMTP,0.9995762137238947,0.0,0.20703983306884766,3761.219357 +60896,Binary classification,Bagging,SMTP,0.999589457262501,0.0,0.2070322036743164,3972.252634 +62799,Binary classification,Bagging,SMTP,0.9995700500015924,0.0,0.20719242095947266,4188.261246 +64702,Binary classification,Bagging,SMTP,0.9995826957852274,0.0,0.20734500885009766,4409.191759 +66605,Binary classification,Bagging,SMTP,0.9995946189418053,0.0,0.20731449127197266,4634.878548000001 +68508,Binary classification,Bagging,SMTP,0.9995766855941729,0.0,0.20736026763916016,4864.934707 +70411,Binary classification,Bagging,SMTP,0.9995881266865502,0.0,0.2072000503540039,5099.2875650000005 +72314,Binary classification,Bagging,SMTP,0.9995989656078437,0.0,0.20736026763916016,5337.886074000001 +74217,Binary classification,Bagging,SMTP,0.99960924867953,0.0,0.20736026763916016,5580.709613000001 +76120,Binary classification,Bagging,SMTP,0.9996190175908775,0.0,0.20746707916259766,5827.7179830000005 +78023,Binary classification,Bagging,SMTP,0.9996283099638563,0.0,0.20746707916259766,6079.015463000001 +79926,Binary classification,Bagging,SMTP,0.9996371598373475,0.0,0.20714664459228516,6334.716663000001 +81829,Binary classification,Bagging,SMTP,0.9996455980837855,0.0,0.20755863189697266,6594.692589000001 +83732,Binary classification,Bagging,SMTP,0.9996536527689864,0.0,0.20795536041259766,6858.666542000001 +85635,Binary classification,Bagging,SMTP,0.999661349463998,0.0,0.2080392837524414,7126.604303000001 +87538,Binary classification,Bagging,SMTP,0.9996687115162731,0.0,0.2078561782836914,7398.595214000001 +89441,Binary classification,Bagging,SMTP,0.9996645796064401,0.0,0.2079019546508789,7674.616155000001 +91344,Binary classification,Bagging,SMTP,0.999671567607808,0.0,0.2078104019165039,7954.656032000001 +93247,Binary classification,Bagging,SMTP,0.9996782703815713,0.0,0.19611454010009766,8238.690655 +95150,Binary classification,Bagging,SMTP,0.9996847050415664,0.0,0.2079477310180664,8526.751857000001 +95156,Binary classification,Bagging,SMTP,0.9996847249224948,0.0,0.20797061920166016,8814.843001000001 +106,Binary classification,Leveraging Bagging,Bananas,0.5142857142857142,0.45161290322580644,0.1802501678466797,1.958268 +212,Binary classification,Leveraging Bagging,Bananas,0.5402843601895735,0.4756756756756757,0.1808605194091797,6.1304110000000005 +318,Binary classification,Leveraging Bagging,Bananas,0.5394321766561514,0.4930555555555555,0.18149375915527344,12.559627 +424,Binary classification,Leveraging Bagging,Bananas,0.5531914893617021,0.4932975871313673,0.1814708709716797,21.158430000000003 +530,Binary classification,Leveraging Bagging,Bananas,0.5614366729678639,0.4703196347031963,0.1814708709716797,31.954501 +636,Binary classification,Leveraging Bagging,Bananas,0.5763779527559055,0.4836852207293666,0.41109561920166016,45.100937 +742,Binary classification,Leveraging Bagging,Bananas,0.5991902834008097,0.4940374787052811,0.5197267532348633,60.647662000000004 +848,Binary classification,Leveraging Bagging,Bananas,0.6210153482880756,0.5201793721973094,0.6145830154418945,78.646382 +954,Binary classification,Leveraging Bagging,Bananas,0.6411332633788038,0.5464190981432361,0.681065559387207,99.167462 +1060,Binary classification,Leveraging Bagging,Bananas,0.6515580736543909,0.555956678700361,0.7228097915649414,122.156435 +1166,Binary classification,Leveraging Bagging,Bananas,0.6626609442060086,0.5732899022801302,0.8111352920532227,147.677601 +1272,Binary classification,Leveraging Bagging,Bananas,0.6766325727773407,0.5958702064896755,0.8519144058227539,175.66982000000002 +1378,Binary classification,Leveraging Bagging,Bananas,0.6877269426289034,0.6062271062271062,0.9361848831176758,206.11220300000002 +1484,Binary classification,Leveraging Bagging,Bananas,0.6999325691166555,0.6238377007607777,0.978398323059082,239.08437400000003 +1590,Binary classification,Leveraging Bagging,Bananas,0.7073631214600378,0.6375681995323461,1.0816278457641602,274.51056400000004 +1696,Binary classification,Leveraging Bagging,Bananas,0.7162241887905605,0.6496722505462491,1.146012306213379,312.20930000000004 +1802,Binary classification,Leveraging Bagging,Bananas,0.7262631871182677,0.6662153012863914,1.231095314025879,352.32891700000005 +1908,Binary classification,Leveraging Bagging,Bananas,0.7320398531725223,0.677602523659306,1.3021745681762695,394.808348 +2014,Binary classification,Leveraging Bagging,Bananas,0.7391952309985097,0.6902654867256638,1.3571443557739258,439.684188 +2120,Binary classification,Leveraging Bagging,Bananas,0.7456347333647947,0.7020453289110005,1.439896583557129,486.825513 +2226,Binary classification,Leveraging Bagging,Bananas,0.750561797752809,0.7080483955812729,1.4615755081176758,536.150148 +2332,Binary classification,Leveraging Bagging,Bananas,0.7554697554697555,0.715,1.4801912307739258,587.578093 +2438,Binary classification,Leveraging Bagging,Bananas,0.7599507591300779,0.7202295552367289,1.5264062881469727,641.095023 +2544,Binary classification,Leveraging Bagging,Bananas,0.7624852536374361,0.7257039055404179,1.5866899490356445,696.665992 +2650,Binary classification,Leveraging Bagging,Bananas,0.7678369195922989,0.7331887201735358,1.6338167190551758,754.18395 +2756,Binary classification,Leveraging Bagging,Bananas,0.7731397459165155,0.7396917950853811,1.718327522277832,813.521765 +2862,Binary classification,Leveraging Bagging,Bananas,0.777350576721426,0.7440739252711932,1.7761125564575195,874.768076 +2968,Binary classification,Leveraging Bagging,Bananas,0.7812605325244355,0.7479611650485437,1.876938819885254,937.8386529999999 +3074,Binary classification,Leveraging Bagging,Bananas,0.7845753335502766,0.7526158445440957,1.974156379699707,1002.618853 +3180,Binary classification,Leveraging Bagging,Bananas,0.7892418999685435,0.7572463768115942,2.0079431533813477,1069.033932 +3286,Binary classification,Leveraging Bagging,Bananas,0.7923896499238965,0.7605337078651686,2.0704050064086914,1137.092928 +3392,Binary classification,Leveraging Bagging,Bananas,0.7938661161899144,0.7636117686844774,2.141594886779785,1206.880705 +3498,Binary classification,Leveraging Bagging,Bananas,0.7966828710323134,0.7657331136738056,2.2472352981567383,1278.374701 +3604,Binary classification,Leveraging Bagging,Bananas,0.7998889814043852,0.7685393258426965,2.2915468215942383,1351.505796 +3710,Binary classification,Leveraging Bagging,Bananas,0.8021029927204099,0.7717661691542288,2.3504953384399414,1426.3168569999998 +3816,Binary classification,Leveraging Bagging,Bananas,0.8055045871559633,0.7761013880506941,2.3972253799438477,1502.8248519999997 +3922,Binary classification,Leveraging Bagging,Bananas,0.8071920428462127,0.7776470588235294,2.4447336196899414,1580.9903339999996 +4028,Binary classification,Leveraging Bagging,Bananas,0.8085423392103303,0.7788930312589618,2.513848304748535,1660.8236829999996 +4134,Binary classification,Leveraging Bagging,Bananas,0.8107911928381321,0.7816862088218872,2.6076173782348633,1742.3313809999995 +4240,Binary classification,Leveraging Bagging,Bananas,0.8136352913422977,0.7852093529091897,2.653599739074707,1825.5059619999995 +4346,Binary classification,Leveraging Bagging,Bananas,0.8161104718066743,0.7881198621055423,2.7111101150512695,1910.3837179999996 +4452,Binary classification,Leveraging Bagging,Bananas,0.8173444169849472,0.7894327894327894,2.7411813735961914,1996.8929369999996 +4558,Binary classification,Leveraging Bagging,Bananas,0.8183015141540487,0.7910146390711761,2.7629594802856445,2085.0686209999994 +4664,Binary classification,Leveraging Bagging,Bananas,0.8205018228608192,0.7941971969510695,2.818455696105957,2174.8872669999996 +4770,Binary classification,Leveraging Bagging,Bananas,0.8209268190396309,0.7942168674698795,2.852097511291504,2266.3018959999995 +4876,Binary classification,Leveraging Bagging,Bananas,0.822974358974359,0.795932844644124,2.940415382385254,2359.3812719999996 +4982,Binary classification,Leveraging Bagging,Bananas,0.825135514956836,0.7990772779700116,2.986912727355957,2454.1200449999997 +5088,Binary classification,Leveraging Bagging,Bananas,0.825437389424022,0.7995485327313769,3.072648048400879,2550.4404699999996 +5194,Binary classification,Leveraging Bagging,Bananas,0.8266897746967071,0.8008849557522125,3.1882104873657227,2648.3615419999996 +5300,Binary classification,Leveraging Bagging,Bananas,0.8282694848084544,0.8026886383347789,3.2357072830200195,2747.9506659999997 +906,Binary classification,Leveraging Bagging,Elec2,0.8895027624309392,0.8873873873873873,2.5379486083984375,35.61841 +1812,Binary classification,Leveraging Bagging,Elec2,0.9127553837658752,0.8941018766756033,3.2672500610351562,98.66906499999999 +2718,Binary classification,Leveraging Bagging,Elec2,0.9013617960986382,0.8815207780725023,2.908538818359375,185.051304 +3624,Binary classification,Leveraging Bagging,Elec2,0.905051062655258,0.8859416445623343,4.239933013916016,291.140366 +4530,Binary classification,Leveraging Bagging,Elec2,0.9059395009935968,0.8829026937877955,4.5028228759765625,414.05054599999994 +5436,Binary classification,Leveraging Bagging,Elec2,0.904691812327507,0.8806451612903227,5.411556243896484,555.0527619999999 +6342,Binary classification,Leveraging Bagging,Elec2,0.904746885349314,0.8810086682427108,3.64324951171875,712.2276919999999 +7248,Binary classification,Leveraging Bagging,Elec2,0.9038222712846695,0.8793908980792524,4.176555633544922,885.4512659999999 +8154,Binary classification,Leveraging Bagging,Elec2,0.9062921623942107,0.8879107981220656,4.873016357421875,1073.942042 +9060,Binary classification,Leveraging Bagging,Elec2,0.9073849210729661,0.8915600361897376,6.068294525146484,1277.3056459999998 +9966,Binary classification,Leveraging Bagging,Elec2,0.9066733567486202,0.8924855491329481,5.883171081542969,1496.0059789999998 +10872,Binary classification,Leveraging Bagging,Elec2,0.9090240088308343,0.8966238110170377,7.123630523681641,1728.5474849999998 +11778,Binary classification,Leveraging Bagging,Elec2,0.9088052984631061,0.8963720571208027,4.904956817626953,1974.076492 +12684,Binary classification,Leveraging Bagging,Elec2,0.9071197666167311,0.8948214285714287,4.745685577392578,2233.08693 +13590,Binary classification,Leveraging Bagging,Elec2,0.908234601515932,0.8972732515034187,5.919612884521484,2504.250556 +14496,Binary classification,Leveraging Bagging,Elec2,0.9082442221455674,0.8976293103448276,4.272552490234375,2787.365554 +15402,Binary classification,Leveraging Bagging,Elec2,0.9089669501980391,0.8979027090008739,4.651363372802734,3081.637323 +16308,Binary classification,Leveraging Bagging,Elec2,0.9085668731219722,0.8971653217463273,5.967304229736328,3386.795808 +17214,Binary classification,Leveraging Bagging,Elec2,0.9075698599895428,0.8943488943488943,5.553913116455078,3703.0592819999997 +18120,Binary classification,Leveraging Bagging,Elec2,0.9077211766653789,0.8943911066195048,7.001399993896484,4030.5410249999995 +19026,Binary classification,Leveraging Bagging,Elec2,0.9078580814717477,0.8932854446947099,7.953182220458984,4368.505934999999 +19932,Binary classification,Leveraging Bagging,Elec2,0.9081832321509207,0.8944636678200691,8.54180908203125,4717.761576999999 +20838,Binary classification,Leveraging Bagging,Elec2,0.9064644622546432,0.8926111631494847,7.284095764160156,5078.966551999999 +21744,Binary classification,Leveraging Bagging,Elec2,0.9064066596145886,0.8909840895698291,8.78485107421875,5451.1446879999985 +22650,Binary classification,Leveraging Bagging,Elec2,0.9054704401960352,0.8890386110391294,9.895774841308594,5834.8071089999985 +23556,Binary classification,Leveraging Bagging,Elec2,0.9032052642751008,0.8860113988601139,9.921958923339844,6231.782156999999 +24462,Binary classification,Leveraging Bagging,Elec2,0.9009443604104493,0.8825098191339766,6.414276123046875,6640.471452999998 +25368,Binary classification,Leveraging Bagging,Elec2,0.8975046320022076,0.87847059923343,7.025360107421875,7059.615553999998 +26274,Binary classification,Leveraging Bagging,Elec2,0.8978418909146272,0.878705712219812,8.249675750732422,7487.941528999998 +27180,Binary classification,Leveraging Bagging,Elec2,0.8983038375216159,0.879888753693725,7.590415954589844,7924.652190999998 +28086,Binary classification,Leveraging Bagging,Elec2,0.8965640021363718,0.8772448763997466,7.862815856933594,8369.790676999999 +28992,Binary classification,Leveraging Bagging,Elec2,0.8963126487530613,0.8762962962962964,9.08489990234375,8823.391086 +29898,Binary classification,Leveraging Bagging,Elec2,0.8952403251162324,0.8748901493968203,2.6490402221679688,9284.744376999999 +30804,Binary classification,Leveraging Bagging,Elec2,0.8947505113138331,0.8736357966947302,3.2276954650878906,9752.904185 +31710,Binary classification,Leveraging Bagging,Elec2,0.8935948784256835,0.8721291594027135,3.8703384399414062,10228.075712 +32616,Binary classification,Leveraging Bagging,Elec2,0.8929940211559099,0.8717194736455194,4.073085784912109,10710.354293 +33522,Binary classification,Leveraging Bagging,Elec2,0.8932311088571343,0.872338148742643,4.776435852050781,11199.826533 +34428,Binary classification,Leveraging Bagging,Elec2,0.8924390739826299,0.8711865585974188,4.868198394775391,11696.46579 +35334,Binary classification,Leveraging Bagging,Elec2,0.8922537005066086,0.8703735231025913,5.445720672607422,12200.310087 +36240,Binary classification,Leveraging Bagging,Elec2,0.8919948122188802,0.8690619563762879,4.9837493896484375,12711.187581 +37146,Binary classification,Leveraging Bagging,Elec2,0.8920985327769552,0.8688996467355751,5.313899993896484,13229.101200000001 +38052,Binary classification,Leveraging Bagging,Elec2,0.8916979842842501,0.8676919125437441,5.129566192626953,13754.199791000001 +38958,Binary classification,Leveraging Bagging,Elec2,0.8912647277767796,0.8674924924924926,5.3661651611328125,14286.524658 +39864,Binary classification,Leveraging Bagging,Elec2,0.8915535709806086,0.8687813021702837,5.776020050048828,14825.184636 +40770,Binary classification,Leveraging Bagging,Elec2,0.8920012754789178,0.8703512852978417,6.964508056640625,15370.438083000001 +41676,Binary classification,Leveraging Bagging,Elec2,0.89250149970006,0.8717728547713092,8.029548645019531,15922.831901000001 +42582,Binary classification,Leveraging Bagging,Elec2,0.8927925600619995,0.8723184068469779,8.723072052001953,16481.133162000002 +43488,Binary classification,Leveraging Bagging,Elec2,0.8927495573389749,0.8722821622213702,8.793426513671875,17045.039295000002 +44394,Binary classification,Leveraging Bagging,Elec2,0.8920775797986169,0.8710467526175545,6.634971618652344,17614.470389000002 +45300,Binary classification,Leveraging Bagging,Elec2,0.8926687123336056,0.8720122143834896,7.5638427734375,18188.843118 +45312,Binary classification,Leveraging Bagging,Elec2,0.8926529981682152,0.8719663069228745,7.565349578857422,18763.342135 +25,Binary classification,Leveraging Bagging,Phishing,0.75,0.75,0.6626491546630859,1.23946 +50,Binary classification,Leveraging Bagging,Phishing,0.8163265306122449,0.8,0.6635112762451172,3.9379920000000004 +75,Binary classification,Leveraging Bagging,Phishing,0.8378378378378378,0.8333333333333334,0.6635112762451172,8.007437 +100,Binary classification,Leveraging Bagging,Phishing,0.8484848484848485,0.8421052631578947,0.6476030349731445,13.398751 +125,Binary classification,Leveraging Bagging,Phishing,0.8467741935483871,0.8403361344537815,0.9203081130981445,19.997869 +150,Binary classification,Leveraging Bagging,Phishing,0.8456375838926175,0.8456375838926175,0.9203310012817383,27.874049 +175,Binary classification,Leveraging Bagging,Phishing,0.867816091954023,0.8588957055214724,1.0861825942993164,37.283539 +200,Binary classification,Leveraging Bagging,Phishing,0.8693467336683417,0.8617021276595744,1.2813997268676758,47.998757 +225,Binary classification,Leveraging Bagging,Phishing,0.8660714285714286,0.8557692307692308,1.3089113235473633,59.952352999999995 +250,Binary classification,Leveraging Bagging,Phishing,0.8554216867469879,0.8434782608695653,1.3089799880981445,73.11976999999999 +275,Binary classification,Leveraging Bagging,Phishing,0.8576642335766423,0.844621513944223,1.2476167678833008,87.72028699999998 +300,Binary classification,Leveraging Bagging,Phishing,0.862876254180602,0.8464419475655431,1.4594087600708008,103.60422699999998 +325,Binary classification,Leveraging Bagging,Phishing,0.8703703703703703,0.851063829787234,1.4950456619262695,120.70292199999997 +350,Binary classification,Leveraging Bagging,Phishing,0.8710601719197708,0.8494983277591974,1.5330171585083008,138.98074599999998 +375,Binary classification,Leveraging Bagging,Phishing,0.8716577540106952,0.8481012658227849,1.809849739074707,158.64485 +400,Binary classification,Leveraging Bagging,Phishing,0.8696741854636592,0.8433734939759037,2.068051338195801,179.64681099999999 +425,Binary classification,Leveraging Bagging,Phishing,0.8702830188679245,0.8405797101449276,2.104710578918457,201.85095299999998 +450,Binary classification,Leveraging Bagging,Phishing,0.8752783964365256,0.845303867403315,2.104527473449707,225.23996699999998 +475,Binary classification,Leveraging Bagging,Phishing,0.8776371308016878,0.8505154639175259,2.132199287414551,249.91758899999996 +500,Binary classification,Leveraging Bagging,Phishing,0.875751503006012,0.8502415458937198,2.1503801345825195,275.75512899999995 +525,Binary classification,Leveraging Bagging,Phishing,0.8778625954198473,0.8497652582159624,2.187130928039551,302.922289 +550,Binary classification,Leveraging Bagging,Phishing,0.8743169398907104,0.8463251670378619,2.2971315383911133,331.41425 +575,Binary classification,Leveraging Bagging,Phishing,0.8763066202090593,0.8479657387580299,2.4067888259887695,361.219435 +600,Binary classification,Leveraging Bagging,Phishing,0.8764607679465777,0.8451882845188285,2.406834602355957,392.26266999999996 +625,Binary classification,Leveraging Bagging,Phishing,0.8782051282051282,0.8442622950819672,2.352017402648926,424.555734 +650,Binary classification,Leveraging Bagging,Phishing,0.8813559322033898,0.850485436893204,2.279099464416504,458.24235899999996 +675,Binary classification,Leveraging Bagging,Phishing,0.8798219584569733,0.8513761467889909,2.54854679107666,493.20967599999994 +700,Binary classification,Leveraging Bagging,Phishing,0.8841201716738197,0.8550983899821109,2.565995216369629,529.352808 +725,Binary classification,Leveraging Bagging,Phishing,0.8812154696132597,0.8537414965986394,2.870518684387207,566.738792 +750,Binary classification,Leveraging Bagging,Phishing,0.8825100133511349,0.8557377049180328,2.941006660461426,605.3090109999999 +775,Binary classification,Leveraging Bagging,Phishing,0.8837209302325582,0.856687898089172,3.0508241653442383,645.053635 +800,Binary classification,Leveraging Bagging,Phishing,0.8836045056320401,0.8584474885844748,3.1606874465942383,686.031259 +825,Binary classification,Leveraging Bagging,Phishing,0.8810679611650486,0.8567251461988304,3.270321846008301,728.1933819999999 +850,Binary classification,Leveraging Bagging,Phishing,0.8833922261484098,0.8591749644381224,3.2882280349731445,771.57935 +875,Binary classification,Leveraging Bagging,Phishing,0.8844393592677345,0.8595271210013908,3.2632036209106445,816.1995999999999 +900,Binary classification,Leveraging Bagging,Phishing,0.8832035595105673,0.8575305291723202,3.380833625793457,861.997768 +925,Binary classification,Leveraging Bagging,Phishing,0.8841991341991342,0.8597640891218873,3.4813432693481445,908.9730649999999 +950,Binary classification,Leveraging Bagging,Phishing,0.8851422550052687,0.8628930817610063,3.5117311477661133,957.1207809999999 +975,Binary classification,Leveraging Bagging,Phishing,0.8870636550308009,0.8651960784313726,3.5666399002075195,1006.3541349999998 +1000,Binary classification,Leveraging Bagging,Phishing,0.8878878878878879,0.8663484486873507,3.645543098449707,1056.728559 +1025,Binary classification,Leveraging Bagging,Phishing,0.8876953125,0.8667439165701043,3.735753059387207,1108.282035 +1050,Binary classification,Leveraging Bagging,Phishing,0.8894184938036225,0.8693693693693694,3.808384895324707,1160.9493009999999 +1075,Binary classification,Leveraging Bagging,Phishing,0.8901303538175046,0.87117903930131,3.9453020095825195,1214.7027389999998 +1100,Binary classification,Leveraging Bagging,Phishing,0.89171974522293,0.873269435569755,3.945347785949707,1269.5439849999998 +1125,Binary classification,Leveraging Bagging,Phishing,0.8905693950177936,0.8730650154798762,3.972836494445801,1325.4243339999998 +1150,Binary classification,Leveraging Bagging,Phishing,0.8920800696257616,0.8747474747474747,3.945645332336426,1382.2374609999997 +1175,Binary classification,Leveraging Bagging,Phishing,0.8909710391822828,0.8732673267326733,3.9732484817504883,1440.0925429999998 +1200,Binary classification,Leveraging Bagging,Phishing,0.8924103419516264,0.8746355685131195,3.9472780227661133,1498.9929549999997 +1225,Binary classification,Leveraging Bagging,Phishing,0.8937908496732027,0.8761904761904762,3.982327461242676,1558.8210809999996 +1250,Binary classification,Leveraging Bagging,Phishing,0.8943154523618895,0.8773234200743495,4.0114030838012695,1619.6535709999996 +1903,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16020870208740234,31.58816 +3806,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16081905364990234,89.620857 +5709,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,167.42750999999998 +7612,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,263.688726 +9515,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,375.65509199999997 +11418,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16203975677490234,502.19041899999996 +13321,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16203975677490234,643.105063 +15224,Binary classification,Leveraging Bagging,SMTP,0.9992117191092426,0.0,0.2446889877319336,797.8947989999999 +17127,Binary classification,Leveraging Bagging,SMTP,0.9991825294873292,0.0,0.1627492904663086,968.1431969999999 +19030,Binary classification,Leveraging Bagging,SMTP,0.9992642808345158,0.0,0.16258907318115234,1153.4864309999998 +20933,Binary classification,Leveraging Bagging,SMTP,0.9993311675902924,0.0,0.16315364837646484,1353.5382149999998 +22836,Binary classification,Leveraging Bagging,SMTP,0.9993869060652507,0.0,0.1632680892944336,1568.3644279999999 +24739,Binary classification,Leveraging Bagging,SMTP,0.9994340690435767,0.0,0.16329097747802734,1796.9469139999999 +26642,Binary classification,Leveraging Bagging,SMTP,0.9994369580721444,0.0,0.1630849838256836,2038.09448 +28545,Binary classification,Leveraging Bagging,SMTP,0.9994744955156951,0.0,0.1630849838256836,2291.954796 +30448,Binary classification,Leveraging Bagging,SMTP,0.999507340624692,0.0,0.16315364837646484,2557.874515 +32351,Binary classification,Leveraging Bagging,SMTP,0.9995363214837713,0.0,0.1631765365600586,2835.65121 +34254,Binary classification,Leveraging Bagging,SMTP,0.999562082153388,0.0,0.16315364837646484,3124.961925 +36157,Binary classification,Leveraging Bagging,SMTP,0.9995851310985728,0.0,0.16310787200927734,3424.787068 +38060,Binary classification,Leveraging Bagging,SMTP,0.9996058750886782,0.0,0.1631307601928711,3734.997034 +39963,Binary classification,Leveraging Bagging,SMTP,0.9996246434112407,0.0,0.16319942474365234,4055.587677 +41866,Binary classification,Leveraging Bagging,SMTP,0.9996417054819061,0.0,0.1638784408569336,4386.458848 +43769,Binary classification,Leveraging Bagging,SMTP,0.9996572838603546,0.0,0.1636495590209961,4726.906731 +45672,Binary classification,Leveraging Bagging,SMTP,0.999671564012174,0.0,0.16385555267333984,5077.03597 +47575,Binary classification,Leveraging Bagging,SMTP,0.9996847017278345,0.0,0.16394710540771484,5436.8723119999995 +49478,Binary classification,Leveraging Bagging,SMTP,0.9996968288295571,0.0,0.16371822357177734,5806.3009919999995 +51381,Binary classification,Leveraging Bagging,SMTP,0.9996691319579603,0.0,0.16394710540771484,6185.544045999999 +53284,Binary classification,Leveraging Bagging,SMTP,0.9996809488955202,0.0,0.1641073226928711,6574.848143999999 +55187,Binary classification,Leveraging Bagging,SMTP,0.9996919508571014,0.0,0.16376399993896484,6975.121680999999 +57090,Binary classification,Leveraging Bagging,SMTP,0.9995445707579393,0.0,0.1638784408569336,7384.584095999999 +58993,Binary classification,Leveraging Bagging,SMTP,0.9995592622728505,0.0,0.16390132904052734,7802.5953629999985 +60896,Binary classification,Leveraging Bagging,SMTP,0.999573035553001,0.0,0.16371822357177734,8228.594131999998 +62799,Binary classification,Leveraging Bagging,SMTP,0.9995541259275773,0.0,0.16390132904052734,8661.618187999999 +64702,Binary classification,Leveraging Bagging,SMTP,0.9995672400735692,0.0,0.16399288177490234,9101.660232999999 +66605,Binary classification,Leveraging Bagging,SMTP,0.9995796048285388,0.0,0.1638326644897461,9548.697329999999 +68508,Binary classification,Leveraging Bagging,SMTP,0.9995620885456961,0.0,0.16380977630615234,10002.655235999999 +70411,Binary classification,Leveraging Bagging,SMTP,0.9995739241585002,0.0,0.16371822357177734,10463.105480999999 +72314,Binary classification,Leveraging Bagging,SMTP,0.9995851368357004,0.0,0.16376399993896484,10929.695224 +74217,Binary classification,Leveraging Bagging,SMTP,0.9995957744960655,0.0,0.16380977630615234,11402.447204 +76120,Binary classification,Leveraging Bagging,SMTP,0.9996058802664249,0.0,0.1638784408569336,11881.476782 +78023,Binary classification,Leveraging Bagging,SMTP,0.9996154930660582,0.0,0.1637411117553711,12366.666901 +79926,Binary classification,Leveraging Bagging,SMTP,0.9996246481076009,0.0,0.16371822357177734,12858.057531 +81829,Binary classification,Leveraging Bagging,SMTP,0.999633377328054,0.0,0.1521596908569336,13355.582794 +83732,Binary classification,Leveraging Bagging,SMTP,0.9996417097610204,0.0,0.15288448333740234,13859.323941 +85635,Binary classification,Leveraging Bagging,SMTP,0.9996496718593082,0.0,0.16432857513427734,14369.189455 +87538,Binary classification,Leveraging Bagging,SMTP,0.9996572877754549,0.0,0.16460323333740234,14885.224126 +89441,Binary classification,Leveraging Bagging,SMTP,0.9996533989266547,0.0,0.16451168060302734,15407.418989999998 +91344,Binary classification,Leveraging Bagging,SMTP,0.9996606198614015,0.0,0.16432857513427734,15935.791255999999 +93247,Binary classification,Leveraging Bagging,SMTP,0.9996675460609571,0.0,0.16451168060302734,16470.041814999997 +95150,Binary classification,Leveraging Bagging,SMTP,0.9996741952096186,0.0,0.1645345687866211,17009.813748999997 +95156,Binary classification,Leveraging Bagging,SMTP,0.9996742157532447,0.0,0.16460323333740234,17549.605714999998 +106,Binary classification,Stacking,Bananas,0.6095238095238096,0.577319587628866,0.7777948379516602,2.119535 +212,Binary classification,Stacking,Bananas,0.7109004739336493,0.6702702702702703,1.3802881240844727,6.931057 +318,Binary classification,Stacking,Bananas,0.7602523659305994,0.7361111111111112,1.8119163513183594,15.160032000000001 +424,Binary classification,Stacking,Bananas,0.7943262411347518,0.772845953002611,2.401026725769043,27.407145 +530,Binary classification,Stacking,Bananas,0.8052930056710775,0.7775377969762419,5.0262651443481445,65.121823 +636,Binary classification,Stacking,Bananas,0.8236220472440945,0.7992831541218638,5.88111686706543,107.288216 +742,Binary classification,Stacking,Bananas,0.8299595141700404,0.8025078369905957,6.734616279602051,153.798119 +848,Binary classification,Stacking,Bananas,0.8347107438016529,0.8087431693989071,7.555168151855469,204.76644700000003 +954,Binary classification,Stacking,Bananas,0.8426023084994754,0.8166259168704157,8.384669303894043,260.019764 +1060,Binary classification,Stacking,Bananas,0.8536355051935789,0.8275862068965517,8.926264762878418,319.31365700000003 +1166,Binary classification,Stacking,Bananas,0.8532188841201717,0.8274470232088799,9.188977241516113,382.58132300000005 +1272,Binary classification,Stacking,Bananas,0.8536585365853658,0.8290441176470588,9.45701789855957,449.39877900000005 +1378,Binary classification,Stacking,Bananas,0.8576615831517792,0.8321917808219177,9.84501838684082,519.6596460000001 +1484,Binary classification,Stacking,Bananas,0.8590694538098449,0.8345209817893903,10.364198684692383,593.3590610000001 +1590,Binary classification,Stacking,Bananas,0.8565135305223411,0.8321060382916053,10.468925476074219,670.9077340000001 +1696,Binary classification,Stacking,Bananas,0.8595870206489675,0.8354080221300139,10.966409683227539,752.0270200000001 +1802,Binary classification,Stacking,Bananas,0.8634092171016102,0.8410852713178295,10.118447303771973,836.636461 +1908,Binary classification,Stacking,Bananas,0.8626114315679078,0.8417874396135265,10.3862943649292,924.5557000000001 +2014,Binary classification,Stacking,Bananas,0.8614008941877794,0.8415672913117546,10.646858215332031,1015.8872450000001 +2120,Binary classification,Stacking,Bananas,0.8640868334119868,0.8459893048128343,10.9229736328125,1110.420791 +2226,Binary classification,Stacking,Bananas,0.8642696629213483,0.8462321792260691,11.325839042663574,1208.24484 +2332,Binary classification,Stacking,Bananas,0.864006864006864,0.8461911693352742,11.659860610961914,1308.9666100000002 +2438,Binary classification,Stacking,Bananas,0.8641772671317193,0.8465461288827074,11.19693660736084,1412.476276 +2544,Binary classification,Stacking,Bananas,0.8651199370821864,0.8484312859036677,11.452000617980957,1518.7765900000002 +2650,Binary classification,Stacking,Bananas,0.864477161192903,0.8480744815911976,11.787381172180176,1627.8671150000002 +2756,Binary classification,Stacking,Bananas,0.8653357531760436,0.849125660837739,12.11353874206543,1739.6750610000001 +2862,Binary classification,Stacking,Bananas,0.8678783642083188,0.8515318146111548,12.359809875488281,1854.1318460000002 +2968,Binary classification,Stacking,Bananas,0.8695652173913043,0.8529076396807297,12.722334861755371,1971.3857770000002 +3074,Binary classification,Stacking,Bananas,0.8682069638789457,0.8515939904727006,13.0479736328125,2091.3191060000004 +3180,Binary classification,Stacking,Bananas,0.8700849323686694,0.8530771967271433,13.308364868164062,2213.9114950000003 +3286,Binary classification,Stacking,Bananas,0.8700152207001522,0.8523002421307506,13.608009338378906,2339.1031470000003 +3392,Binary classification,Stacking,Bananas,0.871129460336184,0.8544788544788545,13.707662582397461,2466.937686 +3498,Binary classification,Stacking,Bananas,0.872748069774092,0.8557536466774717,14.051713943481445,2598.213051 +3604,Binary classification,Stacking,Bananas,0.8742714404662781,0.856872037914692,14.268294334411621,2732.2090540000004 +3710,Binary classification,Stacking,Bananas,0.8751685090320841,0.8583664729275007,14.518733978271484,2868.9613940000004 +3816,Binary classification,Stacking,Bananas,0.8762778505897771,0.8596908442330559,14.742842674255371,3008.2640120000005 +3922,Binary classification,Stacking,Bananas,0.8747768426421831,0.8578048074138431,15.053866386413574,3150.2484420000005 +4028,Binary classification,Stacking,Bananas,0.8733548547305686,0.8560135516657256,15.453622817993164,3294.8873180000005 +4134,Binary classification,Stacking,Bananas,0.874183401887249,0.8569069895432032,15.755488395690918,3442.0965550000005 +4240,Binary classification,Stacking,Bananas,0.8749705119131871,0.8579849946409432,15.976973533630371,3591.8798750000005 +4346,Binary classification,Stacking,Bananas,0.8759493670886076,0.8590849673202615,16.313834190368652,3744.3343260000006 +4452,Binary classification,Stacking,Bananas,0.8755335879577623,0.8585291113381002,16.729196548461914,3899.4355060000007 +4558,Binary classification,Stacking,Bananas,0.8757954794821154,0.8592039800995025,17.032727241516113,4057.1530940000007 +4664,Binary classification,Stacking,Bananas,0.8756165558653227,0.8594961240310078,17.45319175720215,4217.5274930000005 +4770,Binary classification,Stacking,Bananas,0.8754455860767456,0.8590412909349787,17.6948184967041,4380.594611 +4876,Binary classification,Stacking,Bananas,0.8756923076923077,0.8588070829450141,17.917430877685547,4546.383704000001 +4982,Binary classification,Stacking,Bananas,0.8761292913069665,0.8596770525358198,18.09793186187744,4714.847995000001 +5088,Binary classification,Stacking,Bananas,0.8757617456261058,0.8591800356506238,18.51348114013672,4886.014522000001 +5194,Binary classification,Stacking,Bananas,0.876372039283651,0.8598865124399825,18.892748832702637,5059.990176000001 +5300,Binary classification,Stacking,Bananas,0.8762030571806001,0.8596491228070176,19.194592475891113,5236.837813000001 +906,Binary classification,Stacking,Elec2,0.9116022099447514,0.908256880733945,7.041282653808594,59.400144 +1812,Binary classification,Stacking,Elec2,0.906129210381005,0.8855989232839839,9.07800579071045,148.928403 +2718,Binary classification,Stacking,Elec2,0.9002576370997424,0.8772088808337108,9.477606773376465,264.671315 +3624,Binary classification,Stacking,Elec2,0.9064311344189898,0.8849677638276212,10.383838653564453,404.188666 +4530,Binary classification,Stacking,Elec2,0.90527710311327,0.8788477831121152,11.437847137451172,565.129871 +5436,Binary classification,Stacking,Elec2,0.9000919963201472,0.8723854289071681,14.209432601928711,747.817107 +6342,Binary classification,Stacking,Elec2,0.897019397571361,0.8704622098789923,15.688876152038574,951.5612199999999 +7248,Binary classification,Stacking,Elec2,0.8965089002345799,0.8694744169857292,19.837779998779297,1176.633371 +8154,Binary classification,Stacking,Elec2,0.8980743284680486,0.8778839088905216,22.41482448577881,1420.714573 +9060,Binary classification,Stacking,Elec2,0.9000993487139861,0.8828478964401294,25.97023296356201,1683.800751 +9966,Binary classification,Stacking,Elec2,0.8982438534872053,0.8827203331020125,28.783666610717773,1965.440637 +10872,Binary classification,Stacking,Elec2,0.9006531137889798,0.8870765370138016,30.882869720458984,2263.780185 +11778,Binary classification,Stacking,Elec2,0.9021822195805383,0.8888030888030888,33.277831077575684,2578.868179 +12684,Binary classification,Stacking,Elec2,0.9012851848931641,0.8879541793449078,35.50911808013916,2911.085292 +13590,Binary classification,Stacking,Elec2,0.901905953344617,0.8899529431189631,38.6168327331543,3259.512573 +14496,Binary classification,Stacking,Elec2,0.9030010348395998,0.8917128773875539,41.26064682006836,3624.142031 +15402,Binary classification,Stacking,Elec2,0.9039023440036361,0.8922382408620941,43.65532207489014,4004.812957 +16308,Binary classification,Stacking,Elec2,0.9002882197829153,0.8879239040529363,45.1539192199707,4403.507418 +17214,Binary classification,Stacking,Elec2,0.8997850461860222,0.8856176646111001,37.54582214355469,4816.829749 +18120,Binary classification,Stacking,Elec2,0.9001048622992439,0.8858908082209053,41.9152717590332,5243.603799 +19026,Binary classification,Stacking,Elec2,0.9014454664914586,0.886177381169186,44.45838737487793,5683.033974000001 +19932,Binary classification,Stacking,Elec2,0.9011088254477949,0.8867826986041704,48.213175773620605,6135.158415000001 +20838,Binary classification,Stacking,Elec2,0.8992657292316553,0.8848411696933121,47.78572177886963,6598.876461000001 +21744,Binary classification,Stacking,Elec2,0.8986800349537782,0.8824376967821121,49.356743812561035,7073.276106000001 +22650,Binary classification,Stacking,Elec2,0.898361958585368,0.8812912541254125,47.91073036193848,7558.681153000001 +23556,Binary classification,Stacking,Elec2,0.8961579282530249,0.8784656663022956,53.37149906158447,8055.088432000001 +24462,Binary classification,Stacking,Elec2,0.8945259801316381,0.8760211436809228,52.06687641143799,8562.621137000002 +25368,Binary classification,Stacking,Elec2,0.8922221784207829,0.8734610756271406,20.63737678527832,9080.309208000002 +26274,Binary classification,Stacking,Elec2,0.8928177216153466,0.873801201039706,17.83812713623047,9607.290172000003 +27180,Binary classification,Stacking,Elec2,0.8932263880201626,0.874632797649905,19.305461883544922,10142.799192000002 +28086,Binary classification,Stacking,Elec2,0.8915435285739719,0.8721564677243349,18.23539447784424,10686.882271000002 +28992,Binary classification,Stacking,Elec2,0.891345590010693,0.8712498978173793,21.229859352111816,11240.330868000003 +29898,Binary classification,Stacking,Elec2,0.8905575810281968,0.8700246285850481,25.24380111694336,11801.274420000003 +30804,Binary classification,Stacking,Elec2,0.890335356945752,0.8690697674418605,28.836254119873047,12369.623812000003 +31710,Binary classification,Stacking,Elec2,0.8895266328171813,0.8679458664756663,27.00519371032715,12944.308752000003 +32616,Binary classification,Stacking,Elec2,0.8879963207113292,0.86634224872855,30.17805576324463,13524.938061000003 +33522,Binary classification,Stacking,Elec2,0.8870260433757943,0.8654563541407612,32.107930183410645,14111.446119000002 +34428,Binary classification,Stacking,Elec2,0.8857582711244082,0.8638770636486346,32.29031944274902,14703.865141000002 +35334,Binary classification,Stacking,Elec2,0.8850083491353692,0.8622665175090681,36.271653175354004,15302.291934000003 +36240,Binary classification,Stacking,Elec2,0.8850133833715058,0.8612988050461006,35.55355262756348,15906.592759000003 +37146,Binary classification,Stacking,Elec2,0.8836182527931081,0.859061715515274,38.756625175476074,16517.021041000004 +38052,Binary classification,Stacking,Elec2,0.8830516937794013,0.8577547628180539,41.063425064086914,17133.624997000003 +38958,Binary classification,Stacking,Elec2,0.8831788895448828,0.8582198822393221,42.255154609680176,17756.348401000003 +39864,Binary classification,Stacking,Elec2,0.8836765923287259,0.8598797328740216,43.789937019348145,18385.013164000004 +40770,Binary classification,Stacking,Elec2,0.8840295322426354,0.8614708467623791,40.85312080383301,19019.639174000004 +41676,Binary classification,Stacking,Elec2,0.8847030593881223,0.8632106356933413,39.29591369628906,19660.085711000003 +42582,Binary classification,Stacking,Elec2,0.8851130786031328,0.8639675212724542,42.33915042877197,20306.976876000004 +43488,Binary classification,Stacking,Elec2,0.8848391473313864,0.8636091290375292,42.20731544494629,20960.039327000006 +44394,Binary classification,Stacking,Elec2,0.8848467100669024,0.8632350580555408,44.13865566253662,21618.107174000004 +45300,Binary classification,Stacking,Elec2,0.8854720854765006,0.8642027012878233,40.63082981109619,22281.084676000006 +45312,Binary classification,Stacking,Elec2,0.8854582772395224,0.8641574621787154,40.75471591949463,22944.429270000004 +25,Binary classification,Stacking,Phishing,0.6666666666666666,0.7142857142857143,0.6122617721557617,0.640752 +50,Binary classification,Stacking,Phishing,0.7755102040816326,0.7659574468085107,0.7524843215942383,1.992597 +75,Binary classification,Stacking,Phishing,0.8243243243243243,0.8266666666666667,0.9228668212890625,4.151733 +100,Binary classification,Stacking,Phishing,0.8282828282828283,0.8282828282828283,1.193608283996582,7.194986 +125,Binary classification,Stacking,Phishing,0.8306451612903226,0.8292682926829269,1.3295679092407227,11.208746999999999 +150,Binary classification,Stacking,Phishing,0.8389261744966443,0.8441558441558442,1.3798675537109375,16.195196 +175,Binary classification,Stacking,Phishing,0.8563218390804598,0.8520710059171597,1.4546594619750977,22.422242999999998 +200,Binary classification,Stacking,Phishing,0.8542713567839196,0.8497409326424871,1.6083984375,29.888727999999997 +225,Binary classification,Stacking,Phishing,0.8526785714285714,0.8436018957345972,1.7997064590454102,38.482186 +250,Binary classification,Stacking,Phishing,0.8433734939759037,0.8354430379746836,1.9343080520629883,48.311454 +275,Binary classification,Stacking,Phishing,0.8467153284671532,0.8372093023255813,2.053934097290039,59.483297 +300,Binary classification,Stacking,Phishing,0.8461538461538461,0.8333333333333334,2.12460994720459,72.133375 +325,Binary classification,Stacking,Phishing,0.8518518518518519,0.8356164383561644,2.201033592224121,86.30699200000001 +350,Binary classification,Stacking,Phishing,0.8595988538681948,0.8414239482200646,2.2356014251708984,102.05020300000001 +375,Binary classification,Stacking,Phishing,0.8556149732620321,0.8353658536585366,2.328523635864258,119.41383300000001 +400,Binary classification,Stacking,Phishing,0.8571428571428571,0.8347826086956521,2.316814422607422,138.615081 +425,Binary classification,Stacking,Phishing,0.8608490566037735,0.8356545961002786,2.353947639465332,159.65345200000002 +450,Binary classification,Stacking,Phishing,0.8619153674832962,0.8351063829787234,2.4291276931762695,182.47163200000003 +475,Binary classification,Stacking,Phishing,0.8649789029535865,0.8407960199004976,2.5799179077148438,207.23725000000002 +500,Binary classification,Stacking,Phishing,0.8637274549098196,0.841860465116279,4.818408012390137,255.437855 +525,Binary classification,Stacking,Phishing,0.8645038167938931,0.8397291196388261,5.000759124755859,305.327991 +550,Binary classification,Stacking,Phishing,0.8652094717668488,0.8418803418803419,5.177936553955078,356.788916 +575,Binary classification,Stacking,Phishing,0.8658536585365854,0.8425357873210634,5.324765205383301,409.72389599999997 +600,Binary classification,Stacking,Phishing,0.8697829716193656,0.8446215139442231,5.557343482971191,464.11454999999995 +625,Binary classification,Stacking,Phishing,0.8685897435897436,0.8404669260700389,5.701066970825195,520.0238599999999 +650,Binary classification,Stacking,Phishing,0.8721109399075501,0.847145488029466,5.875107765197754,577.5076929999999 +675,Binary classification,Stacking,Phishing,0.8753709198813057,0.8541666666666667,5.993474006652832,636.4901669999999 +700,Binary classification,Stacking,Phishing,0.8798283261802575,0.8576271186440678,6.097118377685547,696.8595059999999 +725,Binary classification,Stacking,Phishing,0.8798342541436464,0.8603531300160514,6.2616376876831055,758.6517739999999 +750,Binary classification,Stacking,Phishing,0.8811748998664887,0.8624420401854715,6.510566711425781,822.0041369999999 +775,Binary classification,Stacking,Phishing,0.8824289405684754,0.8631578947368422,6.659415245056152,886.863173 +800,Binary classification,Stacking,Phishing,0.8823529411764706,0.8645533141210374,6.793304443359375,953.2293709999999 +825,Binary classification,Stacking,Phishing,0.8822815533980582,0.8654646324549237,7.087222099304199,1021.1070179999999 +850,Binary classification,Stacking,Phishing,0.8833922261484098,0.8660351826792964,7.324291229248047,1090.559446 +875,Binary classification,Stacking,Phishing,0.88558352402746,0.8677248677248677,7.470724105834961,1161.497406 +900,Binary classification,Stacking,Phishing,0.8854282536151279,0.8670967741935484,7.810632705688477,1233.983859 +925,Binary classification,Stacking,Phishing,0.8874458874458875,0.8706467661691542,7.971014976501465,1307.96073 +950,Binary classification,Stacking,Phishing,0.8872497365648051,0.8718562874251498,8.080266952514648,1383.548543 +975,Binary classification,Stacking,Phishing,0.8891170431211499,0.8738317757009346,8.205357551574707,1460.7186840000002 +1000,Binary classification,Stacking,Phishing,0.8888888888888888,0.8737201365187712,8.39128303527832,1539.5016600000001 +1025,Binary classification,Stacking,Phishing,0.888671875,0.8738938053097345,8.406519889831543,1619.9124450000002 +1050,Binary classification,Stacking,Phishing,0.8903717826501429,0.8762109795479011,8.400672912597656,1701.87919 +1075,Binary classification,Stacking,Phishing,0.8910614525139665,0.877742946708464,8.453279495239258,1785.406003 +1100,Binary classification,Stacking,Phishing,0.8926296633303002,0.8795918367346939,8.421560287475586,1870.444315 +1125,Binary classification,Stacking,Phishing,0.8932384341637011,0.8811881188118813,8.383057594299316,1956.980231 +1150,Binary classification,Stacking,Phishing,0.8929503916449086,0.8806983511154219,8.433841705322266,2045.031177 +1175,Binary classification,Stacking,Phishing,0.8909710391822828,0.8783269961977185,8.58321475982666,2134.506504 +1200,Binary classification,Stacking,Phishing,0.8932443703085905,0.8803738317757008,8.605175971984863,2225.421876 +1225,Binary classification,Stacking,Phishing,0.8946078431372549,0.8817598533455545,8.70528507232666,2317.752916 +1250,Binary classification,Stacking,Phishing,0.8951160928742994,0.88272157564906,8.721240997314453,2411.4111159999998 +1903,Binary classification,Stacking,SMTP,1.0,0.0,4.7766571044921875,62.937451 +3806,Binary classification,Stacking,SMTP,1.0,0.0,4.703582763671875,162.906939 +5709,Binary classification,Stacking,SMTP,1.0,0.0,4.683967590332031,291.99956099999997 +7612,Binary classification,Stacking,SMTP,1.0,0.0,4.6548919677734375,449.905783 +9515,Binary classification,Stacking,SMTP,1.0,0.0,4.674896240234375,634.264648 +11418,Binary classification,Stacking,SMTP,1.0,0.0,4.68939208984375,844.6132379999999 +13321,Binary classification,Stacking,SMTP,1.0,0.0,4.6727142333984375,1077.972428 +15224,Binary classification,Stacking,SMTP,0.9992774091834724,0.0,4.755153656005859,1334.293209 +17127,Binary classification,Stacking,SMTP,0.9992409202382343,0.0,4.668712615966797,1613.416001 +19030,Binary classification,Stacking,SMTP,0.9993168322034789,0.0,4.707424163818359,1913.210948 +20933,Binary classification,Stacking,SMTP,0.999378941333843,0.0,4.680248260498047,2233.020054 +22836,Binary classification,Stacking,SMTP,0.9994306984891613,0.0,4.695384979248047,2572.124593 +24739,Binary classification,Stacking,SMTP,0.9994744926833212,0.0,4.721019744873047,2930.571035 +26642,Binary classification,Stacking,SMTP,0.9994744942006681,0.0,4.747562408447266,3309.032834 +28545,Binary classification,Stacking,SMTP,0.9995095291479821,0.0,4.741054534912109,3705.221814 +30448,Binary classification,Stacking,SMTP,0.9995401845830459,0.0,4.678241729736328,4115.910057 +32351,Binary classification,Stacking,SMTP,0.9995672333848532,0.0,4.619670867919922,4539.977119 +34254,Binary classification,Stacking,SMTP,0.9995912766764955,0.0,4.749675750732422,4977.188868 +36157,Binary classification,Stacking,SMTP,0.9996127890253347,0.0,4.678524017333984,5426.058059 +38060,Binary classification,Stacking,SMTP,0.9996321500827662,0.0,4.705173492431641,5886.859448 +39963,Binary classification,Stacking,SMTP,0.9996496671838246,0.0,4.729236602783203,6359.258672 +41866,Binary classification,Stacking,SMTP,0.9996655917831124,0.0,4.729305267333984,6843.735511 +43769,Binary classification,Stacking,SMTP,0.9996801316029976,0.0,4.741458892822266,7339.76837 +45672,Binary classification,Stacking,SMTP,0.9996934597446958,0.0,4.677211761474609,7847.750223999999 +47575,Binary classification,Stacking,SMTP,0.9997057216126456,0.0,4.833148956298828,8367.044639 +49478,Binary classification,Stacking,SMTP,0.99971704024092,0.0,4.807292938232422,8898.416265 +51381,Binary classification,Stacking,SMTP,0.9996885947839627,0.0,4.893611907958984,9441.161399999999 +53284,Binary classification,Stacking,SMTP,0.9996997166075484,0.0,4.877178192138672,9993.506038 +55187,Binary classification,Stacking,SMTP,0.999710071394919,0.0,4.888896942138672,10554.524537 +57090,Binary classification,Stacking,SMTP,0.9995620872672494,0.0,4.783634185791016,11123.611551 +58993,Binary classification,Stacking,SMTP,0.9995762137238947,0.0,4.831531524658203,11701.029088 +60896,Binary classification,Stacking,SMTP,0.999589457262501,0.0,4.854015350341797,12286.755545999999 +62799,Binary classification,Stacking,SMTP,0.9995700500015924,0.0,4.858226776123047,12880.441842999999 +64702,Binary classification,Stacking,SMTP,0.9995826957852274,0.0,4.846561431884766,13482.274685999999 +66605,Binary classification,Stacking,SMTP,0.9995946189418053,0.0,4.872089385986328,14092.292626999999 +68508,Binary classification,Stacking,SMTP,0.9995766855941729,0.0,4.843868255615234,14710.448755 +70411,Binary classification,Stacking,SMTP,0.9995881266865502,0.0,4.835132598876953,15336.827121999999 +72314,Binary classification,Stacking,SMTP,0.9995989656078437,0.0,4.892154693603516,15971.964918999998 +74217,Binary classification,Stacking,SMTP,0.99960924867953,0.0,4.812671661376953,16613.982513 +76120,Binary classification,Stacking,SMTP,0.9996190175908775,0.0,4.880641937255859,17262.391145999998 +78023,Binary classification,Stacking,SMTP,0.9996283099638563,0.0,4.831180572509766,17916.095854 +79926,Binary classification,Stacking,SMTP,0.9996371598373475,0.0,4.851375579833984,18574.918078 +81829,Binary classification,Stacking,SMTP,0.9996455980837855,0.0,4.851016998291016,19239.025055 +83732,Binary classification,Stacking,SMTP,0.9996536527689864,0.0,4.869503021240234,19908.351771999998 +85635,Binary classification,Stacking,SMTP,0.999661349463998,0.0,4.886287689208984,20582.843625999998 +87538,Binary classification,Stacking,SMTP,0.9996687115162731,0.0,4.888690948486328,21262.535161 +89441,Binary classification,Stacking,SMTP,0.9996645796064401,0.0,4.888484954833984,21947.343421999998 +91344,Binary classification,Stacking,SMTP,0.999671567607808,0.0,4.876293182373047,22637.362347 +93247,Binary classification,Stacking,SMTP,0.9996782703815713,0.0,4.905620574951172,23332.514825 +95150,Binary classification,Stacking,SMTP,0.9996847050415664,0.0,4.880191802978516,24032.848442 +95156,Binary classification,Stacking,SMTP,0.9996847249224948,0.0,4.888683319091797,24733.238040999997 +106,Binary classification,Voting,Bananas,0.6761904761904762,0.6136363636363638,0.14342212677001953,0.374142 +212,Binary classification,Voting,Bananas,0.7772511848341233,0.7374301675977653,0.23540592193603516,1.3677169999999998 +318,Binary classification,Voting,Bananas,0.7886435331230284,0.7527675276752769,0.3270235061645508,3.238746 +424,Binary classification,Voting,Bananas,0.7990543735224587,0.7658402203856748,0.41901111602783203,6.2520690000000005 +530,Binary classification,Voting,Bananas,0.8015122873345936,0.7575057736720554,2.719620704650879,30.609493999999998 +636,Binary classification,Voting,Bananas,0.8173228346456692,0.7777777777777779,3.159085273742676,56.745796 +742,Binary classification,Voting,Bananas,0.8259109311740891,0.7839195979899498,3.6036806106567383,84.9251 +848,Binary classification,Voting,Bananas,0.8299881936245572,0.7913043478260869,4.0666093826293945,115.11768599999999 +954,Binary classification,Voting,Bananas,0.8352570828961176,0.7963683527885861,4.521588325500488,147.5017 +1060,Binary classification,Voting,Bananas,0.8470254957507082,0.8094117647058824,4.660099983215332,182.008963 +1166,Binary classification,Voting,Bananas,0.8497854077253219,0.8132337246531482,4.474972724914551,218.357961 +1272,Binary classification,Voting,Bananas,0.8489378442171518,0.8135922330097087,4.3258256912231445,256.411001 +1378,Binary classification,Voting,Bananas,0.8482207697893972,0.8112014453477868,4.1847429275512695,296.011707 +1484,Binary classification,Voting,Bananas,0.8530006743088334,0.8180300500834724,4.276310920715332,337.212047 +1590,Binary classification,Voting,Bananas,0.8539962240402769,0.8198757763975156,4.522702217102051,380.36472399999997 +1696,Binary classification,Voting,Bananas,0.8584070796460177,0.8250728862973761,4.5992326736450195,425.08911199999994 +1802,Binary classification,Voting,Bananas,0.8622987229317046,0.8315217391304348,4.6097002029418945,471.42038399999996 +1908,Binary classification,Voting,Bananas,0.8610382800209754,0.8319594166138238,4.583279609680176,519.2335119999999 +2014,Binary classification,Voting,Bananas,0.8584202682563339,0.8302561048243002,4.509037971496582,568.4848139999999 +2120,Binary classification,Voting,Bananas,0.8612553091080698,0.8355704697986577,4.487088203430176,619.150273 +2226,Binary classification,Voting,Bananas,0.8624719101123596,0.8370607028753994,4.479489326477051,671.27983 +2332,Binary classification,Voting,Bananas,0.8614328614328615,0.8357905439755974,4.476758003234863,724.8282939999999 +2438,Binary classification,Voting,Bananas,0.8621255642183012,0.8364167478091528,4.495999336242676,779.843051 +2544,Binary classification,Voting,Bananas,0.8623672827369249,0.8375116063138347,4.492741584777832,836.3752579999999 +2650,Binary classification,Voting,Bananas,0.8618346545866364,0.8374777975133214,4.535428047180176,894.373804 +2756,Binary classification,Voting,Bananas,0.8627949183303085,0.8384615384615384,4.529454231262207,953.727688 +2862,Binary classification,Voting,Bananas,0.8661307235232436,0.842061855670103,4.489285469055176,1014.5232589999999 +2968,Binary classification,Voting,Bananas,0.8678800134816312,0.8437001594896333,4.522076606750488,1076.832167 +3074,Binary classification,Voting,Bananas,0.8662544744549301,0.8419838523644751,4.4861345291137695,1140.498025 +3180,Binary classification,Voting,Bananas,0.8678829820698333,0.8432835820895522,4.490513801574707,1205.597432 +3286,Binary classification,Voting,Bananas,0.8684931506849315,0.8433647570703406,4.5036516189575195,1272.097546 +3392,Binary classification,Voting,Bananas,0.8690651725154822,0.8449720670391062,4.519848823547363,1340.030829 +3498,Binary classification,Voting,Bananas,0.8687446382613668,0.8439306358381503,4.534294128417969,1409.423343 +3604,Binary classification,Voting,Bananas,0.8701082431307244,0.8451356717405691,4.515525817871094,1480.1764939999998 +3710,Binary classification,Voting,Bananas,0.8705850633593961,0.8462524023062139,4.521697998046875,1552.3097799999998 +3816,Binary classification,Voting,Bananas,0.8718217562254259,0.847900466562986,4.52362060546875,1625.8426229999998 +3922,Binary classification,Voting,Bananas,0.8704412139760265,0.845873786407767,4.511474609375,1700.7302089999998 +4028,Binary classification,Voting,Bananas,0.8698783213310156,0.8450620934358367,4.530387878417969,1777.041184 +4134,Binary classification,Voting,Bananas,0.8707960319380595,0.8461981566820277,4.540306091308594,1854.755673 +4240,Binary classification,Voting,Bananas,0.8723755602736495,0.8485018202184262,4.5452880859375,1933.8281619999998 +4346,Binary classification,Voting,Bananas,0.8734177215189873,0.8498088476242489,4.5819854736328125,2014.1792419999997 +4452,Binary classification,Voting,Bananas,0.8732869018198157,0.8494394020288306,4.5782928466796875,2095.8385169999997 +4558,Binary classification,Voting,Bananas,0.8720649550142637,0.8482166102577455,4.539161682128906,2178.6750019999995 +4664,Binary classification,Voting,Bananas,0.8719708342268926,0.8485156051763512,4.509727478027344,2262.6244259999994 +4770,Binary classification,Voting,Bananas,0.8712518347661984,0.8472636815920398,4.5496673583984375,2347.8035639999994 +4876,Binary classification,Voting,Bananas,0.8717948717948718,0.8474493531852575,4.560760498046875,2434.1199849999994 +4982,Binary classification,Voting,Bananas,0.8725155591246737,0.8487735175041676,4.513671875,2521.5491019999995 +5088,Binary classification,Voting,Bananas,0.8718301552978179,0.8480186480186479,4.541267395019531,2610.1395069999994 +5194,Binary classification,Voting,Bananas,0.8725207009435779,0.848927430397079,4.5822906494140625,2699.9579359999993 +5300,Binary classification,Voting,Bananas,0.8726174749952821,0.8491620111731844,4.5840301513671875,2790.9651129999993 +906,Binary classification,Voting,Elec2,0.8795580110497238,0.880351262349067,4.715929985046387,35.551681 +1812,Binary classification,Voting,Elec2,0.8807288790723358,0.8536585365853658,4.9170331954956055,87.613259 +2718,Binary classification,Voting,Elec2,0.8689731321310269,0.8344186046511628,4.988085746765137,154.923184 +3624,Binary classification,Voting,Elec2,0.8793817278498481,0.8493622888659084,4.879870414733887,235.92708 +4530,Binary classification,Voting,Elec2,0.8792227864870833,0.8405712620227338,5.017077445983887,328.79801 +5436,Binary classification,Voting,Elec2,0.8689972401103956,0.8260869565217391,4.985064506530762,432.967134 +6342,Binary classification,Voting,Elec2,0.8680018924459865,0.8269588587967748,4.949084281921387,548.642546 +7248,Binary classification,Voting,Elec2,0.8643576652407893,0.8194010655888295,4.962946891784668,674.769885 +8154,Binary classification,Voting,Elec2,0.8671654605666625,0.8317016317016317,5.020190238952637,811.067026 +9060,Binary classification,Voting,Elec2,0.8711778341980351,0.8417627118644068,5.0752363204956055,957.595858 +9966,Binary classification,Voting,Elec2,0.8706472654290015,0.845165165165165,4.979113578796387,1113.746382 +10872,Binary classification,Voting,Elec2,0.8737006715113605,0.8516156922079325,4.9885969161987305,1279.290866 +11778,Binary classification,Voting,Elec2,0.8733972998216863,0.8507059176930009,5.106616020202637,1454.661274 +12684,Binary classification,Voting,Elec2,0.873294961759836,0.8513551012857274,5.2120466232299805,1640.606202 +13590,Binary classification,Voting,Elec2,0.8755611156082125,0.8560973534167304,5.144991874694824,1836.8358979999998 +14496,Binary classification,Voting,Elec2,0.8765781303897896,0.8583643416989946,5.178118705749512,2042.985996 +15402,Binary classification,Voting,Elec2,0.8766963184208818,0.8575500712624708,5.108157157897949,2257.366998 +16308,Binary classification,Voting,Elec2,0.8711596247010487,0.8493366798135532,5.1558027267456055,2480.0447249999997 +17214,Binary classification,Voting,Elec2,0.8687038865973392,0.8429683157309616,5.140070915222168,2710.925816 +18120,Binary classification,Voting,Elec2,0.8689773166289531,0.8433623647400369,5.172907829284668,2950.660411 +19026,Binary classification,Voting,Elec2,0.8696977660972405,0.8421320766732471,5.328249931335449,3199.829749 +19932,Binary classification,Voting,Elec2,0.8659876574180925,0.8380132209351688,5.355593681335449,3457.907085 +20838,Binary classification,Voting,Elec2,0.8617843259586313,0.8322851153039832,5.442904472351074,3724.5720149999997 +21744,Binary classification,Voting,Elec2,0.862668445016787,0.8307064293003741,5.347712516784668,3998.8413929999997 +22650,Binary classification,Voting,Elec2,0.8610093160845953,0.8268045774647886,5.363558769226074,4280.436632 +23556,Binary classification,Voting,Elec2,0.85434090426661,0.8163571160948456,5.3763532638549805,4569.013267 +24462,Binary classification,Voting,Elec2,0.8534401700666366,0.8138145936120489,5.330439567565918,4864.708005 +25368,Binary classification,Voting,Elec2,0.8518941932431899,0.8121030257564391,5.456484794616699,5167.517117 +26274,Binary classification,Voting,Elec2,0.8530811098846725,0.8130931628897928,5.321070671081543,5477.642376000001 +27180,Binary classification,Voting,Elec2,0.8524964126715479,0.8125672074430782,5.426630973815918,5794.594214000001 +28086,Binary classification,Voting,Elec2,0.8496350364963504,0.8078795323233702,5.3666486740112305,6118.251751000001 +28992,Binary classification,Voting,Elec2,0.8475043979165948,0.8032575319300432,5.4148359298706055,6448.591708000001 +29898,Binary classification,Voting,Elec2,0.8460046158477439,0.8002429711905589,5.3892927169799805,6785.693940000001 +30804,Binary classification,Voting,Elec2,0.8462162776352953,0.7992881657556884,5.532000541687012,7130.317230000001 +31710,Binary classification,Voting,Elec2,0.8429783342268756,0.7938387644403958,5.507189750671387,7481.445887000001 +32616,Binary classification,Voting,Elec2,0.8419745515866932,0.7926122646064703,5.485476493835449,7839.281994000001 +33522,Binary classification,Voting,Elec2,0.8421884788639957,0.7931492922499414,5.629275321960449,8203.623919000001 +34428,Binary classification,Voting,Elec2,0.8400092950300636,0.7894656371837016,5.587969779968262,8574.868737 +35334,Binary classification,Voting,Elec2,0.8398947159878867,0.7879287722586691,5.669405937194824,8954.118864 +36240,Binary classification,Voting,Elec2,0.8408896492728828,0.7879523389232127,5.650286674499512,9340.118817 +37146,Binary classification,Voting,Elec2,0.8397092475434109,0.7854569040069184,5.6525373458862305,9731.872155000001 +38052,Binary classification,Voting,Elec2,0.8398202412551575,0.7854402083993381,5.638819694519043,10129.488521000001 +38958,Binary classification,Voting,Elec2,0.8406704828400544,0.787685992816829,5.6699628829956055,10532.813227 +39864,Binary classification,Voting,Elec2,0.841381732433585,0.7910235647949235,5.623560905456543,10941.028498 +40770,Binary classification,Voting,Elec2,0.8422085408030612,0.7943480067772769,5.627467155456543,11353.937417 +41676,Binary classification,Voting,Elec2,0.8431673665266947,0.7973835947671896,5.641619682312012,11771.547133999999 +42582,Binary classification,Voting,Elec2,0.8438505436697118,0.7987529888919157,5.640711784362793,12193.871152999998 +43488,Binary classification,Voting,Elec2,0.843999356129418,0.7991592160577892,5.725451469421387,12620.815871999997 +44394,Binary classification,Voting,Elec2,0.8432635775910616,0.7972256221950225,5.7456769943237305,13052.460535999997 +45300,Binary classification,Voting,Elec2,0.8436830835117773,0.7980031379261162,5.7547407150268555,13488.904282999996 +45312,Binary classification,Voting,Elec2,0.8436803425216834,0.7979576118892089,5.757502555847168,13925.545040999996 +25,Binary classification,Voting,Phishing,0.5833333333333334,0.7058823529411764,0.17400836944580078,0.162813 +50,Binary classification,Voting,Phishing,0.7346938775510204,0.7636363636363637,0.20249652862548828,0.520257 +75,Binary classification,Voting,Phishing,0.7837837837837838,0.8048780487804877,0.23151493072509766,1.0500919999999998 +100,Binary classification,Voting,Phishing,0.8080808080808081,0.819047619047619,0.26002979278564453,1.7634529999999997 +125,Binary classification,Voting,Phishing,0.8145161290322581,0.8217054263565893,0.2885446548461914,2.7650449999999998 +150,Binary classification,Voting,Phishing,0.8187919463087249,0.830188679245283,0.3175630569458008,4.083864999999999 +175,Binary classification,Voting,Phishing,0.8390804597701149,0.8390804597701148,0.34607791900634766,5.803779 +200,Binary classification,Voting,Phishing,0.8391959798994975,0.8383838383838383,0.3750925064086914,7.866028 +225,Binary classification,Voting,Phishing,0.8348214285714286,0.8294930875576038,0.4036073684692383,10.317012 +250,Binary classification,Voting,Phishing,0.8313253012048193,0.8264462809917356,0.43212223052978516,13.231482 +275,Binary classification,Voting,Phishing,0.8357664233576643,0.8288973384030419,0.46164798736572266,16.680039999999998 +300,Binary classification,Voting,Phishing,0.842809364548495,0.8327402135231317,0.49016284942626953,20.639260999999998 +325,Binary classification,Voting,Phishing,0.8549382716049383,0.8417508417508418,0.5191812515258789,25.174847 +350,Binary classification,Voting,Phishing,0.8624641833810889,0.8471337579617835,0.5476961135864258,30.349829 +375,Binary classification,Voting,Phishing,0.8609625668449198,0.8433734939759037,0.5762109756469727,36.11991 +400,Binary classification,Voting,Phishing,0.8621553884711779,0.8424068767908309,0.605229377746582,42.598397999999996 +425,Binary classification,Voting,Phishing,0.8632075471698113,0.839779005524862,0.6337442398071289,49.849208 +450,Binary classification,Voting,Phishing,0.8663697104677061,0.8412698412698413,0.6627893447875977,57.823257999999996 +475,Binary classification,Voting,Phishing,0.8649789029535865,0.8407960199004976,0.6913042068481445,66.560459 +500,Binary classification,Voting,Phishing,0.8657314629258517,0.8445475638051043,2.87209415435791,96.81871699999999 +525,Binary classification,Voting,Phishing,0.8683206106870229,0.8442437923250564,2.980504035949707,127.96343099999999 +550,Binary classification,Voting,Phishing,0.8688524590163934,0.8461538461538463,3.08364200592041,159.97764899999999 +575,Binary classification,Voting,Phishing,0.8710801393728222,0.848360655737705,3.1883134841918945,192.90880399999998 +600,Binary classification,Voting,Phishing,0.8747913188647746,0.8502994011976048,3.300492286682129,226.71997 +625,Binary classification,Voting,Phishing,0.8733974358974359,0.8460038986354775,3.412938117980957,261.370264 +650,Binary classification,Voting,Phishing,0.8767334360554699,0.8523985239852399,3.522160530090332,296.927054 +675,Binary classification,Voting,Phishing,0.8783382789317508,0.8571428571428572,3.6335840225219727,333.391974 +700,Binary classification,Voting,Phishing,0.882689556509299,0.8605442176870748,3.7482118606567383,370.807982 +725,Binary classification,Voting,Phishing,0.8839779005524862,0.864516129032258,3.8615503311157227,409.129822 +750,Binary classification,Voting,Phishing,0.8851802403204272,0.8664596273291927,3.975522041320801,448.34317699999997 +775,Binary classification,Voting,Phishing,0.8863049095607235,0.8670694864048338,4.095002174377441,488.481904 +800,Binary classification,Voting,Phishing,0.886107634543179,0.8683068017366136,4.146827697753906,529.573688 +825,Binary classification,Voting,Phishing,0.8859223300970874,0.8690807799442897,4.390903472900391,571.6173799999999 +850,Binary classification,Voting,Phishing,0.8869257950530035,0.8695652173913044,4.504707336425781,614.6492939999999 +875,Binary classification,Voting,Phishing,0.8890160183066361,0.8711819389110226,4.624469757080078,658.6172529999999 +900,Binary classification,Voting,Phishing,0.8876529477196885,0.869340232858991,4.741554260253906,703.5317429999999 +925,Binary classification,Voting,Phishing,0.8896103896103896,0.8728179551122195,4.862430572509766,749.5245539999999 +950,Binary classification,Voting,Phishing,0.8904109589041096,0.8752997601918464,4.984291076660156,796.5006999999998 +975,Binary classification,Voting,Phishing,0.8921971252566735,0.8771929824561404,5.102375030517578,844.5149469999998 +1000,Binary classification,Voting,Phishing,0.8928928928928929,0.8779931584948689,5.219093322753906,893.5084249999998 +1025,Binary classification,Voting,Phishing,0.892578125,0.8780487804878048,5.178688049316406,943.5715689999997 +1050,Binary classification,Voting,Phishing,0.894184938036225,0.8802588996763754,5.151969909667969,994.6247309999998 +1075,Binary classification,Voting,Phishing,0.8929236499068901,0.8798328108672936,5.117225646972656,1046.6512989999997 +1100,Binary classification,Voting,Phishing,0.8944494995450409,0.8816326530612245,5.075950622558594,1099.6701919999996 +1125,Binary classification,Voting,Phishing,0.8959074733096085,0.884272997032641,5.007194519042969,1153.6019869999996 +1150,Binary classification,Voting,Phishing,0.896431679721497,0.8845780795344327,4.982025146484375,1208.4750359999996 +1175,Binary classification,Voting,Phishing,0.8952299829642248,0.8829686013320648,4.96966552734375,1264.1689839999997 +1200,Binary classification,Voting,Phishing,0.896580483736447,0.8841121495327102,4.9371490478515625,1320.7795169999997 +1225,Binary classification,Voting,Phishing,0.8970588235294118,0.8844036697247706,4.8813018798828125,1378.3046599999998 +1250,Binary classification,Voting,Phishing,0.8967173738991193,0.8845120859444942,4.820304870605469,1436.7224909999998 +1903,Binary classification,Voting,SMTP,1.0,0.0,4.661611557006836,43.527964 +3806,Binary classification,Voting,SMTP,1.0,0.0,4.557134628295898,113.03314999999999 +5709,Binary classification,Voting,SMTP,1.0,0.0,4.496244430541992,201.747221 +7612,Binary classification,Voting,SMTP,1.0,0.0,4.508665084838867,310.754596 +9515,Binary classification,Voting,SMTP,1.0,0.0,4.565656661987305,436.825284 +11418,Binary classification,Voting,SMTP,1.0,0.0,4.554738998413086,579.964386 +13321,Binary classification,Voting,SMTP,1.0,0.0,4.492513656616211,739.485002 +15224,Binary classification,Voting,SMTP,0.9997372397030808,0.7777777777777778,4.532373428344727,915.25293 +17127,Binary classification,Voting,SMTP,0.9997664369963798,0.8181818181818181,4.528841018676758,1107.42481 +19030,Binary classification,Voting,SMTP,0.9997897945241474,0.8181818181818181,4.520586013793945,1314.036215 +20933,Binary classification,Voting,SMTP,0.9998089050257978,0.8181818181818181,4.519166946411133,1534.1862760000001 +22836,Binary classification,Voting,SMTP,0.9998248303043573,0.8181818181818181,4.512857437133789,1768.1243410000002 +24739,Binary classification,Voting,SMTP,0.9998383054410219,0.8181818181818181,4.568696975708008,2014.7470960000003 +26642,Binary classification,Voting,SMTP,0.9998123193573815,0.782608695652174,4.55253791809082,2273.6909760000003 +28545,Binary classification,Voting,SMTP,0.9998248318385651,0.782608695652174,4.554193496704102,2543.9053730000005 +30448,Binary classification,Voting,SMTP,0.9998357802082307,0.782608695652174,4.487833023071289,2824.6447140000005 +32351,Binary classification,Voting,SMTP,0.9998454404945905,0.782608695652174,4.52525520324707,3116.2234200000003 +34254,Binary classification,Voting,SMTP,0.9998540273844627,0.782608695652174,4.608850479125977,3418.6303260000004 +36157,Binary classification,Voting,SMTP,0.999861710366191,0.782608695652174,4.462549209594727,3731.2161630000005 +38060,Binary classification,Voting,SMTP,0.9998686250295594,0.782608695652174,4.517663955688477,4053.8854360000005 +39963,Binary classification,Voting,SMTP,0.9998748811370802,0.782608695652174,4.596040725708008,4386.480402 +41866,Binary classification,Voting,SMTP,0.9998805684939687,0.782608695652174,4.581964492797852,4729.507439 +43769,Binary classification,Voting,SMTP,0.9998857612867849,0.782608695652174,4.56077766418457,5082.623411 +45672,Binary classification,Voting,SMTP,0.9998905213373913,0.782608695652174,4.554697036743164,5446.283888999999 +47575,Binary classification,Voting,SMTP,0.9998949005759449,0.782608695652174,4.589784622192383,5821.612630999999 +49478,Binary classification,Voting,SMTP,0.9998989429431857,0.782608695652174,4.514947891235352,6206.523862999999 +51381,Binary classification,Voting,SMTP,0.9998637602179836,0.72,4.571069717407227,6600.877267999999 +53284,Binary classification,Voting,SMTP,0.9998686260158024,0.72,4.544900894165039,7002.824473 +55187,Binary classification,Voting,SMTP,0.9998731562352771,0.72,4.490015029907227,7412.266105 +57090,Binary classification,Voting,SMTP,0.9997197358510396,0.5294117647058824,4.520219802856445,7829.0322129999995 +58993,Binary classification,Voting,SMTP,0.9997287767832926,0.5294117647058824,4.567926406860352,8253.169596 +60896,Binary classification,Voting,SMTP,0.9997372526480006,0.5294117647058824,4.602060317993164,8684.574786 +62799,Binary classification,Voting,SMTP,0.9997133666677283,0.5,4.518564224243164,9122.220249 +64702,Binary classification,Voting,SMTP,0.9997217971901516,0.5,4.562410354614258,9566.245368 +66605,Binary classification,Voting,SMTP,0.9997297459612036,0.5,4.587350845336914,10016.770375 +68508,Binary classification,Voting,SMTP,0.9997226560789408,0.5128205128205129,4.58268928527832,10473.685786 +70411,Binary classification,Voting,SMTP,0.9997301519670502,0.5128205128205129,4.552003860473633,10937.213874000001 +72314,Binary classification,Voting,SMTP,0.9997372533292769,0.5128205128205129,4.568490982055664,11407.187031000001 +74217,Binary classification,Voting,SMTP,0.9997439905141748,0.5128205128205129,4.501398086547852,11883.434676 +76120,Binary classification,Voting,SMTP,0.9997503908354025,0.5128205128205129,4.530572891235352,12366.124718000001 +78023,Binary classification,Voting,SMTP,0.999756478941837,0.5128205128205129,4.565195083618164,12855.350972 +79926,Binary classification,Voting,SMTP,0.9997622771348139,0.5128205128205129,4.555276870727539,13350.750206 +81829,Binary classification,Voting,SMTP,0.9997678056411008,0.5128205128205129,4.523683547973633,13852.638924 +83732,Binary classification,Voting,SMTP,0.9997730828486463,0.5128205128205129,4.51640510559082,14360.452684 +85635,Binary classification,Voting,SMTP,0.9997781255108952,0.5128205128205129,4.554742813110352,14874.156807 +87538,Binary classification,Voting,SMTP,0.9997829489244549,0.5128205128205129,4.55351448059082,15393.589182 +89441,Binary classification,Voting,SMTP,0.9997763864042933,0.5,4.576028823852539,15919.905447 +91344,Binary classification,Voting,SMTP,0.9997700973254655,0.4878048780487804,4.509759902954102,16451.368555 +93247,Binary classification,Voting,SMTP,0.9997747892671,0.4878048780487804,4.633722305297852,16987.639193000003 +95150,Binary classification,Voting,SMTP,0.9997792935290964,0.4878048780487804,4.606340408325195,17528.67073 +95156,Binary classification,Voting,SMTP,0.9997793074457464,0.4878048780487804,4.602045059204102,18069.786262 +106,Binary classification,[baseline] Last Class,Bananas,0.5333333333333333,0.5242718446601942,0.0005102157592773438,0.004468 +212,Binary classification,[baseline] Last Class,Bananas,0.5876777251184834,0.5538461538461539,0.0005102157592773438,0.067972 +318,Binary classification,[baseline] Last Class,Bananas,0.5457413249211357,0.5102040816326531,0.0005102157592773438,0.134988 +424,Binary classification,[baseline] Last Class,Bananas,0.5460992907801419,0.5025906735751295,0.0005102157592773438,0.20522 +530,Binary classification,[baseline] Last Class,Bananas,0.5671077504725898,0.5096359743040686,0.0005102157592773438,0.337716 +636,Binary classification,[baseline] Last Class,Bananas,0.5464566929133858,0.4875444839857651,0.0005102157592773438,0.474055 +742,Binary classification,[baseline] Last Class,Bananas,0.5573549257759784,0.4875,0.0005102157592773438,0.646583 +848,Binary classification,[baseline] Last Class,Bananas,0.5501770956316411,0.4816326530612245,0.0005102157592773438,0.822555 +954,Binary classification,[baseline] Last Class,Bananas,0.5487932843651626,0.4794188861985472,0.0005102157592773438,1.00209 +1060,Binary classification,[baseline] Last Class,Bananas,0.5448536355051936,0.46799116997792495,0.0005102157592773438,1.292978 +1166,Binary classification,[baseline] Last Class,Bananas,0.534763948497854,0.4590818363273453,0.0005102157592773438,1.5875979999999998 +1272,Binary classification,[baseline] Last Class,Bananas,0.5287175452399685,0.456935630099728,0.0005102157592773438,1.885535 +1378,Binary classification,[baseline] Last Class,Bananas,0.5286855482933914,0.45232067510548524,0.0005102157592773438,2.211477 +1484,Binary classification,[baseline] Last Class,Bananas,0.5252865812542145,0.44913928012519555,0.0005102157592773438,2.547239 +1590,Binary classification,[baseline] Last Class,Bananas,0.5204531151667715,0.4437956204379563,0.0005102157592773438,2.88734 +1696,Binary classification,[baseline] Last Class,Bananas,0.5227138643067847,0.4455106237148732,0.0005102157592773438,3.258534 +1802,Binary classification,[baseline] Last Class,Bananas,0.524153248195447,0.4523961661341854,0.0005102157592773438,3.633124 +1908,Binary classification,[baseline] Last Class,Bananas,0.5233350812794966,0.456664674237896,0.0005102157592773438,4.01125 +2014,Binary classification,[baseline] Last Class,Bananas,0.5171385991058122,0.4563758389261745,0.0005102157592773438,4.505139000000001 +2120,Binary classification,[baseline] Last Class,Bananas,0.5143935818782445,0.45813586097946285,0.0005102157592773438,5.002779 +2226,Binary classification,[baseline] Last Class,Bananas,0.5114606741573033,0.45459106874059213,0.0005102157592773438,5.503925000000001 +2332,Binary classification,[baseline] Last Class,Bananas,0.510939510939511,0.45506692160611856,0.0005102157592773438,6.074663000000001 +2438,Binary classification,[baseline] Last Class,Bananas,0.5104636848584325,0.4530032095369097,0.0005102157592773438,6.648598000000001 +2544,Binary classification,[baseline] Last Class,Bananas,0.5084545812033032,0.45462478184991273,0.0005102157592773438,7.226634000000001 +2650,Binary classification,[baseline] Last Class,Bananas,0.5096262740656852,0.458072590738423,0.0005102157592773438,7.8632990000000005 +2756,Binary classification,[baseline] Last Class,Bananas,0.5092558983666061,0.45746388443017655,0.0005102157592773438,8.503527 +2862,Binary classification,[baseline] Last Class,Bananas,0.5103110800419434,0.4563445867287544,0.0005102157592773438,9.147193 +2968,Binary classification,[baseline] Last Class,Bananas,0.5133131108864173,0.457957957957958,0.0005102157592773438,9.82546 +3074,Binary classification,[baseline] Last Class,Bananas,0.5099251545720794,0.4563176895306859,0.0005102157592773438,10.507099 +3180,Binary classification,[baseline] Last Class,Bananas,0.5102233406731677,0.45387583304103823,0.0005102157592773438,11.191893 +3286,Binary classification,[baseline] Last Class,Bananas,0.5095890410958904,0.45222713362801764,0.0005102157592773438,11.975438 +3392,Binary classification,[baseline] Last Class,Bananas,0.5107637864936597,0.4558871761233191,0.0005102157592773438,12.764918 +3498,Binary classification,[baseline] Last Class,Bananas,0.5124392336288247,0.45579316948611553,0.0005102157592773438,13.557573 +3604,Binary classification,[baseline] Last Class,Bananas,0.5134610047182903,0.45440398381574854,0.0005102157592773438,14.3795 +3710,Binary classification,[baseline] Last Class,Bananas,0.5122674575357239,0.4546276756104914,0.0005102157592773438,15.204998 +3816,Binary classification,[baseline] Last Class,Bananas,0.510615989515072,0.4536142815335089,0.0005102157592773438,16.116361 +3922,Binary classification,[baseline] Last Class,Bananas,0.5090538128028564,0.45078459343794575,0.0005102157592773438,17.035489000000002 +4028,Binary classification,[baseline] Last Class,Bananas,0.5108020859200397,0.45247359644246804,0.0005102157592773438,17.958008000000003 +4134,Binary classification,[baseline] Last Class,Bananas,0.5102830873457537,0.4517876489707476,0.0005102157592773438,18.927027000000002 +4240,Binary classification,[baseline] Last Class,Bananas,0.5102618542108988,0.4525316455696203,0.0005102157592773438,19.900154000000004 +4346,Binary classification,[baseline] Last Class,Bananas,0.5074798619102416,0.4490216271884655,0.0005102157592773438,20.876623000000006 +4452,Binary classification,[baseline] Last Class,Bananas,0.5099977533138621,0.45132075471698113,0.0005102157592773438,21.913356000000007 +4558,Binary classification,[baseline] Last Class,Bananas,0.5099846390168971,0.45390070921985815,0.0005102157592773438,22.953869000000008 +4664,Binary classification,[baseline] Last Class,Bananas,0.5099721209521767,0.4553039332538737,0.0005102157592773438,23.99911400000001 +4770,Binary classification,[baseline] Last Class,Bananas,0.5110085971901867,0.4556489262371615,0.0005102157592773438,25.08372100000001 +4876,Binary classification,[baseline] Last Class,Bananas,0.5109743589743589,0.4539624370132845,0.0005102157592773438,26.17171900000001 +4982,Binary classification,[baseline] Last Class,Bananas,0.5099377635013049,0.45379279480868207,0.0005102157592773438,27.26320500000001 +5088,Binary classification,[baseline] Last Class,Bananas,0.5099272655789266,0.45364891518737677,0.0005102157592773438,28.44143600000001 +5194,Binary classification,[baseline] Last Class,Bananas,0.5097246293086848,0.4531786941580756,0.0005102157592773438,29.62357400000001 +5300,Binary classification,[baseline] Last Class,Bananas,0.5095301000188714,0.4529572721532309,0.0005102157592773438,30.80903600000001 +906,Binary classification,[baseline] Last Class,Elec2,0.8530386740331491,0.8500563697857948,0.0005102157592773438,0.224121 +1812,Binary classification,[baseline] Last Class,Elec2,0.8619547211485368,0.8287671232876712,0.0005102157592773438,0.785464 +2718,Binary classification,[baseline] Last Class,Elec2,0.8450496871549503,0.80958842152872,0.0005102157592773438,1.64751 +3624,Binary classification,[baseline] Last Class,Elec2,0.8418437758763456,0.8056968463886063,0.0005102157592773438,2.8059529999999997 +4530,Binary classification,[baseline] Last Class,Elec2,0.8388165157871494,0.7960893854748604,0.0005102157592773438,4.158177 +5436,Binary classification,[baseline] Last Class,Elec2,0.8413983440662374,0.7995348837209302,0.0005102157592773438,5.857693 +6342,Binary classification,[baseline] Last Class,Elec2,0.8370919413341744,0.7958094485076103,0.0005102157592773438,7.811494 +7248,Binary classification,[baseline] Last Class,Elec2,0.8359321098385539,0.7948231233822259,0.0005102157592773438,10.005109 +8154,Binary classification,[baseline] Last Class,Elec2,0.8352753587636453,0.8021799970540581,0.0005102157592773438,12.510532 +9060,Binary classification,[baseline] Last Class,Elec2,0.8358538470029805,0.8069081937410726,0.0005102157592773438,15.278307999999999 +9966,Binary classification,[baseline] Last Class,Elec2,0.8372303060712494,0.8118765947575969,0.0005102157592773438,18.289258999999998 +10872,Binary classification,[baseline] Last Class,Elec2,0.8368135406126391,0.8140461215932915,0.0005102157592773438,21.565545999999998 +11778,Binary classification,[baseline] Last Class,Elec2,0.8374798335739153,0.8150724637681159,0.0005102157592773438,25.041396 +12684,Binary classification,[baseline] Last Class,Elec2,0.8384451628163684,0.8161177420802298,0.0005102157592773438,28.814916 +13590,Binary classification,[baseline] Last Class,Elec2,0.842004562513798,0.8223417459660736,0.0005102157592773438,32.85712 +14496,Binary classification,[baseline] Last Class,Elec2,0.8448430493273542,0.8264794383149447,0.0005102157592773438,37.134508000000004 +15402,Binary classification,[baseline] Last Class,Elec2,0.8460489578598792,0.8270983738058776,0.0005102157592773438,41.682175 +16308,Binary classification,[baseline] Last Class,Elec2,0.844851904090268,0.8251313243019076,0.0005102157592773438,46.494991 +17214,Binary classification,[baseline] Last Class,Elec2,0.8443618195549875,0.8222177981286084,0.0005102157592773438,51.515798 +18120,Binary classification,[baseline] Last Class,Elec2,0.8450797505381091,0.8227792158595871,0.0005102157592773438,56.800748 +19026,Binary classification,[baseline] Last Class,Elec2,0.8462023653088042,0.8224083515416363,0.0005102157592773438,62.372633 +19932,Binary classification,[baseline] Last Class,Elec2,0.847523957653906,0.8255753888538139,0.0005102157592773438,68.180409 +20838,Binary classification,[baseline] Last Class,Elec2,0.84661899505687,0.8249917862227577,0.0005102157592773438,74.270057 +21744,Binary classification,[baseline] Last Class,Elec2,0.8452835395299637,0.8209495422610177,0.0005102157592773438,80.612623 +22650,Binary classification,[baseline] Last Class,Elec2,0.8444081416398075,0.8188733552631579,0.0005102157592773438,87.17507 +23556,Binary classification,[baseline] Last Class,Elec2,0.8451284228401613,0.8194595664654062,0.0005102157592773438,93.968638 +24462,Binary classification,[baseline] Last Class,Elec2,0.8464903315481788,0.8198781599270878,0.0005102157592773438,100.983267 +25368,Binary classification,[baseline] Last Class,Elec2,0.8462963692986951,0.8199492034172247,0.0005102157592773438,108.278888 +26274,Binary classification,[baseline] Last Class,Elec2,0.8477524454763445,0.8213168944876262,0.0005102157592773438,115.769594 +27180,Binary classification,[baseline] Last Class,Elec2,0.8495529636851982,0.8240457851026293,0.0005102157592773438,123.465792 +28086,Binary classification,[baseline] Last Class,Elec2,0.8509880719245149,0.825107610012955,0.0005102157592773438,131.36678899999998 +28992,Binary classification,[baseline] Last Class,Elec2,0.8521265220240765,0.8258237516759436,0.0005102157592773438,139.55273799999998 +29898,Binary classification,[baseline] Last Class,Elec2,0.8531959728400843,0.8268160833366216,0.0005102157592773438,147.964309 +30804,Binary classification,[baseline] Last Class,Elec2,0.8537480115573158,0.8267107743201139,0.0005102157592773438,156.664426 +31710,Binary classification,[baseline] Last Class,Elec2,0.8530385694913116,0.8259895444361464,0.0005102157592773438,165.606267 +32616,Binary classification,[baseline] Last Class,Elec2,0.8536869538555879,0.8269760696156635,0.0005102157592773438,174.782391 +33522,Binary classification,[baseline] Last Class,Elec2,0.8541511291429253,0.8276032300151628,0.0005102157592773438,184.217189 +34428,Binary classification,[baseline] Last Class,Elec2,0.8549684840386905,0.8286724084685859,0.0005102157592773438,193.875362 +35334,Binary classification,[baseline] Last Class,Elec2,0.8555175048821215,0.8284321962695346,0.0005102157592773438,203.79136499999998 +36240,Binary classification,[baseline] Last Class,Elec2,0.8545213720025387,0.8259146744155329,0.0005102157592773438,213.957306 +37146,Binary classification,[baseline] Last Class,Elec2,0.854354556467896,0.8252696854208386,0.0005102157592773438,224.37720199999998 +38052,Binary classification,[baseline] Last Class,Elec2,0.8545636119944285,0.8247736052181622,0.0005102157592773438,234.998191 +38958,Binary classification,[baseline] Last Class,Elec2,0.8548142824139435,0.8254213223038459,0.0005102157592773438,245.89740899999998 +39864,Binary classification,[baseline] Last Class,Elec2,0.8546521837292728,0.8262981172802495,0.0005102157592773438,257.034489 +40770,Binary classification,[baseline] Last Class,Elec2,0.8540067207927592,0.8267652366261132,0.0005102157592773438,268.379106 +41676,Binary classification,[baseline] Last Class,Elec2,0.8537012597480504,0.8274320002264302,0.0005102157592773438,279.987419 +42582,Binary classification,[baseline] Last Class,Elec2,0.8536201592259458,0.8277177368086459,0.0005102157592773438,291.808183 +43488,Binary classification,[baseline] Last Class,Elec2,0.853473451836181,0.8276626818845675,0.0005102157592773438,303.899029 +44394,Binary classification,[baseline] Last Class,Elec2,0.8533777847858897,0.8271686890948196,0.0005102157592773438,316.239245 +45300,Binary classification,[baseline] Last Class,Elec2,0.8533521711296055,0.8273155007928462,0.0005102157592773438,328.81397599999997 +45312,Binary classification,[baseline] Last Class,Elec2,0.8533027300214076,0.8272294856132872,0.0005102157592773438,341.389555 +25,Binary classification,[baseline] Last Class,Phishing,0.625,0.64,0.0005102157592773438,0.001863 +50,Binary classification,[baseline] Last Class,Phishing,0.6530612244897959,0.6222222222222223,0.0005102157592773438,0.005016 +75,Binary classification,[baseline] Last Class,Phishing,0.5675675675675675,0.5555555555555556,0.0005102157592773438,0.009415 +100,Binary classification,[baseline] Last Class,Phishing,0.5555555555555556,0.5416666666666666,0.0005102157592773438,0.115037 +125,Binary classification,[baseline] Last Class,Phishing,0.5241935483870968,0.5123966942148761,0.0005102157592773438,0.22212700000000002 +150,Binary classification,[baseline] Last Class,Phishing,0.5234899328859061,0.5298013245033113,0.0005102157592773438,0.330326 +175,Binary classification,[baseline] Last Class,Phishing,0.5229885057471264,0.496969696969697,0.0005102157592773438,0.439628 +200,Binary classification,[baseline] Last Class,Phishing,0.507537688442211,0.47872340425531923,0.0005102157592773438,0.550035 +225,Binary classification,[baseline] Last Class,Phishing,0.5,0.45098039215686275,0.0005102157592773438,0.6616070000000001 +250,Binary classification,[baseline] Last Class,Phishing,0.5180722891566265,0.4782608695652174,0.0005102157592773438,0.774476 +275,Binary classification,[baseline] Last Class,Phishing,0.5218978102189781,0.4738955823293172,0.0005102157592773438,0.8884620000000001 +300,Binary classification,[baseline] Last Class,Phishing,0.5217391304347826,0.460377358490566,0.0005102157592773438,1.0035580000000002 +325,Binary classification,[baseline] Last Class,Phishing,0.5216049382716049,0.44839857651245546,0.0005102157592773438,1.151113 +350,Binary classification,[baseline] Last Class,Phishing,0.5329512893982808,0.4511784511784511,0.0005102157592773438,1.299965 +375,Binary classification,[baseline] Last Class,Phishing,0.5267379679144385,0.4380952380952381,0.0005102157592773438,1.4500600000000001 +400,Binary classification,[baseline] Last Class,Phishing,0.5263157894736842,0.43243243243243246,0.0005102157592773438,1.6013830000000002 +425,Binary classification,[baseline] Last Class,Phishing,0.5424528301886793,0.436046511627907,0.0005102157592773438,1.7539290000000003 +450,Binary classification,[baseline] Last Class,Phishing,0.5367483296213809,0.4222222222222222,0.0005102157592773438,1.9077010000000003 +475,Binary classification,[baseline] Last Class,Phishing,0.5358649789029536,0.43298969072164945,0.0005102157592773438,2.0627030000000004 +500,Binary classification,[baseline] Last Class,Phishing,0.5370741482965932,0.44604316546762596,0.0005102157592773438,2.2669650000000003 +525,Binary classification,[baseline] Last Class,Phishing,0.5400763358778626,0.43822843822843827,0.0005102157592773438,2.491531 +550,Binary classification,[baseline] Last Class,Phishing,0.5391621129326047,0.44150110375275936,0.0005102157592773438,2.717762 +575,Binary classification,[baseline] Last Class,Phishing,0.5418118466898955,0.4416135881104034,0.0005102157592773438,2.945135 +600,Binary classification,[baseline] Last Class,Phishing,0.5509181969949917,0.443064182194617,0.0005102157592773438,3.175983 +625,Binary classification,[baseline] Last Class,Phishing,0.5560897435897436,0.43584521384928715,0.0005102157592773438,3.407996 +650,Binary classification,[baseline] Last Class,Phishing,0.551617873651772,0.4393063583815029,0.0005102157592773438,3.641123 +675,Binary classification,[baseline] Last Class,Phishing,0.5459940652818991,0.44363636363636366,0.0005102157592773438,3.8753569999999997 +700,Binary classification,[baseline] Last Class,Phishing,0.5464949928469242,0.4389380530973452,0.0005102157592773438,4.110698999999999 +725,Binary classification,[baseline] Last Class,Phishing,0.5441988950276243,0.44630872483221484,0.0005102157592773438,4.380075 +750,Binary classification,[baseline] Last Class,Phishing,0.5367156208277704,0.44122383252818037,0.0005102157592773438,4.6504829999999995 +775,Binary classification,[baseline] Last Class,Phishing,0.5310077519379846,0.43369734789391573,0.0005102157592773438,4.940408 +800,Binary classification,[baseline] Last Class,Phishing,0.5294117647058824,0.4388059701492537,0.0005102157592773438,5.231503 +825,Binary classification,[baseline] Last Class,Phishing,0.5266990291262136,0.43965517241379315,0.0005102157592773438,5.523716 +850,Binary classification,[baseline] Last Class,Phishing,0.5241460541813898,0.4341736694677871,0.0005102157592773438,5.817038 +875,Binary classification,[baseline] Last Class,Phishing,0.522883295194508,0.4311050477489768,0.0005102157592773438,6.111637 +900,Binary classification,[baseline] Last Class,Phishing,0.5272525027808677,0.4340878828229028,0.0005102157592773438,6.407366 +925,Binary classification,[baseline] Last Class,Phishing,0.5227272727272727,0.43388960205391536,0.0005102157592773438,6.766113 +950,Binary classification,[baseline] Last Class,Phishing,0.5205479452054794,0.43896424167694204,0.0005102157592773438,7.1265789999999996 +975,Binary classification,[baseline] Last Class,Phishing,0.5174537987679672,0.43373493975903615,0.0005102157592773438,7.4884189999999995 +1000,Binary classification,[baseline] Last Class,Phishing,0.5185185185185185,0.4361078546307151,0.0005102157592773438,7.851253 +1025,Binary classification,[baseline] Last Class,Phishing,0.517578125,0.43863636363636366,0.0005102157592773438,8.215067 +1050,Binary classification,[baseline] Last Class,Phishing,0.5138226882745471,0.4370860927152318,0.0005102157592773438,8.579858 +1075,Binary classification,[baseline] Last Class,Phishing,0.5111731843575419,0.43729903536977494,0.0005102157592773438,8.945611 +1100,Binary classification,[baseline] Last Class,Phishing,0.5122838944494995,0.4393305439330544,0.0005102157592773438,9.312327999999999 +1125,Binary classification,[baseline] Last Class,Phishing,0.5124555160142349,0.44534412955465585,0.0005102157592773438,9.680001999999998 +1150,Binary classification,[baseline] Last Class,Phishing,0.5143603133159269,0.44642857142857145,0.0005102157592773438,10.125450999999998 +1175,Binary classification,[baseline] Last Class,Phishing,0.5187393526405452,0.4509232264334305,0.0005102157592773438,10.572147999999999 +1200,Binary classification,[baseline] Last Class,Phishing,0.5187656380316931,0.448901623686724,0.0005102157592773438,11.020091999999998 +1225,Binary classification,[baseline] Last Class,Phishing,0.5171568627450981,0.4471468662301216,0.0005102157592773438,11.469230999999999 +1250,Binary classification,[baseline] Last Class,Phishing,0.5156124899919936,0.4474885844748858,0.0005102157592773438,11.919638999999998 +1903,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.335236 +3806,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.143886 +5709,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,2.36402 +7612,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,4.028138 +9515,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,6.117771 +11418,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,8.657701 +13321,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,11.631 +15224,Binary classification,[baseline] Last Class,SMTP,0.9985548183669447,0.0,0.0005102157592773438,15.050370000000001 +17127,Binary classification,[baseline] Last Class,SMTP,0.9984818404764685,0.0,0.0005102157592773438,18.876176 +19030,Binary classification,[baseline] Last Class,SMTP,0.9986336644069578,0.0,0.0005102157592773438,23.061029 +20933,Binary classification,[baseline] Last Class,SMTP,0.9987578826676858,0.0,0.0005102157592773438,27.619004 +22836,Binary classification,[baseline] Last Class,SMTP,0.9988613969783228,0.0,0.0005102157592773438,32.587888 +24739,Binary classification,[baseline] Last Class,SMTP,0.9989489853666425,0.0,0.0005102157592773438,37.966612 +26642,Binary classification,[baseline] Last Class,SMTP,0.9989489884013363,0.0,0.0005102157592773438,43.747015999999995 +28545,Binary classification,[baseline] Last Class,SMTP,0.9990190582959642,0.0,0.0005102157592773438,49.923314999999995 +30448,Binary classification,[baseline] Last Class,SMTP,0.9990803691660919,0.0,0.0005102157592773438,56.476617 +32351,Binary classification,[baseline] Last Class,SMTP,0.9991344667697063,0.0,0.0005102157592773438,63.442318 +34254,Binary classification,[baseline] Last Class,SMTP,0.999182553352991,0.0,0.0005102157592773438,70.796392 +36157,Binary classification,[baseline] Last Class,SMTP,0.9992255780506694,0.0,0.0005102157592773438,78.554987 +38060,Binary classification,[baseline] Last Class,SMTP,0.9992643001655325,0.0,0.0005102157592773438,86.688101 +39963,Binary classification,[baseline] Last Class,SMTP,0.9992993343676493,0.0,0.0005102157592773438,95.30254000000001 +41866,Binary classification,[baseline] Last Class,SMTP,0.9993311835662247,0.0,0.0005102157592773438,104.31052400000002 +43769,Binary classification,[baseline] Last Class,SMTP,0.9993602632059952,0.0,0.0005102157592773438,113.72316700000002 +45672,Binary classification,[baseline] Last Class,SMTP,0.9993869194893915,0.0,0.0005102157592773438,123.54962900000001 +47575,Binary classification,[baseline] Last Class,SMTP,0.9994114432252911,0.0,0.0005102157592773438,133.72455100000002 +49478,Binary classification,[baseline] Last Class,SMTP,0.99943408048184,0.0,0.0005102157592773438,144.361296 +51381,Binary classification,[baseline] Last Class,SMTP,0.9994161152199299,0.0625,0.0005102157592773438,155.342577 +53284,Binary classification,[baseline] Last Class,SMTP,0.9994369686391532,0.0625,0.0005102157592773438,166.71976 +55187,Binary classification,[baseline] Last Class,SMTP,0.9994563838654731,0.0625,0.0005102157592773438,178.568204 +57090,Binary classification,[baseline] Last Class,SMTP,0.9994394717020793,0.36,0.0005102157592773438,190.760423 +58993,Binary classification,[baseline] Last Class,SMTP,0.9994575535665853,0.36,0.0005102157592773438,203.318295 +60896,Binary classification,[baseline] Last Class,SMTP,0.9994745052960013,0.36,0.0005102157592773438,216.324675 +62799,Binary classification,[baseline] Last Class,SMTP,0.9994585814834868,0.37037037037037035,0.0005102157592773438,229.75194900000002 +64702,Binary classification,[baseline] Last Class,SMTP,0.9994745058036197,0.37037037037037035,0.0005102157592773438,243.519605 +66605,Binary classification,[baseline] Last Class,SMTP,0.99948952014894,0.37037037037037035,0.0005102157592773438,257.632512 +68508,Binary classification,[baseline] Last Class,SMTP,0.9994745062548352,0.3793103448275862,0.0005102157592773438,272.164854 +70411,Binary classification,[baseline] Last Class,SMTP,0.9994887089902003,0.3793103448275862,0.0005102157592773438,287.040678 +72314,Binary classification,[baseline] Last Class,SMTP,0.9995021642028404,0.3793103448275862,0.0005102157592773438,302.255295 +74217,Binary classification,[baseline] Last Class,SMTP,0.9995149293952786,0.3793103448275862,0.0005102157592773438,317.85437 +76120,Binary classification,[baseline] Last Class,SMTP,0.99952705631971,0.3793103448275862,0.0005102157592773438,333.81575100000003 +78023,Binary classification,[baseline] Last Class,SMTP,0.99953859167927,0.3793103448275862,0.0005102157592773438,350.111597 +79926,Binary classification,[baseline] Last Class,SMTP,0.999549577729121,0.3793103448275862,0.0005102157592773438,366.757049 +81829,Binary classification,[baseline] Last Class,SMTP,0.9995600527936648,0.3793103448275862,0.0005102157592773438,383.769929 +83732,Binary classification,[baseline] Last Class,SMTP,0.9995700517132244,0.3793103448275862,0.0005102157592773438,401.12604899999997 +85635,Binary classification,[baseline] Last Class,SMTP,0.9995796062311698,0.3793103448275862,0.0005102157592773438,418.85540799999995 +87538,Binary classification,[baseline] Last Class,SMTP,0.999588745330546,0.3793103448275862,0.0005102157592773438,436.927356 +89441,Binary classification,[baseline] Last Class,SMTP,0.9995751341681575,0.36666666666666664,0.0005102157592773438,455.364423 +91344,Binary classification,[baseline] Last Class,SMTP,0.9995839856365567,0.36666666666666664,0.0005102157592773438,474.187698 +93247,Binary classification,[baseline] Last Class,SMTP,0.999592475816657,0.36666666666666664,0.0005102157592773438,493.377496 +95150,Binary classification,[baseline] Last Class,SMTP,0.9996006263859841,0.36666666666666664,0.0005102157592773438,512.867881 +95156,Binary classification,[baseline] Last Class,SMTP,0.9996006515684935,0.36666666666666664,0.0005102157592773438,532.358985 diff --git a/benchmarks/config.py b/benchmarks/config.py index 38fa9e90e6..828614deae 100644 --- a/benchmarks/config.py +++ b/benchmarks/config.py @@ -1,16 +1,6 @@ -from model_zoo.torch import ( - TorchLinearRegression, - TorchLogisticRegression, - TorchLSTMClassifier, - TorchLSTMRegressor, - TorchMLPClassifier, - TorchMLPRegressor, -) -from model_zoo.vw import VW2RiverClassifier -from river_torch.classification import Classifier as TorchClassifier -from river_torch.classification import RollingClassifier as TorchRollingClassifier -from river_torch.regression import Regressor as TorchRegressor -from river_torch.regression import RollingRegressor as TorchRollingRegressor +from __future__ import annotations + +from model_adapters.vw import VW2RiverClassifier from sklearn.linear_model import SGDClassifier from river import ( @@ -18,6 +8,7 @@ dummy, ensemble, evaluate, + forest, linear_model, naive_bayes, neighbors, @@ -45,12 +36,13 @@ preprocessing.StandardScaler() | linear_model.LogisticRegression(optimizer=optim.SGD(LEARNING_RATE)) ), + "Aggregated Mondrian Forest": forest.AMFClassifier(seed=42), "ALMA": preprocessing.StandardScaler() | linear_model.ALMAClassifier(), "sklearn SGDClassifier": ( preprocessing.StandardScaler() | compat.SKL2RiverClassifier( SGDClassifier( - loss="log", learning_rate="constant", eta0=LEARNING_RATE, penalty="none" + loss="log_loss", learning_rate="constant", eta0=LEARNING_RATE, penalty=None ), classes=[False, True], ) @@ -73,10 +65,10 @@ "Naive Bayes": naive_bayes.GaussianNB(), "Hoeffding Tree": tree.HoeffdingTreeClassifier(), "Hoeffding Adaptive Tree": tree.HoeffdingAdaptiveTreeClassifier(seed=42), - "Adaptive Random Forest": ensemble.AdaptiveRandomForestClassifier(seed=42), + "Adaptive Random Forest": forest.ARFClassifier(seed=42), + "Aggregated Mondrian Forest": forest.AMFClassifier(seed=42), "Streaming Random Patches": ensemble.SRPClassifier(), - "k-Nearest Neighbors": preprocessing.StandardScaler() - | neighbors.KNNClassifier(window_size=100), + "k-Nearest Neighbors": preprocessing.StandardScaler() | neighbors.KNNClassifier(), "ADWIN Bagging": ensemble.ADWINBaggingClassifier(tree.HoeffdingTreeClassifier(), seed=42), "AdaBoost": ensemble.AdaBoostClassifier(tree.HoeffdingTreeClassifier(), seed=42), "Bagging": ensemble.BaggingClassifier( @@ -90,50 +82,18 @@ preprocessing.StandardScaler() | linear_model.SoftmaxRegression(), naive_bayes.GaussianNB(), tree.HoeffdingTreeClassifier(), - preprocessing.StandardScaler() | neighbors.KNNClassifier(window_size=100), + preprocessing.StandardScaler() | neighbors.KNNClassifier(), ], - meta_classifier=ensemble.AdaptiveRandomForestClassifier(seed=42), + meta_classifier=forest.ARFClassifier(seed=42), ), "Voting": ensemble.VotingClassifier( [ preprocessing.StandardScaler() | linear_model.SoftmaxRegression(), naive_bayes.GaussianNB(), tree.HoeffdingTreeClassifier(), - preprocessing.StandardScaler() | neighbors.KNNClassifier(window_size=100), + preprocessing.StandardScaler() | neighbors.KNNClassifier(), ] ), - "Torch Logistic Regression": ( - preprocessing.StandardScaler() - | TorchClassifier( - module=TorchLogisticRegression, - loss_fn="binary_cross_entropy", - optimizer_fn="adam", - is_class_incremental=True, - lr=LEARNING_RATE, - ) - ), - "Torch MLP": ( - preprocessing.StandardScaler() - | TorchClassifier( - module=TorchMLPClassifier, - loss_fn="binary_cross_entropy", - optimizer_fn="adam", - is_class_incremental=True, - lr=LEARNING_RATE, - ) - ), - "Torch LSTM": ( - preprocessing.StandardScaler() - | TorchRollingClassifier( - module=TorchLSTMClassifier, - loss_fn="binary_cross_entropy", - optimizer_fn="adam", - is_class_incremental=True, - lr=LEARNING_RATE, - window_size=20, - append_predict=False, - ) - ), # Baseline "[baseline] Last Class": dummy.NoChangeClassifier(), }, @@ -147,14 +107,13 @@ | linear_model.PARegressor(mode=1), "Passive-Aggressive Regressor, mode 2": preprocessing.StandardScaler() | linear_model.PARegressor(mode=2), - "k-Nearest Neighbors": preprocessing.StandardScaler() - | neighbors.KNNRegressor(window_size=100), + "k-Nearest Neighbors": preprocessing.StandardScaler() | neighbors.KNNRegressor(), "Hoeffding Tree": preprocessing.StandardScaler() | tree.HoeffdingTreeRegressor(), "Hoeffding Adaptive Tree": preprocessing.StandardScaler() | tree.HoeffdingAdaptiveTreeRegressor(seed=42), "Stochastic Gradient Tree": tree.SGTRegressor(), - "Adaptive Random Forest": preprocessing.StandardScaler() - | ensemble.AdaptiveRandomForestRegressor(seed=42), + "Adaptive Random Forest": preprocessing.StandardScaler() | forest.ARFRegressor(seed=42), + "Aggregated Mondrian Forest": forest.AMFRegressor(seed=42), "Adaptive Model Rules": preprocessing.StandardScaler() | rules.AMRules(), "Streaming Random Patches": preprocessing.StandardScaler() | ensemble.SRPRegressor(seed=42), "Bagging": preprocessing.StandardScaler() @@ -166,28 +125,10 @@ models=[ linear_model.LinearRegression(), tree.HoeffdingAdaptiveTreeRegressor(), - neighbors.KNNRegressor(window_size=100), + neighbors.KNNRegressor(), rules.AMRules(), ], ), - "Torch Linear Regression": ( - preprocessing.StandardScaler() - | TorchRegressor( - module=TorchLinearRegression, - loss_fn="mse", - optimizer_fn="adam", - learning_rate=LEARNING_RATE, - ) - ), - "Torch MLP": ( - preprocessing.StandardScaler() - | TorchRegressor( - module=TorchMLPRegressor, - loss_fn="mse", - optimizer_fn="adam", - learning_rate=LEARNING_RATE, - ) - ), "River MLP": preprocessing.StandardScaler() | neural_net.MLPRegressor( hidden_dims=(5,), @@ -199,17 +140,6 @@ optimizer=optim.SGD(1e-3), seed=42, ), - "Torch LSTM": ( - preprocessing.StandardScaler() - | TorchRollingRegressor( - module=TorchLSTMRegressor, - loss_fn="mse", - optimizer_fn="adam", - learning_rate=LEARNING_RATE, - window_size=20, - append_predict=False, - ) - ), # Baseline "[baseline] Mean predictor": dummy.StatisticRegressor(stats.Mean()), }, diff --git a/benchmarks/details.json b/benchmarks/details.json index 2dbd175e31..f649b6536e 100644 --- a/benchmarks/details.json +++ b/benchmarks/details.json @@ -1,57 +1,59 @@ { "Binary classification": { "Dataset": { - "Bananas": "Bananas dataset.\n\nAn artificial dataset where instances belongs to several clusters with a banana shape.\nThere are two attributes that correspond to the x and y axis, respectively.\n\n Name Bananas \n Task Binary classification \n Samples 5,300 \nFeatures 2 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/banana.zip", - "Elec2": "Electricity prices in New South Wales.\n\nThis is a binary classification task, where the goal is to predict if the price of electricity\nwill go up or down.\n\nThis data was collected from the Australian New South Wales Electricity Market. In this market,\nprices are not fixed and are affected by demand and supply of the market. They are set every\nfive minutes. Electricity transfers to/from the neighboring state of Victoria were done to\nalleviate fluctuations.\n\n Name Elec2 \n Task Binary classification \n Samples 45,312 \n Features 8 \n Sparse False \n Path /home/kulbach/river_data/Elec2/electricity.csv \n URL https://maxhalford.github.io/files/datasets/electricity.zip\n Size 2.95 MB \nDownloaded True ", - "Phishing": "Phishing websites.\n\nThis dataset contains features from web pages that are classified as phishing or not.\n\n Name Phishing \n Task Binary classification \n Samples 1,250 \nFeatures 9 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/phishing.csv.gz", - "SMTP": "SMTP dataset from the KDD 1999 cup.\n\nThe goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only\ncontains 2,211 (0.4%) positive labels.\n\n Name SMTP \n Task Binary classification \n Samples 95,156 \n Features 3 \n Sparse False \n Path /home/kulbach/river_data/SMTP/smtp.csv \n URL https://maxhalford.github.io/files/datasets/smtp.zip\n Size 5.23 MB \nDownloaded True " + "Bananas": "Bananas dataset.\n\nAn artificial dataset where instances belongs to several clusters with a banana shape.\nThere are two attributes that correspond to the x and y axis, respectively.\n\n Name Bananas \n Task Binary classification \n Samples 5,300 \nFeatures 2 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/banana.zip", + "Elec2": "Electricity prices in New South Wales.\n\nThis is a binary classification task, where the goal is to predict if the price of electricity\nwill go up or down.\n\nThis data was collected from the Australian New South Wales Electricity Market. In this market,\nprices are not fixed and are affected by demand and supply of the market. They are set every\nfive minutes. Electricity transfers to/from the neighboring state of Victoria were done to\nalleviate fluctuations.\n\n Name Elec2 \n Task Binary classification \n Samples 45,312 \n Features 8 \n Sparse False \n Path /Users/mastelini/river_data/Elec2/electricity.csv \n URL https://maxhalford.github.io/files/datasets/electricity.zip\n Size 2.95 MB \nDownloaded True ", + "Phishing": "Phishing websites.\n\nThis dataset contains features from web pages that are classified as phishing or not.\n\n Name Phishing \n Task Binary classification \n Samples 1,250 \nFeatures 9 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/phishing.csv.gz", + "SMTP": "SMTP dataset from the KDD 1999 cup.\n\nThe goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only\ncontains 2,211 (0.4%) positive labels.\n\n Name SMTP \n Task Binary classification \n Samples 95,156 \n Features 3 \n Sparse False \n Path /Users/mastelini/river_data/SMTP/smtp.csv \n URL https://maxhalford.github.io/files/datasets/smtp.zip\n Size 5.23 MB \nDownloaded True " }, "Model": { "Logistic regression": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n LogisticRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.005\n )\n )\n loss=Log (\n weight_pos=1.\n weight_neg=1.\n )\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n)", + "Aggregated Mondrian Forest": "[]", "ALMA": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n ALMAClassifier (\n p=2\n alpha=0.9\n B=1.111111\n C=1.414214\n )\n)", - "sklearn SGDClassifier": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n SKL2RiverClassifier (\n estimator=SGDClassifier(eta0=0.005, learning_rate='constant', loss='log', penalty='none')\n classes=[False, True]\n )\n)", + "sklearn SGDClassifier": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n SKL2RiverClassifier (\n estimator=SGDClassifier(eta0=0.005, learning_rate='constant', loss='log_loss',\n penalty=None)\n classes=[False, True]\n )\n)", "Vowpal Wabbit logistic regression": "VW2RiverClassifier ()", "Naive Bayes": "GaussianNB ()", - "Hoeffding Tree": "HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)", - "Hoeffding Adaptive Tree": "HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n)", + "Hoeffding Tree": "HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)", + "Hoeffding Adaptive Tree": "HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n)", "Adaptive Random Forest": "[]", - "Streaming Random Patches": "SRPClassifier (\n model=HoeffdingTreeClassifier (\n grace_period=50\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=0.01\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=False\n seed=None\n metric=Accuracy (\n cm=ConfusionMatrix (\n classes=[]\n )\n )\n)", - "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)", - "ADWIN Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "AdaBoost": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "Bagging": "[HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n)]", - "Leveraging Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "Stacking": "[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)]", - "Voting": "VotingClassifier (\n models=[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)]\n use_probabilities=True\n)", + "Streaming Random Patches": "SRPClassifier (\n model=HoeffdingTreeClassifier (\n grace_period=50\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=0.01\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=False\n seed=None\n metric=Accuracy (\n cm=ConfusionMatrix (\n classes=[]\n )\n )\n)", + "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)", + "ADWIN Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "AdaBoost": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "Bagging": "[HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n)]", + "Leveraging Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "Stacking": "[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)]", + "Voting": "VotingClassifier (\n models=[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)]\n use_probabilities=True\n)", "[baseline] Last Class": "NoChangeClassifier ()" } }, "Multiclass classification": { "Dataset": { - "ImageSegments": "Image segments classification.\n\nThis dataset contains features that describe image segments into 7 classes: brickface, sky,\nfoliage, cement, window, path, and grass.\n\n Name ImageSegments \n Task Multi-class classification \n Samples 2,310 \nFeatures 18 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/segment.csv.zip", - "Insects": "Insects dataset.\n\nThis dataset has different variants, which are:\n\n- abrupt_balanced\n- abrupt_imbalanced\n- gradual_balanced\n- gradual_imbalanced\n- incremental-abrupt_balanced\n- incremental-abrupt_imbalanced\n- incremental-reoccurring_balanced\n- incremental-reoccurring_imbalanced\n- incremental_balanced\n- incremental_imbalanced\n- out-of-control\n\nThe number of samples and the difficulty change from one variant to another. The number of\nclasses is always the same (6), except for the last variant (24).\n\n Name Insects \n Task Multi-class classification \n Samples 52,848 \n Features 33 \n Classes 6 \n Sparse False \n Path /home/kulbach/river_data/Insects/INSECTS-abrupt_balanced_norm.arff \n URL http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff\n Size 15.66 MB \nDownloaded True \n Variant abrupt_balanced \n\nParameters\n----------\n variant\n Indicates which variant of the dataset to load.", - "Keystroke": "CMU keystroke dataset.\n\nUsers are tasked to type in a password. The task is to determine which user is typing in the\npassword.\n\nThe only difference with the original dataset is that the \"sessionIndex\" and \"rep\" attributes\nhave been dropped.\n\n Name Keystroke \n Task Multi-class classification \n Samples 20,400 \n Features 31 \n Sparse False \n Path /home/kulbach/river_data/Keystroke/DSL-StrongPasswordData.csv\n URL http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv \n Size 4.45 MB \nDownloaded True " + "ImageSegments": "Image segments classification.\n\nThis dataset contains features that describe image segments into 7 classes: brickface, sky,\nfoliage, cement, window, path, and grass.\n\n Name ImageSegments \n Task Multi-class classification \n Samples 2,310 \nFeatures 18 \n Classes 7 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/segment.csv.zip", + "Insects": "Insects dataset.\n\nThis dataset has different variants, which are:\n\n- abrupt_balanced\n- abrupt_imbalanced\n- gradual_balanced\n- gradual_imbalanced\n- incremental-abrupt_balanced\n- incremental-abrupt_imbalanced\n- incremental-reoccurring_balanced\n- incremental-reoccurring_imbalanced\n- incremental_balanced\n- incremental_imbalanced\n- out-of-control\n\nThe number of samples and the difficulty change from one variant to another. The number of\nclasses is always the same (6), except for the last variant (24).\n\n Name Insects \n Task Multi-class classification \n Samples 52,848 \n Features 33 \n Classes 6 \n Sparse False \n Path /Users/mastelini/river_data/Insects/INSECTS-abrupt_balanced_norm.arff \n URL http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff\n Size 15.66 MB \nDownloaded True \n Variant abrupt_balanced \n\nParameters\n----------\n variant\n Indicates which variant of the dataset to load.", + "Keystroke": "CMU keystroke dataset.\n\nUsers are tasked to type in a password. The task is to determine which user is typing in the\npassword.\n\nThe only difference with the original dataset is that the \"sessionIndex\" and \"rep\" attributes\nhave been dropped.\n\n Name Keystroke \n Task Multi-class classification \n Samples 20,400 \n Features 31 \n Classes 51 \n Sparse False \n Path /Users/mastelini/river_data/Keystroke/DSL-StrongPasswordData.csv\n URL http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv \n Size 4.45 MB \nDownloaded True " }, "Model": { "Naive Bayes": "GaussianNB ()", - "Hoeffding Tree": "HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)", - "Hoeffding Adaptive Tree": "HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n)", + "Hoeffding Tree": "HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)", + "Hoeffding Adaptive Tree": "HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n)", "Adaptive Random Forest": "[]", - "Streaming Random Patches": "SRPClassifier (\n model=HoeffdingTreeClassifier (\n grace_period=50\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=0.01\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=False\n seed=None\n metric=Accuracy (\n cm=ConfusionMatrix (\n classes=[]\n )\n )\n)", - "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)", - "ADWIN Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "AdaBoost": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "Bagging": "[HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n)]", - "Leveraging Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", - "Stacking": "[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)]", - "Voting": "VotingClassifier (\n models=[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n window_size=100\n min_distance_keep=0.\n weighted=True\n cleanup_every=0\n distance_func=functools.partial(, p=2)\n softmax=False\n )\n)]\n use_probabilities=True\n)", + "Aggregated Mondrian Forest": "[]", + "Streaming Random Patches": "SRPClassifier (\n model=HoeffdingTreeClassifier (\n grace_period=50\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=0.01\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=False\n seed=None\n metric=Accuracy (\n cm=ConfusionMatrix (\n classes=[]\n )\n )\n)", + "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)", + "ADWIN Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "AdaBoost": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "Bagging": "[HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n), HoeffdingAdaptiveTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n)]", + "Leveraging Bagging": "[HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n)]", + "Stacking": "[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)]", + "Voting": "VotingClassifier (\n models=[Pipeline (\n StandardScaler (\n with_std=True\n ),\n SoftmaxRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=CrossEntropy (\n class_weight={}\n )\n l2=0\n )\n), GaussianNB (), HoeffdingTreeClassifier (\n grace_period=200\n max_depth=inf\n split_criterion=\"info_gain\"\n delta=1e-07\n tau=0.05\n leaf_prediction=\"nba\"\n nb_threshold=0\n nominal_attributes=None\n splitter=GaussianSplitter (\n n_splits=10\n )\n binary_split=False\n min_branch_fraction=0.01\n max_share_to_split=0.99\n max_size=100.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n), Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNClassifier (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n weighted=True\n cleanup_every=0\n softmax=False\n )\n)]\n use_probabilities=True\n)", "[baseline] Last Class": "NoChangeClassifier ()" } }, "Regression": { "Dataset": { - "ChickWeights": "Chick weights along time.\n\nThe stream contains 578 items and 3 features. The goal is to predict the weight of each chick\nalong time, according to the diet the chick is on. The data is ordered by time and then by\nchick.\n\n Name ChickWeights \n Task Regression \n Samples 578 \nFeatures 3 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/chick-weights.csv", - "TrumpApproval": "Donald Trump approval ratings.\n\nThis dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald\nTrump's approval ratings. It contains 5 features, which are approval ratings collected by\n5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of\nthis task is to see if we can reproduce FiveThirtyEight's model.\n\n Name TrumpApproval \n Task Regression \n Samples 1,001 \nFeatures 6 \n Sparse False \n Path /home/kulbach/projects/river/river/datasets/trump_approval.csv.gz" + "ChickWeights": "Chick weights along time.\n\nThe stream contains 578 items and 3 features. The goal is to predict the weight of each chick\nalong time, according to the diet the chick is on. The data is ordered by time and then by\nchick.\n\n Name ChickWeights \n Task Regression \n Samples 578 \nFeatures 3 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/chick-weights.csv", + "TrumpApproval": "Donald Trump approval ratings.\n\nThis dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald\nTrump's approval ratings. It contains 5 features, which are approval ratings collected by\n5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of\nthis task is to see if we can reproduce FiveThirtyEight's model.\n\n Name TrumpApproval \n Task Regression \n Samples 1,001 \nFeatures 6 \n Sparse False \n Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/trump_approval.csv.gz" }, "Model": { "Linear Regression": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n)", @@ -59,17 +61,18 @@ "Linear Regression with l2 regularization": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=1.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n)", "Passive-Aggressive Regressor, mode 1": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n PARegressor (\n C=1.\n mode=1\n eps=0.1\n learn_intercept=True\n )\n)", "Passive-Aggressive Regressor, mode 2": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n PARegressor (\n C=1.\n mode=2\n eps=0.1\n learn_intercept=True\n )\n)", - "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNRegressor (\n n_neighbors=5\n window_size=100\n aggregation_method=\"mean\"\n min_distance_keep=0.\n distance_func=functools.partial(, p=2)\n )\n)", + "k-Nearest Neighbors": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n KNNRegressor (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n aggregation_method=\"mean\"\n )\n)", "Hoeffding Tree": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n HoeffdingTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n)", "Hoeffding Adaptive Tree": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=42\n )\n)", "Stochastic Gradient Tree": "SGTRegressor (\n delta=1e-07\n grace_period=200\n init_pred=0.\n max_depth=inf\n lambda_value=0.1\n gamma=1.\n nominal_attributes=[]\n feature_quantizer=StaticQuantizer (\n n_bins=64\n warm_start=100\n buckets=None\n )\n)", "Adaptive Random Forest": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n []\n)", + "Aggregated Mondrian Forest": "[]", "Adaptive Model Rules": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n AMRules (\n n_min=200\n delta=1e-07\n tau=0.05\n pred_type=\"adaptive\"\n pred_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n splitter=TEBSTSplitter (\n digits=1\n )\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n fading_factor=0.99\n anomaly_threshold=-0.75\n m_min=30\n ordered_rule_set=True\n min_samples_split=5\n )\n)", "Streaming Random Patches": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n SRPRegressor (\n model=HoeffdingTreeRegressor (\n grace_period=50\n max_depth=inf\n delta=0.01\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n )\n n_models=10\n subspace_size=0.6\n training_method=\"patches\"\n lam=6\n drift_detector=ADWIN (\n delta=1e-05\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n warning_detector=ADWIN (\n delta=0.0001\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n disable_detector=\"off\"\n disable_weighted_vote=True\n drift_detection_criteria=\"error\"\n aggregation_method=\"mean\"\n seed=42\n metric=MAE ()\n )\n)", "Bagging": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n [HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=False\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n )]\n)", - "Exponentially Weighted Average": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n [LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), KNNRegressor (\n n_neighbors=5\n window_size=100\n aggregation_method=\"mean\"\n min_distance_keep=0.\n distance_func=functools.partial(, p=2)\n ), AMRules (\n n_min=200\n delta=1e-07\n tau=0.05\n pred_type=\"adaptive\"\n pred_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n splitter=TEBSTSplitter (\n digits=1\n )\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n fading_factor=0.99\n anomaly_threshold=-0.75\n m_min=30\n ordered_rule_set=True\n min_samples_split=5\n )]\n)", + "Exponentially Weighted Average": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n [LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n ), HoeffdingAdaptiveTreeRegressor (\n grace_period=200\n max_depth=inf\n delta=1e-07\n tau=0.05\n leaf_prediction=\"adaptive\"\n leaf_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n model_selector_decay=0.95\n nominal_attributes=None\n splitter=TEBSTSplitter (\n digits=1\n )\n min_samples_split=5\n bootstrap_sampling=True\n drift_window_threshold=300\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n switch_significance=0.05\n binary_split=False\n max_size=500.\n memory_estimate_period=1000000\n stop_mem_management=False\n remove_poor_attrs=False\n merit_preprune=True\n seed=None\n ), KNNRegressor (\n n_neighbors=5\n engine=SWINN (\n graph_k=20\n dist_func=FunctionWrapper (\n distance_function=functools.partial(, p=2)\n )\n maxlen=1000\n warm_up=500\n max_candidates=50\n delta=0.0001\n prune_prob=0.\n n_iters=10\n seed=None\n )\n aggregation_method=\"mean\"\n ), AMRules (\n n_min=200\n delta=1e-07\n tau=0.05\n pred_type=\"adaptive\"\n pred_model=LinearRegression (\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.01\n )\n )\n loss=Squared ()\n l2=0.\n l1=0.\n intercept_init=0.\n intercept_lr=Constant (\n learning_rate=0.01\n )\n clip_gradient=1e+12\n initializer=Zeros ()\n )\n splitter=TEBSTSplitter (\n digits=1\n )\n drift_detector=ADWIN (\n delta=0.002\n clock=32\n max_buckets=5\n min_window_length=5\n grace_period=10\n )\n fading_factor=0.99\n anomaly_threshold=-0.75\n m_min=30\n ordered_rule_set=True\n min_samples_split=5\n )]\n)", "River MLP": "Pipeline (\n StandardScaler (\n with_std=True\n ),\n MLPRegressor (\n hidden_dims=(5,)\n activations=(, , )\n loss=Squared ()\n optimizer=SGD (\n lr=Constant (\n learning_rate=0.001\n )\n )\n seed=42\n )\n)", "[baseline] Mean predictor": "StatisticRegressor (\n statistic=Mean ()\n)" } } -} +} \ No newline at end of file diff --git a/benchmarks/model_zoo/__init__.py b/benchmarks/model_adapters/__init__.py similarity index 100% rename from benchmarks/model_zoo/__init__.py rename to benchmarks/model_adapters/__init__.py diff --git a/benchmarks/model_zoo/vw.py b/benchmarks/model_adapters/vw.py similarity index 95% rename from benchmarks/model_zoo/vw.py rename to benchmarks/model_adapters/vw.py index c09ae0e2e2..381b9ea4a8 100644 --- a/benchmarks/model_zoo/vw.py +++ b/benchmarks/model_adapters/vw.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from vowpalwabbit import pyvw from river import base @@ -13,7 +15,6 @@ def _format_x(self, x): class VW2RiverClassifier(VW2RiverBase, base.Classifier): def learn_one(self, x, y): - # Convert {False, True} to {-1, 1} y = int(y) y_vw = 2 * y - 1 @@ -21,7 +22,6 @@ def learn_one(self, x, y): ex = self._format_x(x) ex = f"{y_vw} | {ex}" self.vw.learn(ex) - return self def predict_proba_one(self, x): ex = "| " + self._format_x(x) diff --git a/benchmarks/model_zoo/torch.py b/benchmarks/model_zoo/torch.py deleted file mode 100644 index dfc89771d0..0000000000 --- a/benchmarks/model_zoo/torch.py +++ /dev/null @@ -1,91 +0,0 @@ -import torch - - -class TorchMLPClassifier(torch.nn.Module): - def __init__(self, n_features: int, hidden_size: int = 5): - super().__init__() - self.linear1 = torch.nn.Linear(n_features, hidden_size) - self.nonlin = torch.nn.ReLU() - self.linear2 = torch.nn.Linear(hidden_size, 2) - self.softmax = torch.nn.Softmax(dim=-1) - - def forward(self, x): - x = self.nonlin(self.linear1(x)) - x = self.nonlin(self.linear2(x)) - x = self.softmax(x) - return x - - -class TorchMLPRegressor(torch.nn.Module): - def __init__(self, n_features: int, hidden_size: int = 5): - super().__init__() - self.linear1 = torch.nn.Linear(n_features, hidden_size) - self.nonlin = torch.nn.ReLU() - self.linear2 = torch.nn.Linear(hidden_size, 1) - - def forward(self, x): - x = self.nonlin(self.linear1(x)) - x = self.nonlin(self.linear2(x)) - return x - - -class TorchLogisticRegression(torch.nn.Module): - def __init__(self, n_features: int, n_classes: int = 2): - super().__init__() - self.linear = torch.nn.Linear(n_features, n_classes) - self.softmax = torch.nn.Softmax(dim=-1) - - def forward(self, X): - X = self.linear(X) - return self.softmax(X) - - -class TorchLinearRegression(torch.nn.Module): - def __init__(self, n_features: int): - super().__init__() - self.linear = torch.nn.Linear(n_features, 1) - - def forward(self, X): - return self.linear(X) - - -class TorchLSTMClassifier(torch.nn.Module): - def __init__(self, n_features, num_layers=1, hidden_size=1): - super().__init__() - self.n_features = n_features - self.hidden_size = hidden_size - self.num_layers = num_layers - self.lstm = torch.nn.LSTM( - input_size=n_features, - num_layers=num_layers, - hidden_size=hidden_size, - batch_first=False, - bias=True, - ) - self.fc = torch.nn.Linear(hidden_size, 1) - self.softmax = torch.nn.Softmax(dim=-1) - - def forward(self, X, **kwargs): - out, (hn, cn) = self.lstm(X) - X = self.fc(out[-1, :]) - return self.softmax(X) - - -class TorchLSTMRegressor(torch.nn.Module): - def __init__(self, n_features, num_layers=1, hidden_size=1): - super().__init__() - self.n_features = n_features - self.hidden_size = hidden_size - self.num_layers = num_layers - self.lstm = torch.nn.LSTM( - input_size=n_features, - num_layers=num_layers, - hidden_size=hidden_size, - batch_first=False, - bias=True, - ) - self.fc = torch.nn.Linear(hidden_size, 1) - - def forward(self, X, **kwargs): - out, (hn, cn) = self.lstm(X) - return self.fc(out[-1, :]) diff --git a/benchmarks/multiclass_classification.csv b/benchmarks/multiclass_classification.csv index 852451616e..9394fc8dfd 100644 --- a/benchmarks/multiclass_classification.csv +++ b/benchmarks/multiclass_classification.csv @@ -1,1951 +1,2129 @@ step,track,model,dataset,Accuracy,MicroF1,MacroF1,Memory in Mb,Time in s -46,Multiclass classification,Naive Bayes,ImageSegments,0.4666666666666667,0.4666666666666667,0.4009102009102009,0.3899507522583008,0.163216 -92,Multiclass classification,Naive Bayes,ImageSegments,0.5604395604395604,0.5604395604395604,0.5279334700387331,0.3899507522583008,0.349738 -138,Multiclass classification,Naive Bayes,ImageSegments,0.5474452554744526,0.5474452554744526,0.5191892873237388,0.38997745513916016,0.5584899999999999 -184,Multiclass classification,Naive Bayes,ImageSegments,0.5573770491803278,0.5573770491803278,0.5225713529323662,0.3899507522583008,0.789485 -230,Multiclass classification,Naive Bayes,ImageSegments,0.5545851528384279,0.5545851528384279,0.5217226223148511,0.38997745513916016,1.042858 -276,Multiclass classification,Naive Bayes,ImageSegments,0.56,0.56,0.5450388711329708,0.38997745513916016,1.324703 -322,Multiclass classification,Naive Bayes,ImageSegments,0.5825545171339563,0.5825545171339563,0.5566705826058684,0.39000415802001953,1.637036 -368,Multiclass classification,Naive Bayes,ImageSegments,0.5940054495912807,0.5940054495912807,0.5613773296963412,0.39000415802001953,1.979491 -414,Multiclass classification,Naive Bayes,ImageSegments,0.5980629539951574,0.5980629539951574,0.5624927052752284,0.39000415802001953,2.352111 -460,Multiclass classification,Naive Bayes,ImageSegments,0.599128540305011,0.599128540305011,0.5669821167583783,0.38997745513916016,2.754918 -506,Multiclass classification,Naive Bayes,ImageSegments,0.6099009900990099,0.6099009900990099,0.5922286190986811,0.39000415802001953,3.188186 -552,Multiclass classification,Naive Bayes,ImageSegments,0.6116152450090744,0.6116152450090744,0.5983340184133136,0.3899507522583008,3.651555 -598,Multiclass classification,Naive Bayes,ImageSegments,0.6180904522613065,0.6180904522613065,0.611527101723203,0.38997745513916016,4.145135 -644,Multiclass classification,Naive Bayes,ImageSegments,0.6158631415241057,0.6158631415241057,0.6113311881078581,0.38997745513916016,4.668896 -690,Multiclass classification,Naive Bayes,ImageSegments,0.6182873730043541,0.6182873730043541,0.615018998714676,0.38997745513916016,5.223075 -736,Multiclass classification,Naive Bayes,ImageSegments,0.617687074829932,0.617687074829932,0.6157912419016742,0.38997745513916016,5.807397 -782,Multiclass classification,Naive Bayes,ImageSegments,0.6274007682458387,0.6274007682458387,0.6216325704223051,0.38997745513916016,6.422078 -828,Multiclass classification,Naive Bayes,ImageSegments,0.6324062877871826,0.6324062877871826,0.6280704917469789,0.38997745513916016,7.066915 -874,Multiclass classification,Naive Bayes,ImageSegments,0.6426116838487973,0.6426116838487973,0.6349558095046656,0.38997745513916016,7.742184 -920,Multiclass classification,Naive Bayes,ImageSegments,0.6485310119695321,0.6485310119695321,0.6384515982514894,0.38997745513916016,8.447577 -966,Multiclass classification,Naive Bayes,ImageSegments,0.6507772020725389,0.6507772020725389,0.6399118827528387,0.38997745513916016,9.183146 -1012,Multiclass classification,Naive Bayes,ImageSegments,0.6508407517309595,0.6508407517309595,0.6387857120889422,0.38997745513916016,9.95137 -1058,Multiclass classification,Naive Bayes,ImageSegments,0.6537369914853358,0.6537369914853358,0.6398811322847952,0.38997745513916016,10.747402000000001 -1104,Multiclass classification,Naive Bayes,ImageSegments,0.658204895738894,0.658204895738894,0.6463297068165035,0.38997745513916016,11.559914000000001 -1150,Multiclass classification,Naive Bayes,ImageSegments,0.6640557006092254,0.6640557006092254,0.6508930463144657,0.39000415802001953,12.388643000000002 -1196,Multiclass classification,Naive Bayes,ImageSegments,0.6702928870292887,0.6702928870292887,0.6599370641329335,0.39000415802001953,13.233598000000002 -1242,Multiclass classification,Naive Bayes,ImageSegments,0.6736502820306205,0.6736502820306205,0.669511465798708,0.39000415802001953,14.094776000000003 -1288,Multiclass classification,Naive Bayes,ImageSegments,0.6822066822066822,0.6822066822066822,0.6790074545382362,0.39000415802001953,14.972203000000004 -1334,Multiclass classification,Naive Bayes,ImageSegments,0.6841710427606902,0.6841710427606902,0.6834974476087325,0.39000415802001953,15.866030000000004 -1380,Multiclass classification,Naive Bayes,ImageSegments,0.6874546773023931,0.6874546773023931,0.6876766922721351,0.39000415802001953,16.775981000000005 -1426,Multiclass classification,Naive Bayes,ImageSegments,0.6919298245614035,0.6919298245614035,0.6930786661709784,0.39000415802001953,17.702176000000005 -1472,Multiclass classification,Naive Bayes,ImageSegments,0.698844323589395,0.698844323589395,0.6985606658027722,0.38997745513916016,18.644575000000003 -1518,Multiclass classification,Naive Bayes,ImageSegments,0.7027027027027027,0.7027027027027027,0.7017787722939461,0.39000415802001953,19.603248000000004 -1564,Multiclass classification,Naive Bayes,ImageSegments,0.7056941778630839,0.7056941778630839,0.7062915374924865,0.38997745513916016,20.578282000000005 -1610,Multiclass classification,Naive Bayes,ImageSegments,0.7078931013051585,0.7078931013051585,0.7081385387673029,0.38997745513916016,21.573844000000005 -1656,Multiclass classification,Naive Bayes,ImageSegments,0.7093655589123867,0.7093655589123867,0.7109488618373111,0.3899507522583008,22.586424000000004 -1702,Multiclass classification,Naive Bayes,ImageSegments,0.7101704879482658,0.7101704879482658,0.7132092257742534,0.38997745513916016,23.615335000000005 -1748,Multiclass classification,Naive Bayes,ImageSegments,0.7143674871207785,0.7143674871207784,0.7178399485500211,0.3899507522583008,24.660526000000004 -1794,Multiclass classification,Naive Bayes,ImageSegments,0.7172336865588399,0.7172336865588399,0.7191260584555578,0.38997745513916016,25.721983000000005 -1840,Multiclass classification,Naive Bayes,ImageSegments,0.7199564980967917,0.7199564980967917,0.7217017555070445,0.39000415802001953,26.799680000000006 -1886,Multiclass classification,Naive Bayes,ImageSegments,0.7204244031830239,0.7204244031830238,0.7234495525792994,0.39000415802001953,27.893629000000004 -1932,Multiclass classification,Naive Bayes,ImageSegments,0.7219057483169342,0.7219057483169342,0.723848351214801,0.39000415802001953,29.003837000000004 -1978,Multiclass classification,Naive Bayes,ImageSegments,0.723823975720789,0.723823975720789,0.725139923863974,0.39000415802001953,30.130512000000003 -2024,Multiclass classification,Naive Bayes,ImageSegments,0.726643598615917,0.726643598615917,0.7268553573885639,0.39000415802001953,31.273399000000005 -2070,Multiclass classification,Naive Bayes,ImageSegments,0.7269212179797003,0.7269212179797003,0.7276782991451582,0.39000415802001953,32.432577 -2116,Multiclass classification,Naive Bayes,ImageSegments,0.7286052009456265,0.7286052009456266,0.7283656039279266,0.39000415802001953,33.608017000000004 -2162,Multiclass classification,Naive Bayes,ImageSegments,0.7306802406293382,0.7306802406293383,0.7303992643507475,0.39000415802001953,34.7997 -2208,Multiclass classification,Naive Bayes,ImageSegments,0.733574988672406,0.733574988672406,0.7322842940126589,0.39000415802001953,36.007612 -2254,Multiclass classification,Naive Bayes,ImageSegments,0.7314691522414558,0.7314691522414558,0.7300322879925133,0.39000415802001953,37.231763 -2300,Multiclass classification,Naive Bayes,ImageSegments,0.7316224445411048,0.7316224445411048,0.7300416811383057,0.39000415802001953,38.472431 -1056,Multiclass classification,Naive Bayes,Insects,0.623696682464455,0.623696682464455,0.5870724729616661,0.6116933822631836,0.909568 -2112,Multiclass classification,Naive Bayes,Insects,0.6148744670772146,0.6148744670772146,0.5800776869595597,0.6116933822631836,2.67356 -3168,Multiclass classification,Naive Bayes,Insects,0.6065677297126618,0.6065677297126618,0.5714781230184183,0.6116933822631836,5.143102000000001 -4224,Multiclass classification,Naive Bayes,Insects,0.6043097324177126,0.6043097324177126,0.5697541737710122,0.6116933822631836,7.993857 -5280,Multiclass classification,Naive Bayes,Insects,0.6088274294373934,0.6088274294373934,0.5727560614138387,0.6116933822631836,11.225513 -6336,Multiclass classification,Naive Bayes,Insects,0.6023677979479084,0.6023677979479084,0.5679597008529512,0.6116933822631836,14.839337 -7392,Multiclass classification,Naive Bayes,Insects,0.5995129211202814,0.5995129211202814,0.5652603100832261,0.6116933822631836,18.839998 -8448,Multiclass classification,Naive Bayes,Insects,0.6019888717888008,0.6019888717888008,0.5673514925692325,0.6116933822631836,23.223853000000002 -9504,Multiclass classification,Naive Bayes,Insects,0.5993896664211301,0.5993896664211301,0.5644951651039589,0.6116933822631836,27.990643000000002 -10560,Multiclass classification,Naive Bayes,Insects,0.5994885879344635,0.5994885879344635,0.5645655385998631,0.6116933822631836,33.140509 -11616,Multiclass classification,Naive Bayes,Insects,0.5972449418854929,0.5972449418854929,0.5631227877868952,0.6116933822631836,38.672833000000004 -12672,Multiclass classification,Naive Bayes,Insects,0.6001894088864336,0.6001894088864336,0.5684733590606373,0.6116933822631836,44.58831000000001 -13728,Multiclass classification,Naive Bayes,Insects,0.6120783856632913,0.6120783856632913,0.5935173038317552,0.6116933822631836,50.89270200000001 -14784,Multiclass classification,Naive Bayes,Insects,0.6024487587093282,0.6024487587093282,0.5841270876002981,0.6116933822631836,57.581734000000004 -15840,Multiclass classification,Naive Bayes,Insects,0.5676494728202538,0.5676494728202538,0.5507155080701159,0.6116933822631836,64.65553700000001 -16896,Multiclass classification,Naive Bayes,Insects,0.5418762947617638,0.5418762947617638,0.5256197352354143,0.6116933822631836,72.114698 -17952,Multiclass classification,Naive Bayes,Insects,0.5232020500250683,0.5232020500250683,0.5066898143269706,0.6116933822631836,79.958388 -19008,Multiclass classification,Naive Bayes,Insects,0.5118640500868101,0.5118640500868101,0.4926543583964285,0.6116933822631836,88.190503 -20064,Multiclass classification,Naive Bayes,Insects,0.5103922643672432,0.5103922643672432,0.4900586962359796,0.6116933822631836,96.808684 -21120,Multiclass classification,Naive Bayes,Insects,0.5115772527108291,0.5115772527108291,0.4910837640903744,0.6116933822631836,105.81178 -22176,Multiclass classification,Naive Bayes,Insects,0.5140022547914318,0.5140022547914318,0.49325418882319566,0.6116933822631836,115.205863 -23232,Multiclass classification,Naive Bayes,Insects,0.5154319659076234,0.5154319659076234,0.4943013417599926,0.6116933822631836,124.990845 -24288,Multiclass classification,Naive Bayes,Insects,0.5184254951208466,0.5184254951208466,0.4965832238311332,0.6116933822631836,135.166218 -25344,Multiclass classification,Naive Bayes,Insects,0.5225111470623052,0.5225111470623052,0.499893079239698,0.6116933822631836,145.739141 -26400,Multiclass classification,Naive Bayes,Insects,0.5257396113489148,0.5257396113489148,0.5022487669255871,0.6116933822631836,156.702601 -27456,Multiclass classification,Naive Bayes,Insects,0.5301402294663996,0.5301402294663996,0.5051550433324518,0.6116933822631836,168.057909 -28512,Multiclass classification,Naive Bayes,Insects,0.5277261407877661,0.5277261407877661,0.5036945145235057,0.6116933822631836,179.80420999999998 -29568,Multiclass classification,Naive Bayes,Insects,0.5204450908107011,0.5204450908107011,0.49890087123127674,0.6116933822631836,191.94450099999997 -30624,Multiclass classification,Naive Bayes,Insects,0.5147111648107632,0.5147111648107632,0.49582684007363204,0.6116933822631836,204.47849899999997 -31680,Multiclass classification,Naive Bayes,Insects,0.5105590454244137,0.5105590454244137,0.4941101813344875,0.6116933822631836,217.40209199999998 -32736,Multiclass classification,Naive Bayes,Insects,0.5075607148312204,0.5075607148312204,0.4931947798921405,0.6116933822631836,230.71620099999998 -33792,Multiclass classification,Naive Bayes,Insects,0.5044538486579266,0.5044538486579266,0.4905626123916189,0.6116933822631836,244.42088399999997 -34848,Multiclass classification,Naive Bayes,Insects,0.5020231296811777,0.5020231296811777,0.48787984248812405,0.6116933822631836,258.51509 -35904,Multiclass classification,Naive Bayes,Insects,0.49987466228448874,0.49987466228448874,0.48534350611524757,0.6116933822631836,273.003699 -36960,Multiclass classification,Naive Bayes,Insects,0.49679374441949187,0.49679374441949187,0.4819418474093529,0.6116933822631836,287.88352199999997 -38016,Multiclass classification,Naive Bayes,Insects,0.49559384453505195,0.49559384453505195,0.4801892436835747,0.6116933822631836,303.152298 -39072,Multiclass classification,Naive Bayes,Insects,0.49402370044278365,0.49402370044278365,0.47838078382052607,0.6116933822631836,318.80769699999996 -40128,Multiclass classification,Naive Bayes,Insects,0.493508111745209,0.493508111745209,0.47852138016706713,0.6116933822631836,334.85222999999996 -41184,Multiclass classification,Naive Bayes,Insects,0.49369885632421145,0.49369885632421145,0.47942014994272747,0.6116933822631836,351.28664399999997 -42240,Multiclass classification,Naive Bayes,Insects,0.4938800634484718,0.4938800634484718,0.48023774975329364,0.6116933822631836,368.10561099999995 -43296,Multiclass classification,Naive Bayes,Insects,0.49437579397159026,0.49437579397159026,0.4812132921167227,0.6116933822631836,385.31069299999996 -44352,Multiclass classification,Naive Bayes,Insects,0.49403621113390905,0.49403621113390905,0.48123889196184183,0.6116933822631836,402.906414 -45408,Multiclass classification,Naive Bayes,Insects,0.4944832294580131,0.4944832294580131,0.4818441874360224,0.6116933822631836,420.888505 -46464,Multiclass classification,Naive Bayes,Insects,0.4945225232981082,0.4945225232981082,0.4820791268335544,0.6116933822631836,439.259743 -47520,Multiclass classification,Naive Bayes,Insects,0.4956333256171216,0.4956333256171216,0.48331686360214987,0.6116933822631836,458.01736800000003 -48576,Multiclass classification,Naive Bayes,Insects,0.4970869788986104,0.4970869788986104,0.48467037716343636,0.6116933822631836,477.16088800000006 -49632,Multiclass classification,Naive Bayes,Insects,0.4987608551107171,0.4987608551107171,0.4862426724473749,0.6116933822631836,496.69293600000003 -50688,Multiclass classification,Naive Bayes,Insects,0.5009568528419516,0.5009568528419516,0.4881725476999718,0.6116933822631836,516.6094800000001 -51744,Multiclass classification,Naive Bayes,Insects,0.5034497419940862,0.5034497419940862,0.4903712806540024,0.6116933822631836,536.9146260000001 -52800,Multiclass classification,Naive Bayes,Insects,0.5068467205818292,0.5068467205818292,0.4930025316136313,0.6116933822631836,557.6057650000001 -408,Multiclass classification,Naive Bayes,Keystroke,0.9852579852579852,0.9852579852579852,0.6962686567164179,0.19356441497802734,0.122414 -816,Multiclass classification,Naive Bayes,Keystroke,0.947239263803681,0.947239263803681,0.7418606503288051,0.28890228271484375,0.375804 -1224,Multiclass classification,Naive Bayes,Keystroke,0.884709730171709,0.884709730171709,0.8705899666065842,0.38424015045166016,0.764348 -1632,Multiclass classification,Naive Bayes,Keystroke,0.8933169834457388,0.8933169834457388,0.8791291775937072,0.47957801818847656,1.3033160000000001 -2040,Multiclass classification,Naive Bayes,Keystroke,0.8921039725355566,0.8921039725355566,0.8831785360852743,0.575160026550293,2.01214 -2448,Multiclass classification,Naive Bayes,Keystroke,0.851655087862689,0.851655087862689,0.858198428951664,0.6704978942871094,2.906585 -2856,Multiclass classification,Naive Bayes,Keystroke,0.8598949211908932,0.8598949211908932,0.8469962214365345,0.7658357620239258,3.994802 -3264,Multiclass classification,Naive Bayes,Keystroke,0.8513637756665645,0.8513637756665645,0.8281280134770848,0.8611736297607422,5.296884 -3672,Multiclass classification,Naive Bayes,Keystroke,0.8422773086352493,0.8422773086352493,0.8409307955747314,0.9565114974975586,6.831079000000001 -4080,Multiclass classification,Naive Bayes,Keystroke,0.8367246874233881,0.8367246874233881,0.8249418657104467,1.0523834228515625,8.617788000000001 -4488,Multiclass classification,Naive Bayes,Keystroke,0.8203699576554491,0.8203699576554491,0.8300896799820437,1.147721290588379,10.679552000000001 -4896,Multiclass classification,Naive Bayes,Keystroke,0.8192032686414709,0.8192032686414709,0.8269731591910484,1.2430591583251953,13.032163 -5304,Multiclass classification,Naive Bayes,Keystroke,0.8172732415613804,0.8172732415613804,0.8027823390848743,1.3383970260620117,15.695238 -5712,Multiclass classification,Naive Bayes,Keystroke,0.7961828051129399,0.7961828051129399,0.8002006091139847,1.4337348937988281,18.689224 -6120,Multiclass classification,Naive Bayes,Keystroke,0.793920575257395,0.793920575257395,0.7746960355921346,1.5290727615356445,22.034543 -6528,Multiclass classification,Naive Bayes,Keystroke,0.7688064960931515,0.7688064960931515,0.7622487598340326,1.624410629272461,25.755146 -6936,Multiclass classification,Naive Bayes,Keystroke,0.7568853640951694,0.7568853640951694,0.757813781660983,1.7197484970092773,29.876127 -7344,Multiclass classification,Naive Bayes,Keystroke,0.7669889690862045,0.7669889690862046,0.7643943615019535,1.8150863647460938,34.413227 -7752,Multiclass classification,Naive Bayes,Keystroke,0.7676428847890595,0.7676428847890595,0.7655695901071293,1.9104242324829102,39.374485 -8160,Multiclass classification,Naive Bayes,Keystroke,0.7714180659394534,0.7714180659394533,0.7672011803374248,2.0057621002197266,44.773425 -8568,Multiclass classification,Naive Bayes,Keystroke,0.7702813120112058,0.7702813120112058,0.7699263138193526,2.1021223068237305,50.625164000000005 -8976,Multiclass classification,Naive Bayes,Keystroke,0.7680222841225627,0.7680222841225627,0.7682287234686137,2.197460174560547,56.940867000000004 -9384,Multiclass classification,Naive Bayes,Keystroke,0.7659597143770649,0.7659597143770649,0.7643546547243015,2.2927980422973633,63.725868000000006 -9792,Multiclass classification,Naive Bayes,Keystroke,0.7586559084873864,0.7586559084873864,0.7552148692020618,2.3881359100341797,70.991963 -10200,Multiclass classification,Naive Bayes,Keystroke,0.7505637807628199,0.7505637807628199,0.7430512224080149,2.483473777770996,78.748505 -10608,Multiclass classification,Naive Bayes,Keystroke,0.7290468558499105,0.7290468558499106,0.715756093271779,2.5788116455078125,87.01168299999999 -11016,Multiclass classification,Naive Bayes,Keystroke,0.7217430776214253,0.7217430776214253,0.7173640789896896,2.674149513244629,95.78731699999999 -11424,Multiclass classification,Naive Bayes,Keystroke,0.7151361288628206,0.7151361288628206,0.7011862635194492,2.7694873809814453,105.08400199999998 -11832,Multiclass classification,Naive Bayes,Keystroke,0.705603921900093,0.705603921900093,0.6976881379682605,2.8648252487182617,114.91729899999999 -12240,Multiclass classification,Naive Bayes,Keystroke,0.7094533867146009,0.7094533867146009,0.705840538940343,2.960163116455078,125.31674099999998 -12648,Multiclass classification,Naive Bayes,Keystroke,0.7053846762077963,0.7053846762077963,0.6965736948063981,3.0555009841918945,136.28361299999997 -13056,Multiclass classification,Naive Bayes,Keystroke,0.6927613941018766,0.6927613941018766,0.6842255816736497,3.150838851928711,147.832836 -13464,Multiclass classification,Naive Bayes,Keystroke,0.6890737577063062,0.6890737577063062,0.6845669389392289,3.2461767196655273,159.980064 -13872,Multiclass classification,Naive Bayes,Keystroke,0.6873332852714296,0.6873332852714296,0.6839054551822702,3.3415145874023438,172.74228 -14280,Multiclass classification,Naive Bayes,Keystroke,0.682960991666083,0.682960991666083,0.6781566371919946,3.43685245513916,186.135321 -14688,Multiclass classification,Naive Bayes,Keystroke,0.686185061619119,0.686185061619119,0.6843713776162116,3.5321903228759766,200.177651 -15096,Multiclass classification,Naive Bayes,Keystroke,0.6928784365684001,0.6928784365684001,0.6911392400672977,3.627528190612793,214.888654 -15504,Multiclass classification,Naive Bayes,Keystroke,0.6913500612784622,0.6913500612784622,0.687359772989117,3.7228660583496094,230.279565 -15912,Multiclass classification,Naive Bayes,Keystroke,0.6819810194205267,0.6819810194205267,0.6749159449359359,3.818203926086426,246.36567399999998 -16320,Multiclass classification,Naive Bayes,Keystroke,0.6726515105092223,0.6726515105092223,0.6670192172011686,3.913541793823242,263.163212 -16728,Multiclass classification,Naive Bayes,Keystroke,0.6695163508100676,0.6695163508100676,0.6664051037977978,4.008879661560059,280.68755699999997 -17136,Multiclass classification,Naive Bayes,Keystroke,0.6650131310183834,0.6650131310183834,0.6608988619616459,4.1063079833984375,298.952273 -17544,Multiclass classification,Naive Bayes,Keystroke,0.6568431853160804,0.6568431853160804,0.653138289771919,4.201645851135254,317.97399 -17952,Multiclass classification,Naive Bayes,Keystroke,0.6556180714166342,0.6556180714166342,0.6538448358590967,4.29698371887207,337.769402 -18360,Multiclass classification,Naive Bayes,Keystroke,0.6614194672912468,0.6614194672912468,0.6603186829199905,4.392321586608887,358.361854 -18768,Multiclass classification,Naive Bayes,Keystroke,0.6669686151222891,0.6669686151222891,0.666229361655457,4.487659454345703,379.763277 -19176,Multiclass classification,Naive Bayes,Keystroke,0.6579921773142112,0.6579921773142112,0.6554177118629491,4.5829973220825195,401.98609400000004 -19584,Multiclass classification,Naive Bayes,Keystroke,0.6622580809886126,0.6622580809886126,0.6609360990360077,4.678335189819336,425.04707400000007 -19992,Multiclass classification,Naive Bayes,Keystroke,0.6562453103896754,0.6562453103896754,0.6545704957554573,4.773673057556152,448.96292700000004 -20400,Multiclass classification,Naive Bayes,Keystroke,0.6525319868621011,0.6525319868621011,0.6515767870317881,4.869010925292969,473.747426 -46,Multiclass classification,Hoeffding Tree,ImageSegments,0.35555555555555557,0.35555555555555557,0.25379424497071557,0.41910839080810547,0.164966 -92,Multiclass classification,Hoeffding Tree,ImageSegments,0.4945054945054945,0.4945054945054945,0.5043329927491419,0.41910457611083984,0.35564300000000004 -138,Multiclass classification,Hoeffding Tree,ImageSegments,0.5328467153284672,0.5328467153284672,0.5564033878668025,0.41919994354248047,0.571134 -184,Multiclass classification,Hoeffding Tree,ImageSegments,0.6010928961748634,0.6010928961748634,0.6227664965396451,0.41919994354248047,0.8113900000000001 -230,Multiclass classification,Hoeffding Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6539827168809461,0.4192228317260742,1.079154 -276,Multiclass classification,Hoeffding Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.6671561759164943,0.41927242279052734,1.371943 -322,Multiclass classification,Hoeffding Tree,ImageSegments,0.67601246105919,0.67601246105919,0.6756614325426025,0.41927242279052734,1.689575 -368,Multiclass classification,Hoeffding Tree,ImageSegments,0.7029972752043597,0.7029972752043597,0.6993447851636565,0.41924571990966797,2.032058 -414,Multiclass classification,Hoeffding Tree,ImageSegments,0.7142857142857143,0.7142857142857143,0.7108606838045498,0.41916561126708984,2.4019660000000003 -460,Multiclass classification,Hoeffding Tree,ImageSegments,0.7145969498910676,0.7145969498910676,0.7090365931960759,0.41924190521240234,2.796914 -506,Multiclass classification,Hoeffding Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7126631585949763,0.41924190521240234,3.216844 -552,Multiclass classification,Hoeffding Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7157730164623107,0.41913509368896484,3.6616 -598,Multiclass classification,Hoeffding Tree,ImageSegments,0.7286432160804021,0.7286432160804021,0.7216745323124732,0.4191579818725586,4.131175 -644,Multiclass classification,Hoeffding Tree,ImageSegments,0.7278382581648523,0.7278382581648523,0.72291051830875,0.4191312789916992,4.6280079999999995 -690,Multiclass classification,Hoeffding Tree,ImageSegments,0.7314949201741655,0.7314949201741654,0.7263583447448078,0.4191312789916992,5.149870999999999 -736,Multiclass classification,Hoeffding Tree,ImageSegments,0.7333333333333333,0.7333333333333333,0.729431071218305,0.4191579818725586,5.696603 -782,Multiclass classification,Hoeffding Tree,ImageSegments,0.7387964148527529,0.7387964148527529,0.7349287389986899,0.4191579818725586,6.268242 -828,Multiclass classification,Hoeffding Tree,ImageSegments,0.7376058041112454,0.7376058041112454,0.7356226390109742,0.4191579818725586,6.867156 -874,Multiclass classification,Hoeffding Tree,ImageSegments,0.7445589919816724,0.7445589919816724,0.7409366047432264,0.4191579818725586,7.49107 -920,Multiclass classification,Hoeffding Tree,ImageSegments,0.7453754080522307,0.7453754080522307,0.7408438328939173,0.4191312789916992,8.139827 -966,Multiclass classification,Hoeffding Tree,ImageSegments,0.7471502590673575,0.7471502590673575,0.7416651838589269,0.4191312789916992,8.813418 -1012,Multiclass classification,Hoeffding Tree,ImageSegments,0.7467853610286844,0.7467853610286844,0.7416356251822,0.4191312789916992,9.514287 -1058,Multiclass classification,Hoeffding Tree,ImageSegments,0.7492904446546831,0.7492904446546831,0.7430778844390782,0.4191312789916992,10.240045 -1104,Multiclass classification,Hoeffding Tree,ImageSegments,0.7515865820489573,0.7515865820489573,0.7451256886686588,0.41918087005615234,10.990683 -1150,Multiclass classification,Hoeffding Tree,ImageSegments,0.7536988685813751,0.7536988685813751,0.7468312166689606,0.41918087005615234,11.766057 -1196,Multiclass classification,Hoeffding Tree,ImageSegments,0.7564853556485356,0.7564853556485356,0.7503479321738039,0.41918087005615234,12.566171 -1242,Multiclass classification,Hoeffding Tree,ImageSegments,0.7566478646253022,0.7566478646253022,0.7509717522131719,0.41918087005615234,13.393734 -1288,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614607614607615,0.7614607614607615,0.7547643483779538,0.41918087005615234,14.246394 -1334,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614403600900225,0.7614403600900225,0.7551060921605869,0.41918087005615234,15.123846 -1380,Multiclass classification,Hoeffding Tree,ImageSegments,0.7621464829586657,0.7621464829586658,0.7562209880685911,0.41918087005615234,16.026049 -1426,Multiclass classification,Hoeffding Tree,ImageSegments,0.7642105263157895,0.7642105263157895,0.7575332274919562,0.41918087005615234,16.955566 -1472,Multiclass classification,Hoeffding Tree,ImageSegments,0.7688647178789939,0.768864717878994,0.760438686053582,0.41918087005615234,17.910383 -1518,Multiclass classification,Hoeffding Tree,ImageSegments,0.7705998681608438,0.7705998681608438,0.7612069012840875,0.41918087005615234,18.890183 -1564,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709532949456174,0.7709532949456174,0.7622701654854867,0.41918087005615234,19.895086 -1610,Multiclass classification,Hoeffding Tree,ImageSegments,0.7712865133623369,0.771286513362337,0.7617247271717752,0.4192037582397461,20.927569 -1656,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709969788519637,0.7709969788519637,0.7615629120572474,0.4192037582397461,21.985291999999998 -1702,Multiclass classification,Hoeffding Tree,ImageSegments,0.770135214579659,0.770135214579659,0.7627316365695141,0.4192037582397461,23.068120999999998 -1748,Multiclass classification,Hoeffding Tree,ImageSegments,0.7727532913566113,0.7727532913566113,0.7649467707214076,0.4192037582397461,24.176004999999996 -1794,Multiclass classification,Hoeffding Tree,ImageSegments,0.7741215839375348,0.7741215839375348,0.7649332326562147,0.4191770553588867,25.309107999999995 -1840,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754214246873301,0.7754214246873301,0.7664700790631906,0.4191770553588867,26.470048999999996 -1886,Multiclass classification,Hoeffding Tree,ImageSegments,0.7740053050397878,0.7740053050397878,0.7655121135276625,0.4191770553588867,27.656614999999995 -1932,Multiclass classification,Hoeffding Tree,ImageSegments,0.7742102537545313,0.7742102537545313,0.7648034036287765,0.4191770553588867,28.868293999999995 -1978,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754172989377845,0.7754172989377845,0.7656013068970458,0.4191770553588867,30.105037999999997 -2024,Multiclass classification,Hoeffding Tree,ImageSegments,0.7770637666831438,0.7770637666831438,0.7660878232247856,0.4191770553588867,31.369529999999997 -2070,Multiclass classification,Hoeffding Tree,ImageSegments,0.7762203963267279,0.7762203963267279,0.7654829214385931,0.4191770553588867,32.658967 -2116,Multiclass classification,Hoeffding Tree,ImageSegments,0.7768321513002364,0.7768321513002364,0.7653071619305024,0.4191770553588867,33.973288999999994 -2162,Multiclass classification,Hoeffding Tree,ImageSegments,0.7778806108283203,0.7778806108283203,0.7659351904174981,0.4191770553588867,35.312507 -2208,Multiclass classification,Hoeffding Tree,ImageSegments,0.7797915722700498,0.7797915722700498,0.7668192864082087,0.4191770553588867,36.679283999999996 -2254,Multiclass classification,Hoeffding Tree,ImageSegments,0.7767421216156236,0.7767421216156236,0.7637794374955548,0.4191770553588867,38.070969 -2300,Multiclass classification,Hoeffding Tree,ImageSegments,0.7759895606785558,0.7759895606785558,0.763026662835187,0.4191770553588867,39.487871999999996 -1056,Multiclass classification,Hoeffding Tree,Insects,0.6218009478672986,0.6218009478672986,0.585266310719421,0.6617898941040039,1.016292 -2112,Multiclass classification,Hoeffding Tree,Insects,0.6153481762198011,0.6153481762198011,0.5806436317780949,0.6617898941040039,2.820671 -3168,Multiclass classification,Hoeffding Tree,Insects,0.6071992421850332,0.6071992421850332,0.572248584718361,0.6617898941040039,5.468586999999999 -4224,Multiclass classification,Hoeffding Tree,Insects,0.6043097324177126,0.6043097324177126,0.5697573109597247,0.6617898941040039,8.970813999999999 -5280,Multiclass classification,Hoeffding Tree,Insects,0.6088274294373934,0.6088274294373934,0.5727379077413696,0.6617898941040039,13.304298999999999 -6336,Multiclass classification,Hoeffding Tree,Insects,0.6026835043409629,0.6026835043409629,0.568251333238805,0.6617898941040039,18.451532999999998 -7392,Multiclass classification,Hoeffding Tree,Insects,0.600189419564335,0.600189419564335,0.5659762112716077,0.6617898941040039,24.373814999999997 -8448,Multiclass classification,Hoeffding Tree,Insects,0.60258079791642,0.60258079791642,0.5679781484640409,0.6617898941040039,31.061275999999996 -9504,Multiclass classification,Hoeffding Tree,Insects,0.5998105861306956,0.5998105861306956,0.5649597336877693,0.6617898941040039,38.490334999999995 -10560,Multiclass classification,Hoeffding Tree,Insects,0.5998674116867128,0.5998674116867128,0.5650173260529011,0.6617898941040039,46.63726 -11616,Multiclass classification,Hoeffding Tree,Insects,0.5974171330176495,0.5974171330176495,0.5633067089377386,0.6617898941040039,55.514266 -12672,Multiclass classification,Hoeffding Tree,Insects,0.6001894088864336,0.6001894088864336,0.5684760329567131,0.6617898941040039,65.102691 -13728,Multiclass classification,Hoeffding Tree,Insects,0.6120783856632913,0.6120783856632913,0.5935956771555828,0.6617898941040039,75.408233 -14784,Multiclass classification,Hoeffding Tree,Insects,0.6024487587093282,0.6024487587093282,0.5842148300149193,0.6617898941040039,86.426133 -15840,Multiclass classification,Hoeffding Tree,Insects,0.5677757434181451,0.5677757434181451,0.5509250187877572,0.6617898941040039,98.15845499999999 -16896,Multiclass classification,Hoeffding Tree,Insects,0.5419354838709678,0.5419354838709678,0.5257359157219257,0.6617898941040039,110.605121 -17952,Multiclass classification,Hoeffding Tree,Insects,0.5233691716338923,0.5233691716338923,0.506858183835206,0.6617898941040039,123.763106 -19008,Multiclass classification,Hoeffding Tree,Insects,0.5121271110643447,0.5121271110643447,0.49292899065094153,0.6617898941040039,137.636213 -20064,Multiclass classification,Hoeffding Tree,Insects,0.5120370831879579,0.5120370831879579,0.4920970323041603,1.317840576171875,152.19804 -21120,Multiclass classification,Hoeffding Tree,Insects,0.5173066906577016,0.5173066906577016,0.497344716983625,1.3185958862304688,167.375493 -22176,Multiclass classification,Hoeffding Tree,Insects,0.5229312288613304,0.5229312288613304,0.5026343687424488,1.3185958862304688,183.138263 -23232,Multiclass classification,Hoeffding Tree,Insects,0.5301536739701261,0.5301536739701261,0.5095132087733324,1.3185958862304688,199.493958 -24288,Multiclass classification,Hoeffding Tree,Insects,0.5351422571746202,0.5351422571746202,0.5135975374357353,1.3185958862304688,216.43581799999998 -25344,Multiclass classification,Hoeffding Tree,Insects,0.5403069881229531,0.5403069881229531,0.5180803411538233,1.3185958862304688,233.97305899999998 -26400,Multiclass classification,Hoeffding Tree,Insects,0.5441493995984696,0.5441493995984696,0.5209012984387186,1.3185958862304688,252.09929999999997 -27456,Multiclass classification,Hoeffding Tree,Insects,0.5475869604807867,0.5475869604807867,0.5230407124785976,1.3185958862304688,270.82611499999996 -28512,Multiclass classification,Hoeffding Tree,Insects,0.5442460804601733,0.5442460804601733,0.5199893698637053,1.3185958862304688,290.12573499999996 -29568,Multiclass classification,Hoeffding Tree,Insects,0.5439848479724017,0.5439848479724017,0.5225387960194382,1.3185958862304688,310.131151 -30624,Multiclass classification,Hoeffding Tree,Insects,0.5449825294713124,0.5449825294713124,0.5260472440529832,1.3185958862304688,330.869455 -31680,Multiclass classification,Hoeffding Tree,Insects,0.5469238296663405,0.5469238296663405,0.5300194392617626,1.3185958862304688,352.339648 -32736,Multiclass classification,Hoeffding Tree,Insects,0.5492286543455017,0.5492286543455017,0.5337692045397759,1.3185958862304688,374.544388 -33792,Multiclass classification,Hoeffding Tree,Insects,0.5448196265277737,0.5448196265277737,0.5298516474077152,1.3185958862304688,397.480297 -34848,Multiclass classification,Hoeffding Tree,Insects,0.539357763939507,0.539357763939507,0.5246413689313029,1.3185958862304688,421.148709 -35904,Multiclass classification,Hoeffding Tree,Insects,0.5352756037099964,0.5352756037099964,0.5204658240271912,1.3185958862304688,445.552724 -36960,Multiclass classification,Hoeffding Tree,Insects,0.5307232338537298,0.5307232338537298,0.5158458403074863,1.3185958862304688,470.685377 -38016,Multiclass classification,Hoeffding Tree,Insects,0.5287912666052874,0.5287912666052874,0.5138605376143625,1.8598642349243164,496.54465300000004 -39072,Multiclass classification,Hoeffding Tree,Insects,0.5245322617798367,0.5245322617798367,0.5100329616180462,1.9744834899902344,523.1337460000001 -40128,Multiclass classification,Hoeffding Tree,Insects,0.5244847608841927,0.5244847608841927,0.5114466799524962,1.9744834899902344,550.3794180000001 -41184,Multiclass classification,Hoeffding Tree,Insects,0.5269650098341548,0.5269650098341548,0.5145630920489553,1.9744834899902344,578.1701970000001 -42240,Multiclass classification,Hoeffding Tree,Insects,0.5290608205686688,0.5290608205686688,0.5171452370879218,1.9744834899902344,606.4941780000001 -43296,Multiclass classification,Hoeffding Tree,Insects,0.5316318281556762,0.5316318281556762,0.5200714653059242,1.9744834899902344,635.3594070000001 -44352,Multiclass classification,Hoeffding Tree,Insects,0.5332912448422809,0.5332912448422809,0.521951703681177,1.9752388000488281,664.7773900000002 -45408,Multiclass classification,Hoeffding Tree,Insects,0.5350937080185875,0.5350937080185875,0.5236272112757866,1.9752388000488281,694.7425150000001 -46464,Multiclass classification,Hoeffding Tree,Insects,0.5374168693368917,0.5374168693368917,0.5257977177437826,1.9752388000488281,725.2648820000002 -47520,Multiclass classification,Hoeffding Tree,Insects,0.5359540394368568,0.5359540394368568,0.5247049329892776,1.9752388000488281,756.3925470000001 -48576,Multiclass classification,Hoeffding Tree,Insects,0.5333196088522902,0.5333196088522902,0.5224640186909638,1.9752388000488281,788.1537450000002 -49632,Multiclass classification,Hoeffding Tree,Insects,0.5314017448771937,0.5314017448771937,0.5209076603734538,1.9752388000488281,820.5431960000002 -50688,Multiclass classification,Hoeffding Tree,Insects,0.5321877404462683,0.5321877404462683,0.5219332135179457,2.097897529602051,853.5752100000002 -51744,Multiclass classification,Hoeffding Tree,Insects,0.5376959202210927,0.5376959202210927,0.5274519689249669,2.335637092590332,887.2128290000002 -52800,Multiclass classification,Hoeffding Tree,Insects,0.5370177465482301,0.5370177465482301,0.5270712327692165,2.5391950607299805,921.3507020000002 -408,Multiclass classification,Hoeffding Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.22765445709228516,0.136786 -816,Multiclass classification,Hoeffding Tree,Keystroke,0.9423312883435583,0.9423312883435583,0.7661667470992702,0.32327842712402344,0.43594199999999994 -1224,Multiclass classification,Hoeffding Tree,Keystroke,0.8830744071954211,0.883074407195421,0.8761191747044462,0.4189023971557617,0.9269379999999999 -1632,Multiclass classification,Hoeffding Tree,Keystroke,0.8902513795217658,0.8902513795217658,0.8767853151263398,0.5150146484375,1.637883 -2040,Multiclass classification,Hoeffding Tree,Keystroke,0.8891613536047082,0.8891613536047082,0.8807858055314012,0.6221132278442383,2.570345 -2448,Multiclass classification,Hoeffding Tree,Keystroke,0.848385778504291,0.848385778504291,0.8522513926518692,0.7177371978759766,3.758757 -2856,Multiclass classification,Hoeffding Tree,Keystroke,0.8563922942206655,0.8563922942206655,0.8440193478447515,0.8133611679077148,5.245901 -3264,Multiclass classification,Hoeffding Tree,Keystroke,0.8482991112473184,0.8482991112473184,0.8269786301577753,0.9089851379394531,7.065474 -3672,Multiclass classification,Hoeffding Tree,Keystroke,0.8392808499046581,0.8392808499046581,0.8374924160046074,1.0046091079711914,9.269679 -4080,Multiclass classification,Hoeffding Tree,Keystroke,0.8323118411375338,0.8323118411375338,0.8182261307945194,1.1253337860107422,11.897269999999999 -4488,Multiclass classification,Hoeffding Tree,Keystroke,0.8159126365054602,0.8159126365054602,0.8260965842218733,1.2209577560424805,14.983422 -4896,Multiclass classification,Hoeffding Tree,Keystroke,0.8149131767109296,0.8149131767109296,0.8221314665977922,1.3165817260742188,18.566921 -5304,Multiclass classification,Hoeffding Tree,Keystroke,0.8125589289081652,0.8125589289081652,0.797613058026624,1.412205696105957,22.693048 -5712,Multiclass classification,Hoeffding Tree,Keystroke,0.7907546839432674,0.7907546839432674,0.7936708037520237,1.5078296661376953,27.396131 -6120,Multiclass classification,Hoeffding Tree,Keystroke,0.7886909625755842,0.7886909625755842,0.7694478218498494,1.6034536361694336,32.715078 -6528,Multiclass classification,Hoeffding Tree,Keystroke,0.7635973647924008,0.7635973647924008,0.75687960152136,1.6990776062011719,38.687416 -6936,Multiclass classification,Hoeffding Tree,Keystroke,0.75155010814708,0.7515501081470799,0.7521509466338958,1.7947015762329102,45.356366 -7344,Multiclass classification,Hoeffding Tree,Keystroke,0.7611330518861501,0.7611330518861501,0.7576671162861804,1.8917903900146484,52.757111 -7752,Multiclass classification,Hoeffding Tree,Keystroke,0.7617081666881693,0.7617081666881692,0.7593340838982118,1.9874143600463867,60.928470000000004 -8160,Multiclass classification,Hoeffding Tree,Keystroke,0.7655349920333374,0.7655349920333374,0.7610505848438686,2.083038330078125,69.910689 -8568,Multiclass classification,Hoeffding Tree,Keystroke,0.7644449632310026,0.7644449632310025,0.7639417799779614,2.226712226867676,79.742469 -8976,Multiclass classification,Hoeffding Tree,Keystroke,0.7624512534818941,0.7624512534818941,0.7625605608371231,2.322336196899414,90.464241 -9384,Multiclass classification,Hoeffding Tree,Keystroke,0.7605243525524885,0.7605243525524885,0.7588384348689571,2.4179601669311523,102.115634 -9792,Multiclass classification,Hoeffding Tree,Keystroke,0.753344908589521,0.753344908589521,0.7499438215834663,2.5135841369628906,114.735409 -10200,Multiclass classification,Hoeffding Tree,Keystroke,0.7450730463770958,0.7450730463770959,0.7369660419615973,2.609208106994629,128.375943 -10608,Multiclass classification,Hoeffding Tree,Keystroke,0.7240501555576506,0.7240501555576506,0.7111305646829175,2.704832077026367,143.06648900000002 -11016,Multiclass classification,Hoeffding Tree,Keystroke,0.7166591012256015,0.7166591012256015,0.7122511515574345,2.8004560470581055,158.84720500000003 -11424,Multiclass classification,Hoeffding Tree,Keystroke,0.710146196270682,0.710146196270682,0.6963016796632095,2.8960800170898438,175.75710000000004 -11832,Multiclass classification,Hoeffding Tree,Keystroke,0.7005324993660722,0.7005324993660722,0.6925666211338902,2.991703987121582,193.83910100000003 -12240,Multiclass classification,Hoeffding Tree,Keystroke,0.7043876133671052,0.7043876133671052,0.7007845610449206,3.0873279571533203,213.15240600000004 -12648,Multiclass classification,Hoeffding Tree,Keystroke,0.7004032576895707,0.7004032576895707,0.6915775762792659,3.1829519271850586,233.71252100000004 -13056,Multiclass classification,Hoeffding Tree,Keystroke,0.6877058598238223,0.6877058598238223,0.6789768292873962,3.278575897216797,255.55614900000003 -13464,Multiclass classification,Hoeffding Tree,Keystroke,0.6838743222164451,0.6838743222164451,0.6791243465680946,3.374199867248535,278.72697300000004 -13872,Multiclass classification,Hoeffding Tree,Keystroke,0.6822146925239708,0.6822146925239708,0.6786558938530484,3.4698238372802734,303.265051 -14280,Multiclass classification,Hoeffding Tree,Keystroke,0.6777085230058127,0.6777085230058127,0.6725285130045525,3.5654478073120117,329.21132600000004 -14688,Multiclass classification,Hoeffding Tree,Keystroke,0.6807380676788997,0.6807380676788997,0.6786761142186741,3.66107177734375,356.60277 -15096,Multiclass classification,Hoeffding Tree,Keystroke,0.6873799271281882,0.6873799271281882,0.68548393064844,3.7566957473754883,385.483239 -15504,Multiclass classification,Hoeffding Tree,Keystroke,0.6858027478552539,0.6858027478552539,0.6816808496509055,3.8523197174072266,415.890234 -15912,Multiclass classification,Hoeffding Tree,Keystroke,0.6765759537426937,0.6765759537426937,0.6694713281964944,3.947943687438965,447.86386200000004 -16320,Multiclass classification,Hoeffding Tree,Keystroke,0.6673815797536614,0.6673815797536614,0.6617321933140904,4.043567657470703,481.44086400000003 -16728,Multiclass classification,Hoeffding Tree,Keystroke,0.6643151790518323,0.6643151790518323,0.6611780293584051,4.139191627502441,516.667277 -17136,Multiclass classification,Hoeffding Tree,Keystroke,0.6598774438284214,0.6598774438284214,0.655734247886306,4.333066940307617,553.571784 -17544,Multiclass classification,Hoeffding Tree,Keystroke,0.6518269395200365,0.6518269395200365,0.6481085155228207,4.4286909103393555,592.193317 -17952,Multiclass classification,Hoeffding Tree,Keystroke,0.6507158375577963,0.6507158375577963,0.648936899585426,4.524314880371094,632.57823 -18360,Multiclass classification,Hoeffding Tree,Keystroke,0.6566806470940683,0.6566806470940683,0.6555764711123697,4.619938850402832,674.762883 -18768,Multiclass classification,Hoeffding Tree,Keystroke,0.662279533223211,0.662279533223211,0.6615432060687811,4.71556282043457,718.781471 -19176,Multiclass classification,Hoeffding Tree,Keystroke,0.6534028683181226,0.6534028683181226,0.6508089832432515,4.811186790466309,764.6790530000001 -19584,Multiclass classification,Hoeffding Tree,Keystroke,0.6577643874789358,0.6577643874789358,0.6564201177589184,4.906810760498047,812.4977690000001 -19992,Multiclass classification,Hoeffding Tree,Keystroke,0.6518433294982742,0.6518433294982742,0.6501496360982538,5.002434730529785,862.2665020000001 -20400,Multiclass classification,Hoeffding Tree,Keystroke,0.6482180499044071,0.6482180499044071,0.6472493759146579,5.098058700561523,914.036687 -46,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.4,0.4000000000000001,0.2926704014939309,0.4254798889160156,0.179349 -92,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5274725274725275,0.5274725274725275,0.5399541634835753,0.425537109375,0.395098 -138,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5547445255474452,0.5547445255474452,0.5795767508697842,0.4256591796875,0.646414 -184,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6174863387978142,0.6174863387978142,0.6398140932417979,0.42574310302734375,0.9360970000000001 -230,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6419213973799127,0.6419213973799127,0.6592174177506214,0.42574310302734375,1.2615820000000002 -276,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6545454545454545,0.6545454545454545,0.6716869228432982,0.4257926940917969,1.6228100000000003 -322,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6791277258566978,0.6791277258566978,0.6806263486692059,0.4258537292480469,2.022508 -368,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7029972752043597,0.7029972752043597,0.7008299817149242,0.4258270263671875,2.4581020000000002 -414,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7142857142857143,0.7142857142857143,0.7121569327354127,0.4257469177246094,2.92926 -460,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7145969498910676,0.7145969498910676,0.7103106155638,0.4258232116699219,3.4385440000000003 -506,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7227722772277227,0.7227722772277227,0.715881182832702,0.4258232116699219,3.9835350000000003 -552,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7241379310344828,0.7241379310344829,0.7187949260386588,0.4257164001464844,4.564144000000001 -598,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7286432160804021,0.7286432160804021,0.7227601649788371,0.4257392883300781,5.1830560000000006 -644,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7278382581648523,0.7278382581648523,0.7240595992457829,0.42577362060546875,5.837887 -690,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7314949201741655,0.7314949201741654,0.727547508877315,0.42577362060546875,6.528431 -736,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7333333333333333,0.7333333333333333,0.730585229165138,0.4258003234863281,7.2573300000000005 -782,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7387964148527529,0.7387964148527529,0.7359626710287273,0.4258003234863281,8.022590000000001 -828,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7376058041112454,0.7376058041112454,0.7367699509780541,0.4258003234863281,8.823569 -874,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7434135166093929,0.7434135166093929,0.7406779161411566,0.4258003234863281,9.663167000000001 -920,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7431991294885746,0.7431991294885745,0.7396284921253597,0.42577362060546875,10.538696000000002 -966,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7430051813471502,0.7430051813471502,0.7386475429248082,0.42577362060546875,11.449986 -1012,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7428288822947576,0.7428288822947575,0.7387392151852316,0.42577362060546875,12.399906000000001 -1058,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7445600756859035,0.7445600756859035,0.7397141356071754,0.42577362060546875,13.385988000000001 -1104,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7470534904805077,0.7470534904805077,0.7419829508197956,0.4258232116699219,14.408007000000001 -1150,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7484769364664926,0.7484769364664926,0.7430153502407321,0.4258232116699219,15.46854 -1196,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7514644351464436,0.7514644351464436,0.7466450927602833,0.4254570007324219,16.565517 -1242,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7518130539887188,0.7518130539887188,0.7475811251410989,0.4255790710449219,17.698596 -1288,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7567987567987567,0.7567987567987567,0.7515585748403605,0.4256401062011719,18.868108 -1334,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7576894223555889,0.7576894223555888,0.7527145732365901,0.4256401062011719,20.076794 -1380,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7592458303118201,0.7592458303118201,0.754880899709855,0.4257011413574219,21.321463 -1426,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7621052631578947,0.7621052631578947,0.7572480123106181,0.4257011413574219,22.601949 -1472,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7661454792658056,0.7661454792658056,0.7596240117389202,0.4257011413574219,23.921025 -1518,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7679630850362558,0.7679630850362558,0.7604664202984912,0.4257621765136719,25.275945 -1564,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7683941138835573,0.7683941138835573,0.7616623934037686,0.4257621765136719,26.66678 -1610,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7681789931634556,0.7681789931634556,0.7606779105029744,0.4257850646972656,28.096857 -1656,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7685800604229607,0.7685800604229607,0.7611818346958917,0.4257850646972656,29.563118 -1702,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7683715461493239,0.768371546149324,0.7630805397579306,0.4257850646972656,31.065673 -1748,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7716084716657127,0.7716084716657126,0.7661058855209445,0.4257850646972656,32.607308 -1794,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7730061349693251,0.7730061349693251,0.76613283717613,0.42575836181640625,34.185135 -1840,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7743338771071234,0.7743338771071234,0.7676486165305356,0.42581939697265625,35.798944000000006 -1886,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7729442970822281,0.7729442970822282,0.7669643117326908,0.42581939697265625,37.451807 -1932,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7736923873640601,0.7736923873640601,0.7669808567090198,0.42581939697265625,39.140782 -1978,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7744056651492159,0.7744056651492159,0.7669005381948409,0.42581939697265625,40.865953000000005 -2024,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7755808205635195,0.7755808205635196,0.7665616644775576,0.42581939697265625,42.627552 -2070,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7752537457709038,0.7752537457709039,0.7663566554091733,0.42581939697265625,44.428542 -2116,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.775886524822695,0.775886524822695,0.7661827507972012,0.42581939697265625,46.266451 -2162,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7764923646459972,0.7764923646459972,0.7663510353808046,0.42581939697265625,48.14124 -2208,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7784322609877662,0.7784322609877662,0.767276937076619,0.42581939697265625,50.054953000000005 -2254,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.775410563692854,0.775410563692854,0.7642399015136985,0.42581939697265625,52.004875000000006 -2300,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7746846454980426,0.7746846454980426,0.7634961218545901,0.42581939697265625,53.99744400000001 -1056,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6161137440758294,0.6161137440758294,0.5813841513331479,0.6684322357177734,1.380603 -2112,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6120322122216959,0.6120322122216959,0.5792161554760864,0.6684932708740234,3.9722109999999997 -3168,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6049889485317335,0.6049889485317335,0.5721633809277146,0.6685543060302734,7.807885 -4224,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.603125739995264,0.603125739995264,0.5703574432462962,0.6685543060302734,12.856231999999999 -5280,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6061754120098504,0.6061754120098504,0.5722430970062696,0.6686153411865234,19.046561999999998 -6336,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5995264404104184,0.5995264404104184,0.5671511237518186,0.6686153411865234,26.345702999999997 -7392,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5972128264104992,0.5972128264104992,0.5650210504998666,0.6686153411865234,34.702495 -8448,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5989108559251806,0.5989108559251806,0.566418690076869,0.6686153411865234,44.110113999999996 -9504,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5962327685993897,0.5962327685993897,0.5633780031885508,0.6686153411865234,54.569630999999994 -10560,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5964579979164694,0.5964579979164694,0.5634236596216465,0.6686763763427734,66.07704199999999 -11616,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.594317692638829,0.594317692638829,0.5620068495149612,0.6686763763427734,78.631408 -12672,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5975061163286244,0.5975061163286244,0.567518061449456,0.6686763763427734,92.23293299999999 -13728,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6097472135207984,0.6097472135207984,0.5927729676671933,0.6686763763427734,106.88971799999999 -14784,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001488195900697,0.6001488195900697,0.5832911478837771,0.6683712005615234,122.59496599999999 -15840,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5673969316244712,0.5673969316244712,0.5522471754341497,0.8954944610595703,139.36423 -16896,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5712340929269014,0.5712340929269014,0.559038323684958,1.4438505172729492,157.28485799999999 -17952,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5741184335134533,0.5741184335134533,0.5632919959429029,1.874833106994629,176.64490099999998 -19008,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5867312042931552,0.5867312042931552,0.5723846445183199,0.48981285095214844,197.01838199999997 -20064,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5939789662562927,0.5939789662562927,0.5773993022741072,0.6687717437744141,218.48549899999998 -21120,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.595908897201572,0.595908897201572,0.5788762098776178,0.6688938140869141,241.01084999999998 -22176,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5977452085682075,0.5977452085682075,0.5801804614049403,1.2152299880981445,264.60362299999997 -23232,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5997158968619517,0.5997158968619517,0.5818597835760811,1.3294572830200195,289.45174199999997 -24288,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6025033968789888,0.6025033968789888,0.5841484049015139,1.3295183181762695,315.702926 -25344,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6047823856686264,0.6047823856686264,0.5859943093850892,1.3296403884887695,343.366363 -26400,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6074472517898405,0.6074472517898405,0.5878557237787366,1.3296403884887695,372.430483 -27456,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6086323074121289,0.6086323074121289,0.5880340775890752,1.3298234939575195,402.90288599999997 -28512,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6087124267826453,0.6087124267826453,0.5895354690395743,1.3298234939575195,434.78023099999996 -29568,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6080765718537559,0.6080765718537559,0.5920130278134075,1.3298234939575195,468.06684599999994 -30624,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6071253632890311,0.6071253632890311,0.5937369304389161,1.3293352127075195,502.75580299999996 -31680,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6071845702200196,0.6071845702200196,0.5960066132315273,1.3295793533325195,538.8589619999999 -32736,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6079425691156255,0.6079425691156255,0.59836863034629,1.3296403884887695,576.3667869999999 -33792,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6027936432777958,0.6027936432777958,0.5936321389881086,0.6688251495361328,615.5159199999999 -34848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6018882543690992,0.6018882543690992,0.5927698243358274,0.6689472198486328,655.7217059999999 -35904,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.601398211848592,0.601398211848592,0.592182344393812,0.6690082550048828,696.988077 -36960,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5999080061689981,0.5999080061689981,0.5906275041314122,0.6690082550048828,739.3140189999999 -38016,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5996054189135868,0.5996054189135868,0.5899615119365567,0.6690082550048828,782.70332 -39072,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5989608661155332,0.5989608661155332,0.5889868403975307,0.6687030792236328,827.150323 -40128,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5947865526951928,0.5947865526951928,0.5855600636799734,0.6687030792236328,872.66104 -41184,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5926717334822621,0.5926717334822621,0.5840930914391779,0.6688861846923828,919.237173 -42240,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5913018774118706,0.5913018774118706,0.5832685369240246,0.6689472198486328,966.874788 -43296,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5898833583554683,0.5898833583554683,0.5823904732646675,0.6690082550048828,1015.577362 -44352,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5883745575071588,0.5883745575071588,0.5813207633940128,1.112539291381836,1065.314826 -45408,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5863853590856035,0.5863853590856035,0.5797569747943008,1.3286066055297852,1116.2698050000001 -46464,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5850461657663086,0.5850461657663086,0.5780695197887614,1.3287897109985352,1168.116792 -47520,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5867968602032871,0.5867968602032871,0.5799343284152632,1.328934669494629,1221.156775 -48576,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5917035512094699,0.5917035512094699,0.5847625919047718,1.329483985900879,1275.6038119999998 -49632,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5968447139892405,0.5968447139892405,0.5895877351185161,1.329422950744629,1331.4562389999999 -50688,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.601673012804072,0.601673012804072,0.5939045014873635,1.329606056213379,1388.7152379999998 -51744,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6067487389598593,0.6067487389598593,0.5983547975185618,1.329606056213379,1447.3877459999999 -52800,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6119623477717381,0.6119623477717381,0.6029934068442723,0.14767932891845703,1507.071307 -408,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.2342357635498047,0.174096 -816,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,0.32985973358154297,0.617741 -1224,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8863450531479967,0.8863450531479967,0.8786592421362931,0.42548370361328125,1.425386 -1632,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.891477621091355,0.891477621091355,0.8818548670971932,0.5215349197387695,2.789107 -2040,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.889651790093183,0.889651790093183,0.8812768038030504,0.6287555694580078,4.864698000000001 -2448,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8414384961176952,0.8414384961176952,0.8420581397672002,0.7242574691772461,7.621751000000001 -2856,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8500875656742557,0.8500875656742557,0.834558203718852,0.8199424743652344,10.917147 -3264,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8406374501992032,0.8406374501992032,0.8151418555553325,0.9155054092407227,14.806837 -3672,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8321983110868973,0.8321983110868973,0.8307198315203921,1.011190414428711,19.353183 -4080,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.826182887962736,0.826182887962736,0.812376785603362,1.1319761276245117,24.603589 -4488,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.809226654780477,0.809226654780477,0.8196273526663149,1.2275390625,30.588099 -4896,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8081716036772216,0.8081716036772216,0.815232111826365,1.3230409622192383,37.350443 -5304,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8057703186875353,0.8057703186875353,0.7903391475861199,1.4186649322509766,44.935392 -5712,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7860269655051655,0.7860269655051656,0.7895763142947655,1.5144109725952148,53.372574 -6120,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.784441902271613,0.784441902271613,0.7657785418705475,1.6098518371582031,62.716148000000004 -6528,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7585414432357898,0.7585414432357898,0.751418836389106,1.7056589126586914,73.022548 -6936,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7473684210526316,0.7473684210526316,0.7484284412750403,1.8010997772216797,84.351871 -7344,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7565027917744791,0.7565027917744791,0.7526701844923946,1.898371696472168,96.7592 -7752,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7577086827506129,0.7577086827506129,0.7557350658705178,1.9939956665039062,110.303168 -8160,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7617355068023042,0.7617355068023042,0.7576049653668415,2.0895586013793945,125.047569 -8568,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7604762460604646,0.7604762460604646,0.7596175662696861,2.2332935333251953,141.03972299999998 -8976,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.756991643454039,0.7569916434540391,0.7575313939177277,2.3289785385131836,158.34463899999997 -9384,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7558350207822658,0.7558350207822658,0.7548436696787698,2.424480438232422,177.02417899999998 -9792,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.748340312531917,0.7483403125319169,0.7443908596260193,2.52004337310791,197.13977899999998 -10200,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7393862143347387,0.7393862143347387,0.7315892779928432,2.6156673431396484,218.76351099999997 -10608,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7196191194494201,0.7196191194494201,0.7089541376321257,2.7114133834838867,241.93025799999998 -11016,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7123921924648207,0.7123921924648208,0.7092068316988943,2.806976318359375,266.699543 -11424,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7062943184802591,0.7062943184802591,0.694671323095531,2.9026002883911133,293.16281 -11832,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6967289324655566,0.6967289324655566,0.6902328307983061,2.9981632232666016,321.35092599999996 -12240,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7007108423890841,0.7007108423890841,0.6983689907908355,3.09378719329834,351.321335 -12648,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6969241717403337,0.6969241717403337,0.6892508246262707,3.189472198486328,383.13879899999995 -13056,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6836461126005362,0.6836461126005362,0.6755391962059191,3.2851572036743164,416.86083699999995 -13464,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6793433855752804,0.6793433855752804,0.6754035266161622,3.3807201385498047,452.54592499999995 -13872,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6769519140653161,0.6769519140653161,0.6742482232309566,3.476466178894043,490.25119899999993 -14280,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6728762518383641,0.6728762518383641,0.6689356443053496,3.5720291137695312,530.0355519999999 -14688,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6762442976782188,0.6762442976782188,0.6753292472514647,3.6675920486450195,571.9481539999999 -15096,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6830076184166942,0.6830076184166942,0.6822311287838643,3.763277053833008,616.057788 -15504,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6818035218989873,0.6818035218989873,0.6788656596145115,3.858839988708496,662.434182 -15912,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6816039218150964,0.6816039218150964,0.6801525397911032,0.2779512405395508,710.461266 -16320,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6858263373981249,0.6858263373981249,0.6851912800185752,0.4695272445678711,759.2533930000001 -16728,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6896634184253004,0.6896634184253004,0.6890226069872225,0.6609811782836914,808.8937860000001 -17136,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6925007295010213,0.6925007295010213,0.691863544221197,0.9803314208984375,859.476205 -17544,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6990252522373597,0.6990252522373597,0.6986638608261282,0.27229881286621094,910.5065460000001 -17952,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7040833379756003,0.7040833379756003,0.7034973599095433,0.1428661346435547,961.7687770000001 -18360,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7102783376000872,0.7102783376000872,0.7096708693716106,0.23855113983154297,1013.1870050000001 -18768,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7155645548036447,0.7155645548036447,0.714820465744771,0.33417510986328125,1064.8262280000001 -19176,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7183833116036505,0.7183833116036505,0.7174783905571958,0.42961597442626953,1116.742805 -19584,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7229229433692489,0.7229229433692489,0.7220221994049509,0.5253620147705078,1168.993402 -19992,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7224751138012105,0.7224751138012104,0.7211832505275634,0.6323385238647461,1221.6363350000001 -20400,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7237119466640521,0.7237119466640521,0.7223930256436224,0.7279014587402344,1274.7271140000003 -46,Multiclass classification,Adaptive Random Forest,ImageSegments,0.4222222222222222,0.4222222222222222,0.3590236094437775,0.9732446670532227,0.50913 -92,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5604395604395604,0.5604395604395604,0.5746538615446178,1.0627803802490234,1.324173 -138,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5766423357664233,0.5766423357664233,0.598257695340355,1.3550586700439453,2.399595 -184,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6229508196721312,0.6229508196721312,0.6451744040758779,1.4249095916748047,3.72413 -230,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6506550218340611,0.6506550218340611,0.668065528002595,1.5721073150634766,5.289042 -276,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6727272727272727,0.6727272727272727,0.6900672130049011,1.7710065841674805,7.016464 -322,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7040498442367601,0.7040498442367601,0.7087861936875777,1.8489313125610352,8.876771 -368,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7302452316076294,0.7302452316076294,0.7285991575377422,1.9874763488769531,10.883345 -414,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7457627118644068,0.7457627118644068,0.7430362907281778,2.008787155151367,13.045158 -460,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7342047930283224,0.7342047930283224,0.7271744800226859,1.8246965408325195,15.362221000000002 -506,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7405940594059406,0.7405940594059406,0.7304322149686578,1.7282800674438477,17.823546 -552,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7368421052631579,0.7368421052631579,0.7267508109083203,1.5214414596557617,20.437237 -598,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7403685092127303,0.7403685092127302,0.7318978254380312,1.6621322631835938,23.204591 -644,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7325038880248833,0.7325038880248833,0.7248107612258206,1.7895660400390625,26.125323 -690,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7242380261248186,0.7242380261248187,0.7153272190465999,1.9295940399169922,29.195315 -736,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7251700680272108,0.725170068027211,0.7148466398758337,2.079819679260254,32.416287000000004 -782,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7259923175416133,0.7259923175416134,0.7134712280209222,2.0407657623291016,35.785816000000004 -828,Multiclass classification,Adaptive Random Forest,ImageSegments,0.727932285368803,0.727932285368803,0.7177600265828429,2.245401382446289,39.307391 -874,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7353951890034365,0.7353951890034366,0.7262567978322628,2.3208675384521484,42.982338000000006 -920,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7431991294885746,0.7431991294885745,0.7345004589126253,2.463038444519043,46.81357700000001 -966,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7471502590673575,0.7471502590673575,0.7368855656689401,2.4979677200317383,50.81254500000001 -1012,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7546983184965381,0.754698318496538,0.7446216664767904,2.5897722244262695,54.96693400000001 -1058,Multiclass classification,Adaptive Random Forest,ImageSegments,0.760643330179754,0.760643330179754,0.7502594177262459,2.824686050415039,59.28579400000001 -1104,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7624660018132366,0.7624660018132366,0.7523020427630668,2.512765884399414,63.76907700000001 -1150,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7650130548302873,0.7650130548302874,0.7555087521342715,2.350802421569824,68.40298100000001 -1196,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7690376569037657,0.7690376569037657,0.7603504370239863,2.0774078369140625,73.17908000000001 -1242,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7719580983078163,0.7719580983078163,0.7638249032322542,2.143113136291504,78.09633200000002 -1288,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7746697746697747,0.7746697746697747,0.7668828628349821,2.3053293228149414,83.14236000000002 -1334,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7771942985746436,0.7771942985746436,0.7696789046658701,2.4279375076293945,88.31606900000003 -1380,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7817258883248731,0.7817258883248731,0.7754511149783998,2.350360870361328,93.61768400000003 -1426,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7866666666666666,0.7866666666666666,0.7797171864703156,2.461531639099121,99.04413800000003 -1472,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7912984364377974,0.7912984364377974,0.7836430453045393,2.5941333770751953,104.59649900000004 -1518,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7963085036255768,0.7963085036255768,0.7883976288226552,2.7080554962158203,110.30036700000004 -1564,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7984644913627639,0.7984644913627639,0.7915512335737709,2.379396438598633,116.13112600000004 -1610,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798011187072716,0.7980111870727161,0.7913527809122488,2.557906150817871,122.09210800000004 -1656,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7987915407854985,0.7987915407854985,0.7921693301011166,2.5870275497436523,128.19249900000003 -1702,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7995296884185773,0.7995296884185774,0.7947635312368726,2.4413909912109375,134.42240900000002 -1748,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8019461934745278,0.8019461934745278,0.7968342396743014,2.619420051574707,140.777643 -1794,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8059118795315114,0.8059118795315114,0.8002313091513137,2.7039318084716797,147.25850100000002 -1840,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8058727569331158,0.8058727569331158,0.8006185305294855,3.167543411254883,153.88034500000003 -1886,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8084880636604774,0.8084880636604774,0.8041348438460234,3.187774658203125,160.64099900000002 -1932,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8089073019161056,0.8089073019161055,0.8042053366874767,3.4328765869140625,167.537774 -1978,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8108244815376834,0.8108244815376834,0.8062422218151643,3.621993064880371,174.56717600000002 -2024,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8111715274345032,0.8111715274345032,0.805670935248126,3.7835464477539062,181.73003400000002 -2070,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8134364427259546,0.8134364427259546,0.8085538776813638,3.7409582138061523,189.02955300000002 -2116,Multiclass classification,Adaptive Random Forest,ImageSegments,0.816548463356974,0.816548463356974,0.8113031614777911,3.760796546936035,196.461518 -2162,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8167515039333642,0.8167515039333642,0.8113905234748385,4.035200119018555,204.02457600000002 -2208,Multiclass classification,Adaptive Random Forest,ImageSegments,0.818305391934753,0.818305391934753,0.8126353495892602,4.192110061645508,211.71705500000002 -2254,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8184642698624057,0.8184642698624057,0.8136291554244021,4.486760139465332,219.55329600000002 -2300,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8190517616354936,0.8190517616354936,0.8144252010220491,4.66081428527832,227.54068500000002 -1056,Multiclass classification,Adaptive Random Forest,Insects,0.6701421800947868,0.6701421800947868,0.6068786932307204,6.831533432006836,4.539822 -2112,Multiclass classification,Adaptive Random Forest,Insects,0.6887730933207011,0.6887730933207011,0.6229217946585527,10.195775032043457,12.638872 -3168,Multiclass classification,Adaptive Random Forest,Insects,0.6962425007893905,0.6962425007893905,0.622910390568452,16.60274887084961,24.371093 -4224,Multiclass classification,Adaptive Random Forest,Insects,0.7054226852948141,0.7054226852948141,0.6279874627708885,17.471903800964355,39.890989 -5280,Multiclass classification,Adaptive Random Forest,Insects,0.7099829513165372,0.7099829513165372,0.6301031937879839,20.888835906982422,58.938013999999995 -6336,Multiclass classification,Adaptive Random Forest,Insects,0.7108129439621153,0.7108129439621153,0.6300557461749893,23.72772216796875,81.51329799999999 -7392,Multiclass classification,Adaptive Random Forest,Insects,0.7126234609660398,0.7126234609660397,0.6287819651813062,28.070199966430664,107.68036799999999 -8448,Multiclass classification,Adaptive Random Forest,Insects,0.7168225405469397,0.7168225405469397,0.6299159911335922,31.613859176635742,137.61638299999998 -9504,Multiclass classification,Adaptive Random Forest,Insects,0.7214563821950963,0.7214563821950963,0.6314635817104112,35.43206214904785,171.294053 -10560,Multiclass classification,Adaptive Random Forest,Insects,0.7230798371057865,0.7230798371057865,0.6311670445333952,35.676584243774414,208.90076499999998 -11616,Multiclass classification,Adaptive Random Forest,Insects,0.7247524752475247,0.7247524752475247,0.6314302971551563,41.6592378616333,250.15747499999998 -12672,Multiclass classification,Adaptive Random Forest,Insects,0.7252781943019493,0.7252781943019494,0.6359647238599803,42.66780757904053,295.204683 -13728,Multiclass classification,Adaptive Random Forest,Insects,0.7402928535004006,0.7402928535004006,0.7348419335996624,24.025733947753906,342.696854 -14784,Multiclass classification,Adaptive Random Forest,Insects,0.7468714063451262,0.7468714063451262,0.7455387452701401,2.272738456726074,392.69755799999996 -15840,Multiclass classification,Adaptive Random Forest,Insects,0.7388092682618852,0.7388092682618853,0.7393651674564367,6.547223091125488,445.901262 -16896,Multiclass classification,Adaptive Random Forest,Insects,0.7343000887836638,0.7343000887836638,0.7364396291092657,11.097336769104004,502.213806 -17952,Multiclass classification,Adaptive Random Forest,Insects,0.7271461199933151,0.7271461199933151,0.7303078098029304,16.88492202758789,561.702561 -19008,Multiclass classification,Adaptive Random Forest,Insects,0.7373073078339559,0.7373073078339558,0.7369507693389319,6.800461769104004,623.6949609999999 -20064,Multiclass classification,Adaptive Random Forest,Insects,0.7412650152021133,0.7412650152021133,0.7370000710650216,3.369675636291504,688.4094249999999 -21120,Multiclass classification,Adaptive Random Forest,Insects,0.7425540982054074,0.7425540982054074,0.7353012584659039,5.941231727600098,756.7149029999999 -22176,Multiclass classification,Adaptive Random Forest,Insects,0.7435851183765502,0.7435851183765501,0.7334480812988377,8.846389770507812,828.508796 -23232,Multiclass classification,Adaptive Random Forest,Insects,0.7452111402866859,0.7452111402866859,0.7324964055744654,9.471121788024902,903.877525 -24288,Multiclass classification,Adaptive Random Forest,Insects,0.7463663688392967,0.7463663688392966,0.7310050929424414,12.406947135925293,982.696529 -25344,Multiclass classification,Adaptive Random Forest,Insects,0.7474647831748412,0.7474647831748412,0.7298615493429103,15.979948043823242,1064.886272 -26400,Multiclass classification,Adaptive Random Forest,Insects,0.7483616803666806,0.7483616803666806,0.7285096183890708,19.665884017944336,1150.466447 -27456,Multiclass classification,Adaptive Random Forest,Insects,0.749626661810235,0.749626661810235,0.7275235594970662,24.26569175720215,1239.482455 -28512,Multiclass classification,Adaptive Random Forest,Insects,0.7465188874469503,0.7465188874469504,0.7258897093263847,8.914395332336426,1332.2242660000002 -29568,Multiclass classification,Adaptive Random Forest,Insects,0.7451550715324518,0.7451550715324519,0.7292330017207805,8.459691047668457,1428.2455670000002 -30624,Multiclass classification,Adaptive Random Forest,Insects,0.7443751428664729,0.7443751428664729,0.7327893612754602,12.943696022033691,1527.2067200000001 -31680,Multiclass classification,Adaptive Random Forest,Insects,0.7437103443921841,0.7437103443921841,0.7357305076230832,18.80640697479248,1629.3633260000001 -32736,Multiclass classification,Adaptive Random Forest,Insects,0.7432717275087827,0.7432717275087827,0.7381285892142362,16.162379264831543,1734.350472 -33792,Multiclass classification,Adaptive Random Forest,Insects,0.7377408185611554,0.7377408185611554,0.7340057348640155,11.18346881866455,1842.7318770000002 -34848,Multiclass classification,Adaptive Random Forest,Insects,0.7340373633311332,0.7340373633311332,0.7302084976112027,5.613262176513672,1955.0234710000002 -35904,Multiclass classification,Adaptive Random Forest,Insects,0.7312759379439044,0.7312759379439044,0.7271196230245338,9.756120681762695,2071.1719540000004 -36960,Multiclass classification,Adaptive Random Forest,Insects,0.7278335452799047,0.7278335452799047,0.7234434079919367,11.990450859069824,2191.1586210000005 -38016,Multiclass classification,Adaptive Random Forest,Insects,0.7254241746678942,0.7254241746678942,0.7207605796154644,14.404197692871094,2314.6725880000004 -39072,Multiclass classification,Adaptive Random Forest,Insects,0.7250390315067441,0.7250390315067441,0.7205508934526729,7.651473045349121,2441.6663320000002 -40128,Multiclass classification,Adaptive Random Forest,Insects,0.7236524036185112,0.7236524036185111,0.7196200887167502,7.583705902099609,2572.286548 -41184,Multiclass classification,Adaptive Random Forest,Insects,0.7235995435009591,0.7235995435009591,0.7199895911465058,12.209360122680664,2706.295895 -42240,Multiclass classification,Adaptive Random Forest,Insects,0.7235966760576719,0.7235966760576719,0.7203672841246517,15.002169609069824,2843.875227 -43296,Multiclass classification,Adaptive Random Forest,Insects,0.7241713823767179,0.7241713823767179,0.7213145862540888,17.433518409729004,2985.305163 -44352,Multiclass classification,Adaptive Random Forest,Insects,0.7245608892696895,0.7245608892696895,0.7219384327675483,20.337363243103027,3130.446815 -45408,Multiclass classification,Adaptive Random Forest,Insects,0.7253947629220164,0.7253947629220163,0.7227741676779873,20.507991790771484,3279.062075 -46464,Multiclass classification,Adaptive Random Forest,Insects,0.7263198674213891,0.7263198674213891,0.7236028172229397,24.947001457214355,3431.085655 -47520,Multiclass classification,Adaptive Random Forest,Insects,0.7259622466802753,0.7259622466802753,0.7234132526915972,9.389252662658691,3586.9794589999997 -48576,Multiclass classification,Adaptive Random Forest,Insects,0.7297581060216161,0.7297581060216161,0.7273884829439242,9.12541389465332,3745.9515599999995 -49632,Multiclass classification,Adaptive Random Forest,Insects,0.7336543692450284,0.7336543692450284,0.7312645046388119,8.804935455322266,3907.5042229999995 -50688,Multiclass classification,Adaptive Random Forest,Insects,0.7372501824925524,0.7372501824925524,0.7346466630802606,12.506796836853027,4071.2963689999997 -51744,Multiclass classification,Adaptive Random Forest,Insects,0.741182382157973,0.741182382157973,0.7382896911640772,15.31224250793457,4237.296913 -52800,Multiclass classification,Adaptive Random Forest,Insects,0.7442565200098487,0.7442565200098487,0.7419321396565435,0.3696470260620117,4404.707415 -408,Multiclass classification,Adaptive Random Forest,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.3514842987060547,0.595241 -816,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730061349693252,0.9730061349693252,0.7867307803099512,1.312638282775879,2.098583 -1224,Multiclass classification,Adaptive Random Forest,Keystroke,0.9705641864268193,0.9705641864268193,0.9370502919558801,2.2586374282836914,4.31371 -1632,Multiclass classification,Adaptive Random Forest,Keystroke,0.9711833231146536,0.9711833231146536,0.9377953913100077,3.394951820373535,7.3386510000000005 -2040,Multiclass classification,Adaptive Random Forest,Keystroke,0.969592937714566,0.969592937714566,0.9445939973353387,5.254854202270508,11.230817 -2448,Multiclass classification,Adaptive Random Forest,Keystroke,0.9701675521046179,0.9701675521046179,0.9654865811906564,2.048126220703125,15.860121 -2856,Multiclass classification,Adaptive Random Forest,Keystroke,0.9726795096322242,0.9726795096322242,0.9705770446236133,2.732625961303711,21.099127 -3264,Multiclass classification,Adaptive Random Forest,Keystroke,0.9718050873429359,0.9718050873429359,0.9627836140542233,2.790935516357422,26.971176 -3672,Multiclass classification,Adaptive Random Forest,Keystroke,0.9733042767638246,0.9733042767638246,0.9719148371902758,2.987569808959961,33.477629 -4080,Multiclass classification,Adaptive Random Forest,Keystroke,0.9698455503799951,0.9698455503799951,0.9588020505656979,4.571287155151367,40.764845 -4488,Multiclass classification,Adaptive Random Forest,Keystroke,0.9710274125250724,0.9710274125250724,0.9701901425551159,1.7459039688110352,48.697823 -4896,Multiclass classification,Adaptive Random Forest,Keystroke,0.9722165474974463,0.9722165474974463,0.9719364174281581,2.7892093658447266,57.250967 -5304,Multiclass classification,Adaptive Random Forest,Keystroke,0.9720912690929663,0.9720912690929663,0.970282662152698,2.895453453063965,66.54505900000001 -5712,Multiclass classification,Adaptive Random Forest,Keystroke,0.9723340921029592,0.9723340921029592,0.9718828908328702,4.064221382141113,76.51574600000001 -6120,Multiclass classification,Adaptive Random Forest,Keystroke,0.9718908318352673,0.9718908318352673,0.9703726237787478,5.130434989929199,87.20768000000001 -6528,Multiclass classification,Adaptive Random Forest,Keystroke,0.9719626168224299,0.9719626168224299,0.9714458378209955,2.455193519592285,98.59605300000001 -6936,Multiclass classification,Adaptive Random Forest,Keystroke,0.9733237202595529,0.9733237202595529,0.9740372626056705,2.4587574005126953,110.61869200000001 -7344,Multiclass classification,Adaptive Random Forest,Keystroke,0.9735802805392891,0.9735802805392891,0.973376514333954,3.893580436706543,123.504669 -7752,Multiclass classification,Adaptive Random Forest,Keystroke,0.9729067217133273,0.9729067217133273,0.9721109941692119,4.6176652908325195,137.14807100000002 -8160,Multiclass classification,Adaptive Random Forest,Keystroke,0.9721779629856601,0.9721779629856601,0.9713389113158797,5.2350358963012695,151.61678500000002 -8568,Multiclass classification,Adaptive Random Forest,Keystroke,0.9725691607330454,0.9725691607330454,0.9726516232305997,3.659168243408203,166.92608600000003 -8976,Multiclass classification,Adaptive Random Forest,Keystroke,0.9733704735376044,0.9733704735376044,0.973745927183376,5.072476387023926,182.94001300000002 -9384,Multiclass classification,Adaptive Random Forest,Keystroke,0.9733560694873707,0.9733560694873707,0.9732604538569353,5.722126007080078,199.76705900000002 -9792,Multiclass classification,Adaptive Random Forest,Keystroke,0.9731385966704116,0.9731385966704116,0.9729642609350583,4.404660224914551,217.43706000000003 -10200,Multiclass classification,Adaptive Random Forest,Keystroke,0.9725463280713795,0.9725463280713795,0.9722080895483168,2.6527090072631836,235.89310200000003 -10608,Multiclass classification,Adaptive Random Forest,Keystroke,0.9726595644385783,0.9726595644385783,0.9727080848172961,1.1834087371826172,254.95803500000002 -11016,Multiclass classification,Adaptive Random Forest,Keystroke,0.9729459827507944,0.9729459827507944,0.9730830184440419,1.520833969116211,274.61158 -11424,Multiclass classification,Adaptive Random Forest,Keystroke,0.9724240567276548,0.9724240567276548,0.972236101273467,2.823396682739258,294.969552 -11832,Multiclass classification,Adaptive Random Forest,Keystroke,0.9720226523539853,0.9720226523539853,0.9719096687987197,2.493410110473633,316.08929700000004 -12240,Multiclass classification,Adaptive Random Forest,Keystroke,0.9726284827191765,0.9726284827191765,0.9728780734732722,2.3478269577026367,337.99406600000003 -12648,Multiclass classification,Adaptive Random Forest,Keystroke,0.9727998734877836,0.9727998734877836,0.9729097588140673,2.5516576766967773,360.586004 -13056,Multiclass classification,Adaptive Random Forest,Keystroke,0.9726541554959786,0.9726541554959786,0.9726709194030315,3.304943084716797,383.87924 -13464,Multiclass classification,Adaptive Random Forest,Keystroke,0.9722201589541707,0.9722201589541707,0.9721650267620997,4.003572463989258,407.93215299999997 -13872,Multiclass classification,Adaptive Random Forest,Keystroke,0.9724605291615601,0.9724605291615601,0.9725910057046059,4.270735740661621,432.78625999999997 -14280,Multiclass classification,Adaptive Random Forest,Keystroke,0.9713565375726592,0.9713565375726592,0.9711654862365111,4.102839469909668,458.452707 -14688,Multiclass classification,Adaptive Random Forest,Keystroke,0.9718118063593654,0.9718118063593654,0.9719655808676524,3.971695899963379,484.888355 -15096,Multiclass classification,Adaptive Random Forest,Keystroke,0.9724412056972508,0.9724412056972508,0.9726138064055022,4.612870216369629,512.116119 -15504,Multiclass classification,Adaptive Random Forest,Keystroke,0.9723279365284139,0.9723279365284139,0.9723669009986284,3.2941598892211914,540.133825 -15912,Multiclass classification,Adaptive Random Forest,Keystroke,0.97240902520269,0.97240902520269,0.9724748506273226,5.215278625488281,569.064511 -16320,Multiclass classification,Adaptive Random Forest,Keystroke,0.9718119982842086,0.9718119982842086,0.9717822259045504,2.705050468444824,598.831932 -16728,Multiclass classification,Adaptive Random Forest,Keystroke,0.9713636635379924,0.9713636635379924,0.971358198091739,1.4999914169311523,629.214947 -17136,Multiclass classification,Adaptive Random Forest,Keystroke,0.9716953603735046,0.9716953603735046,0.9717778191727772,1.5952835083007812,660.25813 -17544,Multiclass classification,Adaptive Random Forest,Keystroke,0.9716696118109788,0.9716696118109788,0.971712982907841,2.6761178970336914,692.002463 -17952,Multiclass classification,Adaptive Random Forest,Keystroke,0.9709765472675617,0.9709765472675617,0.970966525204854,3.6711130142211914,724.550435 -18360,Multiclass classification,Adaptive Random Forest,Keystroke,0.9709679176425732,0.9709679176425732,0.9710033330464194,4.9129533767700195,757.867405 -18768,Multiclass classification,Adaptive Random Forest,Keystroke,0.9710129482602441,0.9710129482602441,0.9710485326344032,5.05178165435791,792.058633 -19176,Multiclass classification,Adaptive Random Forest,Keystroke,0.9711603650586701,0.9711603650586701,0.9711938240802871,5.466279983520508,827.148813 -19584,Multiclass classification,Adaptive Random Forest,Keystroke,0.9707909921871011,0.9707909921871011,0.9708057459916863,5.881702423095703,863.142165 -19992,Multiclass classification,Adaptive Random Forest,Keystroke,0.9705867640438197,0.9705867640438197,0.9706070593086332,5.831451416015625,900.075184 -20400,Multiclass classification,Adaptive Random Forest,Keystroke,0.9698514633070249,0.9698514633070249,0.9698673821244657,2.3371658325195312,937.846308 -46,Multiclass classification,Streaming Random Patches,ImageSegments,0.3111111111111111,0.3111111111111111,0.22202380952380954,2.606511116027832,1.431937 -92,Multiclass classification,Streaming Random Patches,ImageSegments,0.4945054945054945,0.4945054945054945,0.5053729602697932,2.609585762023926,3.886705 -138,Multiclass classification,Streaming Random Patches,ImageSegments,0.5109489051094891,0.5109489051094891,0.5310665055578762,2.6113672256469727,7.103774 -184,Multiclass classification,Streaming Random Patches,ImageSegments,0.5737704918032787,0.5737704918032787,0.5886643910747036,2.6136903762817383,11.095616999999999 -230,Multiclass classification,Streaming Random Patches,ImageSegments,0.6026200873362445,0.6026200873362445,0.6106719627755607,2.614529609680176,15.833751999999999 -276,Multiclass classification,Streaming Random Patches,ImageSegments,0.6181818181818182,0.6181818181818182,0.6264208209498925,2.6147661209106445,21.302563 -322,Multiclass classification,Streaming Random Patches,ImageSegments,0.6448598130841121,0.6448598130841121,0.6378728366046057,2.616147041320801,27.471138 -368,Multiclass classification,Streaming Random Patches,ImageSegments,0.667574931880109,0.667574931880109,0.6581306320431076,2.6166696548461914,34.32642 -414,Multiclass classification,Streaming Random Patches,ImageSegments,0.6803874092009685,0.6803874092009685,0.6704325632692101,2.6175050735473633,41.86551 -460,Multiclass classification,Streaming Random Patches,ImageSegments,0.6884531590413944,0.6884531590413944,0.6760149332924277,2.617680549621582,50.055222 -506,Multiclass classification,Streaming Random Patches,ImageSegments,0.691089108910891,0.691089108910891,0.6769247074861785,2.617680549621582,58.91147 -552,Multiclass classification,Streaming Random Patches,ImageSegments,0.691470054446461,0.691470054446461,0.6803521213965826,2.6178178787231445,68.422832 -598,Multiclass classification,Streaming Random Patches,ImageSegments,0.6968174204355109,0.6968174204355109,0.6854975219125513,2.617863655090332,78.595742 -644,Multiclass classification,Streaming Random Patches,ImageSegments,0.6936236391912908,0.6936236391912908,0.6835764097697864,2.6192026138305664,89.423453 -690,Multiclass classification,Streaming Random Patches,ImageSegments,0.6966618287373004,0.6966618287373004,0.6871604229696352,2.6194162368774414,100.90701299999999 -736,Multiclass classification,Streaming Random Patches,ImageSegments,0.6965986394557823,0.6965986394557823,0.6884795420777536,2.6194887161254883,113.056901 -782,Multiclass classification,Streaming Random Patches,ImageSegments,0.7016645326504481,0.7016645326504481,0.6927955715819348,2.6197519302368164,125.87172799999999 -828,Multiclass classification,Streaming Random Patches,ImageSegments,0.7037484885126964,0.7037484885126964,0.6971811816445675,2.61989688873291,139.34805 -874,Multiclass classification,Streaming Random Patches,ImageSegments,0.7124856815578465,0.7124856815578465,0.7027179013602759,2.61989688873291,153.488556 -920,Multiclass classification,Streaming Random Patches,ImageSegments,0.7127312295973884,0.7127312295973884,0.7019247882761857,2.61989688873291,168.286881 -966,Multiclass classification,Streaming Random Patches,ImageSegments,0.7119170984455958,0.7119170984455958,0.7013991197312313,2.61989688873291,183.731934 -1012,Multiclass classification,Streaming Random Patches,ImageSegments,0.7111770524233432,0.7111770524233432,0.7000689942734505,2.61989688873291,199.829198 -1058,Multiclass classification,Streaming Random Patches,ImageSegments,0.7123935666982024,0.7123935666982024,0.700757485135609,2.620041847229004,216.594079 -1104,Multiclass classification,Streaming Random Patches,ImageSegments,0.7116953762466002,0.7116953762466002,0.6997536275311635,2.6201601028442383,234.008444 -1150,Multiclass classification,Streaming Random Patches,ImageSegments,0.7136640557006092,0.7136640557006092,0.7002507718266925,2.6201601028442383,252.079326 -1196,Multiclass classification,Streaming Random Patches,ImageSegments,0.7154811715481172,0.7154811715481171,0.7029614354817431,2.530026435852051,270.790044 -1242,Multiclass classification,Streaming Random Patches,ImageSegments,0.717163577759871,0.717163577759871,0.7059650228666394,2.753697395324707,290.035925 -1288,Multiclass classification,Streaming Random Patches,ImageSegments,0.7187257187257188,0.7187257187257188,0.706699668165461,3.6648149490356445,309.61323300000004 -1334,Multiclass classification,Streaming Random Patches,ImageSegments,0.719429857464366,0.719429857464366,0.7094425115390415,4.463783264160156,329.52005700000007 -1380,Multiclass classification,Streaming Random Patches,ImageSegments,0.7251631617113851,0.725163161711385,0.7174387625572534,4.938790321350098,349.76822000000004 -1426,Multiclass classification,Streaming Random Patches,ImageSegments,0.7319298245614035,0.7319298245614035,0.7244482628352659,5.045901298522949,370.34098700000004 -1472,Multiclass classification,Streaming Random Patches,ImageSegments,0.7335146159075459,0.7335146159075459,0.7247675805597543,5.884430885314941,391.25731 -1518,Multiclass classification,Streaming Random Patches,ImageSegments,0.7251153592617007,0.7251153592617007,0.7184902268106362,6.2875261306762695,412.53433 -1564,Multiclass classification,Streaming Random Patches,ImageSegments,0.7204094689699296,0.7204094689699295,0.7171509654034274,6.316588401794434,434.180383 -1610,Multiclass classification,Streaming Random Patches,ImageSegments,0.7165941578620261,0.7165941578620262,0.7136076251491865,6.364602088928223,456.188448 -1656,Multiclass classification,Streaming Random Patches,ImageSegments,0.7202416918429003,0.7202416918429003,0.7179265770125135,6.460197448730469,478.554693 -1702,Multiclass classification,Streaming Random Patches,ImageSegments,0.721928277483833,0.7219282774838331,0.7220156076184944,6.666633605957031,501.28713 -1748,Multiclass classification,Streaming Random Patches,ImageSegments,0.7263880938752146,0.7263880938752146,0.7263874723147012,6.882956504821777,524.384863 -1794,Multiclass classification,Streaming Random Patches,ImageSegments,0.7328499721137758,0.7328499721137758,0.7320714565315939,6.874361991882324,547.829739 -1840,Multiclass classification,Streaming Random Patches,ImageSegments,0.734094616639478,0.734094616639478,0.7334477172925166,7.857270240783691,571.634536 -1886,Multiclass classification,Streaming Random Patches,ImageSegments,0.7358090185676393,0.7358090185676393,0.736235296466255,8.041683197021484,595.832215 -1932,Multiclass classification,Streaming Random Patches,ImageSegments,0.7369238736406007,0.7369238736406007,0.7364098924240724,8.212060928344727,620.406916 -1978,Multiclass classification,Streaming Random Patches,ImageSegments,0.7369752149721801,0.73697521497218,0.7356260672719533,8.416284561157227,645.365163 -2024,Multiclass classification,Streaming Random Patches,ImageSegments,0.7409787444389521,0.7409787444389521,0.7385453010661254,8.869349479675293,670.737914 -2070,Multiclass classification,Streaming Random Patches,ImageSegments,0.7438376027066216,0.7438376027066217,0.7418803204845174,9.001053810119629,696.540108 -2116,Multiclass classification,Streaming Random Patches,ImageSegments,0.7475177304964539,0.7475177304964539,0.7450940881618369,9.427652359008789,722.759269 -2162,Multiclass classification,Streaming Random Patches,ImageSegments,0.7482646922720962,0.7482646922720962,0.7457425826498583,9.724228858947754,749.427824 -2208,Multiclass classification,Streaming Random Patches,ImageSegments,0.7521522428636158,0.7521522428636158,0.7492034954191574,9.71615219116211,776.532359 -2254,Multiclass classification,Streaming Random Patches,ImageSegments,0.7532179316466933,0.7532179316466933,0.7508205496072249,10.198495864868164,804.090452 -2300,Multiclass classification,Streaming Random Patches,ImageSegments,0.7546759460635059,0.754675946063506,0.7527273841922961,10.425667762756348,832.069921 -1056,Multiclass classification,Streaming Random Patches,Insects,0.6265402843601896,0.6265402843601896,0.5882776540607534,10.90817928314209,22.534162 -2112,Multiclass classification,Streaming Random Patches,Insects,0.6570345807674088,0.6570345807674088,0.61544126739188,21.709880828857422,65.027277 -3168,Multiclass classification,Streaming Random Patches,Insects,0.6684559520050521,0.6684559520050521,0.6242294974630811,28.635205268859863,131.537407 -4224,Multiclass classification,Streaming Random Patches,Insects,0.6810324413923751,0.6810324413923751,0.6325456686453049,36.43542194366455,221.773783 -5280,Multiclass classification,Streaming Random Patches,Insects,0.6910399696912294,0.6910399696912294,0.6411255615252124,45.614484786987305,335.089181 -6336,Multiclass classification,Streaming Random Patches,Insects,0.6937647987371744,0.6937647987371744,0.6440375279924044,53.59738254547119,471.883196 -7392,Multiclass classification,Streaming Random Patches,Insects,0.6988228927073468,0.6988228927073468,0.6494865599203364,66.1818675994873,633.159586 -8448,Multiclass classification,Streaming Random Patches,Insects,0.7001302237480762,0.7001302237480762,0.6494906800979877,76.50763607025146,819.592493 -9504,Multiclass classification,Streaming Random Patches,Insects,0.7055666631590024,0.7055666631590024,0.6515748182594757,80.03414821624756,1031.228873 -10560,Multiclass classification,Streaming Random Patches,Insects,0.7099157117151246,0.7099157117151246,0.6536141909419667,75.23120212554932,1268.49353 -11616,Multiclass classification,Streaming Random Patches,Insects,0.7112354713732243,0.7112354713732243,0.6532930257397846,87.85937118530273,1530.837041 -12672,Multiclass classification,Streaming Random Patches,Insects,0.7140715018546286,0.7140715018546285,0.6586632134486646,96.90367698669434,1818.32799 -13728,Multiclass classification,Streaming Random Patches,Insects,0.7196765498652291,0.7196765498652291,0.7110222921473365,56.69392013549805,2124.564995 -14784,Multiclass classification,Streaming Random Patches,Insects,0.7275248596360685,0.7275248596360685,0.7243727970733626,23.290308952331543,2446.9953570000002 -15840,Multiclass classification,Streaming Random Patches,Insects,0.7219521434433992,0.7219521434433992,0.7204121258981635,12.419946670532227,2789.3598920000004 -16896,Multiclass classification,Streaming Random Patches,Insects,0.7180822728617934,0.7180822728617934,0.7177336146344276,18.459078788757324,3150.9630650000004 -17952,Multiclass classification,Streaming Random Patches,Insects,0.7130521976491561,0.713052197649156,0.7136298242976093,32.946556091308594,3531.3081450000004 -19008,Multiclass classification,Streaming Random Patches,Insects,0.7221023833324565,0.7221023833324565,0.7193994629254835,14.42181396484375,3928.2542010000006 -20064,Multiclass classification,Streaming Random Patches,Insects,0.7273089767233215,0.7273089767233214,0.721146893328104,20.33617401123047,4340.108142000001 -21120,Multiclass classification,Streaming Random Patches,Insects,0.7289170888773142,0.7289170888773142,0.7201390592471967,29.718432426452637,4775.145968000001 -22176,Multiclass classification,Streaming Random Patches,Insects,0.7305524239007892,0.7305524239007891,0.719265816341323,21.72282314300537,5233.162328 -23232,Multiclass classification,Streaming Random Patches,Insects,0.7328569583745856,0.7328569583745856,0.7192472788421966,31.907146453857422,5712.0379140000005 -24288,Multiclass classification,Streaming Random Patches,Insects,0.7349610902952196,0.7349610902952196,0.7190161489472059,36.71180248260498,6211.046404000001 -25344,Multiclass classification,Streaming Random Patches,Insects,0.7382314643096713,0.7382314643096713,0.7202655895968563,44.65183067321777,6729.485654000001 -26400,Multiclass classification,Streaming Random Patches,Insects,0.7397249895829388,0.7397249895829386,0.7198095986730461,54.357375144958496,7266.820082000001 -27456,Multiclass classification,Streaming Random Patches,Insects,0.7418685121107267,0.7418685121107267,0.7199133187431289,60.99125003814697,7822.667714000001 -28512,Multiclass classification,Streaming Random Patches,Insects,0.7388727157939041,0.7388727157939041,0.7182833957431396,26.944812774658203,8398.518895000001 -29568,Multiclass classification,Streaming Random Patches,Insects,0.7376128792234586,0.7376128792234586,0.7214769633664444,24.290247917175293,8990.352305 -30624,Multiclass classification,Streaming Random Patches,Insects,0.7372889658100121,0.7372889658100121,0.7255972176885724,19.85909652709961,9598.262147000001 -31680,Multiclass classification,Streaming Random Patches,Insects,0.7371444805707251,0.737144480570725,0.7291466686667684,32.85751724243164,10221.399895 -32736,Multiclass classification,Streaming Random Patches,Insects,0.7374064457003208,0.7374064457003208,0.7322831246511409,38.75182342529297,10860.215509 -33792,Multiclass classification,Streaming Random Patches,Insects,0.7329170489183511,0.7329170489183511,0.7291423789419403,76.79454803466797,11517.947008 -34848,Multiclass classification,Streaming Random Patches,Insects,0.7290728039716475,0.7290728039716475,0.7252059051088736,37.93787670135498,12198.376918999998 -35904,Multiclass classification,Streaming Random Patches,Insects,0.726791633011169,0.7267916330111689,0.72277521319889,30.2938232421875,12898.613100999999 -36960,Multiclass classification,Streaming Random Patches,Insects,0.7233150247571634,0.7233150247571634,0.7191521630945247,34.07670021057129,13619.767748999999 -38016,Multiclass classification,Streaming Random Patches,Insects,0.7210837827173484,0.7210837827173484,0.7166085958184295,39.338196754455566,14364.864177 -39072,Multiclass classification,Streaming Random Patches,Insects,0.7203040618361445,0.7203040618361445,0.7160627724850469,41.774664878845215,15133.386085 -40128,Multiclass classification,Streaming Random Patches,Insects,0.7183193361078576,0.7183193361078576,0.7145670483840382,44.649410247802734,15926.835433 -41184,Multiclass classification,Streaming Random Patches,Insects,0.7176990505791224,0.7176990505791223,0.7142800617937591,38.580246925354004,16742.181512 -42240,Multiclass classification,Streaming Random Patches,Insects,0.7177489997395772,0.7177489997395772,0.7147225222929322,44.2959041595459,17577.282826 -43296,Multiclass classification,Streaming Random Patches,Insects,0.7185818223813374,0.7185818223813374,0.7159354160738768,44.74843406677246,18431.175396 -44352,Multiclass classification,Streaming Random Patches,Insects,0.7192171540664246,0.7192171540664247,0.7168891106233332,50.63485240936279,19303.296412 -45408,Multiclass classification,Streaming Random Patches,Insects,0.7197128196093113,0.7197128196093113,0.7173999204613543,48.77041816711426,20195.421521 -46464,Multiclass classification,Streaming Random Patches,Insects,0.7207240169597314,0.7207240169597314,0.7184187872009821,56.04546070098877,21107.071122 -47520,Multiclass classification,Streaming Random Patches,Insects,0.7212062543403691,0.7212062543403692,0.7191280088329424,48.19489002227783,22037.88723 -48576,Multiclass classification,Streaming Random Patches,Insects,0.7252084405558414,0.7252084405558414,0.7232782847500743,54.32844257354736,22988.011007 -49632,Multiclass classification,Streaming Random Patches,Insects,0.7291813584251778,0.7291813584251778,0.7271951034706091,53.6518030166626,23956.634362 -50688,Multiclass classification,Streaming Random Patches,Insects,0.7326928009154221,0.7326928009154221,0.7304439468758875,27.42653465270996,24940.316756 -51744,Multiclass classification,Streaming Random Patches,Insects,0.7367180101656262,0.7367180101656263,0.7341247480346391,36.39958953857422,25936.798718000002 -52800,Multiclass classification,Streaming Random Patches,Insects,0.7395784011060815,0.7395784011060814,0.737512125998823,8.341936111450195,26942.262482000002 -408,Multiclass classification,Streaming Random Patches,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.551915168762207,2.576854 -816,Multiclass classification,Streaming Random Patches,Keystroke,0.9742331288343559,0.9742331288343559,0.8747406597440331,4.161267280578613,7.918686 -1224,Multiclass classification,Streaming Random Patches,Keystroke,0.9672935404742437,0.9672935404742437,0.9345378451161834,7.9047441482543945,16.17177 -1632,Multiclass classification,Streaming Random Patches,Keystroke,0.9662783568362967,0.9662783568362967,0.920078959712528,12.156608581542969,27.617862 -2040,Multiclass classification,Streaming Random Patches,Keystroke,0.9632172633643943,0.9632172633643943,0.9392069284616192,18.052184104919434,42.519185 -2448,Multiclass classification,Streaming Random Patches,Keystroke,0.9591336330200245,0.9591336330200245,0.9527072671889639,17.44593620300293,61.058317 -2856,Multiclass classification,Streaming Random Patches,Keystroke,0.9600700525394046,0.9600700525394046,0.9487475492194611,23.22895908355713,83.383302 -3264,Multiclass classification,Streaming Random Patches,Keystroke,0.9589334967821024,0.9589334967821024,0.9481804303110769,30.014172554016113,110.001368 -3672,Multiclass classification,Streaming Random Patches,Keystroke,0.957504767093435,0.957504767093435,0.948270905442242,37.68842315673828,141.307336 -4080,Multiclass classification,Streaming Random Patches,Keystroke,0.9529296396175533,0.9529296396175533,0.9350591426916868,43.92499256134033,178.285036 -4488,Multiclass classification,Streaming Random Patches,Keystroke,0.9558725206151103,0.9558725206151103,0.958348874105129,23.460043907165527,220.514164 -4896,Multiclass classification,Streaming Random Patches,Keystroke,0.957711950970378,0.957711950970378,0.9572545884780326,21.815909385681152,267.806579 -5304,Multiclass classification,Streaming Random Patches,Keystroke,0.9581369036394494,0.9581369036394494,0.9564558175945329,29.116984367370605,320.079148 -5712,Multiclass classification,Streaming Random Patches,Keystroke,0.9593766415689021,0.9593766415689021,0.9590743474150507,34.215229988098145,377.359359 -6120,Multiclass classification,Streaming Random Patches,Keystroke,0.9576728223565942,0.9576728223565942,0.9540539138154063,42.245460510253906,440.30303599999996 -6528,Multiclass classification,Streaming Random Patches,Keystroke,0.9572544813850161,0.9572544813850162,0.9569914463415943,19.71925640106201,508.40118299999995 -6936,Multiclass classification,Streaming Random Patches,Keystroke,0.9586157173756309,0.9586157173756308,0.9593505106134974,22.396859169006348,580.778744 -7344,Multiclass classification,Streaming Random Patches,Keystroke,0.9592809478414817,0.9592809478414817,0.9593459120031487,26.322874069213867,657.980924 -7752,Multiclass classification,Streaming Random Patches,Keystroke,0.9600051606244355,0.9600051606244355,0.9601169971762602,30.259758949279785,740.2744439999999 -8160,Multiclass classification,Streaming Random Patches,Keystroke,0.9574702782203701,0.95747027822037,0.9549133730963548,37.68336868286133,827.6717389999999 -8568,Multiclass classification,Streaming Random Patches,Keystroke,0.9564608380996849,0.9564608380996849,0.9560990529914857,43.737112045288086,921.0091999999999 -8976,Multiclass classification,Streaming Random Patches,Keystroke,0.9568802228412256,0.9568802228412256,0.9569984740230398,33.59728527069092,1020.6233179999998 -9384,Multiclass classification,Streaming Random Patches,Keystroke,0.9575828626238942,0.9575828626238942,0.9578510301970171,34.01332950592041,1125.214274 -9792,Multiclass classification,Streaming Random Patches,Keystroke,0.9576141354304974,0.9576141354304974,0.9575892724596199,40.18074893951416,1234.457491 -10200,Multiclass classification,Streaming Random Patches,Keystroke,0.9558780272575743,0.9558780272575743,0.9547878392234919,48.97087860107422,1349.2361799999999 -10608,Multiclass classification,Streaming Random Patches,Keystroke,0.9524842085415292,0.9524842085415292,0.9506853107984292,30.6993989944458,1470.1246939999999 -11016,Multiclass classification,Streaming Random Patches,Keystroke,0.9539718565592374,0.9539718565592374,0.9545620457235888,26.549206733703613,1595.074551 -11424,Multiclass classification,Streaming Random Patches,Keystroke,0.9543902652543115,0.9543902652543115,0.9545363240408884,33.6107063293457,1724.543611 -11832,Multiclass classification,Streaming Random Patches,Keystroke,0.9541881497760122,0.9541881497760122,0.9541408405790519,25.182985305786133,1859.013318 -12240,Multiclass classification,Streaming Random Patches,Keystroke,0.955061688046409,0.955061688046409,0.9554321262858616,27.34038543701172,1997.8708550000001 -12648,Multiclass classification,Streaming Random Patches,Keystroke,0.9546928125247094,0.9546928125247094,0.9546233453975913,35.30395698547363,2141.6119080000003 -13056,Multiclass classification,Streaming Random Patches,Keystroke,0.953887399463807,0.953887399463807,0.9537532269202632,33.51621055603027,2290.943904 -13464,Multiclass classification,Streaming Random Patches,Keystroke,0.9540221347396568,0.9540221347396568,0.954138309472004,33.38596153259277,2445.0119170000003 -13872,Multiclass classification,Streaming Random Patches,Keystroke,0.9546535938288515,0.9546535938288515,0.9549190485054234,31.360337257385254,2603.6856470000002 -14280,Multiclass classification,Streaming Random Patches,Keystroke,0.9534281112122698,0.9534281112122698,0.9532093226981456,38.61776542663574,2767.1788560000005 -14688,Multiclass classification,Streaming Random Patches,Keystroke,0.9540409886294001,0.9540409886294001,0.9542688403803362,42.8822660446167,2935.9642900000003 -15096,Multiclass classification,Streaming Random Patches,Keystroke,0.9547532295462073,0.9547532295462073,0.9549723528375391,41.949758529663086,3110.4045410000003 -15504,Multiclass classification,Streaming Random Patches,Keystroke,0.9549764561697736,0.9549764561697736,0.9551012466300322,36.29027271270752,3290.0043080000005 -15912,Multiclass classification,Streaming Random Patches,Keystroke,0.9551253849538055,0.9551253849538055,0.9552372796273361,33.26945877075195,3474.8467850000006 -16320,Multiclass classification,Streaming Random Patches,Keystroke,0.9555119799007292,0.9555119799007292,0.9556369370454034,38.47606945037842,3664.7140100000006 -16728,Multiclass classification,Streaming Random Patches,Keystroke,0.954923178095295,0.954923178095295,0.9549151106032768,38.78229522705078,3859.8566950000004 -17136,Multiclass classification,Streaming Random Patches,Keystroke,0.955587977823169,0.955587977823169,0.9557184838324558,44.56228828430176,4060.2677570000005 -17544,Multiclass classification,Streaming Random Patches,Keystroke,0.9550817990081514,0.9550817990081514,0.9550944582439086,49.72221755981445,4266.497535 -17952,Multiclass classification,Streaming Random Patches,Keystroke,0.9547657512116317,0.9547657512116317,0.9547923955213531,44.72002029418945,4478.609232000001 -18360,Multiclass classification,Streaming Random Patches,Keystroke,0.9553897271093197,0.9553897271093197,0.955476322048541,52.00297737121582,4696.983244000001 -18768,Multiclass classification,Streaming Random Patches,Keystroke,0.9555070069803379,0.9555070069803379,0.9555572955831596,59.27475929260254,4921.837298000001 -19176,Multiclass classification,Streaming Random Patches,Keystroke,0.9548891786179922,0.9548891786179922,0.9549038695373787,71.70181655883789,5153.196879000001 -19584,Multiclass classification,Streaming Random Patches,Keystroke,0.954858806107338,0.954858806107338,0.9548865417655429,78.58561515808105,5390.578061000001 -19992,Multiclass classification,Streaming Random Patches,Keystroke,0.9539292681706768,0.9539292681706768,0.9539347026376765,85.24763870239258,5634.910465000001 -20400,Multiclass classification,Streaming Random Patches,Keystroke,0.9532330016177264,0.9532330016177264,0.9532392337717847,74.55205345153809,5886.477404000001 -46,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.5555555555555556,0.5555555555555556,0.4458032432860809,0.06141090393066406,0.019654 -92,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6483516483516484,0.6483516483516484,0.646491610589355,0.11540794372558594,0.062314999999999995 -138,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.708029197080292,0.708029197080292,0.7216654146545566,0.1258535385131836,0.135768 -184,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7431693989071039,0.743169398907104,0.7576794034998369,0.12633037567138672,0.24048 -230,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7641921397379913,0.7641921397379913,0.7751275973499576,0.12631702423095703,0.376459 -276,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7672727272727272,0.7672727272727272,0.7799448750812884,0.1262655258178711,0.543817 -322,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7757009345794392,0.7757009345794392,0.781311030606134,0.12674331665039062,0.742198 -368,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.782016348773842,0.782016348773842,0.7830988277979799,0.12674713134765625,0.9720690000000001 -414,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7893462469733656,0.7893462469733655,0.7891834545778567,0.12626934051513672,1.233319 -460,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7799564270152506,0.7799564270152506,0.778762654261754,0.12626266479492188,1.526064 -506,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7841584158415842,0.7841584158415842,0.7830263284725031,0.12677955627441406,1.8499750000000001 -552,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7840290381125227,0.7840290381125228,0.7833214841514466,0.12677383422851562,2.2053070000000004 -598,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7839195979899497,0.7839195979899497,0.7851401823229054,0.1262836456298828,2.5919950000000003 -644,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7884914463452566,0.7884914463452566,0.790931132142264,0.12628936767578125,3.0100190000000002 -690,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.795355587808418,0.795355587808418,0.7973717331367783,0.12679672241210938,3.459217 -736,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7918367346938775,0.7918367346938775,0.79371924750244,0.12629222869873047,3.93961 -782,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8015364916773368,0.8015364916773368,0.8027236936866887,0.12627696990966797,4.451398 -828,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7980652962515115,0.7980652962515115,0.8001612113332863,0.12677764892578125,4.994585 -874,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8041237113402062,0.8041237113402062,0.8058476562214167,0.12676525115966797,5.568929 -920,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8063112078346029,0.8063112078346029,0.8071524109530731,0.1262378692626953,6.174556 -966,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8072538860103627,0.8072538860103627,0.8069383576906736,0.1262502670288086,6.811568 -1012,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8120672601384767,0.8120672601384767,0.8103691514865562,0.12676239013671875,7.479958 -1058,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8117313150425733,0.8117313150425733,0.8093057999862455,0.12675857543945312,8.179363 -1104,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8105167724388033,0.8105167724388033,0.8087453181575575,0.12626075744628906,8.909729 -1150,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8120104438642297,0.8120104438642298,0.8093458779132273,0.12674808502197266,9.671618 -1196,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8125523012552301,0.8125523012552303,0.8098995946687924,0.1267566680908203,10.464471 -1242,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8170829975825947,0.8170829975825946,0.8146737825459542,0.12630462646484375,11.288497999999999 -1288,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8174048174048174,0.8174048174048174,0.8149699191034137,0.12631511688232422,12.143769999999998 -1334,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8169542385596399,0.8169542385596399,0.8144172630221828,0.12679100036621094,13.030243999999998 -1380,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8165337200870196,0.8165337200870196,0.8142638589810781,0.12678813934326172,13.947682999999998 -1426,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8210526315789474,0.8210526315789475,0.8177443463463022,0.1262807846069336,14.896382999999998 -1472,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.822569680489463,0.822569680489463,0.8180682540474884,0.1267719268798828,15.876335 -1518,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8233355306526038,0.8233355306526038,0.8183049909694801,0.12675857543945312,16.887748 -1564,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8227767114523352,0.8227767114523352,0.8180063024943973,0.1262645721435547,17.930424 -1610,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8228713486637663,0.8228713486637663,0.818440484251979,0.1262655258178711,19.004458 -1656,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.824773413897281,0.824773413897281,0.8207684581521858,0.12678241729736328,20.109849999999998 -1702,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.824808935920047,0.824808935920047,0.8222541912553749,0.1268024444580078,21.24685 -1748,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8259874069834001,0.8259874069834001,0.8228660744170171,0.12630653381347656,22.415483 -1794,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.826547685443391,0.826547685443391,0.8226613560637924,0.12677478790283203,23.615758 -1840,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8254486133768353,0.8254486133768353,0.8217381124058762,0.12675857543945312,24.847279 -1886,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8249336870026526,0.8249336870026526,0.8216008133499116,0.12628459930419922,26.110083 -1932,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8234075608493009,0.8234075608493009,0.8193527544316537,0.12627792358398438,27.403914999999998 -1978,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8234699038947901,0.8234699038947901,0.8195124114516217,0.12675857543945312,28.728914999999997 -2024,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8220464656450815,0.8220464656450815,0.8172381305352,0.12677478790283203,30.084906999999998 -2070,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8206863218946351,0.8206863218946351,0.8164336862763343,0.12628936767578125,31.472198999999996 -2116,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8217494089834515,0.8217494089834515,0.8168455585843762,0.1262645721435547,32.891028 -2162,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8204534937528922,0.8204534937528921,0.8154843900985335,0.12677288055419922,34.340933 -2208,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.822383325781604,0.822383325781604,0.8171788245797035,0.1262683868408203,35.822171 -2254,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.821127385707945,0.821127385707945,0.8170261701336431,0.12625598907470703,37.335093 -2300,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8199217050891692,0.8199217050891693,0.8158945802523674,0.12676048278808594,38.879423 -1056,Multiclass classification,k-Nearest Neighbors,Insects,0.6322274881516587,0.6322274881516587,0.5639948035153092,0.21594810485839844,1.014879 -2112,Multiclass classification,k-Nearest Neighbors,Insects,0.636191378493605,0.636191378493605,0.5686546251961576,0.2164621353149414,3.11274 -3168,Multiclass classification,k-Nearest Neighbors,Insects,0.6274076413009156,0.6274076413009156,0.5664829980315041,0.2159862518310547,6.352824 -4224,Multiclass classification,k-Nearest Neighbors,Insects,0.6317783566185177,0.6317783566185177,0.5676004628647836,0.216461181640625,10.71472 -5280,Multiclass classification,k-Nearest Neighbors,Insects,0.6277704110627013,0.6277704110627013,0.5651907052085646,0.21597766876220703,16.153805 -6336,Multiclass classification,k-Nearest Neighbors,Insects,0.6244672454617206,0.6244672454617206,0.5642758642399058,0.2164630889892578,22.622425999999997 -7392,Multiclass classification,k-Nearest Neighbors,Insects,0.621160871329996,0.621160871329996,0.5621999618118433,0.2159862518310547,30.095943999999996 -8448,Multiclass classification,k-Nearest Neighbors,Insects,0.6183260329110927,0.6183260329110927,0.560545956984929,0.21646785736083984,38.53514199999999 -9504,Multiclass classification,k-Nearest Neighbors,Insects,0.6195938124802693,0.6195938124802693,0.5612689785887882,0.2159566879272461,47.930054999999996 -10560,Multiclass classification,k-Nearest Neighbors,Insects,0.6209868358746093,0.6209868358746093,0.5626902992589761,0.2164936065673828,58.279528 -11616,Multiclass classification,k-Nearest Neighbors,Insects,0.6196297890658631,0.6196297890658631,0.5618958864151227,0.21594715118408203,69.583507 -12672,Multiclass classification,k-Nearest Neighbors,Insects,0.6235498382132428,0.6235498382132428,0.577401509815314,0.21659374237060547,81.841211 -13728,Multiclass classification,k-Nearest Neighbors,Insects,0.6524368033801996,0.6524368033801996,0.656066758247117,0.21615219116210938,95.05061 -14784,Multiclass classification,k-Nearest Neighbors,Insects,0.6639383075153893,0.6639383075153893,0.6656513873636037,0.21651744842529297,109.208982 -15840,Multiclass classification,k-Nearest Neighbors,Insects,0.6599532798787803,0.6599532798787803,0.6660828271082423,0.21599388122558594,124.323768 -16896,Multiclass classification,k-Nearest Neighbors,Insects,0.6583012725658479,0.6583012725658479,0.6678320995738946,0.21648120880126953,140.393226 -17952,Multiclass classification,k-Nearest Neighbors,Insects,0.6558966074313409,0.6558966074313409,0.6676009154715022,0.2160320281982422,157.416689 -19008,Multiclass classification,k-Nearest Neighbors,Insects,0.6731730415110223,0.6731730415110223,0.6774302820037228,0.21651268005371094,175.38966399999998 -20064,Multiclass classification,k-Nearest Neighbors,Insects,0.6798086029008623,0.6798086029008623,0.6780616401383449,0.21589279174804688,194.31697799999998 -21120,Multiclass classification,k-Nearest Neighbors,Insects,0.680382593872816,0.680382593872816,0.6752117016598617,0.2164011001586914,214.20054599999997 -22176,Multiclass classification,k-Nearest Neighbors,Insects,0.6805862457722661,0.6805862457722661,0.6722568877045599,0.2158823013305664,235.03782499999997 -23232,Multiclass classification,k-Nearest Neighbors,Insects,0.6813740260858336,0.6813740260858336,0.6702824994179433,0.21639060974121094,256.831775 -24288,Multiclass classification,k-Nearest Neighbors,Insects,0.6815992094536172,0.6815992094536172,0.6677450869096582,0.21591758728027344,279.574148 -25344,Multiclass classification,k-Nearest Neighbors,Insects,0.6821212958213313,0.6821212958213313,0.6660355323582295,0.21636676788330078,303.273869 -26400,Multiclass classification,k-Nearest Neighbors,Insects,0.6831319368157884,0.6831319368157884,0.6646803813034555,0.21590614318847656,327.922368 -27456,Multiclass classification,k-Nearest Neighbors,Insects,0.6836277545073757,0.6836277545073757,0.6627124931293528,0.2164020538330078,353.52686 -28512,Multiclass classification,k-Nearest Neighbors,Insects,0.6834905825821612,0.6834905825821612,0.664548122616301,0.2161540985107422,380.080866 -29568,Multiclass classification,k-Nearest Neighbors,Insects,0.6814692055331958,0.6814692055331958,0.6671975305669872,0.2166757583618164,407.59582800000004 -30624,Multiclass classification,k-Nearest Neighbors,Insects,0.6796525487378767,0.6796525487378767,0.669471411791397,0.21617984771728516,436.06293600000004 -31680,Multiclass classification,k-Nearest Neighbors,Insects,0.6779570062186306,0.6779570062186306,0.6711290718417154,0.21667957305908203,465.48818300000005 -32736,Multiclass classification,k-Nearest Neighbors,Insects,0.6768901787078051,0.6768901787078051,0.6727094382078547,0.21613597869873047,495.86473400000006 -33792,Multiclass classification,k-Nearest Neighbors,Insects,0.6734337545500281,0.6734337545500281,0.6702378074852682,0.2164754867553711,527.2018280000001 -34848,Multiclass classification,k-Nearest Neighbors,Insects,0.6690676385341636,0.6690676385341636,0.6661382581729155,0.21594715118408203,559.4897450000001 -35904,Multiclass classification,k-Nearest Neighbors,Insects,0.6663510013090828,0.6663510013090828,0.6633778558128317,0.21650028228759766,592.7366190000001 -36960,Multiclass classification,k-Nearest Neighbors,Insects,0.662409697232068,0.662409697232068,0.6597878724618786,0.215972900390625,626.9366260000002 -38016,Multiclass classification,k-Nearest Neighbors,Insects,0.6594239116138366,0.6594239116138366,0.6567102170776443,0.21648025512695312,662.1000680000002 -39072,Multiclass classification,k-Nearest Neighbors,Insects,0.662409459701569,0.662409459701569,0.6591983036871739,0.21597957611083984,698.2204010000002 -40128,Multiclass classification,k-Nearest Neighbors,Insects,0.6615495800832357,0.6615495800832357,0.658372148729009,0.21650981903076172,735.3021140000002 -41184,Multiclass classification,k-Nearest Neighbors,Insects,0.6616079450258602,0.6616079450258602,0.6583203582230679,0.21601200103759766,773.3352610000002 -42240,Multiclass classification,k-Nearest Neighbors,Insects,0.6620895381045953,0.6620895381045953,0.6586855795305535,0.21649646759033203,812.3294790000002 -43296,Multiclass classification,k-Nearest Neighbors,Insects,0.6626862224275321,0.6626862224275321,0.6591267371039767,0.21601295471191406,852.2739140000002 -44352,Multiclass classification,k-Nearest Neighbors,Insects,0.6625104281752384,0.6625104281752384,0.6587853710847982,0.21649742126464844,893.1794370000002 -45408,Multiclass classification,k-Nearest Neighbors,Insects,0.6629374325544519,0.6629374325544519,0.6587077344895959,0.21599388122558594,935.0370030000003 -46464,Multiclass classification,k-Nearest Neighbors,Insects,0.6634311172330671,0.6634311172330671,0.6587873315408634,0.21650028228759766,977.8531990000002 -47520,Multiclass classification,k-Nearest Neighbors,Insects,0.666217723436941,0.666217723436941,0.6621071051846,0.21592044830322266,1021.6188110000003 -48576,Multiclass classification,k-Nearest Neighbors,Insects,0.6698507462686567,0.6698507462686567,0.6663907774790556,0.2164478302001953,1066.3421490000003 -49632,Multiclass classification,k-Nearest Neighbors,Insects,0.6739940762829683,0.6739940762829683,0.6709516060662618,0.2159433364868164,1112.0155100000002 -50688,Multiclass classification,k-Nearest Neighbors,Insects,0.6774715410262987,0.6774715410262987,0.6745572423992897,0.21648406982421875,1158.6520200000002 -51744,Multiclass classification,k-Nearest Neighbors,Insects,0.6814834856888855,0.6814834856888855,0.6786206144243011,0.21599388122558594,1206.2391940000002 -52800,Multiclass classification,k-Nearest Neighbors,Insects,0.6865470936949564,0.6865470936949564,0.6836613373539585,0.21665573120117188,1254.7848490000001 -408,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,0.2092580795288086,0.626636 -816,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9828220858895705,0.9828220858895705,0.9550926410288757,0.20981216430664062,1.943188 -1224,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9852820932134096,0.9852820932134096,0.9672695079711997,0.20935916900634766,3.654696 -1632,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840588595953402,0.9840588595953402,0.9604409213604835,0.2098979949951172,5.778702 -2040,Multiclass classification,k-Nearest Neighbors,Keystroke,0.984796468857283,0.984796468857283,0.9791423790442798,0.21045207977294922,8.327232 -2448,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9861054352268084,0.9861054352268084,0.9837809767868474,0.20999908447265625,11.304767 -2856,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9859894921190894,0.9859894921190894,0.9813641447908843,0.21055316925048828,14.706237 -3264,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9871284094391665,0.9871284094391665,0.9868437405314092,0.2106037139892578,18.525325 -3672,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9880141650776355,0.9880141650776355,0.9878382173613446,0.21015071868896484,22.761229999999998 -4080,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9877420936504046,0.9877420936504046,0.9857777629944036,0.21070480346679688,27.408752 -4488,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9881880989525296,0.9881880989525296,0.9878235870948694,0.2102518081665039,32.463238 -4896,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9885597548518897,0.9885597548518897,0.9882962361329112,0.21030235290527344,37.912155999999996 -5304,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9867999245709975,0.9867999245709975,0.9836140972543967,0.210906982421875,43.748231 -5712,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9873927508317283,0.9873927508317283,0.9875632488318824,0.21045398712158203,49.983610999999996 -6120,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9872528190880863,0.9872528190880863,0.986679154193125,0.21100807189941406,56.586138999999996 -6528,Multiclass classification,k-Nearest Neighbors,Keystroke,0.987130381492263,0.987130381492263,0.9866769113371191,0.2110586166381836,63.546493 -6936,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9875991348233598,0.9875991348233598,0.9877805463370743,0.21060562133789062,70.86485 -7344,Multiclass classification,k-Nearest Neighbors,Keystroke,0.986926324390576,0.986926324390576,0.9861386596476129,0.21262454986572266,78.54037100000001 -7752,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9865823764675525,0.9865823764675525,0.9861511169160879,0.2121715545654297,86.582909 -8160,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9861502635126854,0.9861502635126854,0.9857089041873667,0.21222209930419922,94.988236 -8568,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9863429438543247,0.9863429438543247,0.9863829773026439,0.21277618408203125,103.754213 -8976,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9865181058495822,0.9865181058495822,0.9865643235024212,0.21232318878173828,112.879589 -9384,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9858254289672812,0.9858254289672812,0.9853734936692787,0.2128772735595703,122.365291 -9792,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9857011541211317,0.9857011541211317,0.9856081881161903,0.21292781829833984,132.212374 -10200,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9860770663790568,0.9860770663790568,0.9862471716083434,0.21247482299804688,142.422221 -10608,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9853870085792401,0.9853870085792401,0.9850628829106897,0.2130289077758789,152.99283200000002 -11016,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9855651384475715,0.9855651384475715,0.9856470830770893,0.21257591247558594,163.926329 -11424,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9857305436400245,0.9857305436400245,0.9858087969497247,0.21262645721435547,175.222393 -11832,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9858845406136422,0.9858845406136422,0.9859589489459036,0.2131805419921875,186.880683 -12240,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9861099763052537,0.9861099763052537,0.9862068987479334,0.21272754669189453,198.90524100000002 -12648,Multiclass classification,k-Nearest Neighbors,Keystroke,0.986241796473472,0.986241796473472,0.9863073128720755,0.21328163146972656,211.292323 -13056,Multiclass classification,k-Nearest Neighbors,Keystroke,0.985905783224818,0.985905783224818,0.9858386074980298,0.2133321762084961,224.04148700000002 -13464,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9857386912278095,0.9857386912278095,0.985725098817589,0.21287918090820312,237.153453 -13872,Multiclass classification,k-Nearest Neighbors,Keystroke,0.985725614591594,0.985725614591594,0.9857526199764752,0.21343326568603516,250.627393 -14280,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9845927585965404,0.9845927585965404,0.9843691165759658,0.2129802703857422,264.463365 -14688,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9848845918158916,0.9848845918158916,0.9849709956409892,0.21303081512451172,278.661824 -15096,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9851606492215965,0.9851606492215965,0.9852374033885689,0.21358489990234375,293.222186 -15504,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9843901180416693,0.9843901180416693,0.9842921251481087,0.21313190460205078,308.144297 -15912,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840362013701213,0.9840362013701213,0.9840127534225097,0.2136859893798828,323.428421 -16320,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840676512041179,0.9840676512041179,0.9840971764012499,0.21373653411865234,339.073895 -16728,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9838584324744425,0.9838584324744425,0.9838587519327452,0.21328353881835938,355.08225799999997 -17136,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840676976947768,0.9840676976947768,0.9841085979018743,0.2138376235961914,371.45446999999996 -17544,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840962207148151,0.9840962207148151,0.9841170088782344,0.21338462829589844,388.18776499999996 -17952,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9840120327558354,0.9840120327558354,0.98402212072501,0.21343517303466797,405.28127299999994 -18360,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9842039326760716,0.9842039326760716,0.9842275892846344,0.2139892578125,422.73574599999995 -18768,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9842809186337721,0.9842809186337721,0.9842944297848302,0.21353626251220703,440.55097099999995 -19176,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9843024771838331,0.9843024771838331,0.9843104669951571,0.21409034729003906,458.72564299999993 -19584,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9843742021140786,0.9843742021140786,0.9843801024949197,0.2141408920288086,477.2614029999999 -19992,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9845430443699665,0.9845430443699665,0.984546236206973,0.21368789672851562,496.1581819999999 -20400,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9845090445610079,0.9845090445610079,0.984507607652182,0.21424198150634766,515.4151939999999 -46,Multiclass classification,ADWIN Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.24576497265572897,4.137397766113281,1.064188 -92,Multiclass classification,ADWIN Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.49347523955818895,4.140613555908203,2.6316629999999996 -138,Multiclass classification,ADWIN Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646677,4.140277862548828,4.836076 -184,Multiclass classification,ADWIN Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.614143164890895,4.141227722167969,7.573955 -230,Multiclass classification,ADWIN Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,3.9138870239257812,10.681288 -276,Multiclass classification,ADWIN Bagging,ImageSegments,0.64,0.64,0.6559607038460422,4.028352737426758,14.131211 -322,Multiclass classification,ADWIN Bagging,ImageSegments,0.6697819314641744,0.6697819314641744,0.6706320385346652,4.144774436950684,17.917192 -368,Multiclass classification,ADWIN Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6897433526546474,4.144762992858887,22.052519 -414,Multiclass classification,ADWIN Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.706570530482581,4.148934364318848,26.547524 -460,Multiclass classification,ADWIN Bagging,ImageSegments,0.7145969498910676,0.7145969498910676,0.7071122267088653,4.148022651672363,31.388582999999997 -506,Multiclass classification,ADWIN Bagging,ImageSegments,0.7247524752475247,0.7247524752475247,0.7147973207987898,4.147336006164551,36.558333999999995 -552,Multiclass classification,ADWIN Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7210771168277493,4.147068977355957,42.057708 -598,Multiclass classification,ADWIN Bagging,ImageSegments,0.7336683417085427,0.7336683417085426,0.7250288715672424,4.1468400955200195,47.891459999999995 -644,Multiclass classification,ADWIN Bagging,ImageSegments,0.7325038880248833,0.7325038880248833,0.725892488365903,4.150084495544434,54.057886999999994 -690,Multiclass classification,ADWIN Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.730253637873586,4.1498517990112305,60.540488999999994 -736,Multiclass classification,ADWIN Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7329631379486717,4.149523735046387,67.34570099999999 -782,Multiclass classification,ADWIN Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7387105187530085,4.149043083190918,74.478308 -828,Multiclass classification,ADWIN Bagging,ImageSegments,0.7460701330108828,0.7460701330108827,0.7425025596154724,4.1487531661987305,81.934305 -874,Multiclass classification,ADWIN Bagging,ImageSegments,0.7514318442153494,0.7514318442153494,0.7467163857842192,4.148730278015137,89.700464 -920,Multiclass classification,ADWIN Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7453933609147307,4.148531913757324,97.776444 -966,Multiclass classification,ADWIN Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.7451117895470661,4.148127555847168,106.157006 -1012,Multiclass classification,ADWIN Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.7449630804815479,4.147826194763184,114.848603 -1058,Multiclass classification,ADWIN Bagging,ImageSegments,0.7511825922421949,0.7511825922421949,0.7446315489945474,4.149008750915527,123.845956 -1104,Multiclass classification,ADWIN Bagging,ImageSegments,0.7533998186763372,0.7533998186763373,0.7466082689908061,4.149382591247559,133.146638 -1150,Multiclass classification,ADWIN Bagging,ImageSegments,0.7563098346388164,0.7563098346388164,0.7491651771194965,4.148917198181152,142.738156 -1196,Multiclass classification,ADWIN Bagging,ImageSegments,0.7589958158995815,0.7589958158995815,0.7526420027035882,4.148730278015137,152.636035 -1242,Multiclass classification,ADWIN Bagging,ImageSegments,0.75825946817083,0.7582594681708301,0.7524016178277559,4.148566246032715,162.845279 -1288,Multiclass classification,ADWIN Bagging,ImageSegments,0.7637917637917638,0.7637917637917638,0.75666252908711,4.14877986907959,173.368823 -1334,Multiclass classification,ADWIN Bagging,ImageSegments,0.7636909227306826,0.7636909227306825,0.7569484848610158,4.148688316345215,184.20083499999998 -1380,Multiclass classification,ADWIN Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7590436403579585,4.1487226486206055,195.341139 -1426,Multiclass classification,ADWIN Bagging,ImageSegments,0.767719298245614,0.767719298245614,0.7612112896959209,4.148562431335449,206.790743 -1472,Multiclass classification,ADWIN Bagging,ImageSegments,0.7722637661454793,0.7722637661454793,0.7640566966433581,4.148623466491699,218.54670099999998 -1518,Multiclass classification,ADWIN Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.7642341334147652,4.148673057556152,230.6041 -1564,Multiclass classification,ADWIN Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7653316001442942,4.148703575134277,242.96114799999998 -1610,Multiclass classification,ADWIN Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7647353044337892,4.148566246032715,255.63800099999997 -1656,Multiclass classification,ADWIN Bagging,ImageSegments,0.7734138972809668,0.7734138972809667,0.7645730180903106,4.148055076599121,268.628995 -1702,Multiclass classification,ADWIN Bagging,ImageSegments,0.7724867724867724,0.7724867724867724,0.7656182355666586,4.148245811462402,281.916269 -1748,Multiclass classification,ADWIN Bagging,ImageSegments,0.7750429307384087,0.7750429307384087,0.7677424040514297,4.148360252380371,295.50082 -1794,Multiclass classification,ADWIN Bagging,ImageSegments,0.7763524818739542,0.7763524818739542,0.7677176136548695,4.148287773132324,309.399686 -1840,Multiclass classification,ADWIN Bagging,ImageSegments,0.7775965198477434,0.7775965198477434,0.7691578918725354,4.147894859313965,323.61456 -1886,Multiclass classification,ADWIN Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7681560201617949,4.147856712341309,338.130858 -1932,Multiclass classification,ADWIN Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7674170460709654,4.147791862487793,352.957512 -1978,Multiclass classification,ADWIN Bagging,ImageSegments,0.7769347496206374,0.7769347496206374,0.7672843880004774,4.147627830505371,368.093168 -2024,Multiclass classification,ADWIN Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7681802739952505,4.147582054138184,383.545184 -2070,Multiclass classification,ADWIN Bagging,ImageSegments,0.778153697438376,0.7781536974383759,0.7675304391667319,4.147578239440918,399.300197 -2116,Multiclass classification,ADWIN Bagging,ImageSegments,0.7787234042553192,0.778723404255319,0.7673415220519754,4.147555351257324,415.36676400000005 -2162,Multiclass classification,ADWIN Bagging,ImageSegments,0.7797316057380842,0.7797316057380842,0.7679341969633587,4.147627830505371,431.73852700000003 -2208,Multiclass classification,ADWIN Bagging,ImageSegments,0.7816039873130947,0.7816039873130947,0.7687944234581563,4.1476240158081055,448.43594900000005 -2254,Multiclass classification,ADWIN Bagging,ImageSegments,0.7785175321793165,0.7785175321793165,0.7657018899401807,4.147597312927246,465.43417500000004 -2300,Multiclass classification,ADWIN Bagging,ImageSegments,0.7777294475859069,0.7777294475859068,0.7649119672933201,4.14768123626709,482.736348 -1056,Multiclass classification,ADWIN Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762561,6.54005241394043,11.482596 -2112,Multiclass classification,ADWIN Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.540731430053711,32.930503 -3168,Multiclass classification,ADWIN Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244892,6.541685104370117,64.407359 -4224,Multiclass classification,ADWIN Bagging,Insects,0.6107032914989344,0.6107032914989344,0.574850135712032,6.54176139831543,105.889955 -5280,Multiclass classification,ADWIN Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225518,6.542509078979492,157.317574 -6336,Multiclass classification,ADWIN Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789627,6.541296005249023,218.70625900000002 -7392,Multiclass classification,ADWIN Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867538,6.541265487670898,290.118972 -8448,Multiclass classification,ADWIN Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.541204452514648,371.535386 -9504,Multiclass classification,ADWIN Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297488,6.541570663452148,462.975881 -10560,Multiclass classification,ADWIN Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.541746139526367,564.403412 -11616,Multiclass classification,ADWIN Bagging,Insects,0.6005165733964701,0.6005165733964701,0.5651283239572901,6.541906356811523,675.845474 -12672,Multiclass classification,ADWIN Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306639,6.542104721069336,797.3461219999999 -13728,Multiclass classification,ADWIN Bagging,Insects,0.6152108982297662,0.6152108982297662,0.5959760515786451,6.026429176330566,928.2423849999999 -14784,Multiclass classification,ADWIN Bagging,Insects,0.6060339579246432,0.6060339579246432,0.5869142505177357,6.546758651733398,1068.594912 -15840,Multiclass classification,ADWIN Bagging,Insects,0.5713744554580465,0.5713744554580465,0.5537658591956377,6.547109603881836,1218.8956600000001 -16896,Multiclass classification,ADWIN Bagging,Insects,0.545546019532406,0.545546019532406,0.5286479939306437,6.431658744812012,1379.1626680000002 -17952,Multiclass classification,ADWIN Bagging,Insects,0.526767311013314,0.526767311013314,0.509587529402725,6.54762077331543,1549.227957 -19008,Multiclass classification,ADWIN Bagging,Insects,0.517756615983585,0.517756615983585,0.4976462434137419,4.743686676025391,1728.135143 -20064,Multiclass classification,ADWIN Bagging,Insects,0.5296815032647162,0.5296815032647162,0.5080882715573688,10.447637557983398,1914.608483 -21120,Multiclass classification,ADWIN Bagging,Insects,0.539750935176855,0.539750935176855,0.5184934777423561,11.000249862670898,2110.852221 -22176,Multiclass classification,ADWIN Bagging,Insects,0.5468771138669674,0.5468771138669674,0.5259709774382829,10.998456954956055,2316.603266 -23232,Multiclass classification,ADWIN Bagging,Insects,0.5551633593043778,0.5551633593043778,0.5340735310276195,12.317106246948242,2531.7014360000003 -24288,Multiclass classification,ADWIN Bagging,Insects,0.5615761518507844,0.5615761518507844,0.5396852076547555,12.966436386108398,2756.048529 -25344,Multiclass classification,ADWIN Bagging,Insects,0.5679280274632048,0.5679280274632048,0.5455634192548012,13.622279167175293,2989.801767 -26400,Multiclass classification,ADWIN Bagging,Insects,0.5727868479866661,0.5727868479866661,0.5496374434570931,13.72577953338623,3232.991513 -27456,Multiclass classification,ADWIN Bagging,Insects,0.5754143143325442,0.5754143143325442,0.5513680135969626,13.724169731140137,3485.670293 -28512,Multiclass classification,ADWIN Bagging,Insects,0.5772859598049875,0.5772859598049875,0.5551350356863173,13.7214994430542,3747.946766 -29568,Multiclass classification,ADWIN Bagging,Insects,0.577772516657084,0.577772516657084,0.559086133229251,13.720248222351074,4020.178573 -30624,Multiclass classification,ADWIN Bagging,Insects,0.578225516768442,0.578225516768442,0.5625516131192055,12.85925006866455,4302.520044 -31680,Multiclass classification,ADWIN Bagging,Insects,0.5795637488557088,0.5795637488557088,0.5663363640160618,12.858540534973145,4594.920222 -32736,Multiclass classification,ADWIN Bagging,Insects,0.5811211241790133,0.5811211241790133,0.5696723582178382,12.857670783996582,4897.257331 -33792,Multiclass classification,ADWIN Bagging,Insects,0.575804208221124,0.575804208221124,0.5647934119551398,13.070902824401855,5209.806865 -34848,Multiclass classification,ADWIN Bagging,Insects,0.5701495107182828,0.5701495107182828,0.559068023359177,13.0708646774292,5532.790806 -35904,Multiclass classification,ADWIN Bagging,Insects,0.5657744478177311,0.5657744478177311,0.5542573482740074,13.072970390319824,5866.228602 -36960,Multiclass classification,ADWIN Bagging,Insects,0.5611894261208366,0.5611894261208366,0.5493152777162592,13.618464469909668,6210.1901960000005 -38016,Multiclass classification,ADWIN Bagging,Insects,0.558779429172695,0.558779429172695,0.5463982360776033,13.620196342468262,6564.708960000001 -39072,Multiclass classification,ADWIN Bagging,Insects,0.5546825010877633,0.5546825010877633,0.5426283860139581,14.406596183776855,6929.528370000001 -40128,Multiclass classification,ADWIN Bagging,Insects,0.5542153662122761,0.5542153662122761,0.5429626632180721,15.257904052734375,7304.280479000001 -41184,Multiclass classification,ADWIN Bagging,Insects,0.5541364155112547,0.5541364155112547,0.5435420562964656,15.358447074890137,7688.491709000001 -42240,Multiclass classification,ADWIN Bagging,Insects,0.5542981604678141,0.5542981604678141,0.5443914000180358,15.357035636901855,8082.149982000001 -43296,Multiclass classification,ADWIN Bagging,Insects,0.554151749624668,0.554151749624668,0.5448486588729108,13.518179893493652,8485.318247000001 -44352,Multiclass classification,ADWIN Bagging,Insects,0.5536290049829767,0.5536290049829767,0.5448029815059025,13.742095947265625,8897.925593000002 -45408,Multiclass classification,ADWIN Bagging,Insects,0.5541436342414165,0.5541436342414165,0.5454957405719211,14.286569595336914,9319.685836000002 -46464,Multiclass classification,ADWIN Bagging,Insects,0.5553020683124207,0.5553020683124207,0.546961663735647,15.266200065612793,9750.326660000002 -47520,Multiclass classification,ADWIN Bagging,Insects,0.5579662871693428,0.5579662871693428,0.5498636684303295,14.323851585388184,10190.236686000002 -48576,Multiclass classification,ADWIN Bagging,Insects,0.5627586206896552,0.5627586206896552,0.5545030394801858,14.950955390930176,10639.519510000002 -49632,Multiclass classification,ADWIN Bagging,Insects,0.5677701436602124,0.5677701436602124,0.5591808574875289,15.350643157958984,11098.085262000002 -50688,Multiclass classification,ADWIN Bagging,Insects,0.5730463432438297,0.5730463432438297,0.5639878919164368,16.015583038330078,11565.627356000003 -51744,Multiclass classification,ADWIN Bagging,Insects,0.5791894555785324,0.5791894555785324,0.5695807960578061,16.33325481414795,12041.828478000003 -52800,Multiclass classification,ADWIN Bagging,Insects,0.5794238527244834,0.5794238527244834,0.5701364277094956,15.444610595703125,12525.893185000003 -408,Multiclass classification,ADWIN Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.2430334091186523,1.187339 -816,Multiclass classification,ADWIN Bagging,Keystroke,0.9411042944785276,0.9411042944785276,0.7377235942917068,3.1903820037841797,4.698555 -1224,Multiclass classification,ADWIN Bagging,Keystroke,0.8879803761242846,0.8879803761242846,0.873420796574987,4.134529113769531,10.466629000000001 -1632,Multiclass classification,ADWIN Bagging,Keystroke,0.8988350705088902,0.8988350705088902,0.8792834531664682,5.086630821228027,18.824542 -2040,Multiclass classification,ADWIN Bagging,Keystroke,0.8950465914664051,0.8950465914664051,0.8828407845486113,6.147420883178711,30.316821 -2448,Multiclass classification,ADWIN Bagging,Keystroke,0.856559051900286,0.856559051900286,0.8543242501248514,6.5137739181518555,45.490026 -2856,Multiclass classification,ADWIN Bagging,Keystroke,0.8640980735551663,0.8640980735551663,0.8525227127090282,7.461577415466309,64.600537 -3264,Multiclass classification,ADWIN Bagging,Keystroke,0.855654305853509,0.855654305853509,0.8307453339686874,8.407819747924805,88.28106700000001 -3672,Multiclass classification,ADWIN Bagging,Keystroke,0.8469081994007083,0.8469081994007084,0.8445950801753395,9.06855297088623,117.06845200000001 -4080,Multiclass classification,ADWIN Bagging,Keystroke,0.839911743074283,0.839911743074283,0.8273018519986841,10.241823196411133,151.440392 -4488,Multiclass classification,ADWIN Bagging,Keystroke,0.8279474036104302,0.8279474036104302,0.8381848634946416,11.187081336975098,191.91936800000002 -4896,Multiclass classification,ADWIN Bagging,Keystroke,0.8294177732379979,0.8294177732379979,0.8370944525285466,8.72095775604248,237.68305700000002 -5304,Multiclass classification,ADWIN Bagging,Keystroke,0.832736187063926,0.832736187063926,0.8304665020850452,9.573864936828613,288.722663 -5712,Multiclass classification,ADWIN Bagging,Keystroke,0.8254246191560147,0.8254246191560147,0.8318293629616008,10.363824844360352,345.486273 -6120,Multiclass classification,ADWIN Bagging,Keystroke,0.8238274227815002,0.8238274227815002,0.8134447828524414,11.40614128112793,408.45138 -6528,Multiclass classification,ADWIN Bagging,Keystroke,0.8043511567335683,0.8043511567335683,0.8054460603633147,12.15697956085205,478.20571299999995 -6936,Multiclass classification,ADWIN Bagging,Keystroke,0.8005767844268205,0.8005767844268206,0.8067791986535922,11.58870792388916,555.142755 -7344,Multiclass classification,ADWIN Bagging,Keystroke,0.8081165736075173,0.8081165736075173,0.8106639227074198,12.00939655303955,638.5654069999999 -7752,Multiclass classification,ADWIN Bagging,Keystroke,0.8097019739388466,0.8097019739388466,0.8127585051729247,13.156713485717773,728.9317779999999 -8160,Multiclass classification,ADWIN Bagging,Keystroke,0.8134575315602403,0.8134575315602401,0.8148392057777913,13.882833480834961,826.9412199999999 -8568,Multiclass classification,ADWIN Bagging,Keystroke,0.817672464106455,0.817672464106455,0.8208026583224199,15.191060066223145,933.116233 -8976,Multiclass classification,ADWIN Bagging,Keystroke,0.8213927576601672,0.8213927576601672,0.8243856825821874,16.25563907623291,1048.180156 -9384,Multiclass classification,ADWIN Bagging,Keystroke,0.8219119684535863,0.8219119684535864,0.8243183344026902,17.071918487548828,1172.5966959999998 -9792,Multiclass classification,ADWIN Bagging,Keystroke,0.8224900418751915,0.8224900418751915,0.8248306232761192,18.233381271362305,1306.7477949999998 -10200,Multiclass classification,ADWIN Bagging,Keystroke,0.819197960584371,0.819197960584371,0.8170259665463304,19.36789894104004,1451.4292829999997 -10608,Multiclass classification,ADWIN Bagging,Keystroke,0.805788630149901,0.8057886301499011,0.8022367569175978,20.506345748901367,1607.1296199999997 -11016,Multiclass classification,ADWIN Bagging,Keystroke,0.802088061733999,0.8020880617339992,0.8038074645550285,19.16464138031006,1773.9352609999996 -11424,Multiclass classification,ADWIN Bagging,Keystroke,0.8018909218243894,0.8018909218243894,0.8005729972530424,19.133995056152344,1951.2468279999996 -11832,Multiclass classification,ADWIN Bagging,Keystroke,0.8002704758684811,0.800270475868481,0.8004166941842216,20.079543113708496,2139.4866899999997 -12240,Multiclass classification,ADWIN Bagging,Keystroke,0.8035787237519405,0.8035787237519405,0.8060123607032721,17.827110290527344,2338.9088759999995 -12648,Multiclass classification,ADWIN Bagging,Keystroke,0.8088084130623864,0.8088084130623864,0.8108606005777994,15.629319190979004,2547.8446989999993 -13056,Multiclass classification,ADWIN Bagging,Keystroke,0.8079662964381463,0.8079662964381463,0.8077709771623751,16.39698314666748,2766.5130659999995 -13464,Multiclass classification,ADWIN Bagging,Keystroke,0.8068038327267325,0.8068038327267325,0.807905549135964,17.24907112121582,2995.4784789999994 -13872,Multiclass classification,ADWIN Bagging,Keystroke,0.810107418354841,0.810107418354841,0.8115061911206084,18.023069381713867,3234.9291429999994 -14280,Multiclass classification,ADWIN Bagging,Keystroke,0.813432313187198,0.813432313187198,0.814709519180665,19.25601577758789,3485.4148369999994 -14688,Multiclass classification,ADWIN Bagging,Keystroke,0.8169810036086335,0.8169810036086335,0.8183348126971706,19.792034149169922,3747.4763309999994 -15096,Multiclass classification,ADWIN Bagging,Keystroke,0.8210665783371978,0.8210665783371978,0.8224533109934684,20.76410961151123,4021.7266099999993 -15504,Multiclass classification,ADWIN Bagging,Keystroke,0.8227439850351544,0.8227439850351544,0.8236860076332361,21.707032203674316,4308.908110999999 -15912,Multiclass classification,ADWIN Bagging,Keystroke,0.8177361573754006,0.8177361573754006,0.8170714187961161,22.97433376312256,4609.3886379999985 -16320,Multiclass classification,ADWIN Bagging,Keystroke,0.8135915190881794,0.8135915190881794,0.8136474897036394,23.700613975524902,4923.859395999999 -16728,Multiclass classification,ADWIN Bagging,Keystroke,0.8133556525378132,0.8133556525378132,0.8142218072403056,24.764389038085938,5252.522612999999 -17136,Multiclass classification,ADWIN Bagging,Keystroke,0.8092792529909542,0.8092792529909542,0.8090411402278314,26.198601722717285,5595.9090209999995 -17544,Multiclass classification,ADWIN Bagging,Keystroke,0.8062475061278003,0.8062475061278003,0.8065701979489333,25.60452175140381,5954.543495 -17952,Multiclass classification,ADWIN Bagging,Keystroke,0.8078101498523759,0.8078101498523759,0.8084559739072698,26.552149772644043,6328.536349 -18360,Multiclass classification,ADWIN Bagging,Keystroke,0.8103927229151915,0.8103927229151915,0.8111272646261444,27.50138759613037,6718.347567 -18768,Multiclass classification,ADWIN Bagging,Keystroke,0.813022859274258,0.813022859274258,0.8138485204649677,28.353083610534668,7124.657721 -19176,Multiclass classification,ADWIN Bagging,Keystroke,0.8090743155149935,0.8090743155149935,0.8093701596568051,29.32584285736084,7547.838048 -19584,Multiclass classification,ADWIN Bagging,Keystroke,0.8110606137976817,0.8110606137976817,0.8116953495842238,30.333707809448242,7988.342591 -19992,Multiclass classification,ADWIN Bagging,Keystroke,0.8081136511430144,0.8081136511430144,0.8084718836746521,31.28043556213379,8446.728501 -20400,Multiclass classification,ADWIN Bagging,Keystroke,0.8058238148928869,0.805823814892887,0.8062504565207905,32.1812219619751,8923.60629 -46,Multiclass classification,AdaBoost,ImageSegments,0.1111111111111111,0.1111111111111111,0.0815018315018315,3.4461936950683594,0.804099 -92,Multiclass classification,AdaBoost,ImageSegments,0.23076923076923078,0.23076923076923078,0.22263917712834122,4.129319190979004,2.027411 -138,Multiclass classification,AdaBoost,ImageSegments,0.4233576642335766,0.4233576642335766,0.4463537718619156,4.129193305969238,3.5999849999999998 -184,Multiclass classification,AdaBoost,ImageSegments,0.5355191256830601,0.5355191256830601,0.5617062146473911,4.129368782043457,5.452412 -230,Multiclass classification,AdaBoost,ImageSegments,0.5938864628820961,0.5938864628820961,0.6236530662596055,4.12935733795166,7.651963 -276,Multiclass classification,AdaBoost,ImageSegments,0.6290909090909091,0.6290909090909091,0.6558170665459355,4.129300117492676,10.207424 -322,Multiclass classification,AdaBoost,ImageSegments,0.660436137071651,0.660436137071651,0.6785747202615152,4.128628730773926,13.08877 -368,Multiclass classification,AdaBoost,ImageSegments,0.6920980926430518,0.6920980926430518,0.7041680355881775,4.12868595123291,16.291428 -414,Multiclass classification,AdaBoost,ImageSegments,0.7167070217917676,0.7167070217917676,0.7259075149442815,4.128170967102051,19.832325 -460,Multiclass classification,AdaBoost,ImageSegments,0.7254901960784313,0.7254901960784313,0.732501171084948,4.128491401672363,23.76135 -506,Multiclass classification,AdaBoost,ImageSegments,0.7386138613861386,0.7386138613861386,0.7428621938273078,4.1287431716918945,28.024553 -552,Multiclass classification,AdaBoost,ImageSegments,0.7422867513611615,0.7422867513611615,0.7453719085253248,4.128548622131348,32.646215 -598,Multiclass classification,AdaBoost,ImageSegments,0.7487437185929648,0.7487437185929648,0.7504522188790484,4.128659248352051,37.596323 -644,Multiclass classification,AdaBoost,ImageSegments,0.7465007776049767,0.7465007776049767,0.7482323503576439,4.128731727600098,42.92857 -690,Multiclass classification,AdaBoost,ImageSegments,0.7489114658925979,0.748911465892598,0.7488472102580619,4.128785133361816,48.576103 -736,Multiclass classification,AdaBoost,ImageSegments,0.7523809523809524,0.7523809523809524,0.75182837230991,4.1286211013793945,54.551686000000004 -782,Multiclass classification,AdaBoost,ImageSegments,0.7541613316261203,0.7541613316261204,0.7531089046321313,4.128552436828613,60.838379 -828,Multiclass classification,AdaBoost,ImageSegments,0.7557436517533253,0.7557436517533253,0.7552013614952863,4.1284990310668945,67.464563 -874,Multiclass classification,AdaBoost,ImageSegments,0.7617411225658648,0.7617411225658649,0.7601066395856337,4.128571510314941,74.378096 -920,Multiclass classification,AdaBoost,ImageSegments,0.763873775843308,0.763873775843308,0.7623480483274478,4.1285905838012695,81.591089 -966,Multiclass classification,AdaBoost,ImageSegments,0.7678756476683938,0.7678756476683938,0.7646598072570266,4.128613471984863,89.10581599999999 -1012,Multiclass classification,AdaBoost,ImageSegments,0.7705242334322453,0.7705242334322453,0.7668271197983112,4.128720283508301,96.93086299999999 -1058,Multiclass classification,AdaBoost,ImageSegments,0.7757805108798487,0.7757805108798487,0.7714920336037776,4.128579139709473,105.05195599999999 -1104,Multiclass classification,AdaBoost,ImageSegments,0.7760652765185857,0.7760652765185856,0.7719206139767609,4.128727912902832,113.49129199999999 -1150,Multiclass classification,AdaBoost,ImageSegments,0.7789382071366405,0.7789382071366405,0.7750313949659529,4.128632545471191,122.21725699999999 -1196,Multiclass classification,AdaBoost,ImageSegments,0.7849372384937239,0.7849372384937239,0.782000389047251,4.128678321838379,131.239072 -1242,Multiclass classification,AdaBoost,ImageSegments,0.7856567284448026,0.7856567284448026,0.7827470902102025,4.128628730773926,140.604336 -1288,Multiclass classification,AdaBoost,ImageSegments,0.7894327894327894,0.7894327894327894,0.785982924599392,4.128533363342285,150.25346299999998 -1334,Multiclass classification,AdaBoost,ImageSegments,0.7906976744186046,0.7906976744186046,0.7876424482584368,4.128628730773926,160.232262 -1380,Multiclass classification,AdaBoost,ImageSegments,0.7933284989122552,0.7933284989122552,0.7906471924204203,4.128582954406738,170.496442 -1426,Multiclass classification,AdaBoost,ImageSegments,0.7978947368421052,0.7978947368421052,0.7945020166797493,4.128670692443848,181.024488 -1472,Multiclass classification,AdaBoost,ImageSegments,0.8028552005438477,0.8028552005438477,0.7982243751921435,4.128663063049316,191.820653 -1518,Multiclass classification,AdaBoost,ImageSegments,0.8035596572181938,0.8035596572181938,0.7981876534181911,4.1286821365356445,202.942587 -1564,Multiclass classification,AdaBoost,ImageSegments,0.8035828534868842,0.8035828534868842,0.798634974540431,4.128708839416504,214.370001 -1610,Multiclass classification,AdaBoost,ImageSegments,0.8048477315102548,0.8048477315102549,0.7997380784882049,4.128571510314941,226.09596 -1656,Multiclass classification,AdaBoost,ImageSegments,0.8066465256797583,0.8066465256797583,0.80161945439383,4.128567695617676,238.115396 -1702,Multiclass classification,AdaBoost,ImageSegments,0.8059964726631393,0.8059964726631393,0.8024858564723996,4.128705024719238,250.457722 -1748,Multiclass classification,AdaBoost,ImageSegments,0.8070978820835718,0.8070978820835718,0.8029124203507954,4.128613471984863,263.107237 -1794,Multiclass classification,AdaBoost,ImageSegments,0.8081427774679308,0.8081427774679307,0.8029834045630978,4.1286516189575195,276.028168 -1840,Multiclass classification,AdaBoost,ImageSegments,0.8069603045133225,0.8069603045133223,0.8019276227162541,4.128785133361816,289.253241 -1886,Multiclass classification,AdaBoost,ImageSegments,0.8053050397877984,0.8053050397877984,0.8006727596367826,4.1285905838012695,302.793979 -1932,Multiclass classification,AdaBoost,ImageSegments,0.8047643707923355,0.8047643707923355,0.7995493059800364,4.128586769104004,316.637791 -1978,Multiclass classification,AdaBoost,ImageSegments,0.8057663125948407,0.8057663125948407,0.8003960406612561,4.12862491607666,330.791782 -2024,Multiclass classification,AdaBoost,ImageSegments,0.8072170044488384,0.8072170044488384,0.8005625942078284,4.1286211013793945,345.23827800000004 -2070,Multiclass classification,AdaBoost,ImageSegments,0.8066698888351861,0.8066698888351861,0.8002110568368,4.128506660461426,360.0031 -2116,Multiclass classification,AdaBoost,ImageSegments,0.807565011820331,0.807565011820331,0.8005131307885663,4.128533363342285,375.05937700000004 -2162,Multiclass classification,AdaBoost,ImageSegments,0.8079592781119852,0.8079592781119852,0.8006755955605838,4.128510475158691,390.39582400000006 -2208,Multiclass classification,AdaBoost,ImageSegments,0.8087902129587675,0.8087902129587675,0.8009921695193861,4.128510475158691,405.99760000000003 -2254,Multiclass classification,AdaBoost,ImageSegments,0.8060363959165557,0.8060363959165557,0.7987732120640717,4.128533363342285,421.95304000000004 -2300,Multiclass classification,AdaBoost,ImageSegments,0.8051326663766856,0.8051326663766856,0.7980778928096751,4.128533363342285,438.21900000000005 -1056,Multiclass classification,AdaBoost,Insects,0.6360189573459716,0.6360189573459716,0.5992691812827112,6.522543907165527,11.237994 -2112,Multiclass classification,AdaBoost,Insects,0.6110847939365229,0.6110847939365229,0.5773210074897359,6.522406578063965,32.531305 -3168,Multiclass classification,AdaBoost,Insects,0.6043574360593622,0.6043574360593622,0.5704368753709179,6.521971702575684,63.87417000000001 -4224,Multiclass classification,AdaBoost,Insects,0.6014681506038362,0.6014681506038362,0.5676969561642587,6.521697044372559,105.21576400000001 -5280,Multiclass classification,AdaBoost,Insects,0.6057965523773442,0.6057965523773442,0.5710016183775801,6.521697044372559,156.441097 -6336,Multiclass classification,AdaBoost,Insects,0.5966850828729282,0.5966850828729282,0.5635903588556204,6.521857261657715,217.68026400000002 -7392,Multiclass classification,AdaBoost,Insects,0.5957245298335814,0.5957245298335814,0.5625002603439991,6.52231502532959,288.908384 -8448,Multiclass classification,AdaBoost,Insects,0.5982005445720374,0.5982005445720374,0.5646892369665863,6.522658348083496,370.082682 -9504,Multiclass classification,AdaBoost,Insects,0.596337998526781,0.596337998526781,0.5627085514562804,6.523001670837402,461.259323 -10560,Multiclass classification,AdaBoost,Insects,0.5965527038545316,0.5965527038545316,0.5631320282838163,6.523184776306152,562.3727710000001 -11616,Multiclass classification,AdaBoost,Insects,0.5953508394317693,0.5953508394317693,0.562671447170627,6.523184776306152,673.482974 -12672,Multiclass classification,AdaBoost,Insects,0.5979796385447084,0.5979796385447084,0.5680559575776837,6.522841453552246,794.598712 -13728,Multiclass classification,AdaBoost,Insects,0.610767101333139,0.610767101333139,0.5941277335666079,6.522337913513184,925.382156 -14784,Multiclass classification,AdaBoost,Insects,0.6019752418318338,0.6019752418318338,0.5851264744797858,6.522246360778809,1066.407983 -15840,Multiclass classification,AdaBoost,Insects,0.5705536965717533,0.5705536965717533,0.5545059657048704,6.522475242614746,1217.74448 -16896,Multiclass classification,AdaBoost,Insects,0.548091151228174,0.548091151228174,0.5320735507355622,6.522887229919434,1378.917078 -17952,Multiclass classification,AdaBoost,Insects,0.5307225224221492,0.5307225224221492,0.5138536287616571,6.523138999938965,1549.794627 -19008,Multiclass classification,AdaBoost,Insects,0.5182827379386542,0.5182827379386542,0.4990809738484312,6.523367881774902,1730.586936 -20064,Multiclass classification,AdaBoost,Insects,0.5182176145142801,0.5182176145142801,0.497867701567998,8.711265563964844,1921.389763 -21120,Multiclass classification,AdaBoost,Insects,0.5272503432927695,0.5272503432927695,0.5067114684709674,15.550712585449219,2121.77156 -22176,Multiclass classification,AdaBoost,Insects,0.533032694475761,0.533032694475761,0.5127471323280748,16.9340763092041,2331.759567 -23232,Multiclass classification,AdaBoost,Insects,0.5410442942619775,0.5410442942619775,0.5207771198745245,17.15799903869629,2551.0181620000003 -24288,Multiclass classification,AdaBoost,Insects,0.5459710956478775,0.5459710956478775,0.5251711652768186,17.155046463012695,2778.8449820000005 -25344,Multiclass classification,AdaBoost,Insects,0.5532099593576135,0.5532099593576135,0.5314216535856217,17.15360450744629,3015.0560080000005 -26400,Multiclass classification,AdaBoost,Insects,0.5607788173794462,0.5607788173794462,0.5375130024626694,17.265982627868652,3259.2678140000003 -27456,Multiclass classification,AdaBoost,Insects,0.5667091604443635,0.5667091604443635,0.5418496825562071,17.378803253173828,3511.9238520000004 -28512,Multiclass classification,AdaBoost,Insects,0.5692890463329943,0.5692890463329943,0.5455529487931667,17.379924774169922,3773.544178 -29568,Multiclass classification,AdaBoost,Insects,0.5688436432509216,0.5688436432509216,0.5481992899375988,17.380359649658203,4045.250953 -30624,Multiclass classification,AdaBoost,Insects,0.5687228553701467,0.5687228553701467,0.5505043481720591,17.380290985107422,4327.317909 -31680,Multiclass classification,AdaBoost,Insects,0.5691467533697402,0.5691467533697402,0.5529220328647554,17.37978744506836,4619.673811000001 -32736,Multiclass classification,AdaBoost,Insects,0.5703986558729189,0.5703986558729189,0.5556828084411201,17.37948989868164,4922.186926 -33792,Multiclass classification,AdaBoost,Insects,0.5650025154626972,0.5650025154626972,0.5507695387439543,17.604686737060547,5235.061695 -34848,Multiclass classification,AdaBoost,Insects,0.5587568513788849,0.5587568513788849,0.5445559443415654,18.05030918121338,5558.17951 -35904,Multiclass classification,AdaBoost,Insects,0.554215525165028,0.554215525165028,0.5396701176441828,18.150463104248047,5891.808229 -36960,Multiclass classification,AdaBoost,Insects,0.5490408290267594,0.5490408290267594,0.5342475234810463,19.505443572998047,6235.988455000001 -38016,Multiclass classification,AdaBoost,Insects,0.5476522425358411,0.5476522425358411,0.5324130893403342,21.324423789978027,6590.662011 -39072,Multiclass classification,AdaBoost,Insects,0.5427810908346344,0.5427810908346344,0.5280992603544316,22.076537132263184,6955.865912 -40128,Multiclass classification,AdaBoost,Insects,0.5417300072270541,0.5417300072270541,0.5282649533846114,22.738471031188965,7330.794870000001 -41184,Multiclass classification,AdaBoost,Insects,0.5417283830706845,0.5417283830706845,0.5295529576867488,23.164944648742676,7714.553666000001 -42240,Multiclass classification,AdaBoost,Insects,0.5419635881531286,0.5419635881531286,0.5308394560628455,23.606464385986328,8107.062243 -43296,Multiclass classification,AdaBoost,Insects,0.5438734264926666,0.5438734264926666,0.5334569208328087,23.706249237060547,8507.305821 -44352,Multiclass classification,AdaBoost,Insects,0.5453315596040675,0.5453315596040675,0.5354029875943346,24.299342155456543,8915.303832 -45408,Multiclass classification,AdaBoost,Insects,0.547140308762966,0.547140308762966,0.5374156745075451,24.818781852722168,9330.915411 -46464,Multiclass classification,AdaBoost,Insects,0.5492327228116997,0.5492327228116997,0.5397202270950943,25.030532836914062,9754.309095 -47520,Multiclass classification,AdaBoost,Insects,0.5481596834950231,0.5481596834950231,0.5387960204161004,25.669864654541016,10186.398449 -48576,Multiclass classification,AdaBoost,Insects,0.5455275347400926,0.5455275347400926,0.5361266295596548,25.668834686279297,10627.539344 -49632,Multiclass classification,AdaBoost,Insects,0.5440752755334367,0.5440752755334367,0.534604738581891,26.10423469543457,11077.630938 -50688,Multiclass classification,AdaBoost,Insects,0.5484443742971571,0.5484443742971571,0.538570218508335,27.224528312683105,11536.66667 -51744,Multiclass classification,AdaBoost,Insects,0.5534081904798717,0.5534081904798717,0.5429607704191827,28.064788818359375,12004.483564 -52800,Multiclass classification,AdaBoost,Insects,0.5540824636830243,0.5540824636830243,0.543927330204892,28.290183067321777,12481.284297 -408,Multiclass classification,AdaBoost,Keystroke,0.9877149877149877,0.9877149877149877,0.7696139476961394,2.1705713272094727,1.169245 -816,Multiclass classification,AdaBoost,Keystroke,0.9889570552147239,0.9889570552147239,0.9592655637573824,2.994051933288574,4.333642 -1224,Multiclass classification,AdaBoost,Keystroke,0.9852820932134096,0.9852820932134096,0.9482751483180805,4.729727745056152,12.346709 -1632,Multiclass classification,AdaBoost,Keystroke,0.9822194972409565,0.9822194972409565,0.9509896151723367,5.999781608581543,25.091939 -2040,Multiclass classification,AdaBoost,Keystroke,0.9725355566454145,0.9725355566454145,0.928775026512405,7.915155410766602,43.495481 -2448,Multiclass classification,AdaBoost,Keystroke,0.9550469963220269,0.9550469963220269,0.9404929408648164,9.985001564025879,68.010219 -2856,Multiclass classification,AdaBoost,Keystroke,0.9516637478108582,0.9516637478108582,0.9265706247083845,13.281692504882812,95.83503300000001 -3264,Multiclass classification,AdaBoost,Keystroke,0.9457554397793442,0.9457554397793442,0.9273434636455653,16.35391616821289,127.20590200000001 -3672,Multiclass classification,AdaBoost,Keystroke,0.9417052574230454,0.9417052574230454,0.925978466853896,18.91156578063965,163.340984 -4080,Multiclass classification,AdaBoost,Keystroke,0.9355234126011277,0.9355234126011277,0.9181372267911062,22.338744163513184,205.235807 -4488,Multiclass classification,AdaBoost,Keystroke,0.931580120347671,0.931580120347671,0.9327276252021246,25.310707092285156,252.959286 -4896,Multiclass classification,AdaBoost,Keystroke,0.9303370786516854,0.9303370786516854,0.9257176086775135,28.274658203125,306.688529 -5304,Multiclass classification,AdaBoost,Keystroke,0.9253252875730719,0.9253252875730719,0.9165251784293146,32.214202880859375,367.43293400000005 -5712,Multiclass classification,AdaBoost,Keystroke,0.9226054981614429,0.9226054981614429,0.9209111845314155,34.49547863006592,436.87881300000004 -6120,Multiclass classification,AdaBoost,Keystroke,0.9181238764504004,0.9181238764504004,0.9091206319047903,38.86995029449463,513.269664 -6528,Multiclass classification,AdaBoost,Keystroke,0.9129768653286349,0.9129768653286349,0.9114007831703169,42.32428169250488,602.212119 -6936,Multiclass classification,AdaBoost,Keystroke,0.9114635904830569,0.9114635904830569,0.9134311944430067,44.19167232513428,700.8190040000001 -7344,Multiclass classification,AdaBoost,Keystroke,0.9116165055154569,0.9116165055154569,0.9097332482243848,44.84274196624756,807.0835780000001 -7752,Multiclass classification,AdaBoost,Keystroke,0.9112372597084247,0.9112372597084247,0.9111242959524108,46.848572731018066,921.3564090000001 -8160,Multiclass classification,AdaBoost,Keystroke,0.9094251746537566,0.9094251746537566,0.9076128910354778,51.167396545410156,1044.662094 -8568,Multiclass classification,AdaBoost,Keystroke,0.9066184195167504,0.9066184195167505,0.9066450469749987,55.86186981201172,1177.672199 -8976,Multiclass classification,AdaBoost,Keystroke,0.9056267409470752,0.9056267409470752,0.9063353807566539,58.41574668884277,1322.047745 -9384,Multiclass classification,AdaBoost,Keystroke,0.9030160929340296,0.9030160929340296,0.9022077684947395,62.26175117492676,1477.2987620000001 -9792,Multiclass classification,AdaBoost,Keystroke,0.8986824634868757,0.8986824634868757,0.8984090939041232,65.20822143554688,1646.0177680000002 -10200,Multiclass classification,AdaBoost,Keystroke,0.8947936072163938,0.8947936072163937,0.8926613887647973,69.96524620056152,1829.707382 -10608,Multiclass classification,AdaBoost,Keystroke,0.8881870462901857,0.8881870462901857,0.8865702773222168,73.60436820983887,2032.787653 -11016,Multiclass classification,AdaBoost,Keystroke,0.8849750340444847,0.8849750340444847,0.8859866133359942,77.43610191345215,2252.324419 -11424,Multiclass classification,AdaBoost,Keystroke,0.8823426420379935,0.8823426420379935,0.8811651142625456,81.9732666015625,2486.561888 -11832,Multiclass classification,AdaBoost,Keystroke,0.8789620488547037,0.8789620488547037,0.8783725809837211,87.50129985809326,2738.033946 -12240,Multiclass classification,AdaBoost,Keystroke,0.8803823841817142,0.8803823841817142,0.8815469015078649,88.71501064300537,3002.626444 -12648,Multiclass classification,AdaBoost,Keystroke,0.878231991776706,0.878231991776706,0.8774838611192476,93.73236656188965,3282.43662 -13056,Multiclass classification,AdaBoost,Keystroke,0.8737648410570663,0.8737648410570663,0.8731746930338653,98.24647235870361,3580.919978 -13464,Multiclass classification,AdaBoost,Keystroke,0.872168164599272,0.872168164599272,0.8726091982990895,102.17141056060791,3896.583194 -13872,Multiclass classification,AdaBoost,Keystroke,0.8693677456564054,0.8693677456564054,0.869586320653203,106.91088581085205,4229.831886 -14280,Multiclass classification,AdaBoost,Keystroke,0.8653967364661391,0.8653967364661391,0.8650950015616714,111.61231708526611,4581.249889 -14688,Multiclass classification,AdaBoost,Keystroke,0.8650507251310683,0.8650507251310683,0.8661026270223636,115.34651947021484,4948.063453 -15096,Multiclass classification,AdaBoost,Keystroke,0.8667770785028155,0.8667770785028155,0.8680260482593213,118.24315452575684,5329.445739 -15504,Multiclass classification,AdaBoost,Keystroke,0.8673805069986454,0.8673805069986454,0.8683176015232197,121.64087104797363,5727.804435999999 -15912,Multiclass classification,AdaBoost,Keystroke,0.8630507196279303,0.8630507196279303,0.8630078198630903,126.92564392089844,6152.632774 -16320,Multiclass classification,AdaBoost,Keystroke,0.8588761566272444,0.8588761566272444,0.8591617021179835,132.20891761779785,6602.417036 -16728,Multiclass classification,AdaBoost,Keystroke,0.8546661086865547,0.8546661086865547,0.8551483908890608,137.6782627105713,7072.757626 -17136,Multiclass classification,AdaBoost,Keystroke,0.850539830755763,0.850539830755763,0.8506967692771638,139.40350437164307,7574.584865 -17544,Multiclass classification,AdaBoost,Keystroke,0.8456934389785099,0.8456934389785099,0.845900693576748,144.32332038879395,8103.947966 -17952,Multiclass classification,AdaBoost,Keystroke,0.8442983677789538,0.8442983677789538,0.8449597117669952,149.55280876159668,8653.396068 -18360,Multiclass classification,AdaBoost,Keystroke,0.8462879241788769,0.8462879241788769,0.8472097835611015,154.20918083190918,9220.958091 -18768,Multiclass classification,AdaBoost,Keystroke,0.8472851281504769,0.8472851281504769,0.8482490326871865,158.50969696044922,9805.946569 -19176,Multiclass classification,AdaBoost,Keystroke,0.845632333767927,0.845632333767927,0.8464906194356719,163.69990730285645,10414.744709999999 -19584,Multiclass classification,AdaBoost,Keystroke,0.8470612265740693,0.8470612265740693,0.8481337525939465,167.61861419677734,11041.283472 -19992,Multiclass classification,AdaBoost,Keystroke,0.8455805112300535,0.8455805112300535,0.8467102165017473,172.522611618042,11691.217596999999 -20400,Multiclass classification,AdaBoost,Keystroke,0.8424922790332859,0.8424922790332859,0.8436347186891262,177.38492488861084,12366.859336 -46,Multiclass classification,Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.24576497265572897,4.181334495544434,1.091398 -92,Multiclass classification,Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.49347523955818895,4.1845502853393555,2.768304 -138,Multiclass classification,Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646677,4.1842756271362305,4.818241 -184,Multiclass classification,Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.614143164890895,4.184859275817871,7.25269 -230,Multiclass classification,Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,4.184233665466309,10.061851 -276,Multiclass classification,Bagging,ImageSegments,0.64,0.64,0.6559607038460422,4.184771537780762,13.254704 -322,Multiclass classification,Bagging,ImageSegments,0.6666666666666666,0.6666666666666666,0.6673617488913626,4.184481620788574,16.809438 -368,Multiclass classification,Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6911959597548877,4.184699058532715,20.719577 -414,Multiclass classification,Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.7079630503641953,4.1850385665893555,25.02075 -460,Multiclass classification,Bagging,ImageSegments,0.7124183006535948,0.7124183006535948,0.7065500352371009,4.184954643249512,29.692393 -506,Multiclass classification,Bagging,ImageSegments,0.7207920792079208,0.7207920792079208,0.7127593158348896,4.184813499450684,34.691523 -552,Multiclass classification,Bagging,ImageSegments,0.7259528130671506,0.7259528130671506,0.7192025503807162,4.184779167175293,40.034434999999995 -598,Multiclass classification,Bagging,ImageSegments,0.7319932998324958,0.7319932998324957,0.7251188986558661,4.185019493103027,45.725680999999994 -644,Multiclass classification,Bagging,ImageSegments,0.7309486780715396,0.7309486780715396,0.7259740406437202,4.184813499450684,51.77256499999999 -690,Multiclass classification,Bagging,ImageSegments,0.7358490566037735,0.7358490566037735,0.7304359912942561,4.184943199157715,58.14505499999999 -736,Multiclass classification,Bagging,ImageSegments,0.7374149659863946,0.7374149659863947,0.7331499347170709,4.185004234313965,64.846683 -782,Multiclass classification,Bagging,ImageSegments,0.7426376440460948,0.7426376440460948,0.7385597120510639,4.184893608093262,71.895225 -828,Multiclass classification,Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7412375783772317,4.184882164001465,79.27328899999999 -874,Multiclass classification,Bagging,ImageSegments,0.7491408934707904,0.7491408934707904,0.7454343548790068,4.185431480407715,86.96748099999999 -920,Multiclass classification,Bagging,ImageSegments,0.7486398258977149,0.7486398258977149,0.7441307384051415,4.185576438903809,94.98590999999999 -966,Multiclass classification,Bagging,ImageSegments,0.7492227979274612,0.749222797927461,0.7439306216964365,4.185370445251465,103.316786 -1012,Multiclass classification,Bagging,ImageSegments,0.7487636003956478,0.7487636003956478,0.7437900284473965,4.185484886169434,111.97774899999999 -1058,Multiclass classification,Bagging,ImageSegments,0.750236518448439,0.7502365184484389,0.7448138061687654,4.185519218444824,120.95312499999999 -1104,Multiclass classification,Bagging,ImageSegments,0.7524932003626473,0.7524932003626473,0.7468314646869902,4.185484886169434,130.25113499999998 -1150,Multiclass classification,Bagging,ImageSegments,0.7554395126196692,0.7554395126196692,0.7493227137357602,4.185664176940918,139.83544499999996 -1196,Multiclass classification,Bagging,ImageSegments,0.7581589958158996,0.7581589958158996,0.7527652773681007,4.185568809509277,149.73636899999997 -1242,Multiclass classification,Bagging,ImageSegments,0.7574536663980661,0.7574536663980661,0.7525915384194215,4.185683250427246,159.94850499999995 -1288,Multiclass classification,Bagging,ImageSegments,0.7622377622377622,0.7622377622377621,0.7563448085202399,4.185866355895996,170.48539899999994 -1334,Multiclass classification,Bagging,ImageSegments,0.7621905476369092,0.7621905476369092,0.7566636999776912,4.186026573181152,181.34153899999995 -1380,Multiclass classification,Bagging,ImageSegments,0.7635968092820885,0.7635968092820886,0.7587252257765656,4.1860761642456055,192.51808799999995 -1426,Multiclass classification,Bagging,ImageSegments,0.7663157894736842,0.7663157894736842,0.7609139797315135,4.186099052429199,204.01178799999994 -1472,Multiclass classification,Bagging,ImageSegments,0.7709041468388851,0.7709041468388851,0.763768994920769,4.186240196228027,215.81223399999993 -1518,Multiclass classification,Bagging,ImageSegments,0.7719182597231378,0.7719182597231378,0.7639714255563932,4.186617851257324,227.92141499999994 -1564,Multiclass classification,Bagging,ImageSegments,0.7722328854766475,0.7722328854766475,0.765072133508071,4.186800956726074,240.33772499999995 -1610,Multiclass classification,Bagging,ImageSegments,0.7725295214418894,0.7725295214418892,0.764505787280341,4.186892509460449,253.08285699999996 -1656,Multiclass classification,Bagging,ImageSegments,0.7716012084592145,0.7716012084592145,0.7634170612719107,4.1867780685424805,266.15585699999997 -1702,Multiclass classification,Bagging,ImageSegments,0.7713109935332157,0.7713109935332157,0.7652815676598499,4.187075614929199,279.52232499999997 -1748,Multiclass classification,Bagging,ImageSegments,0.77389811104751,0.77389811104751,0.7674409436090757,4.187258720397949,293.187056 -1794,Multiclass classification,Bagging,ImageSegments,0.7752370329057445,0.7752370329057446,0.7674318582149376,4.1872968673706055,307.180551 -1840,Multiclass classification,Bagging,ImageSegments,0.7765089722675367,0.7765089722675368,0.7688731808749575,4.187228202819824,321.49742299999997 -1886,Multiclass classification,Bagging,ImageSegments,0.7750663129973475,0.7750663129973475,0.7678921362145585,4.187155723571777,336.11504499999995 -1932,Multiclass classification,Bagging,ImageSegments,0.7752459865354738,0.7752459865354739,0.7671636716269125,4.187251091003418,351.05391499999996 -1978,Multiclass classification,Bagging,ImageSegments,0.7759231158320687,0.7759231158320687,0.7670573130332382,4.187151908874512,366.316945 -2024,Multiclass classification,Bagging,ImageSegments,0.7775580820563519,0.7775580820563519,0.7671264358471986,4.187129020690918,381.896141 -2070,Multiclass classification,Bagging,ImageSegments,0.77670372160464,0.7767037216046399,0.7665050383810529,4.1872053146362305,397.783945 -2116,Multiclass classification,Bagging,ImageSegments,0.7773049645390071,0.7773049645390071,0.7663404166149341,4.1872053146362305,413.995271 -2162,Multiclass classification,Bagging,ImageSegments,0.7783433595557612,0.7783433595557612,0.7669657147488861,4.187277793884277,430.524022 -2208,Multiclass classification,Bagging,ImageSegments,0.780244676030811,0.780244676030811,0.7678552364681829,4.187273979187012,447.387318 -2254,Multiclass classification,Bagging,ImageSegments,0.7776298268974701,0.7776298268974701,0.7652407320979201,4.187224388122559,464.558569 -2300,Multiclass classification,Bagging,ImageSegments,0.7768595041322314,0.7768595041322314,0.7644610611003249,4.18729305267334,482.036228 -1056,Multiclass classification,Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762561,6.583989143371582,11.628861 -2112,Multiclass classification,Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.584485054016113,33.423105 -3168,Multiclass classification,Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244892,6.5851945877075195,65.495981 -4224,Multiclass classification,Bagging,Insects,0.6107032914989344,0.6107032914989344,0.574850135712032,6.585576057434082,107.816588 -5280,Multiclass classification,Bagging,Insects,0.615078613373745,0.615078613373745,0.5779184071248228,6.5863847732543945,160.258759 -6336,Multiclass classification,Bagging,Insects,0.6091554853985793,0.6091554853985793,0.5734262289926554,6.586209297180176,222.866435 -7392,Multiclass classification,Bagging,Insects,0.6058720064943851,0.6058720064943851,0.5704339658550047,6.585629463195801,295.76759400000003 -8448,Multiclass classification,Bagging,Insects,0.6070794364863265,0.6070794364863265,0.5712261057542335,6.585507392883301,378.90789100000006 -9504,Multiclass classification,Bagging,Insects,0.6040197832263495,0.6040197832263495,0.567883906637128,6.585629463195801,472.31115100000005 -10560,Multiclass classification,Bagging,Insects,0.6035609432711431,0.6035609432711431,0.5674913890030829,6.5859880447387695,575.935454 -11616,Multiclass classification,Bagging,Insects,0.6006026689625484,0.6006026689625484,0.5651886352361905,6.585965156555176,689.792083 -12672,Multiclass classification,Bagging,Insects,0.6032673032909794,0.6032673032909794,0.5704386423232538,6.585919380187988,813.953016 -13728,Multiclass classification,Bagging,Insects,0.6147738034530488,0.6147738034530488,0.5955647708468143,6.584591865539551,948.29318 -14784,Multiclass classification,Bagging,Insects,0.6052222147060813,0.6052222147060813,0.586323857604342,6.583569526672363,1092.793426 -15840,Multiclass classification,Bagging,Insects,0.570427425973862,0.570427425973862,0.5530515395071289,6.584805488586426,1247.37522 -16896,Multiclass classification,Bagging,Insects,0.5441254809115122,0.5441254809115122,0.5274626123277456,6.5833024978637695,1412.138663 -17952,Multiclass classification,Bagging,Insects,0.5247061445044844,0.5247061445044844,0.5077849244821269,6.5842180252075195,1587.005582 -19008,Multiclass classification,Bagging,Insects,0.5143368232756353,0.5143368232756353,0.4945891921842289,5.490016937255859,1771.530278 -20064,Multiclass classification,Bagging,Insects,0.5203110202860988,0.5203110202860988,0.4996705647403201,13.561949729919434,1966.283082 -21120,Multiclass classification,Bagging,Insects,0.5285288129172783,0.5285288129172783,0.5082662721949724,14.332747459411621,2175.2000869999997 -22176,Multiclass classification,Bagging,Insects,0.5345208568207441,0.5345208568207441,0.5149076376433322,14.875703811645508,2397.3423569999995 -23232,Multiclass classification,Bagging,Insects,0.5431104989023288,0.5431104989023288,0.5234265380967914,14.787662506103516,2632.3046249999998 -24288,Multiclass classification,Bagging,Insects,0.550253221888253,0.550253221888253,0.5298759738824472,16.32009983062744,2880.082024 -25344,Multiclass classification,Bagging,Insects,0.5564455668231859,0.5564455668231859,0.5355827199778521,16.310463905334473,3141.997661 -26400,Multiclass classification,Bagging,Insects,0.5614985416114247,0.5614985416114247,0.5398687013453174,16.303704261779785,3417.9057679999996 -27456,Multiclass classification,Bagging,Insects,0.5647787288289929,0.5647787288289929,0.5421799635248432,15.336288452148438,3708.1186949999997 -28512,Multiclass classification,Bagging,Insects,0.5680965241485743,0.5680965241485743,0.5473162851674372,13.81827449798584,4011.6853579999997 -29568,Multiclass classification,Bagging,Insects,0.5701626813677411,0.5701626813677411,0.5529817475842932,11.429868698120117,4328.316425 -30624,Multiclass classification,Bagging,Insects,0.5724455474643242,0.5724455474643242,0.5586057023406553,9.4625883102417,4656.70214 -31680,Multiclass classification,Bagging,Insects,0.5750181508254679,0.5750181508254679,0.5636300266647484,9.46137523651123,4996.240557 -32736,Multiclass classification,Bagging,Insects,0.5782190316175347,0.5782190316175347,0.5684825891024486,9.4603910446167,5346.796886 -33792,Multiclass classification,Bagging,Insects,0.5756858335059631,0.5756858335059631,0.5663669622675245,7.919375419616699,5710.001071 -34848,Multiclass classification,Bagging,Insects,0.5754871294516027,0.5754871294516027,0.5660193869557423,7.260138511657715,6084.365793 -35904,Multiclass classification,Bagging,Insects,0.5764142272233518,0.5764142272233518,0.5665362650344427,6.60003662109375,6469.590309 -36960,Multiclass classification,Bagging,Insects,0.575908439081144,0.575908439081144,0.5657420280651625,6.597938537597656,6865.284174 -38016,Multiclass classification,Bagging,Insects,0.5767723267131396,0.5767723267131396,0.5661330182942309,6.597076416015625,7271.517818 -39072,Multiclass classification,Bagging,Insects,0.5764889560031737,0.5764889560031737,0.5659501482422926,6.5939178466796875,7688.178552 -40128,Multiclass classification,Bagging,Insects,0.5734792035287961,0.5734792035287961,0.5636824355748769,8.576746940612793,8115.281794 -41184,Multiclass classification,Bagging,Insects,0.5726634776485443,0.5726634776485443,0.563417094879665,8.779536247253418,8552.341759 -42240,Multiclass classification,Bagging,Insects,0.5723383602831507,0.5723383602831507,0.5635995837049609,10.21227741241455,8998.890652 -43296,Multiclass classification,Bagging,Insects,0.5718212264695692,0.5718212264695692,0.5636175088230181,10.21111011505127,9455.260723 -44352,Multiclass classification,Bagging,Insects,0.571125791977633,0.571125791977633,0.5633830644644046,10.209759712219238,9921.572486 -45408,Multiclass classification,Bagging,Insects,0.5712555332878191,0.5712555332878191,0.5638292127585011,11.4169340133667,10398.038321 -46464,Multiclass classification,Bagging,Insects,0.5728429072595399,0.5728429072595399,0.565898409914518,12.449102401733398,10884.980897 -47520,Multiclass classification,Bagging,Insects,0.5768850354595004,0.5768850354595004,0.57039744062367,16.244935989379883,11383.832016999999 -48576,Multiclass classification,Bagging,Insects,0.5828718476582604,0.5828718476582604,0.5764217258661826,15.377230644226074,11896.186787999999 -49632,Multiclass classification,Bagging,Insects,0.5890471681005823,0.5890471681005823,0.5823842044431963,14.950640678405762,12421.939031 -50688,Multiclass classification,Bagging,Insects,0.594728431353207,0.594728431353207,0.5876258810149467,12.564711570739746,12959.806661999999 -51744,Multiclass classification,Bagging,Insects,0.6007382641130201,0.6007382641130201,0.5930524375976366,11.987659454345703,13508.895543999999 -52800,Multiclass classification,Bagging,Insects,0.606053144945927,0.606053144945927,0.5982224401760299,3.750063896179199,14067.159334999998 -408,Multiclass classification,Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.2869701385498047,1.329477 -816,Multiclass classification,Bagging,Keystroke,0.9411042944785276,0.9411042944785276,0.7377235942917068,3.233952522277832,4.406974 -1224,Multiclass classification,Bagging,Keystroke,0.8879803761242846,0.8879803761242846,0.873420796574987,4.178282737731934,9.866805 -1632,Multiclass classification,Bagging,Keystroke,0.8988350705088902,0.8988350705088902,0.8792834531664682,5.13068962097168,18.093902999999997 -2040,Multiclass classification,Bagging,Keystroke,0.8950465914664051,0.8950465914664051,0.8828407845486113,6.191357612609863,29.527086999999998 -2448,Multiclass classification,Bagging,Keystroke,0.8561503882304863,0.8561503882304863,0.8521381720173345,7.13577938079834,44.765435999999994 -2856,Multiclass classification,Bagging,Keystroke,0.8623467600700525,0.8623467600700525,0.8461129037988256,8.082098007202148,64.393699 -3264,Multiclass classification,Bagging,Keystroke,0.8528961078761875,0.8528961078761875,0.828204357625989,9.02734088897705,89.05171 -3672,Multiclass classification,Bagging,Keystroke,0.8428221193135386,0.8428221193135386,0.8381978174360706,9.972960472106934,119.28990999999999 -4080,Multiclass classification,Bagging,Keystroke,0.8350085805344447,0.8350085805344447,0.8208915725311207,11.171079635620117,155.799996 -4488,Multiclass classification,Bagging,Keystroke,0.822821484287943,0.822821484287943,0.832874806475175,12.116948127746582,198.957143 -4896,Multiclass classification,Bagging,Keystroke,0.8212461695607763,0.8212461695607763,0.8275900848879882,13.061814308166504,249.493693 -5304,Multiclass classification,Bagging,Keystroke,0.8178389590797661,0.8178389590797661,0.8022229037941512,14.007365226745605,307.913741 -5712,Multiclass classification,Bagging,Keystroke,0.7974085098931886,0.7974085098931886,0.8005324816804641,14.95396614074707,374.81365400000004 -6120,Multiclass classification,Bagging,Keystroke,0.7947377022389279,0.7947377022389279,0.7763699164747573,15.899590492248535,450.78850600000004 -6528,Multiclass classification,Bagging,Keystroke,0.7695725448138502,0.7695725448138502,0.7646092489325799,16.84642791748047,536.475639 -6936,Multiclass classification,Bagging,Keystroke,0.7614996395097332,0.7614996395097332,0.7633186803137438,17.791536331176758,632.653421 -7344,Multiclass classification,Bagging,Keystroke,0.770393572109492,0.770393572109492,0.7679684376178252,18.753963470458984,739.829913 -7752,Multiclass classification,Bagging,Keystroke,0.7709972906721714,0.7709972906721715,0.7694364393340193,19.70015239715576,858.3068800000001 -8160,Multiclass classification,Bagging,Keystroke,0.7739919107733791,0.7739919107733791,0.7702264725589797,20.646059036254883,989.0801780000002 -8568,Multiclass classification,Bagging,Keystroke,0.770631492938018,0.770631492938018,0.7706502591714904,22.072673797607422,1132.67323 -8976,Multiclass classification,Bagging,Keystroke,0.7691364902506964,0.7691364902506964,0.7697475673922982,23.017619132995605,1289.930532 -9384,Multiclass classification,Bagging,Keystroke,0.7679846530960247,0.7679846530960248,0.7675735514139922,23.96499538421631,1461.233929 -9792,Multiclass classification,Bagging,Keystroke,0.7634562353181493,0.7634562353181493,0.7626887405791724,24.91041660308838,1647.0514269999999 -10200,Multiclass classification,Bagging,Keystroke,0.7552701245220119,0.7552701245220118,0.7474447650479976,25.855456352233887,1848.111209 -10608,Multiclass classification,Bagging,Keystroke,0.734326388234185,0.734326388234185,0.7218544335091276,26.80277729034424,2064.993893 -11016,Multiclass classification,Bagging,Keystroke,0.727099409895597,0.727099409895597,0.7232704418570853,27.74752426147461,2298.467217 -11424,Multiclass classification,Bagging,Keystroke,0.7203011468090694,0.7203011468090693,0.7069709690618045,28.693537712097168,2549.106467 -11832,Multiclass classification,Bagging,Keystroke,0.7107598681430141,0.7107598681430141,0.7032019097144009,29.638681411743164,2817.5197820000003 -12240,Multiclass classification,Bagging,Keystroke,0.7152545142577008,0.7152545142577007,0.7117335483783439,30.584209442138672,3104.4910310000005 -12648,Multiclass classification,Bagging,Keystroke,0.7121056377006405,0.7121056377006404,0.7043178518121461,31.53076934814453,3410.5360360000004 -13056,Multiclass classification,Bagging,Keystroke,0.7005744925315971,0.7005744925315971,0.6932522175542292,32.476640701293945,3735.5420080000004 -13464,Multiclass classification,Bagging,Keystroke,0.6985070192379114,0.6985070192379114,0.6945196760058037,33.421990394592285,4080.277425 -13872,Multiclass classification,Bagging,Keystroke,0.6980751207555331,0.6980751207555331,0.6949558493849793,34.36870098114014,4445.365121 -14280,Multiclass classification,Bagging,Keystroke,0.6936760277330345,0.6936760277330345,0.6891645690411646,35.313669204711914,4831.313826 -14688,Multiclass classification,Bagging,Keystroke,0.6963300878327773,0.6963300878327773,0.6946500105809528,36.259453773498535,5238.529619 -15096,Multiclass classification,Bagging,Keystroke,0.7024180192116595,0.7024180192116595,0.7008836593188431,37.20665740966797,5667.803474 -15504,Multiclass classification,Bagging,Keystroke,0.702509191769335,0.702509191769335,0.6995855030221436,38.15153884887695,6119.5687720000005 -15912,Multiclass classification,Bagging,Keystroke,0.6934824963861479,0.6934824963861479,0.687175788748239,39.09754180908203,6594.573754000001 -16320,Multiclass classification,Bagging,Keystroke,0.6848458851645322,0.6848458851645322,0.6802460349069701,40.04375648498535,7093.395211000001 -16728,Multiclass classification,Bagging,Keystroke,0.6819513361630896,0.6819513361630896,0.6795788912922722,40.98903942108154,7616.277784000001 -17136,Multiclass classification,Bagging,Keystroke,0.6779107090749927,0.6779107090749927,0.6747648209169417,42.91780757904053,8164.158576000001 -17544,Multiclass classification,Bagging,Keystroke,0.6705808584620646,0.6705808584620646,0.6680341530684186,43.864423751831055,8737.125467000002 -17952,Multiclass classification,Bagging,Keystroke,0.6695448721519692,0.6695448721519692,0.6687363294804706,44.8110933303833,9335.759485000002 -18360,Multiclass classification,Bagging,Keystroke,0.674927828313089,0.674927828313089,0.6747300618557481,45.75679397583008,9961.084735000002 -18768,Multiclass classification,Bagging,Keystroke,0.6799701603879149,0.6799701603879149,0.6801519832282531,46.703369140625,10614.122071000002 -19176,Multiclass classification,Bagging,Keystroke,0.6720730117340287,0.6720730117340287,0.6711666831354974,47.648451805114746,11294.958186000002 -19584,Multiclass classification,Bagging,Keystroke,0.6760455497114845,0.6760455497114845,0.6762772840246767,48.59414768218994,12004.137364000002 -19992,Multiclass classification,Bagging,Keystroke,0.6715521984893202,0.6715521984893202,0.6718362805013157,49.540550231933594,12741.947206000003 -20400,Multiclass classification,Bagging,Keystroke,0.6679739202902103,0.6679739202902103,0.6688529665037395,50.48721218109131,13509.112779000003 -46,Multiclass classification,Leveraging Bagging,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,4.12965202331543,2.162623 -92,Multiclass classification,Leveraging Bagging,ImageSegments,0.5164835164835165,0.5164835164835165,0.5316649744849407,4.130231857299805,5.385739 -138,Multiclass classification,Leveraging Bagging,ImageSegments,0.5547445255474452,0.5547445255474452,0.5804654781117262,4.130353927612305,9.498090999999999 -184,Multiclass classification,Leveraging Bagging,ImageSegments,0.6174863387978142,0.6174863387978142,0.6394923756219437,4.130964279174805,14.447809999999999 -230,Multiclass classification,Leveraging Bagging,ImageSegments,0.6506550218340611,0.6506550218340611,0.66859135700569,4.130964279174805,20.213143 -276,Multiclass classification,Leveraging Bagging,ImageSegments,0.6618181818181819,0.6618181818181819,0.6795855359270878,4.131082534790039,26.807817999999997 -322,Multiclass classification,Leveraging Bagging,ImageSegments,0.6853582554517134,0.6853582554517134,0.6872635633687633,4.131624221801758,34.210100999999995 -368,Multiclass classification,Leveraging Bagging,ImageSegments,0.7111716621253406,0.7111716621253404,0.7098417316927395,4.131597518920898,42.42663699999999 -414,Multiclass classification,Leveraging Bagging,ImageSegments,0.7215496368038741,0.7215496368038742,0.7201557312728714,4.13151741027832,51.47481599999999 -460,Multiclass classification,Leveraging Bagging,ImageSegments,0.7211328976034859,0.721132897603486,0.7175330036146421,4.131570816040039,61.34488599999999 -506,Multiclass classification,Leveraging Bagging,ImageSegments,0.7287128712871287,0.7287128712871287,0.7233455022590812,4.131570816040039,72.04824799999999 -552,Multiclass classification,Leveraging Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7255599965917697,4.131490707397461,83.59783199999998 -598,Multiclass classification,Leveraging Bagging,ImageSegments,0.7353433835845896,0.7353433835845896,0.7308494254186014,4.131513595581055,95.96874199999998 -644,Multiclass classification,Leveraging Bagging,ImageSegments,0.7340590979782271,0.7340590979782271,0.7314183982762247,4.132074356079102,109.15469699999997 -690,Multiclass classification,Leveraging Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7343909641298695,4.132074356079102,123.16042199999997 -736,Multiclass classification,Leveraging Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7369557659594496,4.132101058959961,138.00293199999996 -782,Multiclass classification,Leveraging Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7419020281650245,4.132101058959961,153.67249999999996 -828,Multiclass classification,Leveraging Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7432199627682998,4.132101058959961,170.15907299999995 -874,Multiclass classification,Leveraging Bagging,ImageSegments,0.7502863688430699,0.7502863688430699,0.7482089866208982,4.132101058959961,187.48903599999994 -920,Multiclass classification,Leveraging Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7477650187313973,4.132074356079102,205.64141899999993 -966,Multiclass classification,Leveraging Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.747322646811651,4.132074356079102,224.60247399999992 -1012,Multiclass classification,Leveraging Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.7469783619055548,4.132074356079102,244.38522099999992 -1058,Multiclass classification,Leveraging Bagging,ImageSegments,0.7530747398297067,0.7530747398297066,0.7482363934596314,4.132074356079102,264.9944789999999 -1104,Multiclass classification,Leveraging Bagging,ImageSegments,0.7552130553037172,0.7552130553037172,0.750118495060715,4.132123947143555,286.4318919999999 -1150,Multiclass classification,Leveraging Bagging,ImageSegments,0.7571801566579635,0.7571801566579635,0.7516199800653578,4.132123947143555,308.6933539999999 -1196,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598326359832636,0.7598326359832636,0.7548841797367704,4.132123947143555,331.8138849999999 -1242,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598710717163578,0.7598710717163577,0.7553301531902636,4.132123947143555,355.7543559999999 -1288,Multiclass classification,Leveraging Bagging,ImageSegments,0.7645687645687645,0.7645687645687647,0.7590078532621816,4.132734298706055,380.5041089999999 -1334,Multiclass classification,Leveraging Bagging,ImageSegments,0.7644411102775694,0.7644411102775694,0.7591993978414527,4.132757186889648,406.0948849999999 -1380,Multiclass classification,Leveraging Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7601575050520946,4.132757186889648,432.50441499999994 -1426,Multiclass classification,Leveraging Bagging,ImageSegments,0.7670175438596492,0.7670175438596492,0.7613339877221927,4.132757186889648,459.75425999999993 -1472,Multiclass classification,Leveraging Bagging,ImageSegments,0.7715839564921821,0.7715839564921821,0.76413964752182,4.132802963256836,487.81331299999994 -1518,Multiclass classification,Leveraging Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.7648275341801108,4.132802963256836,516.6922259999999 -1564,Multiclass classification,Leveraging Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7657569341108763,4.132802963256836,546.3975239999999 -1610,Multiclass classification,Leveraging Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7651494083475014,4.13282585144043,576.9286479999998 -1656,Multiclass classification,Leveraging Bagging,ImageSegments,0.7740181268882175,0.7740181268882175,0.7654813489818475,4.132780075073242,608.2971799999998 -1702,Multiclass classification,Leveraging Bagging,ImageSegments,0.7730746619635509,0.7730746619635509,0.7664930279619061,4.132780075073242,640.4789209999998 -1748,Multiclass classification,Leveraging Bagging,ImageSegments,0.7756153405838581,0.7756153405838581,0.7686072256536652,4.132780075073242,673.4948829999998 -1794,Multiclass classification,Leveraging Bagging,ImageSegments,0.7769102063580591,0.7769102063580591,0.7685414235990153,4.132753372192383,707.3642379999999 -1840,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781402936378466,0.7781402936378466,0.7699957723931324,4.132753372192383,742.0792459999999 -1886,Multiclass classification,Leveraging Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7684985598909853,4.132753372192383,777.6304799999999 -1932,Multiclass classification,Leveraging Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.767743441804642,4.132753372192383,814.0157899999999 -1978,Multiclass classification,Leveraging Bagging,ImageSegments,0.7774405665149215,0.7774405665149215,0.7684788817649146,4.132753372192383,851.2372119999999 -2024,Multiclass classification,Leveraging Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7689103339153599,4.132753372192383,889.2963629999999 -2070,Multiclass classification,Leveraging Bagging,ImageSegments,0.7786370227162881,0.7786370227162881,0.7686288077529282,4.132753372192383,928.2025699999999 -2116,Multiclass classification,Leveraging Bagging,ImageSegments,0.7791962174940898,0.7791962174940898,0.768391950800897,4.132753372192383,967.9450939999999 -2162,Multiclass classification,Leveraging Bagging,ImageSegments,0.7801943544655252,0.7801943544655253,0.768962628827985,4.132776260375977,1008.5034619999999 -2208,Multiclass classification,Leveraging Bagging,ImageSegments,0.7820570910738559,0.7820570910738559,0.7698068761587117,4.132749557495117,1049.8865549999998 -2254,Multiclass classification,Leveraging Bagging,ImageSegments,0.7789613848202397,0.7789613848202397,0.7667173742344939,4.132749557495117,1092.1021469999998 -2300,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781644193127447,0.7781644193127447,0.7659138381656089,4.132749557495117,1135.1554539999997 -1056,Multiclass classification,Leveraging Bagging,Insects,0.6218009478672986,0.6218009478672986,0.5857016652718547,6.522056579589844,30.289418 -2112,Multiclass classification,Leveraging Bagging,Insects,0.6196115585030791,0.6196115585030791,0.5856756432415232,10.389650344848633,88.669185 -3168,Multiclass classification,Leveraging Bagging,Insects,0.628986422481844,0.628986422481844,0.5949930595607558,19.16711711883545,174.550284 -4224,Multiclass classification,Leveraging Bagging,Insects,0.6294103717736207,0.6294103717736207,0.5952675443708706,19.668034553527832,287.918965 -5280,Multiclass classification,Leveraging Bagging,Insects,0.6364841826103429,0.6364841826103429,0.5994911272790604,18.96163558959961,428.854975 -6336,Multiclass classification,Leveraging Bagging,Insects,0.6352012628255722,0.6352012628255722,0.5993891820807257,20.14603328704834,597.190787 -7392,Multiclass classification,Leveraging Bagging,Insects,0.638749830875389,0.638749830875389,0.6030343276880051,21.10132884979248,793.049033 -8448,Multiclass classification,Leveraging Bagging,Insects,0.6405824553095774,0.6405824553095774,0.6028521616895871,24.152769088745117,1016.521492 -9504,Multiclass classification,Leveraging Bagging,Insects,0.6449542249815847,0.6449542249815847,0.6055705492028415,24.86981773376465,1266.726445 -10560,Multiclass classification,Leveraging Bagging,Insects,0.6485462638507434,0.6485462638507434,0.6081614166360886,28.971991539001465,1544.137884 -11616,Multiclass classification,Leveraging Bagging,Insects,0.6490744726646578,0.6490744726646578,0.6078786452761632,31.018654823303223,1848.95866 -12672,Multiclass classification,Leveraging Bagging,Insects,0.6514876489621971,0.6514876489621971,0.6111938480023121,35.39500713348389,2179.477442 -13728,Multiclass classification,Leveraging Bagging,Insects,0.6707947840023312,0.6707947840023312,0.6607574394823456,17.66313648223877,2527.7545929999997 -14784,Multiclass classification,Leveraging Bagging,Insects,0.6814584319826829,0.6814584319826829,0.6724584381879511,11.128533363342285,2896.354015 -15840,Multiclass classification,Leveraging Bagging,Insects,0.6762421870067554,0.6762421870067554,0.6688785181435096,14.811795234680176,3290.113809 -16896,Multiclass classification,Leveraging Bagging,Insects,0.6741639538324948,0.6741639538324948,0.6676833597101233,15.365425109863281,3710.779607 -17952,Multiclass classification,Leveraging Bagging,Insects,0.670491894601972,0.670491894601972,0.6643621029883554,15.98740005493164,4158.727334 -19008,Multiclass classification,Leveraging Bagging,Insects,0.6754353659178197,0.6754353659178197,0.6656526175716114,17.100504875183105,4627.925426 -20064,Multiclass classification,Leveraging Bagging,Insects,0.6800079748791308,0.6800079748791308,0.6670489534490986,26.370519638061523,5118.761149 -21120,Multiclass classification,Leveraging Bagging,Insects,0.6835550925706709,0.6835550925706709,0.6685883462655132,32.78877353668213,5635.605831 -22176,Multiclass classification,Leveraging Bagging,Insects,0.6869447576099211,0.6869447576099211,0.6701495347804184,36.297407150268555,6178.169973 -23232,Multiclass classification,Leveraging Bagging,Insects,0.6912745899875167,0.6912745899875167,0.6726358783249661,38.26123523712158,6746.46947 -24288,Multiclass classification,Leveraging Bagging,Insects,0.6940338452670153,0.6940338452670153,0.673442702110033,39.100372314453125,7340.085466 -25344,Multiclass classification,Leveraging Bagging,Insects,0.6976679951071302,0.6976679951071302,0.67525701759611,42.24958515167236,7958.6023620000005 -26400,Multiclass classification,Leveraging Bagging,Insects,0.7000643963786507,0.7000643963786507,0.6759116206749555,41.52747917175293,8602.02664 -27456,Multiclass classification,Leveraging Bagging,Insects,0.7027135312329266,0.7027135312329266,0.6765494742782628,43.561981201171875,9269.998307 -28512,Multiclass classification,Leveraging Bagging,Insects,0.7018343797130931,0.7018343797130931,0.6771545550561098,24.23386573791504,9962.817287 -29568,Multiclass classification,Leveraging Bagging,Insects,0.7013224202658369,0.7013224202658369,0.681362451564682,5.156903266906738,10676.641313 -30624,Multiclass classification,Leveraging Bagging,Insects,0.699702837736342,0.699702837736342,0.6839521261644582,8.359548568725586,11409.958608 -31680,Multiclass classification,Leveraging Bagging,Insects,0.6993907635973358,0.6993907635973358,0.6874853197903658,12.837088584899902,12162.299998 -32736,Multiclass classification,Leveraging Bagging,Insects,0.7005651443409195,0.7005651443409195,0.692127614099415,14.392640113830566,12933.190651 -33792,Multiclass classification,Leveraging Bagging,Insects,0.6971678849397769,0.6971678849397769,0.6903104823999882,22.114407539367676,13726.605059000001 -34848,Multiclass classification,Leveraging Bagging,Insects,0.6941487072057853,0.6941487072057853,0.6871648754350796,16.369569778442383,14546.496494 -35904,Multiclass classification,Leveraging Bagging,Insects,0.6917527783193606,0.6917527783193606,0.684473708604621,15.783265113830566,15393.169285 -36960,Multiclass classification,Leveraging Bagging,Insects,0.6883303119673151,0.6883303119673151,0.6807777972894504,18.195876121520996,16266.308937 -38016,Multiclass classification,Leveraging Bagging,Insects,0.6865973957648297,0.6865973957648297,0.6786744939637405,21.092598915100098,17165.821217 -39072,Multiclass classification,Leveraging Bagging,Insects,0.6857259860254409,0.6857259860254409,0.6778492437957201,16.29904079437256,18090.697656 -40128,Multiclass classification,Leveraging Bagging,Insects,0.6837540807934807,0.6837540807934807,0.6766238977666043,13.538718223571777,19041.492123 -41184,Multiclass classification,Leveraging Bagging,Insects,0.6814462278124469,0.6814462278124469,0.675074837604149,15.844508171081543,20015.794843 -42240,Multiclass classification,Leveraging Bagging,Insects,0.6790643717891048,0.6790643717891048,0.6733686277261395,15.962260246276855,21014.495285 -43296,Multiclass classification,Leveraging Bagging,Insects,0.6762443700196328,0.6762443700196328,0.6713719096586489,17.128825187683105,22038.291411000002 -44352,Multiclass classification,Leveraging Bagging,Insects,0.6738066785416338,0.6738066785416338,0.6696205967919768,16.462289810180664,23087.153485000003 -45408,Multiclass classification,Leveraging Bagging,Insects,0.6717686700288502,0.6717686700288502,0.6680705737277651,17.22057342529297,24160.866409000002 -46464,Multiclass classification,Leveraging Bagging,Insects,0.6708994253492025,0.6708994253492025,0.6677330044499646,17.752578735351562,25258.281990000003 -47520,Multiclass classification,Leveraging Bagging,Insects,0.6729939603106126,0.6729939603106126,0.6699611714455135,18.93515110015869,26380.044308000004 -48576,Multiclass classification,Leveraging Bagging,Insects,0.6767061245496655,0.6767061245496655,0.6733691077464542,20.549713134765625,27525.907792000005 -49632,Multiclass classification,Leveraging Bagging,Insects,0.6807237412101308,0.6807237412101308,0.6769109137483648,20.974443435668945,28695.603524000006 -50688,Multiclass classification,Leveraging Bagging,Insects,0.6845147671000453,0.6845147671000453,0.6800104952374638,22.97932243347168,29888.264116000006 -51744,Multiclass classification,Leveraging Bagging,Insects,0.6885182536768258,0.6885182536768258,0.6832561756017089,24.11430263519287,31103.175398000007 -52800,Multiclass classification,Leveraging Bagging,Insects,0.6915471883937195,0.6915471883937195,0.6864107325641782,18.141328811645508,32334.061725000007 -408,Multiclass classification,Leveraging Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.238800048828125,2.971982 -816,Multiclass classification,Leveraging Bagging,Keystroke,0.9460122699386503,0.9460122699386503,0.8367492469040564,4.44326114654541,9.847778 -1224,Multiclass classification,Leveraging Bagging,Keystroke,0.9411283728536386,0.9411283728536386,0.9276213812296338,6.153376579284668,20.932147 -1632,Multiclass classification,Leveraging Bagging,Keystroke,0.950337216431637,0.950337216431637,0.9330502878949443,7.8991851806640625,36.717851 -2040,Multiclass classification,Leveraging Bagging,Keystroke,0.9494850416871016,0.9494850416871016,0.932928877406915,10.965654373168945,57.922702 -2448,Multiclass classification,Leveraging Bagging,Keystroke,0.9525950143032285,0.9525950143032285,0.9502305130509757,10.694184303283691,83.450682 -2856,Multiclass classification,Leveraging Bagging,Keystroke,0.9544658493870403,0.9544658493870403,0.943855127765724,15.53213119506836,113.412194 -3264,Multiclass classification,Leveraging Bagging,Keystroke,0.9515783021759118,0.9515783021759118,0.944582727256988,15.652314186096191,149.290909 -3672,Multiclass classification,Leveraging Bagging,Keystroke,0.9526014709888314,0.9526014709888314,0.9497542235388343,16.11695098876953,190.98460699999998 -4080,Multiclass classification,Leveraging Bagging,Keystroke,0.9499877420936504,0.9499877420936504,0.9391633661003513,16.578293800354004,238.59921099999997 -4488,Multiclass classification,Leveraging Bagging,Keystroke,0.9474036104301314,0.9474036104301314,0.9496969875723204,13.190230369567871,292.48392399999994 -4896,Multiclass classification,Leveraging Bagging,Keystroke,0.9493360572012257,0.9493360572012257,0.9494027577495958,12.864276885986328,353.18946299999993 -5304,Multiclass classification,Leveraging Bagging,Keystroke,0.951159720912691,0.951159720912691,0.9518992835106977,11.604743957519531,419.93206699999996 -5712,Multiclass classification,Leveraging Bagging,Keystroke,0.951146909472947,0.951146909472947,0.9505351682914018,13.577879905700684,492.70239499999997 -6120,Multiclass classification,Leveraging Bagging,Keystroke,0.9478672985781991,0.9478672985781991,0.9429356622084736,16.35688304901123,572.32886 -6528,Multiclass classification,Leveraging Bagging,Keystroke,0.9480618967366324,0.9480618967366324,0.9478348775735732,10.670846939086914,659.058815 -6936,Multiclass classification,Leveraging Bagging,Keystroke,0.9495313626532084,0.9495313626532084,0.9511497142125283,10.614124298095703,751.777349 -7344,Multiclass classification,Leveraging Bagging,Keystroke,0.9500204276181398,0.9500204276181398,0.9502583235097111,12.824416160583496,851.689186 -7752,Multiclass classification,Leveraging Bagging,Keystroke,0.9505870210295446,0.9505870210295446,0.9508630550075082,11.99438190460205,957.810381 -8160,Multiclass classification,Leveraging Bagging,Keystroke,0.9487682314009069,0.9487682314009069,0.9466937008923911,16.34542465209961,1069.487963 -8568,Multiclass classification,Leveraging Bagging,Keystroke,0.9491070386366289,0.9491070386366289,0.9496258519963295,14.096193313598633,1187.33597 -8976,Multiclass classification,Leveraging Bagging,Keystroke,0.9504178272980501,0.9504178272980501,0.95112303337496,8.487105369567871,1310.539589 -9384,Multiclass classification,Leveraging Bagging,Keystroke,0.9507620164126612,0.9507620164126612,0.9509680125568912,10.491826057434082,1439.276187 -9792,Multiclass classification,Leveraging Bagging,Keystroke,0.9505668471044837,0.9505668471044837,0.9508008066421794,12.578843116760254,1574.391157 -10200,Multiclass classification,Leveraging Bagging,Keystroke,0.9496029022453182,0.9496029022453182,0.9490825188137642,15.329971313476562,1716.648577 -10608,Multiclass classification,Leveraging Bagging,Keystroke,0.9462619025172057,0.9462619025172057,0.9448381382156613,14.149526596069336,1865.207745 -11016,Multiclass classification,Leveraging Bagging,Keystroke,0.9473445301861099,0.9473445301861099,0.9480489849360165,15.43016529083252,2018.8519179999998 -11424,Multiclass classification,Leveraging Bagging,Keystroke,0.9476494791210716,0.9476494791210716,0.9477632560487921,19.339406967163086,2178.069416 -11832,Multiclass classification,Leveraging Bagging,Keystroke,0.9466655396838813,0.9466655396838813,0.9465646854570324,20.836685180664062,2343.241513 -12240,Multiclass classification,Leveraging Bagging,Keystroke,0.9473813220034316,0.9473813220034316,0.9477056335712672,22.594761848449707,2516.1915169999997 -12648,Multiclass classification,Leveraging Bagging,Keystroke,0.9481299913022851,0.9481299913022851,0.9484695727303012,17.12001132965088,2696.1117459999996 -13056,Multiclass classification,Leveraging Bagging,Keystroke,0.9465338950593642,0.9465338950593642,0.9461537407653537,16.317899703979492,2881.2856319999996 -13464,Multiclass classification,Leveraging Bagging,Keystroke,0.9475599792022581,0.9475599792022581,0.9479124389900309,17.175325393676758,3071.3018549999997 -13872,Multiclass classification,Leveraging Bagging,Keystroke,0.9480931439694327,0.9480931439694327,0.9483129032895908,20.13454818725586,3267.097668 -14280,Multiclass classification,Leveraging Bagging,Keystroke,0.9472652146508859,0.9472652146508858,0.9472495958535089,23.271190643310547,3470.2386189999997 -14688,Multiclass classification,Leveraging Bagging,Keystroke,0.9470960713556206,0.9470960713556206,0.9472715831304289,24.70554256439209,3681.555614 -15096,Multiclass classification,Leveraging Bagging,Keystroke,0.9477310367671414,0.9477310367671414,0.9480235232823461,12.28943920135498,3901.326497 -15504,Multiclass classification,Leveraging Bagging,Keystroke,0.9481390698574469,0.9481390698574469,0.9483821660022894,8.167351722717285,4127.852482 -15912,Multiclass classification,Leveraging Bagging,Keystroke,0.9489661240651122,0.9489661240651122,0.949259317367439,10.055158615112305,4359.152379 -16320,Multiclass classification,Leveraging Bagging,Keystroke,0.9489552055885777,0.9489552055885777,0.949102505659295,8.927614212036133,4595.973694 -16728,Multiclass classification,Leveraging Bagging,Keystroke,0.948645901835356,0.948645901835356,0.9487532899546077,9.772565841674805,4838.16456 -17136,Multiclass classification,Leveraging Bagging,Keystroke,0.9491683688357164,0.9491683688357164,0.9493664270655614,8.885259628295898,5086.602422 -17544,Multiclass classification,Leveraging Bagging,Keystroke,0.9497235364532862,0.9497235364532862,0.9498997400720455,7.432655334472656,5340.103049 -17952,Multiclass classification,Leveraging Bagging,Keystroke,0.9492507381204389,0.9492507381204389,0.9493299482291901,6.564939498901367,5598.267976 -18360,Multiclass classification,Leveraging Bagging,Keystroke,0.9498883381447791,0.949888338144779,0.9500369712738612,9.445448875427246,5861.742373 -18768,Multiclass classification,Leveraging Bagging,Keystroke,0.9501252198007141,0.9501252198007141,0.950244810756275,8.985580444335938,6130.741927 -19176,Multiclass classification,Leveraging Bagging,Keystroke,0.9502477183833116,0.9502477183833116,0.9503577107154481,10.539984703063965,6405.708121 -19584,Multiclass classification,Leveraging Bagging,Keystroke,0.9504672419956084,0.9504672419956084,0.9505675543483478,12.433100700378418,6686.90918 -19992,Multiclass classification,Leveraging Bagging,Keystroke,0.9504777149717373,0.9504777149717373,0.9505659657035201,12.245397567749023,6973.48828 -20400,Multiclass classification,Leveraging Bagging,Keystroke,0.950389724986519,0.950389724986519,0.9504675266923703,10.420146942138672,7265.024106 -46,Multiclass classification,Stacking,ImageSegments,0.4,0.4000000000000001,0.3289160825620571,1.8703498840332031,0.761019 -92,Multiclass classification,Stacking,ImageSegments,0.5494505494505495,0.5494505494505495,0.5607526488856412,2.0432376861572266,2.058459 -138,Multiclass classification,Stacking,ImageSegments,0.5620437956204379,0.5620437956204379,0.5814352652080846,2.2601184844970703,3.877596 -184,Multiclass classification,Stacking,ImageSegments,0.6174863387978142,0.6174863387978142,0.6349823285289026,2.5773630142211914,5.988367 -230,Multiclass classification,Stacking,ImageSegments,0.6550218340611353,0.6550218340611353,0.6697464616246889,2.673569679260254,8.36838 -276,Multiclass classification,Stacking,ImageSegments,0.68,0.68,0.6977451412884614,2.705929756164551,11.015152 -322,Multiclass classification,Stacking,ImageSegments,0.7040498442367601,0.7040498442367601,0.708655608864303,2.747677803039551,13.922748 -368,Multiclass classification,Stacking,ImageSegments,0.7302452316076294,0.7302452316076294,0.731555248839775,2.9195823669433594,17.085856 -414,Multiclass classification,Stacking,ImageSegments,0.7481840193704601,0.7481840193704601,0.7498869297449521,3.2087087631225586,20.515594 -460,Multiclass classification,Stacking,ImageSegments,0.7429193899782135,0.7429193899782135,0.7431113090395209,2.8742523193359375,24.226579 -506,Multiclass classification,Stacking,ImageSegments,0.7465346534653465,0.7465346534653465,0.7453691625646783,3.051929473876953,28.205653 -552,Multiclass classification,Stacking,ImageSegments,0.7531760435571688,0.7531760435571688,0.7537204076398122,3.133829116821289,32.454626000000005 -598,Multiclass classification,Stacking,ImageSegments,0.7587939698492462,0.7587939698492462,0.7612399908296416,3.1490001678466797,36.970245000000006 -644,Multiclass classification,Stacking,ImageSegments,0.7589424572317263,0.7589424572317262,0.7628637146980985,3.4707136154174805,41.75427400000001 -690,Multiclass classification,Stacking,ImageSegments,0.7619738751814223,0.7619738751814223,0.76530464273308,3.455944061279297,46.80492900000001 -736,Multiclass classification,Stacking,ImageSegments,0.7687074829931972,0.7687074829931972,0.7727990926768868,3.6800451278686523,52.11999800000001 -782,Multiclass classification,Stacking,ImageSegments,0.7733674775928298,0.7733674775928298,0.7767963295410655,3.8801565170288086,57.706528000000006 -828,Multiclass classification,Stacking,ImageSegments,0.7738814993954051,0.7738814993954051,0.7787678467755003,3.8676557540893555,63.567513000000005 -874,Multiclass classification,Stacking,ImageSegments,0.7812142038946163,0.7812142038946163,0.7848289172220594,3.691183090209961,69.704029 -920,Multiclass classification,Stacking,ImageSegments,0.7878128400435256,0.7878128400435256,0.7905661589338376,3.770216941833496,76.10799700000001 -966,Multiclass classification,Stacking,ImageSegments,0.7917098445595855,0.7917098445595855,0.7936972979049142,3.8226003646850586,82.78424000000001 -1012,Multiclass classification,Stacking,ImageSegments,0.7952522255192879,0.7952522255192878,0.796484514345152,4.098711967468262,89.73293500000001 -1058,Multiclass classification,Stacking,ImageSegments,0.8032166508987701,0.8032166508987703,0.8038465931831994,4.173297882080078,96.95259300000001 -1104,Multiclass classification,Stacking,ImageSegments,0.8041704442429737,0.8041704442429737,0.8051724065917674,4.3957414627075195,104.442345 -1150,Multiclass classification,Stacking,ImageSegments,0.8102697998259356,0.8102697998259357,0.8109646011887589,4.552497863769531,112.202361 -1196,Multiclass classification,Stacking,ImageSegments,0.8142259414225942,0.8142259414225941,0.8149917549940485,4.571473121643066,120.23406399999999 -1242,Multiclass classification,Stacking,ImageSegments,0.8186946011281225,0.8186946011281225,0.8196592056494876,4.626148223876953,128.53886899999998 -1288,Multiclass classification,Stacking,ImageSegments,0.8212898212898213,0.8212898212898213,0.822176577441966,5.001523017883301,137.11683999999997 -1334,Multiclass classification,Stacking,ImageSegments,0.8229557389347337,0.8229557389347337,0.8237863794336502,5.1421356201171875,145.97222099999996 -1380,Multiclass classification,Stacking,ImageSegments,0.8245105148658448,0.8245105148658448,0.8256018780761997,5.339564323425293,155.11012299999996 -1426,Multiclass classification,Stacking,ImageSegments,0.8287719298245614,0.8287719298245614,0.8290084946618356,5.337568283081055,164.53666099999995 -1472,Multiclass classification,Stacking,ImageSegments,0.8334466349422162,0.8334466349422162,0.8325983603187124,5.299435615539551,174.25653999999994 -1518,Multiclass classification,Stacking,ImageSegments,0.8358602504943968,0.8358602504943968,0.8344617749849152,5.345264434814453,184.26794299999995 -1564,Multiclass classification,Stacking,ImageSegments,0.8387715930902111,0.8387715930902111,0.837784263767798,4.9267168045043945,194.57830699999994 -1610,Multiclass classification,Stacking,ImageSegments,0.839030453697949,0.839030453697949,0.838065870841574,4.685762405395508,205.19165999999993 -1656,Multiclass classification,Stacking,ImageSegments,0.8416918429003021,0.8416918429003022,0.8408915736149335,4.737677574157715,216.09684699999994 -1702,Multiclass classification,Stacking,ImageSegments,0.8418577307466196,0.8418577307466195,0.8423710518418951,4.180461883544922,227.29100499999993 -1748,Multiclass classification,Stacking,ImageSegments,0.8431597023468803,0.8431597023468804,0.8432643493367186,4.331151962280273,238.7660419999999 -1794,Multiclass classification,Stacking,ImageSegments,0.8455103179029559,0.8455103179029559,0.8449435902582664,4.424459457397461,250.52640599999992 -1840,Multiclass classification,Stacking,ImageSegments,0.8466557911908646,0.8466557911908648,0.8462222022075542,4.3217973709106445,262.5845499999999 -1886,Multiclass classification,Stacking,ImageSegments,0.8477453580901857,0.8477453580901856,0.84772474367672,4.364754676818848,274.94178099999993 -1932,Multiclass classification,Stacking,ImageSegments,0.8487830139823925,0.8487830139823925,0.8484572581714136,4.410244941711426,287.5965719999999 -1978,Multiclass classification,Stacking,ImageSegments,0.849772382397572,0.849772382397572,0.8495372758679525,4.436578750610352,300.5458569999999 -2024,Multiclass classification,Stacking,ImageSegments,0.8507167572911517,0.8507167572911517,0.8496927624131454,4.292850494384766,313.7872069999999 -2070,Multiclass classification,Stacking,ImageSegments,0.8511358144030933,0.8511358144030933,0.8503705992191455,4.422323226928711,327.3192999999999 -2116,Multiclass classification,Stacking,ImageSegments,0.8515366430260047,0.8515366430260047,0.850305284692234,4.440757751464844,341.1384159999999 -2162,Multiclass classification,Stacking,ImageSegments,0.8505321610365572,0.850532161036557,0.84908675540822,4.611151695251465,355.24865599999987 -2208,Multiclass classification,Stacking,ImageSegments,0.851835070231083,0.851835070231083,0.8501011345319502,4.809813499450684,369.64566299999984 -2254,Multiclass classification,Stacking,ImageSegments,0.8490901020861075,0.8490901020861075,0.847799327251759,4.949430465698242,384.32755499999985 -2300,Multiclass classification,Stacking,ImageSegments,0.8490648107872988,0.8490648107872988,0.8479218608351832,5.295671463012695,399.28900199999987 -1056,Multiclass classification,Stacking,Insects,0.6454976303317536,0.6454976303317536,0.5867724425586438,7.441350936889648,8.583896 -2112,Multiclass classification,Stacking,Insects,0.6826148744670772,0.6826148744670772,0.6053874539212664,11.234929084777832,25.378114999999998 -3168,Multiclass classification,Stacking,Insects,0.6896116198294916,0.6896116198294916,0.6083758872885286,13.408919334411621,50.237849 -4224,Multiclass classification,Stacking,Insects,0.6954771489462468,0.6954771489462468,0.6085129807470798,18.096717834472656,83.102519 -5280,Multiclass classification,Stacking,Insects,0.7014586095851487,0.7014586095851487,0.6122692721162352,21.446727752685547,123.914838 -6336,Multiclass classification,Stacking,Insects,0.7021310181531176,0.7021310181531176,0.6116513676781078,27.182113647460938,172.776504 -7392,Multiclass classification,Stacking,Insects,0.7054525774590719,0.7054525774590719,0.6129808753663538,26.876797676086426,229.478895 -8448,Multiclass classification,Stacking,Insects,0.70853557476027,0.70853557476027,0.6147213044531655,31.851184844970703,293.916345 -9504,Multiclass classification,Stacking,Insects,0.7137745974955277,0.7137745974955277,0.6175531178778296,22.848219871520996,366.23076499999996 -10560,Multiclass classification,Stacking,Insects,0.7174921867601098,0.7174921867601098,0.619713417782018,19.317788124084473,446.24860299999995 -11616,Multiclass classification,Stacking,Insects,0.717606543263022,0.717606543263022,0.618960125586482,19.568995475769043,533.7141509999999 -12672,Multiclass classification,Stacking,Insects,0.7184121221687317,0.7184121221687317,0.6302774396409263,23.59817409515381,628.5255009999998 -13728,Multiclass classification,Stacking,Insects,0.7373060391928317,0.7373060391928317,0.7337291247132964,6.318717002868652,729.5138609999999 -14784,Multiclass classification,Stacking,Insects,0.744774403030508,0.7447744030305079,0.7439388578060665,4.747281074523926,836.7314339999999 -15840,Multiclass classification,Stacking,Insects,0.7374834269840268,0.7374834269840268,0.7388535634976899,8.635688781738281,951.47119 -16896,Multiclass classification,Stacking,Insects,0.7324652263983427,0.7324652263983427,0.736003592775451,13.742908477783203,1073.59215 -17952,Multiclass classification,Stacking,Insects,0.7253077822962509,0.7253077822962509,0.7305072565778182,20.007770538330078,1203.171307 -19008,Multiclass classification,Stacking,Insects,0.7387804493081497,0.7387804493081497,0.7395324944779035,6.087196350097656,1339.8258620000001 -20064,Multiclass classification,Stacking,Insects,0.7439066939141704,0.7439066939141704,0.7399287274487314,7.841000556945801,1483.477757 -21120,Multiclass classification,Stacking,Insects,0.7456792461764288,0.7456792461764288,0.738136498436516,11.804688453674316,1634.941518 -22176,Multiclass classification,Stacking,Insects,0.7464712514092446,0.7464712514092445,0.7355899333520025,17.346091270446777,1793.971452 -23232,Multiclass classification,Stacking,Insects,0.7486548146872714,0.7486548146872714,0.7347795423630049,20.6541748046875,1960.617459 -24288,Multiclass classification,Stacking,Insects,0.7502367521719439,0.7502367521719437,0.7334324471857778,21.727876663208008,2134.847723 -25344,Multiclass classification,Stacking,Insects,0.7523576530008287,0.7523576530008288,0.7330792890892175,28.11653995513916,2316.991759 -26400,Multiclass classification,Stacking,Insects,0.7535891511042085,0.7535891511042085,0.731955812013067,28.993709564208984,2507.189995 -27456,Multiclass classification,Stacking,Insects,0.7540338736113641,0.7540338736113641,0.7298780765329144,35.52100467681885,2705.597193 -28512,Multiclass classification,Stacking,Insects,0.7522710532776823,0.7522710532776823,0.7301216768723076,19.597237586975098,2912.282888 -29568,Multiclass classification,Stacking,Insects,0.7502621165488551,0.7502621165488552,0.733319854895679,15.064599990844727,3126.360865 -30624,Multiclass classification,Stacking,Insects,0.7500244913953564,0.7500244913953564,0.7381499467352403,21.998522758483887,3348.070871 -31680,Multiclass classification,Stacking,Insects,0.7493292086240096,0.7493292086240096,0.7414716120706107,28.992523193359375,3577.2433 -32736,Multiclass classification,Stacking,Insects,0.7494424927447686,0.7494424927447686,0.7447602446394828,36.39131259918213,3813.876585 -33792,Multiclass classification,Stacking,Insects,0.7448137077920156,0.7448137077920156,0.7415559043607837,7.406244277954102,4059.132247 -34848,Multiclass classification,Stacking,Insects,0.7397193445633771,0.739719344563377,0.7363475181006618,8.795232772827148,4312.474973 -35904,Multiclass classification,Stacking,Insects,0.7365679748210456,0.7365679748210455,0.7329849736783064,11.101387023925781,4573.597154 -36960,Multiclass classification,Stacking,Insects,0.7330014340214832,0.7330014340214832,0.7293557861681861,15.830912590026855,4842.477951 -38016,Multiclass classification,Stacking,Insects,0.7302643693278968,0.7302643693278967,0.7264691718738406,19.795815467834473,5119.095735 -39072,Multiclass classification,Stacking,Insects,0.7309513449873307,0.7309513449873307,0.7270525503986339,9.05908489227295,5403.358583 -40128,Multiclass classification,Stacking,Insects,0.729284521643781,0.729284521643781,0.7256952486493923,11.242535591125488,5695.769582 -41184,Multiclass classification,Stacking,Insects,0.7294029089672923,0.7294029089672922,0.7260996194485368,9.506610870361328,5995.177374 -42240,Multiclass classification,Stacking,Insects,0.7298941736310045,0.7298941736310045,0.7269475794208268,15.258689880371094,6301.374087 -43296,Multiclass classification,Stacking,Insects,0.7306848365862109,0.7306848365862109,0.7280100891072271,20.2097225189209,6614.284978 -44352,Multiclass classification,Stacking,Insects,0.7312574688282113,0.7312574688282113,0.7287466644577517,26.378506660461426,6934.278340999999 -45408,Multiclass classification,Stacking,Insects,0.7317814433897857,0.7317814433897857,0.7291491859846939,32.061384201049805,7261.498196999999 -46464,Multiclass classification,Stacking,Insects,0.732776617954071,0.732776617954071,0.7299865007540453,32.25613784790039,7595.964266999999 -47520,Multiclass classification,Stacking,Insects,0.7334329426124288,0.7334329426124286,0.7309449816547512,16.162960052490234,7937.466337999999 -48576,Multiclass classification,Stacking,Insects,0.7373751930005147,0.7373751930005147,0.7352697035426822,12.93554973602295,8285.412244 -49632,Multiclass classification,Stacking,Insects,0.741210130765046,0.741210130765046,0.739269872700679,15.798639297485352,8639.863329999998 -50688,Multiclass classification,Stacking,Insects,0.7446682581332491,0.7446682581332491,0.7426657147430288,14.252553939819336,9000.792304999999 -51744,Multiclass classification,Stacking,Insects,0.7485650232881742,0.7485650232881743,0.7463959215624629,16.087495803833008,9368.035474999999 -52800,Multiclass classification,Stacking,Insects,0.752154396863577,0.752154396863577,0.7502511872752614,11.339015007019043,9741.135739 -408,Multiclass classification,Stacking,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.0347824096679688,1.562947 -816,Multiclass classification,Stacking,Keystroke,0.9840490797546012,0.9840490797546012,0.9559273479637392,2.137660026550293,5.08284 -1224,Multiclass classification,Stacking,Keystroke,0.9836467702371219,0.9836467702371219,0.9660207101584454,3.2939910888671875,10.794145 -1632,Multiclass classification,Stacking,Keystroke,0.9803801348865726,0.9803801348865726,0.9452685517164727,4.760180473327637,18.719227 -2040,Multiclass classification,Stacking,Keystroke,0.973516429622364,0.973516429622364,0.9361195161551138,6.6425981521606445,28.938428000000002 -2448,Multiclass classification,Stacking,Keystroke,0.9730281977932161,0.9730281977932161,0.9615988180290456,5.552071571350098,41.439403 -2856,Multiclass classification,Stacking,Keystroke,0.9747810858143607,0.9747810858143607,0.9713591464752813,6.8436784744262695,56.378536 -3264,Multiclass classification,Stacking,Keystroke,0.974869751762182,0.974869751762182,0.9692034094625394,6.567030906677246,73.934123 -3672,Multiclass classification,Stacking,Keystroke,0.9743938981204031,0.9743938981204031,0.9689232613591287,8.48116397857666,94.245901 -4080,Multiclass classification,Stacking,Keystroke,0.9722971316499142,0.9722971316499142,0.9642661054824399,5.934853553771973,117.581237 -4488,Multiclass classification,Stacking,Keystroke,0.9732560731000669,0.9732560731000669,0.9722719909296184,3.644045829772949,143.695153 -4896,Multiclass classification,Stacking,Keystroke,0.9748723186925434,0.9748723186925434,0.9754037061196345,4.787886619567871,172.738198 -5304,Multiclass classification,Stacking,Keystroke,0.9741655666603809,0.9741655666603809,0.9716360242916738,5.742301940917969,204.85114700000003 -5712,Multiclass classification,Stacking,Keystroke,0.9746104009805638,0.9746104009805638,0.9740216295290517,6.782421112060547,240.11050800000004 -6120,Multiclass classification,Stacking,Keystroke,0.9738519365909463,0.9738519365909463,0.9722333406974255,8.176769256591797,278.69027800000003 -6528,Multiclass classification,Stacking,Keystroke,0.9742607629845258,0.9742607629845258,0.9741504405159309,4.433716773986816,320.74209900000005 -6936,Multiclass classification,Stacking,Keystroke,0.9751982696467195,0.9751982696467195,0.9755523782693606,5.133135795593262,366.1265460000001 -7344,Multiclass classification,Stacking,Keystroke,0.9757592264741931,0.9757592264741931,0.9758485662267349,5.760107040405273,415.04014400000005 -7752,Multiclass classification,Stacking,Keystroke,0.9760030963746613,0.9760030963746613,0.9758957983961688,6.842521667480469,467.58770000000004 -8160,Multiclass classification,Stacking,Keystroke,0.9758548841769825,0.9758548841769825,0.9755087152005796,8.403876304626465,523.9395460000001 -8568,Multiclass classification,Stacking,Keystroke,0.9751371541963347,0.9751371541963347,0.9744422302091884,8.9804105758667,584.299164 -8976,Multiclass classification,Stacking,Keystroke,0.9755988857938719,0.9755988857938719,0.9757626053423433,8.348807334899902,648.764254 -9384,Multiclass classification,Stacking,Keystroke,0.97527443248428,0.97527443248428,0.9749874884381717,8.780474662780762,717.435188 -9792,Multiclass classification,Stacking,Keystroke,0.9751812889388214,0.9751812889388214,0.9751287694103772,7.27089786529541,790.366072 -10200,Multiclass classification,Stacking,Keystroke,0.9748994999509756,0.9748994999509756,0.9747198913701116,8.182950019836426,867.658962 -10608,Multiclass classification,Stacking,Keystroke,0.9744508343546714,0.9744508343546714,0.9742218409220015,8.212386131286621,949.401841 -11016,Multiclass classification,Stacking,Keystroke,0.9747616886064457,0.9747616886064457,0.9748981365239817,7.736974716186523,1035.676852 -11424,Multiclass classification,Stacking,Keystroke,0.9748752516851965,0.9748752516851965,0.9749367981815978,8.583486557006836,1126.650079 -11832,Multiclass classification,Stacking,Keystroke,0.9749809821654974,0.9749809821654974,0.9750463661723393,8.887914657592773,1222.478726 -12240,Multiclass classification,Stacking,Keystroke,0.9755698995015932,0.9755698995015932,0.9757989853757533,9.402573585510254,1323.224256 -12648,Multiclass classification,Stacking,Keystroke,0.9760417490313908,0.9760417490313908,0.9762258400907322,9.833843231201172,1429.020384 -13056,Multiclass classification,Stacking,Keystroke,0.9760245116813482,0.9760245116813482,0.9760626338918788,9.939188957214355,1539.962955 -13464,Multiclass classification,Stacking,Keystroke,0.9762311520463492,0.9762311520463492,0.9763300455625981,10.46349811553955,1656.191893 -13872,Multiclass classification,Stacking,Keystroke,0.9763535433638526,0.9763535433638526,0.9764287224231292,11.428705215454102,1777.90617 -14280,Multiclass classification,Stacking,Keystroke,0.9758386441627565,0.9758386441627565,0.9757700210755773,10.687178611755371,1905.2009699999999 -14688,Multiclass classification,Stacking,Keystroke,0.9761013140872881,0.9761013140872881,0.9761996431080103,10.750173568725586,2038.1161399999999 -15096,Multiclass classification,Stacking,Keystroke,0.9764822789002982,0.9764822789002982,0.9765941858257003,10.87511920928955,2176.860193 -15504,Multiclass classification,Stacking,Keystroke,0.9762626588402245,0.9762626588402245,0.9762697293829714,11.144217491149902,2321.518715 -15912,Multiclass classification,Stacking,Keystroke,0.976305700458802,0.976305700458802,0.9763523962033862,10.881969451904297,2472.196158 -16320,Multiclass classification,Stacking,Keystroke,0.9761627550707764,0.9761627550707764,0.9761821898526978,10.72462272644043,2629.0181850000004 -16728,Multiclass classification,Stacking,Keystroke,0.9760267830453757,0.9760267830453757,0.9760462981867313,10.397873878479004,2792.0314960000005 -17136,Multiclass classification,Stacking,Keystroke,0.9763641669098336,0.9763641669098336,0.976427628373518,11.5784912109375,2961.4220420000006 -17544,Multiclass classification,Stacking,Keystroke,0.9762868380550647,0.9762868380550647,0.9763077393136289,10.780138969421387,3137.2839260000005 -17952,Multiclass classification,Stacking,Keystroke,0.9763801459528717,0.9763801459528717,0.9764101118400772,11.24526309967041,3319.6223500000006 -18360,Multiclass classification,Stacking,Keystroke,0.9766327142001199,0.9766327142001199,0.976666198082788,11.450252532958984,3508.4984120000004 -18768,Multiclass classification,Stacking,Keystroke,0.9769808706772526,0.9769808706772526,0.9770112706505792,12.824438095092773,3704.1108500000005 -19176,Multiclass classification,Stacking,Keystroke,0.9767926988265971,0.9767926988265971,0.976797459665624,13.463789939880371,3906.6546070000004 -19584,Multiclass classification,Stacking,Keystroke,0.9766123678700914,0.9766123678700914,0.97661532368473,12.60595417022705,4116.138220000001 -19992,Multiclass classification,Stacking,Keystroke,0.9765894652593667,0.9765894652593667,0.976591825772772,11.445868492126465,4332.655771000001 -20400,Multiclass classification,Stacking,Keystroke,0.9765184567870974,0.9765184567870974,0.9765167109502483,12.220311164855957,4556.333966000001 -46,Multiclass classification,Voting,ImageSegments,0.4888888888888889,0.4888888888888889,0.4138888888888889,0.8855724334716797,0.380739 -92,Multiclass classification,Voting,ImageSegments,0.6263736263736264,0.6263736263736264,0.6295417331131617,0.9400959014892578,0.906366 -138,Multiclass classification,Voting,ImageSegments,0.6788321167883211,0.6788321167883211,0.6955125455614023,0.9512205123901367,1.596335 -184,Multiclass classification,Voting,ImageSegments,0.7158469945355191,0.7158469945355191,0.7293605295181818,0.9506902694702148,2.451892 -230,Multiclass classification,Voting,ImageSegments,0.74235807860262,0.74235807860262,0.7560849066334576,0.9507265090942383,3.478975 -276,Multiclass classification,Voting,ImageSegments,0.7490909090909091,0.7490909090909091,0.7654899494294127,0.9522123336791992,4.6524790000000005 -322,Multiclass classification,Voting,ImageSegments,0.7632398753894081,0.7632398753894081,0.7699967547900484,0.9522132873535156,5.915859 -368,Multiclass classification,Voting,ImageSegments,0.782016348773842,0.782016348773842,0.7847454642968661,0.9517135620117188,7.268692 -414,Multiclass classification,Voting,ImageSegments,0.7869249394673123,0.7869249394673122,0.7891209865588749,0.9521627426147461,8.714221 -460,Multiclass classification,Voting,ImageSegments,0.7821350762527233,0.7821350762527233,0.7829889615631377,0.9522056579589844,10.249346000000001 -506,Multiclass classification,Voting,ImageSegments,0.7861386138613862,0.7861386138613862,0.7872755051739567,0.9517154693603516,11.874447 -552,Multiclass classification,Voting,ImageSegments,0.7858439201451906,0.7858439201451906,0.7876565639439724,0.9515762329101562,13.588949 -598,Multiclass classification,Voting,ImageSegments,0.7872696817420436,0.7872696817420435,0.7897468061485311,0.9521427154541016,15.393404 -644,Multiclass classification,Voting,ImageSegments,0.7822706065318819,0.7822706065318819,0.7858452362125997,0.9521217346191406,17.2908 -690,Multiclass classification,Voting,ImageSegments,0.7851959361393324,0.7851959361393324,0.788215888108031,0.9515953063964844,19.280843 -736,Multiclass classification,Voting,ImageSegments,0.7836734693877551,0.783673469387755,0.7873581098337732,0.9521245956420898,21.362069 -782,Multiclass classification,Voting,ImageSegments,0.7861715749039693,0.7861715749039692,0.7892834149474556,0.9521360397338867,23.534270000000003 -828,Multiclass classification,Voting,ImageSegments,0.7847642079806529,0.7847642079806529,0.7891292080670234,0.951629638671875,25.799776 -874,Multiclass classification,Voting,ImageSegments,0.7892325315005727,0.7892325315005727,0.7922023317831084,0.9516172409057617,28.155901 -920,Multiclass classification,Voting,ImageSegments,0.7889009793253536,0.7889009793253536,0.7905862723276574,0.9520702362060547,30.602138 -966,Multiclass classification,Voting,ImageSegments,0.78860103626943,0.78860103626943,0.7894031693051725,0.952082633972168,33.138731 -1012,Multiclass classification,Voting,ImageSegments,0.7873392680514342,0.7873392680514342,0.7878835011583499,0.9515609741210938,35.768379 -1058,Multiclass classification,Voting,ImageSegments,0.7899716177861873,0.7899716177861873,0.7897146415510686,0.9520339965820312,38.488246000000004 -1104,Multiclass classification,Voting,ImageSegments,0.7905711695376246,0.7905711695376246,0.7902707663283154,0.9521427154541016,41.298010000000005 -1150,Multiclass classification,Voting,ImageSegments,0.7919930374238469,0.7919930374238469,0.7910217164829003,0.9516496658325195,44.198117 -1196,Multiclass classification,Voting,ImageSegments,0.793305439330544,0.793305439330544,0.7926565595792737,0.9516582489013672,47.188338 -1242,Multiclass classification,Voting,ImageSegments,0.7921031426269137,0.7921031426269137,0.791644431462719,0.9522132873535156,50.271512 -1288,Multiclass classification,Voting,ImageSegments,0.7964257964257965,0.7964257964257965,0.7949172523959339,0.9522237777709961,53.444669000000005 -1334,Multiclass classification,Voting,ImageSegments,0.795198799699925,0.7951987996999249,0.7938516970082157,0.9516925811767578,56.707980000000006 -1380,Multiclass classification,Voting,ImageSegments,0.7955039883973894,0.7955039883973894,0.794312731896104,0.9516897201538086,60.06121 -1426,Multiclass classification,Voting,ImageSegments,0.7971929824561403,0.7971929824561403,0.7952130436298935,0.9521360397338867,63.507281000000006 -1472,Multiclass classification,Voting,ImageSegments,0.8008157715839564,0.8008157715839563,0.7971305683653547,0.9521236419677734,67.043451 -1518,Multiclass classification,Voting,ImageSegments,0.8015820698747528,0.8015820698747528,0.7969787037511136,0.95166015625,70.670162 -1564,Multiclass classification,Voting,ImageSegments,0.8016634676903391,0.8016634676903392,0.7975983332578384,0.9521465301513672,74.387065 -1610,Multiclass classification,Voting,ImageSegments,0.8017402113113735,0.8017402113113735,0.7969541458804642,0.9521703720092773,78.19725000000001 -1656,Multiclass classification,Voting,ImageSegments,0.8018126888217523,0.8018126888217523,0.7970318311622571,0.9516267776489258,82.09764400000002 -1702,Multiclass classification,Voting,ImageSegments,0.8018812463256908,0.8018812463256908,0.7992301124377234,0.9516735076904297,86.09102400000002 -1748,Multiclass classification,Voting,ImageSegments,0.8036634230108758,0.8036634230108759,0.8004815801809151,0.9521579742431641,90.17551900000002 -1794,Multiclass classification,Voting,ImageSegments,0.8042387060791969,0.8042387060791969,0.799787639242423,0.9521493911743164,94.35067000000002 -1840,Multiclass classification,Voting,ImageSegments,0.8053289831430125,0.8053289831430125,0.8009597766649573,0.9516563415527344,98.61893200000003 -1886,Multiclass classification,Voting,ImageSegments,0.803183023872679,0.8031830238726789,0.799227837217116,0.9521894454956055,102.97762100000003 -1932,Multiclass classification,Voting,ImageSegments,0.8032107716209218,0.8032107716209218,0.7985344176802335,0.9521827697753906,107.42655600000003 -1978,Multiclass classification,Voting,ImageSegments,0.8042488619119879,0.8042488619119877,0.7992002826592023,0.9516563415527344,111.96600400000003 -2024,Multiclass classification,Voting,ImageSegments,0.8057340583292141,0.8057340583292142,0.799488243695578,0.9516725540161133,116.59842900000002 -2070,Multiclass classification,Voting,ImageSegments,0.80521991300145,0.80521991300145,0.7990099218703556,0.9521942138671875,121.31866500000002 -2116,Multiclass classification,Voting,ImageSegments,0.8056737588652483,0.8056737588652483,0.798658845250099,0.9521694183349609,126.12612700000003 -2162,Multiclass classification,Voting,ImageSegments,0.8061082832022212,0.8061082832022212,0.7986518526284686,0.9516706466674805,131.02082400000003 -2208,Multiclass classification,Voting,ImageSegments,0.8078840054372451,0.8078840054372451,0.7995103660963299,0.9521732330322266,136.00538900000004 -2254,Multiclass classification,Voting,ImageSegments,0.8047048379937861,0.8047048379937861,0.7963417515999387,0.9521608352661133,141.07727400000005 -2300,Multiclass classification,Voting,ImageSegments,0.8033927794693345,0.8033927794693345,0.7949752803158223,0.9516582489013672,146.23634000000004 -1056,Multiclass classification,Voting,Insects,0.6293838862559241,0.6293838862559241,0.5939725193500994,1.5110340118408203,3.200552 -2112,Multiclass classification,Voting,Insects,0.62482235907153,0.62482235907153,0.5894737350922559,1.5110177993774414,9.230037 -3168,Multiclass classification,Voting,Insects,0.6198294916324597,0.6198294916324597,0.5838888884930272,1.5110721588134766,17.854201 -4224,Multiclass classification,Voting,Insects,0.6192280369405636,0.6192280369405636,0.5835519631382228,1.5110435485839844,28.950406 -5280,Multiclass classification,Voting,Insects,0.6256866830839174,0.6256866830839174,0.5887468172490868,1.511063575744629,42.489771000000005 -6336,Multiclass classification,Voting,Insects,0.6187845303867403,0.6187845303867403,0.5833486573822239,1.5110454559326172,58.469284 -7392,Multiclass classification,Voting,Insects,0.6180489784873495,0.6180489784873495,0.5826198728106428,1.5110721588134766,76.881409 -8448,Multiclass classification,Voting,Insects,0.619746655617379,0.619746655617379,0.5840081546383048,1.5110502243041992,97.728436 -9504,Multiclass classification,Voting,Insects,0.6190676628433126,0.6190676628433126,0.5828637425505069,1.511042594909668,121.00604200000001 -10560,Multiclass classification,Voting,Insects,0.6198503646178616,0.6198503646178616,0.5836946750940745,1.5110759735107422,146.711441 -11616,Multiclass classification,Voting,Insects,0.6175634954799828,0.6175634954799828,0.5822534545682404,1.511033058166504,174.85184600000002 -12672,Multiclass classification,Voting,Insects,0.6204719438086971,0.6204719438086971,0.5879866433279776,1.5111761093139648,205.42585100000002 -13728,Multiclass classification,Voting,Insects,0.6369199388067313,0.6369199388067313,0.618745437324273,1.5112380981445312,238.42557100000002 -14784,Multiclass classification,Voting,Insects,0.630386254481499,0.630386254481499,0.6115259179282228,1.5110998153686523,273.85181700000004 -15840,Multiclass classification,Voting,Insects,0.5992171222930741,0.5992171222930741,0.581747071745844,1.5110797882080078,311.71395800000005 -16896,Multiclass classification,Voting,Insects,0.5783959751405742,0.5783959751405742,0.5619501594422388,1.511063575744629,352.00364300000007 -17952,Multiclass classification,Voting,Insects,0.5631998217369506,0.5631998217369506,0.5464708450044057,1.511117935180664,394.7217830000001 -19008,Multiclass classification,Voting,Insects,0.565528489503867,0.565528489503867,0.5447789723081985,1.5110950469970703,439.87157300000007 -20064,Multiclass classification,Voting,Insects,0.5725464785924338,0.5725464785924338,0.5493312346450109,2.16702938079834,487.43608700000004 -21120,Multiclass classification,Voting,Insects,0.5819404327856432,0.5819404327856432,0.5575973426297249,2.1677894592285156,537.344767 -22176,Multiclass classification,Voting,Insects,0.5905298759864712,0.5905298759864712,0.5648531785235197,2.167774200439453,589.565521 -23232,Multiclass classification,Voting,Insects,0.5995867590719297,0.5995867590719297,0.5728007753824246,2.167778968811035,644.107989 -24288,Multiclass classification,Voting,Insects,0.6068678717009099,0.6068678717009099,0.578555560305262,2.16780948638916,700.966552 -25344,Multiclass classification,Voting,Insects,0.6143313735548278,0.6143313735548278,0.5848116898462843,2.167755126953125,760.153761 -26400,Multiclass classification,Voting,Insects,0.621084131974696,0.621084131974696,0.5900605973096019,2.1677980422973633,821.662998 -27456,Multiclass classification,Voting,Insects,0.6266618102349298,0.6266618102349298,0.5936647802901621,2.167790412902832,885.506266 -28512,Multiclass classification,Voting,Insects,0.6295114166462067,0.6295114166462067,0.5991480792709615,2.168045997619629,951.65113 -29568,Multiclass classification,Voting,Insects,0.6294517536442655,0.6294517536442655,0.6037001563215106,2.1680641174316406,1020.2499009999999 -30624,Multiclass classification,Voting,Insects,0.6287104463964993,0.6287104463964993,0.6068237930795873,2.168071746826172,1091.329507 -31680,Multiclass classification,Voting,Insects,0.6292496606584804,0.6292496606584804,0.6106666463743293,2.1680679321289062,1164.8865779999999 -32736,Multiclass classification,Voting,Insects,0.6302734076676341,0.6302734076676341,0.614251388937007,2.168027877807617,1240.9182979999998 -33792,Multiclass classification,Voting,Insects,0.6266165547039152,0.6266165547039152,0.6112639299818544,2.1678638458251953,1319.4287269999998 -34848,Multiclass classification,Voting,Insects,0.6216604011823113,0.6216604011823113,0.6060150865308916,2.1678390502929688,1400.4187529999997 -35904,Multiclass classification,Voting,Insects,0.6181377600757597,0.6181377600757597,0.6018714875673907,2.167888641357422,1483.8935999999997 -36960,Multiclass classification,Voting,Insects,0.6138153088557591,0.6138153088557591,0.5971057932031453,2.1678647994995117,1569.8367669999996 -38016,Multiclass classification,Voting,Insects,0.6116796001578324,0.6116796001578324,0.5945381289951768,2.709075927734375,1658.2515679999995 -39072,Multiclass classification,Voting,Insects,0.6122187811932124,0.6122187811932124,0.5950787740952911,2.823759078979492,1749.1539719999994 -40128,Multiclass classification,Voting,Insects,0.6125052956861963,0.6125052956861963,0.5964110573184415,2.8237857818603516,1842.4635979999994 -41184,Multiclass classification,Voting,Insects,0.6145254109705461,0.6145254109705461,0.5992770713855892,2.82379150390625,1938.0560169999994 -42240,Multiclass classification,Voting,Insects,0.6163024692819432,0.6163024692819432,0.601670854132613,2.823772430419922,2035.9261369999995 -43296,Multiclass classification,Voting,Insects,0.6181776186626631,0.6181776186626631,0.6041281005310094,2.8237924575805664,2136.0724539999997 -44352,Multiclass classification,Voting,Insects,0.6197605465491195,0.6197605465491195,0.6062005996937425,2.824528694152832,2238.51149 -45408,Multiclass classification,Voting,Insects,0.6214019864778558,0.6214019864778558,0.607792464273323,2.824528694152832,2343.241606 -46464,Multiclass classification,Voting,Insects,0.6233992639304393,0.6233992639304393,0.6097993182820672,2.8245315551757812,2450.26566 -47520,Multiclass classification,Voting,Insects,0.6260864075422463,0.6260864075422463,0.6129939002712749,2.8244552612304688,2559.63137 -48576,Multiclass classification,Voting,Insects,0.6300154400411735,0.6300154400411735,0.6173873766747581,2.824479103088379,2671.379304 -49632,Multiclass classification,Voting,Insects,0.6343011424311418,0.6343011424311418,0.621931196280001,2.8244781494140625,2785.498386 -50688,Multiclass classification,Voting,Insects,0.638506914988064,0.638506914988064,0.6263145143911814,2.947113037109375,2902.0058780000004 -51744,Multiclass classification,Voting,Insects,0.6434686817540537,0.6434686817540537,0.6313977027921706,3.1849002838134766,3020.8771950000005 -52800,Multiclass classification,Voting,Insects,0.6479289380480691,0.6479289380480691,0.635943324049664,3.3886165618896484,3141.9869480000007 -408,Multiclass classification,Voting,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,0.6423864364624023,0.703596 -816,Multiclass classification,Voting,Keystroke,0.9546012269938651,0.9546012269938651,0.7993954329623859,0.8351936340332031,2.165771 -1224,Multiclass classification,Voting,Keystroke,0.9206868356500408,0.9206868356500408,0.9055597826779512,1.029007911682129,4.467328 -1632,Multiclass classification,Voting,Keystroke,0.9307173513182097,0.9307173513182097,0.9172597570917439,1.2232952117919922,7.6892 -2040,Multiclass classification,Voting,Keystroke,0.9303580186365865,0.9303580186365865,0.919916287137026,1.428065299987793,11.917356 -2448,Multiclass classification,Voting,Keystroke,0.9060073559460564,0.9060073559460564,0.9093956340782631,1.6218795776367188,17.211204 -2856,Multiclass classification,Voting,Keystroke,0.9103327495621716,0.9103327495621716,0.8980697688452707,1.8146867752075195,23.617224 -3264,Multiclass classification,Voting,Keystroke,0.9043824701195219,0.9043824701195219,0.888202704220525,2.0085010528564453,31.150342000000002 -3672,Multiclass classification,Voting,Keystroke,0.8994824298556252,0.8994824298556252,0.8972334256598172,2.2018117904663086,39.868796 -4080,Multiclass classification,Voting,Keystroke,0.8945820053934788,0.8945820053934787,0.8851783489415491,2.420787811279297,49.793690000000005 -4488,Multiclass classification,Voting,Keystroke,0.8914642299977713,0.8914642299977713,0.898372373723482,2.6146020889282227,61.011475000000004 -4896,Multiclass classification,Voting,Keystroke,0.8880490296220633,0.8880490296220633,0.8932697641963906,2.807912826538086,73.572163 -5304,Multiclass classification,Voting,Keystroke,0.883085046200264,0.883085046200264,0.8680917053752625,3.0007705688476562,87.52603 -5712,Multiclass classification,Voting,Keystroke,0.8746279110488531,0.8746279110488531,0.8792177397015432,3.194584846496582,102.92236500000001 -6120,Multiclass classification,Voting,Keystroke,0.8695865337473443,0.8695865337473442,0.8546904737358852,3.387392044067383,119.81998700000001 -6528,Multiclass classification,Voting,Keystroke,0.8579745671824728,0.8579745671824728,0.858067415232278,3.5812063217163086,138.28125400000002 -6936,Multiclass classification,Voting,Keystroke,0.8537851478010093,0.8537851478010093,0.8590096923865055,3.774517059326172,158.37518500000002 -7344,Multiclass classification,Voting,Keystroke,0.8594579871986926,0.8594579871986926,0.8620220702364139,3.9702539443969727,180.16443600000002 -7752,Multiclass classification,Voting,Keystroke,0.8593729841310799,0.8593729841310799,0.8617576440335053,4.164068222045898,203.71390800000003 -8160,Multiclass classification,Voting,Keystroke,0.8601544306900355,0.8601544306900355,0.8605355806611993,4.357378959655762,229.09093700000003 -8568,Multiclass classification,Voting,Keystroke,0.8596941753239173,0.8596941753239173,0.8627767842417701,4.60028076171875,256.36638600000003 -8976,Multiclass classification,Voting,Keystroke,0.8599442896935933,0.8599442896935933,0.8629838037923419,4.794095039367676,285.60935500000005 -9384,Multiclass classification,Voting,Keystroke,0.8581477139507621,0.8581477139507621,0.8592031021693959,4.986902236938477,316.88668100000007 -9792,Multiclass classification,Voting,Keystroke,0.8539475028087019,0.8539475028087019,0.8546213426549989,5.180716514587402,350.2631590000001 -10200,Multiclass classification,Voting,Keystroke,0.8465535836846749,0.8465535836846749,0.8431270001478435,5.374000549316406,385.8123490000001 -10608,Multiclass classification,Voting,Keystroke,0.8300179126991609,0.8300179126991609,0.8240754775818138,5.566834449768066,423.5940000000001 -11016,Multiclass classification,Voting,Keystroke,0.8254198819791194,0.8254198819791194,0.8271925616445298,5.760648727416992,463.67191300000013 -11424,Multiclass classification,Voting,Keystroke,0.820449969360063,0.820449969360063,0.8166393841205931,5.9539594650268555,506.1140620000001 -11832,Multiclass classification,Voting,Keystroke,0.8169216465218494,0.8169216465218494,0.8172029683603622,6.146766662597656,550.9879090000001 -12240,Multiclass classification,Voting,Keystroke,0.8200016341204347,0.8200016341204347,0.8225884010623591,6.340580940246582,598.362302 -12648,Multiclass classification,Voting,Keystroke,0.8167154265833795,0.8167154265833795,0.8162987105601626,6.533388137817383,648.30112 -13056,Multiclass classification,Voting,Keystroke,0.8121792416698583,0.8121792416698584,0.8136075732214813,6.727202415466309,700.874955 -13464,Multiclass classification,Voting,Keystroke,0.8099234940206492,0.8099234940206492,0.8122480630127521,6.920539855957031,756.151971 -13872,Multiclass classification,Voting,Keystroke,0.810539975488429,0.810539975488429,0.8134726777385565,7.113347053527832,814.194174 -14280,Multiclass classification,Voting,Keystroke,0.8103508649065061,0.810350864906506,0.8130549704062812,7.307161331176758,875.070018 -14688,Multiclass classification,Voting,Keystroke,0.8133042826989855,0.8133042826989855,0.8168484225511677,7.500472068786621,938.856287 -15096,Multiclass classification,Voting,Keystroke,0.8174229877442862,0.8174229877442862,0.8208616131428813,7.693279266357422,1005.6082389999999 -15504,Multiclass classification,Voting,Keystroke,0.8175191898342257,0.8175191898342257,0.8200404227627133,7.887093544006348,1075.3914499999998 -15912,Multiclass classification,Voting,Keystroke,0.8100685060649865,0.8100685060649865,0.8105704783549956,8.079900741577148,1148.2720969999998 -16320,Multiclass classification,Voting,Keystroke,0.8058704577486365,0.8058704577486365,0.8082920647955453,8.273715019226074,1224.3182599999998 -16728,Multiclass classification,Voting,Keystroke,0.8029533090213428,0.8029533090213428,0.8061756731743527,8.467025756835938,1303.6034769999999 -17136,Multiclass classification,Voting,Keystroke,0.7992996790195507,0.7992996790195507,0.8021910628966759,8.7622652053833,1386.1896219999999 -17544,Multiclass classification,Voting,Keystroke,0.7934218776720059,0.7934218776720059,0.7969041071406875,8.956079483032227,1472.1458429999998 -17952,Multiclass classification,Voting,Keystroke,0.7934933986964514,0.7934933986964514,0.7978100866424277,9.14939022064209,1561.5342959999998 -18360,Multiclass classification,Voting,Keystroke,0.7969933002886868,0.7969933002886866,0.8014382450066739,9.34219741821289,1654.4248979999998 -18768,Multiclass classification,Voting,Keystroke,0.7999147439654714,0.7999147439654714,0.8043799341405246,9.536011695861816,1750.8812689999997 -19176,Multiclass classification,Voting,Keystroke,0.7945241199478488,0.7945241199478488,0.7987282715896407,9.728818893432617,1850.9734509999998 -19584,Multiclass classification,Voting,Keystroke,0.797375274472757,0.797375274472757,0.8021140041360401,9.922633171081543,1954.769568 -19992,Multiclass classification,Voting,Keystroke,0.7945075283877745,0.7945075283877745,0.7995475233856788,10.115943908691406,2062.333925 -20400,Multiclass classification,Voting,Keystroke,0.793274180106868,0.793274180106868,0.7984237858213096,10.308751106262207,2173.749777 -46,Multiclass classification,[baseline] Last Class,ImageSegments,0.17777777777777778,0.17777777777777778,0.15260266049739735,0.0013666152954101562,0.007048 -92,Multiclass classification,[baseline] Last Class,ImageSegments,0.13186813186813187,0.13186813186813187,0.1213108980966124,0.0013637542724609375,0.018168 -138,Multiclass classification,[baseline] Last Class,ImageSegments,0.12408759124087591,0.12408759124087591,0.11874455065544491,0.001369476318359375,0.031716 -184,Multiclass classification,[baseline] Last Class,ImageSegments,0.12568306010928962,0.12568306010928962,0.12262983423071581,0.0013647079467773438,0.047654 -230,Multiclass classification,[baseline] Last Class,ImageSegments,0.12663755458515283,0.12663755458515283,0.12503852041208066,0.0013637542724609375,0.065983 -276,Multiclass classification,[baseline] Last Class,ImageSegments,0.12727272727272726,0.12727272727272726,0.12427907918144998,0.0013666152954101562,0.086242 -322,Multiclass classification,[baseline] Last Class,ImageSegments,0.13395638629283488,0.13395638629283488,0.13210036596246022,0.0013666152954101562,0.108232 -368,Multiclass classification,[baseline] Last Class,ImageSegments,0.13896457765667575,0.13896457765667575,0.13745011462972964,0.0013675689697265625,0.131958 -414,Multiclass classification,[baseline] Last Class,ImageSegments,0.14043583535108958,0.14043583535108958,0.14035813096947544,0.0013666152954101562,0.15742099999999998 -460,Multiclass classification,[baseline] Last Class,ImageSegments,0.14596949891067537,0.14596949891067537,0.14563148710727947,0.00136566162109375,0.18458599999999997 -506,Multiclass classification,[baseline] Last Class,ImageSegments,0.13861386138613863,0.13861386138613863,0.1383381610231494,0.0013666152954101562,0.21348499999999998 -552,Multiclass classification,[baseline] Last Class,ImageSegments,0.1397459165154265,0.1397459165154265,0.13938652491777898,0.0013666152954101562,0.24411 -598,Multiclass classification,[baseline] Last Class,ImageSegments,0.1373534338358459,0.1373534338358459,0.13727981043458612,0.0013675689697265625,0.276463 -644,Multiclass classification,[baseline] Last Class,ImageSegments,0.13996889580093314,0.13996889580093314,0.14017571709017965,0.0013666152954101562,0.310533 -690,Multiclass classification,[baseline] Last Class,ImageSegments,0.1378809869375907,0.1378809869375907,0.13801517784553324,0.001369476318359375,0.346313 -736,Multiclass classification,[baseline] Last Class,ImageSegments,0.1401360544217687,0.1401360544217687,0.14031088927958282,0.0013675689697265625,0.38382 -782,Multiclass classification,[baseline] Last Class,ImageSegments,0.14212548015364918,0.14212548015364918,0.1420930265541123,0.0013647079467773438,0.423095 -828,Multiclass classification,[baseline] Last Class,ImageSegments,0.14268440145102781,0.14268440145102781,0.14229874553046912,0.0013666152954101562,0.464082 -874,Multiclass classification,[baseline] Last Class,ImageSegments,0.13860252004581902,0.13860252004581902,0.13845352694595275,0.0013647079467773438,0.506788 -920,Multiclass classification,[baseline] Last Class,ImageSegments,0.13492927094668117,0.13492927094668117,0.1348083913046733,0.0013666152954101562,0.551195 -966,Multiclass classification,[baseline] Last Class,ImageSegments,0.13367875647668392,0.13367875647668392,0.13349177774445278,0.0013637542724609375,0.597302 -1012,Multiclass classification,[baseline] Last Class,ImageSegments,0.13254203758654798,0.13254203758654798,0.1324936677659038,0.0013675689697265625,0.645131 -1058,Multiclass classification,[baseline] Last Class,ImageSegments,0.13339640491958374,0.13339640491958374,0.1331834965440007,0.00136566162109375,0.69466 -1104,Multiclass classification,[baseline] Last Class,ImageSegments,0.13417951042611062,0.13417951042611062,0.13402826529501538,0.0013666152954101562,0.7459020000000001 -1150,Multiclass classification,[baseline] Last Class,ImageSegments,0.134029590948651,0.134029590948651,0.13406391150519123,0.0013637542724609375,0.7988440000000001 -1196,Multiclass classification,[baseline] Last Class,ImageSegments,0.13640167364016736,0.13640167364016736,0.1363948420172951,0.001369476318359375,0.8534870000000001 -1242,Multiclass classification,[baseline] Last Class,ImageSegments,0.13940370668815472,0.13940370668815472,0.13919772383892226,0.0013637542724609375,0.9098240000000001 -1288,Multiclass classification,[baseline] Last Class,ImageSegments,0.1414141414141414,0.1414141414141414,0.14118715023210152,0.0013666152954101562,0.9678680000000001 -1334,Multiclass classification,[baseline] Last Class,ImageSegments,0.14328582145536384,0.14328582145536384,0.14302553278156668,0.0013637542724609375,1.027625 -1380,Multiclass classification,[baseline] Last Class,ImageSegments,0.14358230601885424,0.14358230601885424,0.1433209000486506,0.001369476318359375,1.089079 -1426,Multiclass classification,[baseline] Last Class,ImageSegments,0.14175438596491227,0.14175438596491227,0.14145466559291123,0.001369476318359375,1.152253 -1472,Multiclass classification,[baseline] Last Class,ImageSegments,0.13936097892590074,0.13936097892590074,0.13907629713942624,0.0013647079467773438,1.217139 -1518,Multiclass classification,[baseline] Last Class,ImageSegments,0.13974950560316415,0.13974950560316415,0.13951366685898453,0.0013666152954101562,1.283725 -1564,Multiclass classification,[baseline] Last Class,ImageSegments,0.13691618682021753,0.13691618682021753,0.13664170474395118,0.0013666152954101562,1.352073 -1610,Multiclass classification,[baseline] Last Class,ImageSegments,0.13610938471100062,0.13610938471100062,0.13597683881903072,0.0013637542724609375,1.422125 -1656,Multiclass classification,[baseline] Last Class,ImageSegments,0.1365558912386707,0.1365558912386707,0.13633224623774592,0.001369476318359375,1.4938960000000001 -1702,Multiclass classification,[baseline] Last Class,ImageSegments,0.13932980599647266,0.13932980599647266,0.13901296274399097,0.0013675689697265625,1.5673830000000002 -1748,Multiclass classification,[baseline] Last Class,ImageSegments,0.14195764167143674,0.14195764167143674,0.14147197312723644,0.00136566162109375,1.6425530000000002 -1794,Multiclass classification,[baseline] Last Class,ImageSegments,0.14221974344673732,0.14221974344673732,0.14194103966110075,0.0013647079467773438,1.7194410000000002 -1840,Multiclass classification,[baseline] Last Class,ImageSegments,0.14138118542686243,0.14138118542686243,0.14114329766598663,0.0013675689697265625,1.7980130000000003 -1886,Multiclass classification,[baseline] Last Class,ImageSegments,0.140053050397878,0.140053050397878,0.13973258713820758,0.0013666152954101562,1.8782870000000003 -1932,Multiclass classification,[baseline] Last Class,ImageSegments,0.14293112377006734,0.14293112377006734,0.14275229229825853,0.0013666152954101562,1.9602450000000002 -1978,Multiclass classification,[baseline] Last Class,ImageSegments,0.14618108244815378,0.14618108244815378,0.14597158151605963,0.001369476318359375,2.043928 -2024,Multiclass classification,[baseline] Last Class,ImageSegments,0.14434008897676717,0.14434008897676717,0.14416625237761066,0.001369476318359375,2.12929 -2070,Multiclass classification,[baseline] Last Class,ImageSegments,0.14403093281778637,0.14403093281778637,0.1438554349712762,0.0013666152954101562,2.216361 -2116,Multiclass classification,[baseline] Last Class,ImageSegments,0.14468085106382977,0.14468085106382977,0.1446036231777657,0.0013637542724609375,2.305147 -2162,Multiclass classification,[baseline] Last Class,ImageSegments,0.14530310041647385,0.14530310041647385,0.14520465913821792,0.001369476318359375,2.395629 -2208,Multiclass classification,[baseline] Last Class,ImageSegments,0.14499320344358857,0.14499320344358857,0.14491109851991696,0.001369476318359375,2.487817 -2254,Multiclass classification,[baseline] Last Class,ImageSegments,0.14647137150466044,0.14647137150466044,0.14640425534129609,0.0013666152954101562,2.5817110000000003 -2300,Multiclass classification,[baseline] Last Class,ImageSegments,0.14789038712483688,0.14789038712483688,0.14788688524810298,0.0013675689697265625,2.6773210000000005 -1056,Multiclass classification,[baseline] Last Class,Insects,0.15829383886255924,0.15829383886255924,0.1376212379233521,0.0013856887817382812,0.055672 -2112,Multiclass classification,[baseline] Last Class,Insects,0.16579819990525818,0.16579819990525818,0.15110451064118433,0.0013856887817382812,0.157206 -3168,Multiclass classification,[baseline] Last Class,Insects,0.17019261130407326,0.17019261130407326,0.15681512355039637,0.0013885498046875,0.304619 -4224,Multiclass classification,[baseline] Last Class,Insects,0.16599573762727918,0.16599573762727918,0.1525443315605067,0.0013856887817382812,0.49782699999999996 -5280,Multiclass classification,[baseline] Last Class,Insects,0.17086569426027656,0.17086569426027656,0.1567667911399359,0.0013837814331054688,0.736912 -6336,Multiclass classification,[baseline] Last Class,Insects,0.17379636937647988,0.17379636937647988,0.16137568195972998,0.0013837814331054688,1.021646 -7392,Multiclass classification,[baseline] Last Class,Insects,0.1752130970098769,0.1752130970098769,0.16189407904134775,0.0013837814331054688,1.3518970000000001 -8448,Multiclass classification,[baseline] Last Class,Insects,0.17722268260921037,0.17722268260921037,0.163740045170864,0.0013818740844726562,1.7276090000000002 -9504,Multiclass classification,[baseline] Last Class,Insects,0.17731242765442493,0.17731242765442493,0.16374929744530958,0.0013885498046875,2.148941 -10560,Multiclass classification,[baseline] Last Class,Insects,0.17908892887584052,0.17908892887584052,0.16564210767474952,0.0013837814331054688,2.615755 -11616,Multiclass classification,[baseline] Last Class,Insects,0.17899268187688333,0.17899268187688333,0.16559253835337612,0.0013856887817382812,3.128148 -12672,Multiclass classification,[baseline] Last Class,Insects,0.18530502722752742,0.1853050272275274,0.18269809988409802,0.0013866424560546875,3.685883 -13728,Multiclass classification,[baseline] Last Class,Insects,0.24797843665768193,0.24797843665768193,0.26603936845528803,0.0013866424560546875,4.288806 -14784,Multiclass classification,[baseline] Last Class,Insects,0.2795778935263478,0.2795778935263478,0.28229742751715126,0.0013818740844726562,4.937051 -15840,Multiclass classification,[baseline] Last Class,Insects,0.27615379758823155,0.27615379758823155,0.28473758533654364,0.0013818740844726562,5.631085000000001 -16896,Multiclass classification,[baseline] Last Class,Insects,0.2723290914471737,0.2723290914471737,0.2859139704285301,0.0013856887817382812,6.370871000000001 -17952,Multiclass classification,[baseline] Last Class,Insects,0.2720739791655061,0.2720739791655061,0.28801432065038773,0.0013866424560546875,7.156724000000001 -19008,Multiclass classification,[baseline] Last Class,Insects,0.28252748987215237,0.28252748987215237,0.28775044293210866,0.0013866424560546875,7.988744000000001 -20064,Multiclass classification,[baseline] Last Class,Insects,0.28724517769027563,0.28724517769027563,0.28667392366619265,0.0013818740844726562,8.866412000000002 -21120,Multiclass classification,[baseline] Last Class,Insects,0.28306264501160094,0.28306264501160094,0.28164766024255256,0.0013837814331054688,9.789517000000002 -22176,Multiclass classification,[baseline] Last Class,Insects,0.2805411499436302,0.2805411499436302,0.27862960725280095,0.0013866424560546875,10.758806000000002 -23232,Multiclass classification,[baseline] Last Class,Insects,0.2797124531875511,0.2797124531875511,0.27719419757933417,0.0013856887817382812,11.774485000000002 -24288,Multiclass classification,[baseline] Last Class,Insects,0.2777205912628155,0.2777205912628155,0.2745878480946635,0.0013866424560546875,12.836246000000003 -25344,Multiclass classification,[baseline] Last Class,Insects,0.2756579726157124,0.2756579726157124,0.27233803052028965,0.0013818740844726562,13.944171000000003 -26400,Multiclass classification,[baseline] Last Class,Insects,0.27394977082465244,0.27394977082465244,0.26996904425699914,0.0013837814331054688,15.098386000000003 -27456,Multiclass classification,[baseline] Last Class,Insects,0.27189947186304864,0.27189947186304864,0.26719485323886244,0.0013866424560546875,16.299309000000004 -28512,Multiclass classification,[baseline] Last Class,Insects,0.2723860965942969,0.2723860965942969,0.2686965366571338,0.0013885498046875,17.546694000000006 -29568,Multiclass classification,[baseline] Last Class,Insects,0.2738187844556431,0.2738187844556431,0.2720266804437783,0.0013885498046875,18.840271000000005 -30624,Multiclass classification,[baseline] Last Class,Insects,0.27538124938771513,0.27538124938771513,0.2748698663810352,0.0013885498046875,20.179938000000003 -31680,Multiclass classification,[baseline] Last Class,Insects,0.2780390795163989,0.2780390795163989,0.2784141751235631,0.0013856887817382812,21.565237000000003 -32736,Multiclass classification,[baseline] Last Class,Insects,0.279670077898274,0.279670077898274,0.2802192251245276,0.0013837814331054688,22.996618000000005 -33792,Multiclass classification,[baseline] Last Class,Insects,0.2808440117190968,0.2808440117190968,0.28119627453717067,0.0013856887817382812,24.474295000000005 -34848,Multiclass classification,[baseline] Last Class,Insects,0.2772405085086234,0.2772405085086234,0.27819051828647573,0.0013837814331054688,25.998543000000005 -35904,Multiclass classification,[baseline] Last Class,Insects,0.2739325404562293,0.2739325404562293,0.2754200456137155,0.0013856887817382812,27.569042000000007 -36960,Multiclass classification,[baseline] Last Class,Insects,0.271246516410076,0.271246516410076,0.27333283767820205,0.0013818740844726562,29.185425000000006 -38016,Multiclass classification,[baseline] Last Class,Insects,0.26855188741286334,0.26855188741286334,0.2710722002891223,0.0013856887817382812,30.847968000000005 -39072,Multiclass classification,[baseline] Last Class,Insects,0.277034117376059,0.277034117376059,0.2770619820799866,0.0013866424560546875,32.556678000000005 -40128,Multiclass classification,[baseline] Last Class,Insects,0.27617315024796274,0.27617315024796274,0.2760769006623073,0.0013837814331054688,34.31145000000001 -41184,Multiclass classification,[baseline] Last Class,Insects,0.27567200058276475,0.27567200058276475,0.2754352632972117,0.0013837814331054688,36.11334600000001 -42240,Multiclass classification,[baseline] Last Class,Insects,0.27401216884869434,0.27401216884869434,0.2735946193588543,0.0013885498046875,37.96222800000001 -43296,Multiclass classification,[baseline] Last Class,Insects,0.2738422450629403,0.2738422450629403,0.27319488690835786,0.0013856887817382812,39.85759000000001 -44352,Multiclass classification,[baseline] Last Class,Insects,0.2729588960790061,0.2729588960790061,0.27209116538690487,0.0013866424560546875,41.79925400000001 -45408,Multiclass classification,[baseline] Last Class,Insects,0.27205056489087587,0.27205056489087587,0.2708084959373003,0.0013866424560546875,43.79182700000001 -46464,Multiclass classification,[baseline] Last Class,Insects,0.27137722488862104,0.27137722488862104,0.26986314104154363,0.0013837814331054688,45.834688000000014 -47520,Multiclass classification,[baseline] Last Class,Insects,0.27235421620825356,0.27235421620825356,0.27170627983222856,0.0013837814331054688,47.92837500000002 -48576,Multiclass classification,[baseline] Last Class,Insects,0.2741327843540916,0.2741327843540916,0.27449463409742436,0.0013818740844726562,50.07231500000002 -49632,Multiclass classification,[baseline] Last Class,Insects,0.27535209848683284,0.27535209848683284,0.27650368764304034,0.0013818740844726562,52.26665400000002 -50688,Multiclass classification,[baseline] Last Class,Insects,0.2768362696549411,0.2768362696549411,0.27863440912734966,0.0013837814331054688,54.51115200000002 -51744,Multiclass classification,[baseline] Last Class,Insects,0.27827918752294994,0.27827918752294994,0.2805971515128955,0.0013885498046875,56.805577000000014 -52800,Multiclass classification,[baseline] Last Class,Insects,0.28911532415386654,0.28911532415386654,0.2892953202729756,0.0013866424560546875,59.150289000000015 -408,Multiclass classification,[baseline] Last Class,Keystroke,0.9975429975429976,0.9975429975429976,0.9660408844388819,0.0006122589111328125,0.026957 -816,Multiclass classification,[baseline] Last Class,Keystroke,0.9975460122699387,0.9975460122699387,0.9879967903427672,0.0006628036499023438,0.073338 -1224,Multiclass classification,[baseline] Last Class,Keystroke,0.9975470155355682,0.9975470155355682,0.9931179599499375,0.000713348388671875,0.138405 -1632,Multiclass classification,[baseline] Last Class,Keystroke,0.9975475168608215,0.9975475168608215,0.9950750839342831,0.0012521743774414062,0.22203499999999998 -2040,Multiclass classification,[baseline] Last Class,Keystroke,0.9975478175576263,0.9975478175576263,0.9960150346160549,0.0013027191162109375,0.32420699999999997 -2448,Multiclass classification,[baseline] Last Class,Keystroke,0.9975480179812015,0.9975480179812015,0.9965317313935653,0.0013532638549804688,0.44525699999999996 -2856,Multiclass classification,[baseline] Last Class,Keystroke,0.9975481611208407,0.9975481611208407,0.9968424283169279,0.00140380859375,0.58488 -3264,Multiclass classification,[baseline] Last Class,Keystroke,0.9975482684646031,0.9975482684646031,0.9970416021996,0.0014543533325195312,0.7430509999999999 -3672,Multiclass classification,[baseline] Last Class,Keystroke,0.9975483519476982,0.9975483519476982,0.9971755428551425,0.0015048980712890625,0.9196609999999998 -4080,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484187300809,0.9975484187300809,0.9972690115789393,0.0015554428100585938,1.1148029999999998 -4488,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484733675062,0.9975484733675062,0.9973361791525123,0.001605987548828125,1.3284669999999998 -4896,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485188968335,0.9975485188968335,0.9973856025730918,0.0016565322875976562,1.5608199999999999 -5304,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485574203281,0.9975485574203281,0.997422679833574,0.0017070770263671875,1.81175 -5712,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485904395027,0.9975485904395027,0.99745094204078,0.0017576217651367188,2.0815 -6120,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486190554013,0.9975486190554013,0.9974727709453766,0.00180816650390625,2.369724 -6528,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486440937643,0.9975486440937643,0.997489815700999,0.0018587112426757812,2.6764910000000004 -6936,Multiclass classification,[baseline] Last Class,Keystroke,0.997548666186013,0.997548666186013,0.9975032443691146,0.0019092559814453125,3.0019130000000005 -7344,Multiclass classification,[baseline] Last Class,Keystroke,0.997548685823233,0.997548685823233,0.9975139007887863,0.0034246444702148438,3.3461420000000004 -7752,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487033931105,0.9975487033931105,0.9975224052755713,0.003475189208984375,3.7089170000000005 -8160,Multiclass classification,[baseline] Last Class,Keystroke,0.997548719205785,0.997548719205785,0.9975292209193422,0.0035257339477539062,4.090185000000001 -8568,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487335123147,0.9975487335123147,0.9975346982235256,0.0035762786865234375,4.489997000000001 -8976,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487465181059,0.9975487465181059,0.9975391057693663,0.0036268234252929688,4.908346000000001 -9384,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487583928381,0.9975487583928381,0.997542651662671,0.0036773681640625,5.3453800000000005 -9792,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487692779083,0.9975487692779083,0.9975454987794794,0.0037279129028320312,5.8012950000000005 -10200,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487792920874,0.9975487792920874,0.9975477757646256,0.0037784576416015625,6.275930000000001 -10608,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487885358726,0.9975487885358726,0.9975495850737114,0.0038290023803710938,6.769232000000001 -11016,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487970948707,0.9975487970948707,0.9975510089260561,0.003879547119140625,7.281090000000001 -11424,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488050424582,0.9975488050424582,0.997552113761348,0.003930091857910156,7.811594 -11832,Multiclass classification,[baseline] Last Class,Keystroke,0.99754881244189,0.99754881244189,0.9975529536110198,0.0039806365966796875,8.360849 -12240,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488193479859,0.9975488193479859,0.9975535726732964,0.004031181335449219,8.928801 -12648,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488258084921,0.9975488258084921,0.9975540072976318,0.00408172607421875,9.515298 -13056,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488318651857,0.9975488318651857,0.997554287526727,0.004132270812988281,10.120334999999999 -13464,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488375547797,0.9975488375547797,0.9975544383040469,0.0041828155517578125,10.744062999999999 -13872,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488429096676,0.9975488429096676,0.9975544804262364,0.004233360290527344,11.386615999999998 -14280,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488479585405,0.9975488479585405,0.9975544312994103,0.004283905029296875,12.048032999999998 -14688,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488527269013,0.9975488527269013,0.9975543055435039,0.004334449768066406,12.728291999999998 -15096,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488572374959,0.9975488572374959,0.9975541154780816,0.0043849945068359375,13.427186999999998 -15504,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488615106753,0.9975488615106753,0.9975538715150369,0.004435539245605469,14.144781999999998 -15912,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488655647037,0.9975488655647037,0.997553582477696,0.004486083984375,14.881121999999998 -16320,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488694160182,0.9975488694160182,0.997553255861403,0.004536628723144531,15.636483999999998 -16728,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488730794524,0.9975488730794524,0.997552898047314,0.0045871734619140625,16.410486 -17136,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488765684272,0.9975488765684272,0.997552514478575,0.004637718200683594,17.203509999999998 -17544,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488798951149,0.9975488798951149,0.997552109806108,0.004688262939453125,18.015261 -17952,Multiclass classification,[baseline] Last Class,Keystroke,0.997548883070581,0.997548883070581,0.997551688009728,0.004738807678222656,18.845896 -18360,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488861049077,0.9975488861049077,0.9975512524991371,0.0047893524169921875,19.695493 -18768,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488890073001,0.9975488890073001,0.9975508061984417,0.004839897155761719,20.563921999999998 -19176,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488917861799,0.9975488917861799,0.9975503516171185,0.00489044189453125,21.451134 -19584,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488944492672,0.9975488944492672,0.997549890909789,0.004940986633300781,22.357399 -19992,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488970036517,0.9975488970036517,0.9975494259267257,0.0049915313720703125,23.282656 -20400,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488994558557,0.9975488994558557,0.9975489582566448,0.005042076110839844,24.227045999999998 +46,Multiclass classification,Naive Bayes,ImageSegments,0.4666666666666667,0.4666666666666667,0.4009102009102009,0.3899507522583008,0.450679 +92,Multiclass classification,Naive Bayes,ImageSegments,0.5604395604395604,0.5604395604395604,0.5279334700387331,0.3899507522583008,1.152847 +138,Multiclass classification,Naive Bayes,ImageSegments,0.5474452554744526,0.5474452554744526,0.5191892873237387,0.38997745513916016,2.278305 +184,Multiclass classification,Naive Bayes,ImageSegments,0.5573770491803278,0.5573770491803278,0.5225713529323662,0.3899507522583008,3.449742 +230,Multiclass classification,Naive Bayes,ImageSegments,0.5545851528384279,0.5545851528384279,0.5217226223148511,0.38997745513916016,4.939578 +276,Multiclass classification,Naive Bayes,ImageSegments,0.56,0.56,0.5450388711329709,0.38997745513916016,6.667081 +322,Multiclass classification,Naive Bayes,ImageSegments,0.5825545171339563,0.5825545171339563,0.5566705826058684,0.39000415802001953,8.548779999999999 +368,Multiclass classification,Naive Bayes,ImageSegments,0.5940054495912807,0.5940054495912807,0.5613773296963412,0.39000415802001953,10.607026 +414,Multiclass classification,Naive Bayes,ImageSegments,0.5980629539951574,0.5980629539951574,0.5624927052752284,0.39000415802001953,12.811145 +460,Multiclass classification,Naive Bayes,ImageSegments,0.599128540305011,0.599128540305011,0.5669821167583783,0.38997745513916016,15.144022 +506,Multiclass classification,Naive Bayes,ImageSegments,0.6099009900990099,0.6099009900990099,0.592228619098681,0.39000415802001953,17.683543 +552,Multiclass classification,Naive Bayes,ImageSegments,0.6116152450090744,0.6116152450090744,0.5983340184133136,0.3899507522583008,20.357047 +598,Multiclass classification,Naive Bayes,ImageSegments,0.6180904522613065,0.6180904522613065,0.611527101723203,0.38997745513916016,23.213992 +644,Multiclass classification,Naive Bayes,ImageSegments,0.6158631415241057,0.6158631415241057,0.6113311881078581,0.38997745513916016,26.205369 +690,Multiclass classification,Naive Bayes,ImageSegments,0.6182873730043541,0.6182873730043541,0.6150189987146761,0.38997745513916016,29.350024 +736,Multiclass classification,Naive Bayes,ImageSegments,0.617687074829932,0.617687074829932,0.6157912419016742,0.38997745513916016,32.567265 +782,Multiclass classification,Naive Bayes,ImageSegments,0.6274007682458387,0.6274007682458387,0.6216325704223051,0.38997745513916016,36.027093 +828,Multiclass classification,Naive Bayes,ImageSegments,0.6324062877871826,0.6324062877871826,0.6280704917469789,0.38997745513916016,39.646129 +874,Multiclass classification,Naive Bayes,ImageSegments,0.6426116838487973,0.6426116838487973,0.6349558095046656,0.38997745513916016,43.417442 +920,Multiclass classification,Naive Bayes,ImageSegments,0.6485310119695321,0.6485310119695321,0.6384515982514894,0.38997745513916016,47.360213 +966,Multiclass classification,Naive Bayes,ImageSegments,0.6507772020725389,0.6507772020725389,0.6399118827528387,0.38997745513916016,51.459671 +1012,Multiclass classification,Naive Bayes,ImageSegments,0.6508407517309595,0.6508407517309595,0.6387857120889422,0.38997745513916016,55.677121 +1058,Multiclass classification,Naive Bayes,ImageSegments,0.6537369914853358,0.6537369914853358,0.6398811322847953,0.38997745513916016,60.129657 +1104,Multiclass classification,Naive Bayes,ImageSegments,0.658204895738894,0.658204895738894,0.6463297068165035,0.38997745513916016,64.716333 +1150,Multiclass classification,Naive Bayes,ImageSegments,0.6640557006092254,0.6640557006092254,0.6508930463144657,0.39000415802001953,69.425449 +1196,Multiclass classification,Naive Bayes,ImageSegments,0.6702928870292887,0.6702928870292887,0.6599370641329333,0.39000415802001953,74.368592 +1242,Multiclass classification,Naive Bayes,ImageSegments,0.6736502820306205,0.6736502820306205,0.669511465798708,0.39000415802001953,79.42749 +1288,Multiclass classification,Naive Bayes,ImageSegments,0.6822066822066822,0.6822066822066822,0.6790074545382362,0.39000415802001953,84.676077 +1334,Multiclass classification,Naive Bayes,ImageSegments,0.6841710427606902,0.6841710427606902,0.6834974476087327,0.39000415802001953,90.04929600000001 +1380,Multiclass classification,Naive Bayes,ImageSegments,0.6874546773023931,0.6874546773023931,0.687676692272135,0.39000415802001953,95.54439700000002 +1426,Multiclass classification,Naive Bayes,ImageSegments,0.6919298245614035,0.6919298245614035,0.6930786661709784,0.39000415802001953,101.25523300000002 +1472,Multiclass classification,Naive Bayes,ImageSegments,0.698844323589395,0.698844323589395,0.6985606658027719,0.38997745513916016,107.09626300000002 +1518,Multiclass classification,Naive Bayes,ImageSegments,0.7027027027027027,0.7027027027027027,0.7017787722939461,0.39000415802001953,113.17857300000003 +1564,Multiclass classification,Naive Bayes,ImageSegments,0.7056941778630839,0.7056941778630839,0.7062915374924865,0.38997745513916016,119.38367200000003 +1610,Multiclass classification,Naive Bayes,ImageSegments,0.7078931013051585,0.7078931013051585,0.7081385387673028,0.38997745513916016,125.72760100000004 +1656,Multiclass classification,Naive Bayes,ImageSegments,0.7093655589123867,0.7093655589123867,0.7109488618373111,0.3899507522583008,132.27559300000004 +1702,Multiclass classification,Naive Bayes,ImageSegments,0.7101704879482658,0.7101704879482658,0.7132092257742534,0.38997745513916016,138.94755600000005 +1748,Multiclass classification,Naive Bayes,ImageSegments,0.7143674871207785,0.7143674871207784,0.7178399485500211,0.3899507522583008,145.68584300000003 +1794,Multiclass classification,Naive Bayes,ImageSegments,0.7172336865588399,0.7172336865588399,0.7191260584555579,0.38997745513916016,152.67811600000005 +1840,Multiclass classification,Naive Bayes,ImageSegments,0.7199564980967917,0.7199564980967917,0.7217017555070446,0.39000415802001953,159.82058900000004 +1886,Multiclass classification,Naive Bayes,ImageSegments,0.7204244031830239,0.7204244031830238,0.7234495525792994,0.39000415802001953,167.13449700000004 +1932,Multiclass classification,Naive Bayes,ImageSegments,0.7219057483169342,0.7219057483169342,0.7238483512148008,0.39000415802001953,174.57489300000003 +1978,Multiclass classification,Naive Bayes,ImageSegments,0.723823975720789,0.723823975720789,0.7251399238639739,0.39000415802001953,182.21825900000005 +2024,Multiclass classification,Naive Bayes,ImageSegments,0.726643598615917,0.726643598615917,0.7268553573885639,0.39000415802001953,189.97396200000006 +2070,Multiclass classification,Naive Bayes,ImageSegments,0.7269212179797003,0.7269212179797003,0.7276782991451582,0.39000415802001953,197.92708900000005 +2116,Multiclass classification,Naive Bayes,ImageSegments,0.7286052009456265,0.7286052009456266,0.7283656039279267,0.39000415802001953,206.04766600000005 +2162,Multiclass classification,Naive Bayes,ImageSegments,0.7306802406293382,0.7306802406293383,0.7303992643507475,0.39000415802001953,214.36632800000004 +2208,Multiclass classification,Naive Bayes,ImageSegments,0.733574988672406,0.733574988672406,0.7322842940126589,0.39000415802001953,222.77231300000003 +2254,Multiclass classification,Naive Bayes,ImageSegments,0.7314691522414558,0.7314691522414558,0.7300322879925133,0.39000415802001953,231.40748800000003 +2300,Multiclass classification,Naive Bayes,ImageSegments,0.7316224445411048,0.7316224445411048,0.7300416811383057,0.39000415802001953,240.14309800000004 +2310,Multiclass classification,Naive Bayes,ImageSegments,0.7319185794716327,0.7319185794716329,0.7304188192194185,0.39000415802001953,248.95897400000004 +1056,Multiclass classification,Naive Bayes,Insects,0.623696682464455,0.623696682464455,0.5870724729616662,0.6116933822631836,4.116407 +2112,Multiclass classification,Naive Bayes,Insects,0.6148744670772146,0.6148744670772146,0.5800776869595596,0.6116933822631836,12.008893 +3168,Multiclass classification,Naive Bayes,Insects,0.6065677297126618,0.6065677297126618,0.5714781230184183,0.6116933822631836,23.636521000000002 +4224,Multiclass classification,Naive Bayes,Insects,0.6043097324177126,0.6043097324177126,0.5697541737710122,0.6116933822631836,38.735534 +5280,Multiclass classification,Naive Bayes,Insects,0.6088274294373934,0.6088274294373934,0.5727560614138387,0.6116933822631836,57.253764000000004 +6336,Multiclass classification,Naive Bayes,Insects,0.6023677979479084,0.6023677979479084,0.5679597008529512,0.6116933822631836,79.038555 +7392,Multiclass classification,Naive Bayes,Insects,0.5995129211202814,0.5995129211202814,0.5652603100832261,0.6116933822631836,104.109779 +8448,Multiclass classification,Naive Bayes,Insects,0.6019888717888008,0.6019888717888008,0.5673514925692325,0.6116933822631836,132.296427 +9504,Multiclass classification,Naive Bayes,Insects,0.5993896664211301,0.5993896664211301,0.5644951651039589,0.6116933822631836,163.68164199999998 +10560,Multiclass classification,Naive Bayes,Insects,0.5994885879344635,0.5994885879344635,0.564565538599863,0.6116933822631836,198.25211399999998 +11616,Multiclass classification,Naive Bayes,Insects,0.5972449418854929,0.5972449418854929,0.5631227877868952,0.6116933822631836,235.999104 +12672,Multiclass classification,Naive Bayes,Insects,0.6001894088864336,0.6001894088864336,0.5684733590606373,0.6116933822631836,276.973484 +13728,Multiclass classification,Naive Bayes,Insects,0.6120783856632913,0.6120783856632913,0.5935173038317552,0.6116933822631836,321.087465 +14784,Multiclass classification,Naive Bayes,Insects,0.6024487587093282,0.6024487587093282,0.5841270876002982,0.6116933822631836,368.414891 +15840,Multiclass classification,Naive Bayes,Insects,0.5676494728202538,0.5676494728202538,0.5507155080701159,0.6116933822631836,418.92674800000003 +16896,Multiclass classification,Naive Bayes,Insects,0.5418762947617638,0.5418762947617638,0.5256197352354142,0.6116933822631836,472.67283100000003 +17952,Multiclass classification,Naive Bayes,Insects,0.5232020500250683,0.5232020500250683,0.5066898143269706,0.6116933822631836,529.5973250000001 +19008,Multiclass classification,Naive Bayes,Insects,0.5118640500868101,0.5118640500868101,0.4926543583964285,0.6116933822631836,589.87103 +20064,Multiclass classification,Naive Bayes,Insects,0.5103922643672432,0.5103922643672432,0.4900586962359796,0.6116933822631836,653.257514 +21120,Multiclass classification,Naive Bayes,Insects,0.5115772527108291,0.5115772527108291,0.4910837640903744,0.6116933822631836,719.720849 +22176,Multiclass classification,Naive Bayes,Insects,0.5140022547914318,0.5140022547914318,0.49325418882319577,0.6116933822631836,789.2473650000001 +23232,Multiclass classification,Naive Bayes,Insects,0.5154319659076234,0.5154319659076234,0.49430134175999263,0.6116933822631836,861.9200270000001 +24288,Multiclass classification,Naive Bayes,Insects,0.5184254951208466,0.5184254951208466,0.4965832238311332,0.6116933822631836,937.6283820000001 +25344,Multiclass classification,Naive Bayes,Insects,0.5225111470623052,0.5225111470623052,0.499893079239698,0.6116933822631836,1016.4339050000001 +26400,Multiclass classification,Naive Bayes,Insects,0.5257396113489148,0.5257396113489148,0.5022487669255871,0.6116933822631836,1098.325454 +27456,Multiclass classification,Naive Bayes,Insects,0.5301402294663996,0.5301402294663996,0.5051550433324518,0.6116933822631836,1183.302333 +28512,Multiclass classification,Naive Bayes,Insects,0.5277261407877661,0.5277261407877661,0.5036945145235058,0.6116933822631836,1271.323869 +29568,Multiclass classification,Naive Bayes,Insects,0.5204450908107011,0.5204450908107011,0.4989008712312768,0.6116933822631836,1362.446785 +30624,Multiclass classification,Naive Bayes,Insects,0.5147111648107632,0.5147111648107632,0.49582684007363204,0.6116933822631836,1456.7074690000002 +31680,Multiclass classification,Naive Bayes,Insects,0.5105590454244137,0.5105590454244137,0.4941101813344875,0.6116933822631836,1553.9918670000002 +32736,Multiclass classification,Naive Bayes,Insects,0.5075607148312204,0.5075607148312204,0.4931947798921405,0.6116933822631836,1654.3550870000001 +33792,Multiclass classification,Naive Bayes,Insects,0.5044538486579266,0.5044538486579266,0.4905626123916189,0.6116933822631836,1757.6376 +34848,Multiclass classification,Naive Bayes,Insects,0.5020231296811777,0.5020231296811777,0.48787984248812405,0.6116933822631836,1863.925375 +35904,Multiclass classification,Naive Bayes,Insects,0.49987466228448874,0.49987466228448874,0.48534350611524757,0.6116933822631836,1973.177917 +36960,Multiclass classification,Naive Bayes,Insects,0.49679374441949187,0.49679374441949187,0.4819418474093529,0.6116933822631836,2085.445724 +38016,Multiclass classification,Naive Bayes,Insects,0.49559384453505195,0.49559384453505195,0.4801892436835747,0.6116933822631836,2200.821931 +39072,Multiclass classification,Naive Bayes,Insects,0.49402370044278365,0.49402370044278365,0.47838078382052607,0.6116933822631836,2319.178703 +40128,Multiclass classification,Naive Bayes,Insects,0.493508111745209,0.493508111745209,0.47852138016706713,0.6116933822631836,2440.443075 +41184,Multiclass classification,Naive Bayes,Insects,0.49369885632421145,0.49369885632421145,0.47942014994272747,0.6116933822631836,2564.583087 +42240,Multiclass classification,Naive Bayes,Insects,0.4938800634484718,0.4938800634484718,0.48023774975329353,0.6116933822631836,2691.651665 +43296,Multiclass classification,Naive Bayes,Insects,0.49437579397159026,0.49437579397159026,0.4812132921167227,0.6116933822631836,2821.6013359999997 +44352,Multiclass classification,Naive Bayes,Insects,0.49403621113390905,0.49403621113390905,0.48123889196184183,0.6116933822631836,2954.3777659999996 +45408,Multiclass classification,Naive Bayes,Insects,0.4944832294580131,0.4944832294580131,0.4818441874360225,0.6116933822631836,3089.8310679999995 +46464,Multiclass classification,Naive Bayes,Insects,0.4945225232981082,0.4945225232981082,0.4820791268335544,0.6116933822631836,3227.9665449999993 +47520,Multiclass classification,Naive Bayes,Insects,0.4956333256171216,0.4956333256171216,0.48331686360214987,0.6116933822631836,3368.688097999999 +48576,Multiclass classification,Naive Bayes,Insects,0.4970869788986104,0.4970869788986104,0.48467037716343636,0.6116933822631836,3511.887438999999 +49632,Multiclass classification,Naive Bayes,Insects,0.4987608551107171,0.4987608551107171,0.4862426724473749,0.6116933822631836,3657.6494079999993 +50688,Multiclass classification,Naive Bayes,Insects,0.5009568528419516,0.5009568528419516,0.4881725476999718,0.6116933822631836,3806.0112589999994 +51744,Multiclass classification,Naive Bayes,Insects,0.5034497419940862,0.5034497419940862,0.4903712806540024,0.6116933822631836,3956.8935159999996 +52800,Multiclass classification,Naive Bayes,Insects,0.5068467205818292,0.5068467205818292,0.4930025316136313,0.6116933822631836,4110.278735 +52848,Multiclass classification,Naive Bayes,Insects,0.5068972694760346,0.5068972694760346,0.49301906278314944,0.6116933822631836,4263.766907 +408,Multiclass classification,Naive Bayes,Keystroke,0.9852579852579852,0.9852579852579852,0.6962686567164179,0.19356441497802734,0.780775 +816,Multiclass classification,Naive Bayes,Keystroke,0.947239263803681,0.947239263803681,0.7418606503288051,0.28890228271484375,2.463269 +1224,Multiclass classification,Naive Bayes,Keystroke,0.884709730171709,0.884709730171709,0.8705899666065842,0.38424015045166016,5.15507 +1632,Multiclass classification,Naive Bayes,Keystroke,0.8933169834457388,0.8933169834457388,0.8791291775937072,0.47957801818847656,8.960951 +2040,Multiclass classification,Naive Bayes,Keystroke,0.8921039725355566,0.8921039725355566,0.8831785360852743,0.575160026550293,14.051639 +2448,Multiclass classification,Naive Bayes,Keystroke,0.851655087862689,0.851655087862689,0.8581984289516641,0.6704978942871094,20.582881999999998 +2856,Multiclass classification,Naive Bayes,Keystroke,0.8598949211908932,0.8598949211908932,0.8469962214365346,0.7658357620239258,28.649143 +3264,Multiclass classification,Naive Bayes,Keystroke,0.8513637756665645,0.8513637756665645,0.8281280134770846,0.8611736297607422,38.532046 +3672,Multiclass classification,Naive Bayes,Keystroke,0.8422773086352493,0.8422773086352493,0.8409307955747314,0.9565114974975586,50.273206 +4080,Multiclass classification,Naive Bayes,Keystroke,0.8367246874233881,0.8367246874233881,0.8249418657104467,1.0523834228515625,63.882498 +4488,Multiclass classification,Naive Bayes,Keystroke,0.8203699576554491,0.8203699576554491,0.8300896799820437,1.147721290588379,79.531469 +4896,Multiclass classification,Naive Bayes,Keystroke,0.8192032686414709,0.8192032686414709,0.8269731591910484,1.2430591583251953,97.310117 +5304,Multiclass classification,Naive Bayes,Keystroke,0.8172732415613804,0.8172732415613804,0.8027823390848743,1.3383970260620117,117.35519000000001 +5712,Multiclass classification,Naive Bayes,Keystroke,0.7961828051129399,0.7961828051129399,0.8002006091139847,1.4337348937988281,139.817583 +6120,Multiclass classification,Naive Bayes,Keystroke,0.793920575257395,0.793920575257395,0.7746960355921345,1.5290727615356445,164.727582 +6528,Multiclass classification,Naive Bayes,Keystroke,0.7688064960931515,0.7688064960931515,0.7622487598340326,1.624410629272461,192.15170700000002 +6936,Multiclass classification,Naive Bayes,Keystroke,0.7568853640951694,0.7568853640951694,0.757813781660983,1.7197484970092773,222.24358600000002 +7344,Multiclass classification,Naive Bayes,Keystroke,0.7669889690862045,0.7669889690862046,0.7643943615019536,1.8150863647460938,255.230678 +7752,Multiclass classification,Naive Bayes,Keystroke,0.7676428847890595,0.7676428847890595,0.7655695901071293,1.9104242324829102,291.218411 +8160,Multiclass classification,Naive Bayes,Keystroke,0.7714180659394534,0.7714180659394533,0.7672011803374248,2.0057621002197266,330.398823 +8568,Multiclass classification,Naive Bayes,Keystroke,0.7702813120112058,0.7702813120112058,0.7699263138193525,2.1021223068237305,372.82766399999997 +8976,Multiclass classification,Naive Bayes,Keystroke,0.7680222841225627,0.7680222841225627,0.7682287234686136,2.197460174560547,418.63014999999996 +9384,Multiclass classification,Naive Bayes,Keystroke,0.7659597143770649,0.7659597143770649,0.7643546547243014,2.2927980422973633,468.01111099999997 +9792,Multiclass classification,Naive Bayes,Keystroke,0.7586559084873864,0.7586559084873864,0.7552148692020618,2.3881359100341797,521.0847249999999 +10200,Multiclass classification,Naive Bayes,Keystroke,0.7505637807628199,0.7505637807628199,0.7430512224080145,2.483473777770996,577.917349 +10608,Multiclass classification,Naive Bayes,Keystroke,0.7290468558499105,0.7290468558499106,0.715756093271779,2.5788116455078125,638.790947 +11016,Multiclass classification,Naive Bayes,Keystroke,0.7217430776214253,0.7217430776214253,0.7173640789896896,2.674149513244629,703.666983 +11424,Multiclass classification,Naive Bayes,Keystroke,0.7151361288628206,0.7151361288628206,0.7011862635194489,2.7694873809814453,772.6431349999999 +11832,Multiclass classification,Naive Bayes,Keystroke,0.705603921900093,0.705603921900093,0.6976881379682607,2.8648252487182617,845.8350979999999 +12240,Multiclass classification,Naive Bayes,Keystroke,0.7094533867146009,0.7094533867146009,0.7058405389403433,2.960163116455078,923.50335 +12648,Multiclass classification,Naive Bayes,Keystroke,0.7053846762077963,0.7053846762077963,0.6965736948063982,3.0555009841918945,1005.7536769999999 +13056,Multiclass classification,Naive Bayes,Keystroke,0.6927613941018766,0.6927613941018766,0.6842255816736498,3.150838851928711,1092.707972 +13464,Multiclass classification,Naive Bayes,Keystroke,0.6890737577063062,0.6890737577063062,0.6845669389392289,3.2461767196655273,1184.483965 +13872,Multiclass classification,Naive Bayes,Keystroke,0.6873332852714296,0.6873332852714296,0.68390545518227,3.3415145874023438,1281.216395 +14280,Multiclass classification,Naive Bayes,Keystroke,0.682960991666083,0.682960991666083,0.6781566371919944,3.43685245513916,1383.0399089999999 +14688,Multiclass classification,Naive Bayes,Keystroke,0.686185061619119,0.686185061619119,0.6843713776162116,3.5321903228759766,1489.909884 +15096,Multiclass classification,Naive Bayes,Keystroke,0.6928784365684001,0.6928784365684001,0.6911392400672977,3.627528190612793,1601.996709 +15504,Multiclass classification,Naive Bayes,Keystroke,0.6913500612784622,0.6913500612784622,0.687359772989117,3.7228660583496094,1719.445985 +15912,Multiclass classification,Naive Bayes,Keystroke,0.6819810194205267,0.6819810194205267,0.674915944935936,3.818203926086426,1842.197498 +16320,Multiclass classification,Naive Bayes,Keystroke,0.6726515105092223,0.6726515105092223,0.6670192172011686,3.913541793823242,1970.358299 +16728,Multiclass classification,Naive Bayes,Keystroke,0.6695163508100676,0.6695163508100676,0.6664051037977977,4.008879661560059,2103.939399 +17136,Multiclass classification,Naive Bayes,Keystroke,0.6650131310183834,0.6650131310183834,0.6608988619616458,4.1063079833984375,2242.845385 +17544,Multiclass classification,Naive Bayes,Keystroke,0.6568431853160804,0.6568431853160804,0.6531382897719189,4.201645851135254,2386.822189 +17952,Multiclass classification,Naive Bayes,Keystroke,0.6556180714166342,0.6556180714166342,0.6538448358590968,4.29698371887207,2536.044428 +18360,Multiclass classification,Naive Bayes,Keystroke,0.6614194672912468,0.6614194672912468,0.6603186829199909,4.392321586608887,2690.5476860000003 +18768,Multiclass classification,Naive Bayes,Keystroke,0.6669686151222891,0.6669686151222891,0.6662293616554571,4.487659454345703,2850.3140810000004 +19176,Multiclass classification,Naive Bayes,Keystroke,0.6579921773142112,0.6579921773142112,0.6554177118629491,4.5829973220825195,3015.4823350000006 +19584,Multiclass classification,Naive Bayes,Keystroke,0.6622580809886126,0.6622580809886126,0.6609360990360078,4.678335189819336,3186.2814100000005 +19992,Multiclass classification,Naive Bayes,Keystroke,0.6562453103896754,0.6562453103896754,0.6545704957554572,4.773673057556152,3362.6238980000007 +20400,Multiclass classification,Naive Bayes,Keystroke,0.6525319868621011,0.6525319868621011,0.6515767870317885,4.869010925292969,3544.6906370000006 +46,Multiclass classification,Hoeffding Tree,ImageSegments,0.35555555555555557,0.35555555555555557,0.25379424497071557,0.4170856475830078,0.290301 +92,Multiclass classification,Hoeffding Tree,ImageSegments,0.4945054945054945,0.4945054945054945,0.5043329927491418,0.4170818328857422,0.82046 +138,Multiclass classification,Hoeffding Tree,ImageSegments,0.5328467153284672,0.5328467153284672,0.5564033878668025,0.4171772003173828,1.6754229999999999 +184,Multiclass classification,Hoeffding Tree,ImageSegments,0.6010928961748634,0.6010928961748634,0.622766496539645,0.4171772003173828,2.801183 +230,Multiclass classification,Hoeffding Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6539827168809461,0.41720008850097656,4.271522 +276,Multiclass classification,Hoeffding Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.6671561759164943,0.4172496795654297,5.954744 +322,Multiclass classification,Hoeffding Tree,ImageSegments,0.67601246105919,0.67601246105919,0.6756614325426025,0.4172496795654297,7.864603 +368,Multiclass classification,Hoeffding Tree,ImageSegments,0.7029972752043597,0.7029972752043597,0.6993447851636564,0.4172229766845703,10.008665 +414,Multiclass classification,Hoeffding Tree,ImageSegments,0.7142857142857143,0.7142857142857143,0.7108606838045498,0.4171428680419922,12.399438 +460,Multiclass classification,Hoeffding Tree,ImageSegments,0.7145969498910676,0.7145969498910676,0.7090365931960759,0.4172191619873047,15.01004 +506,Multiclass classification,Hoeffding Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7126631585949761,0.4172191619873047,17.873655 +552,Multiclass classification,Hoeffding Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7157730164623107,0.4171123504638672,20.946970999999998 +598,Multiclass classification,Hoeffding Tree,ImageSegments,0.7286432160804021,0.7286432160804021,0.7216745323124732,0.41713523864746094,24.255884 +644,Multiclass classification,Hoeffding Tree,ImageSegments,0.7278382581648523,0.7278382581648523,0.7229105183087501,0.41710853576660156,27.838411999999998 +690,Multiclass classification,Hoeffding Tree,ImageSegments,0.7314949201741655,0.7314949201741654,0.7263583447448078,0.41710853576660156,31.647636 +736,Multiclass classification,Hoeffding Tree,ImageSegments,0.7333333333333333,0.7333333333333333,0.729431071218305,0.41713523864746094,35.743157 +782,Multiclass classification,Hoeffding Tree,ImageSegments,0.7387964148527529,0.7387964148527529,0.7349287389986899,0.41713523864746094,40.063089999999995 +828,Multiclass classification,Hoeffding Tree,ImageSegments,0.7376058041112454,0.7376058041112454,0.7356226390109741,0.41713523864746094,44.599844 +874,Multiclass classification,Hoeffding Tree,ImageSegments,0.7445589919816724,0.7445589919816724,0.7409366047432264,0.41713523864746094,49.398728999999996 +920,Multiclass classification,Hoeffding Tree,ImageSegments,0.7453754080522307,0.7453754080522307,0.7408438328939173,0.41710853576660156,54.404894 +966,Multiclass classification,Hoeffding Tree,ImageSegments,0.7471502590673575,0.7471502590673575,0.7416651838589269,0.41710853576660156,59.665949 +1012,Multiclass classification,Hoeffding Tree,ImageSegments,0.7467853610286844,0.7467853610286844,0.7416356251822,0.41710853576660156,65.211169 +1058,Multiclass classification,Hoeffding Tree,ImageSegments,0.7492904446546831,0.7492904446546831,0.7430778844390783,0.41710853576660156,70.961377 +1104,Multiclass classification,Hoeffding Tree,ImageSegments,0.7515865820489573,0.7515865820489573,0.7451256886686588,0.4171581268310547,76.969446 +1150,Multiclass classification,Hoeffding Tree,ImageSegments,0.7536988685813751,0.7536988685813751,0.7468312166689606,0.4171581268310547,83.201851 +1196,Multiclass classification,Hoeffding Tree,ImageSegments,0.7564853556485356,0.7564853556485356,0.7503479321738041,0.4171581268310547,89.604352 +1242,Multiclass classification,Hoeffding Tree,ImageSegments,0.7566478646253022,0.7566478646253022,0.7509717522131719,0.4171581268310547,96.30702600000001 +1288,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614607614607615,0.7614607614607615,0.7547643483779538,0.4171581268310547,103.26246200000001 +1334,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614403600900225,0.7614403600900225,0.7551060921605869,0.4171581268310547,110.41488900000002 +1380,Multiclass classification,Hoeffding Tree,ImageSegments,0.7621464829586657,0.7621464829586658,0.7562209880685912,0.4171581268310547,117.79988600000001 +1426,Multiclass classification,Hoeffding Tree,ImageSegments,0.7642105263157895,0.7642105263157895,0.7575332274919562,0.4171581268310547,125.46176800000002 +1472,Multiclass classification,Hoeffding Tree,ImageSegments,0.7688647178789939,0.768864717878994,0.760438686053582,0.4171581268310547,133.360363 +1518,Multiclass classification,Hoeffding Tree,ImageSegments,0.7705998681608438,0.7705998681608438,0.7612069012840872,0.4171581268310547,141.48549400000002 +1564,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709532949456174,0.7709532949456174,0.7622701654854867,0.4171581268310547,149.83563600000002 +1610,Multiclass classification,Hoeffding Tree,ImageSegments,0.7712865133623369,0.771286513362337,0.7617247271717752,0.41718101501464844,158.439217 +1656,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709969788519637,0.7709969788519637,0.7615629120572474,0.41718101501464844,167.22864700000002 +1702,Multiclass classification,Hoeffding Tree,ImageSegments,0.770135214579659,0.770135214579659,0.7627316365695143,0.41718101501464844,176.30742800000002 +1748,Multiclass classification,Hoeffding Tree,ImageSegments,0.7727532913566113,0.7727532913566113,0.7649467707214076,0.41718101501464844,185.609237 +1794,Multiclass classification,Hoeffding Tree,ImageSegments,0.7741215839375348,0.7741215839375348,0.7649332326562149,0.41715431213378906,195.10730800000002 +1840,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754214246873301,0.7754214246873301,0.7664700790631908,0.41715431213378906,204.88888000000003 +1886,Multiclass classification,Hoeffding Tree,ImageSegments,0.7740053050397878,0.7740053050397878,0.7655121135276625,0.41715431213378906,214.87796100000003 +1932,Multiclass classification,Hoeffding Tree,ImageSegments,0.7742102537545313,0.7742102537545313,0.7648034036287765,0.41715431213378906,225.10774000000004 +1978,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754172989377845,0.7754172989377845,0.7656013068970459,0.41715431213378906,235.56491900000003 +2024,Multiclass classification,Hoeffding Tree,ImageSegments,0.7770637666831438,0.7770637666831438,0.7660878232247856,0.41715431213378906,246.31694000000005 +2070,Multiclass classification,Hoeffding Tree,ImageSegments,0.7762203963267279,0.7762203963267279,0.7654829214385931,0.41715431213378906,257.28426500000006 +2116,Multiclass classification,Hoeffding Tree,ImageSegments,0.7768321513002364,0.7768321513002364,0.7653071619305024,0.41715431213378906,268.5154150000001 +2162,Multiclass classification,Hoeffding Tree,ImageSegments,0.7778806108283203,0.7778806108283203,0.7659351904174982,0.41715431213378906,279.94414300000005 +2208,Multiclass classification,Hoeffding Tree,ImageSegments,0.7797915722700498,0.7797915722700498,0.7668192864082087,0.41715431213378906,291.65328600000004 +2254,Multiclass classification,Hoeffding Tree,ImageSegments,0.7767421216156236,0.7767421216156236,0.7637794374955548,0.41715431213378906,303.618395 +2300,Multiclass classification,Hoeffding Tree,ImageSegments,0.7759895606785558,0.7759895606785558,0.763026662835187,0.41715431213378906,315.80512400000003 +2310,Multiclass classification,Hoeffding Tree,ImageSegments,0.776093546990039,0.776093546990039,0.7631372452021826,0.41715431213378906,328.06738900000005 +1056,Multiclass classification,Hoeffding Tree,Insects,0.6218009478672986,0.6218009478672986,0.5852663107194211,0.6579360961914062,7.68277 +2112,Multiclass classification,Hoeffding Tree,Insects,0.6153481762198011,0.6153481762198011,0.5806436317780949,0.6579360961914062,22.565114 +3168,Multiclass classification,Hoeffding Tree,Insects,0.6071992421850332,0.6071992421850332,0.572248584718361,0.6579360961914062,43.997682 +4224,Multiclass classification,Hoeffding Tree,Insects,0.6043097324177126,0.6043097324177126,0.5697573109597247,0.6579360961914062,71.858443 +5280,Multiclass classification,Hoeffding Tree,Insects,0.6088274294373934,0.6088274294373934,0.5727379077413696,0.6579360961914062,105.92483999999999 +6336,Multiclass classification,Hoeffding Tree,Insects,0.6026835043409629,0.6026835043409629,0.568251333238805,0.6579360961914062,146.287253 +7392,Multiclass classification,Hoeffding Tree,Insects,0.600189419564335,0.600189419564335,0.5659762112716077,0.6579360961914062,192.863981 +8448,Multiclass classification,Hoeffding Tree,Insects,0.60258079791642,0.60258079791642,0.5679781484640408,0.6579360961914062,245.806734 +9504,Multiclass classification,Hoeffding Tree,Insects,0.5998105861306956,0.5998105861306956,0.5649597336877693,0.6579360961914062,305.14044 +10560,Multiclass classification,Hoeffding Tree,Insects,0.5998674116867128,0.5998674116867128,0.5650173260529011,0.6579360961914062,370.68089100000003 +11616,Multiclass classification,Hoeffding Tree,Insects,0.5974171330176495,0.5974171330176495,0.5633067089377387,0.6579360961914062,442.33844300000004 +12672,Multiclass classification,Hoeffding Tree,Insects,0.6001894088864336,0.6001894088864336,0.5684760329567131,0.6579360961914062,520.121563 +13728,Multiclass classification,Hoeffding Tree,Insects,0.6120783856632913,0.6120783856632913,0.5935956771555828,0.6579360961914062,604.039429 +14784,Multiclass classification,Hoeffding Tree,Insects,0.6024487587093282,0.6024487587093282,0.5842148300149193,0.6579360961914062,694.113241 +15840,Multiclass classification,Hoeffding Tree,Insects,0.5677757434181451,0.5677757434181451,0.5509250187877572,0.6579360961914062,790.19156 +16896,Multiclass classification,Hoeffding Tree,Insects,0.5419354838709678,0.5419354838709678,0.5257359157219258,0.6579360961914062,892.361186 +17952,Multiclass classification,Hoeffding Tree,Insects,0.5233691716338923,0.5233691716338923,0.5068581838352059,0.6579360961914062,1000.4717479999999 +19008,Multiclass classification,Hoeffding Tree,Insects,0.5121271110643447,0.5121271110643447,0.49292899065094153,0.6579360961914062,1114.494528 +20064,Multiclass classification,Hoeffding Tree,Insects,0.5120370831879579,0.5120370831879579,0.4920970323041603,1.3099584579467773,1234.2056499999999 +21120,Multiclass classification,Hoeffding Tree,Insects,0.5173066906577016,0.5173066906577016,0.4973447169836249,1.310713768005371,1358.925583 +22176,Multiclass classification,Hoeffding Tree,Insects,0.5229312288613304,0.5229312288613304,0.5026343687424488,1.310713768005371,1488.370808 +23232,Multiclass classification,Hoeffding Tree,Insects,0.5301536739701261,0.5301536739701261,0.5095132087733324,1.310713768005371,1622.41448 +24288,Multiclass classification,Hoeffding Tree,Insects,0.5351422571746202,0.5351422571746202,0.5135975374357353,1.310713768005371,1760.8970379999998 +25344,Multiclass classification,Hoeffding Tree,Insects,0.5403069881229531,0.5403069881229531,0.5180803411538233,1.310713768005371,1903.5911449999999 +26400,Multiclass classification,Hoeffding Tree,Insects,0.5441493995984696,0.5441493995984696,0.5209012984387186,1.310713768005371,2050.469487 +27456,Multiclass classification,Hoeffding Tree,Insects,0.5475869604807867,0.5475869604807867,0.5230407124785976,1.310713768005371,2201.55681 +28512,Multiclass classification,Hoeffding Tree,Insects,0.5442460804601733,0.5442460804601733,0.5199893698637053,1.310713768005371,2356.711105 +29568,Multiclass classification,Hoeffding Tree,Insects,0.5439848479724017,0.5439848479724017,0.5225387960194383,1.310713768005371,2516.62263 +30624,Multiclass classification,Hoeffding Tree,Insects,0.5449825294713124,0.5449825294713124,0.5260472440529832,1.310713768005371,2681.5460789999997 +31680,Multiclass classification,Hoeffding Tree,Insects,0.5469238296663405,0.5469238296663405,0.5300194392617626,1.310713768005371,2851.622305 +32736,Multiclass classification,Hoeffding Tree,Insects,0.5492286543455017,0.5492286543455017,0.5337692045397758,1.310713768005371,3026.797274 +33792,Multiclass classification,Hoeffding Tree,Insects,0.5448196265277737,0.5448196265277737,0.5298516474077153,1.310713768005371,3207.119826 +34848,Multiclass classification,Hoeffding Tree,Insects,0.539357763939507,0.539357763939507,0.5246413689313029,1.310713768005371,3392.4010240000002 +35904,Multiclass classification,Hoeffding Tree,Insects,0.5352756037099964,0.5352756037099964,0.5204658240271913,1.310713768005371,3582.6817720000004 +36960,Multiclass classification,Hoeffding Tree,Insects,0.5307232338537298,0.5307232338537298,0.5158458403074864,1.310713768005371,3778.3092850000003 +38016,Multiclass classification,Hoeffding Tree,Insects,0.5287912666052874,0.5287912666052874,0.5138605376143625,1.8479537963867188,3978.8224330000003 +39072,Multiclass classification,Hoeffding Tree,Insects,0.5245322617798367,0.5245322617798367,0.5100329616180462,1.9625730514526367,4184.1075280000005 +40128,Multiclass classification,Hoeffding Tree,Insects,0.5244847608841927,0.5244847608841927,0.5114466799524962,1.9625730514526367,4393.646320000001 +41184,Multiclass classification,Hoeffding Tree,Insects,0.5269650098341548,0.5269650098341548,0.5145630920489553,1.9625730514526367,4606.675677000001 +42240,Multiclass classification,Hoeffding Tree,Insects,0.5290608205686688,0.5290608205686688,0.5171452370879218,1.9625730514526367,4823.052294000001 +43296,Multiclass classification,Hoeffding Tree,Insects,0.5316318281556762,0.5316318281556762,0.5200714653059241,1.9625730514526367,5042.794587000001 +44352,Multiclass classification,Hoeffding Tree,Insects,0.5332912448422809,0.5332912448422809,0.521951703681177,1.9633283615112305,5266.308108000001 +45408,Multiclass classification,Hoeffding Tree,Insects,0.5350937080185875,0.5350937080185875,0.5236272112757866,1.9633283615112305,5493.659660000001 +46464,Multiclass classification,Hoeffding Tree,Insects,0.5374168693368917,0.5374168693368917,0.5257977177437826,1.9633283615112305,5724.562244000002 +47520,Multiclass classification,Hoeffding Tree,Insects,0.5359540394368568,0.5359540394368568,0.5247049329892776,1.9633283615112305,5959.275286000002 +48576,Multiclass classification,Hoeffding Tree,Insects,0.5333196088522902,0.5333196088522902,0.5224640186909637,1.9633283615112305,6197.987866000002 +49632,Multiclass classification,Hoeffding Tree,Insects,0.5314017448771937,0.5314017448771937,0.5209076603734537,1.9633283615112305,6440.583835000002 +50688,Multiclass classification,Hoeffding Tree,Insects,0.5322271982954209,0.5322271982954209,0.5219695808096345,2.081958770751953,6687.224874000002 +51744,Multiclass classification,Hoeffding Tree,Insects,0.5377345727924551,0.5377345727924551,0.5274876060436412,2.3156700134277344,6937.746409000002 +52800,Multiclass classification,Hoeffding Tree,Insects,0.5370366863008769,0.5370366863008769,0.5270872650003847,2.519227981567383,7191.466386000002 +52848,Multiclass classification,Hoeffding Tree,Insects,0.5373058073305959,0.5373058073305959,0.5273644947479657,2.519227981567383,7445.3631460000015 +408,Multiclass classification,Hoeffding Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.2240447998046875,0.863228 +816,Multiclass classification,Hoeffding Tree,Keystroke,0.9423312883435583,0.9423312883435583,0.7661667470992702,0.3196687698364258,3.107641 +1224,Multiclass classification,Hoeffding Tree,Keystroke,0.8830744071954211,0.883074407195421,0.8761191747044462,0.41529273986816406,7.048775 +1632,Multiclass classification,Hoeffding Tree,Keystroke,0.8902513795217658,0.8902513795217658,0.8767853151263398,0.5114049911499023,13.087731999999999 +2040,Multiclass classification,Hoeffding Tree,Keystroke,0.8891613536047082,0.8891613536047082,0.8807858055314012,0.6185035705566406,21.551524999999998 +2448,Multiclass classification,Hoeffding Tree,Keystroke,0.848385778504291,0.848385778504291,0.8522513926518692,0.7141275405883789,32.816222999999994 +2856,Multiclass classification,Hoeffding Tree,Keystroke,0.8563922942206655,0.8563922942206655,0.8440193478447516,0.8097515106201172,47.080318999999996 +3264,Multiclass classification,Hoeffding Tree,Keystroke,0.8482991112473184,0.8482991112473184,0.8269786301577753,0.9053754806518555,64.636989 +3672,Multiclass classification,Hoeffding Tree,Keystroke,0.8392808499046581,0.8392808499046581,0.8374924160046072,1.0009994506835938,85.706576 +4080,Multiclass classification,Hoeffding Tree,Keystroke,0.8323118411375338,0.8323118411375338,0.8182261307945194,1.1217241287231445,110.70978199999999 +4488,Multiclass classification,Hoeffding Tree,Keystroke,0.8159126365054602,0.8159126365054602,0.8260965842218733,1.2173480987548828,139.812165 +4896,Multiclass classification,Hoeffding Tree,Keystroke,0.8149131767109296,0.8149131767109296,0.8221314665977922,1.312972068786621,173.369773 +5304,Multiclass classification,Hoeffding Tree,Keystroke,0.8125589289081652,0.8125589289081652,0.797613058026624,1.4085960388183594,211.780209 +5712,Multiclass classification,Hoeffding Tree,Keystroke,0.7907546839432674,0.7907546839432674,0.7936708037520236,1.5042200088500977,255.27399100000002 +6120,Multiclass classification,Hoeffding Tree,Keystroke,0.7886909625755842,0.7886909625755842,0.7694478218498494,1.599843978881836,304.294734 +6528,Multiclass classification,Hoeffding Tree,Keystroke,0.7635973647924008,0.7635973647924008,0.75687960152136,1.6954679489135742,359.144129 +6936,Multiclass classification,Hoeffding Tree,Keystroke,0.75155010814708,0.7515501081470799,0.7521509466338959,1.7910919189453125,420.22114200000004 +7344,Multiclass classification,Hoeffding Tree,Keystroke,0.7611330518861501,0.7611330518861501,0.7576671162861806,1.8881807327270508,487.76956500000006 +7752,Multiclass classification,Hoeffding Tree,Keystroke,0.7617081666881693,0.7617081666881692,0.7593340838982119,1.983804702758789,562.1432000000001 +8160,Multiclass classification,Hoeffding Tree,Keystroke,0.7655349920333374,0.7655349920333374,0.7610505848438686,2.0794286727905273,643.5514560000001 +8568,Multiclass classification,Hoeffding Tree,Keystroke,0.7644449632310026,0.7644449632310025,0.7639417799779614,2.223102569580078,732.3349550000001 +8976,Multiclass classification,Hoeffding Tree,Keystroke,0.7624512534818941,0.7624512534818941,0.7625605608371232,2.3187265396118164,828.9274100000001 +9384,Multiclass classification,Hoeffding Tree,Keystroke,0.7605243525524885,0.7605243525524885,0.7588384348689571,2.4143505096435547,933.4845880000001 +9792,Multiclass classification,Hoeffding Tree,Keystroke,0.753344908589521,0.753344908589521,0.7499438215834663,2.509974479675293,1046.19484 +10200,Multiclass classification,Hoeffding Tree,Keystroke,0.7450730463770958,0.7450730463770959,0.7369660419615974,2.6055984497070312,1167.344916 +10608,Multiclass classification,Hoeffding Tree,Keystroke,0.7240501555576506,0.7240501555576506,0.7111305646829175,2.7012224197387695,1296.919782 +11016,Multiclass classification,Hoeffding Tree,Keystroke,0.7166591012256015,0.7166591012256015,0.7122511515574346,2.796846389770508,1434.7760759999999 +11424,Multiclass classification,Hoeffding Tree,Keystroke,0.710146196270682,0.710146196270682,0.6963016796632095,2.892470359802246,1580.7280859999998 +11832,Multiclass classification,Hoeffding Tree,Keystroke,0.7005324993660722,0.7005324993660722,0.6925666211338901,2.9880943298339844,1735.0271709999997 +12240,Multiclass classification,Hoeffding Tree,Keystroke,0.7043876133671052,0.7043876133671052,0.7007845610449206,3.0837182998657227,1897.6526119999996 +12648,Multiclass classification,Hoeffding Tree,Keystroke,0.7004032576895707,0.7004032576895707,0.6915775762792657,3.179342269897461,2069.0860809999995 +13056,Multiclass classification,Hoeffding Tree,Keystroke,0.6877058598238223,0.6877058598238223,0.6789768292873962,3.274966239929199,2249.3891769999996 +13464,Multiclass classification,Hoeffding Tree,Keystroke,0.6838743222164451,0.6838743222164451,0.6791243465680947,3.3705902099609375,2438.6931489999997 +13872,Multiclass classification,Hoeffding Tree,Keystroke,0.6822146925239708,0.6822146925239708,0.6786558938530485,3.466214179992676,2637.6841019999997 +14280,Multiclass classification,Hoeffding Tree,Keystroke,0.6777085230058127,0.6777085230058127,0.6725285130045525,3.561838150024414,2845.8288079999998 +14688,Multiclass classification,Hoeffding Tree,Keystroke,0.6807380676788997,0.6807380676788997,0.6786761142186741,3.6574621200561523,3062.9942149999997 +15096,Multiclass classification,Hoeffding Tree,Keystroke,0.6873799271281882,0.6873799271281882,0.6854839306484398,3.7530860900878906,3290.055422 +15504,Multiclass classification,Hoeffding Tree,Keystroke,0.6858027478552539,0.6858027478552539,0.6816808496509055,3.848710060119629,3526.69202 +15912,Multiclass classification,Hoeffding Tree,Keystroke,0.6765759537426937,0.6765759537426937,0.6694713281964946,3.944334030151367,3772.997519 +16320,Multiclass classification,Hoeffding Tree,Keystroke,0.6673815797536614,0.6673815797536614,0.6617321933140904,4.0399580001831055,4029.133223 +16728,Multiclass classification,Hoeffding Tree,Keystroke,0.6643151790518323,0.6643151790518323,0.661178029358405,4.135581970214844,4295.086238 +17136,Multiclass classification,Hoeffding Tree,Keystroke,0.6598774438284214,0.6598774438284214,0.655734247886306,4.3294572830200195,4570.827071 +17544,Multiclass classification,Hoeffding Tree,Keystroke,0.6518269395200365,0.6518269395200365,0.6481085155228206,4.425081253051758,4856.254143 +17952,Multiclass classification,Hoeffding Tree,Keystroke,0.6507158375577963,0.6507158375577963,0.6489368995854258,4.520705223083496,5151.869359 +18360,Multiclass classification,Hoeffding Tree,Keystroke,0.6566806470940683,0.6566806470940683,0.6555764711123695,4.616329193115234,5457.498716 +18768,Multiclass classification,Hoeffding Tree,Keystroke,0.662279533223211,0.662279533223211,0.6615432060687808,4.711953163146973,5772.982264 +19176,Multiclass classification,Hoeffding Tree,Keystroke,0.6534028683181226,0.6534028683181226,0.6508089832432514,4.807577133178711,6098.679956 +19584,Multiclass classification,Hoeffding Tree,Keystroke,0.6577643874789358,0.6577643874789358,0.6564201177589184,4.903201103210449,6434.678037 +19992,Multiclass classification,Hoeffding Tree,Keystroke,0.6518433294982742,0.6518433294982742,0.6501496360982542,4.9988250732421875,6781.324361 +20400,Multiclass classification,Hoeffding Tree,Keystroke,0.6482180499044071,0.6482180499044071,0.6472493759146578,5.094449043273926,7138.730487 +46,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,0.42345714569091797,0.325579 +92,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5164835164835165,0.5164835164835165,0.5335477748411618,0.42351436614990234,1.056326 +138,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5474452554744526,0.5474452554744526,0.5743273066802479,0.42363643646240234,2.202996 +184,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6120218579234973,0.6120218579234973,0.6355989308336889,0.4237203598022461,3.699294 +230,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6557923943920432,0.4237203598022461,5.564336 +276,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.66910740948952,0.4237699508666992,7.749814 +322,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.67601246105919,0.67601246105919,0.678427291711157,0.4238309860229492,10.278631 +368,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7002724795640327,0.7002724795640327,0.6988359939675117,0.42380428314208984,13.125556000000001 +414,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.711864406779661,0.711864406779661,0.7104564330601258,0.4237241744995117,16.369918000000002 +460,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7124183006535948,0.7124183006535948,0.7087721216219991,0.4238004684448242,19.921878000000003 +506,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7145025942185106,0.4238004684448242,23.844357000000002 +552,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7174926871575792,0.4236936569213867,28.111685 +598,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7269681742043551,0.7269681742043551,0.7216367248754637,0.42371654510498047,32.752989 +644,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7262830482115086,0.7262830482115085,0.7230014848259525,0.4237508773803711,37.712808 +690,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7300435413642961,0.7300435413642961,0.7265684058467008,0.4237508773803711,43.006145000000004 +736,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7319727891156462,0.7319727891156461,0.7296570819427115,0.42377758026123047,48.68780100000001 +782,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.737516005121639,0.737516005121639,0.7350906419548328,0.42377758026123047,54.691720000000004 +828,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7363966142684402,0.7363966142684402,0.7359651798179677,0.42377758026123047,60.98272 +874,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7422680412371134,0.7422680412371134,0.7398886847335938,0.42377758026123047,67.641769 +920,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7421109902067464,0.7421109902067464,0.738912026501458,0.4237508773803711,74.649906 +966,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7419689119170985,0.7419689119170985,0.7379593683174607,0.4237508773803711,81.98079 +1012,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7418397626112759,0.741839762611276,0.7380802548116379,0.4237508773803711,89.699811 +1058,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7436140018921475,0.7436140018921475,0.7390703652035102,0.4237508773803711,97.73816099999999 +1104,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7461468721668177,0.7461468721668177,0.7413714574148674,0.4238004684448242,106.141078 +1150,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7476066144473456,0.7476066144473456,0.742441565911322,0.4238004684448242,114.87573499999999 +1196,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7506276150627615,0.7506276150627615,0.7460917536510117,0.4234342575073242,123.97312099999999 +1242,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7510072522159549,0.7510072522159549,0.7470578866974922,0.4235563278198242,133.391788 +1288,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.756021756021756,0.7560217560217559,0.7510482446555896,0.4236173629760742,143.113173 +1334,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7569392348087022,0.7569392348087022,0.7522366633133313,0.4236173629760742,153.228885 +1380,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7585206671501088,0.7585206671501088,0.7544196711061472,0.4236783981323242,163.64661999999998 +1426,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7614035087719299,0.7614035087719299,0.7567964121564391,0.4236783981323242,174.36664399999998 +1472,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7654656696125085,0.7654656696125085,0.7591802078998249,0.4236783981323242,185.463757 +1518,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7673038892551087,0.7673038892551087,0.7600352016074767,0.4237394332885742,196.90308 +1564,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7677543186180422,0.7677543186180422,0.7612494392404334,0.4237394332885742,208.647576 +1610,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7675574891236793,0.7675574891236793,0.7602773300593106,0.42376232147216797,220.786107 +1656,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.76797583081571,0.76797583081571,0.7607906010792568,0.42376232147216797,233.21939999999998 +1702,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7677836566725456,0.7677836566725456,0.7627036277641847,0.42376232147216797,245.952092 +1748,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7710360618202633,0.7710360618202633,0.7657334796773966,0.42376232147216797,259.02702999999997 +1794,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7724484104852203,0.7724484104852203,0.7657758298578787,0.4237356185913086,272.39410699999996 +1840,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7737901033170201,0.77379010331702,0.767302943564198,0.4237966537475586,286.06776199999996 +1886,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7724137931034483,0.7724137931034483,0.7666353585191567,0.4237966537475586,300.095471 +1932,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7731745209735889,0.7731745209735889,0.7666634536176192,0.4237966537475586,314.417396 +1978,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7738998482549317,0.7738998482549316,0.7665909326930368,0.4237966537475586,329.067854 +2024,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7750865051903114,0.7750865051903113,0.7662611838286661,0.4237966537475586,344.01511700000003 +2070,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7747704204929918,0.7747704204929918,0.7660645062500586,0.4237966537475586,359.290159 +2116,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7754137115839244,0.7754137115839244,0.7658988206988366,0.4237966537475586,374.882405 +2162,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7760296159185562,0.7760296159185563,0.7660708746783081,0.4237966537475586,390.75768 +2208,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.777979157227005,0.7779791572270048,0.7670029065892423,0.4237966537475586,407.002801 +2254,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7749667110519307,0.7749667110519308,0.7639707440456852,0.4237966537475586,423.546299 +2300,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7742496737712049,0.7742496737712049,0.7632394528829524,0.4237966537475586,440.39336399999996 +2310,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7743611953226505,0.7743611953226506,0.7633622232911937,0.4237966537475586,457.310729 +1056,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6161137440758294,0.6161137440758294,0.581384151333148,0.6645784378051758,11.249192 +2112,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6120322122216959,0.6120322122216959,0.5792161554760864,0.6646394729614258,32.358705 +3168,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6049889485317335,0.6049889485317335,0.5721633809277145,0.6647005081176758,62.851539 +4224,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.603125739995264,0.603125739995264,0.5703574432462961,0.6647005081176758,102.700179 +5280,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6061754120098504,0.6061754120098504,0.5722430970062696,0.6647615432739258,151.914202 +6336,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5995264404104184,0.5995264404104184,0.5671511237518188,0.6647615432739258,210.432187 +7392,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5972128264104992,0.5972128264104992,0.5650210504998666,0.6647615432739258,278.26775499999997 +8448,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5989108559251806,0.5989108559251806,0.566418690076869,0.6647615432739258,355.20493799999997 +9504,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5962327685993897,0.5962327685993897,0.5633780031885509,0.6647615432739258,441.186739 +10560,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5964579979164694,0.5964579979164694,0.5634236596216465,0.6648225784301758,536.283653 +11616,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.594317692638829,0.594317692638829,0.5620068495149612,0.6648225784301758,640.2689049999999 +12672,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5975061163286244,0.5975061163286244,0.567518061449456,0.6648225784301758,753.0441599999999 +13728,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6097472135207984,0.6097472135207984,0.5927729676671933,0.6648225784301758,874.528885 +14784,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001488195900697,0.6001488195900697,0.5832911478837771,0.6645174026489258,1004.5501099999999 +15840,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5673969316244712,0.5673969316244712,0.5522471754341495,0.8876123428344727,1142.6522839999998 +16896,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5712340929269014,0.5712340929269014,0.5590383236849579,1.4319400787353516,1288.8770269999998 +17952,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5741184335134533,0.5741184335134533,0.5632919959429028,1.8629226684570312,1445.4718369999998 +19008,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5867312042931552,0.5867312042931552,0.5723846445183198,0.4819307327270508,1609.073978 +20064,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5966704879629168,0.5966704879629168,0.5796820575913003,0.6649179458618164,1780.2710459999998 +21120,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5984658364505895,0.5984658364505895,0.5810209140208816,0.6650400161743164,1958.8195809999997 +22176,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001803833145434,0.6001803833145434,0.5822125955100945,1.2073478698730469,2144.7260309999997 +23232,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6020403770823468,0.6020403770823468,0.5837921358595156,1.3215751647949219,2339.5310459999996 +24288,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6047268085807221,0.6047268085807221,0.5859785990228289,1.3216361999511719,2543.839083 +25344,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6069131515605887,0.6069131515605887,0.587737290445056,1.3217582702636719,2757.2066809999997 +26400,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6094927838175689,0.6094927838175689,0.5895162861993263,1.3217582702636719,2979.334505 +27456,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6105991622655254,0.6105991622655254,0.5896134687358237,1.3219413757324219,3211.0823579999997 +28512,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6106064326049595,0.6106064326049595,0.5910741826972655,1.3219413757324219,3451.5448549999996 +29568,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6099029323231981,0.6099029323231981,0.5935355609859342,1.3219413757324219,3700.7129539999996 +30624,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6088887437546942,0.6088887437546942,0.5952474102625339,1.3214530944824219,3958.5322249999995 +31680,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6088891694813598,0.6088891694813598,0.5975058139751561,1.3216972351074219,4224.837575 +32736,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6095921796242554,0.6095921796242554,0.5998546240309938,1.3217582702636719,4499.473663 +33792,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6043917019324673,0.6043917019324673,0.595080118632132,0.6649713516235352,4783.331389999999 +34848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6034378856142566,0.6034378856142566,0.5941773754098104,0.6650934219360352,5073.360361999999 +35904,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6029022644347269,0.6029022644347269,0.5935512429191343,0.6651544570922852,5369.406481999999 +36960,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6013690846613815,0.6013690846613815,0.5919623858291095,0.6651544570922852,5671.388488999999 +38016,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6010259108246745,0.6010259108246745,0.5912597483191937,0.6651544570922852,5979.127636999999 +39072,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6003429653707353,0.6003429653707353,0.5902279082897147,0.6648492813110352,6292.481400999999 +40128,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5961322800109652,0.5961322800109652,0.5867765456240649,0.6648492813110352,6611.499413 +41184,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5939829541315591,0.5939829541315591,0.585290407267574,0.6650323867797852,6936.132393 +42240,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5925803167688629,0.5925803167688629,0.5844470095695741,0.6650934219360352,7266.407125 +43296,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5911306155445202,0.5911306155445202,0.5835517912214992,0.6651544570922852,7602.391688 +44352,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.58959211742689,0.58959211742689,0.58246410272577,1.1046571731567383,7943.862096 +45408,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5875746030347744,0.5875746030347744,0.5808874407233396,1.3207244873046875,8291.951918 +46464,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5862083808621914,0.5862083808621914,0.5791892600330408,1.3209075927734375,8644.890712 +47520,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5879332477535302,0.5879332477535302,0.5810233099134106,1.3210525512695312,9004.012781000001 +48576,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5928152341739578,0.5928152341739578,0.5858160887305829,1.3216018676757812,9370.107000000002 +49632,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5979327436481231,0.5979327436481231,0.5906079347867982,1.3215408325195312,9743.028377000002 +50688,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6027383747311934,0.6027383747311934,0.594893758427483,1.3217239379882812,10122.858893000002 +51744,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6077923583866417,0.6077923583866417,0.5993180348311721,1.3217239379882812,10509.572003000003 +52800,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.612985094414667,0.612985094414667,0.6039181082054342,0.14382553100585938,10901.200853000002 +52848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6133366132420005,0.6133366132420005,0.604218855594392,0.14382553100585938,11292.868844000002 +408,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.23062610626220703,0.871514 +816,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,0.3262500762939453,3.583779 +1224,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8863450531479967,0.8863450531479967,0.8786592421362933,0.4218740463256836,8.686347999999999 +1632,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.891477621091355,0.891477621091355,0.8818548670971931,0.5179252624511719,16.685395 +2040,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.889651790093183,0.889651790093183,0.8812768038030504,0.6251459121704102,28.245741 +2448,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8414384961176952,0.8414384961176952,0.8420581397672002,0.7206478118896484,43.571154 +2856,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8500875656742557,0.8500875656742557,0.8345582037188519,0.8163328170776367,63.099422000000004 +3264,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8406374501992032,0.8406374501992032,0.8151418555553325,0.911895751953125,87.33095300000001 +3672,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8321983110868973,0.8321983110868973,0.8307198315203921,1.0075807571411133,116.498805 +4080,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.826182887962736,0.826182887962736,0.8123767856033619,1.128366470336914,151.118073 +4488,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.809226654780477,0.809226654780477,0.8196273526663149,1.2239294052124023,191.82030500000002 +4896,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8081716036772216,0.8081716036772216,0.815232111826365,1.3194313049316406,239.06161600000001 +5304,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8057703186875353,0.8057703186875353,0.7903391475861199,1.415055274963379,293.29488000000003 +5712,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7860269655051655,0.7860269655051656,0.7895763142947654,1.5108013153076172,355.22640600000005 +6120,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.784441902271613,0.784441902271613,0.7657785418705475,1.6062421798706055,425.24061900000004 +6528,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7585414432357898,0.7585414432357898,0.751418836389106,1.7020492553710938,503.46722600000004 +6936,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7473684210526316,0.7473684210526316,0.7484284412750404,1.797490119934082,590.6999010000001 +7344,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7565027917744791,0.7565027917744791,0.7526701844923946,1.8947620391845703,687.248946 +7752,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7577086827506129,0.7577086827506129,0.755735065870518,1.9903860092163086,793.498598 +8160,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7617355068023042,0.7617355068023042,0.7576049653668414,2.085948944091797,909.902095 +8568,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7604762460604646,0.7604762460604646,0.7596175662696861,2.2296838760375977,1036.556796 +8976,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.756991643454039,0.7569916434540391,0.7575313939177277,2.325368881225586,1173.4366320000001 +9384,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7558350207822658,0.7558350207822658,0.7548436696787698,2.420870780944824,1320.145727 +9792,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.748340312531917,0.7483403125319169,0.744390859626019,2.5164337158203125,1476.9925130000001 +10200,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7393862143347387,0.7393862143347387,0.7315892779928432,2.612057685852051,1644.1937280000002 +10608,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7196191194494201,0.7196191194494201,0.7089541376321258,2.707803726196289,1822.1938220000002 +11016,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7123921924648207,0.7123921924648208,0.7092068316988943,2.8033666610717773,2011.0989090000003 +11424,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7062943184802591,0.7062943184802591,0.6946713230955313,2.8989906311035156,2211.8042590000005 +11832,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6967289324655566,0.6967289324655566,0.690232830798306,2.994553565979004,2423.5715250000003 +12240,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7007108423890841,0.7007108423890841,0.6983689907908355,3.090177536010742,2646.6754960000003 +12648,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6969241717403337,0.6969241717403337,0.6892508246262707,3.1858625411987305,2881.7592360000003 +13056,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6836461126005362,0.6836461126005362,0.6755391962059192,3.2815475463867188,3128.5577150000004 +13464,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6793433855752804,0.6793433855752804,0.6754035266161623,3.377110481262207,3387.3558160000002 +13872,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6769519140653161,0.6769519140653161,0.6742482232309566,3.4728565216064453,3658.0697 +14280,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6728762518383641,0.6728762518383641,0.6689356443053495,3.5684194564819336,3940.688111 +14688,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6762442976782188,0.6762442976782188,0.6753292472514647,3.663982391357422,4235.610853 +15096,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6830076184166942,0.6830076184166942,0.6822311287838643,3.75966739654541,4542.8267670000005 +15504,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6818035218989873,0.6818035218989873,0.6788656596145114,3.8552303314208984,4862.152597 +15912,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6816039218150964,0.6816039218150964,0.6801525397911032,0.2705574035644531,5190.397888 +16320,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6858263373981249,0.6858263373981249,0.685191280018575,0.46213340759277344,5522.880902000001 +16728,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6896634184253004,0.6896634184253004,0.6890226069872224,0.6535873413085938,5860.018685000001 +17136,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6925007295010213,0.6925007295010213,0.6918635442211969,0.9691534042358398,6202.345681000001 +17544,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6990252522373597,0.6990252522373597,0.6986638608261282,0.2649049758911133,6547.073149000001 +17952,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7038605091638349,0.7038605091638349,0.7032543903990934,0.579315185546875,6893.121988000001 +18360,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.710114930007081,0.7101149300070809,0.70950849929648,0.2349414825439453,7240.035665000001 +18768,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.715351414717323,0.715351414717323,0.7146010079934133,0.3305654525756836,7588.155090000001 +19176,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7179139504563233,0.7179139504563233,0.7169858006379833,0.4260063171386719,7937.751954000001 +19584,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7223612316805392,0.7223612316805392,0.7214649429496548,0.5217523574829102,8289.115139000001 +19992,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7219248661897854,0.7219248661897855,0.7206428236711905,0.6287288665771484,8642.591702000002 +20400,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7231236825334575,0.7231236825334575,0.7218249685926471,0.7244749069213867,8998.461289 +46,Multiclass classification,Adaptive Random Forest,ImageSegments,0.4222222222222222,0.4222222222222222,0.3590236094437775,0.9685115814208984,1.326052 +92,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5604395604395604,0.5604395604395604,0.5746538615446178,1.0556058883666992,4.053487 +138,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5766423357664233,0.5766423357664233,0.598257695340355,1.344954490661621,8.154789999999998 +184,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6229508196721312,0.6229508196721312,0.6451744040758778,1.4133405685424805,13.553012999999998 +230,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6506550218340611,0.6506550218340611,0.6680655280025949,1.5576086044311523,20.188933 +276,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6727272727272727,0.6727272727272727,0.6900672130049011,1.7550430297851562,28.051384 +322,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7040498442367601,0.7040498442367601,0.7087861936875776,1.832967758178711,37.153949999999995 +368,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7302452316076294,0.7302452316076294,0.7285991575377422,1.971024513244629,47.432601999999996 +414,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7457627118644068,0.7457627118644068,0.7430362907281778,1.991847038269043,58.97377399999999 +460,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7342047930283224,0.7342047930283224,0.7271744800226857,1.8101978302001953,71.823928 +506,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7405940594059406,0.7405940594059406,0.7304322149686578,1.7132930755615234,85.827474 +552,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7368421052631579,0.7368421052631579,0.7267508109083203,1.5079193115234375,101.049314 +598,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7403685092127303,0.7403685092127302,0.7318978254380314,1.6471452713012695,117.42015599999999 +644,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7325038880248833,0.7325038880248833,0.7248107612258207,1.7740907669067383,135.017443 +690,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7242380261248186,0.7242380261248187,0.7153272190465999,1.913142204284668,153.656893 +736,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7251700680272108,0.725170068027211,0.7148466398758337,2.0619029998779297,173.429455 +782,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7259923175416133,0.7259923175416134,0.7134712280209221,2.0208959579467773,194.315292 +828,Multiclass classification,Adaptive Random Forest,ImageSegments,0.727932285368803,0.727932285368803,0.7177600265828429,2.224555015563965,216.352158 +874,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7353951890034365,0.7353951890034366,0.7262567978322628,2.300021171569824,239.599524 +920,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7431991294885746,0.7431991294885745,0.7345004589126253,2.4412155151367188,263.99359400000003 +966,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7471502590673575,0.7471502590673575,0.7368855656689403,2.474191665649414,289.66420500000004 +1012,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7546983184965381,0.754698318496538,0.7446216664767904,2.5655078887939453,316.44421900000003 +1058,Multiclass classification,Adaptive Random Forest,ImageSegments,0.760643330179754,0.760643330179754,0.7502594177262459,2.798956871032715,344.45448600000003 +1104,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7624660018132366,0.7624660018132366,0.7523020427630668,2.48898983001709,373.71735 +1150,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7650130548302873,0.7650130548302874,0.7555087521342715,2.3284912109375,404.061966 +1196,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7690376569037657,0.7690376569037657,0.7603504370239861,2.0560731887817383,435.51003499999996 +1242,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7719580983078163,0.7719580983078163,0.7638249032322542,2.1193370819091797,467.96964099999997 +1288,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7746697746697747,0.7746697746697747,0.7668828628349821,2.277647018432617,501.44875399999995 +1334,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7771942985746436,0.7771942985746436,0.7696789046658701,2.3871631622314453,535.887669 +1380,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7817258883248731,0.7817258883248731,0.7754511149783997,2.3104944229125977,571.357393 +1426,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7866666666666666,0.7866666666666666,0.7797171864703156,2.4089183807373047,607.784244 +1472,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7912984364377974,0.7912984364377974,0.7836430453045393,2.5425024032592773,645.2286509999999 +1518,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7963085036255768,0.7963085036255768,0.7883976288226553,2.6389265060424805,683.7451019999999 +1564,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7978246960972489,0.7978246960972489,0.790949738475821,2.283763885498047,723.3862519999999 +1610,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798011187072716,0.7980111870727161,0.7914720525222512,2.519012451171875,764.1312649999999 +1656,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7981873111782477,0.7981873111782477,0.7919320984228655,2.307619094848633,806.0160599999999 +1702,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798941798941799,0.7989417989417988,0.7945012991620244,2.40640926361084,848.960292 +1748,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8019461934745278,0.8019461934745278,0.797056036319667,2.447686195373535,893.037184 +1794,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8047964305633017,0.8047964305633019,0.7993493873930555,2.5208606719970703,938.202728 +1840,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8069603045133225,0.8069603045133223,0.8019867749609348,2.8025121688842773,984.592034 +1886,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8084880636604774,0.8084880636604774,0.8043300839686539,2.9287471771240234,1032.221691 +1932,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8114966338684619,0.8114966338684619,0.8071482324590065,2.977842330932617,1081.048247 +1978,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8148710166919575,0.8148710166919576,0.8107088256390683,3.110445022583008,1130.9949649999999 +2024,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8161146811665843,0.8161146811665844,0.8110472160986095,3.3117494583129883,1182.226115 +2070,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8173030449492509,0.8173030449492509,0.8127793203399477,2.7790603637695312,1234.703432 +2116,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8193853427895981,0.8193853427895981,0.8144282151100146,2.8652515411376953,1288.356269 +2162,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8199907450254512,0.8199907450254512,0.8150157846003385,2.925917625427246,1343.2838700000002 +2208,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8205709107385591,0.8205709107385591,0.8153449009635614,2.785597801208496,1399.3252850000001 +2254,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8175765645805593,0.8175765645805593,0.813116129924445,2.868098258972168,1456.6402850000002 +2300,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8186167899086559,0.8186167899086559,0.8144518819207099,3.062863349914551,1515.2003170000003 +2310,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8185361628410567,0.8185361628410566,0.8145347387119569,3.063481330871582,1574.1800910000002 +1056,Multiclass classification,Adaptive Random Forest,Insects,0.6682464454976303,0.6682464454976303,0.6049011732627783,7.181946754455566,32.418226 +2112,Multiclass classification,Adaptive Random Forest,Insects,0.6944576030317385,0.6944576030317385,0.6288311688548281,9.897843360900879,94.87356399999999 +3168,Multiclass classification,Adaptive Random Forest,Insects,0.6984527944426903,0.6984527944426903,0.625371849015863,13.448436737060547,186.837042 +4224,Multiclass classification,Adaptive Random Forest,Insects,0.706369879232773,0.706369879232773,0.6266042661686886,17.43436622619629,307.272577 +5280,Multiclass classification,Adaptive Random Forest,Insects,0.7107406705815495,0.7107406705815495,0.6273487761971507,20.93905258178711,452.99825 +6336,Multiclass classification,Adaptive Random Forest,Insects,0.7108129439621153,0.7108129439621153,0.6274052515282983,25.022296905517578,622.602665 +7392,Multiclass classification,Adaptive Random Forest,Insects,0.7127587606548504,0.7127587606548504,0.6273117178459473,28.819257736206055,816.020547 +8448,Multiclass classification,Adaptive Random Forest,Insects,0.7164673848703682,0.7164673848703682,0.6293431255193823,32.802799224853516,1032.257355 +9504,Multiclass classification,Adaptive Random Forest,Insects,0.721666842049879,0.721666842049879,0.63170101976307,32.88048076629639,1271.699652 +10560,Multiclass classification,Adaptive Random Forest,Insects,0.724405720238659,0.724405720238659,0.6339052025360064,29.71586036682129,1533.827375 +11616,Multiclass classification,Adaptive Random Forest,Insects,0.7244080929832114,0.7244080929832114,0.6334336343217646,33.71169948577881,1818.162347 +12672,Multiclass classification,Adaptive Random Forest,Insects,0.7225949017441402,0.7225949017441402,0.6332595599893077,29.649346351623535,2125.062078 +13728,Multiclass classification,Adaptive Random Forest,Insects,0.7416769869600058,0.7416769869600057,0.7385871869253197,11.750191688537598,2443.2361889999997 +14784,Multiclass classification,Adaptive Random Forest,Insects,0.7472096326861936,0.7472096326861937,0.7473000008879964,7.712667465209961,2772.2292589999997 +15840,Multiclass classification,Adaptive Random Forest,Insects,0.7404507860344719,0.7404507860344719,0.7427443120881612,5.854048728942871,3118.2806729999998 +16896,Multiclass classification,Adaptive Random Forest,Insects,0.73666765315182,0.73666765315182,0.7407696345938622,9.543391227722168,3480.1200929999995 +17952,Multiclass classification,Adaptive Random Forest,Insects,0.7295972369227341,0.7295972369227341,0.7347001031972082,14.625198364257812,3856.6322749999995 +19008,Multiclass classification,Adaptive Random Forest,Insects,0.739780081022781,0.7397800810227809,0.7407912307996387,5.110816955566406,4245.133706999999 +20064,Multiclass classification,Adaptive Random Forest,Insects,0.7434581069630664,0.7434581069630664,0.7402037922066672,3.8148155212402344,4646.574114999999 +21120,Multiclass classification,Adaptive Random Forest,Insects,0.745111037454425,0.7451110374544251,0.7386209934273732,7.313493728637695,5063.578879 +22176,Multiclass classification,Adaptive Random Forest,Insects,0.7462006764374295,0.7462006764374295,0.7365944363606786,12.210733413696289,5495.973683 +23232,Multiclass classification,Adaptive Random Forest,Insects,0.7483965391072274,0.7483965391072274,0.7360584061499352,11.241872787475586,5944.2105440000005 +24288,Multiclass classification,Adaptive Random Forest,Insects,0.7495779635195784,0.7495779635195785,0.7345443205753824,12.262273788452148,6407.867088000001 +25344,Multiclass classification,Adaptive Random Forest,Insects,0.7508582251509293,0.7508582251509293,0.7336140903014292,15.815716743469238,6885.995097000001 +26400,Multiclass classification,Adaptive Random Forest,Insects,0.7510890564036516,0.7510890564036516,0.7317409587301968,20.072275161743164,7378.034229000001 +27456,Multiclass classification,Adaptive Random Forest,Insects,0.7520670187579676,0.7520670187579677,0.7304776676466566,23.249674797058105,7884.702304 +28512,Multiclass classification,Adaptive Random Forest,Insects,0.7487285609063169,0.7487285609063169,0.7285292321096271,2.7024307250976562,8406.670172 +29568,Multiclass classification,Adaptive Random Forest,Insects,0.7464741096492712,0.7464741096492712,0.7309964825863351,6.2935638427734375,8940.901067 +30624,Multiclass classification,Adaptive Random Forest,Insects,0.7457793162002416,0.7457793162002416,0.7347045068936117,9.350909233093262,9487.044090000001 +31680,Multiclass classification,Adaptive Random Forest,Insects,0.745036143817671,0.745036143817671,0.7375864352537521,14.599569320678711,10044.836672000001 +32736,Multiclass classification,Adaptive Random Forest,Insects,0.7451962731021842,0.7451962731021842,0.7406480970104784,19.125198364257812,10615.117300000002 +33792,Multiclass classification,Adaptive Random Forest,Insects,0.7402858749371134,0.7402858749371134,0.7370798749337869,6.808139801025391,11202.653786000003 +34848,Multiclass classification,Adaptive Random Forest,Insects,0.7366200820730623,0.7366200820730623,0.7333315604235389,5.8602495193481445,11807.700361000003 +35904,Multiclass classification,Adaptive Random Forest,Insects,0.733921956382475,0.7339219563824751,0.7303171015411175,9.36469554901123,12429.747970000002 +36960,Multiclass classification,Adaptive Random Forest,Insects,0.7304039611461349,0.7304039611461349,0.7265687877692525,14.848862648010254,13069.446785000002 +38016,Multiclass classification,Adaptive Random Forest,Insects,0.7276864395633302,0.7276864395633302,0.7236022807953257,19.807891845703125,13727.939023000003 +39072,Multiclass classification,Adaptive Random Forest,Insects,0.7250134370760922,0.7250134370760921,0.7209989950382084,16.71243381500244,14405.601845000003 +40128,Multiclass classification,Adaptive Random Forest,Insects,0.7235028783612032,0.7235028783612032,0.7198278735760195,8.331427574157715,15101.835691000002 +41184,Multiclass classification,Adaptive Random Forest,Insects,0.723623825364835,0.723623825364835,0.7203262236880287,6.9819841384887695,15814.868539000003 +42240,Multiclass classification,Adaptive Random Forest,Insects,0.7240464973129098,0.7240464973129098,0.7211005399097123,10.71219539642334,16543.112989 +43296,Multiclass classification,Adaptive Random Forest,Insects,0.7245409400623629,0.7245409400623629,0.721844297210525,10.330558776855469,17285.760894000003 +44352,Multiclass classification,Adaptive Random Forest,Insects,0.7248765529525828,0.7248765529525828,0.7223628081683402,13.299851417541504,18041.694028 +45408,Multiclass classification,Adaptive Random Forest,Insects,0.7254167859581122,0.7254167859581122,0.7228420559832612,15.662115097045898,18810.181113000002 +46464,Multiclass classification,Adaptive Random Forest,Insects,0.7263844349267159,0.7263844349267159,0.7236482152790997,19.25161361694336,19591.516438000002 +47520,Multiclass classification,Adaptive Random Forest,Insects,0.7265304404553968,0.7265304404553967,0.7240124567772878,14.065608024597168,20387.990038000004 +48576,Multiclass classification,Adaptive Random Forest,Insects,0.7304374678332476,0.7304374678332476,0.7281756207358935,7.354809761047363,21197.413376000004 +49632,Multiclass classification,Adaptive Random Forest,Insects,0.7344603171404969,0.7344603171404969,0.7322565876518081,7.006095886230469,22016.972025000003 +50688,Multiclass classification,Adaptive Random Forest,Insects,0.7380590684001815,0.7380590684001815,0.7356981427827818,10.14159107208252,22847.182754 +51744,Multiclass classification,Adaptive Random Forest,Insects,0.7420134124422627,0.7420134124422627,0.7394134340953542,13.563420295715332,23688.037606 +52800,Multiclass classification,Adaptive Random Forest,Insects,0.7451466883842497,0.7451466883842497,0.7430487162081567,0.3614501953125,24535.706056000003 +52848,Multiclass classification,Adaptive Random Forest,Insects,0.7453781671618067,0.7453781671618067,0.7433023109254195,0.36179351806640625,25383.518073000003 +408,Multiclass classification,Adaptive Random Forest,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.3354053497314453,3.23067 +816,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730061349693252,0.9730061349693252,0.8116978142719798,0.988037109375,11.21298 +1224,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730171708912511,0.9730171708912511,0.9579161898493525,2.195523262023926,25.427007 +1632,Multiclass classification,Adaptive Random Forest,Keystroke,0.9693439607602697,0.9693439607602697,0.9069773132409142,3.526730537414551,46.453053999999995 +2040,Multiclass classification,Adaptive Random Forest,Keystroke,0.9666503187837175,0.9666503187837175,0.9303026980117671,5.496582984924316,74.431187 +2448,Multiclass classification,Adaptive Random Forest,Keystroke,0.9660809154066203,0.9660809154066203,0.9555178664837441,2.29970645904541,107.969459 +2856,Multiclass classification,Adaptive Random Forest,Keystroke,0.9691768826619965,0.9691768826619965,0.9674134048328416,3.376467704772949,146.96126800000002 +3264,Multiclass classification,Adaptive Random Forest,Keystroke,0.9672080907140668,0.9672080907140668,0.9546197483047236,4.62060546875,192.073824 +3672,Multiclass classification,Adaptive Random Forest,Keystroke,0.9684009806592209,0.968400980659221,0.9654409635782653,3.119338035583496,243.354323 +4080,Multiclass classification,Adaptive Random Forest,Keystroke,0.9644520715861731,0.9644520715861731,0.95030552665756,4.705347061157227,301.433133 +4488,Multiclass classification,Adaptive Random Forest,Keystroke,0.9661243592600847,0.9661243592600847,0.9659906155964958,1.508072853088379,365.412759 +4896,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677221654749745,0.9677221654749745,0.96768641848376,2.487558364868164,434.672843 +5304,Multiclass classification,Adaptive Random Forest,Keystroke,0.9685083914765227,0.9685083914765227,0.9677400809149086,2.8771514892578125,509.85413800000003 +5712,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690071791279986,0.9690071791279986,0.9686984277929261,4.140267372131348,591.7133210000001 +6120,Multiclass classification,Adaptive Random Forest,Keystroke,0.9671514953423762,0.9671514953423762,0.9635575047511442,5.121949195861816,681.1937680000001 +6528,Multiclass classification,Adaptive Random Forest,Keystroke,0.9675195342423778,0.9675195342423778,0.9673223823066149,2.1385393142700195,777.2102420000001 +6936,Multiclass classification,Adaptive Random Forest,Keystroke,0.9685652487382841,0.9685652487382841,0.9688652926813892,2.7864933013916016,879.0640510000001 +7344,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686776521857552,0.9686776521857552,0.9682274153773371,3.314570426940918,987.0629210000001 +7752,Multiclass classification,Adaptive Random Forest,Keystroke,0.9682621597213262,0.9682621597213262,0.9674704101631952,4.690197944641113,1101.141854 +8160,Multiclass classification,Adaptive Random Forest,Keystroke,0.96727540139723,0.96727540139723,0.9662379529396136,5.223731994628906,1221.487909 +8568,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677833547332788,0.9677833547332788,0.9678822443058487,4.885932922363281,1347.980617 +8976,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686908077994429,0.9686908077994429,0.9690861219789195,6.402636528015137,1480.694289 +9384,Multiclass classification,Adaptive Random Forest,Keystroke,0.9683470105509965,0.9683470105509965,0.9680699356268633,6.928671836853027,1620.259773 +9792,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686446736799101,0.9686446736799101,0.9687197530276813,5.552419662475586,1766.078849 +10200,Multiclass classification,Adaptive Random Forest,Keystroke,0.9684282772820865,0.9684282772820866,0.9682582636163196,2.695918083190918,1917.758924 +10608,Multiclass classification,Adaptive Random Forest,Keystroke,0.9673800320543038,0.9673800320543038,0.9668238422002586,3.239151954650879,2074.190769 +11016,Multiclass classification,Adaptive Random Forest,Keystroke,0.9676804357694053,0.9676804357694053,0.9678040910458205,4.023995399475098,2235.420867 +11424,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677842948437363,0.9677842948437363,0.9678364439490078,4.695375442504883,2402.1641919999997 +11832,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677119432000676,0.9677119432000676,0.9677086079179034,5.258674621582031,2574.8706989999996 +12240,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687065936759539,0.9687065936759539,0.9690716756618885,6.001680374145508,2753.3671299999996 +12648,Multiclass classification,Adaptive Random Forest,Keystroke,0.9688463667272871,0.9688463667272871,0.9689334511448673,5.217698097229004,2937.7699639999996 +13056,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687476062811183,0.9687476062811183,0.968764477893114,5.266051292419434,3127.8024029999997 +13464,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687291094109782,0.9687291094109782,0.9687736841624996,6.279603958129883,3323.370395 +13872,Multiclass classification,Adaptive Random Forest,Keystroke,0.9695047220820416,0.9695047220820416,0.9697384724636318,4.041820526123047,3524.041026 +14280,Multiclass classification,Adaptive Random Forest,Keystroke,0.9682750892919673,0.9682750892919673,0.9680357071263168,2.1731691360473633,3729.110149 +14688,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686797848437394,0.9686797848437394,0.9688099431838716,2.4900379180908203,3938.3384300000002 +15096,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692613448161643,0.9692613448161643,0.9694122553904638,2.7789316177368164,4151.996270000001 +15504,Multiclass classification,Adaptive Random Forest,Keystroke,0.9694897761723538,0.9694897761723538,0.969571649124791,3.946505546569824,4370.227344000001 +15912,Multiclass classification,Adaptive Random Forest,Keystroke,0.9694550939601534,0.9694550939601534,0.9694916672888816,4.345325469970703,4594.050341000001 +16320,Multiclass classification,Adaptive Random Forest,Keystroke,0.9695447024940254,0.9695447024940254,0.9695954968773723,3.909954071044922,4823.361799000001 +16728,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692114545345848,0.9692114545345848,0.9692084456743588,1.764338493347168,5057.2303470000015 +17136,Multiclass classification,Adaptive Random Forest,Keystroke,0.9696527575138605,0.9696527575138605,0.9697329621491685,1.7167367935180664,5295.013901000001 +17544,Multiclass classification,Adaptive Random Forest,Keystroke,0.9696745140511885,0.9696745140511885,0.9697082565052514,2.8143720626831055,5537.319837000001 +17952,Multiclass classification,Adaptive Random Forest,Keystroke,0.968748259149908,0.968748259149908,0.968705960089485,2.951136589050293,5784.484584000001 +18360,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690070265264993,0.9690070265264993,0.9690448168177233,3.5441465377807617,6036.117893000001 +18768,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690946874833485,0.9690946874833485,0.9691164520527107,4.379698753356934,6292.729193000001 +19176,Multiclass classification,Adaptive Random Forest,Keystroke,0.968761408083442,0.968761408083442,0.9687617227117352,3.8120603561401367,6554.348831000001 +19584,Multiclass classification,Adaptive Random Forest,Keystroke,0.9689526630240515,0.9689526630240515,0.9689629146490384,2.019772529602051,6819.891372000001 +19992,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692861787804512,0.9692861787804512,0.9692901573177237,1.2564506530761719,7089.584863000001 +20400,Multiclass classification,Adaptive Random Forest,Keystroke,0.9691161331437815,0.9691161331437815,0.9691108096285476,1.6354646682739258,7363.046142000001 +46,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.5333333333333333,0.5333333333333333,0.5005728607232367,0.8510866165161133,0.941842 +92,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6153846153846154,0.6153846153846154,0.596131344383025,1.5052366256713867,2.918201 +138,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6496350364963503,0.6496350364963503,0.6567305057749026,2.146304130554199,6.147886 +184,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6994535519125683,0.6994535519125683,0.7070190759413217,2.7665939331054688,10.824064 +230,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7379912663755459,0.7379912663755459,0.7433871451842025,3.2484235763549805,16.931166 +276,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7490909090909091,0.7490909090909091,0.7566070103930901,3.776392936706543,24.729994 +322,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7694704049844237,0.7694704049844237,0.7681721604320974,4.142314910888672,34.173162000000005 +368,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.784741144414169,0.7847411444141691,0.7789718513534348,4.497910499572754,45.384105000000005 +414,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7990314769975787,0.7990314769975787,0.7943771701942021,4.869846343994141,58.265676000000006 +460,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7973856209150327,0.7973856209150327,0.7916511033189314,5.3911848068237305,73.08883800000001 +506,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.805940594059406,0.805940594059406,0.8010859843658406,5.806554794311523,89.87625100000001 +552,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8076225045372051,0.8076225045372051,0.8036838079612314,6.295863151550293,108.59930000000001 +598,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8174204355108877,0.8174204355108878,0.8156009215135775,6.727802276611328,129.48595400000002 +644,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8211508553654744,0.8211508553654744,0.8207645722848749,7.18087100982666,152.525841 +690,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8229317851959361,0.8229317851959362,0.8226135245892084,7.561182022094727,177.86541200000002 +736,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8231292517006803,0.8231292517006803,0.8228959515200417,7.975464820861816,205.49957600000002 +782,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8309859154929577,0.8309859154929577,0.8306123687436626,8.301925659179688,235.39408200000003 +828,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8343409915356711,0.834340991535671,0.835521648488366,8.722038269042969,267.718494 +874,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8407789232531501,0.8407789232531501,0.8414965916969209,9.057206153869629,302.46008700000004 +920,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8443960826985855,0.8443960826985855,0.8446110045111287,9.382828712463379,339.623661 +966,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8466321243523316,0.8466321243523316,0.8462590694093756,9.696897506713867,379.342347 +1012,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8516320474777448,0.8516320474777448,0.8504483916737715,9.949009895324707,421.625642 +1058,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8571428571428571,0.8571428571428571,0.8557487568785946,10.2299222946167,466.542637 +1104,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8603807796917498,0.8603807796917498,0.8594481550185353,10.524299621582031,514.218423 +1150,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8624891209747607,0.8624891209747607,0.8612253786789881,10.737759590148926,564.599929 +1196,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8652719665271966,0.8652719665271966,0.8642881992026393,11.010127067565918,617.836337 +1242,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8670427074939565,0.8670427074939565,0.8663181473795101,11.261144638061523,674.05967 +1288,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8694638694638694,0.8694638694638694,0.8687259920464652,11.505732536315918,733.385389 +1334,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8709677419354839,0.8709677419354839,0.870193396369452,11.826444625854492,796.067675 +1380,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8745467730239304,0.8745467730239304,0.874089581073643,12.086430549621582,861.584672 +1426,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8771929824561403,0.8771929824561403,0.8759011931352845,12.29430866241455,930.2045189999999 +1472,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8796736913664174,0.8796736913664174,0.877566397675441,12.500163078308105,1001.8141389999998 +1518,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8826631509558339,0.8826631509558339,0.8803270226288138,12.740474700927734,1076.4882149999999 +1564,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8841970569417786,0.8841970569417786,0.8822041640143002,12.987508773803711,1154.350794 +1610,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.886886264760721,0.886886264760721,0.8850836875294148,13.252826690673828,1235.463166 +1656,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.888821752265861,0.888821752265861,0.8870702351165313,13.500110626220703,1319.86391 +1702,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8912404467960023,0.8912404467960025,0.8905987472429445,13.767583847045898,1407.541035 +1748,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8929593589009731,0.892959358900973,0.8920318510221457,14.030475616455078,1498.676265 +1794,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.894032348020078,0.894032348020078,0.8925886559949978,14.271255493164062,1593.100327 +1840,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8945078847199565,0.8945078847199565,0.8931986390525462,14.574835777282715,1691.005218 +1886,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.896551724137931,0.896551724137931,0.8956464025201587,14.834091186523438,1792.408733 +1932,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8964267219057483,0.8964267219057483,0.8951782213786073,15.134613037109375,1897.156299 +1978,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8973191704602934,0.8973191704602934,0.8961901832930852,15.326050758361816,2005.31409 +2024,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8986653484923381,0.8986653484923381,0.8970310627029995,15.549851417541504,2116.877653 +2070,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8994683421942967,0.8994683421942967,0.8980105869909577,15.816215515136719,2232.114727 +2116,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.900709219858156,0.900709219858156,0.8989778942952686,15.957537651062012,2350.8625340000003 +2162,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9000462748727441,0.9000462748727441,0.8982611856050026,16.206623077392578,2473.2186990000005 +2208,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9012233801540552,0.9012233801540552,0.8993036839855942,16.400617599487305,2599.1257530000003 +2254,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9014647137150466,0.9014647137150466,0.8999821457114682,16.693093299865723,2728.6827460000004 +2300,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9016963897346673,0.9016963897346673,0.9003174232892135,16.988688468933105,2861.834306 +2310,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9016890428757037,0.9016890428757037,0.9003808534937335,17.050235748291016,2997.696193 +1056,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6511848341232227,0.6511848341232227,0.5805974192561721,27.882014274597168,41.422615 +2112,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6830885836096636,0.6830885836096636,0.6159001145696381,53.900901794433594,137.16292 +3168,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6889801073571203,0.6889801073571203,0.6135176771695448,79.45620250701904,291.10856 +4224,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6954771489462468,0.6954771489462468,0.6159765684907534,104.90542316436768,501.70617000000004 +5280,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7003220306876302,0.7003220306876302,0.6217575035584229,130.7021541595459,768.289754 +6336,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7021310181531176,0.7021310181531176,0.622391174421368,156.0168752670288,1090.319759 +7392,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7027465836828576,0.7027465836828576,0.6232948240709647,180.83974838256836,1466.44078 +8448,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7040369361903634,0.7040369361903634,0.6235946437988805,205.63252925872803,1896.370643 +9504,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7105124697463959,0.7105124697463959,0.6284709935917355,229.19151210784912,2381.431795 +10560,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7140827729898664,0.7140827729898664,0.6302854833117341,253.17632389068604,2925.98814 +11616,Multiclass classification,Aggregated Mondrian Forest,Insects,0.71562634524322,0.7156263452432199,0.6305326785921538,277.4567346572876,3530.8520359999998 +12672,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7145450240707126,0.7145450240707125,0.6284185449457835,301.7114896774292,4201.052974 +13728,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7057623661397247,0.7057623661397247,0.6885364031919957,327.2237205505371,4936.820951 +14784,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6967462625989312,0.6967462625989312,0.69194472505998,352.6018476486206,5738.465999 +15840,Multiclass classification,Aggregated Mondrian Forest,Insects,0.676684134099375,0.676684134099375,0.673854549025314,384.9730758666992,6613.285946 +16896,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6698431488606097,0.6698431488606097,0.668750254945471,415.2214603424072,7559.921071 +17952,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6646983454960727,0.6646983454960727,0.6646134205077884,444.32067584991455,8589.520858 +19008,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6620192560635555,0.6620192560635555,0.6605985532750915,472.75781440734863,9705.665905 +20064,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6597717190848826,0.6597717190848826,0.6570293922418718,499.48760890960693,10901.076562 +21120,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6539608882996354,0.6539608882996354,0.6496192149174075,528.8777961730957,12166.701144 +22176,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6547463359639233,0.6547463359639233,0.6484047117859243,557.1920728683472,13501.384366 +23232,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6583444535319185,0.6583444535319185,0.6499882024630633,584.0554361343384,14901.095396 +24288,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6611767612302878,0.6611767612302878,0.6506059068013808,610.3706150054932,16366.700533000001 +25344,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6659827171210986,0.6659827171210986,0.6532433614752314,635.6853046417236,17901.739193 +26400,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6702526610856472,0.6702526610856472,0.6554263220708306,660.4025926589966,19504.786084 +27456,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6745947914769623,0.6745947914769623,0.6575507550972549,684.36501121521,21172.086014 +28512,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6705482094630143,0.6705482094630143,0.6539581966383304,712.6770572662354,22902.746936 +29568,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6644231744850678,0.6644231744850678,0.6512239029866641,743.5559530258179,24691.477434 +30624,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6622799856317148,0.6622799856317148,0.6527566844616065,772.5478630065918,26538.585641 +31680,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6621736797247388,0.6621736797247388,0.6557760097374935,800.5439138412476,28440.416189000003 +32736,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6623797159004124,0.6623797159004124,0.6584479912704261,827.4998264312744,30418.714512000002 +33792,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6575123553608949,0.6575123553608949,0.6541419435809196,857.7161102294922,32439.386127 +34848,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6519069073377909,0.6519069073377909,0.6481893367707658,888.8327789306641,34499.720344 +35904,Multiclass classification,Aggregated Mondrian Forest,Insects,0.647550343982397,0.647550343982397,0.643407015045196,919.6311988830566,36599.09766 +36960,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6444438431775752,0.6444438431775752,0.6400224052225335,949.7819452285767,38735.650911 +38016,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6425358411153492,0.6425358411153492,0.6377821595167165,979.4456567764282,40896.763764999996 +39072,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6414476209976709,0.6414476209976709,0.6370415360917451,1009.0255756378174,43085.847267 +40128,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6409898572033793,0.6409898572033793,0.636858231937463,1037.841980934143,45303.144751 +41184,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6414782798727631,0.6414782798727631,0.637272014233453,1065.1163549423218,47540.369251 +42240,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6428419233409882,0.6428419233409882,0.6385110475108609,1091.8334274291992,49803.522006 +43296,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6441159487238711,0.6441159487238711,0.6396283228479406,1118.1560363769531,52086.93226 +44352,Multiclass classification,Aggregated Mondrian Forest,Insects,0.645058735992424,0.645058735992424,0.6403851797193834,1144.4119939804077,54391.61903 +45408,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6469266853128373,0.6469266853128373,0.6418265850265934,1169.9601306915283,56719.958571 +46464,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6487742935238792,0.6487742935238792,0.643191402092947,1194.6403436660767,59073.094705 +47520,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6459521454576065,0.6459521454576065,0.6406800374556137,1224.6073780059814,61451.967815 +48576,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6443643849716932,0.6443643849716932,0.6398250343320808,1254.4350862503052,63857.884093 +49632,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6446172754931394,0.6446172754931394,0.6406945505071863,1282.3891849517822,66293.298766 +50688,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6461222798745241,0.6461222798745241,0.6426238276925219,1309.4736614227295,68755.018108 +51744,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6489186943161394,0.6489186943161394,0.6457243405011626,1334.444143295288,71244.151045 +52800,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6470577094263149,0.6470577094263149,0.6443966707674731,1363.999231338501,73759.934152 +52848,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6469809071470471,0.6469809071470471,0.6443518314696601,1365.409776687622,76295.692169 +408,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9901719901719902,0.9901719901719902,0.8308395677472984,0.12276840209960938,1.485322 +816,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9914110429447853,0.9914110429447853,0.960934413925625,0.41584110260009766,6.729082 +1224,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9893704006541292,0.9893704006541292,0.9580466011674303,1.2467107772827148,20.148490000000002 +1632,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9889638258736971,0.9889638258736971,0.9786672150923964,2.28104305267334,50.264957 +2040,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.988719960765081,0.988719960765081,0.9803510904896324,3.352717399597168,91.64343099999999 +2448,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9885574172456069,0.9885574172456069,0.9830468792370581,4.983606338500977,148.278076 +2856,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9852889667250437,0.9852889667250437,0.9737767108051043,6.963967323303223,227.073424 +3264,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9825314128102973,0.9825314128102973,0.9734338986941852,9.8344087600708,324.702985 +3672,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9822936529555979,0.9822936529555979,0.9788760747631073,12.7888765335083,446.35643500000003 +4080,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9806325079676391,0.9806325079676391,0.9749453255203757,16.445659637451172,594.71846 +4488,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9801649208825496,0.9801649208825496,0.9779116862524243,20.943636894226074,768.8230350000001 +4896,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9801838610827375,0.9801838610827375,0.978782474664832,24.856953620910645,967.6114420000001 +5304,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9768055817461814,0.9768055817461814,0.9702080932270808,28.10527801513672,1191.0213970000002 +5712,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9746104009805638,0.9746104009805638,0.9718234131704067,32.14579772949219,1440.171835 +6120,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9697663016832816,0.9697663016832816,0.9621279568251032,36.40912055969238,1717.813161 +6528,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9656810173127011,0.9656810173127011,0.9634765255010708,42.20043754577637,2023.330442 +6936,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9653929343907715,0.9653929343907715,0.9646253117338193,46.972042083740234,2355.811343 +7344,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9635026555903582,0.9635026555903582,0.9611034281810401,50.8284969329834,2716.693574 +7752,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9610372855115469,0.9610372855115469,0.9585597537512924,55.062747955322266,3108.019641 +8160,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9593087388160314,0.9593087388160314,0.9577319445930262,59.75967216491699,3529.9614579999998 +8568,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9598459203922026,0.9598459203922026,0.9601713780248281,65.88526916503906,3981.5521329999997 +8976,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.959108635097493,0.959108635097493,0.9586518345557712,71.85272026062012,4465.222941 +9384,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9573697111797932,0.9573697111797932,0.9561353164275519,78.18439388275146,4984.801354 +9792,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9555714431620876,0.9555714431620876,0.9546392488298882,85.86389446258545,5537.4752260000005 +10200,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9486224139621532,0.9486224139621532,0.9433099305923253,94.35744285583496,6127.477763000001 +10608,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9431507495050439,0.9431507495050439,0.9403442056943527,104.1574821472168,6756.480909000001 +11016,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9408987743985474,0.9408987743985474,0.9399975161043574,113.0038013458252,7421.284759000001 +11424,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9380197846450145,0.9380197846450145,0.936341059397272,121.46645069122314,8125.715779000001 +11832,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9322965091708224,0.9322965091708224,0.9294143034054053,131.1031150817871,8872.899296000001 +12240,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9326742380913473,0.9326742380913473,0.9327603226303838,137.88959789276123,9652.703167000001 +12648,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.927571756147703,0.927571756147703,0.9249549620362734,145.5888376235962,10475.658214000001 +13056,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9247797778628878,0.9247797778628878,0.9237072084771099,154.53871536254883,11346.213643000001 +13464,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9238654088984625,0.9238654088984625,0.9233692422863465,161.3583574295044,12266.045404 +13872,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9202653017086007,0.9202653017086007,0.9191663953636944,170.12918186187744,13231.481182 +14280,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9163106660130261,0.9163106660130261,0.9150341930556871,179.0350112915039,14245.599713 +14688,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9161162933206237,0.9161162933206237,0.9160540991607554,184.834698677063,15311.95464 +15096,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9145412388208016,0.9145412388208016,0.91429667624259,191.58009719848633,16433.239395 +15504,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9105979487841063,0.9105979487841064,0.9097163708309961,200.08039951324463,17613.909715 +15912,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9068568914587393,0.9068568914587393,0.9060681758481206,210.5000762939453,18848.20155 +16320,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9031190636681169,0.9031190636681169,0.9023660107991418,221.55222129821777,20131.794746000003 +16728,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9005799007592515,0.9005799007592515,0.9001704241319546,231.28063201904297,21466.799965000002 +17136,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8989203384884739,0.8989203384884739,0.8987537815839572,248.11264038085938,22840.808564000003 +17544,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.893746793592886,0.8937467935928861,0.892807745348426,267.53482723236084,24265.711089000004 +17952,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8894212021614395,0.8894212021614395,0.8884694521151855,281.7739496231079,25739.620728000005 +18360,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8911705430579008,0.8911705430579007,0.8908032768807751,288.0978307723999,27256.627666000004 +18768,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8911387009111739,0.8911387009111739,0.8906428613252552,296.05272102355957,28820.747713000004 +19176,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8886049543676662,0.8886049543676662,0.8879368647002966,307.68266773223877,30435.231458000006 +19584,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8895470561201042,0.8895470561201042,0.889061241536932,313.4344787597656,32089.799369000008 +19992,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8862488119653844,0.8862488119653844,0.8855123768505595,324.9442596435547,33786.733744000005 +20400,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8810726015981175,0.8810726015981175,0.8799282628097613,338.1390075683594,35528.434162000005 +46,Multiclass classification,Streaming Random Patches,ImageSegments,0.35555555555555557,0.35555555555555557,0.24684873949579833,2.5926971435546875,5.672061 +92,Multiclass classification,Streaming Random Patches,ImageSegments,0.5274725274725275,0.5274725274725275,0.5392220990960486,2.5963096618652344,17.740863 +138,Multiclass classification,Streaming Random Patches,ImageSegments,0.5401459854014599,0.5401459854014599,0.5661177456005042,2.5979232788085938,35.937815 +184,Multiclass classification,Streaming Random Patches,ImageSegments,0.5956284153005464,0.5956284153005464,0.6144104879239446,2.6004638671875,59.975895 +230,Multiclass classification,Streaming Random Patches,ImageSegments,0.6200873362445415,0.6200873362445415,0.6319742698014011,2.6008224487304688,89.681265 +276,Multiclass classification,Streaming Random Patches,ImageSegments,0.6327272727272727,0.6327272727272727,0.6440706793955739,2.601276397705078,125.043898 +322,Multiclass classification,Streaming Random Patches,ImageSegments,0.6573208722741433,0.6573208722741433,0.6535377647060517,2.6028709411621094,166.036362 +368,Multiclass classification,Streaming Random Patches,ImageSegments,0.6784741144414169,0.6784741144414169,0.6717418242612484,2.6031723022460938,212.735146 +414,Multiclass classification,Streaming Random Patches,ImageSegments,0.6900726392251816,0.6900726392251816,0.6823551618652942,2.603717803955078,265.17548899999997 +460,Multiclass classification,Streaming Random Patches,ImageSegments,0.6971677559912854,0.6971677559912854,0.686858403065277,2.6037940979003906,323.486791 +506,Multiclass classification,Streaming Random Patches,ImageSegments,0.699009900990099,0.699009900990099,0.6869845800125663,2.604084014892578,387.600808 +552,Multiclass classification,Streaming Random Patches,ImageSegments,0.6987295825771325,0.6987295825771325,0.6895132041566728,2.6040496826171875,457.32381699999996 +598,Multiclass classification,Streaming Random Patches,ImageSegments,0.7035175879396985,0.7035175879396985,0.6939747146282641,2.6041183471679688,532.682096 +644,Multiclass classification,Streaming Random Patches,ImageSegments,0.6998444790046656,0.6998444790046656,0.6913714585468268,2.6053123474121094,613.490084 +690,Multiclass classification,Streaming Random Patches,ImageSegments,0.7024673439767779,0.7024673439767779,0.6944906634267102,2.6058921813964844,699.880084 +736,Multiclass classification,Streaming Random Patches,ImageSegments,0.7020408163265306,0.7020408163265306,0.69548275919944,2.605987548828125,791.65329 +782,Multiclass classification,Streaming Random Patches,ImageSegments,0.706786171574904,0.706786171574904,0.6991539785967766,2.6064224243164062,888.981823 +828,Multiclass classification,Streaming Random Patches,ImageSegments,0.7085852478839177,0.7085852478839177,0.70309750989463,2.6064682006835938,991.6474969999999 +874,Multiclass classification,Streaming Random Patches,ImageSegments,0.715922107674685,0.7159221076746849,0.7073525059690206,2.6064682006835938,1099.573439 +920,Multiclass classification,Streaming Random Patches,ImageSegments,0.7170837867247007,0.7170837867247007,0.707165908654469,2.6064682006835938,1212.915424 +966,Multiclass classification,Streaming Random Patches,ImageSegments,0.7160621761658031,0.716062176165803,0.7063689525089133,2.6064682006835938,1331.5593390000001 +1012,Multiclass classification,Streaming Random Patches,ImageSegments,0.7151335311572701,0.7151335311572701,0.7047830593764105,2.6064910888671875,1455.3800170000002 +1058,Multiclass classification,Streaming Random Patches,ImageSegments,0.7152317880794702,0.7152317880794702,0.7037726227430311,2.606658935546875,1584.3270810000001 +1104,Multiclass classification,Streaming Random Patches,ImageSegments,0.71441523118767,0.71441523118767,0.7026447500373862,2.6067771911621094,1718.2459470000001 +1150,Multiclass classification,Streaming Random Patches,ImageSegments,0.7162750217580505,0.7162750217580505,0.7030218527348165,2.6067771911621094,1857.0229310000002 +1196,Multiclass classification,Streaming Random Patches,ImageSegments,0.7179916317991631,0.7179916317991631,0.705575475090573,2.379610061645508,2000.273372 +1242,Multiclass classification,Streaming Random Patches,ImageSegments,0.7155519742143432,0.7155519742143431,0.7053749246401603,3.185004234313965,2147.034729 +1288,Multiclass classification,Streaming Random Patches,ImageSegments,0.7156177156177156,0.7156177156177156,0.7041730806550314,3.633350372314453,2296.192092 +1334,Multiclass classification,Streaming Random Patches,ImageSegments,0.7149287321830458,0.7149287321830458,0.7045092702498074,4.368736267089844,2447.850078 +1380,Multiclass classification,Streaming Random Patches,ImageSegments,0.7186366932559826,0.7186366932559827,0.7102131417787841,4.724300384521484,2601.871177 +1426,Multiclass classification,Streaming Random Patches,ImageSegments,0.7256140350877193,0.7256140350877193,0.7174099613082184,4.89253044128418,2758.036552 +1472,Multiclass classification,Streaming Random Patches,ImageSegments,0.7273963290278722,0.7273963290278722,0.7183919320082559,5.412370681762695,2916.512936 +1518,Multiclass classification,Streaming Random Patches,ImageSegments,0.7211601845748187,0.7211601845748187,0.7136134581802791,6.487729072570801,3077.492565 +1564,Multiclass classification,Streaming Random Patches,ImageSegments,0.7172104926423545,0.7172104926423546,0.7129536273040751,6.405126571655273,3241.109 +1610,Multiclass classification,Streaming Random Patches,ImageSegments,0.7209446861404599,0.7209446861404599,0.7163536024764182,6.857941627502441,3407.317179 +1656,Multiclass classification,Streaming Random Patches,ImageSegments,0.7238670694864048,0.7238670694864048,0.7196892738307762,7.034061431884766,3576.2587320000002 +1702,Multiclass classification,Streaming Random Patches,ImageSegments,0.7260435038212816,0.7260435038212816,0.7238533950478148,7.623349189758301,3747.8646120000003 +1748,Multiclass classification,Streaming Random Patches,ImageSegments,0.7309673726388094,0.7309673726388093,0.7286270619416129,8.106144905090332,3922.0639630000005 +1794,Multiclass classification,Streaming Random Patches,ImageSegments,0.7361963190184049,0.7361963190184049,0.7329274067865035,8.185744285583496,4098.8506050000005 +1840,Multiclass classification,Streaming Random Patches,ImageSegments,0.7389885807504079,0.7389885807504077,0.7360694376974826,8.929247856140137,4278.2789060000005 +1886,Multiclass classification,Streaming Random Patches,ImageSegments,0.7411140583554376,0.7411140583554376,0.7396669191579938,9.100563049316406,4460.600751000001 +1932,Multiclass classification,Streaming Random Patches,ImageSegments,0.7431382703262558,0.7431382703262558,0.7411378754700444,9.223885536193848,4645.683986000001 +1978,Multiclass classification,Streaming Random Patches,ImageSegments,0.7465857359635811,0.746585735963581,0.744200926808846,9.401692390441895,4833.458731000001 +2024,Multiclass classification,Streaming Random Patches,ImageSegments,0.7508650519031141,0.7508650519031143,0.7476945996538615,9.481804847717285,5024.230846 +2070,Multiclass classification,Streaming Random Patches,ImageSegments,0.7549540840985983,0.7549540840985983,0.7524477298078486,9.431160926818848,5217.969956000001 +2116,Multiclass classification,Streaming Random Patches,ImageSegments,0.7583924349881797,0.7583924349881797,0.7554386161495508,9.549637794494629,5414.604524000001 +2162,Multiclass classification,Streaming Random Patches,ImageSegments,0.7607589079130033,0.7607589079130033,0.7577216433051415,10.151451110839844,5614.378132000002 +2208,Multiclass classification,Streaming Random Patches,ImageSegments,0.7648391481649298,0.7648391481649298,0.7614528787516565,9.14443588256836,5817.274926000002 +2254,Multiclass classification,Streaming Random Patches,ImageSegments,0.7652019529516201,0.7652019529516201,0.762166830901651,8.801234245300293,6023.198429000002 +2300,Multiclass classification,Streaming Random Patches,ImageSegments,0.7672901261418008,0.7672901261418008,0.7647372124971393,8.857858657836914,6232.074878000002 +2310,Multiclass classification,Streaming Random Patches,ImageSegments,0.7669987007362494,0.7669987007362494,0.7647069285577738,8.926526069641113,6441.814766000002 +1056,Multiclass classification,Streaming Random Patches,Insects,0.6388625592417062,0.6388625592417062,0.6031100134310133,8.730474472045898,177.190345 +2112,Multiclass classification,Streaming Random Patches,Insects,0.659403126480341,0.659403126480341,0.6244477305834598,21.138185501098633,477.66892900000005 +3168,Multiclass classification,Streaming Random Patches,Insects,0.6722450268392801,0.6722450268392801,0.6321534006670183,28.433568000793457,884.814326 +4224,Multiclass classification,Streaming Random Patches,Insects,0.680322045938906,0.680322045938906,0.6340126191391743,39.2259521484375,1406.7405990000002 +5280,Multiclass classification,Streaming Random Patches,Insects,0.6878196628149271,0.6878196628149271,0.6395508722492685,32.51231288909912,2046.4837620000003 +6336,Multiclass classification,Streaming Random Patches,Insects,0.6876085240726125,0.6876085240726125,0.641396967542699,34.576416969299316,2792.1074670000003 +7392,Multiclass classification,Streaming Random Patches,Insects,0.6924638073332431,0.6924638073332431,0.6467777725107727,40.06835174560547,3634.422347 +8448,Multiclass classification,Streaming Random Patches,Insects,0.6949212738250267,0.6949212738250267,0.6476372139610082,43.01965808868408,4571.1184410000005 +9504,Multiclass classification,Streaming Random Patches,Insects,0.6992528675155214,0.6992528675155214,0.6494082466298291,45.71251583099365,5608.992399000001 +10560,Multiclass classification,Streaming Random Patches,Insects,0.7013921772895161,0.7013921772895161,0.6506452100316108,50.310431480407715,6744.395149000001 +11616,Multiclass classification,Streaming Random Patches,Insects,0.7043478260869566,0.7043478260869566,0.6524912605091605,59.279197692871094,7964.619750000001 +12672,Multiclass classification,Streaming Random Patches,Insects,0.7079946334148843,0.7079946334148843,0.6596828376773001,72.61201000213623,9269.589572 +13728,Multiclass classification,Streaming Random Patches,Insects,0.7208421359364756,0.7208421359364756,0.7145906055666686,38.78250694274902,10625.218377000001 +14784,Multiclass classification,Streaming Random Patches,Insects,0.7285395386592708,0.7285395386592708,0.7256542392368915,15.546477317810059,12027.231464 +15840,Multiclass classification,Streaming Random Patches,Insects,0.7206263021655408,0.7206263021655408,0.7196216319492748,14.88278579711914,13491.676191 +16896,Multiclass classification,Streaming Random Patches,Insects,0.7171352471145309,0.7171352471145309,0.7175260611854538,21.28149700164795,15025.812845 +17952,Multiclass classification,Streaming Random Patches,Insects,0.7121051751991533,0.7121051751991533,0.7136617513297842,29.336480140686035,16621.157643 +19008,Multiclass classification,Streaming Random Patches,Insects,0.7205240174672489,0.720524017467249,0.7180961996594418,20.976608276367188,18267.655244999998 +20064,Multiclass classification,Streaming Random Patches,Insects,0.7261625878482779,0.7261625878482779,0.7198561207408494,15.994047164916992,19960.279520999997 +21120,Multiclass classification,Streaming Random Patches,Insects,0.7272598134381363,0.7272598134381363,0.7183389579277755,17.52824878692627,21708.029386999995 +22176,Multiclass classification,Streaming Random Patches,Insects,0.7281623449830891,0.7281623449830891,0.7167723651435352,22.240838050842285,23503.558300999994 +23232,Multiclass classification,Streaming Random Patches,Insects,0.7307477078042272,0.7307477078042272,0.7170791531651185,26.114503860473633,25348.434525999994 +24288,Multiclass classification,Streaming Random Patches,Insects,0.7325318071396221,0.7325318071396222,0.7165563330554671,27.974491119384766,27240.683159999993 +25344,Multiclass classification,Streaming Random Patches,Insects,0.7353904431203883,0.7353904431203884,0.7174524973348954,37.12833023071289,29182.513668999993 +26400,Multiclass classification,Streaming Random Patches,Insects,0.7367703322095533,0.7367703322095533,0.7168965346030137,31.575971603393555,31184.058177999992 +27456,Multiclass classification,Streaming Random Patches,Insects,0.738371881260244,0.738371881260244,0.7164257197178175,35.22733116149902,33213.34443999999 +28512,Multiclass classification,Streaming Random Patches,Insects,0.7366279681526429,0.7366279681526429,0.7161250847684691,17.50509262084961,35271.15690999999 +29568,Multiclass classification,Streaming Random Patches,Insects,0.7354483038522678,0.7354483038522677,0.719616514898752,22.40646266937256,37354.25785299999 +30624,Multiclass classification,Streaming Random Patches,Insects,0.7348724814681775,0.7348724814681775,0.7237598149406717,31.226743698120117,39459.628930999985 +31680,Multiclass classification,Streaming Random Patches,Insects,0.7347769815966413,0.7347769815966413,0.7275990709197302,35.24118995666504,41587.294746999985 +32736,Multiclass classification,Streaming Random Patches,Insects,0.7351458683366427,0.7351458683366427,0.7308983066693725,48.40772724151611,43729.930447999985 +33792,Multiclass classification,Streaming Random Patches,Insects,0.7303423988636027,0.7303423988636027,0.7274356410957497,77.28174114227295,45887.55412199999 +34848,Multiclass classification,Streaming Random Patches,Insects,0.726805750853732,0.726805750853732,0.723911701718825,53.16175174713135,48068.300588999984 +35904,Multiclass classification,Streaming Random Patches,Insects,0.7248976408656658,0.7248976408656659,0.7218080521646734,41.53026580810547,50265.619736999986 +36960,Multiclass classification,Streaming Random Patches,Insects,0.7215833761735978,0.7215833761735979,0.7182506744185386,35.33352756500244,52485.185911999986 +38016,Multiclass classification,Streaming Random Patches,Insects,0.7196369854004998,0.7196369854004999,0.7160236415660819,44.00273513793945,54721.05808499999 +39072,Multiclass classification,Streaming Random Patches,Insects,0.7175142688950884,0.7175142688950884,0.713988650041017,46.12203025817871,56978.30571199999 +40128,Multiclass classification,Streaming Random Patches,Insects,0.7158023276098388,0.7158023276098388,0.7126852582249207,27.841010093688965,59249.54765999999 +41184,Multiclass classification,Streaming Random Patches,Insects,0.7157322196051769,0.715732219605177,0.7129296468122535,21.849401473999023,61534.70422899999 +42240,Multiclass classification,Streaming Random Patches,Insects,0.7156656170837378,0.7156656170837377,0.7131576552849198,28.021278381347656,63833.596648999985 +43296,Multiclass classification,Streaming Random Patches,Insects,0.715925626515764,0.715925626515764,0.7137513847694824,36.50454139709473,66146.66403399999 +44352,Multiclass classification,Streaming Random Patches,Insects,0.7161958016730176,0.7161958016730177,0.7143198962298327,46.888444900512695,68474.34057599999 +45408,Multiclass classification,Streaming Random Patches,Insects,0.7170260092056291,0.7170260092056291,0.7151715877390813,47.08374786376953,70816.527322 +46464,Multiclass classification,Streaming Random Patches,Insects,0.7181843617502098,0.7181843617502098,0.7162864260409335,43.18325901031494,73172.526917 +47520,Multiclass classification,Streaming Random Patches,Insects,0.7179023127591069,0.7179023127591069,0.716246618663062,54.80090522766113,75543.857611 +48576,Multiclass classification,Streaming Random Patches,Insects,0.7211528564076171,0.7211528564076171,0.719707905487922,60.4919376373291,77931.086228 +49632,Multiclass classification,Streaming Random Patches,Insects,0.7250710241582882,0.7250710241582882,0.7236001513027165,35.55128765106201,80332.853368 +50688,Multiclass classification,Streaming Random Patches,Insects,0.7288259316984631,0.7288259316984631,0.7271241427068512,21.017152786254883,82746.274567 +51744,Multiclass classification,Streaming Random Patches,Insects,0.7329107318864387,0.7329107318864386,0.7308784460773333,26.768343925476074,85169.877802 +52800,Multiclass classification,Streaming Random Patches,Insects,0.7359230288452433,0.7359230288452432,0.7343606492383059,9.57052230834961,87600.592492 +52848,Multiclass classification,Streaming Random Patches,Insects,0.7361628853104244,0.7361628853104244,0.7346220154259927,9.63199520111084,90031.55993999999 +408,Multiclass classification,Streaming Random Patches,Keystroke,0.9901719901719902,0.9901719901719902,0.8308395677472984,1.027322769165039,17.348128 +816,Multiclass classification,Streaming Random Patches,Keystroke,0.9877300613496932,0.9877300613496932,0.9320293882508496,2.6651391983032227,54.406226000000004 +1224,Multiclass classification,Streaming Random Patches,Keystroke,0.9828291087489779,0.9828291087489779,0.9464059415055075,5.679329872131348,114.007104 +1632,Multiclass classification,Streaming Random Patches,Keystroke,0.9828326180257511,0.9828326180257511,0.9632097550305241,8.335807800292969,201.582874 +2040,Multiclass classification,Streaming Random Patches,Keystroke,0.9749877390877881,0.9749877390877881,0.9373958892668122,12.631415367126465,318.920922 +2448,Multiclass classification,Streaming Random Patches,Keystroke,0.9701675521046179,0.9701675521046179,0.957381800109682,16.891732215881348,465.74626 +2856,Multiclass classification,Streaming Random Patches,Keystroke,0.9660245183887916,0.9660245183887916,0.9394754450400101,22.937668800354004,641.328845 +3264,Multiclass classification,Streaming Random Patches,Keystroke,0.9616916947594238,0.9616916947594238,0.9454054748805115,29.12161636352539,847.207258 +3672,Multiclass classification,Streaming Random Patches,Keystroke,0.9607736311631708,0.9607736311631708,0.953605417859829,28.669262886047363,1081.626023 +4080,Multiclass classification,Streaming Random Patches,Keystroke,0.9578328021573915,0.9578328021573915,0.9463612240153171,34.20732402801514,1345.493591 +4488,Multiclass classification,Streaming Random Patches,Keystroke,0.9589926454201025,0.9589926454201025,0.9613092683363473,18.652557373046875,1636.499529 +4896,Multiclass classification,Streaming Random Patches,Keystroke,0.9607763023493361,0.9607763023493361,0.9605208703626084,20.81053066253662,1952.783692 +5304,Multiclass classification,Streaming Random Patches,Keystroke,0.9615312087497643,0.9615312087497643,0.9603033149830379,27.91543483734131,2294.145458 +5712,Multiclass classification,Streaming Random Patches,Keystroke,0.9616529504465068,0.9616529504465068,0.9605387671994151,32.60424041748047,2660.691575 +6120,Multiclass classification,Streaming Random Patches,Keystroke,0.9597973525085798,0.9597973525085798,0.9561203427932812,39.11091995239258,3053.1932039999997 +6528,Multiclass classification,Streaming Random Patches,Keystroke,0.9589397885705531,0.9589397885705531,0.9571591040678328,29.255366325378418,3470.643733 +6936,Multiclass classification,Streaming Random Patches,Keystroke,0.959913482335977,0.959913482335977,0.9605956598361813,31.930577278137207,3910.602191 +7344,Multiclass classification,Streaming Random Patches,Keystroke,0.9602342366880022,0.9602342366880022,0.9598619882355601,26.562703132629395,4374.151898 +7752,Multiclass classification,Streaming Random Patches,Keystroke,0.9601341762353245,0.9601341762353245,0.9596510454605859,31.588034629821777,4858.190889 +8160,Multiclass classification,Streaming Random Patches,Keystroke,0.9584507905380562,0.9584507905380562,0.9567204261955369,39.12565612792969,5363.543193 +8568,Multiclass classification,Streaming Random Patches,Keystroke,0.9579782887825377,0.9579782887825377,0.957794146577291,44.816758155822754,5892.06228 +8976,Multiclass classification,Streaming Random Patches,Keystroke,0.9579944289693594,0.9579944289693594,0.9581242571113369,49.5586576461792,6445.036349 +9384,Multiclass classification,Streaming Random Patches,Keystroke,0.9570499840136417,0.9570499840136417,0.9565283447410108,50.185367584228516,7025.065903 +9792,Multiclass classification,Streaming Random Patches,Keystroke,0.9563885200694515,0.9563885200694515,0.9560487952418978,54.40623474121094,7630.795018999999 +10200,Multiclass classification,Streaming Random Patches,Keystroke,0.9532307088930287,0.9532307088930287,0.9512518567217172,66.82855319976807,8267.200675 +10608,Multiclass classification,Streaming Random Patches,Keystroke,0.9519185443574998,0.9519185443574998,0.9512557409849248,41.17615795135498,8934.408603 +11016,Multiclass classification,Streaming Random Patches,Keystroke,0.9528824330458465,0.9528824330458465,0.953398407731189,32.87209510803223,9625.903537 +11424,Multiclass classification,Streaming Random Patches,Keystroke,0.953689923837871,0.953689923837871,0.9540175301991308,28.078542709350586,10339.782646 +11832,Multiclass classification,Streaming Random Patches,Keystroke,0.9542726734849125,0.9542726734849125,0.9545119777330118,20.280012130737305,11070.86282 +12240,Multiclass classification,Streaming Random Patches,Keystroke,0.955470218155078,0.955470218155078,0.9559406438939211,21.54300308227539,11820.445116 +12648,Multiclass classification,Streaming Random Patches,Keystroke,0.9559579346880683,0.9559579346880683,0.9561632451269845,26.89114284515381,12588.394717000001 +13056,Multiclass classification,Streaming Random Patches,Keystroke,0.9558789735733435,0.9558789735733435,0.9559075747932771,21.382742881774902,13375.587705000002 +13464,Multiclass classification,Streaming Random Patches,Keystroke,0.9563247418851668,0.9563247418851668,0.9565051554876024,21.864919662475586,14180.689980000001 +13872,Multiclass classification,Streaming Random Patches,Keystroke,0.9569605652079879,0.9569605652079879,0.9571856017401091,25.72835636138916,15004.794290000002 +14280,Multiclass classification,Streaming Random Patches,Keystroke,0.9566496253239022,0.9566496253239022,0.9566382966080723,21.764866828918457,15848.339318000002 +14688,Multiclass classification,Streaming Random Patches,Keystroke,0.957241097569279,0.9572410975692791,0.957426459656079,25.14582061767578,16708.048820000004 +15096,Multiclass classification,Streaming Random Patches,Keystroke,0.9580655846306724,0.9580655846306724,0.9582773620158959,26.658535957336426,17588.164126000003 +15504,Multiclass classification,Streaming Random Patches,Keystroke,0.9584596529703928,0.9584596529703928,0.9585840009788793,30.767892837524414,18489.703328000003 +15912,Multiclass classification,Streaming Random Patches,Keystroke,0.9580793161963421,0.9580793161963421,0.9580713134265897,27.786094665527344,19412.324967000004 +16320,Multiclass classification,Streaming Random Patches,Keystroke,0.958514614866107,0.958514614866107,0.9586173296332885,25.79348850250244,20355.979039000005 +16728,Multiclass classification,Streaming Random Patches,Keystroke,0.9577330065164106,0.9577330065164106,0.9576699214368118,33.630208015441895,21321.132478000007 +17136,Multiclass classification,Streaming Random Patches,Keystroke,0.9576305806828129,0.9576305806828129,0.9576693803774444,33.920249938964844,22315.653729000005 +17544,Multiclass classification,Streaming Random Patches,Keystroke,0.956506868836573,0.956506868836573,0.9564470129227677,31.505155563354492,23335.132930000003 +17952,Multiclass classification,Streaming Random Patches,Keystroke,0.9563812600969306,0.9563812600969306,0.9564135249623555,19.79563045501709,24372.247222 +18360,Multiclass classification,Streaming Random Patches,Keystroke,0.9569148646440437,0.9569148646440437,0.9569804233582649,23.70892333984375,25428.663478000002 +18768,Multiclass classification,Streaming Random Patches,Keystroke,0.9574252677572335,0.9574252677572335,0.957475477736454,21.893744468688965,26504.246634000003 +19176,Multiclass classification,Streaming Random Patches,Keystroke,0.9568187744458931,0.9568187744458931,0.956806677474395,28.04871368408203,27598.635240000003 +19584,Multiclass classification,Streaming Random Patches,Keystroke,0.9567992646683348,0.9567992646683348,0.9568012672257533,32.11082458496094,28712.463090000005 +19992,Multiclass classification,Streaming Random Patches,Keystroke,0.9565304386974138,0.9565304386974138,0.9565268274864178,40.13526153564453,29849.822929000005 +20400,Multiclass classification,Streaming Random Patches,Keystroke,0.9559292122162851,0.9559292122162851,0.9559196349550496,39.63601016998291,31009.846621000004 +46,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.5111111111111111,0.5111111111111111,0.40938578329882686,0.09116363525390625,0.155273 +92,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6043956043956044,0.6043956043956044,0.5940974230447915,0.16827392578125,0.72683 +138,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6715328467153284,0.6715328467153284,0.6806196928151186,0.24543190002441406,1.742293 +184,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7049180327868853,0.7049180327868853,0.7184732466987995,0.32204627990722656,3.3407109999999998 +230,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.74235807860262,0.74235807860262,0.7523809662907407,0.39917659759521484,5.610709 +276,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7490909090909091,0.7490909090909091,0.7611097615339608,0.47675609588623047,8.7745 +322,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7663551401869159,0.766355140186916,0.7725898650917747,0.5538606643676758,12.918764 +368,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.784741144414169,0.7847411444141691,0.7844949397573193,0.6304874420166016,18.189003 +414,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7990314769975787,0.7990314769975787,0.7976353129150817,0.7076187133789062,24.731741 +460,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7952069716775599,0.7952069716775599,0.7930763833747545,0.7847471237182617,32.681045 +506,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7960396039603961,0.7960396039603961,0.7941234022368324,3.003793716430664,61.211909999999996 +552,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8021778584392014,0.8021778584392014,0.8007250644998717,3.2264842987060547,91.16202899999999 +598,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8090452261306532,0.8090452261306531,0.8095532779239047,3.4499826431274414,122.67972799999998 +644,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8164852255054432,0.8164852255054433,0.8176018556357175,3.6760778427124023,155.77468699999997 +690,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8214804063860668,0.8214804063860668,0.8221151176242331,3.8941650390625,190.53727699999996 +736,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8272108843537415,0.8272108843537415,0.8281233770721121,4.128121376037598,227.08550299999996 +782,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8361075544174136,0.8361075544174136,0.8364659566156888,4.367749214172363,265.419762 +828,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8403869407496977,0.8403869407496977,0.8412749002251585,4.601743698120117,305.543518 +874,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.845360824742268,0.845360824742268,0.8465057584066101,4.840575218200684,347.501906 +920,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8487486398258978,0.8487486398258978,0.8489576083149123,5.074535369873047,391.43023400000004 +966,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8538860103626943,0.8538860103626943,0.8530581393966605,5.3079938888549805,437.316456 +1012,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8585558852621167,0.8585558852621167,0.8570252804249208,5.479596138000488,485.23685 +1058,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8628192999053926,0.8628192999053927,0.8611045332429007,5.435150146484375,535.278485 +1104,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8631006346328196,0.8631006346328196,0.8616288881212748,5.355225563049316,587.436372 +1150,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8668407310704961,0.8668407310704961,0.8650902600877293,5.281754493713379,641.538536 +1196,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8719665271966527,0.8719665271966527,0.8702683106604537,5.235520362854004,697.554251 +1242,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8759065269943593,0.8759065269943593,0.8740479640614998,5.142333984375,755.471933 +1288,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8787878787878788,0.8787878787878788,0.8772603222806128,5.092559814453125,815.138635 +1334,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8777194298574643,0.8777194298574643,0.8760741143565023,5.055940628051758,876.491339 +1380,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8796229151559101,0.8796229151559101,0.8783130803325612,4.964084625244141,939.483975 +1426,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8785964912280702,0.8785964912280702,0.8768931648451159,4.951287269592285,1004.010152 +1472,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8769544527532291,0.8769544527532291,0.8748964905672628,4.969002723693848,1070.168576 +1518,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8727752142386289,0.8727752142386289,0.8705110235515202,5.101251602172852,1138.304608 +1564,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8688419705694178,0.8688419705694178,0.8667015278861958,5.262187957763672,1208.4659419999998 +1610,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8651336233685519,0.8651336233685519,0.8631350462642483,5.320252418518066,1280.5305069999997 +1656,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8640483383685801,0.8640483383685801,0.8620479268968886,5.35189151763916,1354.3819709999998 +1702,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8647854203409759,0.8647854203409759,0.8635043959538364,5.359102249145508,1430.0286029999997 +1748,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.866056096164854,0.866056096164854,0.864439618601765,5.402237892150879,1507.5136589999997 +1794,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8683770217512549,0.8683770217512549,0.8664209902402824,5.3993330001831055,1586.7199259999998 +1840,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8694942903752039,0.8694942903752039,0.867597342266498,5.4049272537231445,1667.6587319999999 +1886,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8710875331564987,0.8710875331564986,0.8694766742923737,5.4121294021606445,1750.3169159999998 +1932,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8705334023821854,0.8705334023821854,0.8686918451193435,5.405803680419922,1834.6099799999997 +1978,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8715225088517956,0.8715225088517956,0.8698703895904014,5.395906448364258,1920.5171279999997 +2024,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8729609490855166,0.8729609490855166,0.870902914954928,5.386837959289551,2008.1064549999996 +2070,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8733687771870469,0.8733687771870469,0.8714525187304558,5.375288963317871,2097.403794 +2116,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.875177304964539,0.875177304964539,0.8730645404016979,5.353263854980469,2188.326937 +2162,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8745950948634891,0.8745950948634891,0.872417325547954,5.322790145874023,2280.8823749999997 +2208,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8753964657906661,0.8753964657906661,0.8732500176589647,5.30323600769043,2374.9757969999996 +2254,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8748335552596538,0.8748335552596538,0.8732733602208504,5.278659820556641,2470.7327649999997 +2300,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8742931709438887,0.8742931709438887,0.8727466012343671,5.262259483337402,2568.119206 +2310,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8735383282806409,0.8735383282806409,0.8721361121313428,5.268708229064941,2666.293295 +1056,Multiclass classification,k-Nearest Neighbors,Insects,0.6597156398104266,0.6597156398104266,0.5853273709738578,6.371035575866699,65.756564 +2112,Multiclass classification,k-Nearest Neighbors,Insects,0.6807200378967314,0.6807200378967314,0.5992086579995298,6.2783002853393555,182.184591 +3168,Multiclass classification,k-Nearest Neighbors,Insects,0.6842437638143354,0.6842437638143354,0.6001715208792017,6.298460006713867,341.998975 +4224,Multiclass classification,k-Nearest Neighbors,Insects,0.6848212171442103,0.6848212171442103,0.6051604277089342,6.265153884887695,541.5068689999999 +5280,Multiclass classification,k-Nearest Neighbors,Insects,0.6872513733661678,0.6872513733661678,0.611100448555976,6.2555742263793945,777.52113 +6336,Multiclass classification,k-Nearest Neighbors,Insects,0.6842936069455406,0.6842936069455406,0.6118525331169307,6.3140106201171875,1048.588472 +7392,Multiclass classification,k-Nearest Neighbors,Insects,0.6852929238262752,0.6852929238262752,0.6157762907660722,6.288516998291016,1352.458798 +8448,Multiclass classification,k-Nearest Neighbors,Insects,0.6828459808215934,0.6828459808215934,0.6148503710479976,6.31680965423584,1687.096094 +9504,Multiclass classification,k-Nearest Neighbors,Insects,0.6851520572450805,0.6851520572450805,0.6155258331015067,6.223039627075195,2051.649807 +10560,Multiclass classification,k-Nearest Neighbors,Insects,0.6861445212614831,0.6861445212614831,0.6169474950376627,6.253497123718262,2444.130945 +11616,Multiclass classification,k-Nearest Neighbors,Insects,0.6873009040034438,0.6873009040034438,0.6200568175672779,6.251482009887695,2863.1289039999997 +12672,Multiclass classification,k-Nearest Neighbors,Insects,0.6866072133217583,0.6866072133217583,0.623883491026523,6.276742935180664,3309.0829679999997 +13728,Multiclass classification,k-Nearest Neighbors,Insects,0.7020470605376266,0.7020470605376266,0.6991473808978487,6.26933479309082,3781.2775089999996 +14784,Multiclass classification,k-Nearest Neighbors,Insects,0.7077724413177299,0.7077724413177299,0.7078402863830927,6.244691848754883,4278.402760999999 +15840,Multiclass classification,k-Nearest Neighbors,Insects,0.7016857124818486,0.7016857124818486,0.704840832390747,6.350223541259766,4805.403565999999 +16896,Multiclass classification,k-Nearest Neighbors,Insects,0.6992009470257473,0.6992009470257473,0.7048178275842342,6.243149757385254,5357.683726999999 +17952,Multiclass classification,k-Nearest Neighbors,Insects,0.6922734109520361,0.6922734109520361,0.6995766929659905,6.218992233276367,5935.240105999998 +19008,Multiclass classification,k-Nearest Neighbors,Insects,0.6974272636397116,0.6974272636397116,0.7006862112488368,6.24652099609375,6538.276384999998 +20064,Multiclass classification,k-Nearest Neighbors,Insects,0.699845486716842,0.699845486716842,0.6985118222305657,6.205791473388672,7167.459726999999 +21120,Multiclass classification,k-Nearest Neighbors,Insects,0.7016904209479615,0.7016904209479615,0.6971610909052677,6.218420028686523,7825.840650999999 +22176,Multiclass classification,k-Nearest Neighbors,Insects,0.7039909808342728,0.7039909808342728,0.6964197759629052,6.236072540283203,8511.079801999998 +23232,Multiclass classification,k-Nearest Neighbors,Insects,0.7076320433902974,0.7076320433902974,0.697368621848442,6.279313087463379,9222.986801999998 +24288,Multiclass classification,k-Nearest Neighbors,Insects,0.7098447729237863,0.7098447729237863,0.6967477548491564,6.2948198318481445,9960.927248999997 +25344,Multiclass classification,k-Nearest Neighbors,Insects,0.7127017322337529,0.712701732233753,0.6972185032799825,6.224791526794434,10724.419586999997 +26400,Multiclass classification,k-Nearest Neighbors,Insects,0.7145346414636918,0.7145346414636918,0.6967850611237018,6.263523101806641,11512.986172999998 +27456,Multiclass classification,k-Nearest Neighbors,Insects,0.7156437807321071,0.7156437807321071,0.6955595874776194,6.272575378417969,12326.628602999997 +28512,Multiclass classification,k-Nearest Neighbors,Insects,0.7130931921012942,0.7130931921012942,0.6943090782068162,6.22489070892334,13165.433758999998 +29568,Multiclass classification,k-Nearest Neighbors,Insects,0.7117732607298678,0.7117732607298677,0.6978751959025926,6.217726707458496,14028.837757999998 +30624,Multiclass classification,k-Nearest Neighbors,Insects,0.7122097769650263,0.7122097769650264,0.7026862643890369,6.243690490722656,14916.319238999999 +31680,Multiclass classification,k-Nearest Neighbors,Insects,0.7113545250797058,0.7113545250797058,0.7052714328980031,6.277059555053711,15827.904481999998 +32736,Multiclass classification,k-Nearest Neighbors,Insects,0.7111959676187567,0.7111959676187566,0.7078689284492299,6.295280456542969,16762.400180999997 +33792,Multiclass classification,k-Nearest Neighbors,Insects,0.7067562368678051,0.7067562368678051,0.704703743720216,6.183221817016602,17721.950532 +34848,Multiclass classification,k-Nearest Neighbors,Insects,0.7030734353028956,0.7030734353028956,0.7010614031639846,6.343389511108398,18710.094933 +35904,Multiclass classification,k-Nearest Neighbors,Insects,0.6998022449377489,0.6998022449377489,0.6976694331042329,6.273009300231934,19725.980479 +36960,Multiclass classification,k-Nearest Neighbors,Insects,0.6967179847939609,0.6967179847939609,0.6945045780432343,6.264690399169922,20767.047301000002 +38016,Multiclass classification,k-Nearest Neighbors,Insects,0.6941470472182033,0.6941470472182033,0.6917813776610243,6.265054702758789,21835.556239 +39072,Multiclass classification,k-Nearest Neighbors,Insects,0.691996621535154,0.691996621535154,0.6898060776768534,6.200959205627441,22932.108791000002 +40128,Multiclass classification,k-Nearest Neighbors,Insects,0.6904328756199068,0.6904328756199068,0.6882031611963276,6.413609504699707,24054.967875000002 +41184,Multiclass classification,k-Nearest Neighbors,Insects,0.6916446106403128,0.6916446106403128,0.6892941373261507,6.3101043701171875,25204.026441 +42240,Multiclass classification,k-Nearest Neighbors,Insects,0.692535334643339,0.692535334643339,0.6900712004452627,6.22797966003418,26378.286294 +43296,Multiclass classification,k-Nearest Neighbors,Insects,0.6935904838895947,0.6935904838895947,0.6909354899104013,6.220904350280762,27574.213319000002 +44352,Multiclass classification,k-Nearest Neighbors,Insects,0.6941895334941715,0.6941895334941715,0.691322114366645,6.22946834564209,28791.926615000004 +45408,Multiclass classification,k-Nearest Neighbors,Insects,0.6950690422181602,0.6950690422181602,0.6917362410920441,6.2850341796875,30030.670852000003 +46464,Multiclass classification,k-Nearest Neighbors,Insects,0.6964466349568473,0.6964466349568473,0.6926338572817136,6.2335004806518555,31290.345387 +47520,Multiclass classification,k-Nearest Neighbors,Insects,0.6963530377322755,0.6963530377322755,0.6929015597977773,6.2439117431640625,32571.46119 +48576,Multiclass classification,k-Nearest Neighbors,Insects,0.7006073082861555,0.7006073082861555,0.697843135408715,6.247167587280273,33874.398319 +49632,Multiclass classification,k-Nearest Neighbors,Insects,0.7046805424029337,0.7046805424029337,0.7023003034160373,6.246943473815918,35198.416197 +50688,Multiclass classification,k-Nearest Neighbors,Insects,0.7083867658373942,0.7083867658373942,0.7061355873839065,6.210485458374023,36536.506394 +51744,Multiclass classification,k-Nearest Neighbors,Insects,0.7126567844925884,0.7126567844925883,0.7104085577951368,6.270394325256348,37890.628371 +52800,Multiclass classification,k-Nearest Neighbors,Insects,0.7128544101214038,0.7128544101214038,0.7110869129037599,6.247260093688965,39264.666928 +52848,Multiclass classification,k-Nearest Neighbors,Insects,0.7131152194069673,0.7131152194069672,0.7113808258412672,6.272693634033203,40639.937472 +408,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.0294876098632812,8.610537 +816,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9251533742331288,0.9251533742331288,0.8588670451436246,5.3865966796875,60.879099000000004 +1224,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9247751430907605,0.9247751430907604,0.888226412135106,6.246823310852051,137.71097600000002 +1632,Multiclass classification,k-Nearest Neighbors,Keystroke,0.927038626609442,0.927038626609442,0.893336805209695,6.212030410766602,236.990146 +2040,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9298675821481118,0.9298675821481118,0.911424130088645,6.329436302185059,359.011082 +2448,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9239885574172456,0.9239885574172456,0.9121555472954921,6.208271026611328,503.2068 +2856,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9169877408056042,0.9169877408056042,0.8816257260944811,6.284844398498535,667.387655 +3264,Multiclass classification,k-Nearest Neighbors,Keystroke,0.908979466748391,0.908979466748391,0.9011431783951355,6.232160568237305,850.70223 +3672,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133751021520021,0.9133751021520021,0.9125908871445695,6.234919548034668,1054.346916 +4080,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9141946555528315,0.9141946555528315,0.9054789816810689,6.308258056640625,1277.35851 +4488,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9057276576777357,0.9057276576777357,0.9087691557812896,6.2749834060668945,1518.241674 +4896,Multiclass classification,k-Nearest Neighbors,Keystroke,0.908682328907048,0.908682328907048,0.9101970481905531,6.210176467895508,1774.8436390000002 +5304,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9092966245521403,0.9092966245521403,0.9045962329696907,6.311163902282715,2048.2991580000003 +5712,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9110488530905271,0.9110488530905271,0.9114244990736602,6.304790496826172,2337.0378760000003 +6120,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9089720542572316,0.9089720542572316,0.9032533666541098,6.217576026916504,2640.3521840000003 +6528,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9077677340278841,0.9077677340278841,0.9071900335968285,6.258184432983398,2958.617579 +6936,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9105984138428262,0.9105984138428262,0.9126270814361048,6.1720380783081055,3289.9253120000003 +7344,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9120250578782514,0.9120250578782514,0.9125633522308233,6.2164154052734375,3633.8320570000005 +7752,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133015094826474,0.9133015094826474,0.9136330015220733,6.260525703430176,3992.0508240000004 +8160,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9148179924010296,0.9148179924010296,0.9154195917586071,6.291139602661133,4363.477247000001 +8568,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9166569394186996,0.9166569394186995,0.9177086422960681,6.216147422790527,4747.269196000001 +8976,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9179944289693593,0.9179944289693593,0.9193727618453186,6.204837799072266,5142.613476000001 +9384,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9180432697431525,0.9180432697431525,0.9181590265081533,6.197464942932129,5549.1165660000015 +9792,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9166581554488816,0.9166581554488816,0.9168210748678531,6.232473373413086,5968.023607000002 +10200,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9154819099911756,0.9154819099911756,0.9145218669909496,6.197787284851074,6399.011787000002 +10608,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9126048835674555,0.9126048835674555,0.9111025938131312,6.2185258865356445,6842.990199000003 +11016,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9106672719019518,0.9106672719019518,0.911227786024665,6.261377334594727,7300.041981000002 +11424,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9103562986956141,0.9103562986956141,0.9101104800687125,6.227293968200684,7769.979179000002 +11832,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9092215366410278,0.9092215366410278,0.9094186121236619,6.287784576416016,8252.975566000001 +12240,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9110221423318898,0.9110221423318898,0.9118339797691071,6.31197452545166,8747.696417000001 +12648,Multiclass classification,k-Nearest Neighbors,Keystroke,0.912627500593026,0.912627500593026,0.9131272841889786,6.279851913452148,9255.008595000001 +13056,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9129069322098813,0.9129069322098813,0.913006147591119,6.346117973327637,9774.692327 +13464,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9136150932184506,0.9136150932184506,0.9138210444048112,6.238006591796875,10306.725428000002 +13872,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9137769447047798,0.9137769447047797,0.913931693448659,6.288792610168457,10851.686584000001 +14280,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9121787240002801,0.9121787240002801,0.9118234284090696,6.252389907836914,11409.551357 +14688,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133247089262613,0.9133247089262613,0.9136581918124823,6.268362998962402,11980.223294 +15096,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9134150380920835,0.9134150380920835,0.9134700562544149,6.304409027099609,12563.090191 +15504,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9127910726956073,0.9127910726956073,0.9127632118282708,6.222077369689941,13158.500651999999 +15912,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9127019043429074,0.9127019043429074,0.9127365496247325,6.263586044311523,13766.752283999998 +16320,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113303511244562,0.9113303511244562,0.9110956080213111,6.256609916687012,14388.121761999999 +16728,Multiclass classification,k-Nearest Neighbors,Keystroke,0.910384408441442,0.9103844084414419,0.9103324360258119,6.2080230712890625,15023.088924 +17136,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113510358914503,0.9113510358914503,0.9114963483082135,6.223179817199707,15670.063673 +17544,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113606566721769,0.9113606566721769,0.9113826667045093,6.23558235168457,16331.265293 +17952,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9114255473232689,0.911425547323269,0.9114384409485988,6.255133628845215,17006.553944 +18360,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9126858761370445,0.9126858761370445,0.9127656000580755,6.270404815673828,17693.792261 +18768,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9125059945649278,0.9125059945649276,0.9124701420883569,6.180520057678223,18393.849908 +19176,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9125945241199479,0.9125945241199479,0.9125632790621417,6.265439987182617,19106.227212 +19584,Multiclass classification,k-Nearest Neighbors,Keystroke,0.913547464637696,0.913547464637696,0.9135225066457016,6.303133964538574,19831.701162 +19992,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9111099994997749,0.9111099994997749,0.910917465793804,6.225028991699219,20571.760391 +20400,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9104858081278494,0.9104858081278494,0.9103279821226861,6.325108528137207,21326.45228 +46,Multiclass classification,ADWIN Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.245764972655729,4.105147361755371,2.153154 +92,Multiclass classification,ADWIN Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.4934752395581889,4.108363151550293,6.907408 +138,Multiclass classification,ADWIN Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646677,4.108027458190918,14.639156 +184,Multiclass classification,ADWIN Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.614143164890895,4.108977317810059,25.443956 +230,Multiclass classification,ADWIN Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,3.881842613220215,39.254234 +276,Multiclass classification,ADWIN Bagging,ImageSegments,0.64,0.64,0.6559607038460422,3.996514320373535,55.768073 +322,Multiclass classification,ADWIN Bagging,ImageSegments,0.6697819314641744,0.6697819314641744,0.6706320385346652,4.112936019897461,74.877199 +368,Multiclass classification,ADWIN Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6897433526546475,4.112924575805664,96.687005 +414,Multiclass classification,ADWIN Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.706570530482581,4.117301940917969,121.290167 +460,Multiclass classification,ADWIN Bagging,ImageSegments,0.7145969498910676,0.7145969498910676,0.7071122267088654,4.116390228271484,148.551711 +506,Multiclass classification,ADWIN Bagging,ImageSegments,0.7247524752475247,0.7247524752475247,0.7147973207987898,4.115703582763672,178.365171 +552,Multiclass classification,ADWIN Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7210771168277493,4.115436553955078,210.796119 +598,Multiclass classification,ADWIN Bagging,ImageSegments,0.7336683417085427,0.7336683417085426,0.7250288715672424,4.115207672119141,245.953277 +644,Multiclass classification,ADWIN Bagging,ImageSegments,0.7325038880248833,0.7325038880248833,0.7258924883659029,4.118658065795898,283.80303000000004 +690,Multiclass classification,ADWIN Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7302536378735861,4.118425369262695,324.296282 +736,Multiclass classification,ADWIN Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7329631379486719,4.118097305297852,367.43284 +782,Multiclass classification,ADWIN Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7387105187530085,4.117616653442383,413.28289 +828,Multiclass classification,ADWIN Bagging,ImageSegments,0.7460701330108828,0.7460701330108827,0.7425025596154723,4.117326736450195,461.953497 +874,Multiclass classification,ADWIN Bagging,ImageSegments,0.7514318442153494,0.7514318442153494,0.7467163857842193,4.117303848266602,513.2937440000001 +920,Multiclass classification,ADWIN Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7453933609147309,4.117105484008789,567.431634 +966,Multiclass classification,ADWIN Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.7451117895470661,4.116701126098633,624.2209760000001 +1012,Multiclass classification,ADWIN Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.744963080481548,4.116399765014648,683.8404210000001 +1058,Multiclass classification,ADWIN Bagging,ImageSegments,0.7511825922421949,0.7511825922421949,0.7446315489945475,4.117582321166992,746.2097300000001 +1104,Multiclass classification,ADWIN Bagging,ImageSegments,0.7533998186763372,0.7533998186763373,0.7466082689908061,4.117956161499023,811.1743250000002 +1150,Multiclass classification,ADWIN Bagging,ImageSegments,0.7563098346388164,0.7563098346388164,0.7491651771194966,4.117490768432617,878.6809830000002 +1196,Multiclass classification,ADWIN Bagging,ImageSegments,0.7589958158995815,0.7589958158995815,0.7526420027035883,4.117303848266602,948.8627130000002 +1242,Multiclass classification,ADWIN Bagging,ImageSegments,0.75825946817083,0.7582594681708301,0.7524016178277559,4.11713981628418,1021.5806660000002 +1288,Multiclass classification,ADWIN Bagging,ImageSegments,0.7637917637917638,0.7637917637917638,0.75666252908711,4.117353439331055,1096.9757510000002 +1334,Multiclass classification,ADWIN Bagging,ImageSegments,0.7636909227306826,0.7636909227306825,0.7569484848610158,4.11726188659668,1175.015685 +1380,Multiclass classification,ADWIN Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7590436403579585,4.11729621887207,1255.693831 +1426,Multiclass classification,ADWIN Bagging,ImageSegments,0.767719298245614,0.767719298245614,0.761211289695921,4.117136001586914,1338.965745 +1472,Multiclass classification,ADWIN Bagging,ImageSegments,0.7722637661454793,0.7722637661454793,0.764056696643358,4.117197036743164,1424.822234 +1518,Multiclass classification,ADWIN Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.764234133414765,4.117246627807617,1513.291691 +1564,Multiclass classification,ADWIN Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7653316001442944,4.117277145385742,1604.2857379999998 +1610,Multiclass classification,ADWIN Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7647353044337893,4.11713981628418,1697.9838939999997 +1656,Multiclass classification,ADWIN Bagging,ImageSegments,0.7734138972809668,0.7734138972809667,0.7645730180903108,4.116628646850586,1794.3974679999997 +1702,Multiclass classification,ADWIN Bagging,ImageSegments,0.7724867724867724,0.7724867724867724,0.7656182355666586,4.116819381713867,1893.3012909999998 +1748,Multiclass classification,ADWIN Bagging,ImageSegments,0.7750429307384087,0.7750429307384087,0.7677424040514297,4.116933822631836,1994.6627269999997 +1794,Multiclass classification,ADWIN Bagging,ImageSegments,0.7763524818739542,0.7763524818739542,0.7677176136548693,4.116861343383789,2098.663831 +1840,Multiclass classification,ADWIN Bagging,ImageSegments,0.7775965198477434,0.7775965198477434,0.7691578918725354,4.11646842956543,2205.2268019999997 +1886,Multiclass classification,ADWIN Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7681560201617949,4.116430282592773,2314.3219019999997 +1932,Multiclass classification,ADWIN Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7674170460709655,4.116365432739258,2425.9767019999995 +1978,Multiclass classification,ADWIN Bagging,ImageSegments,0.7769347496206374,0.7769347496206374,0.7672843880004774,4.116201400756836,2540.2532379999993 +2024,Multiclass classification,ADWIN Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7681802739952505,4.116155624389648,2657.1841359999994 +2070,Multiclass classification,ADWIN Bagging,ImageSegments,0.778153697438376,0.7781536974383759,0.767530439166732,4.116151809692383,2776.6077329999994 +2116,Multiclass classification,ADWIN Bagging,ImageSegments,0.7787234042553192,0.778723404255319,0.7673415220519754,4.116128921508789,2898.5867789999993 +2162,Multiclass classification,ADWIN Bagging,ImageSegments,0.7797316057380842,0.7797316057380842,0.7679341969633587,4.116201400756836,3023.0016299999993 +2208,Multiclass classification,ADWIN Bagging,ImageSegments,0.7816039873130947,0.7816039873130947,0.7687944234581563,4.11619758605957,3150.0382249999993 +2254,Multiclass classification,ADWIN Bagging,ImageSegments,0.7785175321793165,0.7785175321793165,0.7657018899401807,4.116170883178711,3279.4760369999995 +2300,Multiclass classification,ADWIN Bagging,ImageSegments,0.7777294475859069,0.7777294475859068,0.7649119672933203,4.116254806518555,3411.2017669999996 +2310,Multiclass classification,ADWIN Bagging,ImageSegments,0.7778258986574275,0.7778258986574276,0.765010539660814,4.116277694702148,3543.5486899999996 +1056,Multiclass classification,ADWIN Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762562,6.4894914627075195,90.340432 +2112,Multiclass classification,ADWIN Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.490170478820801,257.284509 +3168,Multiclass classification,ADWIN Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244894,6.491124153137207,490.807794 +4224,Multiclass classification,ADWIN Bagging,Insects,0.6107032914989344,0.6107032914989344,0.574850135712032,6.4912004470825195,783.3577379999999 +5280,Multiclass classification,ADWIN Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225517,6.491948127746582,1129.937274 +6336,Multiclass classification,ADWIN Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789626,6.490735054016113,1525.7763839999998 +7392,Multiclass classification,ADWIN Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867537,6.490704536437988,1967.9282259999998 +8448,Multiclass classification,ADWIN Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.490643501281738,2456.1055429999997 +9504,Multiclass classification,ADWIN Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297488,6.491009712219238,2990.761064 +10560,Multiclass classification,ADWIN Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.491185188293457,3571.5807019999997 +11616,Multiclass classification,ADWIN Bagging,Insects,0.6005165733964701,0.6005165733964701,0.56512832395729,6.491345405578613,4198.711386999999 +12672,Multiclass classification,ADWIN Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306638,6.491543769836426,4874.277407999999 +13728,Multiclass classification,ADWIN Bagging,Insects,0.6152108982297662,0.6152108982297662,0.5959760515786451,5.97607421875,5593.125681999999 +14784,Multiclass classification,ADWIN Bagging,Insects,0.6060339579246432,0.6060339579246432,0.5869142505177357,6.496403694152832,6355.050274999999 +15840,Multiclass classification,ADWIN Bagging,Insects,0.5713744554580465,0.5713744554580465,0.5537658591956377,6.4967546463012695,7160.569073999999 +16896,Multiclass classification,ADWIN Bagging,Insects,0.545546019532406,0.545546019532406,0.5286479939306438,6.381303787231445,8010.073855999999 +17952,Multiclass classification,ADWIN Bagging,Insects,0.526767311013314,0.526767311013314,0.509587529402725,6.497265815734863,8901.233847 +19008,Multiclass classification,ADWIN Bagging,Insects,0.517756615983585,0.517756615983585,0.4976462434137419,4.6858930587768555,9829.81162 +20064,Multiclass classification,ADWIN Bagging,Insects,0.5296815032647162,0.5296815032647162,0.5080882715573688,10.369908332824707,10791.950057 +21120,Multiclass classification,ADWIN Bagging,Insects,0.539750935176855,0.539750935176855,0.5184934777423561,10.92272663116455,11801.930673 +22176,Multiclass classification,ADWIN Bagging,Insects,0.5468771138669674,0.5468771138669674,0.525970977438283,10.920933723449707,12856.592968 +23232,Multiclass classification,ADWIN Bagging,Insects,0.5551633593043778,0.5551633593043778,0.5340735310276195,12.231526374816895,13957.460176 +24288,Multiclass classification,ADWIN Bagging,Insects,0.5615761518507844,0.5615761518507844,0.5396852076547556,12.87682819366455,15100.290122 +25344,Multiclass classification,ADWIN Bagging,Insects,0.5679280274632048,0.5679280274632048,0.5455634192548013,13.528642654418945,16285.362621 +26400,Multiclass classification,ADWIN Bagging,Insects,0.5727868479866661,0.5727868479866661,0.5496374434570932,13.632143020629883,17508.053461 +27456,Multiclass classification,ADWIN Bagging,Insects,0.5754143143325442,0.5754143143325442,0.5513680135969626,13.630533218383789,18766.96928 +28512,Multiclass classification,ADWIN Bagging,Insects,0.5772859598049875,0.5772859598049875,0.5551350356863173,13.627862930297852,20061.957755000003 +29568,Multiclass classification,ADWIN Bagging,Insects,0.577772516657084,0.577772516657084,0.5590861332292512,13.626611709594727,21394.440888000005 +30624,Multiclass classification,ADWIN Bagging,Insects,0.578225516768442,0.578225516768442,0.5625516131192055,12.769641876220703,22755.311286000004 +31680,Multiclass classification,ADWIN Bagging,Insects,0.5795637488557088,0.5795637488557088,0.5663363640160616,12.768932342529297,24150.336726000005 +32736,Multiclass classification,ADWIN Bagging,Insects,0.5811211241790133,0.5811211241790133,0.5696723582178381,12.768062591552734,25577.802283000005 +33792,Multiclass classification,ADWIN Bagging,Insects,0.575804208221124,0.575804208221124,0.5647934119551398,12.981294631958008,27039.455958000006 +34848,Multiclass classification,ADWIN Bagging,Insects,0.5701495107182828,0.5701495107182828,0.559068023359177,12.981256484985352,28537.493337000007 +35904,Multiclass classification,ADWIN Bagging,Insects,0.5657744478177311,0.5657744478177311,0.5542573482740075,12.983362197875977,30069.702466000006 +36960,Multiclass classification,ADWIN Bagging,Insects,0.5611894261208366,0.5611894261208366,0.5493152777162592,13.52482795715332,31635.746830000007 +38016,Multiclass classification,ADWIN Bagging,Insects,0.558779429172695,0.558779429172695,0.5463982360776033,13.526559829711914,33235.41425300001 +39072,Multiclass classification,ADWIN Bagging,Insects,0.5546825010877633,0.5546825010877633,0.5426283860139581,14.304903030395508,34865.73115700001 +40128,Multiclass classification,ADWIN Bagging,Insects,0.5542153662122761,0.5542153662122761,0.5429626632180721,15.152182579040527,36525.24862800001 +41184,Multiclass classification,ADWIN Bagging,Insects,0.5541364155112547,0.5541364155112547,0.5435420562964655,15.252725601196289,38211.297236000006 +42240,Multiclass classification,ADWIN Bagging,Insects,0.5542981604678141,0.5542981604678141,0.544391400018036,15.251314163208008,39923.86574200001 +43296,Multiclass classification,ADWIN Bagging,Insects,0.554151749624668,0.554151749624668,0.5448486588729107,13.424749374389648,41664.47056700001 +44352,Multiclass classification,ADWIN Bagging,Insects,0.5536290049829767,0.5536290049829767,0.5448029815059025,13.648665428161621,43429.160590000014 +45408,Multiclass classification,ADWIN Bagging,Insects,0.5541436342414165,0.5541436342414165,0.5454957405719211,14.18911075592041,45215.90786900002 +46464,Multiclass classification,ADWIN Bagging,Insects,0.5553020683124207,0.5553020683124207,0.546961663735647,15.156656265258789,47024.86244100002 +47520,Multiclass classification,ADWIN Bagging,Insects,0.5579662871693428,0.5579662871693428,0.5498636684303295,14.218542098999023,48856.78116300002 +48576,Multiclass classification,ADWIN Bagging,Insects,0.5627586206896552,0.5627586206896552,0.5545030394801858,14.845645904541016,50711.770017000024 +49632,Multiclass classification,ADWIN Bagging,Insects,0.5677701436602124,0.5677701436602124,0.5591808574875289,15.233248710632324,52589.43568900003 +50688,Multiclass classification,ADWIN Bagging,Insects,0.5730463432438297,0.5730463432438297,0.5639878919164368,15.890131950378418,54487.48381000003 +51744,Multiclass classification,ADWIN Bagging,Insects,0.5791894555785324,0.5791894555785324,0.5695807960578061,16.1916446685791,56406.77001200003 +52800,Multiclass classification,ADWIN Bagging,Insects,0.5794427924771303,0.5794427924771303,0.5701512686040561,15.307219505310059,58342.70756100003 +52848,Multiclass classification,ADWIN Bagging,Insects,0.5794652487369197,0.5794652487369197,0.5701984940722999,15.307356834411621,60279.413314000034 +408,Multiclass classification,ADWIN Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.100947380065918,5.705318 +816,Multiclass classification,ADWIN Bagging,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,3.048105239868164,25.352994000000002 +1224,Multiclass classification,ADWIN Bagging,Keystroke,0.8912510220768601,0.8912510220768601,0.8617021305177772,3.9921913146972656,63.032035 +1632,Multiclass classification,ADWIN Bagging,Keystroke,0.9031269160024524,0.9031269160024524,0.8868998230762756,4.944231986999512,123.60027500000001 +2040,Multiclass classification,ADWIN Bagging,Keystroke,0.898970083374203,0.898970083374203,0.888705938214812,5.993730545043945,211.611387 +2448,Multiclass classification,ADWIN Bagging,Keystroke,0.8598283612586841,0.8598283612586841,0.8569666636755086,6.37155818939209,330.620683 +2856,Multiclass classification,ADWIN Bagging,Keystroke,0.8669001751313485,0.8669001751313484,0.8547854134985733,7.318934440612793,479.663409 +3264,Multiclass classification,ADWIN Bagging,Keystroke,0.8581060373889059,0.8581060373889059,0.8327540420876277,8.264909744262695,660.474615 +3672,Multiclass classification,ADWIN Bagging,Keystroke,0.8490874421138654,0.8490874421138654,0.8463961237855363,8.926459312438965,875.371562 +4080,Multiclass classification,ADWIN Bagging,Keystroke,0.8421181662172101,0.84211816621721,0.8299816031455575,10.074895858764648,1125.854174 +4488,Multiclass classification,ADWIN Bagging,Keystroke,0.8301760641854246,0.8301760641854244,0.8400819204125556,11.044804573059082,1412.144879 +4896,Multiclass classification,ADWIN Bagging,Keystroke,0.8314606741573034,0.8314606741573034,0.8387821748480373,8.862092971801758,1728.261118 +5304,Multiclass classification,ADWIN Bagging,Keystroke,0.8333019045823119,0.8333019045823119,0.8299513887279447,9.715648651123047,2074.079546 +5712,Multiclass classification,ADWIN Bagging,Keystroke,0.8255997198389073,0.8255997198389075,0.831498100235552,10.513428688049316,2449.732459 +6120,Multiclass classification,ADWIN Bagging,Keystroke,0.8241542735741134,0.8241542735741134,0.813971923025991,11.555256843566895,2856.066254 +6528,Multiclass classification,ADWIN Bagging,Keystroke,0.8043511567335683,0.8043511567335683,0.8048077550156274,12.298343658447266,3295.046229 +6936,Multiclass classification,ADWIN Bagging,Keystroke,0.8002883922134102,0.8002883922134101,0.8062362865697692,11.726844787597656,3768.4733180000003 +7344,Multiclass classification,ADWIN Bagging,Keystroke,0.8093422306959008,0.8093422306959008,0.813125473572493,9.433514595031738,4267.304275 +7752,Multiclass classification,ADWIN Bagging,Keystroke,0.8157657076506257,0.8157657076506257,0.8184378776785012,10.539642333984375,4791.9677950000005 +8160,Multiclass classification,ADWIN Bagging,Keystroke,0.8199534256649099,0.81995342566491,0.8213128379144453,11.364830017089844,5345.1630700000005 +8568,Multiclass classification,ADWIN Bagging,Keystroke,0.8247928096183028,0.8247928096183028,0.8275146627418534,12.672901153564453,5929.400246 +8976,Multiclass classification,ADWIN Bagging,Keystroke,0.8295264623955432,0.8295264623955433,0.8318915513040454,13.833264350891113,6547.972923 +9384,Multiclass classification,ADWIN Bagging,Keystroke,0.8319300863263348,0.8319300863263348,0.8336463894938194,14.741169929504395,7204.877164 +9792,Multiclass classification,ADWIN Bagging,Keystroke,0.8342355224185476,0.8342355224185476,0.8362542817352725,16.02083969116211,7900.9214680000005 +10200,Multiclass classification,ADWIN Bagging,Keystroke,0.8343955289734287,0.8343955289734286,0.833886744496364,17.251243591308594,8638.28045 +10608,Multiclass classification,ADWIN Bagging,Keystroke,0.8258697086829452,0.8258697086829452,0.823298887298616,18.484009742736816,9418.818077 +11016,Multiclass classification,ADWIN Bagging,Keystroke,0.825692237857467,0.825692237857467,0.827229896548608,17.053231239318848,10241.512178 +11424,Multiclass classification,ADWIN Bagging,Keystroke,0.8263153287227524,0.8263153287227524,0.8251000136898328,18.097841262817383,11105.510711 +11832,Multiclass classification,ADWIN Bagging,Keystroke,0.8257966359563857,0.8257966359563859,0.8251092059206939,19.1904354095459,12012.672379000001 +12240,Multiclass classification,ADWIN Bagging,Keystroke,0.8289892965111528,0.8289892965111528,0.8300645161883343,17.2155818939209,12963.554855000002 +12648,Multiclass classification,ADWIN Bagging,Keystroke,0.8324503834901558,0.8324503834901558,0.8328446288662702,17.090572357177734,13955.688030000003 +13056,Multiclass classification,ADWIN Bagging,Keystroke,0.8295672156261968,0.8295672156261968,0.8279815503081916,18.284998893737793,14990.870602000003 +13464,Multiclass classification,ADWIN Bagging,Keystroke,0.828121518235163,0.828121518235163,0.8279872572314477,18.759904861450195,16071.989638000003 +13872,Multiclass classification,ADWIN Bagging,Keystroke,0.8300771393554899,0.8300771393554899,0.8300312724960401,19.937789916992188,17198.739284000003 +14280,Multiclass classification,ADWIN Bagging,Keystroke,0.8329714966034036,0.8329714966034036,0.8330653900337638,21.172300338745117,18373.548754000003 +14688,Multiclass classification,ADWIN Bagging,Keystroke,0.8360454823994008,0.8360454823994008,0.8362319050195895,22.12139320373535,19591.296889000005 +15096,Multiclass classification,ADWIN Bagging,Keystroke,0.8391520370983769,0.8391520370983769,0.8393677597260801,22.688467979431152,20852.364231000003 +15504,Multiclass classification,ADWIN Bagging,Keystroke,0.8400309617493389,0.8400309617493388,0.8398031059873,23.806550979614258,22157.639176000004 +15912,Multiclass classification,ADWIN Bagging,Keystroke,0.8335114072025642,0.8335114072025642,0.8310693286634668,25.06292724609375,23499.792247000005 +16320,Multiclass classification,ADWIN Bagging,Keystroke,0.8283595808566702,0.8283595808566702,0.826721014765785,26.060873985290527,24886.958001000006 +16728,Multiclass classification,ADWIN Bagging,Keystroke,0.826747175225683,0.8267471752256829,0.8259678903415486,27.245673179626465,26317.085564000008 +17136,Multiclass classification,ADWIN Bagging,Keystroke,0.821943390720747,0.821943390720747,0.8202405231953956,28.675668716430664,27793.820666000007 +17544,Multiclass classification,ADWIN Bagging,Keystroke,0.8182180926865417,0.8182180926865417,0.8170173651382093,29.74225902557373,29319.213378000008 +17952,Multiclass classification,ADWIN Bagging,Keystroke,0.81878446883182,0.81878446883182,0.8179349229322325,30.879840850830078,30892.431607000006 +18360,Multiclass classification,ADWIN Bagging,Keystroke,0.821123154855929,0.821123154855929,0.8204502524156659,32.019548416137695,32513.434007000007 +18768,Multiclass classification,ADWIN Bagging,Keystroke,0.8235200085256035,0.8235200085256035,0.8229965581236837,33.157379150390625,34181.929457000006 +19176,Multiclass classification,ADWIN Bagging,Keystroke,0.819973924380704,0.819973924380704,0.8189812465563673,34.295823097229004,35895.74071300001 +19584,Multiclass classification,ADWIN Bagging,Keystroke,0.821733135883164,0.821733135883164,0.8211010404575377,35.31475067138672,37655.039070000006 +19992,Multiclass classification,ADWIN Bagging,Keystroke,0.8188684908208694,0.8188684908208694,0.8180458262517715,36.57470226287842,39458.702899 +20400,Multiclass classification,ADWIN Bagging,Keystroke,0.816559635276239,0.816559635276239,0.8159075588016685,37.855767250061035,41308.014952000005 +46,Multiclass classification,AdaBoost,ImageSegments,0.1111111111111111,0.1111111111111111,0.0815018315018315,3.4160032272338867,1.208286 +92,Multiclass classification,AdaBoost,ImageSegments,0.23076923076923078,0.23076923076923078,0.2226391771283412,4.099128723144531,4.553382 +138,Multiclass classification,AdaBoost,ImageSegments,0.4233576642335766,0.4233576642335766,0.44635377186191566,4.099002838134766,10.351245 +184,Multiclass classification,AdaBoost,ImageSegments,0.5355191256830601,0.5355191256830601,0.5617062146473912,4.099178314208984,18.765494 +230,Multiclass classification,AdaBoost,ImageSegments,0.5938864628820961,0.5938864628820961,0.6236530662596055,4.0991668701171875,30.180838 +276,Multiclass classification,AdaBoost,ImageSegments,0.6290909090909091,0.6290909090909091,0.6558170665459355,4.099109649658203,44.412895 +322,Multiclass classification,AdaBoost,ImageSegments,0.660436137071651,0.660436137071651,0.678574720261515,4.098438262939453,61.214822 +368,Multiclass classification,AdaBoost,ImageSegments,0.6920980926430518,0.6920980926430518,0.7041680355881775,4.0984954833984375,80.427396 +414,Multiclass classification,AdaBoost,ImageSegments,0.7167070217917676,0.7167070217917676,0.7259075149442813,4.097980499267578,102.254145 +460,Multiclass classification,AdaBoost,ImageSegments,0.7254901960784313,0.7254901960784313,0.7325011710849479,4.098300933837891,127.09125599999999 +506,Multiclass classification,AdaBoost,ImageSegments,0.7386138613861386,0.7386138613861386,0.7428621938273078,4.098552703857422,154.35838199999998 +552,Multiclass classification,AdaBoost,ImageSegments,0.7422867513611615,0.7422867513611615,0.7453719085253248,4.098358154296875,184.30369299999998 +598,Multiclass classification,AdaBoost,ImageSegments,0.7487437185929648,0.7487437185929648,0.7504522188790486,4.098468780517578,216.734559 +644,Multiclass classification,AdaBoost,ImageSegments,0.7465007776049767,0.7465007776049767,0.7482323503576439,4.098541259765625,252.13907799999998 +690,Multiclass classification,AdaBoost,ImageSegments,0.7489114658925979,0.748911465892598,0.7488472102580618,4.098594665527344,290.044846 +736,Multiclass classification,AdaBoost,ImageSegments,0.7523809523809524,0.7523809523809524,0.7518283723099097,4.098430633544922,330.661237 +782,Multiclass classification,AdaBoost,ImageSegments,0.7541613316261203,0.7541613316261204,0.7531089046321314,4.098361968994141,373.819675 +828,Multiclass classification,AdaBoost,ImageSegments,0.7557436517533253,0.7557436517533253,0.7552013614952863,4.098308563232422,419.68676000000005 +874,Multiclass classification,AdaBoost,ImageSegments,0.7617411225658648,0.7617411225658649,0.7601066395856337,4.098381042480469,467.91378800000007 +920,Multiclass classification,AdaBoost,ImageSegments,0.763873775843308,0.763873775843308,0.7623480483274478,4.098400115966797,518.622959 +966,Multiclass classification,AdaBoost,ImageSegments,0.7678756476683938,0.7678756476683938,0.7646598072570266,4.098423004150391,571.868767 +1012,Multiclass classification,AdaBoost,ImageSegments,0.7705242334322453,0.7705242334322453,0.7668271197983111,4.098529815673828,627.754669 +1058,Multiclass classification,AdaBoost,ImageSegments,0.7757805108798487,0.7757805108798487,0.7714920336037777,4.098388671875,686.3790640000001 +1104,Multiclass classification,AdaBoost,ImageSegments,0.7760652765185857,0.7760652765185856,0.7719206139767609,4.098537445068359,747.748718 +1150,Multiclass classification,AdaBoost,ImageSegments,0.7789382071366405,0.7789382071366405,0.7750313949659527,4.098442077636719,811.629001 +1196,Multiclass classification,AdaBoost,ImageSegments,0.7849372384937239,0.7849372384937239,0.7820003890472508,4.098487854003906,878.060098 +1242,Multiclass classification,AdaBoost,ImageSegments,0.7856567284448026,0.7856567284448026,0.7827470902102026,4.098438262939453,947.241797 +1288,Multiclass classification,AdaBoost,ImageSegments,0.7894327894327894,0.7894327894327894,0.785982924599392,4.0983428955078125,1018.793017 +1334,Multiclass classification,AdaBoost,ImageSegments,0.7906976744186046,0.7906976744186046,0.7876424482584368,4.098438262939453,1093.0782689999999 +1380,Multiclass classification,AdaBoost,ImageSegments,0.7933284989122552,0.7933284989122552,0.7906471924204205,4.098392486572266,1169.7117919999998 +1426,Multiclass classification,AdaBoost,ImageSegments,0.7978947368421052,0.7978947368421052,0.7945020166797493,4.098480224609375,1248.577134 +1472,Multiclass classification,AdaBoost,ImageSegments,0.8028552005438477,0.8028552005438477,0.7982243751921434,4.098472595214844,1329.680821 +1518,Multiclass classification,AdaBoost,ImageSegments,0.8035596572181938,0.8035596572181938,0.7981876534181912,4.098491668701172,1413.4983189999998 +1564,Multiclass classification,AdaBoost,ImageSegments,0.8035828534868842,0.8035828534868842,0.798634974540431,4.098518371582031,1499.9052179999999 +1610,Multiclass classification,AdaBoost,ImageSegments,0.8048477315102548,0.8048477315102549,0.7997380784882049,4.098381042480469,1588.8368169999999 +1656,Multiclass classification,AdaBoost,ImageSegments,0.8066465256797583,0.8066465256797583,0.80161945439383,4.098377227783203,1680.2495139999999 +1702,Multiclass classification,AdaBoost,ImageSegments,0.8059964726631393,0.8059964726631393,0.8024858564723997,4.098514556884766,1774.345382 +1748,Multiclass classification,AdaBoost,ImageSegments,0.8070978820835718,0.8070978820835718,0.8029124203507955,4.098423004150391,1871.065136 +1794,Multiclass classification,AdaBoost,ImageSegments,0.8081427774679308,0.8081427774679307,0.8029834045630979,4.098461151123047,1970.0377899999999 +1840,Multiclass classification,AdaBoost,ImageSegments,0.8069603045133225,0.8069603045133223,0.801927622716254,4.098594665527344,2071.588776 +1886,Multiclass classification,AdaBoost,ImageSegments,0.8053050397877984,0.8053050397877984,0.8006727596367825,4.098400115966797,2175.772942 +1932,Multiclass classification,AdaBoost,ImageSegments,0.8047643707923355,0.8047643707923355,0.7995493059800365,4.098396301269531,2282.41088 +1978,Multiclass classification,AdaBoost,ImageSegments,0.8057663125948407,0.8057663125948407,0.8003960406612564,4.0984344482421875,2391.59611 +2024,Multiclass classification,AdaBoost,ImageSegments,0.8072170044488384,0.8072170044488384,0.8005625942078284,4.098430633544922,2503.16971 +2070,Multiclass classification,AdaBoost,ImageSegments,0.8066698888351861,0.8066698888351861,0.8002110568368,4.098316192626953,2617.3694960000003 +2116,Multiclass classification,AdaBoost,ImageSegments,0.807565011820331,0.807565011820331,0.8005131307885663,4.0983428955078125,2733.922308 +2162,Multiclass classification,AdaBoost,ImageSegments,0.8079592781119852,0.8079592781119852,0.8006755955605837,4.098320007324219,2852.747139 +2208,Multiclass classification,AdaBoost,ImageSegments,0.8087902129587675,0.8087902129587675,0.8009921695193862,4.098320007324219,2973.740681 +2254,Multiclass classification,AdaBoost,ImageSegments,0.8060363959165557,0.8060363959165557,0.7987732120640717,4.0983428955078125,3097.6149410000003 +2300,Multiclass classification,AdaBoost,ImageSegments,0.8051326663766856,0.8051326663766856,0.798077892809675,4.0983428955078125,3223.9293610000004 +2310,Multiclass classification,AdaBoost,ImageSegments,0.8046773495019489,0.8046773495019489,0.7977695866822911,4.098388671875,3350.8763620000004 +1056,Multiclass classification,AdaBoost,Insects,0.6360189573459716,0.6360189573459716,0.5992691812827112,6.474042892456055,88.969298 +2112,Multiclass classification,AdaBoost,Insects,0.6110847939365229,0.6110847939365229,0.5773210074897359,6.473905563354492,256.045675 +3168,Multiclass classification,AdaBoost,Insects,0.6043574360593622,0.6043574360593622,0.5704368753709179,6.473470687866211,489.83548 +4224,Multiclass classification,AdaBoost,Insects,0.6014681506038362,0.6014681506038362,0.5676969561642586,6.473196029663086,783.3519140000001 +5280,Multiclass classification,AdaBoost,Insects,0.6057965523773442,0.6057965523773442,0.5710016183775801,6.473196029663086,1130.801617 +6336,Multiclass classification,AdaBoost,Insects,0.5966850828729282,0.5966850828729282,0.5635903588556204,6.473356246948242,1527.884634 +7392,Multiclass classification,AdaBoost,Insects,0.5957245298335814,0.5957245298335814,0.5625002603439991,6.473814010620117,1971.450931 +8448,Multiclass classification,AdaBoost,Insects,0.5982005445720374,0.5982005445720374,0.5646892369665863,6.474157333374023,2461.351589 +9504,Multiclass classification,AdaBoost,Insects,0.596337998526781,0.596337998526781,0.5627085514562804,6.47450065612793,2997.75158 +10560,Multiclass classification,AdaBoost,Insects,0.5965527038545316,0.5965527038545316,0.5631320282838163,6.47468376159668,3580.189969 +11616,Multiclass classification,AdaBoost,Insects,0.5953508394317693,0.5953508394317693,0.562671447170627,6.47468376159668,4209.202801 +12672,Multiclass classification,AdaBoost,Insects,0.5979796385447084,0.5979796385447084,0.5680559575776837,6.474340438842773,4886.872526 +13728,Multiclass classification,AdaBoost,Insects,0.610767101333139,0.610767101333139,0.5941277335666079,6.473836898803711,5609.570035 +14784,Multiclass classification,AdaBoost,Insects,0.6019752418318338,0.6019752418318338,0.5851264744797859,6.473745346069336,6378.998584999999 +15840,Multiclass classification,AdaBoost,Insects,0.5705536965717533,0.5705536965717533,0.5545059657048704,6.473974227905273,7193.860588999999 +16896,Multiclass classification,AdaBoost,Insects,0.548091151228174,0.548091151228174,0.5320735507355622,6.474386215209961,8051.816493999999 +17952,Multiclass classification,AdaBoost,Insects,0.5307225224221492,0.5307225224221492,0.5138536287616571,6.474637985229492,8952.059017 +19008,Multiclass classification,AdaBoost,Insects,0.5182827379386542,0.5182827379386542,0.4990809738484312,6.47486686706543,9893.706361 +20064,Multiclass classification,AdaBoost,Insects,0.5182176145142801,0.5182176145142801,0.497867701567998,8.642622947692871,10881.966771000001 +21120,Multiclass classification,AdaBoost,Insects,0.5272503432927695,0.5272503432927695,0.5067114684709674,15.437758445739746,11917.076256 +22176,Multiclass classification,AdaBoost,Insects,0.533032694475761,0.533032694475761,0.5127471323280748,16.81709384918213,12999.258268 +23232,Multiclass classification,AdaBoost,Insects,0.5410442942619775,0.5410442942619775,0.5207771198745245,17.041016578674316,14124.681759 +24288,Multiclass classification,AdaBoost,Insects,0.5459710956478775,0.5459710956478775,0.5251711652768184,17.038064002990723,15290.808705 +25344,Multiclass classification,AdaBoost,Insects,0.5532099593576135,0.5532099593576135,0.5314216535856217,17.036622047424316,16491.42669 +26400,Multiclass classification,AdaBoost,Insects,0.5607788173794462,0.5607788173794462,0.5375130024626694,17.14900016784668,17723.115047 +27456,Multiclass classification,AdaBoost,Insects,0.5667091604443635,0.5667091604443635,0.5418496825562071,17.261820793151855,18986.675500999998 +28512,Multiclass classification,AdaBoost,Insects,0.5692890463329943,0.5692890463329943,0.5455529487931667,17.26294231414795,20283.404340999998 +29568,Multiclass classification,AdaBoost,Insects,0.5688436432509216,0.5688436432509216,0.5481992899375988,17.26337718963623,21617.011828 +30624,Multiclass classification,AdaBoost,Insects,0.5687228553701467,0.5687228553701467,0.5505043481720591,17.26330852508545,22980.237824 +31680,Multiclass classification,AdaBoost,Insects,0.5691467533697402,0.5691467533697402,0.5529220328647554,17.262804985046387,24378.053405 +32736,Multiclass classification,AdaBoost,Insects,0.5703986558729189,0.5703986558729189,0.5556828084411201,17.262507438659668,25809.039082 +33792,Multiclass classification,AdaBoost,Insects,0.5650025154626972,0.5650025154626972,0.5507695387439543,17.487704277038574,27274.413313999998 +34848,Multiclass classification,AdaBoost,Insects,0.5587281545039745,0.5587281545039745,0.5445349362041821,17.929275512695312,28775.770082 +35904,Multiclass classification,AdaBoost,Insects,0.5541876723393588,0.5541876723393588,0.5396635045593164,18.030207633972168,30311.158156999998 +36960,Multiclass classification,AdaBoost,Insects,0.549122000054114,0.549122000054114,0.5343517375956978,19.681435585021973,31880.342243 +38016,Multiclass classification,AdaBoost,Insects,0.5473628830724714,0.5473628830724714,0.5321033552605493,21.718143463134766,33482.866823 +39072,Multiclass classification,AdaBoost,Insects,0.5426787131120269,0.5426787131120269,0.52803892360078,22.487850189208984,35116.535538 +40128,Multiclass classification,AdaBoost,Insects,0.5419293742367982,0.5419293742367982,0.5284857300708793,23.76238250732422,36778.160188999995 +41184,Multiclass classification,AdaBoost,Insects,0.5417769467984362,0.5417769467984362,0.5296361895775551,23.860816955566406,38464.674338 +42240,Multiclass classification,AdaBoost,Insects,0.5422240109851085,0.5422240109851085,0.5313111510734391,24.196860313415527,40175.576412999995 +43296,Multiclass classification,AdaBoost,Insects,0.5444970550871925,0.5444970550871925,0.5344195798463859,24.296037673950195,41906.99288399999 +44352,Multiclass classification,AdaBoost,Insects,0.5463461928705102,0.5463461928705102,0.5369578677381479,24.84640598297119,43659.477484999996 +45408,Multiclass classification,AdaBoost,Insects,0.5482855066399454,0.5482855066399454,0.5392181145139481,25.28636360168457,45430.571796 +46464,Multiclass classification,AdaBoost,Insects,0.5506532079288896,0.5506532079288896,0.5419048601727473,25.498303413391113,47220.970484 +47520,Multiclass classification,AdaBoost,Insects,0.5514846692901787,0.5514846692901787,0.5429796926051395,25.823355674743652,49033.125825999996 +48576,Multiclass classification,AdaBoost,Insects,0.5515388574369532,0.5515388574369532,0.543031483592694,25.821112632751465,50867.580247 +49632,Multiclass classification,AdaBoost,Insects,0.551953416211642,0.551953416211642,0.5433574148660688,26.098894119262695,52723.913942 +50688,Multiclass classification,AdaBoost,Insects,0.5563359441276856,0.5563359441276856,0.5472854805195191,26.366034507751465,54600.511786999996 +51744,Multiclass classification,AdaBoost,Insects,0.5623562607502464,0.5623562607502464,0.552981536157949,27.10032081604004,56496.789585 +52800,Multiclass classification,AdaBoost,Insects,0.5634576412432054,0.5634576412432054,0.5545218292020726,27.943178176879883,58415.671716 +52848,Multiclass classification,AdaBoost,Insects,0.5635324616345299,0.5635324616345299,0.5546220283668154,27.942995071411133,60335.727695999994 +408,Multiclass classification,AdaBoost,Keystroke,0.9877149877149877,0.9877149877149877,0.7696139476961394,2.1207275390625,4.211814 +816,Multiclass classification,AdaBoost,Keystroke,0.9889570552147239,0.9889570552147239,0.9592655637573824,2.9369373321533203,23.739993000000002 +1224,Multiclass classification,AdaBoost,Keystroke,0.9836467702371219,0.9836467702371219,0.9326470331192014,4.590028762817383,86.527265 +1632,Multiclass classification,AdaBoost,Keystroke,0.9828326180257511,0.9828326180257511,0.9594506659780556,5.819695472717285,184.232691 +2040,Multiclass classification,AdaBoost,Keystroke,0.9705738106915155,0.9705738106915155,0.9304838721924584,8.549582481384277,323.308574 +2448,Multiclass classification,AdaBoost,Keystroke,0.9607682876992235,0.9607682876992235,0.9455756842664337,10.061903953552246,491.40663400000005 +2856,Multiclass classification,AdaBoost,Keystroke,0.9541155866900175,0.9541155866900175,0.9254688528922778,12.678574562072754,687.150288 +3264,Multiclass classification,AdaBoost,Keystroke,0.9436101746858719,0.9436101746858719,0.9191430707434157,16.086813926696777,906.458431 +3672,Multiclass classification,AdaBoost,Keystroke,0.9403432307273223,0.9403432307273223,0.9284235615798526,18.255277633666992,1151.002639 +4080,Multiclass classification,AdaBoost,Keystroke,0.9338073057121844,0.9338073057121844,0.9182429705382059,21.65336036682129,1427.8669570000002 +4488,Multiclass classification,AdaBoost,Keystroke,0.9318029864051705,0.9318029864051705,0.9319119487505448,22.768765449523926,1733.8095280000002 +4896,Multiclass classification,AdaBoost,Keystroke,0.9317671092951992,0.9317671092951992,0.9296889978700974,24.966033935546875,2062.8329900000003 +5304,Multiclass classification,AdaBoost,Keystroke,0.9281538751650009,0.9281538751650009,0.9197653564039141,29.062508583068848,2423.7318920000002 +5712,Multiclass classification,AdaBoost,Keystroke,0.9227805988443355,0.9227805988443355,0.9201475418022375,32.12655830383301,2815.063536 +6120,Multiclass classification,AdaBoost,Keystroke,0.9177970256577872,0.9177970256577872,0.9072843264203106,37.27707767486572,3238.118927 +6528,Multiclass classification,AdaBoost,Keystroke,0.9115979776313774,0.9115979776313774,0.909931232789514,41.43412208557129,3706.915394 +6936,Multiclass classification,AdaBoost,Keystroke,0.9129055515501081,0.9129055515501081,0.9153430596364791,44.48411560058594,4207.758914 +7344,Multiclass classification,AdaBoost,Keystroke,0.9135230832084978,0.9135230832084978,0.9124682676754273,47.44067192077637,4740.722969 +7752,Multiclass classification,AdaBoost,Keystroke,0.9121403689846471,0.9121403689846471,0.9121831707972875,51.03960132598877,5307.755902000001 +8160,Multiclass classification,AdaBoost,Keystroke,0.9086897904154921,0.9086897904154921,0.9062633734460517,56.064818382263184,5918.019803000001 +8568,Multiclass classification,AdaBoost,Keystroke,0.9059180576631259,0.9059180576631259,0.9058259471519292,61.820496559143066,6575.962782000001 +8976,Multiclass classification,AdaBoost,Keystroke,0.9042896935933148,0.9042896935933148,0.9043251050138335,64.74030494689941,7280.842530000002 +9384,Multiclass classification,AdaBoost,Keystroke,0.9018437599914739,0.9018437599914739,0.9009662752730246,68.81300067901611,8035.1031440000015 +9792,Multiclass classification,AdaBoost,Keystroke,0.8971504442855683,0.8971504442855683,0.8956423708961025,74.25286674499512,8855.073479000002 +10200,Multiclass classification,AdaBoost,Keystroke,0.8926365329934307,0.8926365329934307,0.8903074227158838,79.6785535812378,9744.976722000003 +10608,Multiclass classification,AdaBoost,Keystroke,0.8846987838220043,0.8846987838220043,0.8819820059100918,85.28873825073242,10726.537155000004 +11016,Multiclass classification,AdaBoost,Keystroke,0.8791647753064004,0.8791647753064004,0.8795835231396919,89.59383392333984,11788.438997000003 +11424,Multiclass classification,AdaBoost,Keystroke,0.8759520266129738,0.8759520266129738,0.8744149508862001,94.86630344390869,12917.703791000004 +11832,Multiclass classification,AdaBoost,Keystroke,0.872200152142676,0.872200152142676,0.8717012117300328,100.15169906616211,14117.771334000003 +12240,Multiclass classification,AdaBoost,Keystroke,0.8736824903995425,0.8736824903995425,0.8749440738646468,101.93578433990479,15365.994884000003 +12648,Multiclass classification,AdaBoost,Keystroke,0.8717482406894915,0.8717482406894915,0.8710221211412438,107.46908473968506,16670.526324000002 +13056,Multiclass classification,AdaBoost,Keystroke,0.8661815396399847,0.8661815396399847,0.8651994621744733,112.71690273284912,18043.485216 +13464,Multiclass classification,AdaBoost,Keystroke,0.8642204560647702,0.8642204560647702,0.8645487273027374,116.56386756896973,19480.298866 +13872,Multiclass classification,AdaBoost,Keystroke,0.8619421815298104,0.8619421815298104,0.862314869215492,121.52821636199951,20980.471725 +14280,Multiclass classification,AdaBoost,Keystroke,0.859163806989285,0.859163806989285,0.8592780138529494,125.80194187164307,22534.622977 +14688,Multiclass classification,AdaBoost,Keystroke,0.8591952066453326,0.8591952066453326,0.8604793833246808,129.6016607284546,24135.768583999998 +15096,Multiclass classification,AdaBoost,Keystroke,0.8607485922490891,0.8607485922490891,0.8621609789956539,132.68816757202148,25779.863983 +15504,Multiclass classification,AdaBoost,Keystroke,0.8604141133974069,0.8604141133974069,0.8613237595899307,136.05841445922852,27480.057181 +15912,Multiclass classification,AdaBoost,Keystroke,0.8536861290930803,0.8536861290930803,0.853192144751886,141.70578575134277,29254.423593 +16320,Multiclass classification,AdaBoost,Keystroke,0.8493167473497151,0.849316747349715,0.8496464102754333,147.49746799468994,31089.50572 +16728,Multiclass classification,AdaBoost,Keystroke,0.846296407006636,0.846296407006636,0.8470383589757107,153.1935510635376,32973.577156 +17136,Multiclass classification,AdaBoost,Keystroke,0.8411438576014006,0.8411438576014006,0.8410396667771575,156.28132915496826,34948.88082 +17544,Multiclass classification,AdaBoost,Keystroke,0.8365729920766117,0.8365729920766117,0.8367907010021001,161.03080940246582,36967.519165 +17952,Multiclass classification,AdaBoost,Keystroke,0.8355523369171634,0.8355523369171634,0.8362918425397341,166.63249397277832,39016.229588999995 +18360,Multiclass classification,AdaBoost,Keystroke,0.837572852551882,0.8375728525518821,0.8385662484273668,171.47760772705078,41092.028592999995 +18768,Multiclass classification,AdaBoost,Keystroke,0.8390259498055097,0.8390259498055097,0.8401126675526959,175.70373821258545,43194.947863999994 +19176,Multiclass classification,AdaBoost,Keystroke,0.8376531942633637,0.8376531942633637,0.838676297522501,180.87701034545898,45330.650987999994 +19584,Multiclass classification,AdaBoost,Keystroke,0.8390951335341879,0.8390951335341879,0.8403338496937821,185.05438709259033,47482.84587799999 +19992,Multiclass classification,AdaBoost,Keystroke,0.8372767745485469,0.8372767745485468,0.8385640183306876,190.1383810043335,49661.235199999996 +20400,Multiclass classification,AdaBoost,Keystroke,0.8347958233246727,0.8347958233246727,0.8360623278174891,194.794171333313,51861.27850099999 +46,Multiclass classification,Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.24576497265572897,4.149084091186523,2.196675 +92,Multiclass classification,Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.4934752395581889,4.152299880981445,7.023639 +138,Multiclass classification,Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646678,4.15202522277832,15.046926 +184,Multiclass classification,Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.6141431648908949,4.152608871459961,26.297795 +230,Multiclass classification,Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,4.151983261108398,40.50873 +276,Multiclass classification,Bagging,ImageSegments,0.64,0.64,0.6559607038460421,4.152521133422852,57.698206 +322,Multiclass classification,Bagging,ImageSegments,0.6666666666666666,0.6666666666666666,0.6673617488913626,4.152231216430664,77.585785 +368,Multiclass classification,Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6911959597548878,4.152448654174805,100.185488 +414,Multiclass classification,Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.7079630503641953,4.152788162231445,125.71728800000001 +460,Multiclass classification,Bagging,ImageSegments,0.7124183006535948,0.7124183006535948,0.7065500352371009,4.152704238891602,154.000542 +506,Multiclass classification,Bagging,ImageSegments,0.7207920792079208,0.7207920792079208,0.7127593158348896,4.152563095092773,184.883226 +552,Multiclass classification,Bagging,ImageSegments,0.7259528130671506,0.7259528130671506,0.7192025503807162,4.152528762817383,218.482328 +598,Multiclass classification,Bagging,ImageSegments,0.7319932998324958,0.7319932998324957,0.7251188986558661,4.152769088745117,254.840787 +644,Multiclass classification,Bagging,ImageSegments,0.7309486780715396,0.7309486780715396,0.7259740406437201,4.152563095092773,294.12903800000004 +690,Multiclass classification,Bagging,ImageSegments,0.7358490566037735,0.7358490566037735,0.7304359912942561,4.152692794799805,336.073433 +736,Multiclass classification,Bagging,ImageSegments,0.7374149659863946,0.7374149659863947,0.733149934717071,4.152753829956055,380.701162 +782,Multiclass classification,Bagging,ImageSegments,0.7426376440460948,0.7426376440460948,0.7385597120510639,4.152643203735352,428.175969 +828,Multiclass classification,Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7412375783772316,4.152631759643555,478.460063 +874,Multiclass classification,Bagging,ImageSegments,0.7491408934707904,0.7491408934707904,0.7454343548790067,4.153181076049805,531.417765 +920,Multiclass classification,Bagging,ImageSegments,0.7486398258977149,0.7486398258977149,0.7441307384051415,4.153326034545898,587.1362770000001 +966,Multiclass classification,Bagging,ImageSegments,0.7492227979274612,0.749222797927461,0.7439306216964366,4.153120040893555,645.6842 +1012,Multiclass classification,Bagging,ImageSegments,0.7487636003956478,0.7487636003956478,0.7437900284473965,4.153234481811523,707.105172 +1058,Multiclass classification,Bagging,ImageSegments,0.750236518448439,0.7502365184484389,0.7448138061687654,4.153268814086914,771.2868930000001 +1104,Multiclass classification,Bagging,ImageSegments,0.7524932003626473,0.7524932003626473,0.7468314646869904,4.153234481811523,838.222518 +1150,Multiclass classification,Bagging,ImageSegments,0.7554395126196692,0.7554395126196692,0.7493227137357602,4.153413772583008,907.556087 +1196,Multiclass classification,Bagging,ImageSegments,0.7581589958158996,0.7581589958158996,0.7527652773681007,4.153318405151367,979.5797180000001 +1242,Multiclass classification,Bagging,ImageSegments,0.7574536663980661,0.7574536663980661,0.7525915384194216,4.153432846069336,1054.216781 +1288,Multiclass classification,Bagging,ImageSegments,0.7622377622377622,0.7622377622377621,0.7563448085202398,4.153615951538086,1131.5718310000002 +1334,Multiclass classification,Bagging,ImageSegments,0.7621905476369092,0.7621905476369092,0.7566636999776912,4.153776168823242,1211.5912470000003 +1380,Multiclass classification,Bagging,ImageSegments,0.7635968092820885,0.7635968092820886,0.7587252257765656,4.153825759887695,1294.4019940000003 +1426,Multiclass classification,Bagging,ImageSegments,0.7663157894736842,0.7663157894736842,0.7609139797315134,4.153848648071289,1379.8910190000004 +1472,Multiclass classification,Bagging,ImageSegments,0.7709041468388851,0.7709041468388851,0.7637689949207689,4.153989791870117,1467.9946540000003 +1518,Multiclass classification,Bagging,ImageSegments,0.7719182597231378,0.7719182597231378,0.7639714255563932,4.154367446899414,1558.8129900000004 +1564,Multiclass classification,Bagging,ImageSegments,0.7722328854766475,0.7722328854766475,0.7650721335080709,4.154550552368164,1652.2028900000003 +1610,Multiclass classification,Bagging,ImageSegments,0.7725295214418894,0.7725295214418892,0.764505787280341,4.154642105102539,1748.3782850000002 +1656,Multiclass classification,Bagging,ImageSegments,0.7716012084592145,0.7716012084592145,0.7634170612719108,4.15452766418457,1847.3163560000003 +1702,Multiclass classification,Bagging,ImageSegments,0.7713109935332157,0.7713109935332157,0.7652815676598499,4.154825210571289,1948.7028940000002 +1748,Multiclass classification,Bagging,ImageSegments,0.77389811104751,0.77389811104751,0.7674409436090757,4.155008316040039,2052.533374 +1794,Multiclass classification,Bagging,ImageSegments,0.7752370329057445,0.7752370329057446,0.7674318582149376,4.155046463012695,2159.053176 +1840,Multiclass classification,Bagging,ImageSegments,0.7765089722675367,0.7765089722675368,0.7688731808749575,4.154977798461914,2268.233507 +1886,Multiclass classification,Bagging,ImageSegments,0.7750663129973475,0.7750663129973475,0.7678921362145585,4.154905319213867,2379.8377889999997 +1932,Multiclass classification,Bagging,ImageSegments,0.7752459865354738,0.7752459865354739,0.7671636716269125,4.155000686645508,2494.085284 +1978,Multiclass classification,Bagging,ImageSegments,0.7759231158320687,0.7759231158320687,0.7670573130332384,4.154901504516602,2611.0522539999997 +2024,Multiclass classification,Bagging,ImageSegments,0.7775580820563519,0.7775580820563519,0.7671264358471986,4.154878616333008,2730.5624909999997 +2070,Multiclass classification,Bagging,ImageSegments,0.77670372160464,0.7767037216046399,0.7665050383810529,4.15495491027832,2852.4395529999997 +2116,Multiclass classification,Bagging,ImageSegments,0.7773049645390071,0.7773049645390071,0.766340416614934,4.15495491027832,2976.9921299999996 +2162,Multiclass classification,Bagging,ImageSegments,0.7783433595557612,0.7783433595557612,0.766965714748886,4.155027389526367,3104.012504 +2208,Multiclass classification,Bagging,ImageSegments,0.780244676030811,0.780244676030811,0.7678552364681828,4.155023574829102,3233.6609839999996 +2254,Multiclass classification,Bagging,ImageSegments,0.7776298268974701,0.7776298268974701,0.7652407320979201,4.154973983764648,3365.6406429999997 +2300,Multiclass classification,Bagging,ImageSegments,0.7768595041322314,0.7768595041322314,0.764461061100325,4.15504264831543,3499.962334 +2310,Multiclass classification,Bagging,ImageSegments,0.7769597228237333,0.7769597228237333,0.7645642360301897,4.155065536499023,3634.8810719999997 +1056,Multiclass classification,Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762561,6.533428192138672,93.097088 +2112,Multiclass classification,Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.533924102783203,264.682132 +3168,Multiclass classification,Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244892,6.534633636474609,504.28420900000003 +4224,Multiclass classification,Bagging,Insects,0.6107032914989344,0.6107032914989344,0.5748501357120321,6.535015106201172,804.5259470000001 +5280,Multiclass classification,Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225517,6.535823822021484,1159.582019 +6336,Multiclass classification,Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789625,6.535648345947266,1564.000203 +7392,Multiclass classification,Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867537,6.535068511962891,2016.3102330000002 +8448,Multiclass classification,Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.534946441650391,2516.339397 +9504,Multiclass classification,Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297489,6.535068511962891,3064.243813 +10560,Multiclass classification,Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.535427093505859,3659.768381 +11616,Multiclass classification,Bagging,Insects,0.6005165733964701,0.6005165733964701,0.56512832395729,6.535404205322266,4303.8464189999995 +12672,Multiclass classification,Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306639,6.535358428955078,4997.310473 +13728,Multiclass classification,Bagging,Insects,0.6147009543235958,0.6147009543235958,0.5955104002005771,6.534030914306641,5738.022631999999 +14784,Multiclass classification,Bagging,Insects,0.6051545694378678,0.6051545694378678,0.586271708420286,6.533008575439453,6524.316427 +15840,Multiclass classification,Bagging,Insects,0.5703642906749163,0.5703642906749163,0.5530031721301686,6.534244537353516,7355.370967999999 +16896,Multiclass classification,Bagging,Insects,0.5440662918023084,0.5440662918023084,0.5274181049148582,6.532741546630859,8230.882624 +17952,Multiclass classification,Bagging,Insects,0.524650437301543,0.524650437301543,0.5077439094080566,6.533657073974609,9149.482306 +19008,Multiclass classification,Bagging,Insects,0.5142842110801283,0.5142842110801283,0.4945495171544722,5.423342704772949,10110.1367 +20064,Multiclass classification,Bagging,Insects,0.5202611772915317,0.5202611772915317,0.499632175624185,13.463048934936523,11121.939621 +21120,Multiclass classification,Bagging,Insects,0.5284814621904447,0.5284814621904447,0.5082299437323158,14.233846664428711,12202.117709999999 +22176,Multiclass classification,Bagging,Insects,0.5344757609921083,0.5344757609921083,0.5148729059414189,14.772774696350098,13344.394484999999 +23232,Multiclass classification,Bagging,Insects,0.5430674529723215,0.5430674529723215,0.5233933209280776,14.684733390808105,14542.607494 +24288,Multiclass classification,Bagging,Insects,0.5502120475974801,0.5502120475974801,0.5298443248135049,16.20911407470703,15791.070918 +25344,Multiclass classification,Bagging,Insects,0.5564061081955569,0.5564061081955569,0.5355525016331893,16.199478149414062,17093.843057 +26400,Multiclass classification,Bagging,Insects,0.561460661388689,0.561460661388689,0.5398397773012414,16.192718505859375,18441.382026 +27456,Multiclass classification,Bagging,Insects,0.564742305590967,0.564742305590967,0.5421523628031605,15.229331016540527,19838.570208 +28512,Multiclass classification,Bagging,Insects,0.5680614499666795,0.5680614499666795,0.5472893783055924,13.71937370300293,21280.868604 +29568,Multiclass classification,Bagging,Insects,0.5701288598775662,0.5701288598775662,0.55295508639855,11.343052864074707,22768.358846 +30624,Multiclass classification,Bagging,Insects,0.5724128922705156,0.5724128922705156,0.5585792537754973,9.387857437133789,24294.822849 +31680,Multiclass classification,Bagging,Insects,0.5749865841724802,0.5749865841724802,0.5636037623129485,9.38664436340332,25857.719223 +32736,Multiclass classification,Bagging,Insects,0.5781884832747823,0.5781884832747823,0.5684564968293649,9.385660171508789,27456.12944 +33792,Multiclass classification,Bagging,Insects,0.575656239827173,0.575656239827173,0.5663415557568727,7.860757827758789,29092.739018 +34848,Multiclass classification,Bagging,Insects,0.5754584325766924,0.5754584325766924,0.565994999425249,7.205549240112305,30762.023764 +35904,Multiclass classification,Bagging,Insects,0.5763863743976827,0.5763863743976827,0.5665127709334143,6.54947566986084,32461.363070000003 +36960,Multiclass classification,Bagging,Insects,0.5758813820720258,0.5758813820720258,0.56571927622701,6.547377586364746,34189.07998 +38016,Multiclass classification,Bagging,Insects,0.5767460213073786,0.5767460213073786,0.5661110063916132,6.546515464782715,35945.284935 +39072,Multiclass classification,Bagging,Insects,0.5764633615725219,0.5764633615725219,0.5659285794545608,6.543356895446777,37730.818682000005 +40128,Multiclass classification,Bagging,Insects,0.573454282652578,0.573454282652578,0.5636611811263741,8.510072708129883,39542.33547700001 +41184,Multiclass classification,Bagging,Insects,0.5726391957846685,0.5726391957846685,0.5633960246210544,8.712862014770508,41378.34519600001 +42240,Multiclass classification,Bagging,Insects,0.5723146854802433,0.5723146854802433,0.5635786987292998,10.13754653930664,43237.00688100001 +43296,Multiclass classification,Bagging,Insects,0.5717981291142165,0.5717981291142165,0.5635967907133216,10.13637924194336,45117.76335600001 +44352,Multiclass classification,Bagging,Insects,0.571103244571712,0.571103244571712,0.5633625241299441,10.135028839111328,47020.66134100001 +45408,Multiclass classification,Bagging,Insects,0.5712335102517233,0.5712335102517233,0.563808836162261,11.334146499633789,48947.97157600001 +46464,Multiclass classification,Bagging,Insects,0.5728213847577642,0.5728213847577642,0.5658781423773395,12.350201606750488,50897.74209700001 +47520,Multiclass classification,Bagging,Insects,0.576863991245607,0.576863991245607,0.5703778478941884,16.125893592834473,52890.49897200001 +48576,Multiclass classification,Bagging,Insects,0.5828512609366958,0.5828512609366958,0.5764029561430954,15.266244888305664,54904.91240000001 +49632,Multiclass classification,Bagging,Insects,0.5890270194031956,0.5890270194031956,0.5823661991476956,14.839654922485352,56940.07330000001 +50688,Multiclass classification,Bagging,Insects,0.5947087024286306,0.5947087024286306,0.5876086024291545,12.465810775756836,58994.29364000001 +51744,Multiclass classification,Bagging,Insects,0.600718937827339,0.600718937827339,0.5930357853224563,11.884730339050293,61065.77177200001 +52800,Multiclass classification,Bagging,Insects,0.6060342051932802,0.6060342051932802,0.5982060206393416,3.691446304321289,63151.215841000005 +52848,Multiclass classification,Bagging,Insects,0.6063920373909588,0.6063920373909588,0.5985419438128344,3.691621780395508,65236.99615100001 +408,Multiclass classification,Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.1448841094970703,5.867596 +816,Multiclass classification,Bagging,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,3.0916757583618164,25.808269 +1224,Multiclass classification,Bagging,Keystroke,0.8912510220768601,0.8912510220768601,0.8617021305177773,4.035944938659668,63.939426 +1632,Multiclass classification,Bagging,Keystroke,0.9031269160024524,0.9031269160024524,0.8868998230762758,4.988290786743164,125.34339 +2040,Multiclass classification,Bagging,Keystroke,0.898970083374203,0.898970083374203,0.888705938214812,6.037667274475098,214.307845 +2448,Multiclass classification,Bagging,Keystroke,0.8594196975888844,0.8594196975888844,0.8547805855679916,6.993380546569824,335.016386 +2856,Multiclass classification,Bagging,Keystroke,0.8651488616462347,0.8651488616462347,0.8483773016417727,7.939821243286133,488.12155800000005 +3264,Multiclass classification,Bagging,Keystroke,0.8553478394115844,0.8553478394115844,0.8302147847543373,8.885003089904785,675.394352 +3672,Multiclass classification,Bagging,Keystroke,0.8452737673658404,0.8452737673658404,0.8411086163638233,9.830622673034668,899.024814 +4080,Multiclass classification,Bagging,Keystroke,0.8374601618043638,0.8374601618043638,0.8238000521910981,11.003908157348633,1161.3046 +4488,Multiclass classification,Bagging,Keystroke,0.8250501448629374,0.8250501448629373,0.8343531144302688,11.974610328674316,1461.738376 +4896,Multiclass classification,Bagging,Keystroke,0.8232890704800817,0.8232890704800817,0.8292209535545839,12.919659614562988,1801.820426 +5304,Multiclass classification,Bagging,Keystroke,0.8199132566471808,0.819913256647181,0.8044565992905442,13.86521053314209,2181.861898 +5712,Multiclass classification,Bagging,Keystroke,0.7998599194536858,0.7998599194536857,0.8029484507582976,14.811628341674805,2601.779179 +6120,Multiclass classification,Bagging,Keystroke,0.7970256577872201,0.7970256577872201,0.7783451709211457,15.75713062286377,3063.5971010000003 +6528,Multiclass classification,Bagging,Keystroke,0.7720239007200858,0.7720239007200858,0.767005590841987,16.704151153564453,3570.6766780000003 +6936,Multiclass classification,Bagging,Keystroke,0.7645277577505407,0.7645277577505407,0.766187831914561,17.649503707885742,4126.519897 +7344,Multiclass classification,Bagging,Keystroke,0.773389622769985,0.7733896227699851,0.770832075885354,18.61162567138672,4733.5217410000005 +7752,Multiclass classification,Bagging,Keystroke,0.7737066185008385,0.7737066185008385,0.7718493223486268,19.557814598083496,5395.069721000001 +8160,Multiclass classification,Bagging,Keystroke,0.7765657556073048,0.7765657556073047,0.7724710929560354,20.503721237182617,6113.943535 +8568,Multiclass classification,Bagging,Keystroke,0.7730827594257033,0.7730827594257033,0.7727491763630034,21.882675170898438,6890.839823 +8976,Multiclass classification,Bagging,Keystroke,0.7714763231197772,0.7714763231197772,0.7717207236627096,22.87528133392334,7728.212391 +9384,Multiclass classification,Bagging,Keystroke,0.7702227432590856,0.7702227432590856,0.7694267539223918,23.822596549987793,8626.275614 +9792,Multiclass classification,Bagging,Keystroke,0.7656010621999796,0.7656010621999795,0.7644081311179032,24.768078804016113,9586.24664 +10200,Multiclass classification,Bagging,Keystroke,0.757623296401608,0.757623296401608,0.749720417225094,25.71299648284912,10618.940127 +10608,Multiclass classification,Bagging,Keystroke,0.737154709154332,0.737154709154332,0.7245707699101513,26.660439491271973,11726.561153 +11016,Multiclass classification,Bagging,Keystroke,0.729822968679074,0.7298229686790739,0.7256689004292383,27.605186462402344,12907.41343 +11424,Multiclass classification,Bagging,Keystroke,0.7229274271207213,0.7229274271207213,0.7092514304350318,28.551199913024902,14153.769988 +11832,Multiclass classification,Bagging,Keystroke,0.7133801031189249,0.7133801031189249,0.7054771135814562,29.4963436126709,15465.906612 +12240,Multiclass classification,Bagging,Keystroke,0.7177874009314487,0.7177874009314487,0.7138351093258007,30.441871643066406,16835.329364 +12648,Multiclass classification,Bagging,Keystroke,0.7147149521625682,0.7147149521625682,0.7065885995198201,31.388431549072266,18265.757575 +13056,Multiclass classification,Bagging,Keystroke,0.7031788586748372,0.7031788586748372,0.6954173783902821,32.33424186706543,19760.458564 +13464,Multiclass classification,Bagging,Keystroke,0.7011067369828419,0.7011067369828419,0.6966368809795416,33.27959156036377,21319.158047 +13872,Multiclass classification,Bagging,Keystroke,0.7007425564126595,0.7007425564126595,0.6971102154727419,34.22630214691162,22941.129728 +14280,Multiclass classification,Bagging,Keystroke,0.6961972126899643,0.6961972126899643,0.691133802747568,35.17108726501465,24623.129677 +14688,Multiclass classification,Bagging,Keystroke,0.698781235105876,0.698781235105876,0.696592906911097,36.11711597442627,26362.470984 +15096,Multiclass classification,Bagging,Keystroke,0.7048029148724744,0.7048029148724744,0.702773358939844,37.0643196105957,28156.052692 +15504,Multiclass classification,Bagging,Keystroke,0.7047668193252918,0.7047668193252918,0.7013012225519919,38.00920104980469,30004.434818 +15912,Multiclass classification,Bagging,Keystroke,0.6956822324178241,0.6956822324178241,0.6887843659114408,38.955204010009766,31904.325566000003 +16320,Multiclass classification,Bagging,Keystroke,0.6869906244255163,0.6869906244255163,0.6817298949676788,39.901418685913086,33880.147234000004 +16728,Multiclass classification,Bagging,Keystroke,0.6840437615830693,0.6840437615830693,0.6809878840610977,40.84670162200928,35894.19480500001 +17136,Multiclass classification,Bagging,Keystroke,0.6798949518529326,0.6798949518529326,0.6760668667678135,42.678324699401855,37945.35177200001 +17544,Multiclass classification,Bagging,Keystroke,0.6725759562218548,0.6725759562218548,0.6693298574086026,43.72208595275879,40033.19473500001 +17952,Multiclass classification,Bagging,Keystroke,0.6715503314578575,0.6715503314578575,0.6700615486077944,44.668694496154785,42156.41544100001 +18360,Multiclass classification,Bagging,Keystroke,0.6768887194291628,0.6768887194291628,0.6760264883444682,45.61451721191406,44306.462280000014 +18768,Multiclass classification,Bagging,Keystroke,0.6818884211648105,0.6818884211648105,0.6814185274246665,46.561092376708984,46484.07667300002 +19176,Multiclass classification,Bagging,Keystroke,0.6739504563233377,0.6739504563233377,0.6724064481498903,47.50611400604248,48682.185033000016 +19584,Multiclass classification,Bagging,Keystroke,0.677883878874534,0.677883878874534,0.6774885006147249,48.451809883117676,50904.928535000014 +19992,Multiclass classification,Bagging,Keystroke,0.6733530088539843,0.6733530088539843,0.6729949515014169,49.39821243286133,53145.742009000016 +20400,Multiclass classification,Bagging,Keystroke,0.6697387126819943,0.6697387126819943,0.6699810213452306,50.34487438201904,55411.38251600001 +46,Multiclass classification,Leveraging Bagging,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,4.0974016189575195,6.997987 +92,Multiclass classification,Leveraging Bagging,ImageSegments,0.5164835164835165,0.5164835164835165,0.5316649744849407,4.0979814529418945,22.017115 +138,Multiclass classification,Leveraging Bagging,ImageSegments,0.5547445255474452,0.5547445255474452,0.5804654781117263,4.0981035232543945,44.610383999999996 +184,Multiclass classification,Leveraging Bagging,ImageSegments,0.6174863387978142,0.6174863387978142,0.6394923756219437,4.0987138748168945,74.61421299999999 +230,Multiclass classification,Leveraging Bagging,ImageSegments,0.6506550218340611,0.6506550218340611,0.66859135700569,4.0987138748168945,111.65332099999999 +276,Multiclass classification,Leveraging Bagging,ImageSegments,0.6618181818181819,0.6618181818181819,0.6795855359270878,4.098832130432129,156.076644 +322,Multiclass classification,Leveraging Bagging,ImageSegments,0.6853582554517134,0.6853582554517134,0.6872635633687633,4.099373817443848,207.57491499999998 +368,Multiclass classification,Leveraging Bagging,ImageSegments,0.7111716621253406,0.7111716621253404,0.7098417316927395,4.099347114562988,266.34173899999996 +414,Multiclass classification,Leveraging Bagging,ImageSegments,0.7215496368038741,0.7215496368038742,0.7201557312728714,4.09926700592041,332.356571 +460,Multiclass classification,Leveraging Bagging,ImageSegments,0.7211328976034859,0.721132897603486,0.7175330036146421,4.099320411682129,405.380301 +506,Multiclass classification,Leveraging Bagging,ImageSegments,0.7287128712871287,0.7287128712871287,0.7233455022590812,4.099320411682129,485.520305 +552,Multiclass classification,Leveraging Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7255599965917697,4.099240303039551,572.983507 +598,Multiclass classification,Leveraging Bagging,ImageSegments,0.7353433835845896,0.7353433835845896,0.7308494254186014,4.0992631912231445,667.526521 +644,Multiclass classification,Leveraging Bagging,ImageSegments,0.7340590979782271,0.7340590979782271,0.7314183982762247,4.099823951721191,768.914228 +690,Multiclass classification,Leveraging Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7343909641298695,4.099823951721191,877.069835 +736,Multiclass classification,Leveraging Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7369557659594496,4.099850654602051,992.1901310000001 +782,Multiclass classification,Leveraging Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7419020281650245,4.099850654602051,1114.103609 +828,Multiclass classification,Leveraging Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7432199627682998,4.099850654602051,1242.576589 +874,Multiclass classification,Leveraging Bagging,ImageSegments,0.7502863688430699,0.7502863688430699,0.7482089866208982,4.099850654602051,1377.530874 +920,Multiclass classification,Leveraging Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7477650187313974,4.099823951721191,1518.374517 +966,Multiclass classification,Leveraging Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.747322646811651,4.099823951721191,1664.8953589999999 +1012,Multiclass classification,Leveraging Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.7469783619055548,4.099823951721191,1817.1987969999998 +1058,Multiclass classification,Leveraging Bagging,ImageSegments,0.7530747398297067,0.7530747398297066,0.7482363934596314,4.099823951721191,1975.1124209999998 +1104,Multiclass classification,Leveraging Bagging,ImageSegments,0.7552130553037172,0.7552130553037172,0.750118495060715,4.0998735427856445,2138.658016 +1150,Multiclass classification,Leveraging Bagging,ImageSegments,0.7571801566579635,0.7571801566579635,0.7516199800653577,4.0998735427856445,2307.825702 +1196,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598326359832636,0.7598326359832636,0.7548841797367702,4.0998735427856445,2482.820129 +1242,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598710717163578,0.7598710717163577,0.7553301531902636,4.0998735427856445,2663.4478950000002 +1288,Multiclass classification,Leveraging Bagging,ImageSegments,0.7645687645687645,0.7645687645687647,0.7590078532621816,4.1004838943481445,2849.419913 +1334,Multiclass classification,Leveraging Bagging,ImageSegments,0.7644411102775694,0.7644411102775694,0.7591993978414527,4.100506782531738,3040.9965970000003 +1380,Multiclass classification,Leveraging Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7601575050520947,4.100506782531738,3238.5768190000003 +1426,Multiclass classification,Leveraging Bagging,ImageSegments,0.7670175438596492,0.7670175438596492,0.7613339877221927,4.100506782531738,3441.4807240000005 +1472,Multiclass classification,Leveraging Bagging,ImageSegments,0.7715839564921821,0.7715839564921821,0.7641396475218201,4.100552558898926,3649.5015090000006 +1518,Multiclass classification,Leveraging Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.7648275341801108,4.100552558898926,3862.6942700000004 +1564,Multiclass classification,Leveraging Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7657569341108763,4.100552558898926,4080.8910560000004 +1610,Multiclass classification,Leveraging Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7651494083475014,4.1005754470825195,4304.577590000001 +1656,Multiclass classification,Leveraging Bagging,ImageSegments,0.7740181268882175,0.7740181268882175,0.7654813489818475,4.100529670715332,4533.710142000001 +1702,Multiclass classification,Leveraging Bagging,ImageSegments,0.7730746619635509,0.7730746619635509,0.766493027961906,4.100529670715332,4767.793233000001 +1748,Multiclass classification,Leveraging Bagging,ImageSegments,0.7756153405838581,0.7756153405838581,0.7686072256536652,4.100529670715332,5007.029776000001 +1794,Multiclass classification,Leveraging Bagging,ImageSegments,0.7769102063580591,0.7769102063580591,0.7685414235990152,4.100502967834473,5251.440116000002 +1840,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781402936378466,0.7781402936378466,0.7699957723931323,4.100502967834473,5500.964415000001 +1886,Multiclass classification,Leveraging Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7684985598909853,4.100502967834473,5755.503987000001 +1932,Multiclass classification,Leveraging Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7677434418046419,4.100502967834473,6014.862306000001 +1978,Multiclass classification,Leveraging Bagging,ImageSegments,0.7774405665149215,0.7774405665149215,0.7684788817649146,4.100502967834473,6279.121569000001 +2024,Multiclass classification,Leveraging Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7689103339153599,4.100502967834473,6548.278113000001 +2070,Multiclass classification,Leveraging Bagging,ImageSegments,0.7786370227162881,0.7786370227162881,0.7686288077529282,4.100502967834473,6822.363214000001 +2116,Multiclass classification,Leveraging Bagging,ImageSegments,0.7791962174940898,0.7791962174940898,0.768391950800897,4.100502967834473,7101.096348000001 +2162,Multiclass classification,Leveraging Bagging,ImageSegments,0.7801943544655252,0.7801943544655253,0.768962628827985,4.100525856018066,7384.333285000001 +2208,Multiclass classification,Leveraging Bagging,ImageSegments,0.7820570910738559,0.7820570910738559,0.7698068761587117,4.100499153137207,7672.298476000001 +2254,Multiclass classification,Leveraging Bagging,ImageSegments,0.7789613848202397,0.7789613848202397,0.7667173742344939,4.100499153137207,7965.117559000001 +2300,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781644193127447,0.7781644193127447,0.7659138381656089,4.100499153137207,8262.647904000001 +2310,Multiclass classification,Leveraging Bagging,ImageSegments,0.7782589865742746,0.7782589865742745,0.7660163657276376,4.100499153137207,8561.303246000001 +1056,Multiclass classification,Leveraging Bagging,Insects,0.6218009478672986,0.6218009478672986,0.5857016652718549,6.471495628356934,220.837673 +2112,Multiclass classification,Leveraging Bagging,Insects,0.6196115585030791,0.6196115585030791,0.5856756432415233,10.302834510803223,598.297395 +3168,Multiclass classification,Leveraging Bagging,Insects,0.628986422481844,0.628986422481844,0.5949930595607559,19.024110794067383,1103.516793 +4224,Multiclass classification,Leveraging Bagging,Insects,0.6294103717736207,0.6294103717736207,0.5952675443708706,19.52926254272461,1735.893967 +5280,Multiclass classification,Leveraging Bagging,Insects,0.6364841826103429,0.6364841826103429,0.5994911272790603,18.82306957244873,2497.807238 +6336,Multiclass classification,Leveraging Bagging,Insects,0.6352012628255722,0.6352012628255722,0.5993891820807258,20.00343894958496,3379.788115 +7392,Multiclass classification,Leveraging Bagging,Insects,0.638749830875389,0.638749830875389,0.6030343276880051,20.9547061920166,4385.582643 +8448,Multiclass classification,Leveraging Bagging,Insects,0.6405824553095774,0.6405824553095774,0.6028521616895871,23.98197650909424,5520.259032 +9504,Multiclass classification,Leveraging Bagging,Insects,0.6449542249815847,0.6449542249815847,0.6055705492028415,24.687146186828613,6764.141036999999 +10560,Multiclass classification,Leveraging Bagging,Insects,0.6485462638507434,0.6485462638507434,0.6081614166360887,28.76917839050293,8102.806145 +11616,Multiclass classification,Leveraging Bagging,Insects,0.6490744726646578,0.6490744726646578,0.6078786452761632,30.803756713867188,9530.02909 +12672,Multiclass classification,Leveraging Bagging,Insects,0.6514876489621971,0.6514876489621971,0.6111938480023122,35.14385414123535,11044.697830000001 +13728,Multiclass classification,Leveraging Bagging,Insects,0.6707947840023312,0.6707947840023312,0.6607574394823457,17.51351547241211,12617.098737 +14784,Multiclass classification,Leveraging Bagging,Insects,0.6821348846648176,0.6821348846648176,0.6733632096765088,9.275564193725586,14250.678949000001 +15840,Multiclass classification,Leveraging Bagging,Insects,0.6778205694803965,0.6778205694803965,0.670556396248407,11.964457511901855,15956.730999000001 +16896,Multiclass classification,Leveraging Bagging,Insects,0.6754661142349808,0.6754661142349808,0.6690281338426608,12.60369873046875,17732.973803 +17952,Multiclass classification,Leveraging Bagging,Insects,0.6721631106902123,0.6721631106902123,0.6660357480506892,12.93508529663086,19577.321708 +19008,Multiclass classification,Leveraging Bagging,Insects,0.6856947440416689,0.6856947440416689,0.6751812770122833,14.563780784606934,21465.395048 +20064,Multiclass classification,Leveraging Bagging,Insects,0.6926680954991776,0.6926680954991776,0.6785701715539604,23.61655616760254,23398.659989 +21120,Multiclass classification,Leveraging Bagging,Insects,0.6942090061082438,0.6942090061082438,0.6784920731228882,30.020954132080078,25401.280766 +22176,Multiclass classification,Leveraging Bagging,Insects,0.6958737316798196,0.6958737316798196,0.6784853924286285,31.293453216552734,27443.688764 +23232,Multiclass classification,Leveraging Bagging,Insects,0.6989798114588266,0.6989798114588266,0.6799590657327791,29.59604263305664,29526.276676999998 +24288,Multiclass classification,Leveraging Bagging,Insects,0.7011981718614897,0.7011981718614897,0.680282364066019,32.615909576416016,31645.669427999997 +25344,Multiclass classification,Leveraging Bagging,Insects,0.7031527443475516,0.7031527443475516,0.6805566439417602,33.91432285308838,33792.819539 +26400,Multiclass classification,Leveraging Bagging,Insects,0.7051782264479716,0.7051782264479716,0.6809495737401271,35.12977695465088,35966.301701 +27456,Multiclass classification,Leveraging Bagging,Insects,0.7065743944636678,0.7065743944636678,0.6805936316849747,38.84447956085205,38159.78466 +28512,Multiclass classification,Leveraging Bagging,Insects,0.7054820946301428,0.7054820946301428,0.681225779493031,34.570815086364746,40377.715598999996 +29568,Multiclass classification,Leveraging Bagging,Insects,0.7045692833226231,0.7045692833226231,0.6849598194839713,20.382534980773926,42611.23284999999 +30624,Multiclass classification,Leveraging Bagging,Insects,0.7031316330862424,0.7031316330862424,0.6877640955933652,22.55568027496338,44864.71640799999 +31680,Multiclass classification,Leveraging Bagging,Insects,0.7032418952618454,0.7032418952618454,0.6917227552448634,26.177990913391113,47133.482559 +32736,Multiclass classification,Leveraging Bagging,Insects,0.7037421719871697,0.7037421719871697,0.6952024388211077,25.761178016662598,49415.922784999995 +33792,Multiclass classification,Leveraging Bagging,Insects,0.7002160338551685,0.7002160338551685,0.6931280234945141,25.958494186401367,51714.47139399999 +34848,Multiclass classification,Leveraging Bagging,Insects,0.6973627571957414,0.6973627571957414,0.6902163957562899,18.894118309020996,54032.337051999995 +35904,Multiclass classification,Leveraging Bagging,Insects,0.6951786758766677,0.6951786758766677,0.6877287571005829,18.049145698547363,56371.370632 +36960,Multiclass classification,Leveraging Bagging,Insects,0.6919830081982737,0.6919830081982737,0.6843647347906762,22.045016288757324,58731.919307 +38016,Multiclass classification,Leveraging Bagging,Insects,0.6900697093252663,0.6900697093252663,0.68217396069655,25.079078674316406,61114.705623999995 +39072,Multiclass classification,Leveraging Bagging,Insects,0.688720534411712,0.688720534411712,0.6808510434728485,19.794261932373047,63520.517782999996 +40128,Multiclass classification,Leveraging Bagging,Insects,0.6867695068158597,0.6867695068158597,0.6796002866264578,10.854747772216797,65948.78476699999 +41184,Multiclass classification,Leveraging Bagging,Insects,0.6843843333414273,0.6843843333414273,0.6779529807793833,10.474969863891602,68395.63477799999 +42240,Multiclass classification,Leveraging Bagging,Insects,0.6822131205757712,0.6822131205757712,0.6764872431583758,14.707494735717773,70864.05938699999 +43296,Multiclass classification,Leveraging Bagging,Insects,0.6795472918350849,0.6795472918350849,0.674587653669649,12.672552108764648,73351.02096199998 +44352,Multiclass classification,Leveraging Bagging,Insects,0.6769633153705666,0.6769633153705666,0.6725984110786069,13.144417762756348,75857.66838799998 +45408,Multiclass classification,Leveraging Bagging,Insects,0.6748959411544475,0.6748959411544475,0.6710316194917795,14.719610214233398,78383.45415699997 +46464,Multiclass classification,Leveraging Bagging,Insects,0.6743215031315241,0.6743215031315241,0.670959098678123,15.027325630187988,80927.72302099997 +47520,Multiclass classification,Leveraging Bagging,Insects,0.6765293882447021,0.6765293882447021,0.6733002712216741,17.283148765563965,83488.02074099997 +48576,Multiclass classification,Leveraging Bagging,Insects,0.6805970149253732,0.6805970149253732,0.6770692638556323,17.906007766723633,86063.52222099998 +49632,Multiclass classification,Leveraging Bagging,Insects,0.6848340754770205,0.6848340754770205,0.6808344811077705,18.8202543258667,88653.18323199998 +50688,Multiclass classification,Leveraging Bagging,Insects,0.6890524197525992,0.6890524197525992,0.6843657264244208,21.507144927978516,91255.74433499998 +51744,Multiclass classification,Leveraging Bagging,Insects,0.6932531936687089,0.6932531936687089,0.6877873898777546,23.154582023620605,93870.63731899999 +52800,Multiclass classification,Leveraging Bagging,Insects,0.6956002954601412,0.6956002954601412,0.6902433463100389,14.128369331359863,96495.21656399999 +52848,Multiclass classification,Leveraging Bagging,Insects,0.6958578538043787,0.6958578538043787,0.6905081705907102,13.831001281738281,99120.19143899999 +408,Multiclass classification,Leveraging Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.0028390884399414,23.27864 +816,Multiclass classification,Leveraging Bagging,Keystroke,0.9521472392638037,0.9521472392638037,0.8408896590786493,4.076430320739746,76.761208 +1224,Multiclass classification,Leveraging Bagging,Keystroke,0.9533932951757972,0.9533932951757972,0.9542235338779169,5.6716413497924805,164.877928 +1632,Multiclass classification,Leveraging Bagging,Keystroke,0.9589209074187615,0.9589209074187615,0.9361222534860761,8.122180938720703,291.606081 +2040,Multiclass classification,Leveraging Bagging,Keystroke,0.9573320255026974,0.9573320255026974,0.9445755787125868,10.5212984085083,455.912148 +2448,Multiclass classification,Leveraging Bagging,Keystroke,0.9607682876992235,0.9607682876992235,0.9588299190873342,9.065413475036621,649.921126 +2856,Multiclass classification,Leveraging Bagging,Keystroke,0.9618213660245184,0.9618213660245184,0.9516555143941907,13.188368797302246,870.236672 +3264,Multiclass classification,Leveraging Bagging,Keystroke,0.9589334967821024,0.9589334967821024,0.9492703335352553,13.21088695526123,1122.958204 +3672,Multiclass classification,Leveraging Bagging,Keystroke,0.9585943884500137,0.9585943884500137,0.9531276848185062,16.65507411956787,1406.732623 +4080,Multiclass classification,Leveraging Bagging,Keystroke,0.9541554302525129,0.9541554302525129,0.9416377826660955,17.091320037841797,1722.764314 +4488,Multiclass classification,Leveraging Bagging,Keystroke,0.9529752618676176,0.9529752618676176,0.9549694463549354,10.336687088012695,2070.686487 +4896,Multiclass classification,Leveraging Bagging,Keystroke,0.9550561797752809,0.9550561797752809,0.95517907029875,11.520882606506348,2449.358224 +5304,Multiclass classification,Leveraging Bagging,Keystroke,0.9568168960965491,0.9568168960965491,0.9575833276239932,13.737529754638672,2856.6015070000003 +5712,Multiclass classification,Leveraging Bagging,Keystroke,0.9574505340570828,0.9574505340570828,0.9570632809827344,15.842782020568848,3290.691514 +6120,Multiclass classification,Leveraging Bagging,Keystroke,0.9557117176009152,0.9557117176009152,0.9522483041543378,20.042810440063477,3760.43818 +6528,Multiclass classification,Leveraging Bagging,Keystroke,0.9566416424084572,0.9566416424084572,0.9568246790885271,11.687369346618652,4258.095146 +6936,Multiclass classification,Leveraging Bagging,Keystroke,0.9574621485219899,0.9574621485219899,0.9579855320572277,12.487288475036621,4780.363237 +7344,Multiclass classification,Leveraging Bagging,Keystroke,0.9568296336647147,0.9568296336647147,0.9563404233689646,15.327423095703125,5334.336646 +7752,Multiclass classification,Leveraging Bagging,Keystroke,0.9565217391304348,0.9565217391304348,0.9563017119581124,18.70553493499756,5920.99825 +8160,Multiclass classification,Leveraging Bagging,Keystroke,0.9545287412673121,0.9545287412673121,0.9527980459948603,24.086796760559082,6539.621381999999 +8568,Multiclass classification,Leveraging Bagging,Keystroke,0.9545932064900199,0.9545932064900199,0.9549210113442089,21.21516990661621,7187.28418 +8976,Multiclass classification,Leveraging Bagging,Keystroke,0.9550974930362117,0.9550974930362117,0.9553627160759579,17.566545486450195,7867.8027329999995 +9384,Multiclass classification,Leveraging Bagging,Keystroke,0.9555579239049344,0.9555579239049344,0.9558253322166266,15.710055351257324,8578.363562999999 +9792,Multiclass classification,Leveraging Bagging,Keystroke,0.9552650393218262,0.9552650393218262,0.9553715117788079,18.717252731323242,9321.146735999999 +10200,Multiclass classification,Leveraging Bagging,Keystroke,0.9533287577213452,0.9533287577213452,0.9523119157834915,15.605277061462402,10099.276789999998 +10608,Multiclass classification,Leveraging Bagging,Keystroke,0.9521070990855096,0.9521070990855096,0.9515822083565743,11.186952590942383,10903.979313999998 +11016,Multiclass classification,Leveraging Bagging,Keystroke,0.953427144802542,0.953427144802542,0.9541201209142027,7.581887245178223,11728.435273 +11424,Multiclass classification,Leveraging Bagging,Keystroke,0.953689923837871,0.953689923837871,0.9538275342826803,10.15964126586914,12573.844722999998 +11832,Multiclass classification,Leveraging Bagging,Keystroke,0.9535964838137098,0.9535964838137098,0.9538502960885477,11.061944961547852,13441.174568999999 +12240,Multiclass classification,Leveraging Bagging,Keystroke,0.9541629218073372,0.9541629218073372,0.9544632162431566,11.249642372131348,14331.138910999998 +12648,Multiclass classification,Leveraging Bagging,Keystroke,0.9548509527951293,0.9548509527951293,0.9551609875055331,13.203255653381348,15243.410521999998 +13056,Multiclass classification,Leveraging Bagging,Keystroke,0.9551895825354271,0.955189582535427,0.9553883557595891,9.36058521270752,16176.429406999998 +13464,Multiclass classification,Leveraging Bagging,Keystroke,0.955953353635891,0.955953353635891,0.9562606797905644,11.575583457946777,17130.275872 +13872,Multiclass classification,Leveraging Bagging,Keystroke,0.9561675437964098,0.9561675437964098,0.9563487774281333,11.42638874053955,18106.072389999998 +14280,Multiclass classification,Leveraging Bagging,Keystroke,0.9549688353526157,0.9549688353526157,0.9548529395574759,10.249165534973145,19109.380900999997 +14688,Multiclass classification,Leveraging Bagging,Keystroke,0.9552665622659495,0.9552665622659495,0.955472434271787,8.168793678283691,20137.264238999996 +15096,Multiclass classification,Leveraging Bagging,Keystroke,0.9560781715799934,0.9560781715799934,0.9563263247313607,9.020037651062012,21191.151532999997 +15504,Multiclass classification,Leveraging Bagging,Keystroke,0.9563955363478036,0.9563955363478036,0.9565429512012837,8.031278610229492,22273.408126999995 +15912,Multiclass classification,Leveraging Bagging,Keystroke,0.9566337753755264,0.9566337753755264,0.9567672375037608,10.967172622680664,23379.117397999995 +16320,Multiclass classification,Leveraging Bagging,Keystroke,0.9563085973405233,0.9563085973405233,0.9563585840602682,11.29026985168457,24508.785403999995 +16728,Multiclass classification,Leveraging Bagging,Keystroke,0.955580797513003,0.955580797513003,0.9555776398983683,9.525394439697266,25660.924918999994 +17136,Multiclass classification,Leveraging Bagging,Keystroke,0.9564050189670266,0.9564050189670267,0.9565585833577668,10.421767234802246,26839.493165999993 +17544,Multiclass classification,Leveraging Bagging,Keystroke,0.9566778772159836,0.9566778772159836,0.9567660151847867,11.633780479431152,28038.177978999993 +17952,Multiclass classification,Leveraging Bagging,Keystroke,0.9564369672998718,0.9564369672998718,0.9564736297242662,8.448995590209961,29257.620389999993 +18360,Multiclass classification,Leveraging Bagging,Keystroke,0.9567514570510376,0.9567514570510375,0.9568227044222711,7.821832656860352,30499.29393699999 +18768,Multiclass classification,Leveraging Bagging,Keystroke,0.9568924175414291,0.9568924175414291,0.9569505378685396,9.859258651733398,31763.565401999993 +19176,Multiclass classification,Leveraging Bagging,Keystroke,0.9567144719687093,0.9567144719687093,0.956766336746882,11.256629943847656,33053.804573999994 +19584,Multiclass classification,Leveraging Bagging,Keystroke,0.9568503293673084,0.9568503293673084,0.9569026376832065,11.690522193908691,34367.36625199999 +19992,Multiclass classification,Leveraging Bagging,Keystroke,0.9564303936771548,0.9564303936771548,0.9564653381379459,12.451186180114746,35701.899461999994 +20400,Multiclass classification,Leveraging Bagging,Keystroke,0.9566155203686455,0.9566155203686455,0.9566498206969933,7.4099931716918945,37049.10208799999 +46,Multiclass classification,Stacking,ImageSegments,0.4,0.4000000000000001,0.3289160825620571,1.89190673828125,1.901401 +92,Multiclass classification,Stacking,ImageSegments,0.5494505494505495,0.5494505494505495,0.5607526488856412,2.084074020385742,6.467373 +138,Multiclass classification,Stacking,ImageSegments,0.5693430656934306,0.5693430656934306,0.5872103411959265,2.357966423034668,13.822826 +184,Multiclass classification,Stacking,ImageSegments,0.6174863387978142,0.6174863387978142,0.6372989403156369,2.7369613647460938,24.259991 +230,Multiclass classification,Stacking,ImageSegments,0.6375545851528385,0.6375545851528385,0.6548159763148107,2.862431526184082,37.817904999999996 +276,Multiclass classification,Stacking,ImageSegments,0.6618181818181819,0.6618181818181819,0.6802187985971371,2.982741355895996,54.565380999999995 +322,Multiclass classification,Stacking,ImageSegments,0.6915887850467289,0.6915887850467289,0.6955507555363084,3.080752372741699,74.633343 +368,Multiclass classification,Stacking,ImageSegments,0.7111716621253406,0.7111716621253404,0.7105739026832886,3.232259750366211,98.20470399999999 +414,Multiclass classification,Stacking,ImageSegments,0.7263922518159807,0.7263922518159807,0.7261041400072307,3.505929946899414,125.52754499999999 +460,Multiclass classification,Stacking,ImageSegments,0.7276688453159041,0.7276688453159043,0.72519869331257,3.7872886657714844,156.78717 +506,Multiclass classification,Stacking,ImageSegments,0.7425742574257426,0.7425742574257425,0.7379486431795568,6.240692138671875,210.52347600000002 +552,Multiclass classification,Stacking,ImageSegments,0.7422867513611615,0.7422867513611615,0.7388440561615693,6.313092231750488,268.009607 +598,Multiclass classification,Stacking,ImageSegments,0.7520938023450586,0.7520938023450586,0.749839509127547,6.682056427001953,329.26112900000004 +644,Multiclass classification,Stacking,ImageSegments,0.7573872472783826,0.7573872472783826,0.7582793237949303,7.269444465637207,394.37227100000007 +690,Multiclass classification,Stacking,ImageSegments,0.7634252539912917,0.7634252539912917,0.7648953830992049,7.531791687011719,463.2777280000001 +736,Multiclass classification,Stacking,ImageSegments,0.7673469387755102,0.7673469387755102,0.7694390547687558,7.987269401550293,536.1609010000001 +782,Multiclass classification,Stacking,ImageSegments,0.7772087067861716,0.7772087067861717,0.7788980835102386,8.317158699035645,613.0067590000001 +828,Multiclass classification,Stacking,ImageSegments,0.7823458282950423,0.7823458282950423,0.7854763667551727,8.613452911376953,693.8523060000001 +874,Multiclass classification,Stacking,ImageSegments,0.7915234822451317,0.7915234822451317,0.7933203073280156,8.694649696350098,778.7710210000001 +920,Multiclass classification,Stacking,ImageSegments,0.7986942328618063,0.7986942328618062,0.7996826842527437,8.824880599975586,867.8856220000001 +966,Multiclass classification,Stacking,ImageSegments,0.8041450777202073,0.8041450777202073,0.8044659150084363,9.089361190795898,961.2082950000001 +1012,Multiclass classification,Stacking,ImageSegments,0.8100890207715133,0.8100890207715133,0.8093994872208631,9.280214309692383,1058.9817440000002 +1058,Multiclass classification,Stacking,ImageSegments,0.8145695364238411,0.814569536423841,0.8133421993203876,9.165953636169434,1161.0697040000002 +1104,Multiclass classification,Stacking,ImageSegments,0.8213961922030825,0.8213961922030824,0.8206569542548617,8.760258674621582,1267.2341280000003 +1150,Multiclass classification,Stacking,ImageSegments,0.824194952132289,0.824194952132289,0.8228781271733864,8.742037773132324,1377.3471480000003 +1196,Multiclass classification,Stacking,ImageSegments,0.8292887029288702,0.8292887029288704,0.8281638601893785,8.87535572052002,1491.4919770000004 +1242,Multiclass classification,Stacking,ImageSegments,0.8340048348106366,0.8340048348106366,0.833490204478907,8.332135200500488,1609.3898390000004 +1288,Multiclass classification,Stacking,ImageSegments,0.8360528360528361,0.8360528360528361,0.8353480055004047,8.416248321533203,1730.8650650000004 +1334,Multiclass classification,Stacking,ImageSegments,0.8394598649662416,0.8394598649662416,0.8389194005130135,8.469959259033203,1855.8596220000004 +1380,Multiclass classification,Stacking,ImageSegments,0.8419144307469181,0.8419144307469181,0.8414934007209077,8.578604698181152,1984.2269550000003 +1426,Multiclass classification,Stacking,ImageSegments,0.8449122807017544,0.8449122807017544,0.8435602800871403,8.689190864562988,2115.814455 +1472,Multiclass classification,Stacking,ImageSegments,0.8484024473147519,0.8484024473147518,0.8459519552383536,8.800261497497559,2250.6136890000002 +1518,Multiclass classification,Stacking,ImageSegments,0.8503625576796309,0.8503625576796308,0.8475723684173131,9.025433540344238,2388.852428 +1564,Multiclass classification,Stacking,ImageSegments,0.8522072936660269,0.8522072936660269,0.8497128793769615,8.811847686767578,2530.498155 +1610,Multiclass classification,Stacking,ImageSegments,0.8527035425730267,0.8527035425730267,0.8503048238231962,8.729784965515137,2675.5358680000004 +1656,Multiclass classification,Stacking,ImageSegments,0.8531722054380665,0.8531722054380665,0.8508343416398155,8.761359214782715,2823.7565440000003 +1702,Multiclass classification,Stacking,ImageSegments,0.8571428571428571,0.8571428571428571,0.8561317791292776,8.798370361328125,2975.2442650000003 +1748,Multiclass classification,Stacking,ImageSegments,0.8580423583285632,0.8580423583285632,0.8567712479140972,8.86152172088623,3129.874549 +1794,Multiclass classification,Stacking,ImageSegments,0.8611266034578918,0.8611266034578918,0.8591986188286931,8.932531356811523,3287.541657 +1840,Multiclass classification,Stacking,ImageSegments,0.8618814573137574,0.8618814573137574,0.8601172531559075,8.819746017456055,3448.3205970000004 +1886,Multiclass classification,Stacking,ImageSegments,0.8636604774535809,0.8636604774535809,0.8623243992773615,9.007128715515137,3612.1367020000002 +1932,Multiclass classification,Stacking,ImageSegments,0.8648368720870016,0.8648368720870016,0.8630569076841595,9.368453979492188,3779.147863 +1978,Multiclass classification,Stacking,ImageSegments,0.8649468892261002,0.8649468892261002,0.8631362872103546,8.952109336853027,3949.363777 +2024,Multiclass classification,Stacking,ImageSegments,0.8665348492338112,0.8665348492338112,0.8639071890295129,9.146061897277832,4122.536804 +2070,Multiclass classification,Stacking,ImageSegments,0.8680521991300145,0.8680521991300145,0.8658036637930728,8.80567455291748,4298.84894 +2116,Multiclass classification,Stacking,ImageSegments,0.8695035460992908,0.8695035460992909,0.8667661913422944,8.892473220825195,4478.129032 +2162,Multiclass classification,Stacking,ImageSegments,0.8690421101341971,0.869042110134197,0.8663186552920692,8.910783767700195,4660.4030729999995 +2208,Multiclass classification,Stacking,ImageSegments,0.8699592206615315,0.8699592206615315,0.8669965232275297,8.99278450012207,4845.573407999999 +2254,Multiclass classification,Stacking,ImageSegments,0.869063470927652,0.8690634709276521,0.8666022158227548,9.09610366821289,5033.674223999999 +2300,Multiclass classification,Stacking,ImageSegments,0.8686385384949978,0.8686385384949978,0.8662053097556822,9.110825538635254,5224.6921839999995 +2310,Multiclass classification,Stacking,ImageSegments,0.8679081853616284,0.8679081853616284,0.8656034675726049,9.181622505187988,5416.881651 +1056,Multiclass classification,Stacking,Insects,0.6511848341232227,0.6511848341232227,0.5864257754346489,12.51792049407959,137.265242 +2112,Multiclass classification,Stacking,Insects,0.6873519658929418,0.6873519658929418,0.6004104483953082,15.371862411499023,366.367491 +3168,Multiclass classification,Stacking,Insects,0.6978212819703189,0.6978212819703189,0.602242348585179,17.772335052490234,671.574116 +4224,Multiclass classification,Stacking,Insects,0.7054226852948141,0.7054226852948141,0.6059831617919115,20.14197826385498,1043.757912 +5280,Multiclass classification,Stacking,Insects,0.7080886531540065,0.7080886531540066,0.6082411118035554,23.246225357055664,1476.569185 +6336,Multiclass classification,Stacking,Insects,0.708602999210734,0.708602999210734,0.6091818949546898,28.13547992706299,1970.1501170000001 +7392,Multiclass classification,Stacking,Insects,0.7104586659450683,0.7104586659450683,0.6104104212994758,30.164710998535156,2526.789716 +8448,Multiclass classification,Stacking,Insects,0.7130342133301764,0.7130342133301764,0.6119778058667307,27.698996543884277,3146.1270590000004 +9504,Multiclass classification,Stacking,Insects,0.717773334736399,0.717773334736399,0.6149023583636667,27.04288387298584,3829.1831980000006 +10560,Multiclass classification,Stacking,Insects,0.7215645420967894,0.7215645420967894,0.617635708330779,23.96706485748291,4572.729772000001 +11616,Multiclass classification,Stacking,Insects,0.7213086526043909,0.721308652604391,0.6182075626749539,26.15617847442627,5374.612830000001 +12672,Multiclass classification,Stacking,Insects,0.7240943887617394,0.7240943887617394,0.6351065980046956,25.051542282104492,6233.453892000001 +13728,Multiclass classification,Stacking,Insects,0.7432796678079697,0.7432796678079697,0.7402334392509421,15.30208683013916,7142.743199000001 +14784,Multiclass classification,Stacking,Insects,0.7491713454643848,0.7491713454643848,0.7487081677599373,11.128735542297363,8102.097506000001 +15840,Multiclass classification,Stacking,Insects,0.7424079803017867,0.7424079803017867,0.7445532404968841,16.950417518615723,9128.379042 +16896,Multiclass classification,Stacking,Insects,0.7382657591003255,0.7382657591003255,0.7427378731329454,18.26229953765869,10214.572621000001 +17952,Multiclass classification,Stacking,Insects,0.7309342097933262,0.7309342097933262,0.7368436311738037,23.776363372802734,11358.099531000002 +19008,Multiclass classification,Stacking,Insects,0.7429368127531962,0.7429368127531962,0.7441354243297112,12.958039283752441,12553.803014000001 +20064,Multiclass classification,Stacking,Insects,0.7475950755121368,0.7475950755121367,0.7439196968116685,12.612845420837402,13796.415893000001 +21120,Multiclass classification,Stacking,Insects,0.7492305506889531,0.7492305506889531,0.7418613509588597,16.95127773284912,15088.238885 +22176,Multiclass classification,Stacking,Insects,0.7509808342728298,0.7509808342728299,0.7400929587109365,17.926865577697754,16424.025269 +23232,Multiclass classification,Stacking,Insects,0.7532176832680471,0.7532176832680472,0.7391930166872092,20.939698219299316,17798.240955 +24288,Multiclass classification,Stacking,Insects,0.7550129699015935,0.7550129699015935,0.7379653286035112,25.43882942199707,19212.969178 +25344,Multiclass classification,Stacking,Insects,0.7569743124334136,0.7569743124334136,0.7375346698329149,29.94521999359131,20668.368585 +26400,Multiclass classification,Stacking,Insects,0.7580590173870222,0.7580590173870221,0.7363169253318035,34.1699275970459,22166.950006 +27456,Multiclass classification,Stacking,Insects,0.7593880896011656,0.7593880896011656,0.7352131419868576,32.93678665161133,23706.536377 +28512,Multiclass classification,Stacking,Insects,0.7573217354705202,0.7573217354705202,0.7350502568377754,21.273219108581543,25286.984696 +29568,Multiclass classification,Stacking,Insects,0.7555382690161329,0.7555382690161329,0.7386915112539557,20.747055053710938,26906.631088 +30624,Multiclass classification,Stacking,Insects,0.7544982529471312,0.7544982529471312,0.7426503125712552,24.910794258117676,28562.795387 +31680,Multiclass classification,Stacking,Insects,0.7531487736355315,0.7531487736355315,0.7453200395899969,32.13512706756592,30253.076649 +32736,Multiclass classification,Stacking,Insects,0.7530471971895525,0.7530471971895525,0.7484606399297139,36.17057991027832,31977.334616 +33792,Multiclass classification,Stacking,Insects,0.7480986061377289,0.748098606137729,0.7448942365218528,13.298456192016602,33736.870240000004 +34848,Multiclass classification,Stacking,Insects,0.7436795133010016,0.7436795133010016,0.7403442775964885,15.221885681152344,35530.857132000005 +35904,Multiclass classification,Stacking,Insects,0.7404952232403977,0.7404952232403977,0.7368033013057004,16.932289123535156,37356.72126300001 +36960,Multiclass classification,Stacking,Insects,0.7371411564165697,0.7371411564165696,0.7332530467261859,22.237309455871582,39213.91358200001 +38016,Multiclass classification,Stacking,Insects,0.7341049585689859,0.7341049585689859,0.7299460315219516,22.86026954650879,41100.10013700001 +39072,Multiclass classification,Stacking,Insects,0.7343042154027284,0.7343042154027284,0.7301016033872143,21.91624164581299,43017.482867000006 +40128,Multiclass classification,Stacking,Insects,0.7327734443143021,0.7327734443143021,0.728948208474553,20.388718605041504,44961.93393100001 +41184,Multiclass classification,Stacking,Insects,0.7327538061821626,0.7327538061821626,0.7292630064673854,15.630711555480957,46951.12268600001 +42240,Multiclass classification,Stacking,Insects,0.7331849712351145,0.7331849712351144,0.7301128191332076,20.110919952392578,48959.16072000001 +43296,Multiclass classification,Stacking,Insects,0.7337567848481349,0.7337567848481349,0.7309969621648841,24.057676315307617,50985.068017000005 +44352,Multiclass classification,Stacking,Insects,0.7342111790038556,0.7342111790038556,0.731637560144403,28.529647827148438,53028.942305000004 +45408,Multiclass classification,Stacking,Insects,0.7351289448763406,0.7351289448763407,0.7324911060941295,28.861422538757324,55091.119898000004 +46464,Multiclass classification,Stacking,Insects,0.7357682457008803,0.7357682457008803,0.7329742877599967,33.076725006103516,57170.52325500001 +47520,Multiclass classification,Stacking,Insects,0.7366947957659041,0.736694795765904,0.7341498113226347,21.352835655212402,59267.07909500001 +48576,Multiclass classification,Stacking,Insects,0.7403602676273804,0.7403602676273804,0.7381372580344014,19.381468772888184,61379.50627500001 +49632,Multiclass classification,Stacking,Insects,0.7442122866756664,0.7442122866756663,0.742109373234967,21.8067569732666,63507.849119000006 +50688,Multiclass classification,Stacking,Insects,0.7475289521968157,0.7475289521968157,0.7453466445950636,21.65154266357422,65647.81315 +51744,Multiclass classification,Stacking,Insects,0.7510581141410433,0.7510581141410433,0.7487124138061083,22.870601654052734,67797.610737 +52800,Multiclass classification,Stacking,Insects,0.7545218659444307,0.7545218659444307,0.752582163258218,10.554459571838379,69956.07865499999 +52848,Multiclass classification,Stacking,Insects,0.7547448294132117,0.7547448294132117,0.7528178949021433,10.58643913269043,72115.038215 +408,Multiclass classification,Stacking,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.786503791809082,20.578742 +816,Multiclass classification,Stacking,Keystroke,0.9815950920245399,0.98159509202454,0.9278568842209168,6.9002227783203125,101.28008299999999 +1224,Multiclass classification,Stacking,Keystroke,0.9803761242845462,0.9803761242845462,0.9574942570636209,9.112634658813477,223.840162 +1632,Multiclass classification,Stacking,Keystroke,0.9779276517473943,0.9779276517473943,0.9432755457272627,10.40715503692627,381.844655 +2040,Multiclass classification,Stacking,Keystroke,0.973516429622364,0.973516429622364,0.9361356188587967,12.656171798706055,575.269036 +2448,Multiclass classification,Stacking,Keystroke,0.9726195341234164,0.9726195341234164,0.9612590316809274,8.745987892150879,802.257021 +2856,Multiclass classification,Stacking,Keystroke,0.9754816112084063,0.9754816112084063,0.9751469891413959,9.931495666503906,1061.688609 +3264,Multiclass classification,Stacking,Keystroke,0.9754826846460313,0.9754826846460313,0.9697604489278108,10.511832237243652,1352.298412 +3672,Multiclass classification,Stacking,Keystroke,0.9733042767638246,0.9733042767638246,0.9642745555297418,11.800049781799316,1675.3338330000001 +4080,Multiclass classification,Stacking,Keystroke,0.9722971316499142,0.9722971316499142,0.9666413905932107,12.42660903930664,2030.1772580000002 +4488,Multiclass classification,Stacking,Keystroke,0.9734789391575663,0.9734789391575663,0.9728883985144964,9.746350288391113,2413.735444 +4896,Multiclass classification,Stacking,Keystroke,0.9740551583248213,0.9740551583248213,0.9730015599884005,10.666529655456543,2823.5055469999998 +5304,Multiclass classification,Stacking,Keystroke,0.9741655666603809,0.9741655666603809,0.9728266773902405,11.775634765625,3261.739577 +5712,Multiclass classification,Stacking,Keystroke,0.9747855016634565,0.9747855016634565,0.9744326987999562,12.58005428314209,3727.994683 +6120,Multiclass classification,Stacking,Keystroke,0.9751593397613989,0.9751593397613989,0.9747223863351728,13.55466365814209,4223.159482999999 +6528,Multiclass classification,Stacking,Keystroke,0.9751800214493642,0.9751800214493642,0.9745255481694279,11.360074043273926,4747.988555 +6936,Multiclass classification,Stacking,Keystroke,0.9763518385003604,0.9763518385003604,0.9769458779347455,11.155635833740234,5300.577354999999 +7344,Multiclass classification,Stacking,Keystroke,0.9765763311997822,0.9765763311997822,0.9763923596721359,12.33658504486084,5881.2072339999995 +7752,Multiclass classification,Stacking,Keystroke,0.9771642368726616,0.9771642368726616,0.9773496343719735,13.116165161132812,6492.775159 +8160,Multiclass classification,Stacking,Keystroke,0.976590268415247,0.976590268415247,0.9759275084076021,13.303799629211426,7137.299991 +8568,Multiclass classification,Stacking,Keystroke,0.9768880588303958,0.9768880588303958,0.9769304999907085,13.133574485778809,7814.540539 +8976,Multiclass classification,Stacking,Keystroke,0.9774930362116991,0.9774930362116991,0.9777587646121523,13.50635814666748,8523.072866 +9384,Multiclass classification,Stacking,Keystroke,0.9767664925929873,0.9767664925929873,0.9763135719034829,15.166536331176758,9263.702674 +9792,Multiclass classification,Stacking,Keystroke,0.9765090389132877,0.9765090389132877,0.9763153416047449,16.169885635375977,10037.443626 +10200,Multiclass classification,Stacking,Keystroke,0.9758799882341406,0.9758799882341406,0.9755246287395946,14.205968856811523,10844.068015 +10608,Multiclass classification,Stacking,Keystroke,0.9755821627227302,0.9755821627227302,0.9754319444516873,12.997503280639648,11685.064117 +11016,Multiclass classification,Stacking,Keystroke,0.9759418974126192,0.9759418974126192,0.9761027289556774,12.962043762207031,12559.39796 +11424,Multiclass classification,Stacking,Keystroke,0.9760133064869124,0.9760133064869124,0.9760613734021468,14.090433120727539,13467.395857 +11832,Multiclass classification,Stacking,Keystroke,0.9754881244188995,0.9754881244188995,0.9753195915858492,14.295487403869629,14408.853786 +12240,Multiclass classification,Stacking,Keystroke,0.9759784296102623,0.9759784296102623,0.9761779987511395,15.044499397277832,15385.688043 +12648,Multiclass classification,Stacking,Keystroke,0.9762789594370206,0.9762789594370206,0.9764127823145236,15.120206832885742,16404.149055 +13056,Multiclass classification,Stacking,Keystroke,0.9758713136729222,0.9758713136729221,0.975797420384815,15.049361228942871,17460.942559000003 +13464,Multiclass classification,Stacking,Keystroke,0.9757112084973631,0.9757112084973631,0.9757165619520196,15.162266731262207,18558.798501 +13872,Multiclass classification,Stacking,Keystroke,0.9759930790858626,0.9759930790858626,0.9761084708221816,15.711796760559082,19695.838422 +14280,Multiclass classification,Stacking,Keystroke,0.9754884795854052,0.9754884795854052,0.975424480421301,16.988737106323242,20872.643227 +14688,Multiclass classification,Stacking,Keystroke,0.975624702117519,0.975624702117519,0.9757017096421697,17.869779586791992,22084.930025 +15096,Multiclass classification,Stacking,Keystroke,0.9757535607817158,0.9757535607817158,0.9758249143111629,17.579912185668945,23330.568569000003 +15504,Multiclass classification,Stacking,Keystroke,0.9755531187512094,0.9755531187512094,0.9755669148190674,16.59157657623291,24610.336028 +15912,Multiclass classification,Stacking,Keystroke,0.9756772044497517,0.9756772044497517,0.9757389077528199,16.193113327026367,25925.188427 +16320,Multiclass classification,Stacking,Keystroke,0.9759176420123782,0.9759176420123782,0.9759886766110538,16.353660583496094,27266.573062 +16728,Multiclass classification,Stacking,Keystroke,0.9756680815448078,0.9756680815448078,0.9756766431570708,17.00908374786377,28641.859399 +17136,Multiclass classification,Stacking,Keystroke,0.9758389261744966,0.9758389261744966,0.975891563489883,18.364989280700684,30047.550521 +17544,Multiclass classification,Stacking,Keystroke,0.9753747933648749,0.9753747933648749,0.975363882573194,17.298136711120605,31485.782589000002 +17952,Multiclass classification,Stacking,Keystroke,0.9753217090969862,0.9753217090969862,0.9753429667022142,16.72727108001709,32956.927282000004 +18360,Multiclass classification,Stacking,Keystroke,0.9754888610490767,0.9754888610490767,0.9755190387029732,17.51059913635254,34461.639008000006 +18768,Multiclass classification,Stacking,Keystroke,0.9757553151809026,0.9757553151809026,0.9757835195290103,18.871691703796387,35998.873267 +19176,Multiclass classification,Stacking,Keystroke,0.9754367666232073,0.9754367666232073,0.9754369138844643,17.42948341369629,37568.082084 +19584,Multiclass classification,Stacking,Keystroke,0.9754889444926722,0.9754889444926722,0.9754964783302286,17.978480339050293,39170.395427 +19992,Multiclass classification,Stacking,Keystroke,0.9756390375669051,0.9756390375669051,0.975642520227376,19.26256561279297,40805.125646 +20400,Multiclass classification,Stacking,Keystroke,0.9754889945585568,0.9754889945585568,0.9754863274548964,18.711057662963867,42471.761869 +46,Multiclass classification,Voting,ImageSegments,0.4666666666666667,0.4666666666666667,0.3890768588137009,0.9137420654296875,0.663852 +92,Multiclass classification,Voting,ImageSegments,0.6153846153846154,0.6153846153846154,0.617040786788686,0.9906883239746094,2.032737 +138,Multiclass classification,Voting,ImageSegments,0.6715328467153284,0.6715328467153284,0.6884491245817251,1.0679149627685547,4.226265 +184,Multiclass classification,Voting,ImageSegments,0.7049180327868853,0.7049180327868853,0.7194266051408907,1.1443958282470703,7.386208 +230,Multiclass classification,Voting,ImageSegments,0.7292576419213974,0.7292576419213974,0.7448338459304749,1.2214689254760742,11.723904000000001 +276,Multiclass classification,Voting,ImageSegments,0.7381818181818182,0.7381818181818182,0.7559766728000937,1.2995519638061523,17.331033 +322,Multiclass classification,Voting,ImageSegments,0.7538940809968847,0.7538940809968847,0.7616248500949714,1.3766565322875977,24.26159 +368,Multiclass classification,Voting,ImageSegments,0.773841961852861,0.7738419618528611,0.7772939373537765,1.4532833099365234,32.770568000000004 +414,Multiclass classification,Voting,ImageSegments,0.7820823244552058,0.7820823244552059,0.7854200812154107,1.5304145812988281,42.983195 +460,Multiclass classification,Voting,ImageSegments,0.7777777777777778,0.7777777777777778,0.7796254955467015,1.6075658798217773,54.886431 +506,Multiclass classification,Voting,ImageSegments,0.7861386138613862,0.7861386138613862,0.7886239053396241,3.8640270233154297,87.00222099999999 +552,Multiclass classification,Voting,ImageSegments,0.7858439201451906,0.7858439201451906,0.7889431335032357,4.088808059692383,121.00394599999998 +598,Multiclass classification,Voting,ImageSegments,0.7906197654941374,0.7906197654941374,0.7944387660679091,4.304059028625488,157.00397999999998 +644,Multiclass classification,Voting,ImageSegments,0.7853810264385692,0.7853810264385692,0.7901251252871709,4.532710075378418,195.073691 +690,Multiclass classification,Voting,ImageSegments,0.7895500725689405,0.7895500725689405,0.7935315861788143,4.759090423583984,235.272046 +736,Multiclass classification,Voting,ImageSegments,0.7863945578231293,0.7863945578231294,0.7911065855691086,4.991429328918457,277.59961999999996 +782,Multiclass classification,Voting,ImageSegments,0.7887323943661971,0.7887323943661971,0.792926322670609,5.219735145568848,322.07131499999997 +828,Multiclass classification,Voting,ImageSegments,0.7896009673518742,0.7896009673518742,0.7950712422059908,5.452417373657227,368.82718 +874,Multiclass classification,Voting,ImageSegments,0.7938144329896907,0.7938144329896907,0.7979586706142276,5.699496269226074,417.885664 +920,Multiclass classification,Voting,ImageSegments,0.794341675734494,0.794341675734494,0.7973145688626199,5.9376373291015625,469.16999000000004 +966,Multiclass classification,Voting,ImageSegments,0.7937823834196891,0.7937823834196891,0.7958827691316667,6.182188987731934,522.9385980000001 +1012,Multiclass classification,Voting,ImageSegments,0.7912957467853611,0.7912957467853611,0.7931630938612351,6.34267520904541,579.2700850000001 +1058,Multiclass classification,Voting,ImageSegments,0.793755912961211,0.7937559129612108,0.7947921362588558,6.295009613037109,638.2805060000001 +1104,Multiclass classification,Voting,ImageSegments,0.7941976427923844,0.7941976427923844,0.7951664828862093,6.2213640213012695,699.725726 +1150,Multiclass classification,Voting,ImageSegments,0.7954743255004352,0.7954743255004351,0.7958304956922065,6.151959419250488,763.5071 +1196,Multiclass classification,Voting,ImageSegments,0.796652719665272,0.796652719665272,0.7972397572733622,6.087224006652832,829.5043310000001 +1242,Multiclass classification,Voting,ImageSegments,0.7953263497179693,0.7953263497179693,0.795947547023496,6.001987457275391,897.6078460000001 +1288,Multiclass classification,Voting,ImageSegments,0.7995337995337995,0.7995337995337995,0.799082939294124,5.924266815185547,967.7224320000001 +1334,Multiclass classification,Voting,ImageSegments,0.7981995498874719,0.7981995498874719,0.7978549794399667,5.872907638549805,1039.926656 +1380,Multiclass classification,Voting,ImageSegments,0.7991298042059464,0.7991298042059464,0.799072028035076,5.784454345703125,1114.0085680000002 +1426,Multiclass classification,Voting,ImageSegments,0.8007017543859649,0.8007017543859649,0.799801266098334,5.781437873840332,1190.125915 +1472,Multiclass classification,Voting,ImageSegments,0.8042148198504419,0.8042148198504419,0.8016037490391381,5.805401802062988,1268.322504 +1518,Multiclass classification,Voting,ImageSegments,0.8048780487804879,0.8048780487804877,0.8013581039030082,5.915700912475586,1348.933518 +1564,Multiclass classification,Voting,ImageSegments,0.8048624440179143,0.8048624440179143,0.8017038254481382,6.0695037841796875,1431.999326 +1610,Multiclass classification,Voting,ImageSegments,0.8048477315102548,0.8048477315102549,0.8009666848419111,6.138180732727051,1517.316045 +1656,Multiclass classification,Voting,ImageSegments,0.804833836858006,0.804833836858006,0.8009346118743482,6.1542863845825195,1604.689641 +1702,Multiclass classification,Voting,ImageSegments,0.8048206937095826,0.8048206937095828,0.802987300619633,6.14796257019043,1694.105126 +1748,Multiclass classification,Voting,ImageSegments,0.8065254722381225,0.8065254722381225,0.8041280306488863,6.185528755187988,1785.6451299999999 +1794,Multiclass classification,Voting,ImageSegments,0.8070273284997211,0.8070273284997211,0.8033862119520573,6.18717098236084,1879.2213629999999 +1840,Multiclass classification,Voting,ImageSegments,0.8085916258836324,0.8085916258836324,0.8051706679397826,6.228180885314941,1974.8859899999998 +1886,Multiclass classification,Voting,ImageSegments,0.8074270557029177,0.8074270557029178,0.8044133208197751,6.244633674621582,2072.712055 +1932,Multiclass classification,Voting,ImageSegments,0.8073537027446919,0.8073537027446919,0.8036280810428232,6.232837677001953,2172.610836 +1978,Multiclass classification,Voting,ImageSegments,0.808295397066262,0.808295397066262,0.8041943782356388,6.225313186645508,2274.5024089999997 +2024,Multiclass classification,Voting,ImageSegments,0.8096885813148789,0.809688581314879,0.8043903689108628,6.209332466125488,2378.336668 +2070,Multiclass classification,Voting,ImageSegments,0.8086031899468342,0.8086031899468342,0.8034099584264852,6.192641258239746,2484.108554 +2116,Multiclass classification,Voting,ImageSegments,0.808983451536643,0.808983451536643,0.8029929757635029,6.163993835449219,2591.83622 +2162,Multiclass classification,Voting,ImageSegments,0.8093475242943082,0.8093475242943081,0.8028985652670257,6.160528182983398,2701.493184 +2208,Multiclass classification,Voting,ImageSegments,0.8110557317625736,0.8110557317625736,0.8037088502350873,6.127141952514648,2812.975729 +2254,Multiclass classification,Voting,ImageSegments,0.8078118064802485,0.8078118064802485,0.8004652010359966,6.094814300537109,2926.3842619999996 +2300,Multiclass classification,Voting,ImageSegments,0.8064375815571988,0.8064375815571988,0.7990276111502428,6.073050498962402,3041.734776 +2310,Multiclass classification,Voting,ImageSegments,0.8064097011693374,0.8064097011693374,0.7989986920740723,6.073922157287598,3157.9431529999997 +1056,Multiclass classification,Voting,Insects,0.6293838862559241,0.6293838862559241,0.5938169901557457,7.681754112243652,78.197886 +2112,Multiclass classification,Voting,Insects,0.6290857413548081,0.6290857413548081,0.5936238360694311,7.563845634460449,217.436369 +3168,Multiclass classification,Voting,Insects,0.625197347647616,0.625197347647616,0.5890732389154221,7.54627799987793,406.781755 +4224,Multiclass classification,Voting,Insects,0.624437603599337,0.624437603599337,0.5890978975177876,7.509035110473633,643.136123 +5280,Multiclass classification,Voting,Insects,0.6309907179390036,0.6309907179390036,0.5943307513870396,7.529419898986816,922.055301 +6336,Multiclass classification,Voting,Insects,0.6249408050513023,0.6249408050513023,0.5899587518293812,7.541637420654297,1240.879558 +7392,Multiclass classification,Voting,Insects,0.6242727641726424,0.6242727641726424,0.589208790087756,7.5199432373046875,1598.2590730000002 +8448,Multiclass classification,Voting,Insects,0.6266129986977625,0.6266129986977625,0.5910042020201396,7.600367546081543,1990.9287910000003 +9504,Multiclass classification,Voting,Insects,0.6255919183415763,0.6255919183415763,0.5892477749449755,7.551809310913086,2416.671036 +10560,Multiclass classification,Voting,Insects,0.6269533099725353,0.6269533099725353,0.5906555376897765,7.57810115814209,2875.240995 +11616,Multiclass classification,Voting,Insects,0.6254842875591907,0.6254842875591907,0.5899069142128334,7.574300765991211,3366.8452850000003 +12672,Multiclass classification,Voting,Insects,0.6276536974193039,0.6276536974193039,0.5948280902959312,7.593076705932617,3891.533291 +13728,Multiclass classification,Voting,Insects,0.6419465287389816,0.6419465287389816,0.6240594787506325,7.568525314331055,4449.097087 +14784,Multiclass classification,Voting,Insects,0.6349861327200162,0.6349861327200162,0.6168664949740267,7.497129440307617,5038.3500540000005 +15840,Multiclass classification,Voting,Insects,0.6042048109097796,0.6042048109097796,0.5876183517420878,7.622871398925781,5663.9066330000005 +16896,Multiclass classification,Voting,Insects,0.5831311038768866,0.5831311038768866,0.5677288238088704,7.5406084060668945,6323.428796 +17952,Multiclass classification,Voting,Insects,0.5683805916104953,0.5683805916104953,0.5530005563922373,7.511743545532227,7015.247243 +19008,Multiclass classification,Voting,Insects,0.5655811016993739,0.5655811016993739,0.5465928919365096,7.569133758544922,7739.601247 +20064,Multiclass classification,Voting,Insects,0.5718985196630614,0.5718985196630614,0.5506497035356593,8.179316520690918,8496.204598999999 +21120,Multiclass classification,Voting,Insects,0.5817510298783086,0.5817510298783086,0.55937505855693,8.13927173614502,9285.092110999998 +22176,Multiclass classification,Voting,Insects,0.5905298759864712,0.5905298759864712,0.5668099949242361,8.13715648651123,10104.551325999999 +23232,Multiclass classification,Voting,Insects,0.6004907236020834,0.6004907236020834,0.5756153967719769,8.254791259765625,10955.282647999999 +24288,Multiclass classification,Voting,Insects,0.6088854119487792,0.6088854119487792,0.5822871692574689,8.217899322509766,11836.441737999998 +25344,Multiclass classification,Voting,Insects,0.617014560233595,0.617014560233595,0.5890646667396601,8.13050651550293,12747.590801999997 +26400,Multiclass classification,Voting,Insects,0.6237357475661957,0.6237357475661957,0.5942060376379845,8.178851127624512,13688.250944999996 +27456,Multiclass classification,Voting,Insects,0.6299763248952832,0.6299763248952832,0.5983574644866619,8.215079307556152,14661.447404999995 +28512,Multiclass classification,Voting,Insects,0.6312651257409421,0.6312651257409421,0.6016879522351425,8.160200119018555,15669.084531999995 +29568,Multiclass classification,Voting,Insects,0.6310751851726587,0.6310751851726587,0.6062390002054064,8.153844833374023,16709.899933999994 +30624,Multiclass classification,Voting,Insects,0.6313228619011854,0.6313228619011854,0.610710416812842,8.221953392028809,17785.196262999994 +31680,Multiclass classification,Voting,Insects,0.6320590927743931,0.6320590927743931,0.614817700164209,8.237210273742676,18894.010558999995 +32736,Multiclass classification,Voting,Insects,0.6331144035436077,0.6331144035436077,0.6184679282473909,8.208189964294434,20033.816622999995 +33792,Multiclass classification,Voting,Insects,0.6291616110798733,0.6291616110798733,0.6151628967287334,8.149331092834473,21206.789184999994 +34848,Multiclass classification,Voting,Insects,0.6245587855482538,0.6245587855482538,0.6103108800280445,8.270771980285645,22409.569843999994 +35904,Multiclass classification,Voting,Insects,0.6211737180736986,0.6211737180736986,0.6063163580543118,8.246885299682617,23639.112908999996 +36960,Multiclass classification,Voting,Insects,0.6171433209772992,0.6171433209772992,0.6018416894357856,8.222872734069824,24895.212450999996 +38016,Multiclass classification,Voting,Insects,0.6153360515585953,0.6153360515585953,0.5996210858832133,8.711487770080566,26177.407049999994 +39072,Multiclass classification,Voting,Insects,0.613472908295155,0.613472908295155,0.5980758777202522,8.84398365020752,27486.887242999994 +40128,Multiclass classification,Voting,Insects,0.6139008647544048,0.6139008647544048,0.5993833357378361,9.00393295288086,28821.579146999993 +41184,Multiclass classification,Voting,Insects,0.6157395041643396,0.6157395041643396,0.6018873090815099,8.895415306091309,30174.675792999995 +42240,Multiclass classification,Voting,Insects,0.6179833802883591,0.6179833802883591,0.6047393094362844,8.820836067199707,31551.592344999994 +43296,Multiclass classification,Voting,Insects,0.6202101859337106,0.6202101859337106,0.60743097275183,8.80302619934082,32950.21258099999 +44352,Multiclass classification,Voting,Insects,0.6221054767648982,0.6221054767648982,0.6097047537791253,8.807188034057617,34370.71930599999 +45408,Multiclass classification,Voting,Insects,0.623736428304006,0.623736428304006,0.6112415003179203,8.906554222106934,35814.22252799999 +46464,Multiclass classification,Voting,Insects,0.6259389191399608,0.6259389191399608,0.6133867892257391,8.822076797485352,37279.498286999995 +47520,Multiclass classification,Voting,Insects,0.6274542814453166,0.6274542814453166,0.6153714367024555,8.875716209411621,38770.246245999995 +48576,Multiclass classification,Voting,Insects,0.6317858980957283,0.6317858980957283,0.6202967225132047,8.86828327178955,40284.403256 +49632,Multiclass classification,Voting,Insects,0.6360137817090125,0.6360137817090125,0.6247992459885968,8.835649490356445,41820.805007999996 +50688,Multiclass classification,Voting,Insects,0.6403811628228145,0.6403811628228145,0.6293790828873279,8.924153327941895,43378.957976 +51744,Multiclass classification,Voting,Insects,0.6455559206076185,0.6455559206076185,0.6346828420183047,9.218049049377441,44959.107880999996 +52800,Multiclass classification,Voting,Insects,0.648269853595712,0.648269853595712,0.6377385869395499,9.400546073913574,46560.782 +52848,Multiclass classification,Voting,Insects,0.6485325562472799,0.6485325562472799,0.637999701607352,9.406517028808594,48163.738895 +408,Multiclass classification,Voting,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,1.4587059020996094,10.139614 +816,Multiclass classification,Voting,Keystroke,0.9496932515337423,0.9496932515337423,0.7435135353411919,6.019382476806641,66.737739 +1224,Multiclass classification,Voting,Keystroke,0.9149632052330335,0.9149632052330335,0.9012024099743488,7.076447486877441,151.07716299999998 +1632,Multiclass classification,Voting,Keystroke,0.9258123850398529,0.9258123850398529,0.913338738884437,7.232892990112305,261.540164 +2040,Multiclass classification,Voting,Keystroke,0.9230014713094654,0.9230014713094654,0.9086113906821328,7.553393363952637,397.83621500000004 +2448,Multiclass classification,Voting,Keystroke,0.8961994278708623,0.8961994278708623,0.8992132713257572,7.640434265136719,558.733108 +2856,Multiclass classification,Voting,Keystroke,0.9001751313485113,0.9001751313485113,0.8860451027148403,7.9326982498168945,743.600486 +3264,Multiclass classification,Voting,Keystroke,0.8924302788844621,0.8924302788844621,0.8761196773917237,8.074724197387695,952.077233 +3672,Multiclass classification,Voting,Keystroke,0.8874965949332607,0.8874965949332607,0.8846937712308092,8.20841121673584,1184.393658 +4080,Multiclass classification,Voting,Keystroke,0.8815886246629075,0.8815886246629075,0.868452721773406,8.525882720947266,1441.2089369999999 +4488,Multiclass classification,Voting,Keystroke,0.8760864720303098,0.8760864720303098,0.8834419600614621,8.681946754455566,1719.7568239999998 +4896,Multiclass classification,Voting,Keystroke,0.8737487231869254,0.8737487231869254,0.8797220914000274,8.834684371948242,2018.9742069999998 +5304,Multiclass classification,Voting,Keystroke,0.8693192532528757,0.8693192532528757,0.8538682361373632,9.067034721374512,2339.6996679999997 +5712,Multiclass classification,Voting,Keystroke,0.8607949571003327,0.8607949571003327,0.8654889627515672,9.271133422851562,2680.904224 +6120,Multiclass classification,Voting,Keystroke,0.8561856512502043,0.8561856512502043,0.84095068957581,9.378315925598145,3042.663698 +6528,Multiclass classification,Voting,Keystroke,0.8434196414891987,0.8434196414891987,0.8427350578509161,9.608606338500977,3424.478417 +6936,Multiclass classification,Voting,Keystroke,0.8392213410237923,0.8392213410237923,0.8447429510460126,9.751982688903809,3824.86879 +7344,Multiclass classification,Voting,Keystroke,0.8454310227427482,0.8454310227427482,0.847842289102327,9.957889556884766,4243.00141 +7752,Multiclass classification,Voting,Keystroke,0.8456973293768546,0.8456973293768547,0.8480563212460421,10.19985294342041,4680.993142 +8160,Multiclass classification,Voting,Keystroke,0.8469175144012747,0.8469175144012746,0.8472851046009279,10.418806076049805,5138.9878340000005 +8568,Multiclass classification,Voting,Keystroke,0.8469709349830746,0.8469709349830746,0.8501227536717817,10.607142448425293,5616.664707000001 +8976,Multiclass classification,Voting,Keystroke,0.8475766016713092,0.8475766016713092,0.8507851780426926,10.772598266601562,6113.940894000001 +9384,Multiclass classification,Voting,Keystroke,0.8459980816370031,0.8459980816370031,0.8471668648040658,10.97368335723877,6631.342845000001 +9792,Multiclass classification,Voting,Keystroke,0.8418956184250843,0.8418956184250843,0.8426049398612477,11.192140579223633,7169.901201000001 +10200,Multiclass classification,Voting,Keystroke,0.8344935778017453,0.8344935778017454,0.8308153568434791,11.354521751403809,7729.92345 +10608,Multiclass classification,Voting,Keystroke,0.817384745922504,0.817384745922504,0.8105787344487394,11.59365177154541,8312.440227000001 +11016,Multiclass classification,Voting,Keystroke,0.8127099409895597,0.8127099409895597,0.8142119266109252,11.793928146362305,8918.030696000002 +11424,Multiclass classification,Voting,Keystroke,0.8079313665411888,0.8079313665411888,0.8037472320719128,11.945178031921387,9547.170938000001 +11832,Multiclass classification,Voting,Keystroke,0.8040740427689967,0.8040740427689967,0.8039730126613296,12.203582763671875,10200.281645000001 +12240,Multiclass classification,Voting,Keystroke,0.8072554947299616,0.8072554947299616,0.8097160881214022,12.414502143859863,10877.318664 +12648,Multiclass classification,Voting,Keystroke,0.8043014153554202,0.8043014153554202,0.8038043720799647,12.561456680297852,11578.515438 +13056,Multiclass classification,Voting,Keystroke,0.7996936039831483,0.7996936039831483,0.8010057260657798,12.889472007751465,12304.325005 +13464,Multiclass classification,Voting,Keystroke,0.7974448488449826,0.7974448488449826,0.7996515087686575,12.99599838256836,13054.609905000001 +13872,Multiclass classification,Voting,Keystroke,0.7978516329031793,0.7978516329031793,0.8006715750629478,13.20394229888916,13829.291085 +14280,Multiclass classification,Voting,Keystroke,0.797674907206387,0.7976749072063871,0.8002875748518964,13.364522933959961,14628.347686000001 +14688,Multiclass classification,Voting,Keystroke,0.8007761966364813,0.8007761966364813,0.8043248634763072,13.53370189666748,15451.756014 +15096,Multiclass classification,Voting,Keystroke,0.8051010268300762,0.8051010268300763,0.8085780284871096,13.774932861328125,16299.960754 +15504,Multiclass classification,Voting,Keystroke,0.8052634973876024,0.8052634973876024,0.8077470357827514,13.933537483215332,17172.988913 +15912,Multiclass classification,Voting,Keystroke,0.7978756834894098,0.7978756834894098,0.7983136026998061,14.138628005981445,18070.675966000003 +16320,Multiclass classification,Voting,Keystroke,0.793369691770329,0.7933696917703291,0.7956625263629296,14.30509090423584,18993.333450000002 +16728,Multiclass classification,Voting,Keystroke,0.7901596221677527,0.7901596221677527,0.7932579365729884,14.447582244873047,19941.842904 +17136,Multiclass classification,Voting,Keystroke,0.7861686606361249,0.7861686606361248,0.7888822346867281,14.767212867736816,20916.572711 +17544,Multiclass classification,Voting,Keystroke,0.780425240836801,0.780425240836801,0.7838193866310822,14.989240646362305,21922.215184 +17952,Multiclass classification,Voting,Keystroke,0.7802907915993538,0.7802907915993537,0.7845235361146662,15.200251579284668,22957.213951 +18360,Multiclass classification,Voting,Keystroke,0.783975162045863,0.783975162045863,0.7883700169311393,15.375930786132812,24020.765336 +18768,Multiclass classification,Voting,Keystroke,0.7869664837214259,0.7869664837214259,0.7913854757843782,15.5132417678833,25114.453204 +19176,Multiclass classification,Voting,Keystroke,0.7816427640156454,0.7816427640156454,0.7858184292134073,15.77665901184082,26236.293864000003 +19584,Multiclass classification,Voting,Keystroke,0.7846090997293571,0.7846090997293571,0.7893723685613512,15.996115684509277,27388.205854000003 +19992,Multiclass classification,Voting,Keystroke,0.7807013155920164,0.7807013155920164,0.785620728786203,16.12063980102539,28569.915626 +20400,Multiclass classification,Voting,Keystroke,0.7791068189617139,0.7791068189617139,0.7841355172773921,16.39253330230713,29779.243894000003 +46,Multiclass classification,[baseline] Last Class,ImageSegments,0.17777777777777778,0.17777777777777778,0.15260266049739735,0.0013666152954101562,0.110776 +92,Multiclass classification,[baseline] Last Class,ImageSegments,0.13186813186813187,0.13186813186813187,0.1213108980966124,0.0013637542724609375,0.225611 +138,Multiclass classification,[baseline] Last Class,ImageSegments,0.12408759124087591,0.12408759124087591,0.11874455065544491,0.001369476318359375,0.34363900000000003 +184,Multiclass classification,[baseline] Last Class,ImageSegments,0.12568306010928962,0.12568306010928962,0.12262983423071581,0.0013647079467773438,0.48452400000000007 +230,Multiclass classification,[baseline] Last Class,ImageSegments,0.12663755458515283,0.12663755458515283,0.12503852041208066,0.0013637542724609375,0.6292090000000001 +276,Multiclass classification,[baseline] Last Class,ImageSegments,0.12727272727272726,0.12727272727272726,0.12427907918144998,0.0013666152954101562,0.7861950000000001 +322,Multiclass classification,[baseline] Last Class,ImageSegments,0.13395638629283488,0.13395638629283488,0.13210036596246022,0.0013666152954101562,1.0166240000000002 +368,Multiclass classification,[baseline] Last Class,ImageSegments,0.13896457765667575,0.13896457765667575,0.13745011462972964,0.0013675689697265625,1.2507780000000002 +414,Multiclass classification,[baseline] Last Class,ImageSegments,0.14043583535108958,0.14043583535108958,0.14035813096947544,0.0013666152954101562,1.5223060000000002 +460,Multiclass classification,[baseline] Last Class,ImageSegments,0.14596949891067537,0.14596949891067537,0.14563148710727947,0.00136566162109375,1.7974560000000002 +506,Multiclass classification,[baseline] Last Class,ImageSegments,0.13861386138613863,0.13861386138613863,0.13833816102314941,0.0013666152954101562,2.0756200000000002 +552,Multiclass classification,[baseline] Last Class,ImageSegments,0.1397459165154265,0.1397459165154265,0.13938652491777898,0.0013666152954101562,2.402759 +598,Multiclass classification,[baseline] Last Class,ImageSegments,0.1373534338358459,0.1373534338358459,0.13727981043458612,0.0013675689697265625,2.771723 +644,Multiclass classification,[baseline] Last Class,ImageSegments,0.13996889580093314,0.13996889580093314,0.14017571709017965,0.0013666152954101562,3.149556 +690,Multiclass classification,[baseline] Last Class,ImageSegments,0.1378809869375907,0.1378809869375907,0.13801517784553327,0.001369476318359375,3.580436 +736,Multiclass classification,[baseline] Last Class,ImageSegments,0.1401360544217687,0.1401360544217687,0.14031088927958282,0.0013675689697265625,4.0152470000000005 +782,Multiclass classification,[baseline] Last Class,ImageSegments,0.14212548015364918,0.14212548015364918,0.1420930265541123,0.0013647079467773438,4.453992 +828,Multiclass classification,[baseline] Last Class,ImageSegments,0.14268440145102781,0.14268440145102781,0.14229874553046912,0.0013666152954101562,4.959761 +874,Multiclass classification,[baseline] Last Class,ImageSegments,0.13860252004581902,0.13860252004581902,0.13845352694595275,0.0013647079467773438,5.469480000000001 +920,Multiclass classification,[baseline] Last Class,ImageSegments,0.13492927094668117,0.13492927094668117,0.1348083913046733,0.0013666152954101562,6.0005820000000005 +966,Multiclass classification,[baseline] Last Class,ImageSegments,0.13367875647668392,0.13367875647668392,0.13349177774445276,0.0013637542724609375,6.5350530000000004 +1012,Multiclass classification,[baseline] Last Class,ImageSegments,0.13254203758654798,0.13254203758654798,0.1324936677659038,0.0013675689697265625,7.07275 +1058,Multiclass classification,[baseline] Last Class,ImageSegments,0.13339640491958374,0.13339640491958374,0.1331834965440007,0.00136566162109375,7.6454260000000005 +1104,Multiclass classification,[baseline] Last Class,ImageSegments,0.13417951042611062,0.13417951042611062,0.13402826529501538,0.0013666152954101562,8.221471000000001 +1150,Multiclass classification,[baseline] Last Class,ImageSegments,0.134029590948651,0.134029590948651,0.13406391150519123,0.0013637542724609375,8.800858000000002 +1196,Multiclass classification,[baseline] Last Class,ImageSegments,0.13640167364016736,0.13640167364016736,0.1363948420172951,0.001369476318359375,9.430169000000001 +1242,Multiclass classification,[baseline] Last Class,ImageSegments,0.13940370668815472,0.13940370668815472,0.13919772383892226,0.0013637542724609375,10.062783000000001 +1288,Multiclass classification,[baseline] Last Class,ImageSegments,0.1414141414141414,0.1414141414141414,0.14118715023210152,0.0013666152954101562,10.698372 +1334,Multiclass classification,[baseline] Last Class,ImageSegments,0.14328582145536384,0.14328582145536384,0.14302553278156666,0.0013637542724609375,11.387531000000001 +1380,Multiclass classification,[baseline] Last Class,ImageSegments,0.14358230601885424,0.14358230601885424,0.1433209000486506,0.001369476318359375,12.080639000000001 +1426,Multiclass classification,[baseline] Last Class,ImageSegments,0.14175438596491227,0.14175438596491227,0.14145466559291123,0.001369476318359375,12.777602000000002 +1472,Multiclass classification,[baseline] Last Class,ImageSegments,0.13936097892590074,0.13936097892590074,0.13907629713942624,0.0013647079467773438,13.546128000000001 +1518,Multiclass classification,[baseline] Last Class,ImageSegments,0.13974950560316415,0.13974950560316415,0.13951366685898453,0.0013666152954101562,14.318195000000001 +1564,Multiclass classification,[baseline] Last Class,ImageSegments,0.13691618682021753,0.13691618682021753,0.13664170474395118,0.0013666152954101562,15.093811 +1610,Multiclass classification,[baseline] Last Class,ImageSegments,0.13610938471100062,0.13610938471100062,0.13597683881903072,0.0013637542724609375,15.942934000000001 +1656,Multiclass classification,[baseline] Last Class,ImageSegments,0.1365558912386707,0.1365558912386707,0.13633224623774592,0.001369476318359375,16.795246000000002 +1702,Multiclass classification,[baseline] Last Class,ImageSegments,0.13932980599647266,0.13932980599647266,0.13901296274399097,0.0013675689697265625,17.650687 +1748,Multiclass classification,[baseline] Last Class,ImageSegments,0.14195764167143674,0.14195764167143674,0.14147197312723642,0.00136566162109375,18.510738 +1794,Multiclass classification,[baseline] Last Class,ImageSegments,0.14221974344673732,0.14221974344673732,0.14194103966110075,0.0013647079467773438,19.374685 +1840,Multiclass classification,[baseline] Last Class,ImageSegments,0.14138118542686243,0.14138118542686243,0.14114329766598663,0.0013675689697265625,20.242449999999998 +1886,Multiclass classification,[baseline] Last Class,ImageSegments,0.140053050397878,0.140053050397878,0.13973258713820755,0.0013666152954101562,21.182872999999997 +1932,Multiclass classification,[baseline] Last Class,ImageSegments,0.14293112377006734,0.14293112377006734,0.14275229229825853,0.0013666152954101562,22.126859999999997 +1978,Multiclass classification,[baseline] Last Class,ImageSegments,0.14618108244815378,0.14618108244815378,0.14597158151605963,0.001369476318359375,23.074112999999997 +2024,Multiclass classification,[baseline] Last Class,ImageSegments,0.14434008897676717,0.14434008897676717,0.14416625237761066,0.001369476318359375,24.067370999999998 +2070,Multiclass classification,[baseline] Last Class,ImageSegments,0.14403093281778637,0.14403093281778637,0.14385543497127623,0.0013666152954101562,25.063920999999997 +2116,Multiclass classification,[baseline] Last Class,ImageSegments,0.14468085106382977,0.14468085106382977,0.14460362317776573,0.0013637542724609375,26.063629999999996 +2162,Multiclass classification,[baseline] Last Class,ImageSegments,0.14530310041647385,0.14530310041647385,0.14520465913821792,0.001369476318359375,27.083891999999995 +2208,Multiclass classification,[baseline] Last Class,ImageSegments,0.14499320344358857,0.14499320344358857,0.14491109851991696,0.001369476318359375,28.107943999999996 +2254,Multiclass classification,[baseline] Last Class,ImageSegments,0.14647137150466044,0.14647137150466044,0.14640425534129609,0.0013666152954101562,29.207950999999998 +2300,Multiclass classification,[baseline] Last Class,ImageSegments,0.14789038712483688,0.14789038712483688,0.14788688524810298,0.0013675689697265625,30.311507 +2310,Multiclass classification,[baseline] Last Class,ImageSegments,0.14811606756171503,0.14811606756171503,0.14811566784252675,0.001369476318359375,31.415920999999997 +1056,Multiclass classification,[baseline] Last Class,Insects,0.15829383886255924,0.15829383886255924,0.1376212379233521,0.0013856887817382812,0.57267 +2112,Multiclass classification,[baseline] Last Class,Insects,0.16579819990525818,0.16579819990525818,0.15110451064118433,0.0013856887817382812,1.690872 +3168,Multiclass classification,[baseline] Last Class,Insects,0.17019261130407326,0.17019261130407326,0.15681512355039637,0.0013885498046875,3.2981429999999996 +4224,Multiclass classification,[baseline] Last Class,Insects,0.16599573762727918,0.16599573762727918,0.15254433156050665,0.0013856887817382812,5.4736839999999995 +5280,Multiclass classification,[baseline] Last Class,Insects,0.17086569426027656,0.17086569426027656,0.15676679113993588,0.0013837814331054688,8.202311 +6336,Multiclass classification,[baseline] Last Class,Insects,0.17379636937647988,0.17379636937647988,0.16137568195972998,0.0013837814331054688,11.448991 +7392,Multiclass classification,[baseline] Last Class,Insects,0.1752130970098769,0.1752130970098769,0.16189407904134778,0.0013837814331054688,15.242684 +8448,Multiclass classification,[baseline] Last Class,Insects,0.17722268260921037,0.17722268260921037,0.163740045170864,0.0013818740844726562,19.537217000000002 +9504,Multiclass classification,[baseline] Last Class,Insects,0.17731242765442493,0.17731242765442493,0.1637492974453096,0.0013885498046875,24.318802 +10560,Multiclass classification,[baseline] Last Class,Insects,0.17908892887584052,0.17908892887584052,0.16564210767474952,0.0013837814331054688,29.683683000000002 +11616,Multiclass classification,[baseline] Last Class,Insects,0.17899268187688333,0.17899268187688333,0.16559253835337612,0.0013856887817382812,35.598037000000005 +12672,Multiclass classification,[baseline] Last Class,Insects,0.18530502722752742,0.1853050272275274,0.18269809988409802,0.0013866424560546875,41.981502000000006 +13728,Multiclass classification,[baseline] Last Class,Insects,0.24797843665768193,0.24797843665768193,0.26603936845528803,0.0013866424560546875,48.94863000000001 +14784,Multiclass classification,[baseline] Last Class,Insects,0.2795778935263478,0.2795778935263478,0.28229742751715126,0.0013818740844726562,56.43945000000001 +15840,Multiclass classification,[baseline] Last Class,Insects,0.27615379758823155,0.27615379758823155,0.2847375853365436,0.0013818740844726562,64.48233200000001 +16896,Multiclass classification,[baseline] Last Class,Insects,0.2723290914471737,0.2723290914471737,0.2859139704285301,0.0013856887817382812,73.03679300000002 +17952,Multiclass classification,[baseline] Last Class,Insects,0.2720739791655061,0.2720739791655061,0.2880143206503878,0.0013866424560546875,82.10379000000002 +19008,Multiclass classification,[baseline] Last Class,Insects,0.28252748987215237,0.28252748987215237,0.2877504429321087,0.0013866424560546875,91.70347300000002 +20064,Multiclass classification,[baseline] Last Class,Insects,0.28724517769027563,0.28724517769027563,0.28667392366619265,0.0013818740844726562,101.81113500000002 +21120,Multiclass classification,[baseline] Last Class,Insects,0.28306264501160094,0.28306264501160094,0.28164766024255256,0.0013837814331054688,112.42818900000002 +22176,Multiclass classification,[baseline] Last Class,Insects,0.2805411499436302,0.2805411499436302,0.27862960725280095,0.0013866424560546875,123.55266000000002 +23232,Multiclass classification,[baseline] Last Class,Insects,0.2797124531875511,0.2797124531875511,0.27719419757933417,0.0013856887817382812,135.22034100000002 +24288,Multiclass classification,[baseline] Last Class,Insects,0.2777205912628155,0.2777205912628155,0.2745878480946635,0.0013866424560546875,147.32084400000002 +25344,Multiclass classification,[baseline] Last Class,Insects,0.2756579726157124,0.2756579726157124,0.27233803052028965,0.0013818740844726562,159.88729300000003 +26400,Multiclass classification,[baseline] Last Class,Insects,0.27394977082465244,0.27394977082465244,0.26996904425699914,0.0013837814331054688,172.95537600000003 +27456,Multiclass classification,[baseline] Last Class,Insects,0.27189947186304864,0.27189947186304864,0.26719485323886244,0.0013866424560546875,186.52082400000003 +28512,Multiclass classification,[baseline] Last Class,Insects,0.2723860965942969,0.2723860965942969,0.2686965366571337,0.0013885498046875,200.59564800000004 +29568,Multiclass classification,[baseline] Last Class,Insects,0.2738187844556431,0.2738187844556431,0.2720266804437783,0.0013885498046875,215.16150500000003 +30624,Multiclass classification,[baseline] Last Class,Insects,0.27538124938771513,0.27538124938771513,0.27486986638103517,0.0013885498046875,230.19075300000003 +31680,Multiclass classification,[baseline] Last Class,Insects,0.2780390795163989,0.2780390795163989,0.2784141751235631,0.0013856887817382812,245.71900300000004 +32736,Multiclass classification,[baseline] Last Class,Insects,0.279670077898274,0.279670077898274,0.28021922512452757,0.0013837814331054688,261.76959600000004 +33792,Multiclass classification,[baseline] Last Class,Insects,0.2808440117190968,0.2808440117190968,0.28119627453717067,0.0013856887817382812,278.22772000000003 +34848,Multiclass classification,[baseline] Last Class,Insects,0.2772405085086234,0.2772405085086234,0.27819051828647573,0.0013837814331054688,295.19763900000004 +35904,Multiclass classification,[baseline] Last Class,Insects,0.2739325404562293,0.2739325404562293,0.2754200456137155,0.0013856887817382812,312.64260700000005 +36960,Multiclass classification,[baseline] Last Class,Insects,0.271246516410076,0.271246516410076,0.27333283767820205,0.0013818740844726562,330.5037730000001 +38016,Multiclass classification,[baseline] Last Class,Insects,0.26855188741286334,0.26855188741286334,0.2710722002891223,0.0013856887817382812,348.8496650000001 +39072,Multiclass classification,[baseline] Last Class,Insects,0.277034117376059,0.277034117376059,0.2770619820799866,0.0013866424560546875,367.6207990000001 +40128,Multiclass classification,[baseline] Last Class,Insects,0.27617315024796274,0.27617315024796274,0.2760769006623072,0.0013837814331054688,386.8573710000001 +41184,Multiclass classification,[baseline] Last Class,Insects,0.27567200058276475,0.27567200058276475,0.27543526329721163,0.0013837814331054688,406.52795400000014 +42240,Multiclass classification,[baseline] Last Class,Insects,0.27401216884869434,0.27401216884869434,0.27359461935885426,0.0013885498046875,426.69962200000015 +43296,Multiclass classification,[baseline] Last Class,Insects,0.2738422450629403,0.2738422450629403,0.27319488690835786,0.0013856887817382812,447.37129900000014 +44352,Multiclass classification,[baseline] Last Class,Insects,0.2729588960790061,0.2729588960790061,0.27209116538690487,0.0013866424560546875,468.49129600000015 +45408,Multiclass classification,[baseline] Last Class,Insects,0.27205056489087587,0.27205056489087587,0.2708084959373003,0.0013866424560546875,490.06234300000017 +46464,Multiclass classification,[baseline] Last Class,Insects,0.27137722488862104,0.27137722488862104,0.2698631410415437,0.0013837814331054688,512.0778290000002 +47520,Multiclass classification,[baseline] Last Class,Insects,0.27235421620825356,0.27235421620825356,0.27170627983222856,0.0013837814331054688,534.5781510000002 +48576,Multiclass classification,[baseline] Last Class,Insects,0.2741327843540916,0.2741327843540916,0.27449463409742436,0.0013818740844726562,557.5265480000002 +49632,Multiclass classification,[baseline] Last Class,Insects,0.27535209848683284,0.27535209848683284,0.27650368764304034,0.0013818740844726562,580.9705880000001 +50688,Multiclass classification,[baseline] Last Class,Insects,0.2768362696549411,0.2768362696549411,0.27863440912734966,0.0013837814331054688,604.9012140000001 +51744,Multiclass classification,[baseline] Last Class,Insects,0.27827918752294994,0.27827918752294994,0.2805971515128955,0.0013885498046875,629.3033230000001 +52800,Multiclass classification,[baseline] Last Class,Insects,0.28911532415386654,0.28911532415386654,0.28929532027297566,0.0013866424560546875,654.1512880000001 +52848,Multiclass classification,[baseline] Last Class,Insects,0.2897610081934642,0.2897610081934642,0.28976272570313216,0.0013866424560546875,679.0036960000001 +408,Multiclass classification,[baseline] Last Class,Keystroke,0.9975429975429976,0.9975429975429976,0.9660408844388819,0.0006122589111328125,0.255536 +816,Multiclass classification,[baseline] Last Class,Keystroke,0.9975460122699387,0.9975460122699387,0.9879967903427672,0.0006628036499023438,0.794196 +1224,Multiclass classification,[baseline] Last Class,Keystroke,0.9975470155355682,0.9975470155355682,0.9931179599499375,0.000713348388671875,1.53447 +1632,Multiclass classification,[baseline] Last Class,Keystroke,0.9975475168608215,0.9975475168608215,0.9950750839342831,0.0012521743774414062,2.469131 +2040,Multiclass classification,[baseline] Last Class,Keystroke,0.9975478175576263,0.9975478175576263,0.9960150346160551,0.0013027191162109375,3.675833 +2448,Multiclass classification,[baseline] Last Class,Keystroke,0.9975480179812015,0.9975480179812015,0.9965317313935653,0.0013532638549804688,5.030286 +2856,Multiclass classification,[baseline] Last Class,Keystroke,0.9975481611208407,0.9975481611208407,0.9968424283169279,0.00140380859375,6.586031 +3264,Multiclass classification,[baseline] Last Class,Keystroke,0.9975482684646031,0.9975482684646031,0.9970416021996,0.0014543533325195312,8.377109 +3672,Multiclass classification,[baseline] Last Class,Keystroke,0.9975483519476982,0.9975483519476982,0.9971755428551425,0.0015048980712890625,10.331252000000001 +4080,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484187300809,0.9975484187300809,0.9972690115789393,0.0015554428100585938,12.525489 +4488,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484733675062,0.9975484733675062,0.9973361791525123,0.001605987548828125,14.940819000000001 +4896,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485188968335,0.9975485188968335,0.9973856025730918,0.0016565322875976562,17.495259 +5304,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485574203281,0.9975485574203281,0.9974226798335742,0.0017070770263671875,20.336762 +5712,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485904395027,0.9975485904395027,0.99745094204078,0.0017576217651367188,23.402208 +6120,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486190554013,0.9975486190554013,0.9974727709453766,0.00180816650390625,26.661861000000002 +6528,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486440937643,0.9975486440937643,0.997489815700999,0.0018587112426757812,30.164710000000003 +6936,Multiclass classification,[baseline] Last Class,Keystroke,0.997548666186013,0.997548666186013,0.9975032443691146,0.0019092559814453125,33.838397 +7344,Multiclass classification,[baseline] Last Class,Keystroke,0.997548685823233,0.997548685823233,0.9975139007887865,0.0034246444702148438,37.738436 +7752,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487033931105,0.9975487033931105,0.9975224052755716,0.003475189208984375,41.800015 +8160,Multiclass classification,[baseline] Last Class,Keystroke,0.997548719205785,0.997548719205785,0.9975292209193424,0.0035257339477539062,46.105028000000004 +8568,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487335123147,0.9975487335123147,0.9975346982235258,0.0035762786865234375,50.63279300000001 +8976,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487465181059,0.9975487465181059,0.9975391057693664,0.0036268234252929688,55.447067000000004 +9384,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487583928381,0.9975487583928381,0.997542651662671,0.0036773681640625,60.387128000000004 +9792,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487692779083,0.9975487692779083,0.9975454987794795,0.0037279129028320312,65.547582 +10200,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487792920874,0.9975487792920874,0.9975477757646256,0.0037784576416015625,70.981052 +10608,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487885358726,0.9975487885358726,0.9975495850737114,0.0038290023803710938,76.594226 +11016,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487970948707,0.9975487970948707,0.9975510089260562,0.003879547119140625,82.44596800000001 +11424,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488050424582,0.9975488050424582,0.9975521137613483,0.003930091857910156,88.533094 +11832,Multiclass classification,[baseline] Last Class,Keystroke,0.99754881244189,0.99754881244189,0.9975529536110199,0.0039806365966796875,94.81874400000001 +12240,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488193479859,0.9975488193479859,0.9975535726732964,0.004031181335449219,101.331754 +12648,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488258084921,0.9975488258084921,0.9975540072976319,0.00408172607421875,108.05167800000001 +13056,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488318651857,0.9975488318651857,0.997554287526727,0.004132270812988281,114.99668100000001 +13464,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488375547797,0.9975488375547797,0.9975544383040469,0.0041828155517578125,122.1119 +13872,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488429096676,0.9975488429096676,0.9975544804262362,0.004233360290527344,129.47010500000002 +14280,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488479585405,0.9975488479585405,0.9975544312994101,0.004283905029296875,136.988051 +14688,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488527269013,0.9975488527269013,0.9975543055435039,0.004334449768066406,144.742896 +15096,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488572374959,0.9975488572374959,0.9975541154780816,0.0043849945068359375,152.648866 +15504,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488615106753,0.9975488615106753,0.9975538715150367,0.004435539245605469,160.767465 +15912,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488655647037,0.9975488655647037,0.9975535824776959,0.004486083984375,169.09858 +16320,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488694160182,0.9975488694160182,0.9975532558614028,0.004536628723144531,177.653336 +16728,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488730794524,0.9975488730794524,0.997552898047314,0.0045871734619140625,186.438203 +17136,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488765684272,0.9975488765684272,0.9975525144785747,0.004637718200683594,195.44716799999998 +17544,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488798951149,0.9975488798951149,0.9975521098061079,0.004688262939453125,204.56363499999998 +17952,Multiclass classification,[baseline] Last Class,Keystroke,0.997548883070581,0.997548883070581,0.9975516880097278,0.004738807678222656,213.933058 +18360,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488861049077,0.9975488861049077,0.997551252499137,0.0047893524169921875,223.513668 +18768,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488890073001,0.9975488890073001,0.9975508061984416,0.004839897155761719,233.322943 +19176,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488917861799,0.9975488917861799,0.9975503516171184,0.00489044189453125,243.357771 +19584,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488944492672,0.9975488944492672,0.9975498909097889,0.004940986633300781,253.567103 +19992,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488970036517,0.9975488970036517,0.9975494259267257,0.0049915313720703125,264.004285 +20400,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488994558557,0.9975488994558557,0.9975489582566448,0.005042076110839844,274.675054 diff --git a/benchmarks/regression.csv b/benchmarks/regression.csv index 87b6f471b7..6d0b88b0a6 100644 --- a/benchmarks/regression.csv +++ b/benchmarks/regression.csv @@ -1,1633 +1,1769 @@ step,track,model,dataset,MAE,RMSE,R2,Memory in Mb,Time in s -11,Regression,Linear Regression,ChickWeights,32.364564569910335,32.97872020361878,-1398.9905780691188,0.004130363464355469,0.003051 -22,Regression,Linear Regression,ChickWeights,22.977933628105813,25.38362603225939,-681.3960169454474,0.004130363464355469,0.0083 -33,Regression,Linear Regression,ChickWeights,16.216942910977988,20.82463881551788,-300.18738429635704,0.004130363464355469,0.014943 -44,Regression,Linear Regression,ChickWeights,12.450847696587651,18.04722398474583,-255.42929659358055,0.004130363464355469,0.023007 -55,Regression,Linear Regression,ChickWeights,11.888340701788199,18.699705575978978,-67.26141846932143,0.004130363464355469,0.032524 -66,Regression,Linear Regression,ChickWeights,11.481406471145082,17.562600262725994,-24.955493215822358,0.004130363464355469,0.043462 -77,Regression,Linear Regression,ChickWeights,10.781108661026625,16.493572764286025,-14.34295652053857,0.004130363464355469,0.055844000000000005 -88,Regression,Linear Regression,ChickWeights,9.717703273355898,15.46585610846664,-11.231382330967593,0.004130363464355469,0.06965700000000001 -99,Regression,Linear Regression,ChickWeights,8.826979124235404,14.601347274688614,-8.118374730562003,0.004130363464355469,0.084902 -110,Regression,Linear Regression,ChickWeights,8.34720326953035,13.931298318002055,-4.796525071049026,0.004130363464355469,0.101965 -121,Regression,Linear Regression,ChickWeights,8.037877082888846,13.410806382891339,-3.136902586442697,0.004130363464355469,0.120474 -132,Regression,Linear Regression,ChickWeights,7.656433924417384,12.898278689410903,-2.1275837609073576,0.004130363464355469,0.140407 -143,Regression,Linear Regression,ChickWeights,7.307942088554156,12.437137940834392,-1.3553409371460328,0.004130363464355469,0.161783 -154,Regression,Linear Regression,ChickWeights,7.037714222368383,12.042115748312936,-0.8765797740197239,0.004130363464355469,0.184594 -165,Regression,Linear Regression,ChickWeights,7.129031762481882,11.913307711374014,-0.47642580101504595,0.004130363464355469,0.208855 -176,Regression,Linear Regression,ChickWeights,7.184514897799321,11.77636646389892,-0.1632359842489146,0.004130363464355469,0.23453000000000002 -187,Regression,Linear Regression,ChickWeights,7.115123062484693,11.572523949602724,0.08026778192309036,0.004130363464355469,0.261536 -198,Regression,Linear Regression,ChickWeights,7.006474290899419,11.366304822809298,0.2942397460202306,0.004130363464355469,0.289991 -209,Regression,Linear Regression,ChickWeights,7.129008217053805,11.440940870898142,0.41052686032506414,0.004130363464355469,0.319876 -220,Regression,Linear Regression,ChickWeights,7.62421928608864,12.045617517752783,0.42792292503665363,0.004130363464355469,0.351191 -231,Regression,Linear Regression,ChickWeights,7.729844807682863,12.068921171072352,0.5087708730950672,0.004130363464355469,0.383941 -242,Regression,Linear Regression,ChickWeights,7.873703200353374,12.233427305577539,0.5938836560989084,0.004130363464355469,0.41811699999999996 -253,Regression,Linear Regression,ChickWeights,7.894340397324045,12.218207932001457,0.6481596201604607,0.004130363464355469,0.45372499999999993 -264,Regression,Linear Regression,ChickWeights,8.479294890454037,13.126132095776898,0.6289858847198173,0.004210472106933594,0.49076999999999993 -275,Regression,Linear Regression,ChickWeights,8.914096443559163,13.971715828104035,0.6301108693673194,0.004210472106933594,0.529253 -286,Regression,Linear Regression,ChickWeights,9.123963222012373,14.305597328390173,0.6641373552910966,0.004210472106933594,0.569186 -297,Regression,Linear Regression,ChickWeights,9.083791720841957,14.24670706195338,0.7111028643570333,0.004210472106933594,0.610599 -308,Regression,Linear Regression,ChickWeights,9.589205789771716,14.956254664628933,0.716435491318643,0.004210472106933594,0.653398 -319,Regression,Linear Regression,ChickWeights,10.6480954226875,17.335456678654833,0.654294294845865,0.004210472106933594,0.697581 -330,Regression,Linear Regression,ChickWeights,11.061417554605157,17.89416376383148,0.6847745473646168,0.004210472106933594,0.743241 -341,Regression,Linear Regression,ChickWeights,11.240970714084437,17.96809449059472,0.7153933828209167,0.004210472106933594,0.7902760000000001 -352,Regression,Linear Regression,ChickWeights,11.393007763406809,18.07679096199219,0.7381404893604309,0.004210472106933594,0.8386990000000001 -363,Regression,Linear Regression,ChickWeights,12.251680566634816,19.3891577397662,0.7074601934283691,0.004210472106933594,0.8885620000000001 -374,Regression,Linear Regression,ChickWeights,12.75183556333798,20.473547618215626,0.7001526953506461,0.004210472106933594,0.9398400000000001 -385,Regression,Linear Regression,ChickWeights,13.120977867369845,21.06680160073653,0.7191139726408686,0.004210472106933594,0.9925610000000001 -396,Regression,Linear Regression,ChickWeights,13.243904830041805,21.04850718241465,0.7385587649833809,0.004210472106933594,1.046701 -407,Regression,Linear Regression,ChickWeights,14.114140715648693,22.50284796635845,0.7222415724766076,0.004210472106933594,1.102244 -418,Regression,Linear Regression,ChickWeights,14.877176135328032,23.91912678123439,0.7054123344015044,0.004210472106933594,1.159239 -429,Regression,Linear Regression,ChickWeights,15.420211528669606,24.826921056607986,0.71797392321154,0.004210472106933594,1.2176479999999998 -440,Regression,Linear Regression,ChickWeights,15.588380621816588,24.899467271207534,0.7364018543257719,0.004210472106933594,1.277456 -451,Regression,Linear Regression,ChickWeights,16.102138383202178,25.5012042182244,0.73526123694725,0.004210472106933594,1.3387229999999999 -462,Regression,Linear Regression,ChickWeights,17.19666374070754,27.602141070792264,0.7086782414730581,0.004210472106933594,1.4014049999999998 -473,Regression,Linear Regression,ChickWeights,17.97145397683086,28.90516312323801,0.7179019616037816,0.004210472106933594,1.4654839999999998 -484,Regression,Linear Regression,ChickWeights,18.29792978215437,29.184271659667466,0.728186505594778,0.004210472106933594,1.5310409999999999 -495,Regression,Linear Regression,ChickWeights,18.74962346435519,29.709578411858935,0.7350194821983969,0.004210472106933594,1.5979919999999999 -506,Regression,Linear Regression,ChickWeights,19.63242676502778,31.145843529930993,0.717244444772776,0.004210472106933594,1.666343 -517,Regression,Linear Regression,ChickWeights,20.352340621675207,32.13418072986834,0.7159654376794024,0.004210472106933594,1.736162 -528,Regression,Linear Regression,ChickWeights,21.13777985928475,33.324214910779105,0.7253645356808669,0.004210472106933594,1.807369 -539,Regression,Linear Regression,ChickWeights,21.30552841968683,33.32197733500869,0.7367405849979859,0.004210472106933594,1.880014 -550,Regression,Linear Regression,ChickWeights,22.288420935356612,34.93191609140748,0.7196038878445231,0.004210472106933594,1.954091 -561,Regression,Linear Regression,ChickWeights,22.987903852135958,35.84862508987654,0.7176082524890277,0.004210472106933594,2.029559 -572,Regression,Linear Regression,ChickWeights,23.835304128485923,37.028707868367256,0.7213067136137974,0.004210472106933594,2.106474 -20,Regression,Linear Regression,TrumpApproval,23.20376765378399,26.086393589237737,-1595.1823041445402,0.004813194274902344,0.006002 -40,Regression,Linear Regression,TrumpApproval,14.037845165976737,19.010285970857193,-144.292318589198,0.004813194274902344,0.015135000000000001 -60,Regression,Linear Regression,TrumpApproval,11.507970876430278,16.25440462414082,-142.20309184289852,0.004813194274902344,0.026737 -80,Regression,Linear Regression,TrumpApproval,9.557907896578074,14.248619966820991,-109.38231735939875,0.004813194274902344,0.04106 -100,Regression,Linear Regression,TrumpApproval,8.00119890237694,12.784639272000032,-54.757167221204185,0.004813194274902344,0.058228 -120,Regression,Linear Regression,TrumpApproval,6.9642139928158,11.706689332840265,-38.660847151370525,0.004813194274902344,0.078171 -140,Regression,Linear Regression,TrumpApproval,6.158017211594616,10.855926078196223,-34.244125473921144,0.004813194274902344,0.100823 -160,Regression,Linear Regression,TrumpApproval,5.477712824897756,10.159717829752895,-26.222218487939156,0.004813194274902344,0.126265 -180,Regression,Linear Regression,TrumpApproval,5.024407839120485,9.597258286357787,-20.334227408789637,0.004813194274902344,0.154466 -200,Regression,Linear Regression,TrumpApproval,4.585662202332267,9.108145701438088,-18.272229834363905,0.004813194274902344,0.185533 -220,Regression,Linear Regression,TrumpApproval,4.26060909213789,8.692057179629266,-17.933082537971817,0.004813194274902344,0.219335 -240,Regression,Linear Regression,TrumpApproval,3.9717866152166024,8.326248244302885,-16.503720237291063,0.004813194274902344,0.255864 -260,Regression,Linear Regression,TrumpApproval,3.713770572650404,8.00217875002923,-15.385557669694744,0.004973411560058594,0.29514799999999997 -280,Regression,Linear Regression,TrumpApproval,3.519033617242816,7.718418241237259,-14.960370233444369,0.004973411560058594,0.33718899999999996 -300,Regression,Linear Regression,TrumpApproval,3.3459125962612686,7.4642342223287805,-13.679347912302557,0.004973411560058594,0.382005 -320,Regression,Linear Regression,TrumpApproval,3.2142611116185447,7.238080925352425,-13.486769876410833,0.004973411560058594,0.429554 -340,Regression,Linear Regression,TrumpApproval,3.0579195410777067,7.023783188903098,-13.415885727361019,0.004973411560058594,0.479826 -360,Regression,Linear Regression,TrumpApproval,2.945682332324278,6.834004497968132,-12.75946181118139,0.004973411560058594,0.532818 -380,Regression,Linear Regression,TrumpApproval,2.8346230504950753,6.655478314361804,-12.501407484394289,0.004973411560058594,0.588545 -400,Regression,Linear Regression,TrumpApproval,2.750580257859316,6.492898516140861,-12.2130072039923,0.004973411560058594,0.647075 -420,Regression,Linear Regression,TrumpApproval,2.6430441633874873,6.337629923196658,-12.005235448499764,0.004973411560058594,0.708317 -440,Regression,Linear Regression,TrumpApproval,2.55209658354648,6.194505226365406,-11.19965041203295,0.004973411560058594,0.7723099999999999 -460,Regression,Linear Regression,TrumpApproval,2.456089002686458,6.059000096335146,-10.068304379054995,0.004973411560058594,0.8388639999999999 -480,Regression,Linear Regression,TrumpApproval,2.366054305985814,5.93188196569365,-9.364683952709628,0.004973411560058594,0.9079419999999999 -500,Regression,Linear Regression,TrumpApproval,2.2878529832492873,5.812913918334153,-8.7442989221461,0.004973411560058594,0.9796759999999999 -520,Regression,Linear Regression,TrumpApproval,2.2263077878678064,5.701877933590318,-8.391958889485423,0.004973411560058594,1.054282 -540,Regression,Linear Regression,TrumpApproval,2.159275760054389,5.596308740310266,-8.01424271274666,0.004973411560058594,1.13157 -560,Regression,Linear Regression,TrumpApproval,2.121286703179314,5.500929056902255,-7.917124287747498,0.004973411560058594,1.211538 -580,Regression,Linear Regression,TrumpApproval,2.058130800812745,5.4056516105350205,-7.823875188349783,0.004973411560058594,1.294212 -600,Regression,Linear Regression,TrumpApproval,2.010772210317983,5.316298216027806,-7.440165070210115,0.004973411560058594,1.379521 -620,Regression,Linear Regression,TrumpApproval,1.9712240547218982,5.232121316388296,-7.05039272692726,0.004973411560058594,1.467481 -640,Regression,Linear Regression,TrumpApproval,1.9189061166281676,5.150155111235484,-6.654334565315682,0.004973411560058594,1.558 -660,Regression,Linear Regression,TrumpApproval,1.87787066207154,5.072802363597129,-6.372735616761029,0.004973411560058594,1.651027 -680,Regression,Linear Regression,TrumpApproval,1.8268195769848845,4.997758130035794,-6.2693000939147145,0.004973411560058594,1.746732 -700,Regression,Linear Regression,TrumpApproval,1.786028440025988,4.9266674679383895,-6.249499636750513,0.004973411560058594,1.84509 -720,Regression,Linear Regression,TrumpApproval,1.7407901109286728,4.857855812572241,-6.20325679847918,0.004973411560058594,1.9462679999999999 -740,Regression,Linear Regression,TrumpApproval,1.6980813320245582,4.791872643159282,-6.00468916158508,0.004973411560058594,2.050093 -760,Regression,Linear Regression,TrumpApproval,1.6641755726043945,4.729168490426344,-5.896482494172518,0.004973411560058594,2.156434 -780,Regression,Linear Regression,TrumpApproval,1.6304504193200038,4.6685949390255965,-5.751055148180852,0.004973411560058594,2.265329 -800,Regression,Linear Regression,TrumpApproval,1.6024144632936517,4.610765602340218,-5.644394777378336,0.004973411560058594,2.376883 -820,Regression,Linear Regression,TrumpApproval,1.5772046524793362,4.5553425632171916,-5.556843815680599,0.004973411560058594,2.490967 -840,Regression,Linear Regression,TrumpApproval,1.5504230286371175,4.501494961913348,-5.462190804899245,0.004973411560058594,2.6076259999999998 -860,Regression,Linear Regression,TrumpApproval,1.5213504760602443,4.449264316210896,-5.302224210455831,0.004973411560058594,2.7268899999999996 -880,Regression,Linear Regression,TrumpApproval,1.4920620594434295,4.398585386750051,-5.128866413959423,0.004973411560058594,2.8486969999999996 -900,Regression,Linear Regression,TrumpApproval,1.4631535468073946,4.3495699730724,-5.018290064232882,0.004973411560058594,2.973079 -920,Regression,Linear Regression,TrumpApproval,1.4376774845864433,4.302379067062498,-4.985157602999735,0.004973411560058594,3.100144 -940,Regression,Linear Regression,TrumpApproval,1.415413283450155,4.2569033587476754,-4.909007968017405,0.004973411560058594,3.229628 -960,Regression,Linear Regression,TrumpApproval,1.3929189597034646,4.212790914002432,-4.847686152244137,0.004973411560058594,3.361574 -980,Regression,Linear Regression,TrumpApproval,1.3689716642677325,4.1697584324400925,-4.840094251784054,0.004973411560058594,3.49595 -1000,Regression,Linear Regression,TrumpApproval,1.348598310616665,4.128277744647548,-4.8208398605179,0.004973411560058594,3.6327000000000003 -11,Regression,Linear Regression with l1 regularization,ChickWeights,32.42675747760146,33.032143455333795,-1403.5300282096139,0.004361152648925781,0.002172 -22,Regression,Linear Regression with l1 regularization,ChickWeights,23.116711205346814,25.467535638550565,-685.9150105173057,0.004361152648925781,0.005938 -33,Regression,Linear Regression with l1 regularization,ChickWeights,16.40645850052153,20.90890407573329,-302.6297778360383,0.004361152648925781,0.010657 -44,Regression,Linear Regression with l1 regularization,ChickWeights,12.633013937743208,18.123648450153386,-257.60569409037487,0.004361152648925781,0.016312 -55,Regression,Linear Regression with l1 regularization,ChickWeights,12.09340740686418,18.755320878466158,-67.66805855020911,0.004361152648925781,0.022822000000000002 -66,Regression,Linear Regression with l1 regularization,ChickWeights,11.723217014070245,17.64468538999345,-25.198684873207917,0.004361152648925781,0.030046000000000003 -77,Regression,Linear Regression with l1 regularization,ChickWeights,10.995180265837302,16.56912334002292,-14.483838555564008,0.004361152648925781,0.037972000000000006 -88,Regression,Linear Regression with l1 regularization,ChickWeights,9.883907021821408,15.530287677810513,-11.333507781967656,0.004361152648925781,0.04659800000000001 -99,Regression,Linear Regression with l1 regularization,ChickWeights,8.972176235897166,14.66146594146288,-8.193616152032533,0.004361152648925781,0.055927000000000004 -110,Regression,Linear Regression with l1 regularization,ChickWeights,8.50780758363674,13.99831063395296,-4.852424068253989,0.004361152648925781,0.06618700000000001 -121,Regression,Linear Regression with l1 regularization,ChickWeights,8.189643323772653,13.479618530659062,-3.1794651999709123,0.004361152648925781,0.07716500000000001 -132,Regression,Linear Regression with l1 regularization,ChickWeights,7.801679323915957,12.961634417305982,-2.158384304610562,0.004361152648925781,0.08884500000000001 -143,Regression,Linear Regression with l1 regularization,ChickWeights,7.451861020480359,12.498785048420814,-1.3787482214976663,0.004361152648925781,0.101232 -154,Regression,Linear Regression with l1 regularization,ChickWeights,7.149646459280303,12.093459492377487,-0.8926161646086501,0.004361152648925781,0.114319 -165,Regression,Linear Regression with l1 regularization,ChickWeights,7.229782522017506,11.96532542528415,-0.4893471433346175,0.004361152648925781,0.128105 -176,Regression,Linear Regression with l1 regularization,ChickWeights,7.2725124267989045,11.818048782353436,-0.17148507897118015,0.004361152648925781,0.14260699999999998 -187,Regression,Linear Regression with l1 regularization,ChickWeights,7.196746625780547,11.610365671998538,0.07424296733128555,0.004361152648925781,0.15781399999999998 -198,Regression,Linear Regression with l1 regularization,ChickWeights,7.1064479168500405,11.42347112116664,0.28712271717281546,0.004361152648925781,0.173728 -209,Regression,Linear Regression with l1 regularization,ChickWeights,7.187961051781539,11.470757896418931,0.4074503232991815,0.004361152648925781,0.19033999999999998 -220,Regression,Linear Regression with l1 regularization,ChickWeights,7.669011107337858,12.056664202246258,0.426873173509426,0.004361152648925781,0.20765199999999998 -231,Regression,Linear Regression with l1 regularization,ChickWeights,7.786810512364482,12.097059810994589,0.5064776054701067,0.004361152648925781,0.22566199999999997 -242,Regression,Linear Regression with l1 regularization,ChickWeights,7.967587416991401,12.312376354870244,0.5886249568088757,0.004361152648925781,0.24437199999999998 -253,Regression,Linear Regression with l1 regularization,ChickWeights,7.942191618805254,12.251768500136135,0.6462241186586895,0.004361152648925781,0.26377399999999995 -264,Regression,Linear Regression with l1 regularization,ChickWeights,8.532657015260138,13.159069559279287,0.6271215737447722,0.004441261291503906,0.28388899999999995 -275,Regression,Linear Regression with l1 regularization,ChickWeights,8.974527826218258,14.016709692996269,0.6277246858047626,0.004441261291503906,0.30470499999999995 -286,Regression,Linear Regression with l1 regularization,ChickWeights,9.187875132430849,14.367497338174372,0.6612245262436964,0.004441261291503906,0.32623499999999994 -297,Regression,Linear Regression with l1 regularization,ChickWeights,9.146460078204477,14.316362398212213,0.7082709930315267,0.004441261291503906,0.3484709999999999 -308,Regression,Linear Regression with l1 regularization,ChickWeights,9.641370323857412,15.001693346690402,0.7147098761119641,0.004441261291503906,0.3714179999999999 -319,Regression,Linear Regression with l1 regularization,ChickWeights,10.700825117113602,17.38383679543193,0.6523619983610591,0.004441261291503906,0.3950729999999999 -330,Regression,Linear Regression with l1 regularization,ChickWeights,11.121905143066762,17.96551370253039,0.6822557197544588,0.004441261291503906,0.4194319999999999 -341,Regression,Linear Regression with l1 regularization,ChickWeights,11.300130820443067,18.038310133249194,0.7131646675929553,0.004441261291503906,0.4444929999999999 -352,Regression,Linear Regression with l1 regularization,ChickWeights,11.446939695127732,18.134419536696374,0.7364682185789984,0.004441261291503906,0.4702589999999999 -363,Regression,Linear Regression with l1 regularization,ChickWeights,12.306713664516929,19.446501901626007,0.7057272396553849,0.004441261291503906,0.4967349999999999 -374,Regression,Linear Regression with l1 regularization,ChickWeights,12.804714538172899,20.530886427306594,0.6984708214392588,0.004441261291503906,0.523921 -385,Regression,Linear Regression with l1 regularization,ChickWeights,13.174415073298738,21.133761140382827,0.7173255769005842,0.004441261291503906,0.551805 -396,Regression,Linear Regression with l1 regularization,ChickWeights,13.2883642577537,21.10340115690396,0.7371933225224319,0.004441261291503906,0.580392 -407,Regression,Linear Regression with l1 regularization,ChickWeights,14.156717848187574,22.549679209142333,0.7210842693854467,0.004441261291503906,0.6096860000000001 -418,Regression,Linear Regression with l1 regularization,ChickWeights,14.91944953335544,23.967687063528587,0.7042149845564116,0.004441261291503906,0.6396860000000001 -429,Regression,Linear Regression with l1 regularization,ChickWeights,15.467166242517186,24.886955839016704,0.7166083213097005,0.004441261291503906,0.6704000000000001 -440,Regression,Linear Regression with l1 regularization,ChickWeights,15.631989433801651,24.954278611820005,0.735240056765428,0.004441261291503906,0.7018220000000001 -451,Regression,Linear Regression with l1 regularization,ChickWeights,16.140858755557055,25.549476814516595,0.7342580119275103,0.004441261291503906,0.7339490000000001 -462,Regression,Linear Regression with l1 regularization,ChickWeights,17.234115417053438,27.64913352119068,0.7076854506057617,0.004441261291503906,0.766781 -473,Regression,Linear Regression with l1 regularization,ChickWeights,18.0152369206823,28.967470484053976,0.7166844816496443,0.004441261291503906,0.800319 -484,Regression,Linear Regression with l1 regularization,ChickWeights,18.337846632158772,29.233263620963758,0.727273146935064,0.004441261291503906,0.834558 -495,Regression,Linear Regression with l1 regularization,ChickWeights,18.785497932712666,29.755293652524703,0.7342033839127224,0.004441261291503906,0.86951 -506,Regression,Linear Regression with l1 regularization,ChickWeights,19.668627686193567,31.194713202801612,0.7163564282233008,0.004441261291503906,0.90517 -517,Regression,Linear Regression with l1 regularization,ChickWeights,20.38947136061499,32.18441644636668,0.7150766748346216,0.004441261291503906,0.941543 -528,Regression,Linear Regression with l1 regularization,ChickWeights,21.174247281712567,33.375214332088184,0.7245232875146275,0.004441261291503906,0.978621 -539,Regression,Linear Regression with l1 regularization,ChickWeights,21.335113723003413,33.369412847265615,0.7359905254430201,0.004441261291503906,1.016405 -550,Regression,Linear Regression with l1 regularization,ChickWeights,22.319908918673338,34.98038586656285,0.7188252208291055,0.004441261291503906,1.054903 -561,Regression,Linear Regression with l1 regularization,ChickWeights,23.021029445869836,35.899425045778656,0.7168073485461736,0.004441261291503906,1.094104 -572,Regression,Linear Regression with l1 regularization,ChickWeights,23.867950458471242,37.077313876148,0.7205745757906983,0.004441261291503906,1.134008 -20,Regression,Linear Regression with l1 regularization,TrumpApproval,23.431235633428948,26.218144216470428,-1611.3462157506035,0.005043983459472656,0.004147 -40,Regression,Linear Regression with l1 regularization,TrumpApproval,14.103513545807008,19.087149489688155,-145.46960290724672,0.005043983459472656,0.010783000000000001 -60,Regression,Linear Regression with l1 regularization,TrumpApproval,11.461367198760033,16.23222714650599,-141.81258639462544,0.005043983459472656,0.018889000000000003 -80,Regression,Linear Regression with l1 regularization,TrumpApproval,9.44127715126052,14.201674320759095,-108.65615123709478,0.005043983459472656,0.028452000000000005 -100,Regression,Linear Regression with l1 regularization,TrumpApproval,7.797022024035496,12.721240349043525,-54.205539694816935,0.005043983459472656,0.039551 -120,Regression,Linear Regression with l1 regularization,TrumpApproval,6.691147023027369,11.629122321270428,-38.137013046560284,0.005043983459472656,0.052122 -140,Regression,Linear Regression with l1 regularization,TrumpApproval,5.831783229321132,10.770680162674168,-33.69279126993988,0.005043983459472656,0.066155 -160,Regression,Linear Regression with l1 regularization,TrumpApproval,5.153144449790517,10.076741978726949,-25.779378860246055,0.005043983459472656,0.08166000000000001 -180,Regression,Linear Regression with l1 regularization,TrumpApproval,4.653806702754108,9.504457154537077,-19.92363756907144,0.005043983459472656,0.09862900000000001 -200,Regression,Linear Regression with l1 regularization,TrumpApproval,4.222103651464603,9.017588926226491,-17.890910704295415,0.005043983459472656,0.117152 -220,Regression,Linear Regression with l1 regularization,TrumpApproval,3.8867321432249606,8.600346446062781,-17.535660715889627,0.005043983459472656,0.137154 -240,Regression,Linear Regression with l1 regularization,TrumpApproval,3.599340946609919,8.235553698215059,-16.124474758948196,0.005043983459472656,0.15861 -260,Regression,Linear Regression with l1 regularization,TrumpApproval,3.3420538385748753,7.912854669167724,-15.021792727856035,0.005204200744628906,0.181525 -280,Regression,Linear Regression with l1 regularization,TrumpApproval,3.1218767056970433,7.6253908516912965,-14.577959242562436,0.005204200744628906,0.205897 -300,Regression,Linear Regression with l1 regularization,TrumpApproval,2.9443386382725367,7.3683114276631025,-13.30448388815742,0.005204200744628906,0.231796 -320,Regression,Linear Regression with l1 regularization,TrumpApproval,2.815755936756583,7.138230019587049,-13.089830522149688,0.005204200744628906,0.259173 -340,Regression,Linear Regression with l1 regularization,TrumpApproval,2.6933833627330244,6.928597052658441,-13.027805837336182,0.005204200744628906,0.288012 -360,Regression,Linear Regression with l1 regularization,TrumpApproval,2.6051143953909115,6.739167122054826,-12.380223856378365,0.005204200744628906,0.318313 -380,Regression,Linear Regression with l1 regularization,TrumpApproval,2.490520088621087,6.560408055638074,-12.118440379947032,0.005204200744628906,0.350072 -400,Regression,Linear Regression with l1 regularization,TrumpApproval,2.3796000807759676,6.3947378153227445,-11.816514349639151,0.005204200744628906,0.383345 -420,Regression,Linear Regression with l1 regularization,TrumpApproval,2.2783211278818065,6.240996339547436,-11.61166202267384,0.005204200744628906,0.418092 -440,Regression,Linear Regression with l1 regularization,TrumpApproval,2.2145782025697205,6.101000859547544,-10.83412932108482,0.005204200744628906,0.45429800000000004 -460,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1464112051078263,5.968705291729145,-9.740869666058233,0.005204200744628906,0.49195900000000004 -480,Regression,Linear Regression with l1 regularization,TrumpApproval,2.072454283271577,5.843773480387036,-9.059069500744918,0.005204200744628906,0.5310900000000001 -500,Regression,Linear Regression with l1 regularization,TrumpApproval,1.9987939253418097,5.726023081885353,-8.45516263823347,0.005204200744628906,0.5716870000000001 -520,Regression,Linear Regression with l1 regularization,TrumpApproval,1.9284894530733134,5.614979238753044,-8.107866604370331,0.005204200744628906,0.613828 -540,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8708966196370942,5.510640598064868,-7.740375491210358,0.005204200744628906,0.657455 -560,Regression,Linear Regression with l1 regularization,TrumpApproval,1.824494929332068,5.412971505312132,-7.634241934578528,0.005204200744628906,0.702504 -580,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7819511272383441,5.3203622791847724,-7.547628985954546,0.005204200744628906,0.748994 -600,Regression,Linear Regression with l1 regularization,TrumpApproval,1.749732390051898,5.233421242207161,-7.179064952823026,0.005204200744628906,0.796981 -620,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7052484400139905,5.14921027958835,-6.797272491389374,0.005204200744628906,0.846385 -640,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6664817162996513,5.068963198399273,-6.414896594696412,0.005204200744628906,0.897251 -660,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6214900718153242,4.99171743637858,-6.138924064419633,0.005204200744628906,0.949568 -680,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5818233283584242,4.918048415051679,-6.0392717086910395,0.005204200744628906,1.003319 -700,Regression,Linear Regression with l1 regularization,TrumpApproval,1.555259840641907,4.848763195223404,-6.02204294951599,0.005204200744628906,1.0585930000000001 -720,Regression,Linear Regression with l1 regularization,TrumpApproval,1.532054525069809,4.783346185353484,-5.983984772166844,0.005204200744628906,1.1153050000000002 -740,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5146024654094865,4.7215191517857305,-5.800515662606876,0.005204200744628906,1.1734340000000003 -760,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4818289913711118,4.659306491272199,-5.694229888242945,0.005204200744628906,1.2330550000000002 -780,Regression,Linear Regression with l1 regularization,TrumpApproval,1.452402157072035,4.5995629070091395,-5.552882667907455,0.005204200744628906,1.2940880000000001 -800,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4293016341621463,4.542852809132106,-5.450103306475059,0.005204200744628906,1.356613 -820,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4031025761620077,4.487566758646917,-5.363185739412862,0.005204200744628906,1.420603 -840,Regression,Linear Regression with l1 regularization,TrumpApproval,1.381369306663701,4.43470615373713,-5.271853959037296,0.005204200744628906,1.48601 -860,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3592027324075535,4.3834303912536,-5.11710119981194,0.005204200744628906,1.552867 -880,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3365452172324814,4.3337346544721145,-4.949476248649982,0.005204200744628906,1.621167 -900,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3099328416755933,4.285399956971287,-4.842022075269781,0.005204200744628906,1.690888 -920,Regression,Linear Regression with l1 regularization,TrumpApproval,1.286195364366444,4.238743982476022,-4.809417914789597,0.005204200744628906,1.762146 -940,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2707541496507981,4.194574036632911,-4.737236102715975,0.005204200744628906,1.834847 -960,Regression,Linear Regression with l1 regularization,TrumpApproval,1.252007480522268,4.151199175197265,-4.677947706919012,0.005204200744628906,1.9089589999999999 -980,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2327553756112288,4.10900051997898,-4.671141158917876,0.005204200744628906,1.9845519999999999 -1000,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2158544056174447,4.0682125513101886,-4.652689174637866,0.005204200744628906,2.061558 -11,Regression,Linear Regression with l2 regularization,ChickWeights,32.49979765090093,33.085767570527814,-1408.0939351430807,0.004153251647949219,0.00221 -22,Regression,Linear Regression with l2 regularization,ChickWeights,23.548763427243948,25.711783397814365,-699.1539821884553,0.004153251647949219,0.0060149999999999995 -33,Regression,Linear Regression with l2 regularization,ChickWeights,16.994606748791693,21.16216382986949,-310.0297747454571,0.004153251647949219,0.010773 -44,Regression,Linear Regression with l2 regularization,ChickWeights,13.434663043069094,18.386175360023177,-265.1519301746234,0.004153251647949219,0.016452 -55,Regression,Linear Regression with l2 regularization,ChickWeights,12.685726760044922,18.64479618798502,-66.86112450289035,0.004153251647949219,0.023006000000000002 -66,Regression,Linear Regression with l2 regularization,ChickWeights,12.3059577367172,17.58876192176611,-25.03287862681572,0.004153251647949219,0.030252 -77,Regression,Linear Regression with l2 regularization,ChickWeights,11.844694328458633,16.6807536659431,-14.693178357367543,0.004153251647949219,0.03818 -88,Regression,Linear Regression with l2 regularization,ChickWeights,11.154725738202893,15.783555067193374,-11.739056703820452,0.004153251647949219,0.046793 -99,Regression,Linear Regression with l2 regularization,ChickWeights,10.383929359740238,14.970988963724652,-8.585892537614809,0.004153251647949219,0.056087 -110,Regression,Linear Regression with l2 regularization,ChickWeights,9.96579366260941,14.36848414897767,-5.166041463970149,0.004153251647949219,0.066275 -121,Regression,Linear Regression with l2 regularization,ChickWeights,9.69456937849415,13.920059192886765,-3.4570517192093604,0.004153251647949219,0.07716100000000001 -132,Regression,Linear Regression with l2 regularization,ChickWeights,9.550940690791585,13.540299798742515,-2.4466881997122822,0.004153251647949219,0.088733 -143,Regression,Linear Regression with l2 regularization,ChickWeights,9.359343163302276,13.17888693683795,-1.6446630191344274,0.004153251647949219,0.100994 -154,Regression,Linear Regression with l2 regularization,ChickWeights,9.050096583806178,12.809003240652471,-1.1232058766616237,0.004153251647949219,0.113939 -165,Regression,Linear Regression with l2 regularization,ChickWeights,9.017680561209701,12.690905771048246,-0.6754526017728211,0.004153251647949219,0.127575 -176,Regression,Linear Regression with l2 regularization,ChickWeights,9.128420828629457,12.675650490262221,-0.3476766887041909,0.004153251647949219,0.141896 -187,Regression,Linear Regression with l2 regularization,ChickWeights,9.200055067293626,12.61943948921252,-0.0936676208508318,0.004153251647949219,0.156913 -198,Regression,Linear Regression with l2 regularization,ChickWeights,9.131920516304557,12.48608852319409,0.14832986501041,0.004153251647949219,0.172621 -209,Regression,Linear Regression with l2 regularization,ChickWeights,9.262178084838375,12.632807163510389,0.2813122010719644,0.004153251647949219,0.18901 -220,Regression,Linear Regression with l2 regularization,ChickWeights,9.624585089266471,13.14522964439942,0.3187088278286061,0.004153251647949219,0.206079 -231,Regression,Linear Regression with l2 regularization,ChickWeights,9.871229105762628,13.33182219595452,0.4005868940419749,0.004153251647949219,0.22382400000000002 -242,Regression,Linear Regression with l2 regularization,ChickWeights,10.053594156641497,13.615837484576032,0.49691323204665605,0.004153251647949219,0.24225500000000003 -253,Regression,Linear Regression with l2 regularization,ChickWeights,10.103586276318884,13.654347763291469,0.5605873283913356,0.004153251647949219,0.261365 -264,Regression,Linear Regression with l2 regularization,ChickWeights,10.539757430756955,14.361033144769575,0.5558923432468688,0.004233360290527344,0.281177 -275,Regression,Linear Regression with l2 regularization,ChickWeights,11.000663746720075,15.253690514733572,0.5591184315358017,0.004233360290527344,0.301675 -286,Regression,Linear Regression with l2 regularization,ChickWeights,11.301045102393736,15.716738058687294,0.5946085911267809,0.004233360290527344,0.32286000000000004 -297,Regression,Linear Regression with l2 regularization,ChickWeights,11.275670942063872,15.722526759958118,0.648148884657232,0.004233360290527344,0.34473600000000004 -308,Regression,Linear Regression with l2 regularization,ChickWeights,11.737413962135747,16.425717512690383,0.6579773485389515,0.004233360290527344,0.367308 -319,Regression,Linear Regression with l2 regularization,ChickWeights,12.62956768598283,18.523981673419602,0.6052658886851463,0.004233360290527344,0.390571 -330,Regression,Linear Regression with l2 regularization,ChickWeights,13.20835169207171,19.400144953397177,0.6294827674482892,0.004233360290527344,0.41452 -341,Regression,Linear Regression with l2 regularization,ChickWeights,13.34580485099439,19.473222157632836,0.6657152345574865,0.004233360290527344,0.439156 -352,Regression,Linear Regression with l2 regularization,ChickWeights,13.572829398695534,19.644456145190084,0.6907528542453616,0.004233360290527344,0.464482 -363,Regression,Linear Regression with l2 regularization,ChickWeights,14.286348966120116,20.694687599962585,0.666738740088638,0.004233360290527344,0.490494 -374,Regression,Linear Regression with l2 regularization,ChickWeights,14.777113556436731,21.820651771093797,0.6593962849465681,0.004233360290527344,0.517211 -385,Regression,Linear Regression with l2 regularization,ChickWeights,15.215863000720542,22.583610768099227,0.6772102871974224,0.004233360290527344,0.544615 -396,Regression,Linear Regression with l2 regularization,ChickWeights,15.33144965807796,22.564695888148663,0.6995373757169706,0.004233360290527344,0.5727059999999999 -407,Regression,Linear Regression with l2 regularization,ChickWeights,16.156138146741625,23.924755047114473,0.6860306363812331,0.004233360290527344,0.601482 -418,Regression,Linear Regression with l2 regularization,ChickWeights,16.826141311926307,25.281544830782227,0.6708975363085852,0.004233360290527344,0.6309469999999999 -429,Regression,Linear Regression with l2 regularization,ChickWeights,17.40263926327312,26.38004441662919,0.6815842184892376,0.004233360290527344,0.6611069999999999 -440,Regression,Linear Regression with l2 regularization,ChickWeights,17.533400796677356,26.42712307382207,0.7030645738539452,0.004233360290527344,0.6919729999999998 -451,Regression,Linear Regression with l2 regularization,ChickWeights,18.01931805998843,26.98764790902567,0.7034989551644695,0.004233360290527344,0.7235269999999998 -462,Regression,Linear Regression with l2 regularization,ChickWeights,19.020059735828532,28.983219342716673,0.6787962347144068,0.004233360290527344,0.7557739999999998 -473,Regression,Linear Regression with l2 regularization,ChickWeights,19.888963224686226,30.578078926209333,0.6843036130043219,0.004233360290527344,0.7887069999999998 -484,Regression,Linear Regression with l2 regularization,ChickWeights,20.14556805064173,30.710181129007662,0.6990197135707891,0.004233360290527344,0.8223269999999998 -495,Regression,Linear Regression with l2 regularization,ChickWeights,20.606054171923702,31.270986299633186,0.7064351021760091,0.004233360290527344,0.8566339999999998 -506,Regression,Linear Regression with l2 regularization,ChickWeights,21.410220326067673,32.615082621422005,0.6899384474766328,0.004233360290527344,0.8916409999999998 -517,Regression,Linear Regression with l2 regularization,ChickWeights,22.149063292155795,33.66176418126127,0.6883188968774838,0.004233360290527344,0.9273449999999998 -528,Regression,Linear Regression with l2 regularization,ChickWeights,22.923596011881333,34.92960124509041,0.6982661596564212,0.004233360290527344,0.9637369999999997 -539,Regression,Linear Regression with l2 regularization,ChickWeights,23.042465823580866,34.93124976178739,0.7106985365247873,0.004233360290527344,1.0008159999999997 -550,Regression,Linear Regression with l2 regularization,ChickWeights,23.974366627279803,36.47485289150521,0.6942867450600009,0.004233360290527344,1.0385829999999998 -561,Regression,Linear Regression with l2 regularization,ChickWeights,24.688352372874245,37.45551228620605,0.6917248794696187,0.004233360290527344,1.077041 -572,Regression,Linear Regression with l2 regularization,ChickWeights,25.520405522785374,38.65530944983144,0.6962839796503111,0.004233360290527344,1.116183 -20,Regression,Linear Regression with l2 regularization,TrumpApproval,23.51586240468561,26.237375459551668,-1613.712423965852,0.004836082458496094,0.004083 -40,Regression,Linear Regression with l2 regularization,TrumpApproval,14.404588626352647,19.156187724628055,-146.53108046561562,0.004836082458496094,0.01063 -60,Regression,Linear Regression with l2 regularization,TrumpApproval,12.231791081689295,16.474815193156783,-146.113106004694,0.004836082458496094,0.018597000000000002 -80,Regression,Linear Regression with l2 regularization,TrumpApproval,9.897294138330498,14.380930849374858,-111.44182781593443,0.004836082458496094,0.027966000000000005 -100,Regression,Linear Regression with l2 regularization,TrumpApproval,8.086918618304638,12.871841233853624,-55.52038254270653,0.004836082458496094,0.038819000000000006 -120,Regression,Linear Regression with l2 regularization,TrumpApproval,7.090525087262731,11.800543733353924,-39.29933105483862,0.004836082458496094,0.05107300000000001 -140,Regression,Linear Regression with l2 regularization,TrumpApproval,6.252895179240796,10.939748534807466,-34.79049149741283,0.004836082458496094,0.06473100000000001 -160,Regression,Linear Regression with l2 regularization,TrumpApproval,5.613172563674658,10.244365728872303,-26.67772385659461,0.004836082458496094,0.07979200000000002 -180,Regression,Linear Regression with l2 regularization,TrumpApproval,5.139122523864994,9.674573881172426,-20.679349421494624,0.004836082458496094,0.09624700000000001 -200,Regression,Linear Regression with l2 regularization,TrumpApproval,4.970287512907828,9.286148634805688,-19.03287536608377,0.004836082458496094,0.11416100000000001 -220,Regression,Linear Regression with l2 regularization,TrumpApproval,4.714509288646119,8.88589717245386,-18.78694495096694,0.004836082458496094,0.13348100000000002 -240,Regression,Linear Regression with l2 regularization,TrumpApproval,4.3785831664508095,8.51169894931726,-17.292125083299812,0.004836082458496094,0.15421200000000002 -260,Regression,Linear Regression with l2 regularization,TrumpApproval,4.192432902948977,8.203158141559566,-16.21895925323482,0.004996299743652344,0.17633200000000002 -280,Regression,Linear Regression with l2 regularization,TrumpApproval,4.032216676138626,7.92689648751503,-15.834209174335765,0.004996299743652344,0.19986500000000001 -300,Regression,Linear Regression with l2 regularization,TrumpApproval,3.888249411356283,7.680244711632193,-14.54126486396457,0.004996299743652344,0.22485700000000003 -320,Regression,Linear Regression with l2 regularization,TrumpApproval,3.851910342336546,7.48583024126048,-14.495465984230798,0.004996299743652344,0.251273 -340,Regression,Linear Regression with l2 regularization,TrumpApproval,3.7628843932429423,7.2990742240635536,-14.5680671641431,0.004996299743652344,0.27909500000000004 -360,Regression,Linear Regression with l2 regularization,TrumpApproval,3.7414983297472317,7.147194018854174,-14.049499953660261,0.004996299743652344,0.30831700000000006 -380,Regression,Linear Regression with l2 regularization,TrumpApproval,3.6331417160345065,6.972318267910069,-13.817498979914417,0.004996299743652344,0.3389480000000001 -400,Regression,Linear Regression with l2 regularization,TrumpApproval,3.505723677240456,6.801269751447825,-13.497878046830476,0.004996299743652344,0.37103900000000006 -420,Regression,Linear Regression with l2 regularization,TrumpApproval,3.3777899375444775,6.6406714986639335,-13.278693194916952,0.004996299743652344,0.4045350000000001 -440,Regression,Linear Regression with l2 regularization,TrumpApproval,3.2855655851554295,6.496417025168413,-12.417819031510927,0.004996299743652344,0.4394350000000001 -460,Regression,Linear Regression with l2 regularization,TrumpApproval,3.204438206990859,6.363151879091182,-11.207416254643826,0.004996299743652344,0.4757300000000001 -480,Regression,Linear Regression with l2 regularization,TrumpApproval,3.1147239220792944,6.234124280033156,-10.44779843680249,0.004996299743652344,0.5134280000000001 -500,Regression,Linear Regression with l2 regularization,TrumpApproval,3.0674610457420313,6.126558214637352,-9.824203383261038,0.004996299743652344,0.5525380000000001 -520,Regression,Linear Regression with l2 regularization,TrumpApproval,2.990407253638538,6.01302433311803,-9.444947809169491,0.004996299743652344,0.593122 -540,Regression,Linear Regression with l2 regularization,TrumpApproval,2.9353658306947947,5.909270916056388,-9.050640009643079,0.004996299743652344,0.635128 -560,Regression,Linear Regression with l2 regularization,TrumpApproval,2.8633890512526734,5.806679023039649,-8.935926541145857,0.004996299743652344,0.6785490000000001 -580,Regression,Linear Regression with l2 regularization,TrumpApproval,2.8074953384874966,5.7111093740410706,-8.849273711490637,0.004996299743652344,0.723357 -600,Regression,Linear Regression with l2 regularization,TrumpApproval,2.7371272959787114,5.616984296672238,-8.421904346026553,0.004996299743652344,0.769575 -620,Regression,Linear Regression with l2 regularization,TrumpApproval,2.6952794581589052,5.533794104458184,-8.005492094038127,0.004996299743652344,0.817237 -640,Regression,Linear Regression with l2 regularization,TrumpApproval,2.663427445960627,5.457208108806078,-7.594247452705627,0.004996299743652344,0.866263 -660,Regression,Linear Regression with l2 regularization,TrumpApproval,2.6161528712216042,5.378587544649896,-7.28837246231315,0.004996299743652344,0.916653 -680,Regression,Linear Regression with l2 regularization,TrumpApproval,2.5798661012588893,5.305972052989116,-7.193548818831784,0.004996299743652344,0.968481 -700,Regression,Linear Regression with l2 regularization,TrumpApproval,2.537320459903927,5.236098928386573,-7.188742583674767,0.004996299743652344,1.021719 -720,Regression,Linear Regression with l2 regularization,TrumpApproval,2.4875620374759775,5.165214649048708,-7.14359946989749,0.004996299743652344,1.076348 -740,Regression,Linear Regression with l2 regularization,TrumpApproval,2.4359344014471898,5.096521995605296,-6.923665614900413,0.004996299743652344,1.1323770000000002 -760,Regression,Linear Regression with l2 regularization,TrumpApproval,2.407655610747697,5.035258504842907,-6.818106944929671,0.004996299743652344,1.1897670000000002 -780,Regression,Linear Regression with l2 regularization,TrumpApproval,2.359216458068109,4.971257259303496,-6.65476288050581,0.004996299743652344,1.2485520000000003 -800,Regression,Linear Regression with l2 regularization,TrumpApproval,2.316070029510264,4.9101929612142525,-6.535402552442101,0.004996299743652344,1.3087800000000003 -820,Regression,Linear Regression with l2 regularization,TrumpApproval,2.2777366252623445,4.852446886619337,-6.440024000118236,0.004996299743652344,1.3703650000000003 -840,Regression,Linear Regression with l2 regularization,TrumpApproval,2.243368105900121,4.7970088928814505,-6.33849981001193,0.004996299743652344,1.4333480000000003 -860,Regression,Linear Regression with l2 regularization,TrumpApproval,2.208161873346368,4.742699334581194,-6.1609167229990645,0.004996299743652344,1.4977110000000002 -880,Regression,Linear Regression with l2 regularization,TrumpApproval,2.173524312605847,4.690026300839657,-5.967944096931786,0.004996299743652344,1.5634250000000003 -900,Regression,Linear Regression with l2 regularization,TrumpApproval,2.143113317062195,4.639957881226245,-5.848706397355668,0.004996299743652344,1.6305320000000003 -920,Regression,Linear Regression with l2 regularization,TrumpApproval,2.107662636673197,4.590154711256589,-5.812600067991807,0.004996299743652344,1.6990770000000004 -940,Regression,Linear Regression with l2 regularization,TrumpApproval,2.084055644614135,4.5438188766398575,-5.732386133187966,0.004996299743652344,1.7689810000000004 -960,Regression,Linear Regression with l2 regularization,TrumpApproval,2.0573225686306618,4.498049663013517,-5.666421016566231,0.004996299743652344,1.8402960000000004 -980,Regression,Linear Regression with l2 regularization,TrumpApproval,2.029372876222096,4.453478872426294,-5.661880798559547,0.004996299743652344,1.9129870000000004 -1000,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9991816433402003,4.409973662813209,-5.642320489885111,0.004996299743652344,1.9870360000000005 -11,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,29.34636433128918,30.877867366178624,-1226.3038921604407,0.0034055709838867188,0.002755 -22,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.78579478575624,22.219906445728544,-521.8939460594183,0.0034055709838867188,0.007689 -33,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.764406226748013,18.43476392899385,-235.02444355689545,0.0034055709838867188,0.013926999999999998 -44,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.514750373746141,16.140786164803156,-204.11441945614393,0.0034055709838867188,0.021043 -55,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.118868381910097,17.807152193193623,-60.900579144648304,0.0034055709838867188,0.029020999999999998 -66,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.068652098820438,16.444921319285292,-21.75701273895947,0.0034055709838867188,0.037852 -77,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.238561614797426,15.414518181428047,-12.401070176830181,0.0034055709838867188,0.047528999999999995 -88,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.397438800335843,14.475112340817045,-9.714489831884093,0.0034055709838867188,0.05806599999999999 -99,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.833048644024143,13.715858800506394,-7.045954767890709,0.0034055709838867188,0.069447 -110,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.655503029296072,13.224205829308971,-4.223044606682061,0.0034055709838867188,0.08190399999999999 -121,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.616633860178707,12.86945366769748,-2.809655727072758,0.0034055709838867188,0.09521999999999999 -132,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.226514673025618,12.359535435581538,-1.871770505407636,0.0034055709838867188,0.10940099999999998 -143,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.028644030968596,11.955222747545205,-1.1763474084194208,0.0034055709838867188,0.12445599999999998 -154,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.986848640780892,11.69340351574852,-0.7694704308339801,0.0034055709838867188,0.14037099999999997 -165,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.055944609610511,11.647997480703344,-0.41139782315780704,0.0034055709838867188,0.15710899999999997 -176,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.165300854035116,11.693346051910419,-0.14689275551772507,0.0034055709838867188,0.17464399999999997 -187,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.086580322778478,11.479257625599036,0.09503282026869064,0.0034055709838867188,0.19298799999999997 -198,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.0079421524022685,11.279628541389027,0.3049625679809165,0.0034055709838867188,0.21213599999999996 -209,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.136281210496879,11.437970564343967,0.4108328996032877,0.0034055709838867188,0.23209399999999997 -220,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.591733813835971,12.23821647621677,0.40948264143079904,0.0034055709838867188,0.252837 -231,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.6364242427237805,12.197368986664097,0.4982590675647103,0.0034055709838867188,0.274371 -242,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.775639220351206,12.334191292520584,0.5871659255406134,0.0034055709838867188,0.296703 -253,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.787415018822619,12.26821713761009,0.6452735558950271,0.0034055709838867188,0.31983 -264,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.387229506824328,13.12794439290609,0.6288834273883384,0.0034589767456054688,0.343762 -275,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.893165599265544,14.22060275652947,0.6168153604937621,0.0034589767456054688,0.368493 -286,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.145334455404228,14.488680433063887,0.6554856011642076,0.0034589767456054688,0.394025 -297,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.138984650870034,14.40937364029996,0.7044680409221951,0.0034589767456054688,0.420371 -308,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.653721426872565,15.186897141025446,0.7076222812215553,0.0034589767456054688,0.447515 -319,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.688547371510406,17.568442046558065,0.6449394075517852,0.0034589767456054688,0.475457 -330,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.136888804773205,18.130051576409294,0.6764089203981734,0.0034589767456054688,0.5041950000000001 -341,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.389709627918275,18.31814497212097,0.7041960757214223,0.0034589767456054688,0.53373 -352,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.506556624125665,18.357319157972537,0.7299499902857889,0.0034589767456054688,0.56408 -363,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.507799925411486,19.941572040394526,0.6905532928077196,0.0034589767456054688,0.5952430000000001 -374,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.006439137322559,20.915910426515573,0.6870553798396234,0.0034589767456054688,0.6272240000000001 -385,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.44126440090726,21.59107831138786,0.7049595304644938,0.0034589767456054688,0.6600050000000001 -396,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.452600873244053,21.4799043653453,0.7277322681469997,0.0034589767456054688,0.6935880000000001 -407,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,14.30954893105116,22.795153034451378,0.7149787127692513,0.0034589767456054688,0.7279810000000001 -418,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.058465273046647,24.128248961177892,0.7002387244037227,0.0034589767456054688,0.7631730000000001 -429,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.61316127520364,25.007094384238126,0.7138656442877266,0.0034589767456054688,0.799166 -440,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.833192306896644,25.159785721055627,0.7308613212218023,0.0034589767456054688,0.8359580000000001 -451,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.36714766376461,25.770608582556893,0.729638089688956,0.0034589767456054688,0.87355 -462,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,17.39003054773241,27.773207338784317,0.7050560764454472,0.0034589767456054688,0.911945 -473,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.288428268963266,29.396681708172505,0.7082265052542642,0.0034589767456054688,0.951137 -484,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.65703381705754,29.64739580601693,0.7194912595024185,0.0034589767456054688,0.991127 -495,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.170202167844984,30.22319045197901,0.7257784495510498,0.0034589767456054688,1.031919 -506,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,20.001643634540255,31.52072905752619,0.7103967314785831,0.0034589767456054688,1.073505 -517,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,20.7279777099298,32.51187613530653,0.7092492864364832,0.0034589767456054688,1.115928 -528,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.566830336201956,33.84128388534863,0.7167757554755416,0.0034589767456054688,1.159144 -539,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.76383497583081,33.92033428284125,0.727201090633804,0.0034589767456054688,1.203152 -550,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,22.69913149057359,35.42417858076478,0.7116454901231712,0.0034589767456054688,1.247925 -561,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,23.377380034069706,36.327056125710044,0.7100204288247138,0.0034589767456054688,1.293462 -572,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.23392939046311,37.557568322570944,0.7132890208408607,0.0034589767456054688,1.339772 -20,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,19.88769754963664,24.32970381980572,-1387.4429851591376,0.004302024841308594,0.004188 -40,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,13.670966252574736,18.651500155088886,-138.85979610511808,0.004302024841308594,0.011118 -60,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,10.928349961418421,15.667746469337834,-132.052589810652,0.004302024841308594,0.019635 -80,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.678171261463671,14.124417656525663,-107.46634425227307,0.004302024841308594,0.029706999999999997 -100,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.352313294103828,13.38210191485773,-60.090321390572015,0.004302024841308594,0.041437 -120,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.620142702346234,12.447697479286916,-43.84064485890953,0.004302024841308594,0.05473 -140,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.069393725677067,11.747669450243144,-40.272086023690846,0.004302024841308594,0.069574 -160,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.915997483818508,11.323556682786094,-32.81628847194888,0.004302024841308594,0.085987 -180,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.901057285338727,11.048721851620664,-27.27526182903414,0.004302024841308594,0.103947 -200,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.54356570823564,10.615481381947994,-25.17890116869019,0.004302024841308594,0.12354799999999999 -220,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.241521693861677,10.231227443733495,-25.232015334984826,0.004302024841308594,0.14471199999999998 -240,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.0081924215580145,9.935910620042465,-23.925679436807183,0.004302024841308594,0.16741999999999999 -260,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.801990869552961,9.666795781283113,-22.91166502123009,0.004435539245605469,0.191681 -280,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.624313646675292,9.422664085622767,-22.786676328633025,0.004435539245605469,0.217492 -300,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.459069309379183,9.212027904845302,-21.358709628275353,0.004435539245605469,0.244937 -320,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.271769455500056,8.978276576497144,-21.290029949269993,0.004435539245605469,0.27394799999999997 -340,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.106653016686588,8.760106381933458,-21.42424847489592,0.004435539245605469,0.304509 -360,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.987363514041657,8.573066448421043,-20.653260781415113,0.004435539245605469,0.336642 -380,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.8616356212919145,8.393806584529749,-20.475259224624704,0.004435539245605469,0.370328 -400,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.709779552257037,8.216191215364908,-20.15755692495403,0.004435539245605469,0.40563899999999997 -420,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.611842526965273,8.072589482864162,-20.100376445858966,0.004435539245605469,0.442509 -440,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.621266668644241,8.061169250733263,-19.659995268253144,0.004435539245605469,0.480923 -460,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.589374981160082,7.977922595195435,-18.1892858176961,0.004435539245605469,0.520896 -480,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.601949193869223,7.939757215258646,-17.568871361729258,0.004435539245605469,0.5624170000000001 -500,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.5323381947357735,7.8271184711570925,-16.667155417363553,0.004435539245605469,0.6055100000000001 -520,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.482642323931563,7.735647602994712,-16.286764124140948,0.004435539245605469,0.6502260000000001 -540,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.40625682094428,7.63238246035087,-15.7666448554263,0.004435539245605469,0.696447 -560,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.339029679572209,7.532223621796607,-15.718570546376949,0.004435539245605469,0.744222 -580,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.278136676956568,7.444054065699633,-15.733326658615429,0.004435539245605469,0.7935180000000001 -600,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.267448053107655,7.406286860836784,-15.380732931295814,0.004435539245605469,0.8443630000000001 -620,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.236749685364014,7.349650271122191,-14.885277446643839,0.004435539245605469,0.8968030000000001 -640,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.212060707085285,7.303465599867649,-14.393054201831113,0.004435539245605469,0.9507400000000001 -660,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.169763730821092,7.239859498572405,-14.017341318649356,0.004435539245605469,1.0062160000000002 -680,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.126488999027155,7.167741867944588,-13.952260083042017,0.004435539245605469,1.0632270000000001 -700,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.093088048073085,7.110044108134988,-14.098928285386927,0.004435539245605469,1.1217890000000001 -720,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.0907403672935505,7.074302078930798,-14.275901824522656,0.004435539245605469,1.181923 -740,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.094807885255693,7.045283722404726,-14.141723406350016,0.004435539245605469,1.243554 -760,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.043787658869922,6.9756465425414875,-14.00468895273832,0.004435539245605469,1.306712 -780,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.064043270517397,6.970645877797938,-14.050305184338798,0.004435539245605469,1.3714110000000002 -800,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.100755566375936,7.010389089224324,-14.360083784619212,0.004435539245605469,1.4376520000000002 -820,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.088064827359337,6.979627415872227,-14.392786134873418,0.004435539245605469,1.5054450000000001 -840,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.068856460674791,6.93736545783203,-14.348127111309871,0.004435539245605469,1.5747550000000001 -860,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.054699520351681,6.9002481606255905,-14.158172906563095,0.004435539245605469,1.645553 -880,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.051365258335469,6.881479694679562,-14.000915574481752,0.004435539245605469,1.717914 -900,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.025346349320704,6.842894221251935,-13.895672840614656,0.004435539245605469,1.791764 -920,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.9962011230931616,6.800765764747934,-13.95456831471299,0.004435539245605469,1.867216 -940,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.964612353038952,6.764345134580912,-13.920332812605437,0.004435539245605469,1.944198 -960,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.9296416909632255,6.717192667284049,-13.866880687662514,0.004435539245605469,2.02269 -980,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.910978624057615,6.682715130965835,-14.000438748691009,0.004435539245605469,2.102738 -1000,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.906394390750375,6.665596501187553,-14.174901311798424,0.004435539245605469,2.184247 -11,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.361343182089286,50.93510711941157,-3338.580868182736,0.0034055709838867188,0.003338 -22,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,33.77268754890631,41.67984599422324,-1838.8455756180547,0.0034055709838867188,0.00919 -33,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.39258875507137,38.96806999674433,-1053.6287703611629,0.0034055709838867188,0.016390000000000002 -44,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,30.948810318548677,36.76152485506615,-1062.9809670274265,0.0034055709838867188,0.024921000000000002 -55,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,33.955857035779495,41.369655851763525,-333.094700988151,0.0034055709838867188,0.034827000000000004 -66,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.010493836145145,40.92418807176112,-139.93270784533922,0.0034055709838867188,0.046059 -77,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.064338631511326,40.5595538563462,-91.78246602216655,0.0034055709838867188,0.058629 -88,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.363478110253816,40.47408671194747,-82.76869054449229,0.0034055709838867188,0.072516 -99,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.108671766629826,39.953033914579606,-67.2701887367714,0.0034055709838867188,0.087732 -110,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,35.5272246808861,41.294149289689244,-49.9285816695273,0.0034055709838867188,0.10461200000000001 -121,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,35.85052464333277,41.487247497278275,-38.59084342971371,0.0034055709838867188,0.12281900000000001 -132,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.414238795248615,42.10793271457587,-32.332913655998325,0.0034055709838867188,0.14236400000000002 -143,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.270929183098715,43.0841670883325,-27.264953879536876,0.0034055709838867188,0.163229 -154,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.68834060456834,43.351809236536255,-23.320689906510196,0.0034055709838867188,0.18538000000000002 -165,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.63751931524077,43.32469674855668,-18.52621065458175,0.0034055709838867188,0.208847 -176,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,38.77878602757167,44.749537188256824,-15.796635622291838,0.0034055709838867188,0.233634 -187,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,39.47954967522975,45.39032172466195,-13.149195443978995,0.0034055709838867188,0.259707 -198,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.91548075261064,46.964281697881674,-11.049082215962626,0.0034055709838867188,0.287109 -209,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,41.53723737741547,47.716579905431935,-9.253665706385002,0.0034055709838867188,0.31584 -220,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,41.999353809226264,48.63121942098776,-8.324525171754294,0.0034055709838867188,0.345843 -231,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,42.76794868726854,49.65880643089243,-7.316484115890946,0.0034055709838867188,0.377141 -242,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,44.368299619960766,51.88245138837915,-6.304578357179455,0.0034055709838867188,0.409767 -253,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,45.426835880148886,53.192808855117775,-5.668628208432534,0.0034055709838867188,0.443674 -264,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,46.93737170570451,55.9021194350506,-5.729354993233594,0.0034589767456054688,0.478844 -275,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,48.507353400069945,58.8434937271261,-5.560984055006233,0.0034589767456054688,0.515365 -286,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,49.83788736782816,60.74084767697289,-5.054961799673954,0.0034589767456054688,0.5532039999999999 -297,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,51.62361105051165,63.27455882125991,-4.698656745997159,0.0034589767456054688,0.5923289999999999 -308,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,53.1847087657747,65.32139627595005,-4.409001340116382,0.0034589767456054688,0.6328059999999999 -319,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,55.18568520771326,70.36874449488667,-4.696335728503607,0.0034589767456054688,0.6746009999999999 -330,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,56.89081675494835,72.51519786174504,-4.176742159590063,0.0034589767456054688,0.7176629999999999 -341,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,58.741890997125225,75.13624143509449,-3.976681879584251,0.0034589767456054688,0.7620199999999999 -352,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,60.2425190521958,76.8261590755005,-3.729812475420129,0.0034589767456054688,0.8076819999999999 -363,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,62.22918871806441,80.45649530418282,-4.037201263859445,0.0034589767456054688,0.854609 -374,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,64.15805403621584,84.29062360683722,-4.082442538013045,0.0034589767456054688,0.9028449999999999 -385,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,65.35464556519459,85.58979700811152,-3.6363575203718748,0.0034589767456054688,0.9523969999999999 -396,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,67.08652533638559,87.70251677464411,-3.538952097004609,0.0034589767456054688,1.00322 -407,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,68.34565949899115,89.3540667816312,-3.3794635851122994,0.0034589767456054688,1.055319 -418,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,70.95506872236689,94.70758550832085,-3.618420231648276,0.0034589767456054688,1.108722 -429,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,72.57879663609631,96.82281941707701,-3.2894241164712765,0.0034589767456054688,1.163438 -440,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,74.40667828471298,99.12463362784464,-3.1775863007897582,0.0034589767456054688,1.219419 -451,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,75.93202948942836,101.47969042740627,-3.1923199945959375,0.0034589767456054688,1.276691 -462,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,78.59712471455953,106.68213481552291,-3.3518185198786687,0.0034589767456054688,1.335258 -473,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,81.70573920737151,112.24508574603003,-3.253866822086197,0.0034589767456054688,1.395089 -484,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,82.72251416230137,113.16810597159807,-3.0871576545332315,0.0034589767456054688,1.456215 -495,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,84.44211178292737,115.99711612480067,-3.039385949207989,0.0034589767456054688,1.518639 -506,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,86.55149997089892,119.94151559804615,-3.1932428575971077,0.0034589767456054688,1.582321 -517,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,87.81070722823603,121.26627191062052,-3.0449837234699952,0.0034589767456054688,1.64731 -528,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,90.44199567451936,126.238673662019,-2.9411377147279807,0.0034589767456054688,1.713605 -539,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,91.59067370330054,127.24192286613216,-2.8386881284150203,0.0034589767456054688,1.781138 -550,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,93.78609063040562,130.999704877259,-2.943372518937519,0.0034589767456054688,1.8499700000000001 -561,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,96.49865979675555,135.93192637304293,-3.060223463970532,0.0034589767456054688,1.920139 -572,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,99.56805853722271,141.40025114882988,-3.0639630782357843,0.0034589767456054688,1.991547 -20,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,38.256966119949794,53.46437671117289,-6703.762875072117,0.004302024841308594,0.004258 -40,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,36.86518407094958,46.91757933405302,-883.9863015306486,0.004302024841308594,0.011255999999999999 -60,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,34.815387267092916,43.77024226536395,-1037.4083280498467,0.004302024841308594,0.019887 -80,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.82099099523828,40.95636211937148,-911.003802678922,0.004302024841308594,0.030078999999999998 -100,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.41507697560151,39.67328525303196,-535.9329715822871,0.004302024841308594,0.041942 -120,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.126533810299065,38.74392424963554,-433.4111998192701,0.004302024841308594,0.055368 -140,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.578696449953306,37.792227545537656,-426.12792465890055,0.004302024841308594,0.070349 -160,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.74326128631808,36.75147996394125,-355.2131008927076,0.004302024841308594,0.086898 -180,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.01457804514465,35.792387341570375,-295.7316609698654,0.004302024841308594,0.10499800000000001 -200,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.69358993814741,35.287452593511624,-288.27615949420783,0.004302024841308594,0.124741 -220,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.653103799764548,34.981694493871686,-305.6605175079266,0.004302024841308594,0.146064 -240,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.438535707776744,34.575414655319626,-300.8328104705327,0.004302024841308594,0.168934 -260,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.47891951475184,34.56744094622709,-304.7589578451177,0.004435539245605469,0.193372 -280,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.263090992629436,34.21824031120625,-312.6907330516863,0.004435539245605469,0.21937099999999998 -300,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.22587791049338,34.04470159277256,-304.37628668920115,0.004435539245605469,0.246997 -320,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.39018713755992,34.027497016659055,-319.1729973057711,0.004435539245605469,0.276184 -340,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.42027037377748,33.93053373578466,-335.4190027425263,0.004435539245605469,0.306921 -360,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.664307062669348,34.045635299267715,-340.48671423689984,0.004435539245605469,0.339233 -380,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.83498723746747,34.11808971579651,-353.80514851973993,0.004435539245605469,0.373101 -400,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.001082895685222,34.17628998525774,-365.0785427385626,0.004435539245605469,0.408596 -420,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.14554243966483,34.20268208319664,-377.77804236740525,0.004435539245605469,0.445652 -440,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.318997649837996,34.29163258825692,-372.8612574130533,0.004435539245605469,0.484259 -460,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.44816166194883,34.34474274542465,-354.6310813895727,0.004435539245605469,0.524426 -480,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.547707035634,34.3460988289287,-346.47686362533597,0.004435539245605469,0.5661419999999999 -500,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.677713278160862,34.40102042286509,-340.27577766039445,0.004435539245605469,0.6094289999999999 -520,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.774567489967993,34.41616981796676,-341.1727526506248,0.004435539245605469,0.6543389999999999 -540,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.8774276068303,34.45885995564912,-340.7651110108783,0.004435539245605469,0.7007559999999999 -560,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.809160821418953,34.35467967149731,-346.7959646189386,0.004435539245605469,0.7487349999999999 -580,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.794626934327955,34.31319945532044,-354.5377185045104,0.004435539245605469,0.798249 -600,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.95165244409411,34.41180694760753,-352.62847406429205,0.004435539245605469,0.849322 -620,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.05924245979316,34.47559777081781,-348.53049085374005,0.004435539245605469,0.9019860000000001 -640,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.174863467025094,34.55495775749415,-343.57800745174796,0.004435539245605469,0.9561540000000001 -660,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.18138785276698,34.53217565702543,-340.6493968041527,0.004435539245605469,1.011873 -680,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.262098564397714,34.57860649898553,-346.98225985471817,0.004435539245605469,1.06915 -700,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.21351117376332,34.56642775102796,-355.8704038677769,0.004435539245605469,1.127995 -720,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.187412408778744,34.52480046664081,-362.83293670855346,0.004435539245605469,1.188423 -740,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.11951946368673,34.44075758421072,-360.84595808308967,0.004435539245605469,1.250358 -760,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.06286774404838,34.392033812715184,-363.73192746261515,0.004435539245605469,1.3138290000000001 -780,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.122129081397333,34.4147077867913,-365.8490836115921,0.004435539245605469,1.378848 -800,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.106214253085106,34.39160753515401,-368.6700713862459,0.004435539245605469,1.445416 -820,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.221689813214,34.48240321448139,-374.7057202811433,0.004435539245605469,1.513551 -840,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.279425902447173,34.50697415799086,-378.7344488771949,0.004435539245605469,1.583215 -860,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.342876956175303,34.54160049479585,-378.8414458535583,0.004435539245605469,1.65438 -880,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.26946109805924,34.463329541799936,-375.2431199026635,0.004435539245605469,1.727121 -900,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.252963502360984,34.437602544585566,-376.2648040183398,0.004435539245605469,1.801358 -920,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.171400346182477,34.35879469425013,-380.710483609337,0.004435539245605469,1.877212 -940,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.028682196255257,34.23221645186374,-381.1175926718204,0.004435539245605469,1.9546070000000002 -960,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.091829199349828,34.349474902309865,-387.76257537598985,0.004435539245605469,2.033516 -980,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.00094350988743,34.303507421677764,-394.25294990859175,0.004435539245605469,2.113994 -1000,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.128814077399102,34.425663215951964,-403.7738674316034,0.004435539245605469,2.195941 -11,Regression,k-Nearest Neighbors,ChickWeights,4.6439393939393945,12.708027567111456,-206.8805289598106,0.0077915191650390625,0.002301 -22,Regression,k-Nearest Neighbors,ChickWeights,2.7674242424242426,9.021574170013263,-85.19732920009746,0.011640548706054688,0.0070350000000000005 -33,Regression,k-Nearest Neighbors,ChickWeights,2.3661616161616172,7.437810062008745,-37.421294111394644,0.016172409057617188,0.013843000000000001 -44,Regression,k-Nearest Neighbors,ChickWeights,2.0155303030303036,6.463663489621867,-31.893061768560024,0.020200729370117188,0.022662 -55,Regression,k-Nearest Neighbors,ChickWeights,2.2124242424242424,6.080421054665558,-6.217272109648366,0.024396896362304688,0.033503000000000005 -66,Regression,k-Nearest Neighbors,ChickWeights,2.2800505050505055,5.694858940322259,-1.7290883479828647,0.028783798217773438,0.046733000000000004 -77,Regression,k-Nearest Neighbors,ChickWeights,2.61926406926407,5.7079502667942235,-0.8375532519268223,0.03319740295410156,0.062616 -88,Regression,k-Nearest Neighbors,ChickWeights,2.530492424242425,5.412982721609634,-0.4983072905775765,0.03750419616699219,0.08146600000000001 -99,Regression,k-Nearest Neighbors,ChickWeights,2.4755892255892267,5.17010990945742,-0.14322345740966957,0.04228782653808594,0.10359300000000002 -110,Regression,k-Nearest Neighbors,ChickWeights,2.7716666666666683,5.296236752390676,0.16224058779712935,0.04331779479980469,0.129424 -121,Regression,k-Nearest Neighbors,ChickWeights,3.180853994490359,5.621206607854847,0.2731837882445769,0.04387855529785156,0.158775 -132,Regression,k-Nearest Neighbors,ChickWeights,3.3642676767676765,5.706770043255583,0.38775368143556643,0.04362678527832031,0.191664 -143,Regression,k-Nearest Neighbors,ChickWeights,3.6467365967365963,5.919243012407738,0.46648673933101714,0.04393196105957031,0.228099 -154,Regression,k-Nearest Neighbors,ChickWeights,3.7550865800865787,5.97572666401829,0.537892640768072,0.04406929016113281,0.268063 -165,Regression,k-Nearest Neighbors,ChickWeights,4.093838383838381,6.488494998076776,0.562039588096868,0.04465293884277344,0.311593 -176,Regression,k-Nearest Neighbors,ChickWeights,4.458428030303029,6.947478945595657,0.5951448357515823,0.04467964172363281,0.358632 -187,Regression,k-Nearest Neighbors,ChickWeights,4.792959001782529,7.272258331212408,0.6368016898131145,0.04475975036621094,0.40948 -198,Regression,k-Nearest Neighbors,ChickWeights,5.229713804713803,7.766788141562423,0.6704650236153215,0.04428291320800781,0.46388 -209,Regression,k-Nearest Neighbors,ChickWeights,5.61188197767145,8.429860803311705,0.6799768871245477,0.04433631896972656,0.521779 -220,Regression,k-Nearest Neighbors,ChickWeights,6.048560606060604,9.536044923225655,0.6414638231876792,0.04430961608886719,0.583178 -231,Regression,k-Nearest Neighbors,ChickWeights,6.582178932178929,10.203249124116919,0.648905367768132,0.04489326477050781,0.6480619999999999 -242,Regression,k-Nearest Neighbors,ChickWeights,7.071418732782365,10.928542055135825,0.6759002976153703,0.04494667053222656,0.7164429999999999 -253,Regression,k-Nearest Neighbors,ChickWeights,7.4777997364953865,11.323352624926212,0.6978095597045382,0.04505348205566406,0.7883119999999999 -264,Regression,k-Nearest Neighbors,ChickWeights,7.970770202020199,12.283351878677939,0.6750992767833781,0.04463005065917969,0.8636689999999999 -275,Regression,k-Nearest Neighbors,ChickWeights,8.55812121212121,13.382565810664547,0.6606476529151027,0.04468345642089844,0.9425549999999999 -286,Regression,k-Nearest Neighbors,ChickWeights,9.054137529137526,14.013384412631826,0.6777181990167639,0.04484367370605469,1.024953 -297,Regression,k-Nearest Neighbors,ChickWeights,9.468967452300786,14.435360812541292,0.7034011013652389,0.04540061950683594,1.110877 -308,Regression,k-Nearest Neighbors,ChickWeights,9.90871212121212,15.173853281638724,0.7081243055691319,0.04542732238769531,1.200187 -319,Regression,k-Nearest Neighbors,ChickWeights,10.713740856844305,17.013635837866804,0.6670107307192514,0.04553413391113281,1.2928449999999998 -330,Regression,k-Nearest Neighbors,ChickWeights,11.460252525252525,18.125243873896302,0.6765805165314649,0.04508399963378906,1.388826 -341,Regression,k-Nearest Neighbors,ChickWeights,11.901710654936462,18.576691605351197,0.6957870549438744,0.04519081115722656,1.488176 -352,Regression,k-Nearest Neighbors,ChickWeights,12.310464015151513,18.922178666477887,0.7130752857476492,0.04516410827636719,1.591164 -363,Regression,k-Nearest Neighbors,ChickWeights,12.780394857667583,19.823234941774256,0.694215027528111,0.04561424255371094,1.697605 -374,Regression,k-Nearest Neighbors,ChickWeights,13.344073083778964,20.889730456192645,0.6878383009059359,0.04561424255371094,1.807438 -385,Regression,k-Nearest Neighbors,ChickWeights,13.830865800865798,21.557316750546793,0.7058815074667231,0.04569435119628906,1.920665 -396,Regression,k-Nearest Neighbors,ChickWeights,14.08051346801346,21.615344438143325,0.7242879119502419,0.04521751403808594,2.037277 -407,Regression,k-Nearest Neighbors,ChickWeights,14.665069615069608,22.797561920331077,0.714918470135155,0.04524421691894531,2.157231 -418,Regression,k-Nearest Neighbors,ChickWeights,15.362400318979262,24.076729101709564,0.7015174886253861,0.04574775695800781,2.280525 -429,Regression,k-Nearest Neighbors,ChickWeights,15.914413364413358,24.924104546372128,0.7157616535295273,0.04582786560058594,2.4071919999999998 -440,Regression,k-Nearest Neighbors,ChickWeights,16.21655303030303,25.17906749713446,0.730448642009748,0.04585456848144531,2.5372429999999997 -451,Regression,k-Nearest Neighbors,ChickWeights,16.571359940872135,25.529131814454708,0.7346810631079229,0.04588127136230469,2.6706619999999996 -462,Regression,k-Nearest Neighbors,ChickWeights,17.517063492063492,27.458379114103476,0.7117049574257082,0.04537773132324219,2.8074329999999996 -473,Regression,k-Nearest Neighbors,ChickWeights,18.23357998590557,28.586680380220994,0.7240841374900429,0.04535102844238281,2.9476329999999997 -484,Regression,k-Nearest Neighbors,ChickWeights,18.61876721763086,29.038858036362505,0.7308884346272555,0.04588127136230469,3.0911699999999995 -495,Regression,k-Nearest Neighbors,ChickWeights,19.16138047138048,29.754410032566323,0.7342191699916846,0.04588127136230469,3.2380789999999995 -506,Regression,k-Nearest Neighbors,ChickWeights,19.693445322793156,30.658970587616192,0.7260154404176653,0.04585456848144531,3.3883479999999997 -517,Regression,k-Nearest Neighbors,ChickWeights,20.317762733720187,31.53258587823862,0.7265009007981393,0.04537773132324219,3.5419949999999996 -528,Regression,k-Nearest Neighbors,ChickWeights,21.03841540404041,32.63466371480821,0.7366125677104822,0.04543113708496094,3.699025 -539,Regression,k-Nearest Neighbors,ChickWeights,21.282900432900444,32.839173901100196,0.7443140702924032,0.04540443420410156,3.859333 -550,Regression,k-Nearest Neighbors,ChickWeights,21.858333333333338,33.61129662942374,0.7404041745313898,0.04601478576660156,4.022901 -561,Regression,k-Nearest Neighbors,ChickWeights,22.363071895424838,34.18934989679706,0.7431446126310046,0.04606819152832031,4.189756 -572,Regression,k-Nearest Neighbors,ChickWeights,22.904341491841496,34.79445522931405,0.7539238777546076,0.04612159729003906,4.359905 -20,Regression,k-Nearest Neighbors,TrumpApproval,2.5579731333333355,9.79490509533104,-224.03748800996974,0.016119003295898438,0.00492 -40,Regression,k-Nearest Neighbors,TrumpApproval,1.814472516666667,6.975921914401759,-18.564491994995524,0.028676986694335938,0.014556 -60,Regression,k-Nearest Neighbors,TrumpApproval,1.3860128111111119,5.7089304150813796,-16.665248891500116,0.04073143005371094,0.029158 -80,Regression,k-Nearest Neighbors,TrumpApproval,1.1425351583333334,4.950945892995169,-12.326934431680348,0.05278587341308594,0.04994 -100,Regression,k-Nearest Neighbors,TrumpApproval,1.0609460066666667,4.443635225860514,-5.735976224554387,0.06531715393066406,0.078368 -120,Regression,k-Nearest Neighbors,TrumpApproval,1.0173975888888886,4.080152828774464,-3.8177766328983793,0.06587028503417969,0.115087 -140,Regression,k-Nearest Neighbors,TrumpApproval,0.9499007047619044,3.7850734619410638,-3.284514427728187,0.06536674499511719,0.160142 -160,Regression,k-Nearest Neighbors,TrumpApproval,0.8997031916666666,3.548063392436267,-2.320037309218333,0.06536674499511719,0.213621 -180,Regression,k-Nearest Neighbors,TrumpApproval,0.8894699925925924,3.3651237174821174,-1.6229174672077478,0.06587028503417969,0.275231 -200,Regression,k-Nearest Neighbors,TrumpApproval,0.8368248133333331,3.1964619940401167,-1.3736195901717156,0.06536674499511719,0.34510799999999997 -220,Regression,k-Nearest Neighbors,TrumpApproval,0.7973693939393937,3.051737973437425,-1.333837761501293,0.06536674499511719,0.42318599999999995 -240,Regression,k-Nearest Neighbors,TrumpApproval,0.7882918027777774,2.9302063484469683,-1.1678441811700537,0.06587028503417969,0.5093219999999999 -260,Regression,k-Nearest Neighbors,TrumpApproval,0.756480287179487,2.818118800540444,-1.032185390259123,0.06550025939941406,0.603278 -280,Regression,k-Nearest Neighbors,TrumpApproval,0.7152656523809521,2.716213897230066,-0.9765794643185606,0.06550025939941406,0.705058 -300,Regression,k-Nearest Neighbors,TrumpApproval,0.6924420288888888,2.6266701457407935,-0.8178051192110904,0.06600379943847656,0.8148869999999999 -320,Regression,k-Nearest Neighbors,TrumpApproval,0.6685162833333335,2.544831183351663,-0.7907817018280727,0.06600379943847656,0.9324959999999999 -340,Regression,k-Nearest Neighbors,TrumpApproval,0.6729953196078432,2.478015515638401,-0.7943500832815562,0.06550025939941406,1.057867 -360,Regression,k-Nearest Neighbors,TrumpApproval,0.6574798574074076,2.410620514027796,-0.7120191674014065,0.06600379943847656,1.1910109999999998 -380,Regression,k-Nearest Neighbors,TrumpApproval,0.6458375333333335,2.3511270035956984,-0.6848943678054311,0.06600379943847656,1.3319119999999998 -400,Regression,k-Nearest Neighbors,TrumpApproval,0.6474776666666668,2.299895164719867,-0.6578320154352482,0.06550025939941406,1.4805999999999997 -420,Regression,k-Nearest Neighbors,TrumpApproval,0.6263061492063494,2.245732257498697,-0.6329783328857519,0.06600379943847656,1.6370479999999996 -440,Regression,k-Nearest Neighbors,TrumpApproval,0.6161101106060607,2.196509834675512,-0.5339119919932027,0.06600379943847656,1.8010919999999997 -460,Regression,k-Nearest Neighbors,TrumpApproval,0.6114796710144929,2.151899880839346,-0.39612176608758154,0.06550025939941406,1.9727339999999998 -480,Regression,k-Nearest Neighbors,TrumpApproval,0.595679659722222,2.1075134371992843,-0.3083133320099125,0.06550025939941406,2.1519839999999997 -500,Regression,k-Nearest Neighbors,TrumpApproval,0.5871453373333329,2.0674213824343695,-0.23259619346371974,0.06600379943847656,2.338834 -520,Regression,k-Nearest Neighbors,TrumpApproval,0.5787837589743586,2.029002666882104,-0.1892840306210153,0.06550025939941406,2.5333539999999997 -540,Regression,k-Nearest Neighbors,TrumpApproval,0.5648130308641971,1.991660903442925,-0.14171238475006254,0.06550025939941406,2.7354719999999997 -560,Regression,k-Nearest Neighbors,TrumpApproval,0.5728799083333329,1.9649652423985717,-0.1377909554693113,0.06600379943847656,2.9451739999999997 -580,Regression,k-Nearest Neighbors,TrumpApproval,0.5729694735632179,1.9355227317321977,-0.1312531571152491,0.06550025939941406,3.1626749999999997 -600,Regression,k-Nearest Neighbors,TrumpApproval,0.5633090777777773,1.904172510525728,-0.08279153968794839,0.06550025939941406,3.387869 -620,Regression,k-Nearest Neighbors,TrumpApproval,0.5627685010752685,1.877938378401011,-0.037108351625399605,0.06600379943847656,3.620659 -640,Regression,k-Nearest Neighbors,TrumpApproval,0.5527265229166664,1.8490211996046175,0.013378435498877628,0.06600379943847656,3.861011 -660,Regression,k-Nearest Neighbors,TrumpApproval,0.5494711494949491,1.8235613734746332,0.04726187505437851,0.06550025939941406,4.108899 -680,Regression,k-Nearest Neighbors,TrumpApproval,0.5450294392156858,1.7983422134533726,0.05878940572589009,0.06600379943847656,4.364357 -700,Regression,k-Nearest Neighbors,TrumpApproval,0.5469558666666661,1.7765896178823126,0.057295083327486895,0.06600379943847656,4.627459 -720,Regression,k-Nearest Neighbors,TrumpApproval,0.543456514814814,1.7539567687409483,0.06097441190332087,0.06550025939941406,4.898124 -740,Regression,k-Nearest Neighbors,TrumpApproval,0.5378193549549544,1.7312282531924004,0.08570362891572103,0.06600379943847656,5.1763650000000005 -760,Regression,k-Nearest Neighbors,TrumpApproval,0.5339037666666662,1.709797998783685,0.0985374850173486,0.06600379943847656,5.462206 -780,Regression,k-Nearest Neighbors,TrumpApproval,0.5278528008547004,1.688590710651063,0.11682237578267707,0.06550025939941406,5.755616 -800,Regression,k-Nearest Neighbors,TrumpApproval,0.5283612208333329,1.6702821330140922,0.12805516368066394,0.06550025939941406,6.056664 -820,Regression,k-Nearest Neighbors,TrumpApproval,0.5218793715447149,1.650734458460968,0.13899199371192528,0.06600379943847656,6.365303 -840,Regression,k-Nearest Neighbors,TrumpApproval,0.5164909984126979,1.6320321449108954,0.15057770250639546,0.06550025939941406,6.681529 -860,Regression,k-Nearest Neighbors,TrumpApproval,0.5203083496124026,1.617111920842916,0.16747440563774907,0.06550025939941406,7.005502 -880,Regression,k-Nearest Neighbors,TrumpApproval,0.5169651212121207,1.6004898524254525,0.18855338810104805,0.06600379943847656,7.3370109999999995 -900,Regression,k-Nearest Neighbors,TrumpApproval,0.5126542296296291,1.583708561642625,0.20213208493959922,0.06550025939941406,7.6761029999999995 -920,Regression,k-Nearest Neighbors,TrumpApproval,0.5104052702898545,1.5679886127118026,0.20504223486761397,0.06550025939941406,8.022855999999999 -940,Regression,k-Nearest Neighbors,TrumpApproval,0.5051605113475173,1.551953998854848,0.21461123840440588,0.06600379943847656,8.377156 -960,Regression,k-Nearest Neighbors,TrumpApproval,0.5025567965277773,1.5374441378201587,0.22116952746321128,0.06600379943847656,8.739002999999999 -980,Regression,k-Nearest Neighbors,TrumpApproval,0.49825265639455735,1.522650494486571,0.22124917342604244,0.06550025939941406,9.108430999999998 -1000,Regression,k-Nearest Neighbors,TrumpApproval,0.4939745494666663,1.5080707141004983,0.22323214061337782,0.06600379943847656,9.485458999999997 -11,Regression,Hoeffding Tree,ChickWeights,8.042756132756132,17.336048579080593,-385.86349170941764,0.016574859619140625,0.003293 -22,Regression,Hoeffding Tree,ChickWeights,4.456785613727984,12.282422261556867,-158.770726389092,0.018154144287109375,0.009578 -33,Regression,Hoeffding Tree,ChickWeights,3.4353973358733074,10.070376517434479,-69.4325218162971,0.023418426513671875,0.01815 -44,Regression,Hoeffding Tree,ChickWeights,2.736909422894262,8.732393473100391,-59.03623058514604,0.024471282958984375,0.029162 -55,Regression,Hoeffding Tree,ChickWeights,2.788577579622257,8.074088551816661,-11.726025456653014,0.031314849853515625,0.042723 -66,Regression,Hoeffding Tree,ChickWeights,3.3958800855981375,7.878422021930021,-4.223121571879303,0.040790557861328125,0.059358999999999995 -77,Regression,Hoeffding Tree,ChickWeights,3.8895265016210883,7.800910386370324,-2.432180745921895,0.047107696533203125,0.07961599999999999 -88,Regression,Hoeffding Tree,ChickWeights,4.072650698433535,7.572197783925699,-1.9320509270116553,0.052898406982421875,0.103875 -99,Regression,Hoeffding Tree,ChickWeights,4.410984939713907,7.55185413515251,-1.439151418709002,0.053951263427734375,0.132276 -110,Regression,Hoeffding Tree,ChickWeights,4.441558524813062,7.364764038532391,-0.6199522309877294,0.055530548095703125,0.164898 -121,Regression,Hoeffding Tree,ChickWeights,4.487290951327676,7.260940155844585,-0.21269398713682386,0.055530548095703125,0.201538 -132,Regression,Hoeffding Tree,ChickWeights,4.401729970486312,7.0591187066650845,0.06320102490380497,0.055530548095703125,0.242248 -143,Regression,Hoeffding Tree,ChickWeights,4.303599977233167,6.863829202938119,0.28262569921695146,0.056056976318359375,0.28717899999999996 -154,Regression,Hoeffding Tree,ChickWeights,4.2479679761417515,6.717580819449276,0.41603443739821244,0.056056976318359375,0.33616199999999996 -165,Regression,Hoeffding Tree,ChickWeights,4.525268599337025,6.978492074792776,0.49339428301547505,0.056056976318359375,0.38909699999999997 -176,Regression,Hoeffding Tree,ChickWeights,4.7434869323510185,7.161143757518859,0.5698598432460567,0.056583404541015625,0.44603699999999996 -187,Regression,Hoeffding Tree,ChickWeights,4.817684977356876,7.1877471099050325,0.6451941261958376,0.056583404541015625,0.5072089999999999 -198,Regression,Hoeffding Tree,ChickWeights,4.83667165494537,7.176577259975889,0.7186458480933114,0.056583404541015625,0.5725709999999999 -209,Regression,Hoeffding Tree,ChickWeights,5.073405719834179,7.569308518085582,0.7419802486075263,0.02174663543701172,0.645323 -220,Regression,Hoeffding Tree,ChickWeights,5.671396913729996,8.67042326781336,0.7036008152378226,0.02806377410888672,0.719465 -231,Regression,Hoeffding Tree,ChickWeights,5.870013976865108,8.892004937785565,0.7333469233470653,0.03332805633544922,0.7950900000000001 -242,Regression,Hoeffding Tree,ChickWeights,6.098410134572789,9.27860472778795,0.7663748869623117,0.03806591033935547,0.8723510000000001 -253,Regression,Hoeffding Tree,ChickWeights,6.1962339865774,9.406595007903094,0.7914570321252903,0.04175090789794922,0.9513590000000001 -264,Regression,Hoeffding Tree,ChickWeights,6.851942913488504,10.678395276366356,0.7544562538164442,0.041831016540527344,1.031981 -275,Regression,Hoeffding Tree,ChickWeights,7.351838545672251,11.801369148896674,0.7361015298851068,0.041831016540527344,1.114342 -286,Regression,Hoeffding Tree,ChickWeights,7.621792166351879,12.282711040561285,0.7524071035845484,0.042357444763183594,1.1984219999999999 -297,Regression,Hoeffding Tree,ChickWeights,7.6372566302059255,12.295347873811286,0.7848229800793282,0.042357444763183594,1.2842529999999999 -308,Regression,Hoeffding Tree,ChickWeights,8.1943326666584,13.18128308543095,0.7797471460356308,0.042357444763183594,1.3717719999999998 -319,Regression,Hoeffding Tree,ChickWeights,9.301321372784987,15.883856969554804,0.7097662534619076,0.042357444763183594,1.4609289999999997 -330,Regression,Hoeffding Tree,ChickWeights,9.759535973032726,16.540475274696632,0.7306639846914664,0.042357444763183594,1.5518979999999998 -341,Regression,Hoeffding Tree,ChickWeights,9.98108531256273,16.6656027575944,0.7551596457293974,0.042357444763183594,1.6445489999999998 -352,Regression,Hoeffding Tree,ChickWeights,10.172493780682656,16.824682995393008,0.773160093080841,0.042357444763183594,1.7389499999999998 -363,Regression,Hoeffding Tree,ChickWeights,11.068151856114426,18.263714825485387,0.7404354867888504,0.042357444763183594,1.8350029999999997 -374,Regression,Hoeffding Tree,ChickWeights,11.603116520774071,19.443156920913136,0.7295745843003554,0.042357444763183594,1.9329859999999996 -385,Regression,Hoeffding Tree,ChickWeights,12.00507936887919,20.0961554988217,0.744401153020958,0.042357444763183594,2.0327189999999997 -396,Regression,Hoeffding Tree,ChickWeights,12.159003512064782,20.104597547074984,0.7614813985176707,0.042357444763183594,2.1341089999999996 -407,Regression,Hoeffding Tree,ChickWeights,13.058561054914291,21.64678300128301,0.7429728504217219,0.039313316345214844,2.2395339999999995 -418,Regression,Hoeffding Tree,ChickWeights,13.849374886718222,23.13707582414104,0.7243608784812086,0.039839744567871094,2.3466389999999997 -429,Regression,Hoeffding Tree,ChickWeights,14.418764158229274,24.093967285200236,0.7343803671174814,0.040892601013183594,2.4553529999999997 -440,Regression,Hoeffding Tree,ChickWeights,14.611969435637262,24.1872515908579,0.7512659254336986,0.041419029235839844,2.5657939999999995 -451,Regression,Hoeffding Tree,ChickWeights,15.149542104002839,24.823452151261105,0.7491462990276416,0.042998313903808594,2.6778409999999995 -462,Regression,Hoeffding Tree,ChickWeights,16.266748325298664,26.992269976456935,0.7214095667925529,0.043524742126464844,2.7915709999999994 -473,Regression,Hoeffding Tree,ChickWeights,17.063164501315207,28.337029081432476,0.7288823142196059,0.043524742126464844,2.9070249999999995 -484,Regression,Hoeffding Tree,ChickWeights,17.41028324926407,28.63458736095403,0.7383292654808864,0.043524742126464844,3.0241479999999994 -495,Regression,Hoeffding Tree,ChickWeights,17.881702409973563,29.18189849457619,0.7443486730566713,0.043524742126464844,3.1430089999999993 -506,Regression,Hoeffding Tree,ChickWeights,18.783373559654446,30.65392766804094,0.7261055653266129,0.043524742126464844,3.263499999999999 -517,Regression,Hoeffding Tree,ChickWeights,19.52135237811833,31.66784012367412,0.7241496029986892,0.043524742126464844,3.3857279999999994 -528,Regression,Hoeffding Tree,ChickWeights,20.32410387080197,32.88418602247989,0.7325694870351915,0.043524742126464844,3.509655999999999 -539,Regression,Hoeffding Tree,ChickWeights,20.508458063622076,32.89095814819447,0.7435070498169007,0.043524742126464844,3.635277999999999 -550,Regression,Hoeffding Tree,ChickWeights,21.507291986413147,34.52927015042095,0.7260306568753008,0.043524742126464844,3.762600999999999 -561,Regression,Hoeffding Tree,ChickWeights,22.222091157093345,35.46412346985515,0.7236334646353288,0.043524742126464844,3.891587999999999 -572,Regression,Hoeffding Tree,ChickWeights,23.084218600655667,36.66377836765904,0.7267728639741885,0.044051170349121094,4.022359999999999 -20,Regression,Hoeffding Tree,TrumpApproval,4.834704431652337,13.708514217962266,-439.7934984576362,0.05081939697265625,0.006949 -40,Regression,Hoeffding Tree,TrumpApproval,3.4692310697037447,9.813795721313518,-37.72035957928713,0.07398223876953125,0.020525 -60,Regression,Hoeffding Tree,TrumpApproval,2.530247618203559,8.024836796214231,-33.90460110966681,0.08661651611328125,0.041034 -80,Regression,Hoeffding Tree,TrumpApproval,2.1398752670733447,6.982837000856316,-25.510487239912003,0.09661865234375,0.06908 -100,Regression,Hoeffding Tree,TrumpApproval,2.2521629689485394,6.362737158647257,-12.810573390910955,0.1060943603515625,0.105159 -120,Regression,Hoeffding Tree,TrumpApproval,2.2753311831165886,5.895687482983747,-9.059182991303912,0.1103057861328125,0.149228 -140,Regression,Hoeffding Tree,TrumpApproval,2.181766409647037,5.493495699082884,-8.025069637302263,0.1124114990234375,0.201546 -160,Regression,Hoeffding Tree,TrumpApproval,2.0641207293789914,5.165105496730293,-6.03588310696345,0.1166229248046875,0.262282 -180,Regression,Hoeffding Tree,TrumpApproval,1.9901037542149176,4.906162642056599,-4.575276834209563,0.1187286376953125,0.33158200000000004 -200,Regression,Hoeffding Tree,TrumpApproval,1.854788525917255,4.661016718308231,-4.0470005140641305,0.015085220336914062,0.42238300000000006 -220,Regression,Hoeffding Tree,TrumpApproval,1.777996659033335,4.4592908674997,-3.98319384245183,0.031249046325683594,0.517121 -240,Regression,Hoeffding Tree,TrumpApproval,1.696058551537428,4.2776990038095555,-3.6201076872383524,0.037039756774902344,0.616341 -260,Regression,Hoeffding Tree,TrumpApproval,1.6130985138696277,4.114896288193841,-3.3327388943406255,0.044569969177246094,0.720441 -280,Regression,Hoeffding Tree,TrumpApproval,1.5797289220452373,3.9859591491501196,-3.256494063385272,0.05755901336669922,0.829564 -300,Regression,Hoeffding Tree,TrumpApproval,1.5530598651848762,3.8727363700469204,-2.9515921533937095,0.06756114959716797,0.9443549999999999 -320,Regression,Hoeffding Tree,TrumpApproval,1.487708679701559,3.7532986907452788,-2.895390019001044,0.07545757293701172,1.064913 -340,Regression,Hoeffding Tree,TrumpApproval,1.447577018485449,3.651821310152781,-2.8968902417113367,0.08072185516357422,1.191641 -360,Regression,Hoeffding Tree,TrumpApproval,1.4679964351503354,3.577735862687295,-2.771095017816483,0.08861827850341797,1.3247849999999999 -380,Regression,Hoeffding Tree,TrumpApproval,1.4337467115212041,3.490600620499155,-2.7138197592485107,0.09388256072998047,1.4646529999999998 -400,Regression,Hoeffding Tree,TrumpApproval,1.394545215153479,3.4083536810761967,-2.6409419214114362,0.10125255584716797,1.6116329999999999 -420,Regression,Hoeffding Tree,TrumpApproval,1.3582303513295786,3.3304244373469776,-2.5913988321456767,0.10546398162841797,1.7657539999999998 -440,Regression,Hoeffding Tree,TrumpApproval,1.3237600646089562,3.2576165394889824,-2.3739144098898306,0.11125469207763672,1.9272029999999998 -460,Regression,Hoeffding Tree,TrumpApproval,1.313374021763953,3.1966490326870396,-2.080839603370824,0.11967754364013672,2.096272 -480,Regression,Hoeffding Tree,TrumpApproval,1.2871329178228548,3.1331240379472574,-1.891520259695389,0.12757396697998047,2.277254 -500,Regression,Hoeffding Tree,TrumpApproval,1.257405891185914,3.0730152095835095,-1.7232796098685204,0.13231182098388672,2.471873 -520,Regression,Hoeffding Tree,TrumpApproval,1.236691492049667,3.017934049641527,-1.6311150670478525,0.13757610321044922,2.675336 -540,Regression,Hoeffding Tree,TrumpApproval,1.2120036461347103,2.9640451937301795,-1.5286898077621993,0.13968181610107422,2.8858550000000003 -560,Regression,Hoeffding Tree,TrumpApproval,1.1973910792850353,2.9159627023633448,-1.5056283897938432,0.14441967010498047,3.1010690000000003 -580,Regression,Hoeffding Tree,TrumpApproval,1.1729976236433703,2.867868059269123,-1.4835996265310456,0.14705181121826172,3.321073 -600,Regression,Hoeffding Tree,TrumpApproval,1.164383909889498,2.8255860627341494,-1.384236602114556,0.14148998260498047,3.5505340000000003 -620,Regression,Hoeffding Tree,TrumpApproval,1.1607847453289375,2.7880287925830083,-1.2858933574415188,0.14412212371826172,3.784549 -640,Regression,Hoeffding Tree,TrumpApproval,1.1488689392891114,2.748475985188827,-1.179970998158682,0.14622783660888672,4.023051000000001 -660,Regression,Hoeffding Tree,TrumpApproval,1.1353328006378431,2.711500109160092,-1.1064542348150135,0.09333324432373047,4.270219000000001 -680,Regression,Hoeffding Tree,TrumpApproval,1.1237673891934872,2.67586665558629,-1.083872122585975,0.10280895233154297,4.520950000000001 -700,Regression,Hoeffding Tree,TrumpApproval,1.1197870211993892,2.6450513004918434,-1.0896316212520398,0.11070537567138672,4.77542 -720,Regression,Hoeffding Tree,TrumpApproval,1.0988128320741317,2.6090976903580367,-1.0778794022938847,0.11702251434326172,5.033773 -740,Regression,Hoeffding Tree,TrumpApproval,1.0821696401958585,2.5758102479785587,-1.0239793040320149,0.12070751190185547,5.296116 -760,Regression,Hoeffding Tree,TrumpApproval,1.0646778366488154,2.542912274197232,-0.9939800998628658,0.12386608123779297,5.562507999999999 -780,Regression,Hoeffding Tree,TrumpApproval,1.0458773095514022,2.5110610332394967,-0.9530519853191719,0.13018321990966797,5.833025999999999 -800,Regression,Hoeffding Tree,TrumpApproval,1.037522387437363,2.4829468635274075,-0.9268335078372076,0.14051342010498047,6.107975999999999 -820,Regression,Hoeffding Tree,TrumpApproval,1.0344825476074344,2.459713101244265,-0.9117084698305153,0.14683055877685547,6.387556999999999 -840,Regression,Hoeffding Tree,TrumpApproval,1.0265033672562451,2.4338535651506326,-0.8891007921837151,0.15051555633544922,6.671677999999999 -860,Regression,Hoeffding Tree,TrumpApproval,1.0098876748057108,2.406249644034785,-0.8433117943327111,0.15209484100341797,6.960477999999999 -880,Regression,Hoeffding Tree,TrumpApproval,0.9987418826957182,2.3807044173500915,-0.795415858642577,0.15525341033935547,7.253952999999999 -900,Regression,Hoeffding Tree,TrumpApproval,0.9899512872354768,2.3562979624350358,-0.7662040947107138,0.15735912322998047,7.552186999999999 -920,Regression,Hoeffding Tree,TrumpApproval,0.9763510674237786,2.3315103197053233,-0.7576521562914318,0.12545299530029297,7.860606999999999 -940,Regression,Hoeffding Tree,TrumpApproval,0.9721072130212217,2.310027291919755,-0.7400479483856388,0.13442516326904297,8.173459999999999 -960,Regression,Hoeffding Tree,TrumpApproval,0.9655343485152449,2.289186508543074,-0.7266590967915565,0.14021587371826172,8.490537999999999 -980,Regression,Hoeffding Tree,TrumpApproval,0.9583856890611192,2.2686834337047155,-0.7288044208040367,0.14442729949951172,8.812014999999999 -1000,Regression,Hoeffding Tree,TrumpApproval,0.9497447679766952,2.248146643879841,-0.7262238291744263,0.14863872528076172,9.137958999999999 -11,Regression,Hoeffding Adaptive Tree,ChickWeights,8.051220648038832,17.336198122120386,-385.87016600913427,0.023288726806640625,0.00403 -22,Regression,Hoeffding Adaptive Tree,ChickWeights,4.498502947359929,12.285286375364281,-158.84524831763767,0.024929046630859375,0.011609 -33,Regression,Hoeffding Adaptive Tree,ChickWeights,3.4668695042339137,10.074636808082968,-69.49212762837747,0.030193328857421875,0.022078 -44,Regression,Hoeffding Adaptive Tree,ChickWeights,2.7637805804889553,8.735764655686483,-59.08259408516962,0.031307220458984375,0.035307 -55,Regression,Hoeffding Adaptive Tree,ChickWeights,2.814517498310432,8.074396776941786,-11.726997097138026,0.038150787353515625,0.051286 -66,Regression,Hoeffding Adaptive Tree,ChickWeights,3.396900059747575,7.862006773633152,-4.201378762014764,0.047626495361328125,0.070551 -77,Regression,Hoeffding Adaptive Tree,ChickWeights,3.8844336568547537,7.782255505653143,-2.415785129732385,0.054004669189453125,0.093692 -88,Regression,Hoeffding Adaptive Tree,ChickWeights,4.068768385552718,7.555909217267645,-1.9194502155140074,0.059795379638671875,0.121065 -99,Regression,Hoeffding Adaptive Tree,ChickWeights,4.311602452908636,7.487314706483316,-1.3976387620786475,0.060848236083984375,0.152902 -110,Regression,Hoeffding Adaptive Tree,ChickWeights,4.261918758035323,7.240982145259267,-0.5659557565320237,0.062427520751953125,0.189312 -121,Regression,Hoeffding Adaptive Tree,ChickWeights,4.32509570871032,7.149348278127394,-0.17570514229398082,0.062427520751953125,0.22997900000000002 -132,Regression,Hoeffding Adaptive Tree,ChickWeights,4.243770455182887,6.949556168474376,0.0920549285716371,0.062427520751953125,0.275055 -143,Regression,Hoeffding Adaptive Tree,ChickWeights,4.119311205048765,6.7400830594316625,0.3082592266545521,0.024164199829101562,0.330922 -154,Regression,Hoeffding Adaptive Tree,ChickWeights,4.094718549433554,6.618738421062464,0.43309293168511476,0.034926414489746094,0.389383 -165,Regression,Hoeffding Adaptive Tree,ChickWeights,4.3535914858207265,6.858418841195889,0.5106778054828556,0.041365623474121094,0.450937 -176,Regression,Hoeffding Adaptive Tree,ChickWeights,4.494676115333661,6.99651956882687,0.5894091082089881,0.047156333923339844,0.515937 -187,Regression,Hoeffding Adaptive Tree,ChickWeights,4.531460188122701,6.982633238942946,0.6651551017011323,0.052016258239746094,0.584675 -198,Regression,Hoeffding Adaptive Tree,ChickWeights,4.550856564096301,6.9489545654121585,0.736210476532317,0.054648399353027344,0.6572239999999999 -209,Regression,Hoeffding Adaptive Tree,ChickWeights,4.745146525796211,7.286245359964537,0.7609173164436883,0.054648399353027344,0.733675 -220,Regression,Hoeffding Adaptive Tree,ChickWeights,5.330595718754616,8.515887777891804,0.7140722769094752,0.055296897888183594,0.814194 -231,Regression,Hoeffding Adaptive Tree,ChickWeights,5.50563091305374,8.701126762111178,0.7446721431039482,0.055296897888183594,0.898563 -242,Regression,Hoeffding Adaptive Tree,ChickWeights,5.723610994733609,9.068119167211083,0.7768542529953268,0.055296897888183594,0.986859 -253,Regression,Hoeffding Adaptive Tree,ChickWeights,5.834317193911452,9.203767847944404,0.8003533769470821,0.055296897888183594,1.07894 -264,Regression,Hoeffding Adaptive Tree,ChickWeights,6.561326226922799,10.595386608942691,0.7582588922727441,0.05537700653076172,1.17475 -275,Regression,Hoeffding Adaptive Tree,ChickWeights,7.055343281410319,11.793355798881397,0.7364597921881992,0.05537700653076172,1.274735 -286,Regression,Hoeffding Adaptive Tree,ChickWeights,7.331998951413002,12.245186296589463,0.7539176280650666,0.05391216278076172,1.3832309999999999 -297,Regression,Hoeffding Adaptive Tree,ChickWeights,7.416966166983629,12.289761227218738,0.7850184759487971,0.05443859100341797,1.495361 -308,Regression,Hoeffding Adaptive Tree,ChickWeights,7.99448083149163,13.217085318753208,0.7785490451915651,0.05456066131591797,1.6112419999999998 -319,Regression,Hoeffding Adaptive Tree,ChickWeights,9.157233410060112,16.13339057164046,0.7005755447430102,0.05613994598388672,1.73087 -330,Regression,Hoeffding Adaptive Tree,ChickWeights,9.47509121278654,16.446724789755304,0.7337084949314072,0.05613994598388672,1.854306 -341,Regression,Hoeffding Adaptive Tree,ChickWeights,9.757592549477597,16.701217161288277,0.7541120796137563,0.05613994598388672,1.981549 -352,Regression,Hoeffding Adaptive Tree,ChickWeights,9.93501095513177,16.87017564150386,0.7719317193583981,0.05613994598388672,2.112498 -363,Regression,Hoeffding Adaptive Tree,ChickWeights,10.85670423687554,18.405576814105093,0.7363875320655082,0.05613994598388672,2.247206 -374,Regression,Hoeffding Adaptive Tree,ChickWeights,11.59956477803306,20.214372545093333,0.7076961913412723,0.0658864974975586,2.386792 -385,Regression,Hoeffding Adaptive Tree,ChickWeights,12.011712885443343,20.838356414394227,0.7251727140820715,0.0713338851928711,2.5311649999999997 -396,Regression,Hoeffding Adaptive Tree,ChickWeights,12.02179706392092,20.699504894468422,0.7471567277697432,0.07817745208740234,2.6805389999999996 -407,Regression,Hoeffding Adaptive Tree,ChickWeights,12.867904342374958,22.04935022682606,0.733324041761514,0.08257198333740234,2.8351709999999994 -418,Regression,Hoeffding Adaptive Tree,ChickWeights,13.7726214629037,23.76360253855293,0.7092307493255587,0.08467769622802734,2.9951159999999994 -429,Regression,Hoeffding Adaptive Tree,ChickWeights,14.320664029675132,24.677208926329655,0.7213650338700139,0.06568050384521484,3.1646219999999996 -440,Regression,Hoeffding Adaptive Tree,ChickWeights,14.560745017781516,24.854467305977835,0.7373537773889289,0.0706624984741211,3.3387759999999997 -451,Regression,Hoeffding Adaptive Tree,ChickWeights,15.052718157581781,25.416929688531035,0.7370081245929037,0.07874202728271484,3.5178329999999995 -462,Regression,Hoeffding Adaptive Tree,ChickWeights,16.182360709465044,27.521690548068367,0.7103739673524856,0.0857076644897461,3.7020849999999994 -473,Regression,Hoeffding Adaptive Tree,ChickWeights,17.028395162723214,29.038217655150593,0.7152989103492285,0.0888662338256836,3.891670999999999 -484,Regression,Hoeffding Adaptive Tree,ChickWeights,17.39410374569335,29.368989764604034,0.7247347991767862,0.0889272689819336,4.084988999999999 -495,Regression,Hoeffding Adaptive Tree,ChickWeights,17.903720470221085,29.931789274576047,0.7310408495158558,0.0889272689819336,4.281388999999999 -506,Regression,Hoeffding Adaptive Tree,ChickWeights,18.828904303353827,31.360943457558232,0.7133254165496098,0.08951473236083984,4.480870999999999 -517,Regression,Hoeffding Adaptive Tree,ChickWeights,19.65231385288099,32.53535290181733,0.7088292337107098,0.0743856430053711,4.686204999999998 -528,Regression,Hoeffding Adaptive Tree,ChickWeights,20.485248099981963,33.800991401884744,0.7174497851371298,0.0787191390991211,4.894319999999999 -539,Regression,Hoeffding Adaptive Tree,ChickWeights,20.62693587606907,33.76714158926765,0.7296595823775684,0.0872030258178711,5.105511999999998 -550,Regression,Hoeffding Adaptive Tree,ChickWeights,21.611004652382654,35.363345192969206,0.7126350131259431,0.0935201644897461,5.319765999999999 -561,Regression,Hoeffding Adaptive Tree,ChickWeights,22.391562087266266,36.4058649659661,0.708760885936744,0.09343624114990234,5.537231999999999 -572,Regression,Hoeffding Adaptive Tree,ChickWeights,23.25574522992599,37.57896806973795,0.7129622004044582,0.09461116790771484,5.757816999999998 -20,Regression,Hoeffding Adaptive Tree,TrumpApproval,4.828377634536296,13.70786256219322,-439.7515918302183,0.05759429931640625,0.005178 -40,Regression,Hoeffding Adaptive Tree,TrumpApproval,3.453811275213839,9.811073218407973,-37.69887927291551,0.08081817626953125,0.01451 -60,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.5116544078850294,8.021960641037959,-33.879585508404254,0.09345245361328125,0.027873000000000002 -80,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.1224425015381523,6.9797990571526345,-25.487425023640153,0.103515625,0.045557 -100,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.246653919301699,6.363694444016854,-12.814729355257526,0.1129913330078125,0.06794 -120,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.270681160376927,5.896666779393501,-9.062525006956841,0.1172027587890625,0.095003 -140,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.1411489845018936,5.486121567062232,-8.000856498367144,0.1193084716796875,0.126859 -160,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.9595309296437795,5.145701533389061,-5.983118424699933,0.048110008239746094,0.170894 -180,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.8606850760789415,4.874784956472401,-4.504190782470528,0.06455135345458984,0.218268 -200,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.753768292507887,4.635064394721464,-3.990954055000616,0.07511425018310547,0.26933799999999997 -220,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.6442100676088158,4.426753978888705,-3.9107401204837533,0.0809926986694336,0.324221 -240,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.5586094458315778,4.243291414416048,-3.546083102970604,0.0831594467163086,0.383071 -260,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4757349309283123,4.079934399827006,-3.259426129696055,0.08697795867919922,0.445946 -280,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.429182741517093,3.94384178403937,-3.1670173911200505,0.09651470184326172,0.513038 -300,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.395554812880875,3.827170050036498,-2.859150937529973,0.10505962371826172,0.584571 -320,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3420726443092519,3.7085248950294267,-2.8030066952692625,0.11137676239013672,0.660678 -340,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2981450416106572,3.6023493777501,-2.7920215666096264,0.09692668914794922,0.7468359999999999 -360,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3017629599133824,3.52722030011446,-2.665355451896766,0.10482311248779297,0.837109 -380,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2791887566109008,3.4421979658375847,-2.6115379824259644,0.11014842987060547,0.931726 -400,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2309239845123983,3.35653802988408,-2.5310802374824855,0.11751842498779297,1.0309650000000001 -420,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2044881660092321,3.279580566778272,-2.4825797995498564,0.12172985076904297,1.13497 -440,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1805943216757935,3.2099310427881282,-2.2758615914195226,0.12752056121826172,1.2436960000000001 -460,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1747748413206256,3.150463961675437,-1.9924589881120154,0.13547801971435547,1.3572460000000002 -480,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1643181646721343,3.0907225956471227,-1.8137863387673288,0.14337444305419922,1.47581 -500,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1467427258000205,3.033773569773335,-1.6541724792729693,0.13623332977294922,1.6042770000000002 -520,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1310531754619513,2.979836071514613,-1.5651047078316291,0.14155864715576172,1.7377410000000002 -540,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1142715036195536,2.927551584361061,-1.466806182052764,0.14366436004638672,1.8762310000000002 -560,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1052485405576076,2.88137538641146,-1.4465405352520455,0.14840221405029297,2.0198690000000004 -580,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0859142446379713,2.834429565013283,-1.4260211929714153,0.15103435516357422,2.168629 -600,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0769717813954383,2.7922355683541964,-1.3282862925750138,0.15471935272216797,2.322692 -620,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0766885404316004,2.7563858053447157,-1.2342999001530521,0.15787792205810547,2.481953 -640,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.066276571170311,2.7176435544384367,-1.1313354614936588,0.15998363494873047,2.6464339999999997 -660,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0564634149761172,2.6824145051502803,-1.061505763110988,0.16366863250732422,2.8162789999999998 -680,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0488950225738152,2.6482621618515996,-1.0410990478472515,0.16630077362060547,2.9915459999999996 -700,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0494335446356584,2.619695292588707,-1.0497603683353351,0.15471935272216797,3.177748 -720,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0311572549278785,2.584165722213007,-1.0383576153991259,0.15945720672607422,3.3691549999999997 -740,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0166711191684197,2.551525288520744,-0.9859947129346351,0.16320323944091797,3.5658709999999996 -760,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9986122460589341,2.518502326974768,-0.9558825700512557,0.16478252410888672,3.7680209999999996 -780,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9842422617410425,2.4876653972852822,-0.9168282284512221,0.16958141326904297,3.9756869999999997 -800,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9822601758268574,2.462050972175237,-0.8945384291924348,0.16168498992919922,4.194112 -820,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9867607749501652,2.442599600947298,-0.8851995145588711,0.16431713104248047,4.4181 -840,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9793985197413392,2.417271328475225,-0.8634469862274994,0.16800212860107422,4.647504 -860,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9647458553132983,2.3903176435066817,-0.8189831286734526,0.17010784149169922,4.882465 -880,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9550000119490066,2.3652388018081414,-0.7721647401587992,0.17273998260498047,5.123038 -900,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9450286973597519,2.34076619049233,-0.7429966155689833,0.11153697967529297,5.374164 -920,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9340382087332013,2.316653968016665,-0.7353240501222194,0.11639690399169922,5.629799 -940,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9315944774387301,2.295899595899596,-0.7188294143518761,0.12230968475341797,5.8899680000000005 -960,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9266882702688716,2.2758824318695603,-0.7066477496763941,0.12967967987060547,6.154803 -980,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9199947893356224,2.255690063945526,-0.7090584581073034,0.13494396209716797,6.424549 -1000,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.910675347240908,2.2342958873570486,-0.7050189374098543,0.13822460174560547,6.699174 -11,Regression,Stochastic Gradient Tree,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.009614944458007812,0.001833 -22,Regression,Stochastic Gradient Tree,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012609481811523438,0.00539 -33,Regression,Stochastic Gradient Tree,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.015787124633789062,0.009921 -44,Regression,Stochastic Gradient Tree,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.018873214721679688,0.015497 -55,Regression,Stochastic Gradient Tree,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.021825790405273438,0.022338 -66,Regression,Stochastic Gradient Tree,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.024618148803710938,0.030239000000000002 -77,Regression,Stochastic Gradient Tree,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.027502059936523438,0.039152000000000006 -88,Regression,Stochastic Gradient Tree,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.030019760131835938,0.04912300000000001 -99,Regression,Stochastic Gradient Tree,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.03290367126464844,0.060256000000000004 -110,Regression,Stochastic Gradient Tree,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.26967811584472656,0.074892 -121,Regression,Stochastic Gradient Tree,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.26967811584472656,0.095588 -132,Regression,Stochastic Gradient Tree,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.26967811584472656,0.12212100000000001 -143,Regression,Stochastic Gradient Tree,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.26967811584472656,0.15448800000000001 -154,Regression,Stochastic Gradient Tree,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.26967811584472656,0.19265400000000002 -165,Regression,Stochastic Gradient Tree,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.26967811584472656,0.23664700000000002 -176,Regression,Stochastic Gradient Tree,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.26967811584472656,0.28637300000000004 -187,Regression,Stochastic Gradient Tree,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.26967811584472656,0.34182100000000004 -198,Regression,Stochastic Gradient Tree,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.26967811584472656,0.40300900000000006 -209,Regression,Stochastic Gradient Tree,ChickWeights,55.16354936929098,57.0482200725598,-13.656313160472004,0.6838865280151367,0.4890500000000001 -220,Regression,Stochastic Gradient Tree,ChickWeights,53.62203856749311,56.03531795068661,-11.37998411824978,0.6869077682495117,0.5845720000000001 -231,Regression,Stochastic Gradient Tree,ChickWeights,52.77279286370195,55.29408706815337,-9.311090357596036,0.6899290084838867,0.6897040000000001 -242,Regression,Stochastic Gradient Tree,ChickWeights,52.49661908339594,55.007104536867395,-7.210918602421254,0.6929502487182617,0.8041680000000001 -253,Regression,Stochastic Gradient Tree,ChickWeights,52.25631812193077,54.713446605156875,-6.055353919833875,0.6947126388549805,0.9281400000000001 -264,Regression,Stochastic Gradient Tree,ChickWeights,51.62511478420569,54.312843786153664,-5.352168023774992,0.6947126388549805,1.061635 -275,Regression,Stochastic Gradient Tree,ChickWeights,51.4425344352617,54.29364548356293,-4.585603291722447,0.6947126388549805,1.204612 -286,Regression,Stochastic Gradient Tree,ChickWeights,51.75651621106165,54.635705044608144,-3.8989478253777694,0.6947126388549805,1.357118 -297,Regression,Stochastic Gradient Tree,ChickWeights,52.373839404142416,55.25476711535166,-3.3456400671942,0.6947126388549805,1.518976 -308,Regression,Stochastic Gradient Tree,ChickWeights,52.87239275875638,55.86677247417265,-2.9565197175813713,0.6947126388549805,1.6900950000000001 -319,Regression,Stochastic Gradient Tree,ChickWeights,52.69554478958866,56.2770501442128,-2.6433309475704183,0.6947126388549805,1.870449 -330,Regression,Stochastic Gradient Tree,ChickWeights,53.85316804407712,57.75044402630399,-2.2832890424968193,0.6947126388549805,2.06005 -341,Regression,Stochastic Gradient Tree,ChickWeights,54.90678041411178,59.01114057562677,-2.0697921090482247,0.6947126388549805,2.258925 -352,Regression,Stochastic Gradient Tree,ChickWeights,56.00533746556472,60.302245208561004,-1.9140207825503284,0.6947126388549805,2.467073 -363,Regression,Stochastic Gradient Tree,ChickWeights,55.99599298772852,60.54917173074773,-1.852879941931207,0.6947126388549805,2.684461 -374,Regression,Stochastic Gradient Tree,ChickWeights,56.87222492302705,61.81275171085535,-1.7331917323651345,0.6947126388549805,2.911073 -385,Regression,Stochastic Gradient Tree,ChickWeights,58.41786698150333,63.95254893573906,-1.5885028214279253,0.6947126388549805,3.146928 -396,Regression,Stochastic Gradient Tree,ChickWeights,59.7033976124885,65.46926983257002,-1.5293357430909813,0.6947126388549805,3.392032 -407,Regression,Stochastic Gradient Tree,ChickWeights,60.057805647389294,66.17359973042984,-1.4019380007417155,1.1097631454467773,3.672033 -418,Regression,Stochastic Gradient Tree,ChickWeights,59.7070864579051,66.11592086962122,-1.2507954049688483,1.1127843856811523,3.9657679999999997 -429,Regression,Stochastic Gradient Tree,ChickWeights,60.122823673891816,66.73609937588846,-1.0378169857688957,1.1158056259155273,4.272702 -440,Regression,Stochastic Gradient Tree,ChickWeights,60.39504675635191,66.96100690444877,-0.906365593827489,1.1188268661499023,4.593074 -451,Regression,Stochastic Gradient Tree,ChickWeights,60.27126048587789,66.93502892662679,-0.8239085862185902,1.120589256286621,4.926735 -462,Regression,Stochastic Gradient Tree,ChickWeights,60.340686610373176,67.43825007380137,-0.7390015352251049,1.120589256286621,5.273856 -473,Regression,Stochastic Gradient Tree,ChickWeights,61.40703262301831,69.11306667757516,-0.6127592621572406,1.120589256286621,5.634397 -484,Regression,Stochastic Gradient Tree,ChickWeights,61.95796621360106,69.71422620021941,-0.5510154280248158,1.120589256286621,6.008344 -495,Regression,Stochastic Gradient Tree,ChickWeights,62.59018166487368,70.55352405729404,-0.4943708535906215,1.120589256286621,6.395653 -506,Regression,Stochastic Gradient Tree,ChickWeights,62.49664579133251,70.88193125644693,-0.46447524520130457,1.120589256286621,6.796304 -517,Regression,Stochastic Gradient Tree,ChickWeights,63.25224079915844,71.92080214464903,-0.4228062717918979,1.120589256286621,7.21023 -528,Regression,Stochastic Gradient Tree,ChickWeights,64.80783657170488,74.3681944005728,-0.36776422230083305,1.120589256286621,7.637592 -539,Regression,Stochastic Gradient Tree,ChickWeights,65.59959781369417,75.30113885843834,-0.3443906138479853,1.120589256286621,8.078512 -550,Regression,Stochastic Gradient Tree,ChickWeights,65.79684627343133,76.01328745307667,-0.32771909731089166,1.120589256286621,8.532886 -561,Regression,Stochastic Gradient Tree,ChickWeights,66.6512855136148,77.20436469287773,-0.30975691666695093,1.120589256286621,9.000807 -572,Regression,Stochastic Gradient Tree,ChickWeights,68.11975592628174,79.56492566870935,-0.2867456678376987,1.120589256286621,9.482145 -20,Regression,Stochastic Gradient Tree,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.019941329956054688,0.002755 -40,Regression,Stochastic Gradient Tree,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.03173637390136719,0.008058000000000001 -60,Regression,Stochastic Gradient Tree,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.04389762878417969,0.015549 -80,Regression,Stochastic Gradient Tree,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.05624198913574219,0.025294 -100,Regression,Stochastic Gradient Tree,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.5381031036376953,0.041246000000000005 -120,Regression,Stochastic Gradient Tree,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.5386066436767578,0.070023 -140,Regression,Stochastic Gradient Tree,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.5391101837158203,0.110787 -160,Regression,Stochastic Gradient Tree,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.5393619537353516,0.163463 -180,Regression,Stochastic Gradient Tree,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.5396137237548828,0.227995 -200,Regression,Stochastic Gradient Tree,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.5077581405639648,0.320041 -220,Regression,Stochastic Gradient Tree,TrumpApproval,36.497516001377406,38.019453444701035,-361.2329206514933,1.3602590560913086,0.441408 -240,Regression,Stochastic Gradient Tree,TrumpApproval,33.64243104419191,36.40668421494773,-333.65237138497804,1.360762596130371,0.581306 -260,Regression,Stochastic Gradient Tree,TrumpApproval,31.222114965034955,34.98371838354962,-312.16748668977897,1.3610143661499023,0.739627 -280,Regression,Stochastic Gradient Tree,TrumpApproval,29.182059468614717,33.71869814960704,-303.5986275675674,1.361769676208496,0.915776 -300,Regression,Stochastic Gradient Tree,TrumpApproval,27.34275770505051,32.57805191350732,-278.63174197976707,1.3620214462280273,1.109879 -320,Regression,Stochastic Gradient Tree,TrumpApproval,25.81388747443183,31.5521424826706,-274.2849072221064,1.3630285263061523,1.321838 -340,Regression,Stochastic Gradient Tree,TrumpApproval,24.51835124153299,30.62414457186519,-273.0482727941538,1.3640356063842773,1.5516940000000001 -360,Regression,Stochastic Gradient Tree,TrumpApproval,23.451930423400693,29.787924926455332,-260.4155562259403,1.3660497665405273,1.799643 -380,Regression,Stochastic Gradient Tree,TrumpApproval,22.468440533492842,29.014219480552867,-255.59151052979877,1.3665533065795898,2.065559 -400,Regression,Stochastic Gradient Tree,TrumpApproval,21.594907007575774,28.301677882839346,-250.0434007116766,0.510127067565918,2.355987 -420,Regression,Stochastic Gradient Tree,TrumpApproval,20.62268781294523,27.62086591367872,-246.0239415518119,1.3623762130737305,2.6744339999999998 -440,Regression,Stochastic Gradient Tree,TrumpApproval,19.786863931462925,26.990398924900393,-230.60756767519214,1.3643903732299805,3.010878 -460,Regression,Stochastic Gradient Tree,TrumpApproval,19.05732899619648,26.404670160589287,-209.2038511633616,1.3666563034057617,3.365293 -480,Regression,Stochastic Gradient Tree,TrumpApproval,18.376512097202227,25.854792215140314,-195.90337768575387,1.3701810836791992,3.737716 -500,Regression,Stochastic Gradient Tree,TrumpApproval,17.755044410127518,25.338820973360427,-184.15507530651482,1.3716917037963867,4.128280999999999 -520,Regression,Stochastic Gradient Tree,TrumpApproval,17.16611419898163,24.851444862058347,-177.4118263333629,1.3737058639526367,4.537221 -540,Regression,Stochastic Gradient Tree,TrumpApproval,16.628565596068775,24.392285078947275,-170.25012213753183,1.3747129440307617,4.9643749999999995 -560,Regression,Stochastic Gradient Tree,TrumpApproval,16.091244232649693,23.955027361350904,-168.10096043791202,1.3752164840698242,5.410107999999999 -580,Regression,Stochastic Gradient Tree,TrumpApproval,15.590768135673304,23.54051091957351,-166.33817208986073,1.3764753341674805,5.874175999999999 -600,Regression,Stochastic Gradient Tree,TrumpApproval,15.168708628495342,23.15108754841241,-159.05714501634571,0.5124959945678711,6.365194 -620,Regression,Stochastic Gradient Tree,TrumpApproval,14.742446374247313,22.779539618023726,-151.59887848495535,3.0642080307006836,6.921285 -640,Regression,Stochastic Gradient Tree,TrumpApproval,14.319364852585176,22.42187566882095,-144.08105420081068,3.0679845809936523,7.51197 -660,Regression,Stochastic Gradient Tree,TrumpApproval,13.916412195872256,22.080274918425697,-138.68241285181185,3.0712575912475586,8.136981 -680,Regression,Stochastic Gradient Tree,TrumpApproval,13.515604789075645,21.753254558457893,-136.71797028279042,3.074782371520996,8.796442 -700,Regression,Stochastic Gradient Tree,TrumpApproval,13.16391092204058,21.44141764506316,-136.3120101768532,3.0773000717163086,9.490636 -720,Regression,Stochastic Gradient Tree,TrumpApproval,12.828283113852926,21.142484202016185,-135.44313416922282,3.078558921813965,10.219341 -740,Regression,Stochastic Gradient Tree,TrumpApproval,12.504466467012781,20.855361315179096,-131.6825380828392,3.0800695419311523,10.982324 -760,Regression,Stochastic Gradient Tree,TrumpApproval,12.187542748969033,20.57929219886472,-129.592708960364,3.0813283920288086,11.779656000000001 -780,Regression,Stochastic Gradient Tree,TrumpApproval,11.899403743710545,20.31464229706916,-126.82553676745258,3.08359432220459,12.611571000000001 -800,Regression,Stochastic Gradient Tree,TrumpApproval,11.634366305883285,20.06137952581079,-124.7856004590591,3.084601402282715,13.493909000000002 -820,Regression,Stochastic Gradient Tree,TrumpApproval,11.363415331478278,19.815492221289514,-123.0687724200615,3.08560848236084,14.412321000000002 -840,Regression,Stochastic Gradient Tree,TrumpApproval,11.106640469158773,19.57848368678801,-121.24309788996561,3.086615562438965,15.363464000000002 -860,Regression,Stochastic Gradient Tree,TrumpApproval,10.873909665943762,19.350226189127362,-118.20364312373843,3.0871191024780273,16.347229000000002 -880,Regression,Stochastic Gradient Tree,TrumpApproval,10.65545006969969,19.130035299019603,-114.92727947355435,3.0873708724975586,17.36361 -900,Regression,Stochastic Gradient Tree,TrumpApproval,10.439309697188909,18.916827199314994,-112.83532852765143,3.08762264251709,18.412326 -920,Regression,Stochastic Gradient Tree,TrumpApproval,10.21789524284777,18.710158789526105,-112.19133803320567,3.087874412536621,19.493438 -940,Regression,Stochastic Gradient Tree,TrumpApproval,10.012578535125467,18.510293787577226,-110.72583714230211,3.077906608581543,20.736809 -960,Regression,Stochastic Gradient Tree,TrumpApproval,9.811853150109151,18.316579311485903,-109.54344305213982,3.0804243087768555,22.013587 -980,Regression,Stochastic Gradient Tree,TrumpApproval,9.61909067795052,18.12881604876013,-109.39183420714343,3.080927848815918,23.322572 -1000,Regression,Stochastic Gradient Tree,TrumpApproval,9.438738635632271,17.946847607318464,-109.00797869183796,3.0824384689331055,24.663779 -11,Regression,Adaptive Random Forest,ChickWeights,7.837563210503649,16.830121687224917,-363.61289911513376,0.15062332153320312,0.018991 -22,Regression,Adaptive Random Forest,ChickWeights,4.3557641651310055,11.925612892987612,-149.62275175212707,0.17615127563476562,0.051966 -33,Regression,Adaptive Random Forest,ChickWeights,3.371112580593177,9.780386843070694,-65.43453306461763,0.21428680419921875,0.10284099999999999 -44,Regression,Adaptive Random Forest,ChickWeights,2.6950975095192975,8.482165721989492,-55.64483692929184,0.2312774658203125,0.17143399999999998 -55,Regression,Adaptive Random Forest,ChickWeights,2.750371500828058,7.825470627419848,-10.954370249441634,0.2869682312011719,0.25594999999999996 -66,Regression,Adaptive Random Forest,ChickWeights,2.874973614360605,7.312672972792191,-3.4999113348114523,0.33329010009765625,0.36036799999999997 -77,Regression,Adaptive Random Forest,ChickWeights,3.049190601733834,7.064366487423796,-1.814660484448317,0.3119354248046875,0.49218199999999995 -88,Regression,Adaptive Random Forest,ChickWeights,2.9760015514160614,6.690266116634344,-1.2888345310585096,0.3463325500488281,0.6436869999999999 -99,Regression,Adaptive Random Forest,ChickWeights,3.4458208345296213,6.801756467406213,-0.9786716534372655,0.3914375305175781,0.8148389999999999 -110,Regression,Adaptive Random Forest,ChickWeights,3.8036130411377513,6.901055458118455,-0.4223797783359673,0.4219093322753906,1.008913 -121,Regression,Adaptive Random Forest,ChickWeights,4.044286594485068,6.961321070951229,-0.1146764842737158,0.4422340393066406,1.227422 -132,Regression,Adaptive Random Forest,ChickWeights,4.278051046290026,6.992189163715883,0.08088093472531654,0.4695549011230469,1.467051 -143,Regression,Adaptive Random Forest,ChickWeights,4.514698307868387,7.09845673012605,0.23274318106229808,0.5039863586425781,1.72858 -154,Regression,Adaptive Random Forest,ChickWeights,4.7116828167337585,7.225211881769595,0.32444204647582153,0.5465545654296875,2.00923 -165,Regression,Adaptive Random Forest,ChickWeights,5.098965674365188,7.74448667501397,0.3760752970367085,0.5543212890625,2.317031 -176,Regression,Adaptive Random Forest,ChickWeights,5.613573109580669,8.405070008975855,0.4074460804745814,0.5778732299804688,2.650775 -187,Regression,Adaptive Random Forest,ChickWeights,6.0300184245211925,8.816070592615688,0.4662285490406689,0.597381591796875,3.008386 -198,Regression,Adaptive Random Forest,ChickWeights,6.135166503700917,8.873071763991604,0.5699028278126741,0.6156463623046875,3.389767 -209,Regression,Adaptive Random Forest,ChickWeights,6.663870316688535,9.680163486851024,0.5780063435312484,0.6372909545898438,3.797698 -220,Regression,Adaptive Random Forest,ChickWeights,7.114976257039628,10.70304519196575,0.548340525531499,0.6516532897949219,4.233731 -231,Regression,Adaptive Random Forest,ChickWeights,7.558935022860611,11.205896813348822,0.5765126454701623,0.6456108093261719,4.696369 -242,Regression,Adaptive Random Forest,ChickWeights,7.8784966144348445,11.546829616877769,0.6381907286214681,0.6543197631835938,5.1845419999999995 -253,Regression,Adaptive Random Forest,ChickWeights,8.045465222989945,11.7730800696534,0.6733287963504477,0.6542396545410156,5.698149999999999 -264,Regression,Adaptive Random Forest,ChickWeights,8.55861765945315,12.698578392308455,0.6527621165047097,0.7007179260253906,6.239901999999999 -275,Regression,Adaptive Random Forest,ChickWeights,9.012932654808607,13.883768164483953,0.6347528903285174,0.7182159423828125,6.810226999999999 -286,Regression,Adaptive Random Forest,ChickWeights,9.46045520422096,14.421722772543973,0.6586625054492083,0.7397651672363281,7.406784999999999 -297,Regression,Adaptive Random Forest,ChickWeights,9.467084537445258,14.393153360282469,0.7051330126585751,0.6962127685546875,8.032812 -308,Regression,Adaptive Random Forest,ChickWeights,9.993822911601686,15.306673373378164,0.7029922374440833,0.712249755859375,8.684709 -319,Regression,Adaptive Random Forest,ChickWeights,10.906399564516823,17.709412566907307,0.6392184800849823,0.7230567932128906,9.367602999999999 -330,Regression,Adaptive Random Forest,ChickWeights,11.354718196032138,18.25739128586918,0.6718473555535345,0.748199462890625,10.07931 -341,Regression,Adaptive Random Forest,ChickWeights,11.709200775641314,18.709755653596346,0.691413317744465,0.7508468627929688,10.818764 -352,Regression,Adaptive Random Forest,ChickWeights,12.007404346564588,19.024465799394036,0.7099648583835785,0.7794418334960938,11.583692 -363,Regression,Adaptive Random Forest,ChickWeights,12.612646669962949,20.078004824306742,0.6863045708062432,0.8219375610351562,12.374611 -374,Regression,Adaptive Random Forest,ChickWeights,13.305874945245664,21.69198202373745,0.6634013148524545,0.8368568420410156,13.19172 -385,Regression,Adaptive Random Forest,ChickWeights,13.874152914620149,22.398641632517744,0.6824761966895349,0.8399162292480469,14.039444 -396,Regression,Adaptive Random Forest,ChickWeights,14.02164789427361,22.45496751107478,0.7024524705388633,0.8451423645019531,14.916421999999999 -407,Regression,Adaptive Random Forest,ChickWeights,14.881080524519055,23.990711305579165,0.684297135752792,0.844085693359375,15.821423999999999 -418,Regression,Adaptive Random Forest,ChickWeights,15.672731015633323,25.84901428202966,0.6559576619146927,0.8621559143066406,16.75642 -429,Regression,Adaptive Random Forest,ChickWeights,16.52730145447373,27.035273550157452,0.6655701177301542,0.8826904296875,17.718062999999997 -440,Regression,Adaptive Random Forest,ChickWeights,16.835543678126854,27.29532071757395,0.6832339396752392,0.8854255676269531,18.709096 -451,Regression,Adaptive Random Forest,ChickWeights,17.2847401584601,27.83347178305896,0.6846223453976918,0.9156723022460938,19.725565 -462,Regression,Adaptive Random Forest,ChickWeights,18.263556988874104,29.983264015814683,0.6562480279042127,0.9476966857910156,20.769031 -473,Regression,Adaptive Random Forest,ChickWeights,19.168820167599023,31.233753579350584,0.6706197338145063,0.9631462097167969,21.841162999999998 -484,Regression,Adaptive Random Forest,ChickWeights,19.66691155441429,31.662924032965286,0.6800549958221269,0.9797592163085938,22.942597 -495,Regression,Adaptive Random Forest,ChickWeights,20.1980005296915,32.326142080110245,0.6862897411402615,1.0035591125488281,24.073218999999998 -506,Regression,Adaptive Random Forest,ChickWeights,21.038101965066165,33.861783151779306,0.6657814123530009,1.0402488708496094,25.232436 -517,Regression,Adaptive Random Forest,ChickWeights,21.79336863548733,34.905834748448235,0.6648549707242759,1.0591621398925781,26.421057 -528,Regression,Adaptive Random Forest,ChickWeights,22.71415913579816,36.0778798798642,0.6781016272298894,1.0802803039550781,27.640294 -539,Regression,Adaptive Random Forest,ChickWeights,22.973012146363907,36.13494365478664,0.690416970144236,1.1105842590332031,28.88878 -550,Regression,Adaptive Random Forest,ChickWeights,23.822482702164027,37.524990725820025,0.6764299185798421,1.15643310546875,30.168770000000002 -561,Regression,Adaptive Random Forest,ChickWeights,24.754282528765707,38.80763200620001,0.669066082535116,1.1719589233398438,31.481567000000002 -572,Regression,Adaptive Random Forest,ChickWeights,25.964764492717645,40.6034258569803,0.6648997528039327,1.186126708984375,32.828648 -20,Regression,Adaptive Random Forest,TrumpApproval,4.656631863584941,13.301513571178564,-414.0080590272913,0.20159912109375,0.060655 -40,Regression,Adaptive Random Forest,TrumpApproval,3.3092233454389866,9.514642226769206,-35.395716979334736,0.28952789306640625,0.186458 -60,Regression,Adaptive Random Forest,TrumpApproval,2.3702455482222518,7.779742234417967,-31.80504797847069,0.3228263854980469,0.354007 -80,Regression,Adaptive Random Forest,TrumpApproval,2.0011825545667588,6.767036502792006,-23.897224712465306,0.3692893981933594,0.55542 -100,Regression,Adaptive Random Forest,TrumpApproval,2.0568714899324476,6.139015712379544,-11.856454989718417,0.4076881408691406,0.79515 -120,Regression,Adaptive Random Forest,TrumpApproval,2.0030922669838476,5.651046465030358,-8.241693395555247,0.4241790771484375,1.072178 -140,Regression,Adaptive Random Forest,TrumpApproval,1.8854667406427696,5.2532308662388045,-7.2528881392181574,0.44397735595703125,1.385482 -160,Regression,Adaptive Random Forest,TrumpApproval,1.9340845560700954,4.984838031470717,-5.553334350331147,0.45578765869140625,1.738613 -180,Regression,Adaptive Random Forest,TrumpApproval,1.9596878280460812,4.757627458420989,-4.242801539132487,0.47260284423828125,2.128929 -200,Regression,Adaptive Random Forest,TrumpApproval,1.9000266951648126,4.53846274922039,-3.785084131964374,0.5018348693847656,2.555084 -220,Regression,Adaptive Random Forest,TrumpApproval,1.7756062902244436,4.331882031903988,-3.702506681895046,0.5390548706054688,3.014926 -240,Regression,Adaptive Random Forest,TrumpApproval,1.722064335127714,4.161337926832492,-3.3721758647478204,0.5583267211914062,3.510014 -260,Regression,Adaptive Random Forest,TrumpApproval,1.668741133448935,4.009095884933904,-3.112800247055813,0.5867843627929688,4.039729 -280,Regression,Adaptive Random Forest,TrumpApproval,1.596023168515899,3.86837254754146,-3.0090634555073343,0.6034698486328125,4.6068750000000005 -300,Regression,Adaptive Random Forest,TrumpApproval,1.5862781822088614,3.757725401796155,-2.72037165832895,0.6344451904296875,5.213001 -320,Regression,Adaptive Random Forest,TrumpApproval,1.5269819355711358,3.6440152095112572,-2.6718510406861706,0.6524238586425781,5.854158 -340,Regression,Adaptive Random Forest,TrumpApproval,1.4570659608871932,3.537119081966105,-2.6559352799702967,0.6771697998046875,6.534176 -360,Regression,Adaptive Random Forest,TrumpApproval,1.4064005847783463,3.441784227878547,-2.4899419787597266,0.7120590209960938,7.253771 -380,Regression,Adaptive Random Forest,TrumpApproval,1.3534816779602579,3.352627624678765,-2.426029812278527,0.7612266540527344,8.012458 -400,Regression,Adaptive Random Forest,TrumpApproval,1.3254238940946812,3.27422063813923,-2.360008106665105,0.7831077575683594,8.809453000000001 -420,Regression,Adaptive Random Forest,TrumpApproval,1.2822507927277378,3.197239276537114,-2.309899038967682,0.8153495788574219,9.645719000000001 -440,Regression,Adaptive Random Forest,TrumpApproval,1.2469389910195894,3.127027882921648,-2.108834809107039,0.8549957275390625,10.520540000000002 -460,Regression,Adaptive Random Forest,TrumpApproval,1.209007072666545,3.060196802253249,-1.8234356170996366,0.8641319274902344,11.437965000000002 -480,Regression,Adaptive Random Forest,TrumpApproval,1.173565091899577,2.996986036098964,-1.6456994146583188,0.9048995971679688,12.395296000000002 -500,Regression,Adaptive Random Forest,TrumpApproval,1.1545102154576106,2.9412312685847275,-1.494716293761022,0.9429206848144531,13.397268000000002 -520,Regression,Adaptive Random Forest,TrumpApproval,1.1235341152888954,2.885679937630878,-1.4055626483775598,0.9187583923339844,14.455292000000002 -540,Regression,Adaptive Random Forest,TrumpApproval,1.0931325909773704,2.8325152336083046,-1.3092471918321817,0.9785118103027344,15.562633000000002 -560,Regression,Adaptive Random Forest,TrumpApproval,1.080423433010604,2.7861157835692825,-1.2874470666919131,0.8416099548339844,16.721025 -580,Regression,Adaptive Random Forest,TrumpApproval,1.0538460644054608,2.73873459117484,-1.2649736085810765,0.9269638061523438,17.931443 -600,Regression,Adaptive Random Forest,TrumpApproval,1.037762850657022,2.6954140775252133,-1.1696179031883163,1.0152244567871094,19.191903 -620,Regression,Adaptive Random Forest,TrumpApproval,1.022747434499167,2.654976815796094,-1.0729218181826923,0.9687690734863281,20.501534 -640,Regression,Adaptive Random Forest,TrumpApproval,1.006861222675929,2.6151861949722415,-0.9736586966592728,0.803070068359375,21.860298999999998 -660,Regression,Adaptive Random Forest,TrumpApproval,0.9869414277387362,2.5762284524749504,-0.9015227260176624,0.7759437561035156,23.260671 -680,Regression,Adaptive Random Forest,TrumpApproval,0.966244541930286,2.538677410892474,-0.8756731719459285,0.8428535461425781,24.702724 -700,Regression,Adaptive Random Forest,TrumpApproval,0.9583039577617943,2.5060806369029174,-0.8758219540617476,0.9465827941894531,26.183847 -720,Regression,Adaptive Random Forest,TrumpApproval,0.9436352676783603,2.472672874668102,-0.8662636043278715,1.0379486083984375,27.712492 -740,Regression,Adaptive Random Forest,TrumpApproval,0.9279533445219875,2.44025252932111,-0.816552178803118,1.1120071411132812,29.286906000000002 -760,Regression,Adaptive Random Forest,TrumpApproval,0.9160723193407865,2.4102496967776017,-0.7913569678512289,1.17376708984375,30.912860000000002 -780,Regression,Adaptive Random Forest,TrumpApproval,0.8969957702252379,2.379401465215729,-0.753616871824967,1.2616004943847656,32.587144 -800,Regression,Adaptive Random Forest,TrumpApproval,0.8879951793456313,2.351379467833174,-0.7280439441517783,1.3552436828613281,34.314383 -820,Regression,Adaptive Random Forest,TrumpApproval,0.8778121160020841,2.323761508818731,-0.7062232791684944,1.4321250915527344,36.09551 -840,Regression,Adaptive Random Forest,TrumpApproval,0.8674818777939237,2.297554956673723,-0.683441607427212,1.4874954223632812,37.948423 -860,Regression,Adaptive Random Forest,TrumpApproval,0.8596582290799093,2.2724190258224723,-0.6439714465644337,1.5595359802246094,39.857894 -880,Regression,Adaptive Random Forest,TrumpApproval,0.8554226716886886,2.248810009475745,-0.601989379910626,1.6191024780273438,41.823765 -900,Regression,Adaptive Random Forest,TrumpApproval,0.8440517106498798,2.2244180426447486,-0.5740310312005918,1.1261253356933594,43.864200000000004 -920,Regression,Adaptive Random Forest,TrumpApproval,0.8347159483348575,2.2010585469199837,-0.5664676720057789,1.1678504943847656,45.946931000000006 -940,Regression,Adaptive Random Forest,TrumpApproval,0.8262269751007542,2.1790511985851073,-0.5483240613297584,1.119964599609375,48.07255500000001 -960,Regression,Adaptive Random Forest,TrumpApproval,0.8159800926650994,2.1571455676024542,-0.5332153251602936,1.1733894348144531,50.238479000000005 -980,Regression,Adaptive Random Forest,TrumpApproval,0.80675324923832,2.1360469188887925,-0.5325676025464574,1.2283477783203125,52.446400000000004 -1000,Regression,Adaptive Random Forest,TrumpApproval,0.801132918984696,2.1160276454820277,-0.5292923269414216,1.2836189270019531,54.694244000000005 -11,Regression,Adaptive Model Rules,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.019614219665527344,0.002799 -22,Regression,Adaptive Model Rules,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.021178245544433594,0.009348 -33,Regression,Adaptive Model Rules,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.02634716033935547,0.018276 -44,Regression,Adaptive Model Rules,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.027434349060058594,0.029790999999999998 -55,Regression,Adaptive Model Rules,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.034033775329589844,0.044022 -66,Regression,Adaptive Model Rules,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.043257713317871094,0.061336 -77,Regression,Adaptive Model Rules,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.04948711395263672,0.082289 -88,Regression,Adaptive Model Rules,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.05513286590576172,0.107232 -99,Regression,Adaptive Model Rules,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.05624675750732422,0.136317 -110,Regression,Adaptive Model Rules,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.057623863220214844,0.169599 -121,Regression,Adaptive Model Rules,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.05775737762451172,0.206842 -132,Regression,Adaptive Model Rules,ChickWeights,5.247611065864998,7.56430675955835,-0.07568150661018036,0.05781078338623047,0.24815199999999998 -143,Regression,Adaptive Model Rules,ChickWeights,5.0844132960442625,7.343803904848652,0.17878850148449155,0.058394432067871094,0.29376199999999997 -154,Regression,Adaptive Model Rules,ChickWeights,4.973008915037768,7.173430375731751,0.3340904988080935,0.058447837829589844,0.343499 -165,Regression,Adaptive Model Rules,ChickWeights,5.201973475639973,7.389818367745889,0.43191354366781964,0.058447837829589844,0.397279 -176,Regression,Adaptive Model Rules,ChickWeights,5.377897753885034,7.538080975572278,0.5233859928595415,0.059058189392089844,0.455134 -187,Regression,Adaptive Model Rules,ChickWeights,5.414777515271245,7.541781669769663,0.6093812059493195,0.059111595153808594,0.517245 -198,Regression,Adaptive Model Rules,ChickWeights,5.40059238519783,7.511878220104288,0.6917410630009373,0.059058189392089844,0.583523 -209,Regression,Adaptive Model Rules,ChickWeights,5.933708937482518,9.717098931216647,0.5747798982216288,0.025275230407714844,0.662886 -220,Regression,Adaptive Model Rules,ChickWeights,6.498767742677896,10.515698120348512,0.5640139167754625,0.031424522399902344,0.744596 -231,Regression,Adaptive Model Rules,ChickWeights,6.70504504336628,10.673746805737519,0.6157790851383267,0.03654003143310547,0.828906 -242,Regression,Adaptive Model Rules,ChickWeights,7.118759598962231,11.248237924166032,0.6566609789141779,0.04107189178466797,0.9161250000000001 -253,Regression,Adaptive Model Rules,ChickWeights,7.339750662382254,11.398711123846239,0.6937739359440155,0.04459667205810547,1.006337 -264,Regression,Adaptive Model Rules,ChickWeights,7.866457552558359,12.301057885719082,0.6741619364384577,0.04470348358154297,1.099545 -275,Regression,Adaptive Model Rules,ChickWeights,8.421243223038738,13.285884557144795,0.6655331879845522,0.04470348358154297,1.19574 -286,Regression,Adaptive Model Rules,ChickWeights,8.956033363560122,14.109625220244896,0.6732762786849746,0.04520702362060547,1.295357 -297,Regression,Adaptive Model Rules,ChickWeights,9.573413802719209,14.887232530340057,0.6845415314559343,0.045233726501464844,1.398184 -308,Regression,Adaptive Model Rules,ChickWeights,10.140823162094344,15.798657858475249,0.6835926538539661,0.04526042938232422,1.5040010000000001 -319,Regression,Adaptive Model Rules,ChickWeights,11.056460411651761,17.826108509419473,0.6344480842540857,0.04526042938232422,1.6128270000000002 -330,Regression,Adaptive Model Rules,ChickWeights,11.706749123156325,18.647901518188576,0.6576594092850414,0.045287132263183594,1.7247210000000002 -341,Regression,Adaptive Model Rules,ChickWeights,11.849547188265053,18.683751607733633,0.6922705096711406,0.045287132263183594,1.8398310000000002 -352,Regression,Adaptive Model Rules,ChickWeights,11.96088648820193,18.74329807265456,0.7184745225672177,0.045287132263183594,1.9580810000000002 -363,Regression,Adaptive Model Rules,ChickWeights,12.783089048199372,19.958388531582212,0.6900311672733117,0.04531383514404297,2.0793790000000003 -374,Regression,Adaptive Model Rules,ChickWeights,13.27307991721093,20.988857849066502,0.6848686892374445,0.045340538024902344,2.203704 -385,Regression,Adaptive Model Rules,ChickWeights,13.623649100869688,21.545378780740656,0.7062071700264252,0.045340538024902344,2.331151 -396,Regression,Adaptive Model Rules,ChickWeights,13.714864044781413,21.4916185882578,0.7274352207736796,0.045340538024902344,2.4617500000000003 -407,Regression,Adaptive Model Rules,ChickWeights,14.57407318940339,22.903346450438516,0.712266679293069,0.04568004608154297,2.5991990000000005 -418,Regression,Adaptive Model Rules,ChickWeights,15.311297276648313,24.25392212062312,0.6971079497894322,0.04573345184326172,2.7396560000000005 -429,Regression,Adaptive Model Rules,ChickWeights,15.833945440380871,25.12811892959106,0.7110893860103431,0.04573345184326172,2.8832220000000004 -440,Regression,Adaptive Model Rules,ChickWeights,15.995632485589844,25.20571130808328,0.7298778762133054,0.045653343200683594,3.0299210000000003 -451,Regression,Adaptive Model Rules,ChickWeights,16.482571154231422,25.77399383544894,0.7295670550023294,0.04618358612060547,3.179584 -462,Regression,Adaptive Model Rules,ChickWeights,17.556958821758087,27.82207110996992,0.7040173234911381,0.046927452087402344,3.3322830000000003 -473,Regression,Adaptive Model Rules,ChickWeights,18.31809908164516,29.103026344234387,0.7140266770057507,0.046980857849121094,3.488055 -484,Regression,Adaptive Model Rules,ChickWeights,18.645508344467764,29.39095020592674,0.7243229903014706,0.04695415496826172,3.6469690000000003 -495,Regression,Adaptive Model Rules,ChickWeights,19.076944683969508,29.900823849283483,0.7315970559213162,0.04690074920654297,3.8089630000000003 -506,Regression,Adaptive Model Rules,ChickWeights,19.9412049113122,31.299098765867257,0.7144549629170223,0.046980857849121094,3.9740900000000003 -517,Regression,Adaptive Model Rules,ChickWeights,20.652539482762663,32.28122969713156,0.7133599532452177,0.04700756072998047,4.142383000000001 -528,Regression,Adaptive Model Rules,ChickWeights,21.437431132207106,33.471760575953454,0.7229272102715563,0.04700756072998047,4.313758000000001 -539,Regression,Adaptive Model Rules,ChickWeights,21.589008276225865,33.459905509370415,0.7345566785536845,0.04700756072998047,4.488246000000001 -550,Regression,Adaptive Model Rules,ChickWeights,22.551700866868885,35.03737693702089,0.717908278090669,0.047034263610839844,4.665796000000001 -561,Regression,Adaptive Model Rules,ChickWeights,23.243872726229487,35.949191367533466,0.7160216409608307,0.04700756072998047,4.846464000000001 -572,Regression,Adaptive Model Rules,ChickWeights,24.092513885911806,37.13693189688246,0.7196752558485364,0.04695415496826172,5.030284000000001 -20,Regression,Adaptive Model Rules,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.053809165954589844,0.005953 -40,Regression,Adaptive Model Rules,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.07615184783935547,0.016156 -60,Regression,Adaptive Model Rules,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.08839702606201172,0.030248999999999998 -80,Regression,Adaptive Model Rules,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.09804439544677734,0.048479999999999995 -100,Regression,Adaptive Model Rules,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.10713481903076172,0.071134 -120,Regression,Adaptive Model Rules,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.11132335662841797,0.098322 -140,Regression,Adaptive Model Rules,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.11333751678466797,0.13009 -160,Regression,Adaptive Model Rules,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.1174459457397461,0.16650700000000002 -180,Regression,Adaptive Model Rules,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.1194601058959961,0.207686 -200,Regression,Adaptive Model Rules,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.017621994018554688,0.26294300000000004 -220,Regression,Adaptive Model Rules,TrumpApproval,2.0738811328897206,4.417664564856108,-3.890594467356201,0.035803794860839844,0.32065000000000005 -240,Regression,Adaptive Model Rules,TrumpApproval,1.9726100065438286,4.2375242409752385,-3.5337340888030546,0.041502952575683594,0.38109000000000004 -260,Regression,Adaptive Model Rules,TrumpApproval,1.8594315384151245,4.074751007989252,-3.248610147038553,0.04884624481201172,0.44441200000000003 -280,Regression,Adaptive Model Rules,TrumpApproval,1.7773205119132678,3.936654153117972,-3.1518424972300867,0.06379222869873047,0.510957 -300,Regression,Adaptive Model Rules,TrumpApproval,1.8265705896173514,3.8591002097544127,-2.923813511442849,0.0735006332397461,0.581026 -320,Regression,Adaptive Model Rules,TrumpApproval,1.7437620649419607,3.7394874649640353,-2.8667745903740336,0.08108043670654297,0.6546050000000001 -340,Regression,Adaptive Model Rules,TrumpApproval,1.7029951846067328,3.640753753244776,-2.8733054628571226,0.0861959457397461,0.7321480000000001 -360,Regression,Adaptive Model Rules,TrumpApproval,1.691125588823449,3.557868621003357,-2.729329365262769,0.09378337860107422,0.8137190000000001 -380,Regression,Adaptive Model Rules,TrumpApproval,1.641476039217788,3.4678199943963026,-2.665503107324644,0.09889888763427734,0.8994270000000001 -400,Regression,Adaptive Model Rules,TrumpApproval,1.6260112424669562,3.3952504336469187,-2.613000890937967,0.10611629486083984,0.9893730000000001 -420,Regression,Adaptive Model Rules,TrumpApproval,1.6289201270983786,3.3343146523246907,-2.599793842225358,0.11011791229248047,1.08339 -440,Regression,Adaptive Model Rules,TrumpApproval,1.6670601236468519,3.302206999442347,-2.466911261860751,0.11576366424560547,1.1816330000000002 -460,Regression,Adaptive Model Rules,TrumpApproval,1.69667104754334,3.2720484626442996,-2.2278892819413008,0.12384700775146484,1.2846220000000002 -480,Regression,Adaptive Model Rules,TrumpApproval,1.6506098779434177,3.2067821053781245,-2.029074572324324,0.13150691986083984,1.3924660000000002 -500,Regression,Adaptive Model Rules,TrumpApproval,1.6365240614669594,3.1603547309397073,-1.8802784791951508,0.07843685150146484,1.5052050000000001 -520,Regression,Adaptive Model Rules,TrumpApproval,1.6536721067944387,3.126560253923372,-1.823930193625598,0.08357906341552734,1.621512 -540,Regression,Adaptive Model Rules,TrumpApproval,1.6698160029512246,3.0946441969309766,-1.7564325082786318,0.0873861312866211,1.745676 -560,Regression,Adaptive Model Rules,TrumpApproval,1.6408841434358417,3.0465865813662636,-1.735141389893172,0.08855342864990234,1.8735 -580,Regression,Adaptive Model Rules,TrumpApproval,1.6127327645791831,2.9996113742580612,-1.7170225021123482,0.08905696868896484,2.004994 -600,Regression,Adaptive Model Rules,TrumpApproval,1.6269498006919803,2.9730823953265535,-1.6396488808638732,0.09091472625732422,2.1402289999999997 -620,Regression,Adaptive Model Rules,TrumpApproval,1.64112955570108,2.949075530135499,-1.5576036781852802,0.0923452377319336,2.2792199999999996 -640,Regression,Adaptive Model Rules,TrumpApproval,1.6562657927450177,2.9273758724267736,-1.4729982020585646,0.09443950653076172,2.4220089999999996 -660,Regression,Adaptive Model Rules,TrumpApproval,1.6610090165740414,2.900076441293188,-1.409637238697782,0.09603023529052734,2.5686609999999996 -680,Regression,Adaptive Model Rules,TrumpApproval,1.640070345532056,2.8623424740678667,-1.3844340745604549,0.09812450408935547,2.7190639999999995 -700,Regression,Adaptive Model Rules,TrumpApproval,1.6119603204138224,2.8240252200668348,-1.3819830911167421,0.10156917572021484,2.8733429999999993 -720,Regression,Adaptive Model Rules,TrumpApproval,1.589173412563986,2.7883164816052854,-1.3731423466582644,0.1027364730834961,3.0315409999999994 -740,Regression,Adaptive Model Rules,TrumpApproval,1.5872474989945902,2.762320631839069,-1.3276973292362433,0.1038503646850586,3.198810999999999 -760,Regression,Adaptive Model Rules,TrumpApproval,1.573860293324891,2.731605449949154,-1.3008801881813965,0.1038503646850586,3.3700799999999993 -780,Regression,Adaptive Model Rules,TrumpApproval,1.5672492734296428,2.7047187411026274,-1.2659143323804294,0.10642147064208984,3.5453349999999992 -800,Regression,Adaptive Model Rules,TrumpApproval,1.5527653312522924,2.676901954415756,-1.2396196471003753,0.10703182220458984,3.7246999999999995 -820,Regression,Adaptive Model Rules,TrumpApproval,1.5366430278321572,2.648967131435787,-1.2172052322327516,0.1085958480834961,3.9080949999999994 -840,Regression,Adaptive Model Rules,TrumpApproval,1.5234128351461855,2.622526466573217,-1.1933402061449065,0.11015987396240234,4.095543999999999 -860,Regression,Adaptive Model Rules,TrumpApproval,1.520997799745444,2.601206078568585,-1.1541054380753062,0.11077022552490234,4.287108999999999 -880,Regression,Adaptive Model Rules,TrumpApproval,1.4988763963538276,2.573388458013685,-1.0978034673380694,0.11124706268310547,4.482816999999999 -900,Regression,Adaptive Model Rules,TrumpApproval,1.4758663089418147,2.54594713286987,-1.061955243276925,0.11164379119873047,4.682644999999999 -920,Regression,Adaptive Model Rules,TrumpApproval,1.4629376967309233,2.523979038782575,-1.059822201667401,0.11167049407958984,4.886850999999999 -940,Regression,Adaptive Model Rules,TrumpApproval,1.4561394845136584,2.50519739840106,-1.0464959899330828,0.11270427703857422,5.100838999999999 -960,Regression,Adaptive Model Rules,TrumpApproval,1.4393535919618172,2.483254475687026,-1.0318268701719249,0.11320781707763672,5.318975999999998 -980,Regression,Adaptive Model Rules,TrumpApproval,1.4209594300543067,2.4596960058574417,-1.0321742156649796,0.11381816864013672,5.541285999999999 -1000,Regression,Adaptive Model Rules,TrumpApproval,1.4020445673510784,2.4364355463770164,-1.027485181852556,0.11442852020263672,5.767785999999998 -11,Regression,Streaming Random Patches,ChickWeights,4.674710287324511,12.709622005759085,-206.93269654300337,0.14386653900146484,0.043578 -22,Regression,Streaming Random Patches,ChickWeights,2.741934273684416,9.017856101646904,-85.12629469646626,0.16807842254638672,0.114891 -33,Regression,Streaming Random Patches,ChickWeights,2.321314094852809,7.424021720293775,-37.27897402435965,0.20960521697998047,0.217395 -44,Regression,Streaming Random Patches,ChickWeights,1.9425031371298072,6.446443185481759,-31.718029761567877,0.24174785614013672,0.352059 -55,Regression,Streaming Random Patches,ChickWeights,2.220127898780405,6.120501061993398,-6.312733162160137,0.30608272552490234,0.516621 -66,Regression,Streaming Random Patches,ChickWeights,2.3297521261863876,5.733717860182345,-1.7664593315707076,0.35672664642333984,0.719974 -77,Regression,Streaming Random Patches,ChickWeights,2.702798931003846,5.8295610878248265,-0.9166874006339529,0.3732900619506836,0.96062 -88,Regression,Streaming Random Patches,ChickWeights,2.6099619817757915,5.526618942218035,-0.5618763668879856,0.41286373138427734,1.239929 -99,Regression,Streaming Random Patches,ChickWeights,2.746820956366501,5.433915350818854,-0.2628661224764999,0.4623746871948242,1.563224 -110,Regression,Streaming Random Patches,ChickWeights,2.8880448046427323,5.393209741308231,0.131281330475993,0.5318593978881836,1.933351 -121,Regression,Streaming Random Patches,ChickWeights,3.102793618966865,5.4667968998241765,0.31256539815016504,0.5604543685913086,2.356966 -132,Regression,Streaming Random Patches,ChickWeights,3.301393875206229,5.777687225912497,0.3724425463943938,0.1946859359741211,2.8345469999999997 -143,Regression,Streaming Random Patches,ChickWeights,3.2818761969220303,5.651296511520541,0.5136946280960023,0.22883892059326172,3.343438 -154,Regression,Streaming Random Patches,ChickWeights,3.2515588511167266,5.5381909018976145,0.6030852117170243,0.2577199935913086,3.884102 -165,Regression,Streaming Random Patches,ChickWeights,3.5490619378471253,5.901472315915889,0.6377005660204649,0.26273250579833984,4.460716 -176,Regression,Streaming Random Patches,ChickWeights,3.7480992047402877,6.114989482363781,0.6863562530103127,0.29411983489990234,5.073099 -187,Regression,Streaming Random Patches,ChickWeights,3.8553217511154356,6.172883744230479,0.7383134393857906,0.3320951461791992,5.716163 -198,Regression,Streaming Random Patches,ChickWeights,4.064611732985858,6.634154210053631,0.7595694090278171,0.25274181365966797,6.400463 -209,Regression,Streaming Random Patches,ChickWeights,4.2854283677061895,7.034301545827039,0.7771654635449772,0.32021617889404297,7.116785 -220,Regression,Streaming Random Patches,ChickWeights,4.87812017832838,8.464168223538273,0.7175347810295998,0.35108089447021484,7.873189 -231,Regression,Streaming Random Patches,ChickWeights,5.139510271881338,8.709209118107985,0.7441975817879227,0.32001399993896484,8.671972 -242,Regression,Streaming Random Patches,ChickWeights,5.512568989147682,9.214318878962654,0.7696009669773758,0.3710927963256836,9.508629 -253,Regression,Streaming Random Patches,ChickWeights,5.61462831814645,9.300369065456374,0.7961404678287027,0.41926097869873047,10.381303 -264,Regression,Streaming Random Patches,ChickWeights,6.307820522062941,10.632794713133398,0.7565488951511344,0.3416013717651367,11.288641 -275,Regression,Streaming Random Patches,ChickWeights,6.788015101176612,11.834244259749067,0.7346291964126817,0.3569021224975586,12.231102 -286,Regression,Streaming Random Patches,ChickWeights,7.083325494446596,12.279756566760542,0.7525262008661281,0.39655208587646484,13.20812 -297,Regression,Streaming Random Patches,ChickWeights,7.191320030958258,12.343414248948324,0.7831373025283351,0.4258260726928711,14.218150999999999 -308,Regression,Streaming Random Patches,ChickWeights,7.797209174725968,13.278742843330225,0.7764780945249113,0.45011425018310547,15.260696 -319,Regression,Streaming Random Patches,ChickWeights,8.817827198046418,15.969694059781501,0.7066209046531168,0.4718656539916992,16.339578 -330,Regression,Streaming Random Patches,ChickWeights,9.357743221083844,16.830542617885925,0.7211345584533546,0.3236379623413086,17.459623999999998 -341,Regression,Streaming Random Patches,ChickWeights,9.597227793355115,16.978620563243194,0.7458759585671038,0.3676939010620117,18.612920999999996 -352,Regression,Streaming Random Patches,ChickWeights,9.737351596839199,17.046818432657442,0.7671306392320572,0.36295223236083984,19.795535999999995 -363,Regression,Streaming Random Patches,ChickWeights,10.474784553852622,18.420082712462506,0.7359718490826586,0.3571195602416992,21.012741999999996 -374,Regression,Streaming Random Patches,ChickWeights,11.136965297216438,20.001694167276444,0.7138145773791849,0.39987850189208984,22.263944999999996 -385,Regression,Streaming Random Patches,ChickWeights,11.663755213802826,20.723109459955122,0.7282041838356077,0.3288450241088867,23.553921999999996 -396,Regression,Streaming Random Patches,ChickWeights,11.794892571100942,20.71536814309967,0.7467690419180344,0.4024953842163086,24.874664999999997 -407,Regression,Streaming Random Patches,ChickWeights,12.7361976076631,22.396840643020628,0.7248523596518419,0.4475545883178711,26.248018999999996 -418,Regression,Streaming Random Patches,ChickWeights,13.669165628181593,24.17119440887557,0.6991706950778176,0.4968290328979492,27.657995999999997 -429,Regression,Streaming Random Patches,ChickWeights,14.21091951782044,24.907688885339496,0.7161359437094021,0.5376424789428711,29.100111 -440,Regression,Streaming Random Patches,ChickWeights,14.453337266265764,25.002813963201465,0.7342091543116155,0.5657072067260742,30.577019 -451,Regression,Streaming Random Patches,ChickWeights,14.795462221424788,25.396591749368834,0.7374288341656263,0.25832653045654297,32.092868 -462,Regression,Streaming Random Patches,ChickWeights,16.121556592117344,28.147488646444906,0.6970529793362255,0.3047628402709961,33.634783000000006 -473,Regression,Streaming Random Patches,ChickWeights,16.919755395139642,29.31210430009691,0.7099030173412568,0.3544912338256836,35.20760200000001 -484,Regression,Streaming Random Patches,ChickWeights,17.222944705236912,29.519121223412064,0.7219133474565596,0.39832592010498047,36.808400000000006 -495,Regression,Streaming Random Patches,ChickWeights,17.875632407347712,30.34841895302658,0.7235012910446976,0.43242549896240234,38.43633400000001 -506,Regression,Streaming Random Patches,ChickWeights,18.888217233083253,31.835585198521954,0.7045822227904736,0.46784114837646484,40.09290200000001 -517,Regression,Streaming Random Patches,ChickWeights,19.824673890348247,33.232962754926376,0.6962090391160322,0.49755001068115234,41.78457100000001 -528,Regression,Streaming Random Patches,ChickWeights,20.55396214529221,34.09704280994323,0.712478586733055,0.5351285934448242,43.50851800000001 -539,Regression,Streaming Random Patches,ChickWeights,20.794926737672743,34.209729391662115,0.7225264054992676,0.576685905456543,45.26814300000001 -550,Regression,Streaming Random Patches,ChickWeights,21.840113545070352,36.13121679398973,0.7000199686285788,0.4753904342651367,47.07007900000001 -561,Regression,Streaming Random Patches,ChickWeights,22.59682566601873,37.083244303938294,0.6978222813826447,0.5189352035522461,48.90757000000001 -572,Regression,Streaming Random Patches,ChickWeights,23.516206925607502,38.207190389030345,0.703284935102584,0.5585355758666992,50.782863000000006 -20,Regression,Streaming Random Patches,TrumpApproval,2.677140920600926,9.804891856735377,-224.4966127051096,0.2373647689819336,0.10297 -40,Regression,Streaming Random Patches,TrumpApproval,2.2277778132201114,7.083306817310631,-19.171465983096805,0.3270711898803711,0.289755 -60,Regression,Streaming Random Patches,TrumpApproval,1.615301860635012,5.7908261762165685,-17.175707266102673,0.34931087493896484,0.561259 -80,Regression,Streaming Random Patches,TrumpApproval,1.3541232617236425,5.026566774725167,-12.73715546617699,0.3968191146850586,0.918423 -100,Regression,Streaming Random Patches,TrumpApproval,1.2177336486572592,4.516233376106315,-5.957872973758095,0.41078853607177734,1.362708 -120,Regression,Streaming Random Patches,TrumpApproval,1.1154353306650457,4.135716354504269,-3.949887064372274,0.4562673568725586,1.881933 -140,Regression,Streaming Random Patches,TrumpApproval,1.2694693870086102,4.857381406328817,-6.05598089151511,0.20268917083740234,2.479905 -160,Regression,Streaming Random Patches,TrumpApproval,1.1977296704805873,4.553187715474859,-4.4675320024162675,0.32157421112060547,3.138645 -180,Regression,Streaming Random Patches,TrumpApproval,1.1382354235034091,4.3021116541255315,-3.2869252353070264,0.4017667770385742,3.869636 -200,Regression,Streaming Random Patches,TrumpApproval,1.0619299314905082,4.083716699099621,-2.8742107303306135,0.47729015350341797,4.674132999999999 -220,Regression,Streaming Random Patches,TrumpApproval,1.0098981716710287,3.898715068147509,-2.8090719143731833,0.5200605392456055,5.552957999999999 -240,Regression,Streaming Random Patches,TrumpApproval,0.9646445055554522,3.736185930434801,-2.5244277699140394,0.590418815612793,6.501358999999999 -260,Regression,Streaming Random Patches,TrumpApproval,0.9172332240820483,3.5913301496738597,-2.300314999970906,0.677699089050293,7.522147999999999 -280,Regression,Streaming Random Patches,TrumpApproval,0.8713372213927624,3.4617932319016353,-2.2106176891844487,0.7459287643432617,8.615362 -300,Regression,Streaming Random Patches,TrumpApproval,0.8589405406556339,3.350216865329824,-1.9572094631572514,0.8300580978393555,9.791068 -320,Regression,Streaming Random Patches,TrumpApproval,0.8332253596869799,3.2462964384589745,-1.9140771338193199,0.8134641647338867,11.051060999999999 -340,Regression,Streaming Random Patches,TrumpApproval,0.8452804825423809,3.1781241500612394,-1.9514868559348706,0.7938528060913086,12.398121 -360,Regression,Streaming Random Patches,TrumpApproval,0.8633895166787324,3.1040171399305283,-1.8385669899509445,0.8660383224487305,13.839894 -380,Regression,Streaming Random Patches,TrumpApproval,0.8513159306308952,3.0258511220091995,-1.790715802132521,0.9304952621459961,15.372487 -400,Regression,Streaming Random Patches,TrumpApproval,0.8510161564157176,2.9641796059686003,-1.7538068241726803,0.9046812057495117,17.004842 -420,Regression,Streaming Random Patches,TrumpApproval,0.8518447067444684,2.91289276041272,-1.747346648183163,0.2984609603881836,18.737382 -440,Regression,Streaming Random Patches,TrumpApproval,0.8395925714721912,2.8496340177457244,-1.5817388594042834,0.3453207015991211,20.534513 -460,Regression,Streaming Random Patches,TrumpApproval,0.8276812048623222,2.790693875069675,-1.3480296319058032,0.3807516098022461,22.405168 -480,Regression,Streaming Random Patches,TrumpApproval,0.8089244758990292,2.7333778802045163,-1.2007484949677454,0.44824886322021484,24.346508 -500,Regression,Streaming Random Patches,TrumpApproval,0.7928829832001318,2.679994202833863,-1.0712404739849797,0.49751949310302734,26.364916 -520,Regression,Streaming Random Patches,TrumpApproval,0.7968627360462042,2.655906961990266,-1.037727311679443,0.37538814544677734,28.465481 -540,Regression,Streaming Random Patches,TrumpApproval,0.7742648164612971,2.606576025481956,-0.9555400217068246,0.40636730194091797,30.635854000000002 -560,Regression,Streaming Random Patches,TrumpApproval,0.7686196547282114,2.5632583130611786,-0.9361432250468023,0.43744945526123047,32.873421 -580,Regression,Streaming Random Patches,TrumpApproval,0.7604339230100455,2.522208925126442,-0.9209911676265596,0.49677181243896484,35.174013 -600,Regression,Streaming Random Patches,TrumpApproval,0.7505205215384793,2.4816305596178174,-0.839105014250833,0.569575309753418,37.541938 -620,Regression,Streaming Random Patches,TrumpApproval,0.7415649894603582,2.4434454055931187,-0.7557664113175795,0.6584272384643555,39.976025 -640,Regression,Streaming Random Patches,TrumpApproval,0.7318408439896782,2.4068861956825156,-0.6717754169622376,0.7120962142944336,42.479937 -660,Regression,Streaming Random Patches,TrumpApproval,0.7165688958291918,2.37061113672817,-0.6101020988109156,0.8089780807495117,45.058017 -680,Regression,Streaming Random Patches,TrumpApproval,0.7019759043195819,2.336022363731008,-0.5881668313132071,0.8398160934448242,47.720249 -700,Regression,Streaming Random Patches,TrumpApproval,0.7046161229979929,2.307765546298159,-0.5906876272832315,0.9275884628295898,50.471651 -720,Regression,Streaming Random Patches,TrumpApproval,0.699791115281057,2.2802792890719745,-0.5871418479715558,0.9087285995483398,53.310919 -740,Regression,Streaming Random Patches,TrumpApproval,0.6993154625867034,2.2532668970074776,-0.5488294820654547,0.8719320297241211,56.236366 -760,Regression,Streaming Random Patches,TrumpApproval,0.6904813287783395,2.224703478329614,-0.5261679577762028,0.9295186996459961,59.248630999999996 -780,Regression,Streaming Random Patches,TrumpApproval,0.6809571474431378,2.1968956754751456,-0.4949206250779803,1.0446271896362305,62.34821899999999 -800,Regression,Streaming Random Patches,TrumpApproval,0.6801944660295238,2.1726014141558028,-0.4752630155166446,1.1031560897827148,65.54039399999999 -820,Regression,Streaming Random Patches,TrumpApproval,0.6800251543540311,2.1538267014914845,-0.46579848690850234,1.0328702926635742,68.82165799999999 -840,Regression,Streaming Random Patches,TrumpApproval,0.6756860923792902,2.1342936501586,-0.4526954783446735,0.7475957870483398,72.18837399999998 -860,Regression,Streaming Random Patches,TrumpApproval,0.6733560632024954,2.111426905397114,-0.41928475998502446,0.8119535446166992,75.62699399999998 -880,Regression,Streaming Random Patches,TrumpApproval,0.6699617254268444,2.0889651464935617,-0.38234513818638316,0.8655576705932617,79.14156799999998 -900,Regression,Streaming Random Patches,TrumpApproval,0.6641763033522209,2.070781028281848,-0.3641082228169974,0.8467855453491211,82.73512999999998 -920,Regression,Streaming Random Patches,TrumpApproval,0.6581948551182502,2.0492186837607402,-0.35779705060721523,0.9400205612182617,86.40810799999998 -940,Regression,Streaming Random Patches,TrumpApproval,0.6518145768911946,2.0285221107553344,-0.3417959776408932,0.8168668746948242,90.15720199999998 -960,Regression,Streaming Random Patches,TrumpApproval,0.6459384626205039,2.0083339154310043,-0.328972794111009,0.9120321273803711,93.98330699999998 -980,Regression,Streaming Random Patches,TrumpApproval,0.6434185155449816,1.9895342463135757,-0.3295384550375804,0.9782476425170898,97.88845799999999 -1000,Regression,Streaming Random Patches,TrumpApproval,0.6405613133684143,1.9713432953350207,-0.32730996532857737,1.0593442916870117,101.87257699999999 -11,Regression,Bagging,ChickWeights,10.961585696594295,17.742218537059934,-404.203665453531,0.1782550811767578,0.00837 -22,Regression,Bagging,ChickWeights,5.889263372306967,12.56756036562654,-166.2750319368382,0.1910533905029297,0.024461000000000004 -33,Regression,Bagging,ChickWeights,4.385855416063054,10.300574999811516,-72.68935558725912,0.22809410095214844,0.048530000000000004 -44,Regression,Bagging,ChickWeights,3.447994945519213,8.931729546653537,-61.80843214992921,0.2438068389892578,0.081553 -55,Regression,Bagging,ChickWeights,3.35253400433443,8.24824849102831,-12.280953122644297,0.2916851043701172,0.123768 -66,Regression,Bagging,ChickWeights,3.890626377385135,8.046318086318358,-4.448112248710658,0.3612346649169922,0.176552 -77,Regression,Bagging,ChickWeights,4.3386080839549335,7.968508799771546,-2.5812421382870157,0.4169635772705078,0.24268299999999998 -88,Regression,Bagging,ChickWeights,4.490657725713863,7.741140850532905,-2.064344233357061,0.4634113311767578,0.322916 -99,Regression,Bagging,ChickWeights,4.7848487383098295,7.7069639973194795,-1.5403773740733393,0.4844684600830078,0.418203 -110,Regression,Bagging,ChickWeights,4.819508170706054,7.5258766681538125,-0.6916040970280823,0.5139484405517578,0.528794 -121,Regression,Bagging,ChickWeights,4.831868630589585,7.4067264415397105,-0.26188018024053505,0.5271091461181641,0.654758 -132,Regression,Bagging,ChickWeights,4.689813959013141,7.185628463196342,0.029322522663730477,0.5355319976806641,0.796727 -143,Regression,Bagging,ChickWeights,4.575735548989308,6.988996035323699,0.25622347620189123,0.5445156097412109,0.954849 -154,Regression,Bagging,ChickWeights,4.5047654468276,6.840884466051986,0.3943998861813385,0.5490932464599609,1.128949 -165,Regression,Bagging,ChickWeights,4.788922390491115,7.112527411058041,0.4737467226899117,0.5519008636474609,1.318602 -176,Regression,Bagging,ChickWeights,4.970710142849908,7.252656159084941,0.5587960603089545,0.5291376113891602,1.527133 -187,Regression,Bagging,ChickWeights,5.027942129925975,7.276897995624471,0.6363381045701173,0.5012483596801758,1.7549830000000002 -198,Regression,Bagging,ChickWeights,5.049038224539579,7.28413559977408,0.7101491068659838,0.3717927932739258,2.012416 -209,Regression,Bagging,ChickWeights,5.250417000807892,7.635375364934655,0.7374564682191251,0.3473329544067383,2.287127 -220,Regression,Bagging,ChickWeights,5.8380227010176045,8.722095838276593,0.7000574251839753,0.3545808792114258,2.5755529999999998 -231,Regression,Bagging,ChickWeights,6.044258003678201,8.960072203538655,0.7292489015049135,0.39151477813720703,2.875072 -242,Regression,Bagging,ChickWeights,6.276524828158362,9.354066864400572,0.7625593259413487,0.4183511734008789,3.187566 -253,Regression,Bagging,ChickWeights,6.3739207187481774,9.482128758285446,0.7880944389945109,0.43993473052978516,3.5127859999999997 -264,Regression,Bagging,ChickWeights,6.979732945669537,10.70081988088543,0.7534238884125567,0.45258426666259766,3.851311 -275,Regression,Bagging,ChickWeights,7.440978636214291,11.778455343325094,0.7371253175053303,0.4610300064086914,4.203753 -286,Regression,Bagging,ChickWeights,7.722695321089189,12.251182899165162,0.7536765505554787,0.47004032135009766,4.570129 -297,Regression,Bagging,ChickWeights,7.706764271589622,12.242915074252833,0.7866542868859879,0.47332096099853516,4.950063 -308,Regression,Bagging,ChickWeights,8.246398153738868,13.148346181255997,0.7808464902384371,0.4770059585571289,5.342659 -319,Regression,Bagging,ChickWeights,9.3474227240372,15.837966045238385,0.7114408913609181,0.4802255630493164,5.749268000000001 -330,Regression,Bagging,ChickWeights,9.817205251980441,16.4984558080814,0.732030690326682,0.4911470413208008,6.171356000000001 -341,Regression,Bagging,ChickWeights,10.029686613445753,16.618597428793866,0.7565388422982208,0.49631214141845703,6.607662000000001 -352,Regression,Bagging,ChickWeights,10.183424974922595,16.72954607175143,0.7757182197717174,0.5041093826293945,7.058783000000001 -363,Regression,Bagging,ChickWeights,11.087226456936445,18.183293641227465,0.7427163510168195,0.5065813064575195,7.524030000000001 -374,Regression,Bagging,ChickWeights,11.611208342142504,19.353368352464965,0.7320664683002076,0.4931306838989258,8.007171000000001 -385,Regression,Bagging,ChickWeights,12.01727910935522,20.0211446068419,0.7463056879958478,0.4681062698364258,8.510702000000002 -396,Regression,Bagging,ChickWeights,12.181393533243165,20.04691533881536,0.7628481050767555,0.4353647232055664,9.035917000000001 -407,Regression,Bagging,ChickWeights,13.09061742270854,21.647454224788547,0.7429569103775002,0.42530155181884766,9.583169000000002 -418,Regression,Bagging,ChickWeights,13.857583790923206,23.134723844753264,0.7244169153448246,0.44881534576416016,10.147004 -429,Regression,Bagging,ChickWeights,14.415812868423222,24.060867034272455,0.7351096813998451,0.4820413589477539,10.724926 -440,Regression,Bagging,ChickWeights,14.633210713365676,24.146050913573614,0.7521125932778163,0.5092172622680664,11.318068 -451,Regression,Bagging,ChickWeights,15.133020029766048,24.73830229788057,0.7508643131645836,0.5367746353149414,11.926425 -462,Regression,Bagging,ChickWeights,16.308165732303497,27.095114472384648,0.7192825818530082,0.5905351638793945,12.55391 -473,Regression,Bagging,ChickWeights,17.097647362788752,28.427463452620437,0.7271490710401121,0.6096200942993164,13.198999 -484,Regression,Bagging,ChickWeights,17.403233741388874,28.60630060223112,0.7388459944901757,0.6277017593383789,13.862263 -495,Regression,Bagging,ChickWeights,17.845914876737442,29.12415998011611,0.7453593218363636,0.6518182754516602,14.544130000000001 -506,Regression,Bagging,ChickWeights,18.749535538879957,30.62874049978988,0.7265554777237875,0.6593713760375977,15.245298000000002 -517,Regression,Bagging,ChickWeights,19.47487310339954,31.618292560451803,0.7250121198400961,0.6093225479125977,15.967903000000002 -528,Regression,Bagging,ChickWeights,20.253849759201476,32.7838221306768,0.7341994138392889,0.6206216812133789,16.707851 -539,Regression,Bagging,ChickWeights,20.434012229678878,32.81136182644564,0.7447469767293837,0.6340532302856445,17.46759 -550,Regression,Bagging,ChickWeights,21.48271816680656,34.486522216355176,0.7267085961280999,0.6409807205200195,18.246304000000002 -561,Regression,Bagging,ChickWeights,22.214948014440754,35.44362766111904,0.7239528137855742,0.6374177932739258,19.046772 -572,Regression,Bagging,ChickWeights,23.05953013824058,36.58622235196899,0.7279275728793118,0.6435747146606445,19.865794 -20,Regression,Bagging,TrumpApproval,6.585839365171216,13.881516159864546,-450.9893611410228,0.4141368865966797,0.017776 -40,Regression,Bagging,TrumpApproval,4.363712565198605,9.938671227284793,-38.712022235644,0.5953998565673828,0.05778 -60,Regression,Bagging,TrumpApproval,3.1246200328296143,8.126568674182735,-34.79519075888522,0.7211894989013672,0.123528 -80,Regression,Bagging,TrumpApproval,2.5854221987839483,7.070456887280416,-26.179962781345687,0.8254451751708984,0.217362 -100,Regression,Bagging,TrumpApproval,2.612795341566278,6.441452396131832,-13.15439617190778,0.9287128448486328,0.340609 -120,Regression,Bagging,TrumpApproval,2.5650159676967723,5.964319967024998,-9.294746752112971,0.9687213897705078,0.493962 -140,Regression,Bagging,TrumpApproval,2.4246087892523565,5.555151474129083,-8.228790661275799,0.9892597198486328,0.677921 -160,Regression,Bagging,TrumpApproval,2.2372944440918734,5.2113826313001965,-6.162524907472281,1.0282306671142578,0.8947890000000001 -180,Regression,Bagging,TrumpApproval,2.1466345389477697,4.9537003451084844,-4.683842281929501,0.9880685806274414,1.151076 -200,Regression,Bagging,TrumpApproval,1.9892950861944354,4.704683592711833,-4.14200943628533,0.5611734390258789,1.4770729999999999 -220,Regression,Bagging,TrumpApproval,1.8962186057938935,4.4991342088580515,-4.072640393025983,0.39672183990478516,1.8417389999999998 -240,Regression,Bagging,TrumpApproval,1.803503053095204,4.314740779926486,-3.700467691841136,0.45048999786376953,2.2257529999999996 -260,Regression,Bagging,TrumpApproval,1.7080100607704198,4.14999374436193,-3.406965132392963,0.5133523941040039,2.6310909999999996 -280,Regression,Bagging,TrumpApproval,1.6655932342441322,4.017842470146439,-3.324861014485008,0.6190156936645508,3.0590589999999995 -300,Regression,Bagging,TrumpApproval,1.6385421968346507,3.904974440220909,-3.01765496812087,0.7099161148071289,3.5109909999999998 -320,Regression,Bagging,TrumpApproval,1.5504197008133211,3.781681667850968,-2.954527764504537,0.777043342590332,3.9877279999999997 -340,Regression,Bagging,TrumpApproval,1.5047182142648992,3.6774767295200856,-2.9518368180177696,0.8162164688110352,4.4912399999999995 -360,Regression,Bagging,TrumpApproval,1.5167434089128324,3.599404249538014,-2.8169122586309756,0.8943338394165039,5.022537 -380,Regression,Bagging,TrumpApproval,1.47690532831993,3.5095784857397434,-2.754312484791544,0.9456682205200195,5.5820669999999994 -400,Regression,Bagging,TrumpApproval,1.4473785178172314,3.4281100357630656,-2.683273334840131,1.0103578567504883,6.1727229999999995 -420,Regression,Bagging,TrumpApproval,1.40955398667131,3.3497394826483737,-2.633176800681623,0.9896020889282227,6.801780999999999 -440,Regression,Bagging,TrumpApproval,1.3766290707405415,3.2780180874471507,-2.4163065189839363,1.0508508682250977,7.462708999999999 -460,Regression,Bagging,TrumpApproval,1.3473843223583597,3.2105206765417766,-2.1076358108912725,1.1290063858032227,8.158304 -480,Regression,Bagging,TrumpApproval,1.3146601146346069,3.145698486393554,-1.914776430829721,1.2007226943969727,8.887782 -500,Regression,Bagging,TrumpApproval,1.2829933656636,3.0848353870921095,-1.7442697801193798,1.2468271255493164,9.6533 -520,Regression,Bagging,TrumpApproval,1.253253159364902,3.027195393916086,-1.647288417049146,1.2976083755493164,10.456286 -540,Regression,Bagging,TrumpApproval,1.221748892460797,2.9719574458877616,-1.5422080467663055,1.2989130020141602,11.302161 -560,Regression,Bagging,TrumpApproval,1.2084725532442826,2.924408360153415,-1.5201637785773734,1.2362565994262695,12.194852 -580,Regression,Bagging,TrumpApproval,1.1798260089354877,2.8751438072223436,-1.496217338554359,1.0948266983032227,13.14623 -600,Regression,Bagging,TrumpApproval,1.1697523000107772,2.832108990371036,-1.3952574337441268,0.9711389541625977,14.161434999999999 -620,Regression,Bagging,TrumpApproval,1.1550049446613664,2.791430677298195,-1.29147514335963,0.9871377944946289,15.218796 -640,Regression,Bagging,TrumpApproval,1.1314798119108302,2.748614104980716,-1.180190104825623,0.955540657043457,16.316332 -660,Regression,Bagging,TrumpApproval,1.1121913646065007,2.708986615305391,-1.102550782410015,0.9793291091918945,17.449759 -680,Regression,Bagging,TrumpApproval,1.096472283083769,2.6715401075955145,-1.0771388394715324,1.0325212478637695,18.614745 -700,Regression,Bagging,TrumpApproval,1.0884657542544798,2.639348967980305,-1.080631470653255,1.1148195266723633,19.813024 -720,Regression,Bagging,TrumpApproval,1.0667544669913536,2.603003384812668,-1.0681837571806945,1.1698732376098633,21.046602999999998 -740,Regression,Bagging,TrumpApproval,1.0459321867530782,2.5682868281355016,-1.0121733037883445,1.2092561721801758,22.315315 -760,Regression,Bagging,TrumpApproval,1.0276781933457848,2.5353029000371494,-0.9820644398878615,1.2420778274536133,23.623046 -780,Regression,Bagging,TrumpApproval,1.0094276125302497,2.50338787783782,-0.9411341749539492,1.3017473220825195,24.968597 -800,Regression,Bagging,TrumpApproval,1.0023537746213038,2.475512975868657,-0.9153129864924485,1.3318758010864258,26.357820999999998 -820,Regression,Bagging,TrumpApproval,0.9977235614586971,2.450813157375169,-0.8978992844128302,1.3786516189575195,27.784761999999997 -840,Regression,Bagging,TrumpApproval,0.9889333901170725,2.4246168260512984,-0.8747893145143344,1.416356086730957,29.251495999999996 -860,Regression,Bagging,TrumpApproval,0.9712423333697714,2.3967734443047704,-0.8288218633529623,1.4480867385864258,30.757563999999995 -880,Regression,Bagging,TrumpApproval,0.9593965092028914,2.3708401769846086,-0.7805683851951253,1.4688615798950195,32.305296999999996 -900,Regression,Bagging,TrumpApproval,0.9500350341868696,2.3460297253016638,-0.7508441641708208,1.3691072463989258,33.916340999999996 -920,Regression,Bagging,TrumpApproval,0.9362562616709212,2.321054413049932,-0.7419227486050888,1.2137422561645508,35.592853999999996 -940,Regression,Bagging,TrumpApproval,0.9293426247327622,2.2986373047272863,-0.7229310397545319,1.2394838333129883,37.31016399999999 -960,Regression,Bagging,TrumpApproval,0.9223382441623138,2.2770208564096435,-0.7083555493245846,1.2827730178833008,39.06327099999999 -980,Regression,Bagging,TrumpApproval,0.9155630176835056,2.2562776680559558,-0.7099489887647161,1.2986268997192383,40.85878199999999 -1000,Regression,Bagging,TrumpApproval,0.9044154317407225,2.234829366963466,-0.7058332444386559,1.3350114822387695,42.69044199999999 -11,Regression,Exponentially Weighted Average,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.05248260498046875,0.006254 -22,Regression,Exponentially Weighted Average,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.059535980224609375,0.016967 -33,Regression,Exponentially Weighted Average,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.07399749755859375,0.032538 -44,Regression,Exponentially Weighted Average,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.08022689819335938,0.05351499999999999 -55,Regression,Exponentially Weighted Average,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.09786605834960938,0.08048899999999999 -66,Regression,Exponentially Weighted Average,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.121429443359375,0.11473799999999999 -77,Regression,Exponentially Weighted Average,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.13842391967773438,0.15747899999999998 -88,Regression,Exponentially Weighted Average,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.15406036376953125,0.209356 -99,Regression,Exponentially Weighted Average,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.1602935791015625,0.271082 -110,Regression,Exponentially Weighted Average,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.164306640625,0.34316199999999997 -121,Regression,Exponentially Weighted Average,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.16500091552734375,0.42516599999999993 -132,Regression,Exponentially Weighted Average,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.165863037109375,0.5168919999999999 -143,Regression,Exponentially Weighted Average,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.13225841522216797,0.622087 -154,Regression,Exponentially Weighted Average,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.14046764373779297,0.736412 -165,Regression,Exponentially Weighted Average,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.1464834213256836,0.8597999999999999 -176,Regression,Exponentially Weighted Average,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.15288448333740234,0.9928599999999999 -187,Regression,Exponentially Weighted Average,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.15793132781982422,1.135584 -198,Regression,Exponentially Weighted Average,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.16001415252685547,1.287864 -209,Regression,Exponentially Weighted Average,ChickWeights,57.5645933014354,59.46206220864915,-14.922837840066967,0.12554073333740234,1.4549239999999999 -220,Regression,Exponentially Weighted Average,ChickWeights,58.69090909090908,60.81327606250582,-13.581197962556498,0.13231182098388672,1.6307399999999999 -231,Regression,Exponentially Weighted Average,ChickWeights,60.25541125541125,62.66764529032318,-12.244451024360147,0.13700389862060547,1.8153519999999999 -242,Regression,Exponentially Weighted Average,ChickWeights,62.17355371900826,65.06963847478845,-10.489760184397113,0.14158916473388672,2.009009 -253,Regression,Exponentially Weighted Average,ChickWeights,63.936758893280626,67.17295239601157,-9.634560128382748,0.14522075653076172,2.211595 -264,Regression,Exponentially Weighted Average,ChickWeights,65.10606060606062,68.57980310513724,-9.127665748505592,0.14588451385498047,2.423132 -275,Regression,Exponentially Weighted Average,ChickWeights,66.61454545454548,70.46451073219248,-8.408339126213217,0.14593791961669922,2.643805 -286,Regression,Exponentially Weighted Average,ChickWeights,68.48951048951052,72.8020594498525,-7.6983532427125105,0.1282644271850586,2.877713 -297,Regression,Exponentially Weighted Average,ChickWeights,70.55218855218858,75.3669362796119,-7.08492451355157,0.13004207611083984,3.120317 -308,Regression,Exponentially Weighted Average,ChickWeights,72.39285714285718,77.65033596401675,-6.643510181414674,0.1343069076538086,3.37158 -319,Regression,Exponentially Weighted Average,ChickWeights,73.45454545454551,79.15086186624424,-6.206879640065647,0.14070415496826172,3.631653 -330,Regression,Exponentially Weighted Average,ChickWeights,75.77878787878792,82.20832738177494,-5.653192449779911,0.14494609832763672,3.900744 -341,Regression,Exponentially Weighted Average,ChickWeights,77.92375366568919,84.89106353805269,-5.352795814687307,0.1466054916381836,4.1790140000000005 -352,Regression,Exponentially Weighted Average,ChickWeights,80.04545454545458,87.49376601169416,-5.134510311668016,0.14665889739990234,4.46626 -363,Regression,Exponentially Weighted Average,ChickWeights,80.99724517906337,88.57562798692558,-5.105139086016474,0.14615535736083984,4.762509 -374,Regression,Exponentially Weighted Average,ChickWeights,82.77807486631018,90.83029071422122,-4.901675845817959,0.15692806243896484,5.06867 -385,Regression,Exponentially Weighted Average,ChickWeights,85.1766233766234,93.99517810235533,-4.591702735915359,0.16295909881591797,5.384646 -396,Regression,Exponentially Weighted Average,ChickWeights,87.26767676767678,96.48964983485283,-4.494054297851511,0.16985607147216797,5.710516 -407,Regression,Exponentially Weighted Average,ChickWeights,89.00737100737103,98.71879502607636,-4.345544683073043,0.1741170883178711,6.048375 -418,Regression,Exponentially Weighted Average,ChickWeights,90.57416267942587,100.72635724110245,-4.224084264201084,0.17566967010498047,6.396311 -429,Regression,Exponentially Weighted Average,ChickWeights,93.12121212121215,104.19735398794236,-3.9677178403495805,0.17532634735107422,6.754458 -440,Regression,Exponentially Weighted Average,ChickWeights,95.41818181818185,107.03565676064125,-3.8710119659250104,0.15803813934326172,7.124987 -451,Regression,Exponentially Weighted Average,ChickWeights,97.16629711751665,109.07665280092142,-3.843505105397095,0.16715145111083984,7.505263 -462,Regression,Exponentially Weighted Average,ChickWeights,98.71645021645023,111.17636431671961,-3.72620239405422,0.17374706268310547,7.89547 -473,Regression,Exponentially Weighted Average,ChickWeights,101.54122621564484,115.20584573786859,-3.4812404756668593,0.17859935760498047,8.295795 -484,Regression,Exponentially Weighted Average,ChickWeights,103.77066115702482,117.90601559037043,-3.4365483842712585,0.18236827850341797,8.706368 -495,Regression,Exponentially Weighted Average,ChickWeights,106.02424242424244,120.71525892518193,-3.37467008920777,0.1661062240600586,9.13027 -506,Regression,Exponentially Weighted Average,ChickWeights,107.31620553359684,122.26004165941237,-3.356924458603192,0.17095088958740234,9.564033 -517,Regression,Exponentially Weighted Average,ChickWeights,109.39651837524178,124.91233289427785,-3.291877964737682,0.17259502410888672,10.007821 -528,Regression,Exponentially Weighted Average,ChickWeights,112.36553030303028,129.1106745698386,-3.1225038051323804,0.17818355560302734,10.461943999999999 -539,Regression,Exponentially Weighted Average,ChickWeights,114.52504638218922,131.65752925403248,-3.109734667916423,0.18294811248779297,10.926196 -550,Regression,Exponentially Weighted Average,ChickWeights,115.89999999999996,133.35909826820617,-3.0866973064470367,0.18257808685302734,11.400602999999998 -561,Regression,Exponentially Weighted Average,ChickWeights,117.86452762923346,135.8046463151548,-3.0526234314410727,0.18224620819091797,11.885435 -572,Regression,Exponentially Weighted Average,ChickWeights,120.54020979020974,139.4624607986965,-2.953338846956928,0.18338680267333984,12.380554 -20,Regression,Exponentially Weighted Average,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.1279449462890625,0.015909 -40,Regression,Exponentially Weighted Average,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.18556594848632812,0.053644 -60,Regression,Exponentially Weighted Average,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.22249984741210938,0.118601 -80,Regression,Exponentially Weighted Average,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.25476837158203125,0.215522 -100,Regression,Exponentially Weighted Average,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.28536224365234375,0.349036 -120,Regression,Exponentially Weighted Average,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.29428863525390625,0.520816 -140,Regression,Exponentially Weighted Average,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.2989387512207031,0.713599 -160,Regression,Exponentially Weighted Average,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.23287487030029297,0.934381 -180,Regression,Exponentially Weighted Average,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.25080013275146484,1.175315 -200,Regression,Exponentially Weighted Average,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.1596212387084961,1.445583 -220,Regression,Exponentially Weighted Average,TrumpApproval,39.727043136363626,39.77723500009918,-395.50198072931875,0.18315410614013672,1.734898 -240,Regression,Exponentially Weighted Average,TrumpApproval,39.56323079166665,39.61325406766278,-395.19837684116754,0.19051647186279297,2.043551 -260,Regression,Exponentially Weighted Average,TrumpApproval,39.42014538461535,39.46968290441584,-397.63185900832246,0.20207500457763672,2.3716209999999998 -280,Regression,Exponentially Weighted Average,TrumpApproval,39.33200189285712,39.37942345737111,-414.45601593500356,0.22603130340576172,2.7200119999999997 -300,Regression,Exponentially Weighted Average,TrumpApproval,39.18435719999999,39.23275803924839,-404.5402138221895,0.24378108978271484,3.0886229999999997 -320,Regression,Exponentially Weighted Average,TrumpApproval,39.13568690624999,39.1818628962716,-423.5167725219512,0.2581815719604492,3.4776649999999996 -340,Regression,Exponentially Weighted Average,TrumpApproval,39.14620944117645,39.18989510023786,-447.7943063391533,0.2493734359741211,3.8925269999999994 -360,Regression,Exponentially Weighted Average,TrumpApproval,39.24072974999997,39.28395553300239,-453.6543473793619,0.26435375213623047,4.328145999999999 -380,Regression,Exponentially Weighted Average,TrumpApproval,39.29597665789471,39.337699215460226,-470.6701690846498,0.2747945785522461,4.784845 -400,Regression,Exponentially Weighted Average,TrumpApproval,39.35730624999997,39.39781946688104,-485.4842825426507,0.2898855209350586,5.262664 -420,Regression,Exponentially Weighted Average,TrumpApproval,39.40549083333331,39.44465897881697,-502.77995042269276,0.2975950241088867,5.761624 -440,Regression,Exponentially Weighted Average,TrumpApproval,39.49730674999998,39.53710368662846,-495.9856416828035,0.30903148651123047,6.281948 -460,Regression,Exponentially Weighted Average,TrumpApproval,39.61474728260867,39.65658853240579,-473.14358309219216,0.3255758285522461,6.824272 -480,Regression,Exponentially Weighted Average,TrumpApproval,39.71032456249997,39.753047582709755,-464.4916761787406,0.34113216400146484,7.389190999999999 -500,Regression,Exponentially Weighted Average,TrumpApproval,39.80313951999997,39.84667590965187,-456.87508245086684,0.27970027923583984,7.9812069999999995 -520,Regression,Exponentially Weighted Average,TrumpApproval,39.873547134615364,39.916931033645376,-459.2932847271911,0.29067134857177734,8.594204 -540,Regression,Exponentially Weighted Average,TrumpApproval,39.94649651851849,39.98996046818772,-459.28610565666287,0.2965841293334961,9.232432 -560,Regression,Exponentially Weighted Average,TrumpApproval,39.976066142857114,40.018487723609816,-470.92618770667195,0.3019857406616211,9.891907999999999 -580,Regression,Exponentially Weighted Average,TrumpApproval,40.00338510344825,40.044755101652726,-483.2331705341176,0.30562496185302734,10.572818 -600,Regression,Exponentially Weighted Average,TrumpApproval,40.07393431666663,40.11569326301364,-479.5746686678817,0.3106412887573242,11.275212999999999 -620,Regression,Exponentially Weighted Average,TrumpApproval,40.1459417741935,40.18827077358568,-473.96334667177865,0.3147268295288086,11.999135999999998 -640,Regression,Exponentially Weighted Average,TrumpApproval,40.219438156249964,40.26249426545423,-466.8085709746123,0.3194303512573242,12.744586999999997 -660,Regression,Exponentially Weighted Average,TrumpApproval,40.28296777272724,40.32626722721455,-464.9172853497744,0.3247060775756836,13.511768999999997 -680,Regression,Exponentially Weighted Average,TrumpApproval,40.31998279411761,40.36256991107017,-473.1325264408024,0.32892894744873047,14.300919999999998 -700,Regression,Exponentially Weighted Average,TrumpApproval,40.31359012857138,40.35509446667054,-485.40526703956544,0.27628612518310547,15.116748999999999 -720,Regression,Exponentially Weighted Average,TrumpApproval,40.31730695833329,40.357915759594896,-496.1610725544049,0.2866239547729492,15.953536999999999 -740,Regression,Exponentially Weighted Average,TrumpApproval,40.36653568918915,40.407119416424955,-497.07428037101636,0.22740459442138672,16.820776 -760,Regression,Exponentially Weighted Average,TrumpApproval,40.403143671052604,40.443256311482514,-503.3712175162706,0.23808956146240234,17.708028 -780,Regression,Exponentially Weighted Average,TrumpApproval,40.44545064102563,40.48534274444009,-506.6856716110208,0.25345516204833984,18.615627999999997 -800,Regression,Exponentially Weighted Average,TrumpApproval,40.478548249999996,40.518050685964006,-512.1052117095793,0.26721858978271484,19.543924999999998 -820,Regression,Exponentially Weighted Average,TrumpApproval,40.50894034146341,40.5479845946661,-518.5068774177179,0.27524471282958984,20.493024 -840,Regression,Exponentially Weighted Average,TrumpApproval,40.5406558690476,40.579310897365986,-524.140575335229,0.28415584564208984,21.463054 -860,Regression,Exponentially Weighted Average,TrumpApproval,40.58371181395347,40.62239247493601,-524.3496319016275,0.29000377655029297,22.454245 -880,Regression,Exponentially Weighted Average,TrumpApproval,40.62855514772725,40.66738601007716,-522.897851512946,0.27073192596435547,23.471721 -900,Regression,Exponentially Weighted Average,TrumpApproval,40.664104233333326,40.702738445808535,-526.020768835918,0.2802000045776367,24.510012 -920,Regression,Exponentially Weighted Average,TrumpApproval,40.68274704347825,40.72073961991632,-535.1540147256861,0.29017162322998047,25.569536 -940,Regression,Exponentially Weighted Average,TrumpApproval,40.70972619148935,40.74737437775791,-540.4099749760601,0.29757213592529297,26.655616 -960,Regression,Exponentially Weighted Average,TrumpApproval,40.734006364583315,40.771242977826994,-546.7118652484228,0.30805492401123047,27.763053999999997 -980,Regression,Exponentially Weighted Average,TrumpApproval,40.74031829795916,40.776840159239676,-557.5026042066913,0.3134031295776367,28.892280999999997 -1000,Regression,Exponentially Weighted Average,TrumpApproval,40.75359492299998,40.78950075300399,-567.2567645513548,0.31664180755615234,30.043213999999995 -11,Regression,River MLP,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.012152671813964844,0.028772 -22,Regression,River MLP,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012152671813964844,0.079044 -33,Regression,River MLP,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.012152671813964844,0.14985700000000002 -44,Regression,River MLP,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.012152671813964844,0.237747 -55,Regression,River MLP,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.012152671813964844,0.33842500000000003 -66,Regression,River MLP,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.012152671813964844,0.452041 -77,Regression,River MLP,ChickWeights,43.46441536424646,43.6630525281676,-106.5245816691109,0.012152671813964844,0.578307 -88,Regression,River MLP,ChickWeights,43.36686847531974,43.58986852756654,-96.16251088961317,0.012152671813964844,0.71715 -99,Regression,River MLP,ChickWeights,39.3777319762956,41.29293125499619,-71.92610223487543,0.012152671813964844,0.8686729999999999 -110,Regression,River MLP,ChickWeights,36.38865389064824,39.46494228501369,-45.51654802750055,0.012152671813964844,1.032976 -121,Regression,River MLP,ChickWeights,33.47571311046551,37.663819251639204,-31.629785850276598,0.012152671813964844,1.209623 -132,Regression,River MLP,ChickWeights,30.829458151969483,36.06523958622657,-23.452489437409387,0.012152671813964844,1.398231 -143,Regression,River MLP,ChickWeights,28.694004741896396,34.66464981164902,-17.297279097398025,0.012152671813964844,1.59888 -154,Regression,River MLP,ChickWeights,26.901670019158168,33.42439644535526,-13.457346308406352,0.012152671813964844,1.811498 -165,Regression,River MLP,ChickWeights,25.73601455188179,32.42918896219868,-9.940044331027545,0.012152671813964844,2.036514 -176,Regression,River MLP,ChickWeights,24.444168946165895,31.465121114844866,-7.304318935113031,0.012152671813964844,2.273516 -187,Regression,River MLP,ChickWeights,23.31106527863985,30.560727675640003,-5.414053903472148,0.012152671813964844,2.522216 -198,Regression,River MLP,ChickWeights,22.2497659881735,29.729269659264478,-3.828220321961205,0.012152671813964844,2.782587 -209,Regression,River MLP,ChickWeights,21.58385771370322,29.10991604615937,-2.8161237021822814,0.012152671813964844,3.05463 -220,Regression,River MLP,ChickWeights,21.489125733317938,28.89967848441583,-2.2929294700983545,0.012152671813964844,3.338483 -231,Regression,River MLP,ChickWeights,20.960775268964717,28.36167354440183,-1.7127571457874735,0.012152671813964844,3.633999 -242,Regression,River MLP,ChickWeights,20.684837308385994,27.973225917526175,-1.123436519018493,0.012152671813964844,3.9411810000000003 -253,Regression,River MLP,ChickWeights,20.10836609318901,27.433189641281587,-0.7737126718723288,0.012152671813964844,4.259926 -264,Regression,River MLP,ChickWeights,20.477468707260805,27.77140109557264,-0.6607814047377214,0.012312889099121094,4.590346 -275,Regression,River MLP,ChickWeights,20.61134029899391,28.056502395486245,-0.49155454936660403,0.012312889099121094,4.932228 -286,Regression,River MLP,ChickWeights,20.562403371381592,27.861367163045813,-0.27395637869960554,0.012312889099121094,5.285550000000001 -297,Regression,River MLP,ChickWeights,20.280215853243796,27.63742758295081,-0.08720009619051883,0.012312889099121094,5.650314000000001 -308,Regression,River MLP,ChickWeights,20.449355350398,27.920149137510645,0.011807308141553507,0.012312889099121094,6.026518000000001 -319,Regression,River MLP,ChickWeights,21.501341692909694,30.009678165732634,-0.03599735953192962,0.012312889099121094,6.414170000000001 -330,Regression,River MLP,ChickWeights,21.657358056011915,30.050226639054536,0.11101595735800762,0.012312889099121094,6.813245000000001 -341,Regression,River MLP,ChickWeights,21.886687938329302,30.333453058468624,0.18888085688667844,0.012312889099121094,7.223789000000001 -352,Regression,River MLP,ChickWeights,21.886047000767114,30.34044931799507,0.2623171341711791,0.012312889099121094,7.645758000000001 -363,Regression,River MLP,ChickWeights,22.895757506339034,32.0447958992957,0.2009350122171253,0.012312889099121094,8.07951 -374,Regression,River MLP,ChickWeights,23.752495639064158,33.838912051163845,0.18088151494061133,0.012312889099121094,8.524693000000001 -385,Regression,River MLP,ChickWeights,24.560900349589296,34.753482086388026,0.2355843412503995,0.012312889099121094,8.981311000000002 -396,Regression,River MLP,ChickWeights,24.352453583923488,34.473609026370774,0.2986981406442135,0.012312889099121094,9.449373000000001 -407,Regression,River MLP,ChickWeights,25.719275464870663,37.04357441923364,0.2473067090757558,0.012312889099121094,9.928858000000002 -418,Regression,River MLP,ChickWeights,27.096550121003176,39.902565812758986,0.1801666848023049,0.012312889099121094,10.419752000000003 -429,Regression,River MLP,ChickWeights,28.343681876579897,42.0624752869508,0.19046954409903982,0.012312889099121094,10.922067000000002 -440,Regression,River MLP,ChickWeights,29.341897763641,43.65194027092424,0.18984270930397695,0.012312889099121094,11.435807000000002 -451,Regression,River MLP,ChickWeights,30.67273802938304,45.6370688200433,0.15212536617682837,0.012312889099121094,11.960959000000003 -462,Regression,River MLP,ChickWeights,32.56224145473062,49.915337115223885,0.047301683757009605,0.012312889099121094,12.497734000000003 -473,Regression,River MLP,ChickWeights,34.43415680949522,53.78695495416363,0.023205662187861797,0.012312889099121094,13.045956000000004 -484,Regression,River MLP,ChickWeights,37.59302308486845,59.431785855457115,-0.12722563369819695,0.012312889099121094,13.605608000000004 -495,Regression,River MLP,ChickWeights,39.81029832190851,63.2563754686467,-0.20123941420174463,0.012312889099121094,14.176690000000004 -506,Regression,River MLP,ChickWeights,40.81849028215024,64.7117163856453,-0.2206096222811289,0.012312889099121094,14.759208000000005 -517,Regression,River MLP,ChickWeights,42.30053541895924,66.69347450894223,-0.22349841903640066,0.012312889099121094,15.353157000000005 -528,Regression,River MLP,ChickWeights,43.87998520264807,69.22658128427689,-0.18517514283849468,0.012312889099121094,15.958328000000005 -539,Regression,River MLP,ChickWeights,45.090482136166365,70.91807128614899,-0.1924391171188431,0.012312889099121094,16.574693000000003 -550,Regression,River MLP,ChickWeights,46.166033810637174,72.79977252028388,-0.2178315347358042,0.012312889099121094,17.202244000000004 -561,Regression,River MLP,ChickWeights,47.99895001321041,75.49864048778444,-0.2525216922711382,0.012312889099121094,17.841166000000005 -572,Regression,River MLP,ChickWeights,49.57826542612398,77.90262570405233,-0.23354098460271144,0.012312889099121094,18.491295000000004 -20,Regression,River MLP,TrumpApproval,30.45103241731707,33.23585723529438,-2590.0045530336465,0.013110160827636719,0.03648 -40,Regression,River MLP,TrumpApproval,18.3406114455606,24.162855811212598,-233.72636807636488,0.013110160827636719,0.100456 -60,Regression,River MLP,TrumpApproval,14.012302795940927,20.27429916426714,-221.7932161673039,0.013110160827636719,0.19099100000000002 -80,Regression,River MLP,TrumpApproval,11.49107264681655,17.720640796103456,-169.73114207921216,0.013110160827636719,0.30723900000000004 -100,Regression,River MLP,TrumpApproval,9.726608796116105,15.913172800750537,-85.38479390162912,0.013110160827636719,0.4491740000000001 -120,Regression,River MLP,TrumpApproval,8.642897473622819,14.610205599232696,-60.77410396737057,0.013110160827636719,0.6162740000000001 -140,Regression,River MLP,TrumpApproval,7.627861449957184,13.547130531753504,-53.88423494401591,0.013110160827636719,0.8076440000000001 -160,Regression,River MLP,TrumpApproval,6.825616478903957,12.68640242718137,-41.446041076168875,0.013110160827636719,1.023068 -180,Regression,River MLP,TrumpApproval,6.298325880816026,11.99455373608459,-32.32351025206219,0.013110160827636719,1.262663 -200,Regression,River MLP,TrumpApproval,5.791221405645649,11.389261045241726,-29.134439780883504,0.013110160827636719,1.526002 -220,Regression,River MLP,TrumpApproval,5.429217208457164,10.877504011291329,-28.650681734644422,0.013110160827636719,1.812923 -240,Regression,River MLP,TrumpApproval,5.060396678997669,10.421476408956162,-26.4214333861576,0.013110160827636719,2.123256 -260,Regression,River MLP,TrumpApproval,4.7577579662422185,10.01970369652003,-24.68943115642662,0.013350486755371094,2.456738 -280,Regression,River MLP,TrumpApproval,4.47898479527949,9.659781964354623,-23.99890538466576,0.013350486755371094,2.813161 -300,Regression,River MLP,TrumpApproval,4.231628573868652,9.33546881214548,-21.961936464609707,0.013350486755371094,3.192823 -320,Regression,River MLP,TrumpApproval,4.032288217093912,9.046394584789834,-21.629541054113563,0.013350486755371094,3.5954460000000004 -340,Regression,River MLP,TrumpApproval,3.8351551756309497,8.779241117397504,-21.522318216666985,0.013350486755371094,4.021045 -360,Regression,River MLP,TrumpApproval,3.660607443401045,8.536652519215579,-20.469707840891584,0.013350486755371094,4.469678 -380,Regression,River MLP,TrumpApproval,3.5052706051205775,8.311598150636895,-20.056664379019765,0.013350486755371094,4.941134 -400,Regression,River MLP,TrumpApproval,3.360453882251005,8.10307441673556,-19.578991923208985,0.013350486755371094,5.435716 -420,Regression,River MLP,TrumpApproval,3.230603986740656,7.909485894846834,-19.256340082684435,0.013350486755371094,5.953156 -440,Regression,River MLP,TrumpApproval,3.1198347345540465,7.731010347257149,-18.002320884204543,0.013350486755371094,6.493473 -460,Regression,River MLP,TrumpApproval,3.010353198494384,7.5628771106824955,-16.244605442867506,0.013350486755371094,7.056611999999999 -480,Regression,River MLP,TrumpApproval,2.9023580604284582,7.404341650720478,-15.148937801753288,0.013350486755371094,7.642589999999999 -500,Regression,River MLP,TrumpApproval,2.8120368518109427,7.256642134427339,-14.185679409391113,0.013350486755371094,8.251631 -520,Regression,River MLP,TrumpApproval,2.722217494708863,7.11705765703134,-13.632593906904136,0.013350486755371094,8.883484 -540,Regression,River MLP,TrumpApproval,2.6379600337769933,6.984812005374008,-13.042206620144155,0.013350486755371094,9.537690999999999 -560,Regression,River MLP,TrumpApproval,2.5735320859360358,6.8629311186902635,-12.879442173523902,0.013350486755371094,10.214296 -580,Regression,River MLP,TrumpApproval,2.508294944253365,6.745457437445754,-12.739978855164816,0.013350486755371094,10.913295999999999 -600,Regression,River MLP,TrumpApproval,2.4431260057234407,6.633435680824833,-12.140422111767565,0.013350486755371094,11.634953999999999 -620,Regression,River MLP,TrumpApproval,2.386700775516181,6.528113194197335,-11.53247390884172,0.013350486755371094,12.379039999999998 -640,Regression,River MLP,TrumpApproval,2.3242014075410804,6.426013651213371,-10.916538218303476,0.013350486755371094,13.145560999999997 -660,Regression,River MLP,TrumpApproval,2.2685968162113417,6.328883008296775,-10.475904112331508,0.013350486755371094,13.934486999999997 -680,Regression,River MLP,TrumpApproval,2.2077966601853354,6.2353065982608955,-10.31508330333358,0.013350486755371094,14.745848999999998 -700,Regression,River MLP,TrumpApproval,2.1581493026656715,6.146460408814674,-10.283704638807178,0.013350486755371094,15.579853999999997 -720,Regression,River MLP,TrumpApproval,2.1027567473920863,6.060643545524585,-10.211846444382044,0.013350486755371094,16.436290999999997 -740,Regression,River MLP,TrumpApproval,2.051121816682194,5.978350933609188,-9.90287777753909,0.013350486755371094,17.315177999999996 -760,Regression,River MLP,TrumpApproval,2.003155134641025,5.899358540561457,-9.731678337792127,0.013350486755371094,18.216513999999997 -780,Regression,River MLP,TrumpApproval,1.9581628436412764,5.823548668044576,-9.504483044737295,0.013350486755371094,19.140318999999998 -800,Regression,River MLP,TrumpApproval,1.920522972774301,5.751090425279772,-9.337362153707343,0.013350486755371094,20.086782999999997 -820,Regression,River MLP,TrumpApproval,1.884378546616626,5.681435320930898,-9.199265318679966,0.013350486755371094,21.055681999999997 -840,Regression,River MLP,TrumpApproval,1.8462172344878474,5.613649762186833,-9.049787223051387,0.013350486755371094,22.047026999999996 -860,Regression,River MLP,TrumpApproval,1.8114873001009923,5.548504609880171,-8.800976301633217,0.013350486755371094,23.060785999999997 -880,Regression,River MLP,TrumpApproval,1.7737427907909609,5.485176730961097,-8.530931568707373,0.013350486755371094,24.096957999999997 -900,Regression,River MLP,TrumpApproval,1.7378742216236305,5.423968591721732,-8.358684442608821,0.013350486755371094,25.155794999999998 -920,Regression,River MLP,TrumpApproval,1.7058202733924563,5.364926267836746,-8.30648673172487,0.013350486755371094,26.237104 -940,Regression,River MLP,TrumpApproval,1.6771033983092818,5.307938337650396,-8.187106100071555,0.013350486755371094,27.340904 -960,Regression,River MLP,TrumpApproval,1.6470899020587193,5.252615019014566,-8.09065943244505,0.013350486755371094,28.467202999999998 -980,Regression,River MLP,TrumpApproval,1.6179664800066769,5.198922142498095,-8.078721464420347,0.013350486755371094,29.615951999999997 -1000,Regression,River MLP,TrumpApproval,1.591389315944533,5.1470018023734205,-8.048080908594342,0.013350486755371094,30.787286999999996 -11,Regression,[baseline] Mean predictor,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.0004901885986328125,0.002154 -22,Regression,[baseline] Mean predictor,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.0004901885986328125,0.005944 -33,Regression,[baseline] Mean predictor,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.0004901885986328125,0.010037 -44,Regression,[baseline] Mean predictor,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.0004901885986328125,0.014469000000000001 -55,Regression,[baseline] Mean predictor,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.0004901885986328125,0.019181 -66,Regression,[baseline] Mean predictor,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.0004901885986328125,0.024182000000000002 -77,Regression,[baseline] Mean predictor,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.0004901885986328125,0.029453 -88,Regression,[baseline] Mean predictor,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.0004901885986328125,0.034986 -99,Regression,[baseline] Mean predictor,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.0004901885986328125,0.040781000000000005 -110,Regression,[baseline] Mean predictor,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.0004901885986328125,0.046845000000000005 -121,Regression,[baseline] Mean predictor,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.0004901885986328125,0.053171 -132,Regression,[baseline] Mean predictor,ChickWeights,5.875758254207378,8.194624755054596,-0.2624191661321591,0.0004901885986328125,0.05976 -143,Regression,[baseline] Mean predictor,ChickWeights,6.530760796045927,8.870097879563003,-0.19803554240449484,0.0004901885986328125,0.066612 -154,Regression,[baseline] Mean predictor,ChickWeights,7.121466111912466,9.458403141043558,-0.15770278521517955,0.0004901885986328125,0.073726 -165,Regression,[baseline] Mean predictor,ChickWeights,7.772438504082036,10.375670403553157,-0.11989999304508925,0.0004901885986328125,0.081109 -176,Regression,[baseline] Mean predictor,ChickWeights,8.565827130563894,11.410434180005831,-0.09206765686265328,0.0004901885986328125,0.088752 -187,Regression,[baseline] Mean predictor,ChickWeights,9.429958588641576,12.495061319237752,-0.07221531716282037,0.0004901885986328125,0.096665 -198,Regression,[baseline] Mean predictor,ChickWeights,10.47731537859646,13.900491647656429,-0.05555027037575888,0.0004901885986328125,0.104851 -209,Regression,[baseline] Mean predictor,ChickWeights,11.43172675762076,15.229123619635446,-0.04445651287163721,0.0004901885986328125,0.1133 -220,Regression,[baseline] Mean predictor,ChickWeights,11.974320980081139,16.22368260926648,-0.03775608698471111,0.0004901885986328125,0.122025 -231,Regression,[baseline] Mean predictor,ChickWeights,12.9382196746461,17.489503190785292,-0.03157819728271183,0.0004901885986328125,0.131017 -242,Regression,[baseline] Mean predictor,ChickWeights,14.229204186206863,19.43725798629848,-0.02523677186741935,0.0004901885986328125,0.140272 -253,Regression,[baseline] Mean predictor,ChickWeights,15.339413196393396,20.820238312545918,-0.021649789303838762,0.0004901885986328125,0.149788 -264,Regression,[baseline] Mean predictor,ChickWeights,15.948617107030818,21.75817315507082,-0.019440185124094622,0.0004901885986328125,0.159572 -275,Regression,[baseline] Mean predictor,ChickWeights,16.794155127707494,23.16724301729152,-0.016999619323781356,0.0004901885986328125,0.169617 -286,Regression,[baseline] Mean predictor,ChickWeights,17.990009992534457,24.865985915258104,-0.014754713395529917,0.0004901885986328125,0.17992 -297,Regression,[baseline] Mean predictor,ChickWeights,19.34919450213405,26.676209297603677,-0.012890456560007202,0.0004901885986328125,0.190491 -308,Regression,[baseline] Mean predictor,ChickWeights,20.46881241431745,28.248013022827834,-0.011537481517321035,0.0004901885986328125,0.201321 -319,Regression,[baseline] Mean predictor,ChickWeights,20.993702124162965,29.638141143499492,-0.010503673119392376,0.0004901885986328125,0.212414 -330,Regression,[baseline] Mean predictor,ChickWeights,22.586872779548433,32.01796640002603,-0.009220237952050514,0.0004901885986328125,0.223765 -341,Regression,[baseline] Mean predictor,ChickWeights,23.973458872107372,33.821533603903084,-0.008387701903732392,0.0004901885986328125,0.235368 -352,Regression,[baseline] Mean predictor,ChickWeights,25.315991788770976,35.461698606860665,-0.007731302158646702,0.0004901885986328125,0.247225 -363,Regression,[baseline] Mean predictor,ChickWeights,25.615062978866305,35.981300981590465,-0.007443749031205149,0.0004901885986328125,0.259337 -374,Regression,[baseline] Mean predictor,ChickWeights,26.673321526932543,37.51836715700961,-0.006935846124255907,0.0004901885986328125,0.271713 -385,Regression,[baseline] Mean predictor,ChickWeights,28.27694482780972,39.8753298933956,-0.006332510983879436,0.0004901885986328125,0.28435499999999997 -396,Regression,[baseline] Mean predictor,ChickWeights,29.55612496209691,41.288487059450155,-0.005980181891907188,0.0004901885986328125,0.29720599999999997 -407,Regression,[baseline] Mean predictor,ChickWeights,30.561677112682855,42.81802042618151,-0.005646723150046551,0.0004901885986328125,0.31025099999999994 -418,Regression,[baseline] Mean predictor,ChickWeights,31.39346669137945,44.18765357092498,-0.0053697143301307815,0.0004901885986328125,0.3234889999999999 -429,Regression,[baseline] Mean predictor,ChickWeights,33.10612890637694,46.865579751152914,-0.004966366070605188,0.0004901885986328125,0.33691999999999994 -440,Regression,[baseline] Mean predictor,ChickWeights,34.54914638861108,48.61167278858254,-0.004716123854972665,0.0004901885986328125,0.35054899999999994 -451,Regression,[baseline] Mean predictor,ChickWeights,35.43263419295921,49.67507127970072,-0.004553693807187953,0.0004901885986328125,0.36437299999999995 -462,Regression,[baseline] Mean predictor,ChickWeights,36.308550382896186,51.2507761435036,-0.004357377489546899,0.0004901885986328125,0.3783909999999999 -473,Regression,[baseline] Mean predictor,ChickWeights,38.26330298063241,54.532250497281034,-0.004051661204895529,0.0004901885986328125,0.3926029999999999 -484,Regression,[baseline] Mean predictor,ChickWeights,39.598662348008276,56.08659790201894,-0.0039023944795495424,0.0004901885986328125,0.4070059999999999 -495,Regression,[baseline] Mean predictor,ChickWeights,40.94697327298068,57.823326559810994,-0.0037535911132069444,0.0004901885986328125,0.4215999999999999 -506,Regression,[baseline] Mean predictor,ChickWeights,41.42384714758024,58.679845942015916,-0.003665234721119459,0.0004901885986328125,0.4363849999999999 -517,Regression,[baseline] Mean predictor,ChickWeights,42.72663002099646,60.40151056768402,-0.0035345422299792872,0.0004901885986328125,0.45136399999999993 -528,Regression,[baseline] Mean predictor,ChickWeights,44.77321528369677,63.69509749878913,-0.0033415055563215112,0.0004901885986328125,0.4665329999999999 -539,Regression,[baseline] Mean predictor,ChickWeights,45.99579764939489,65.0494992510053,-0.0032526095626370655,0.0004901885986328125,0.4818929999999999 -550,Regression,[baseline] Mean predictor,ChickWeights,46.57020777663759,66.07332710234044,-0.0031815200825582313,0.0004901885986328125,0.4974459999999999 -561,Regression,[baseline] Mean predictor,ChickWeights,47.758257606406204,67.5643396193493,-0.0030950009187136196,0.0004901885986328125,0.5131919999999999 -572,Regression,[baseline] Mean predictor,ChickWeights,49.49138874897682,70.24569214117749,-0.002971942406188699,0.0004901885986328125,0.5291269999999999 -20,Regression,[baseline] Mean predictor,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.0004901885986328125,0.003379 -40,Regression,[baseline] Mean predictor,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.0004901885986328125,0.008178999999999999 -60,Regression,[baseline] Mean predictor,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.0004901885986328125,0.013590999999999999 -80,Regression,[baseline] Mean predictor,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.0004901885986328125,0.019584999999999998 -100,Regression,[baseline] Mean predictor,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.0004901885986328125,0.026254999999999997 -120,Regression,[baseline] Mean predictor,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.0004901885986328125,0.033502 -140,Regression,[baseline] Mean predictor,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.0004901885986328125,0.041316 -160,Regression,[baseline] Mean predictor,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.0004901885986328125,0.049694999999999996 -180,Regression,[baseline] Mean predictor,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.0004901885986328125,0.058637999999999996 -200,Regression,[baseline] Mean predictor,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.0004901885986328125,0.06823299999999999 -220,Regression,[baseline] Mean predictor,TrumpApproval,1.9409519346841397,3.56902990398404,-2.19210047340805,0.0004901885986328125,0.078397 -240,Regression,[baseline] Mean predictor,TrumpApproval,1.9366756524315063,3.4612902974772624,-2.024876884626847,0.0004901885986328125,0.089124 -260,Regression,[baseline] Mean predictor,TrumpApproval,1.9250039777458068,3.363327951159923,-1.8945640461454523,0.0004901885986328125,0.10041399999999999 -280,Regression,[baseline] Mean predictor,TrumpApproval,1.8726934920539138,3.257010428159885,-1.8420037280027222,0.0004901885986328125,0.11227 -300,Regression,[baseline] Mean predictor,TrumpApproval,1.8907476896224937,3.1958821895815714,-1.6910252267675165,0.0004901885986328125,0.124747 -320,Regression,[baseline] Mean predictor,TrumpApproval,1.819623890420079,3.103812605138666,-1.663886258690169,0.0004901885986328125,0.137792 -340,Regression,[baseline] Mean predictor,TrumpApproval,1.7396293145937214,3.014220627768389,-1.654906383755708,0.0004901885986328125,0.151284 -360,Regression,[baseline] Mean predictor,TrumpApproval,1.7350691203787965,2.9569384317632506,-1.5759385016835008,0.0004901885986328125,0.16520200000000002 -380,Regression,[baseline] Mean predictor,TrumpApproval,1.6987131960417108,2.8893997308323693,-1.5446951110541192,0.0004901885986328125,0.179547 -400,Regression,[baseline] Mean predictor,TrumpApproval,1.673610627740774,2.82935583501861,-1.5089937655143242,0.0004901885986328125,0.194363 -420,Regression,[baseline] Mean predictor,TrumpApproval,1.6410137122925974,2.7701802079251965,-1.484737486096575,0.0004901885986328125,0.209602 -440,Regression,[baseline] Mean predictor,TrumpApproval,1.6565972573555454,2.7427790467379385,-1.391750010744973,0.0004901885986328125,0.22526800000000002 -460,Regression,[baseline] Mean predictor,TrumpApproval,1.699464840115161,2.7394674040138396,-1.2626191030939884,0.0004901885986328125,0.24135600000000001 -480,Regression,[baseline] Mean predictor,TrumpApproval,1.7224824441896143,2.7219018737730583,-1.182307732575659,0.0004901885986328125,0.25786400000000004 -500,Regression,[baseline] Mean predictor,TrumpApproval,1.7446092142173422,2.7058035442295596,-1.1113262021905803,0.0004901885986328125,0.27478900000000006 -520,Regression,[baseline] Mean predictor,TrumpApproval,1.7464998751860934,2.677192702589883,-1.0705208906620065,0.0004901885986328125,0.29219700000000004 -540,Regression,[baseline] Mean predictor,TrumpApproval,1.7535492786865425,2.653885630983747,-1.0271707062792519,0.0004901885986328125,0.31002500000000005 -560,Regression,[baseline] Mean predictor,TrumpApproval,1.7201019899937544,2.614359234374483,-1.0141103337708768,0.0004901885986328125,0.32827100000000003 -580,Regression,[baseline] Mean predictor,TrumpApproval,1.6887559504032665,2.5757257291728384,-1.0033760803823184,0.0004901885986328125,0.34693300000000005 -600,Regression,[baseline] Mean predictor,TrumpApproval,1.701917368353294,2.5614247637328695,-0.9592753712060649,0.0004901885986328125,0.36605800000000005 -620,Regression,[baseline] Mean predictor,TrumpApproval,1.7178157166185173,2.5513468959681562,-0.9142580419512063,0.0004901885986328125,0.38561000000000006 -640,Regression,[baseline] Mean predictor,TrumpApproval,1.7365901196485038,2.545046385321895,-0.8692105635365064,0.0004901885986328125,0.40558200000000005 -660,Regression,[baseline] Mean predictor,TrumpApproval,1.7465677425181807,2.532051562790666,-0.8368676529707118,0.0004901885986328125,0.42596800000000007 -680,Regression,[baseline] Mean predictor,TrumpApproval,1.731617734826669,2.5042261861708606,-0.8251107974736909,0.0004901885986328125,0.44678300000000004 -700,Regression,[baseline] Mean predictor,TrumpApproval,1.6973720107412233,2.4702678919797196,-0.8225927549994396,0.0004901885986328125,0.468073 -720,Regression,[baseline] Mean predictor,TrumpApproval,1.6698372433333928,2.4400355004771073,-0.81732226470892,0.0004901885986328125,0.489788 -740,Regression,[baseline] Mean predictor,TrumpApproval,1.6732482399922957,2.425592833263792,-0.7947920429290933,0.0004901885986328125,0.511921 -760,Regression,[baseline] Mean predictor,TrumpApproval,1.6653913599894004,2.404136439714782,-0.7822814452716051,0.0004901885986328125,0.53447 -780,Regression,[baseline] Mean predictor,TrumpApproval,1.6644612180457288,2.387561393188575,-0.7656652158374817,0.0004901885986328125,0.557436 -800,Regression,[baseline] Mean predictor,TrumpApproval,1.6556359332933146,2.368497267913513,-0.7532954885990883,0.0004901885986328125,0.580851 -820,Regression,[baseline] Mean predictor,TrumpApproval,1.6452077788467467,2.348678653798561,-0.7430103139622937,0.0004901885986328125,0.604682 -840,Regression,[baseline] Mean predictor,TrumpApproval,1.6374623223784903,2.3305035344735936,-0.7320713255917544,0.0004901885986328125,0.6289220000000001 -860,Regression,[baseline] Mean predictor,TrumpApproval,1.6419505315856449,2.3202080137162757,-0.7138439732116804,0.0004901885986328125,0.653572 -880,Regression,[baseline] Mean predictor,TrumpApproval,1.6490002164922652,2.3126155324510744,-0.6941855677649247,0.0004901885986328125,0.6786340000000001 -900,Regression,[baseline] Mean predictor,TrumpApproval,1.6474991175923384,2.299197536504521,-0.6816400531907807,0.0004901885986328125,0.704114 -920,Regression,[baseline] Mean predictor,TrumpApproval,1.6301006788336792,2.2779225390149764,-0.6777843948800273,0.0004901885986328125,0.730046 -940,Regression,[baseline] Mean predictor,TrumpApproval,1.6221876471839873,2.2623787372500574,-0.6690049120995847,0.0004901885986328125,0.7563869999999999 -960,Regression,[baseline] Mean predictor,TrumpApproval,1.6124120493571745,2.245866476718547,-0.6619276404267609,0.0004901885986328125,0.7831509999999999 -980,Regression,[baseline] Mean predictor,TrumpApproval,1.5867001120604314,2.223758235975506,-0.661013659831075,0.0004901885986328125,0.810322 -1000,Regression,[baseline] Mean predictor,TrumpApproval,1.5681359363812417,2.2037391763141216,-0.6587014308970958,0.0004901885986328125,0.8379 +11,Regression,Linear Regression,ChickWeights,30.432219699626994,31.267456151778337,-1257.4692714745631,0.004130363464355469,0.000963 +22,Regression,Linear Regression,ChickWeights,20.75760844034268,23.632210645041404,-590.4769976066937,0.004130363464355469,0.002374 +33,Regression,Linear Regression,ChickWeights,14.555240079240876,19.349294933329695,-259.0232069515881,0.004130363464355469,0.004113 +44,Regression,Linear Regression,ChickWeights,11.143633659136759,16.767243978820222,-220.34524244378574,0.004130363464355469,0.006175 +55,Regression,Linear Regression,ChickWeights,10.841164000616114,17.714902804136145,-60.2608923989398,0.004130363464355469,0.008581 +66,Regression,Linear Regression,ChickWeights,10.32598508406065,16.527353468164844,-21.985729074745297,0.004130363464355469,0.01133 +77,Regression,Linear Regression,ChickWeights,9.718401993814265,15.521096390186141,-12.587024696233003,0.004130363464355469,0.014424 +88,Regression,Linear Regression,ChickWeights,8.767755200283737,14.552446235427842,-9.829280875288257,0.004130363464355469,0.017858 +99,Regression,Linear Regression,ChickWeights,7.977130626229444,13.740429605807138,-7.074807888709797,0.004130363464355469,0.021634999999999998 +110,Regression,Linear Regression,ChickWeights,7.506893871110683,13.098273311725844,-4.124041411671393,0.004130363464355469,0.025751999999999997 +121,Regression,Linear Regression,ChickWeights,7.252833276832352,12.607637144454216,-2.6562249812820733,0.004130363464355469,0.030208999999999996 +132,Regression,Linear Regression,ChickWeights,6.896359231575217,12.121970224209305,-1.7624336939368233,0.004130363464355469,0.03500399999999999 +143,Regression,Linear Regression,ChickWeights,6.581914741629191,11.688367143429069,-1.080274127204615,0.004130363464355469,0.040138999999999994 +154,Regression,Linear Regression,ChickWeights,6.347682986169337,11.314945909537578,-0.6567859420078188,0.004130363464355469,0.045612999999999994 +165,Regression,Linear Regression,ChickWeights,6.47676439389405,11.21748999353191,-0.30899590760610374,0.004130363464355469,0.051426999999999994 +176,Regression,Linear Regression,ChickWeights,6.552290709218319,11.100632967129414,-0.03357189497448321,0.004130363464355469,0.05758099999999999 +187,Regression,Linear Regression,ChickWeights,6.503097179992549,10.915357728148932,0.18175912588502985,0.004130363464355469,0.06407299999999999 +198,Regression,Linear Regression,ChickWeights,6.420443618722296,10.727647067877953,0.3713230272376924,0.004130363464355469,0.070904 +209,Regression,Linear Regression,ChickWeights,6.54715053669462,10.814712106795348,0.47329133398018763,0.004130363464355469,0.07807199999999999 +220,Regression,Linear Regression,ChickWeights,7.075852889975692,11.488147441481185,0.479648982578291,0.004130363464355469,0.08557599999999999 +231,Regression,Linear Regression,ChickWeights,7.197265349840174,11.527376107145999,0.5518657524511614,0.004130363464355469,0.09341599999999999 +242,Regression,Linear Regression,ChickWeights,7.359957454348683,11.71365363090123,0.6276606533313056,0.004130363464355469,0.10159099999999999 +253,Regression,Linear Regression,ChickWeights,7.389343614466645,11.704104182671559,0.6771453727427903,0.004130363464355469,0.11010199999999999 +264,Regression,Linear Regression,ChickWeights,8.007684680730522,12.681713023454451,0.6536838584261326,0.004210472106933594,0.11894999999999999 +275,Regression,Linear Regression,ChickWeights,8.456356064016727,13.562457362384485,0.6514630282957669,0.004210472106933594,0.128137 +286,Regression,Linear Regression,ChickWeights,8.682222588679535,13.91372755183948,0.6822857451181047,0.004210472106933594,0.137663 +297,Regression,Linear Regression,ChickWeights,8.656490376145301,13.862729792291397,0.7264657185265005,0.004210472106933594,0.14752700000000002 +308,Regression,Linear Regression,ChickWeights,9.17087534181789,14.586626878398466,0.730278281446047,0.004210472106933594,0.15773800000000002 +319,Regression,Linear Regression,ChickWeights,10.253235573939358,17.040182474587255,0.6659707835095393,0.004210472106933594,0.270641 +330,Regression,Linear Regression,ChickWeights,10.67218268870669,17.597898989920818,0.6951262006904333,0.004210472106933594,0.38498 +341,Regression,Linear Regression,ChickWeights,10.865878827617594,17.684075493652397,0.7243197409220903,0.004210472106933594,0.500381 +352,Regression,Linear Regression,ChickWeights,11.014541487264223,17.788847456042067,0.7464163188501894,0.004210472106933594,0.6168239999999999 +363,Regression,Linear Regression,ChickWeights,11.893125923244742,19.14640328452056,0.7147396000186461,0.004210472106933594,0.7343709999999999 +374,Regression,Linear Regression,ChickWeights,12.40252640363099,20.24468752454989,0.7068188127948265,0.004210472106933594,0.8529599999999999 +385,Regression,Linear Regression,ChickWeights,12.78041264925886,20.84297745742841,0.7250508110390363,0.004210472106933594,0.972583 +396,Regression,Linear Regression,ChickWeights,12.908163646252072,20.82655299121286,0.7440434321899679,0.004210472106933594,1.093238 +407,Regression,Linear Regression,ChickWeights,13.78624220521945,22.297725224665914,0.7272822586077066,0.004210472106933594,1.2149269999999999 +418,Regression,Linear Regression,ChickWeights,14.56231380927385,23.732773749874315,0.7099846963904786,0.004210472106933594,1.3375199999999998 +429,Regression,Linear Regression,ChickWeights,15.109717404902197,24.642068489898374,0.7221580232945248,0.004210472106933594,1.4604629999999998 +440,Regression,Linear Regression,ChickWeights,15.287005413554732,24.721522560240437,0.7401560140604169,0.004210472106933594,1.583729 +451,Regression,Linear Regression,ChickWeights,15.806865735774078,25.331119330890413,0.7387809061287051,0.004210472106933594,1.707315 +462,Regression,Linear Regression,ChickWeights,16.912347710111163,27.450327347193873,0.7118740092210123,0.004210472106933594,1.831218 +473,Regression,Linear Regression,ChickWeights,17.68786801080465,28.748046923071918,0.7209603573249957,0.004210472106933594,1.955435 +484,Regression,Linear Regression,ChickWeights,18.02230431978895,29.040370094251127,0.7308604085348502,0.004210472106933594,2.079964 +495,Regression,Linear Regression,ChickWeights,18.476434617297652,29.565622398548214,0.7375811559076941,0.004210472106933594,2.204806 +506,Regression,Linear Regression,ChickWeights,19.368862660258834,31.016595939650866,0.7195863076124669,0.004210472106933594,2.32996 +517,Regression,Linear Regression,ChickWeights,20.093492725340727,32.00802507821089,0.7181912437784894,0.004210472106933594,2.455434 +528,Regression,Linear Regression,ChickWeights,20.883641447975457,33.20140091570763,0.727385103943677,0.004210472106933594,2.581219 +539,Regression,Linear Regression,ChickWeights,21.055940734584826,33.19901872731025,0.7386798629639011,0.004210472106933594,2.707313 +550,Regression,Linear Regression,ChickWeights,22.046658398851132,34.818142407426606,0.7214274205964286,0.004210472106933594,2.833717 +561,Regression,Linear Regression,ChickWeights,22.750150790689958,35.737018888500465,0.7193638350430389,0.004210472106933594,2.960429 +572,Regression,Linear Regression,ChickWeights,23.60149518688988,36.92142939550449,0.722919218201958,0.004210472106933594,3.087448 +578,Regression,Linear Regression,ChickWeights,23.758656678867762,37.03767126301035,0.7279537206511313,0.004210472106933594,3.2147080000000003 +20,Regression,Linear Regression,TrumpApproval,20.715375599336316,24.276120972986362,-1381.3340079163324,0.004813194274902344,0.003774 +40,Regression,Linear Regression,TrumpApproval,12.956746822999646,17.85530816845139,-127.17403450091604,0.004813194274902344,0.008234 +60,Regression,Linear Regression,TrumpApproval,10.540337295823328,15.264267507077205,-125.28803290438402,0.004813194274902344,0.013346 +80,Regression,Linear Regression,TrumpApproval,8.92648259034571,13.436420463778147,-97.15695382305036,0.004813194274902344,0.019104 +100,Regression,Linear Regression,TrumpApproval,7.5495393499287236,12.076339439187347,-48.75014684916543,0.004813194274902344,0.025552 +120,Regression,Linear Regression,TrumpApproval,6.5712666531069654,11.058195411086313,-34.388513465790076,0.004813194274902344,0.032651 +140,Regression,Linear Regression,TrumpApproval,5.868178209177549,10.265658199354172,-30.515672886293014,0.004813194274902344,0.040397 +160,Regression,Linear Regression,TrumpApproval,5.226493262391851,9.609365926739029,-23.352843972650145,0.004813194274902344,0.048786 +180,Regression,Linear Regression,TrumpApproval,4.806672346419344,9.079121174210673,-18.092824435696784,0.004813194274902344,0.057857000000000006 +200,Regression,Linear Regression,TrumpApproval,4.400421129740624,8.617551092451054,-16.252012396913173,0.004813194274902344,0.06766 +220,Regression,Linear Regression,TrumpApproval,4.083414123099576,8.223437931584808,-15.946617088642817,0.004813194274902344,0.07815899999999999 +240,Regression,Linear Regression,TrumpApproval,3.8235343884157706,7.87966547036827,-14.67643164713968,0.004813194274902344,0.08935399999999999 +260,Regression,Linear Regression,TrumpApproval,3.5733429968046226,7.572887494545769,-13.674649599158814,0.004973411560058594,0.10124299999999999 +280,Regression,Linear Regression,TrumpApproval,3.399764262602937,7.307305033384193,-13.305426773388605,0.004973411560058594,0.11382999999999999 +300,Regression,Linear Regression,TrumpApproval,3.2435269592384794,7.069212717011484,-12.166742621467943,0.004973411560058594,0.21774699999999997 +320,Regression,Linear Regression,TrumpApproval,3.1105754847518408,6.854541649824586,-11.99216513034567,0.004973411560058594,0.416803 +340,Regression,Linear Regression,TrumpApproval,2.9569354047226284,6.651479799277566,-11.928129373171446,0.004973411560058594,0.618037 +360,Regression,Linear Regression,TrumpApproval,2.8537856094930785,6.474036710445056,-11.348131391644102,0.004973411560058594,0.8214119999999999 +380,Regression,Linear Regression,TrumpApproval,2.750449728962714,6.305826559379086,-11.120053648606476,0.004973411560058594,1.026924 +400,Regression,Linear Regression,TrumpApproval,2.6634141155755278,6.151161672136967,-10.858744866397979,0.004973411560058594,1.2341469999999999 +420,Regression,Linear Regression,TrumpApproval,2.556259025339157,6.003825249623929,-10.671335514866263,0.004973411560058594,1.4420449999999998 +440,Regression,Linear Regression,TrumpApproval,2.471571610669061,5.868919367302693,-9.950915405915524,0.004973411560058594,1.6506019999999997 +460,Regression,Linear Regression,TrumpApproval,2.3796807630395826,5.740715994508566,-8.935993501779443,0.004973411560058594,1.8600329999999996 +480,Regression,Linear Regression,TrumpApproval,2.2935423272146473,5.620383847029998,-8.304713733239236,0.004973411560058594,2.0701579999999997 +500,Regression,Linear Regression,TrumpApproval,2.2170719472274363,5.50775327209046,-7.748060324415055,0.004973411560058594,2.2809679999999997 +520,Regression,Linear Regression,TrumpApproval,2.1605380581247338,5.4030519069184955,-7.433320998258445,0.004973411560058594,2.492507 +540,Regression,Linear Regression,TrumpApproval,2.093930365363914,5.302901387269021,-7.093810234661742,0.004973411560058594,2.7047299999999996 +560,Regression,Linear Regression,TrumpApproval,2.0590245226095627,5.213512799867119,-7.009651494197669,0.004973411560058594,2.9176339999999996 +580,Regression,Linear Regression,TrumpApproval,1.9976476082662873,5.1231852511763165,-6.925804791819894,0.004973411560058594,3.1312199999999994 +600,Regression,Linear Regression,TrumpApproval,1.950641059884997,5.038426259116397,-6.58092298894084,0.004973411560058594,3.3455269999999993 +620,Regression,Linear Regression,TrumpApproval,1.9139787950639096,4.959092402037442,-6.2321238970256,0.004973411560058594,3.560656999999999 +640,Regression,Linear Regression,TrumpApproval,1.8644177203659011,4.8815607080230725,-5.87676544995844,0.004973411560058594,3.776474999999999 +660,Regression,Linear Regression,TrumpApproval,1.8242147858959745,4.808190620674182,-5.62363098938706,0.004973411560058594,3.9929739999999994 +680,Regression,Linear Regression,TrumpApproval,1.7745110240786572,4.737042423784333,-5.530654039159668,0.004973411560058594,4.2677439999999995 +700,Regression,Linear Regression,TrumpApproval,1.73663030353679,4.669916427921507,-5.51357997146441,0.004973411560058594,4.544746 +720,Regression,Linear Regression,TrumpApproval,1.692679144669073,4.604703216269991,-5.472066122280332,0.004973411560058594,4.823995 +740,Regression,Linear Regression,TrumpApproval,1.6517738073879173,4.542197076044804,-5.293761488897717,0.004973411560058594,5.105493 +760,Regression,Linear Regression,TrumpApproval,1.6176019850850996,4.482676429973872,-5.196305855003581,0.004973411560058594,5.389141 +780,Regression,Linear Regression,TrumpApproval,1.5865007641193463,4.425455260516019,-5.066178353973196,0.004973411560058594,5.673523 +800,Regression,Linear Regression,TrumpApproval,1.5595678531598225,4.370690133148669,-4.970481738375755,0.004973411560058594,5.9679660000000005 +820,Regression,Linear Regression,TrumpApproval,1.5359483450738913,4.318357063182573,-4.892367885242343,0.004973411560058594,6.2645230000000005 +840,Regression,Linear Regression,TrumpApproval,1.5094802852221159,4.267276732370252,-4.807214337073276,0.004973411560058594,6.563154000000001 +860,Regression,Linear Regression,TrumpApproval,1.4815681878661566,4.217864876321593,-4.663732871777943,0.004973411560058594,6.863868000000001 +880,Regression,Linear Regression,TrumpApproval,1.4526831778170481,4.169823323470254,-4.507942651608292,0.004973411560058594,7.276079000000001 +900,Regression,Linear Regression,TrumpApproval,1.425504815240136,4.123417367951589,-4.4087270270070205,0.004973411560058594,7.880966000000001 +920,Regression,Linear Regression,TrumpApproval,1.401135420694234,4.078757160785335,-4.379153600942964,0.004973411560058594,8.488067000000001 +940,Regression,Linear Regression,TrumpApproval,1.3798894262867005,4.035722473386745,-4.310917809364017,0.004973411560058594,9.096757 +960,Regression,Linear Regression,TrumpApproval,1.3578157698337674,3.993911445090692,-4.255827563021541,0.004973411560058594,9.706153 +980,Regression,Linear Regression,TrumpApproval,1.3349554985290681,3.953168904153961,-4.2491478554421755,0.004973411560058594,10.316233 +1000,Regression,Linear Regression,TrumpApproval,1.3157385915327033,3.9139344489617316,-4.232086679588724,0.004973411560058594,10.926998000000001 +1001,Regression,Linear Regression,TrumpApproval,1.3145482000473083,3.9119809164882438,-4.230354806784151,0.004973411560058594,11.537908000000002 +11,Regression,Linear Regression with l1 regularization,ChickWeights,30.519429760441792,31.341724959881887,-1263.4547929656035,0.004361152648925781,0.001889 +22,Regression,Linear Regression with l1 regularization,ChickWeights,20.93274945698016,23.730069634788823,-595.3856524245364,0.004361152648925781,0.005264 +33,Regression,Linear Regression with l1 regularization,ChickWeights,14.671976905269485,19.432784890847977,-261.2719879213097,0.004361152648925781,0.045495999999999995 +44,Regression,Linear Regression with l1 regularization,ChickWeights,11.206218788565426,16.83704009498573,-222.1918420065333,0.004361152648925781,0.086209 +55,Regression,Linear Regression with l1 regularization,ChickWeights,10.7873677371092,17.69725945175844,-60.138926246201024,0.004361152648925781,0.127302 +66,Regression,Linear Regression with l1 regularization,ChickWeights,10.358479420064798,16.54420972880916,-22.032639310332936,0.004361152648925781,0.168766 +77,Regression,Linear Regression with l1 regularization,ChickWeights,9.753598876381378,15.536347024393615,-12.613738343052718,0.004361152648925781,0.21060099999999998 +88,Regression,Linear Regression with l1 regularization,ChickWeights,8.774706713989955,14.560860647391403,-9.841807755380493,0.004361152648925781,0.252804 +99,Regression,Linear Regression with l1 regularization,ChickWeights,7.976543403311107,13.74760854733656,-7.083247758311314,0.004361152648925781,0.295373 +110,Regression,Linear Regression with l1 regularization,ChickWeights,7.5284067705618165,13.110785837893241,-4.133835882207287,0.004361152648925781,0.338308 +121,Regression,Linear Regression with l1 regularization,ChickWeights,7.271666718491515,12.6229442838289,-2.665108536473531,0.004361152648925781,0.38161 +132,Regression,Linear Regression with l1 regularization,ChickWeights,6.91845605456336,12.134014714075713,-1.7679259750984961,0.004361152648925781,0.425278 +143,Regression,Linear Regression with l1 regularization,ChickWeights,6.610383809165891,11.700505099139125,-1.084596952740374,0.004361152648925781,0.469311 +154,Regression,Linear Regression with l1 regularization,ChickWeights,6.3485668448406924,11.31852948419668,-0.6578355548574832,0.004361152648925781,0.51371 +165,Regression,Linear Regression with l1 regularization,ChickWeights,6.473998962981321,11.222073845492618,-0.3100659276219817,0.004361152648925781,0.558476 +176,Regression,Linear Regression with l1 regularization,ChickWeights,6.543521830550948,11.096254270292283,-0.032756661210885385,0.004361152648925781,0.603607 +187,Regression,Linear Regression with l1 regularization,ChickWeights,6.493894355635018,10.908553918682982,0.18277886707380187,0.004361152648925781,0.649102 +198,Regression,Linear Regression with l1 regularization,ChickWeights,6.432058292739276,10.739983052449066,0.36987633376979445,0.004361152648925781,0.6949599999999999 +209,Regression,Linear Regression with l1 regularization,ChickWeights,6.530905166315106,10.805387069826965,0.47419925648761396,0.004361152648925781,0.74118 +220,Regression,Linear Regression with l1 regularization,ChickWeights,7.049069109840064,11.46222613381468,0.4819945238144716,0.004361152648925781,0.7877609999999999 +231,Regression,Linear Regression with l1 regularization,ChickWeights,7.185364391622807,11.520615160379734,0.5523912707049028,0.004361152648925781,0.834703 +242,Regression,Linear Regression with l1 regularization,ChickWeights,7.384443509591489,11.759466507882767,0.6247424700583044,0.004361152648925781,0.882006 +253,Regression,Linear Regression with l1 regularization,ChickWeights,7.370825288025247,11.706644644448966,0.6770052015955412,0.004361152648925781,0.929669 +264,Regression,Linear Regression with l1 regularization,ChickWeights,7.997212264968545,12.688148058774217,0.6533323093865229,0.004441261291503906,0.977694 +275,Regression,Linear Regression with l1 regularization,ChickWeights,8.45564901988644,13.583827871673952,0.6503637760490552,0.004441261291503906,1.026082 +286,Regression,Linear Regression with l1 regularization,ChickWeights,8.687395226209604,13.953064893865328,0.6804867014487179,0.004441261291503906,1.074833 +297,Regression,Linear Regression with l1 regularization,ChickWeights,8.660171229881424,13.910099225377925,0.7245931722706233,0.004441261291503906,1.1239519999999998 +308,Regression,Linear Regression with l1 regularization,ChickWeights,9.16625719191718,14.612234985526298,0.7293304097140514,0.004441261291503906,1.1734349999999998 +319,Regression,Linear Regression with l1 regularization,ChickWeights,10.250950211093048,17.0718306278326,0.664728869016383,0.004441261291503906,1.2232849999999997 +330,Regression,Linear Regression with l1 regularization,ChickWeights,10.679670450254022,17.65395670255975,0.6931807697512926,0.004441261291503906,1.3407639999999996 +341,Regression,Linear Regression with l1 regularization,ChickWeights,10.873729384474112,17.73873175202587,0.7226130148559202,0.004441261291503906,1.6983979999999996 +352,Regression,Linear Regression with l1 regularization,ChickWeights,11.018541118771262,17.831871437600412,0.745188204067577,0.004441261291503906,2.0571989999999998 +363,Regression,Linear Regression with l1 regularization,ChickWeights,11.899574150448762,19.190338217602402,0.7134289333715201,0.004441261291503906,2.417118 +374,Regression,Linear Regression with l1 regularization,ChickWeights,12.408282768986876,20.289550367060546,0.7055179762102581,0.004441261291503906,2.778154 +385,Regression,Linear Regression with l1 regularization,ChickWeights,12.788104615245373,20.897902847676004,0.7235998101431352,0.004441261291503906,3.1404569999999996 +396,Regression,Linear Regression with l1 regularization,ChickWeights,12.908222014164421,20.86950621812891,0.7429865604297317,0.004441261291503906,3.5031369999999997 +407,Regression,Linear Regression with l1 regularization,ChickWeights,13.785647364051679,22.333927174809716,0.726395986248676,0.004441261291503906,3.8661689999999997 +418,Regression,Linear Regression with l1 regularization,ChickWeights,14.562464823979756,23.771461386342615,0.709038397249883,0.004441261291503906,4.229544 +429,Regression,Linear Regression with l1 regularization,ChickWeights,15.115712915071189,24.692790084324347,0.7210130632693055,0.004441261291503906,4.59326 +440,Regression,Linear Regression with l1 regularization,ChickWeights,15.290646451171162,24.766775019882367,0.7392038606135755,0.004441261291503906,4.9573149999999995 +451,Regression,Linear Regression with l1 regularization,ChickWeights,15.806610158983217,25.370563596297366,0.7379667599208486,0.004441261291503906,5.321708999999999 +462,Regression,Linear Regression with l1 regularization,ChickWeights,16.91167446753811,27.489289014578034,0.711055524573946,0.004441261291503906,5.686440999999999 +473,Regression,Linear Regression with l1 regularization,ChickWeights,17.69453441784174,28.803034656505247,0.7198918720890418,0.004441261291503906,6.051508999999999 +484,Regression,Linear Regression with l1 regularization,ChickWeights,18.025914293879836,29.08166628667707,0.7300944167213836,0.004441261291503906,6.416912999999999 +495,Regression,Linear Regression with l1 regularization,ChickWeights,18.47687089345869,29.604201733284565,0.7368958634072684,0.004441261291503906,6.782651999999999 +506,Regression,Linear Regression with l1 regularization,ChickWeights,19.37032815671457,31.058772984483273,0.7188231637639817,0.004441261291503906,7.148725999999999 +517,Regression,Linear Regression with l1 regularization,ChickWeights,20.096649322747314,32.051830787895724,0.717419357352562,0.004441261291503906,7.515149999999999 +528,Regression,Linear Regression with l1 regularization,ChickWeights,20.88685610593147,33.24610520798377,0.7266504806846955,0.004441261291503906,7.882236999999999 +539,Regression,Linear Regression with l1 regularization,ChickWeights,21.052957054073875,33.24035912136826,0.7380286507287471,0.004441261291503906,8.25334 +550,Regression,Linear Regression with l1 regularization,ChickWeights,22.046178761536364,34.86098206113683,0.7207414968982613,0.004441261291503906,8.62558 +561,Regression,Linear Regression with l1 regularization,ChickWeights,22.751953045975853,35.78242297978339,0.7186502822700677,0.004441261291503906,8.998921 +572,Regression,Linear Regression with l1 regularization,ChickWeights,23.603432973098663,36.96472548228527,0.7222689970347711,0.004441261291503906,9.373355 +578,Regression,Linear Regression with l1 regularization,ChickWeights,23.757667537133976,37.078025255419426,0.7273605875689941,0.004441261291503906,9.748496 +20,Regression,Linear Regression with l1 regularization,TrumpApproval,20.96628233331211,24.387937149248955,-1394.0974368768457,0.005043983459472656,0.003367 +40,Regression,Linear Regression with l1 regularization,TrumpApproval,12.95809265443779,17.886947111698607,-127.62867621055315,0.005043983459472656,0.060679000000000004 +60,Regression,Linear Regression with l1 regularization,TrumpApproval,10.43403375286247,15.198987179765494,-124.2101566950438,0.005043983459472656,0.118829 +80,Regression,Linear Regression with l1 regularization,TrumpApproval,8.76952679896777,13.348146279436204,-95.87145335263979,0.005043983459472656,0.177742 +100,Regression,Linear Regression with l1 regularization,TrumpApproval,7.318348711169017,11.969856517585775,-47.87667264392048,0.005043983459472656,0.237441 +120,Regression,Linear Regression with l1 regularization,TrumpApproval,6.2853039116310185,10.94189036106609,-33.648027646243705,0.005043983459472656,0.297866 +140,Regression,Linear Regression with l1 regularization,TrumpApproval,5.5208355911538485,10.138862242229527,-29.74195117722151,0.005043983459472656,0.35901700000000003 +160,Regression,Linear Regression with l1 regularization,TrumpApproval,4.9080595636493145,9.487746704217276,-22.740310036230184,0.005043983459472656,0.420891 +180,Regression,Linear Regression with l1 regularization,TrumpApproval,4.437342628193194,8.948953859899,-17.549281500204398,0.005043983459472656,0.483487 +200,Regression,Linear Regression with l1 regularization,TrumpApproval,4.020740144728086,8.490404067975657,-15.746680942149272,0.005043983459472656,0.546844 +220,Regression,Linear Regression with l1 regularization,TrumpApproval,3.702540763677515,8.09713522450445,-15.430052960036054,0.005043983459472656,0.610978 +240,Regression,Linear Regression with l1 regularization,TrumpApproval,3.449057445346116,7.7551931287900455,-14.185073150160106,0.005043983459472656,0.675884 +260,Regression,Linear Regression with l1 regularization,TrumpApproval,3.201640426877581,7.451485247160068,-13.20791735379428,0.005204200744628906,0.741568 +280,Regression,Linear Regression with l1 regularization,TrumpApproval,2.9861522146348123,7.180696949733205,-12.814002869999907,0.005204200744628906,0.808037 +300,Regression,Linear Regression with l1 regularization,TrumpApproval,2.8260389726991693,6.939608203297966,-11.688379207589731,0.005204200744628906,0.926618 +320,Regression,Linear Regression with l1 regularization,TrumpApproval,2.694730270614988,6.722171113188908,-11.495217468089896,0.005204200744628906,1.2155880000000001 +340,Regression,Linear Regression with l1 regularization,TrumpApproval,2.572442774284147,6.524300196624447,-11.438471282384336,0.005204200744628906,1.5070640000000002 +360,Regression,Linear Regression with l1 regularization,TrumpApproval,2.4832798669216825,6.3452949037256134,-10.86190793294698,0.005204200744628906,1.8008990000000002 +380,Regression,Linear Regression with l1 regularization,TrumpApproval,2.371542642654472,6.177015076243767,-10.629949316856383,0.005204200744628906,2.0970690000000003 +400,Regression,Linear Regression with l1 regularization,TrumpApproval,2.263251524870982,6.020874949010495,-10.361708857360679,0.005204200744628906,2.4016 +420,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1669018257777095,5.8760767656227735,-10.179937857653263,0.005204200744628906,2.706944 +440,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1025509089011916,5.743886676480224,-9.489284487381322,0.005204200744628906,3.013076 +460,Regression,Linear Regression with l1 regularization,TrumpApproval,2.0360304025506277,5.61908468135586,-8.519416508014515,0.005204200744628906,3.319991 +480,Regression,Linear Regression with l1 regularization,TrumpApproval,1.9657178079674962,5.50138701729293,-7.914879120336785,0.005204200744628906,3.627685 +500,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8948913466896102,5.390446783732167,-7.379388774419297,0.005204200744628906,3.9361800000000002 +520,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8304411336225566,5.286008256480869,-7.071904701569496,0.005204200744628906,4.245555 +540,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7733791235095338,5.187623645241403,-6.74573862520947,0.005204200744628906,4.555757000000001 +560,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7328732375480083,5.096231477200102,-6.653340289034931,0.005204200744628906,4.866786000000001 +580,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6922671720641331,5.009032279128942,-6.5765398617523605,0.005204200744628906,5.1996410000000015 +600,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6600221636451293,4.9270067527590165,-6.249341959517198,0.005204200744628906,5.545038000000002 +620,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6169171465584515,4.847662648980224,-5.910766757861972,0.005204200744628906,5.892753000000002 +640,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5787668849144931,4.771995268006674,-5.5715350899413965,0.005204200744628906,6.242777000000002 +660,Regression,Linear Regression with l1 regularization,TrumpApproval,1.535700232104731,4.69925054984221,-5.326885534626132,0.005204200744628906,6.599796000000002 +680,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5003699975160405,4.630081239411466,-5.239062722957792,0.005204200744628906,6.957619000000002 +700,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4782734303433982,4.565354365023557,-5.225160013321354,0.005204200744628906,7.316272000000002 +720,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4563696019956498,4.503833132228122,-5.19161922746511,0.005204200744628906,7.675697000000002 +740,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4392280778003554,4.445645440595998,-5.02903742417401,0.005204200744628906,8.035893000000002 +760,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4073407178561264,4.387021097703184,-4.9346827726614455,0.005204200744628906,8.396854000000001 +780,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3782504190107006,4.330701361336262,-4.809192109617374,0.005204200744628906,8.758623000000002 +800,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3571814777264213,4.277370073659861,-4.718248073230613,0.005204200744628906,9.121240000000002 +820,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3328025450945626,4.2253925636381995,-4.641399853721709,0.005204200744628906,9.484674000000002 +840,Regression,Linear Regression with l1 regularization,TrumpApproval,1.311715211433691,4.175582527272098,-4.560327645533724,0.005204200744628906,9.848922000000002 +860,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2897432923325247,4.127236925138345,-4.422957957045758,0.005204200744628906,10.213983000000002 +880,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2672991203860131,4.080383024210964,-4.274192362897992,0.005204200744628906,10.579853000000002 +900,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2421842209255052,4.03488734209176,-4.178968845613636,0.005204200744628906,10.965988000000001 +920,Regression,Linear Regression with l1 regularization,TrumpApproval,1.220808929344255,3.991045752926761,-4.15028972045945,0.005204200744628906,11.354575 +940,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2057181063421543,3.9494511154557617,-4.0862825167589865,0.005204200744628906,11.745471 +960,Regression,Linear Regression with l1 regularization,TrumpApproval,1.188437369603739,3.9086583836793856,-4.0338431039290805,0.005204200744628906,12.138778 +980,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1710173649101312,3.8690392813429555,-4.028105053503917,0.005204200744628906,12.545048 +1000,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1544521877618488,3.8306020851942315,-4.0116636492940465,0.005204200744628906,12.952187 +1001,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1537672749321948,3.8287168981917103,-4.010074752320696,0.005204200744628906,13.359495 +11,Regression,Linear Regression with l2 regularization,ChickWeights,30.6062254572366,31.39938120772091,-1268.1112549740517,0.004153251647949219,0.000711 +22,Regression,Linear Regression with l2 regularization,ChickWeights,21.412737763681047,23.97862157826266,-607.9443275975191,0.004153251647949219,0.001889 +33,Regression,Linear Regression with l2 regularization,ChickWeights,15.119104680903606,19.655410372524667,-267.315679768846,0.004153251647949219,0.003406 +44,Regression,Linear Regression with l2 regularization,ChickWeights,11.691588950452092,17.042779535378298,-227.6797328948204,0.004153251647949219,0.00525 +55,Regression,Linear Regression with l2 regularization,ChickWeights,11.128477598777668,17.570968714531574,-59.26944361385635,0.004153251647949219,0.0074210000000000005 +66,Regression,Linear Regression with l2 regularization,ChickWeights,10.755656716101159,16.483156797846284,-21.862958739409084,0.004153251647949219,0.009919 +77,Regression,Linear Regression with l2 regularization,ChickWeights,10.454334080303978,15.644372833730271,-12.803711937026078,0.004153251647949219,0.012745000000000001 +88,Regression,Linear Regression with l2 regularization,ChickWeights,9.893519322025275,14.807378680481822,-10.212022929829027,0.004153251647949219,0.015896 +99,Regression,Linear Regression with l2 regularization,ChickWeights,9.219705201317108,14.044546137802199,-7.436202462041329,0.004153251647949219,0.019372 +110,Regression,Linear Regression with l2 regularization,ChickWeights,8.828389618716818,13.455080798744472,-4.4070097733575375,0.004153251647949219,0.023173 +121,Regression,Linear Regression with l2 regularization,ChickWeights,8.61456960864212,13.037583740326507,-2.9098467157738415,0.004153251647949219,0.027299 +132,Regression,Linear Regression with l2 regularization,ChickWeights,8.52880743945525,12.690080989153241,-2.0274307958032884,0.004153251647949219,0.031782 +143,Regression,Linear Regression with l2 regularization,ChickWeights,8.39143583855712,12.359614263508039,-1.3260696348061907,0.004153251647949219,0.036638 +154,Regression,Linear Regression with l2 regularization,ChickWeights,8.12180315101294,12.009375103170282,-0.866389387173786,0.004153251647949219,0.041874999999999996 +165,Regression,Linear Regression with l2 regularization,ChickWeights,8.136940986261356,11.920551719153746,-0.47822185831879493,0.004153251647949219,0.04749199999999999 +176,Regression,Linear Regression with l2 regularization,ChickWeights,8.284290032332207,11.93362687305613,-0.19451089204457617,0.004153251647949219,0.05348299999999999 +187,Regression,Linear Regression with l2 regularization,ChickWeights,8.390309464431912,11.903488345267945,0.026908395403585694,0.004153251647949219,0.05984699999999999 +198,Regression,Linear Regression with l2 regularization,ChickWeights,8.350219958465262,11.791481226840993,0.2404518209934976,0.004153251647949219,0.06659 +209,Regression,Linear Regression with l2 regularization,ChickWeights,8.499019855105985,11.958125495095471,0.3560283448388185,0.004153251647949219,0.073713 +220,Regression,Linear Regression with l2 regularization,ChickWeights,8.90272187978296,12.527163169679886,0.3812690011074207,0.004153251647949219,0.081218 +231,Regression,Linear Regression with l2 regularization,ChickWeights,9.171291167504231,12.73748746029564,0.45283948771259386,0.004153251647949219,0.08971 +242,Regression,Linear Regression with l2 regularization,ChickWeights,9.37629466014084,13.047657656056804,0.538024139715424,0.004153251647949219,0.099295 +253,Regression,Linear Regression with l2 regularization,ChickWeights,9.440817816219349,13.0964165059942,0.5957634168273553,0.004153251647949219,0.110134 +264,Regression,Linear Regression with l2 regularization,ChickWeights,9.906487060964153,13.855497684527965,0.5866088718530376,0.004233360290527344,0.12224199999999999 +275,Regression,Linear Regression with l2 regularization,ChickWeights,10.387009537918406,14.786939232799543,0.5856869069436603,0.004233360290527344,0.13552899999999998 +286,Regression,Linear Regression with l2 regularization,ChickWeights,10.701469010841246,15.270898285463774,0.6172820078624095,0.004233360290527344,0.14989199999999997 +297,Regression,Linear Regression with l2 regularization,ChickWeights,10.689852199892528,15.284847538688991,0.6674656839655615,0.004233360290527344,0.17720199999999997 +308,Regression,Linear Regression with l2 regularization,ChickWeights,11.168487287417783,16.008183102465477,0.6751444757196481,0.004233360290527344,0.20585199999999998 +319,Regression,Linear Regression with l2 regularization,ChickWeights,12.085867087734242,18.170753499240714,0.6201764868699093,0.004233360290527344,0.23488799999999999 +330,Regression,Linear Regression with l2 regularization,ChickWeights,12.672501856506585,19.05837058612535,0.6424226539377311,0.004233360290527344,0.26428199999999996 +341,Regression,Linear Regression with l2 regularization,ChickWeights,12.822446828447035,19.13937756684808,0.6770787925994421,0.004233360290527344,0.29402799999999996 +352,Regression,Linear Regression with l2 regularization,ChickWeights,13.055746883990931,19.312136445778254,0.7011272480618885,0.004233360290527344,0.32412699999999994 +363,Regression,Linear Regression with l2 regularization,ChickWeights,13.79008745873622,20.396105048894267,0.6762859401979866,0.004233360290527344,0.35457799999999995 +374,Regression,Linear Regression with l2 regularization,ChickWeights,14.293199062265238,21.539399675842862,0.6681199603719434,0.004233360290527344,0.38537999999999994 +385,Regression,Linear Regression with l2 regularization,ChickWeights,14.740320816630273,22.311026164960477,0.6849554171717112,0.004233360290527344,0.42616299999999996 +396,Regression,Linear Regression with l2 regularization,ChickWeights,14.862968645899144,22.294096988116678,0.7067005430463744,0.004233360290527344,0.467315 +407,Regression,Linear Regression with l2 regularization,ChickWeights,15.699705023283963,23.67314903355933,0.6925996644733732,0.004233360290527344,0.508826 +418,Regression,Linear Regression with l2 regularization,ChickWeights,16.38213993729544,25.048095107979137,0.6769473375050636,0.004233360290527344,0.55069 +429,Regression,Linear Regression with l2 regularization,ChickWeights,16.967894830794286,26.153201890569886,0.6870368010887093,0.004233360290527344,0.592905 +440,Regression,Linear Regression with l2 regularization,ChickWeights,17.10728249235129,26.204092785638924,0.7080553660644732,0.004233360290527344,0.6354690000000001 +451,Regression,Linear Regression with l2 regularization,ChickWeights,17.603016925007317,26.772391386711114,0.7082099437723521,0.004233360290527344,0.6783830000000001 +462,Regression,Linear Regression with l2 regularization,ChickWeights,18.614531201761594,28.786744962703725,0.6831362914484524,0.004233360290527344,0.7216460000000001 +473,Regression,Linear Regression with l2 regularization,ChickWeights,19.488293352005442,30.38515335394973,0.6882746780375071,0.004233360290527344,0.7652570000000001 +484,Regression,Linear Regression with l2 regularization,ChickWeights,19.755002868307955,30.52390276571354,0.7026599444855313,0.004233360290527344,0.8092140000000001 +495,Regression,Linear Regression with l2 regularization,ChickWeights,20.22217092676305,31.08727194033441,0.7098743070293987,0.004233360290527344,0.8535240000000001 +506,Regression,Linear Regression with l2 regularization,ChickWeights,21.03670858216615,32.44431034253017,0.6931769059461363,0.004233360290527344,0.898204 +517,Regression,Linear Regression with l2 regularization,ChickWeights,21.78200415465676,33.496021791915204,0.6913806254796178,0.004233360290527344,0.943264 +528,Regression,Linear Regression with l2 regularization,ChickWeights,22.56258004106143,34.768391171729405,0.7010449079513538,0.004233360290527344,0.9886969999999999 +539,Regression,Linear Regression with l2 regularization,ChickWeights,22.68725373887437,34.77075336357408,0.7133508993505916,0.004233360290527344,1.0345 +550,Regression,Linear Regression with l2 regularization,ChickWeights,23.627725892037507,36.324416048782524,0.6968033114915981,0.004233360290527344,1.080674 +561,Regression,Linear Regression with l2 regularization,ChickWeights,24.347376192466918,37.30920796407717,0.6941284720923248,0.004233360290527344,1.127216 +572,Regression,Linear Regression with l2 regularization,ChickWeights,25.18573737545828,38.51358935872805,0.698506895988072,0.004233360290527344,1.174127 +578,Regression,Linear Regression with l2 regularization,ChickWeights,25.27380465992389,38.58852748240754,0.7046942807227952,0.004233360290527344,1.2212839999999998 +20,Regression,Linear Regression with l2 regularization,TrumpApproval,20.994354275814885,24.339467027537435,-1388.5575385664913,0.004836082458496094,0.002841 +40,Regression,Linear Regression with l2 regularization,TrumpApproval,12.808927193108108,17.83271591943186,-126.84988353201342,0.004836082458496094,0.0066630000000000005 +60,Regression,Linear Regression with l2 regularization,TrumpApproval,10.864002308096953,15.320672400398038,-126.22308256175273,0.004836082458496094,0.011298 +80,Regression,Linear Regression with l2 regularization,TrumpApproval,8.882777304938948,13.38981065066765,-96.4771385394691,0.004836082458496094,0.01675 +100,Regression,Linear Regression with l2 regularization,TrumpApproval,7.231639558854497,11.98203471414171,-47.97617801736401,0.004836082458496094,0.023066000000000003 +120,Regression,Linear Regression with l2 regularization,TrumpApproval,6.334108393931037,10.98237795329033,-33.904913895880355,0.004836082458496094,0.030179000000000004 +140,Regression,Linear Regression with l2 regularization,TrumpApproval,5.563493982833803,10.178707085968126,-29.98405233271513,0.004836082458496094,0.038033000000000004 +160,Regression,Linear Regression with l2 regularization,TrumpApproval,5.002122045077101,9.533278572445496,-22.968717144675633,0.004836082458496094,0.04831800000000001 +180,Regression,Linear Regression with l2 regularization,TrumpApproval,4.587842803317817,9.003737317880292,-17.777085610739057,0.004836082458496094,0.07294600000000001 +200,Regression,Linear Regression with l2 regularization,TrumpApproval,4.458683971614509,8.652080760634158,-16.390543570087573,0.004836082458496094,0.09946700000000001 +220,Regression,Linear Regression with l2 regularization,TrumpApproval,4.239995800771734,8.280452519944822,-16.182419642449048,0.004836082458496094,0.129303 +240,Regression,Linear Regression with l2 regularization,TrumpApproval,3.943592784264584,7.932077353220182,-14.885669934585447,0.004836082458496094,0.159907 +260,Regression,Linear Regression with l2 regularization,TrumpApproval,3.7846302486799286,7.646201644169009,-13.960159512708739,0.004996299743652344,0.191262 +280,Regression,Linear Regression with l2 regularization,TrumpApproval,3.6468171672887713,7.389977926170562,-13.630953412847402,0.004996299743652344,0.22336299999999998 +300,Regression,Linear Regression with l2 regularization,TrumpApproval,3.5261123680869226,7.1621871014808685,-12.51535851099468,0.004996299743652344,0.25877399999999995 +320,Regression,Linear Regression with l2 regularization,TrumpApproval,3.5074300839639245,6.985469271455791,-12.493228210862723,0.004996299743652344,0.29652399999999995 +340,Regression,Linear Regression with l2 regularization,TrumpApproval,3.434140699763514,6.814822943627961,-12.570888751300782,0.004996299743652344,0.33652199999999993 +360,Regression,Linear Regression with l2 regularization,TrumpApproval,3.4272200155971797,6.678288393486038,-12.13957341395007,0.004996299743652344,0.3787709999999999 +380,Regression,Linear Regression with l2 regularization,TrumpApproval,3.332029752839207,6.516115548498917,-11.941900403072644,0.004996299743652344,0.4232519999999999 +400,Regression,Linear Regression with l2 regularization,TrumpApproval,3.217390968362987,6.356555790563252,-11.66392028459017,0.004996299743652344,0.4696459999999999 +420,Regression,Linear Regression with l2 regularization,TrumpApproval,3.100825681509746,6.206562691759863,-11.472880484909139,0.004996299743652344,0.516851 +440,Regression,Linear Regression with l2 regularization,TrumpApproval,3.0187726323631243,6.072312098448126,-10.723095644711893,0.004996299743652344,0.5652379999999999 +460,Regression,Linear Regression with l2 regularization,TrumpApproval,2.947022825868371,5.94849802587685,-9.668265577306911,0.004996299743652344,0.6161209999999999 +480,Regression,Linear Regression with l2 regularization,TrumpApproval,2.867282537241402,5.828292237410032,-9.005843404687633,0.004996299743652344,0.6743809999999999 +500,Regression,Linear Regression with l2 regularization,TrumpApproval,2.8281006485905213,5.729646774374514,-8.467133754251039,0.004996299743652344,0.7334769999999999 +520,Regression,Linear Regression with l2 regularization,TrumpApproval,2.759113137285707,5.623931694381955,-8.136932704030892,0.004996299743652344,0.79343 +540,Regression,Linear Regression with l2 regularization,TrumpApproval,2.7113951403332286,5.52770300084093,-7.7945843187225226,0.004996299743652344,0.8541829999999999 +560,Regression,Linear Regression with l2 regularization,TrumpApproval,2.646739535451309,5.4320905955210534,-7.695343452205655,0.004996299743652344,0.9420199999999999 +580,Regression,Linear Regression with l2 regularization,TrumpApproval,2.5972398336076634,5.343168086286508,-7.621065103502998,0.004996299743652344,1.0306859999999998 +600,Regression,Linear Regression with l2 regularization,TrumpApproval,2.533455116608919,5.255265792942869,-7.247487071141652,0.004996299743652344,1.1201999999999999 +620,Regression,Linear Regression with l2 regularization,TrumpApproval,2.497138699914293,5.178243230235351,-6.88544757519055,0.004996299743652344,1.2104949999999999 +640,Regression,Linear Regression with l2 regularization,TrumpApproval,2.4712145738198297,5.107804033669319,-6.528964961790648,0.004996299743652344,1.3015299999999999 +660,Regression,Linear Regression with l2 regularization,TrumpApproval,2.429247896498525,5.0347117637840935,-6.262430681498119,0.004996299743652344,1.3932969999999998 +680,Regression,Linear Regression with l2 regularization,TrumpApproval,2.3980901245026116,4.967521902410674,-6.18160824182094,0.004996299743652344,1.4857959999999997 +700,Regression,Linear Regression with l2 regularization,TrumpApproval,2.360396901712673,4.90286744871834,-6.1796262474179136,0.004996299743652344,1.5790619999999997 +720,Regression,Linear Regression with l2 regularization,TrumpApproval,2.3150393936015323,4.836721469702358,-6.140716883970916,0.004996299743652344,1.6730619999999998 +740,Regression,Linear Regression with l2 regularization,TrumpApproval,2.267679208737699,4.77254376860168,-5.948293801048677,0.004996299743652344,1.7677929999999997 +760,Regression,Linear Regression with l2 regularization,TrumpApproval,2.2434173929652075,4.715867112708654,-5.857742612566196,0.004996299743652344,1.8632789999999997 +780,Regression,Linear Regression with l2 regularization,TrumpApproval,2.199009654391343,4.656030786420359,-5.714767077599112,0.004996299743652344,1.9595409999999998 +800,Regression,Linear Regression with l2 regularization,TrumpApproval,2.1596596811720175,4.59898830049537,-5.610494506343661,0.004996299743652344,2.0566079999999998 +820,Regression,Linear Regression with l2 regularization,TrumpApproval,2.1249482408574707,4.545176313025559,-5.527610390446187,0.004996299743652344,2.175652 +840,Regression,Linear Regression with l2 regularization,TrumpApproval,2.094058354623314,4.493551443258636,-5.439404045425388,0.004996299743652344,2.37025 +860,Regression,Linear Regression with l2 regularization,TrumpApproval,2.062104039794744,4.442864622918497,-5.284107374622643,0.004996299743652344,2.565666 +880,Regression,Linear Regression with l2 regularization,TrumpApproval,2.0307065941401414,4.393695684791879,-5.115247638705276,0.004996299743652344,2.7618669999999996 +900,Regression,Linear Regression with l2 regularization,TrumpApproval,2.003263565299311,4.347049681772325,-5.011317673951863,0.004996299743652344,2.9588469999999996 +920,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9706878923964866,4.300488305941944,-4.979898179552831,0.004996299743652344,3.1566579999999997 +940,Regression,Linear Regression with l2 regularization,TrumpApproval,1.949819924248383,4.257394252920471,-4.91037086709413,0.004996299743652344,3.355253 +960,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9258186229947107,4.214725843085415,-4.85305905428677,0.004996299743652344,3.5546249999999997 +980,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9004260103609922,4.173138113177231,-4.849565137575967,0.004996299743652344,3.754774 +1000,Regression,Linear Regression with l2 regularization,TrumpApproval,1.872733130377695,4.13253797119814,-4.832859832721421,0.004996299743652344,3.955698 +1001,Regression,Linear Regression with l2 regularization,TrumpApproval,1.871510887330926,4.130524228438989,-4.8310671605777085,0.004996299743652344,4.156775 +11,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,26.624124996337724,28.77138517975663,-1064.5628215382144,0.0034055709838867188,0.000572 +22,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.0510878175865,20.931739283093208,-463.02330712701985,0.0034055709838867188,0.001645 +33,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.49930786476168,17.564629142555763,-213.26922094451623,0.0034055709838867188,0.003059 +44,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.378514545021682,15.405121473747096,-185.84310618709696,0.0034055709838867188,0.004809 +55,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.844108697295251,17.128215293517524,-56.27037115396167,0.0034055709838867188,0.0068920000000000006 +66,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.889488781892217,15.88743125142584,-20.240220516271876,0.0034055709838867188,0.009306 +77,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.103343480706034,14.91594241381016,-11.548186613409534,0.0034055709838867188,0.012052 +88,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.288900850158633,14.011374344891147,-9.038968322803399,0.0034055709838867188,0.015129 +99,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.736865157066078,13.281093172283262,-6.5439573170464564,0.0034055709838867188,0.018712 +110,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.618125386224052,12.858171267844924,-3.9379074608874927,0.0034055709838867188,0.022717 +121,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.580936033253089,12.515247629942861,-2.6028352541815996,0.0034055709838867188,0.027127000000000002 +132,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.191573127926202,12.024287681643044,-1.7180920054032294,0.0034055709838867188,0.031928 +143,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.001452140019149,11.63905100750295,-1.062756769701151,0.0034055709838867188,0.037113999999999994 +154,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.959260067984971,11.397763679955697,-0.6811278108981134,0.0034055709838867188,0.04268899999999999 +165,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.036161429677985,11.359538570018055,-0.3423577921849861,0.0034055709838867188,0.04865399999999999 +176,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.141200516910354,11.407680550849575,-0.09154063283497149,0.0034055709838867188,0.05501099999999999 +187,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.061965626679777,11.211626858308708,0.1367382583661435,0.0034055709838867188,0.06175399999999999 +198,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.988600359846859,11.023879576943443,0.33612315722527375,0.0034055709838867188,0.06888499999999999 +209,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.115468527113427,11.18859440458875,0.43624345152336885,0.0034055709838867188,0.07639599999999999 +220,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.571784360381598,11.998798941058181,0.4323613497222297,0.0034055709838867188,0.08428799999999999 +231,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.610536559977233,11.962244483436113,0.5174164020157423,0.0034055709838867188,0.09375399999999999 +242,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.753677752144043,12.109970858596688,0.6020391290764042,0.0034055709838867188,0.10451099999999999 +253,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.763402728464486,12.046916639776326,0.6579556132088519,0.0034055709838867188,0.123112 +264,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.37232599699494,12.938281421109101,0.6395292100633578,0.0034589767456054688,0.147917 +275,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.870502401884236,14.03783628218945,0.6266016212673495,0.0034589767456054688,0.173118 +286,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.125553299295866,14.312481045438886,0.6638140497188074,0.0034589767456054688,0.198688 +297,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.11642729851449,14.234872044017683,0.7115826499817814,0.0034589767456054688,0.225283 +308,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.63053955101658,15.01159987060024,0.7143329631149452,0.0034589767456054688,0.252265 +319,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.671899739762464,17.42953249336733,0.650531972004655,0.0034589767456054688,0.27961600000000003 +330,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.113559839827301,17.980470366868552,0.6817264420663893,0.0034589767456054688,0.307329 +341,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.368994570730054,18.183536514460908,0.7085274545262112,0.0034589767456054688,0.335405 +352,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.47998520043724,18.216810890558104,0.7340681346165732,0.0034589767456054688,0.363842 +363,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.490995837872445,19.84181186896939,0.693641639088806,0.0034589767456054688,0.392636 +374,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.988870134156189,20.81926805033374,0.6899406322869175,0.0034589767456054688,0.42178699999999997 +385,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.420579982415202,21.48960215237335,0.7077263415053474,0.0034589767456054688,0.45129199999999997 +396,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.424816444492956,21.37796604773129,0.7303103668220552,0.0034589767456054688,0.481151 +407,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,14.284688005634004,22.701575115822557,0.7173140296578691,0.0034589767456054688,0.514769 +418,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.038658536726118,24.042516108283174,0.7023651722582169,0.0034589767456054688,0.548784 +429,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.590290098257741,24.916858152232297,0.7159269075042054,0.0034589767456054688,0.583167 +440,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.812702077031824,25.072500493300815,0.7327254930224041,0.0034589767456054688,0.617914 +451,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.346042839206106,25.68091484988461,0.7315167856134144,0.0034589767456054688,0.653021 +462,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,17.370765923434053,27.689388199834635,0.7068336630361484,0.0034589767456054688,0.688487 +473,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.264179516209435,29.30099868065636,0.7101227966068765,0.0034589767456054688,0.724309 +484,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.63502154656571,29.559619400414906,0.7211497930314269,0.0034589767456054688,0.760485 +495,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.145243718121584,30.130361606680754,0.7274603750306702,0.0034589767456054688,0.7970149999999999 +506,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.98075812634153,31.43770898148617,0.7119202509985216,0.0034589767456054688,0.8338999999999999 +517,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,20.7046141289421,32.42665929478992,0.7107714616434232,0.0034589767456054688,0.8711439999999999 +528,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.54126059149082,33.75343345950398,0.7182443212146572,0.0034589767456054688,0.908739 +539,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.736037457517718,33.829762552174344,0.7286559632490104,0.0034589767456054688,0.946687 +550,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,22.674609740448528,35.33904665998618,0.7130297805712756,0.0034589767456054688,0.984985 +561,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,23.350956760305525,36.24046007710213,0.7114012814424304,0.0034589767456054688,1.023633 +572,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.20743030595361,37.47019278346573,0.7146215025224946,0.0034589767456054688,1.06263 +578,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.342328163686027,37.59599019491026,0.7196900586014492,0.0034589767456054688,1.1018649999999999 +20,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,20.806898309502586,26.56763494383828,-1654.6182189603317,0.004302024841308594,0.003003 +40,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,14.866074912822512,20.957300378156614,-175.5777711351631,0.004302024841308594,0.009504 +60,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,11.772648582583251,17.555009093750932,-166.03688377592212,0.004302024841308594,0.016813 +80,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,10.372925375947808,15.758572852966298,-134.01675577859288,0.004302024841308594,0.024890000000000002 +100,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.950999863257042,14.807263848606526,-73.79513907078027,0.004302024841308594,0.033777 +120,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.131163180965077,13.743973626529105,-53.66614209724606,0.004302024841308594,0.043434 +140,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.532294463666167,12.935885124148239,-49.04322437944699,0.004302024841308594,0.05944 +160,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.33219708929472,12.40854626547306,-39.60710527883104,0.004302024841308594,0.077842 +180,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.281092452540433,12.043698542516514,-32.597139849683785,0.004302024841308594,0.09862299999999999 +200,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.889313429527772,11.548268653424005,-29.981738551789036,0.004302024841308594,0.121857 +220,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.555718436766954,11.115454500430198,-29.962115725262894,0.004302024841308594,0.149384 +240,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.300584612865839,10.768588372618428,-28.278525817685868,0.004302024841308594,0.177703 +260,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.073956995660685,10.455941089275187,-26.975057358486694,0.004435539245605469,0.206834 +280,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.879100927439736,10.179149173565092,-26.75935065850941,0.004435539245605469,0.236782 +300,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.698392466938299,9.935855831167725,-25.01038668400382,0.004435539245605469,0.26759 +320,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.496977203333427,9.674599820332077,-24.881575653507443,0.004435539245605469,0.299212 +340,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.319501534649956,9.433800456219284,-25.005937123592886,0.004435539245605469,0.33164299999999997 +360,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.189316591643737,9.224778235838508,-24.070488458586468,0.004435539245605469,0.36488499999999996 +380,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.05373584315195,9.026036673488779,-23.83217081250324,0.004435539245605469,0.39894499999999994 +400,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.893196935767096,8.831009888188627,-23.44247524737401,0.004435539245605469,0.43386499999999995 +420,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.787168115685009,8.669033337133486,-23.33356914323267,0.004435539245605469,0.46960899999999994 +440,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.789860410241021,8.633597516354541,-22.69832962851382,0.004435539245605469,0.5061589999999999 +460,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.751464501173282,8.532971696368575,-20.952287666855003,0.004435539245605469,0.5516559999999999 +480,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.758413491181221,8.476961067588123,-20.166616413985547,0.004435539245605469,0.599555 +500,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.682950272504451,8.3510488559106,-19.111518541810455,0.004435539245605469,0.6519079999999999 +520,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.627995468360723,8.245754355787446,-18.64179334000355,0.004435539245605469,0.705132 +540,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.546541731300828,8.130789587119862,-18.02792190581397,0.004435539245605469,0.7591289999999999 +560,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.474658569482086,8.019262742277965,-17.95054121046633,0.004435539245605469,0.813877 +580,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.409420416004319,7.920158789530457,-17.94222667017848,0.004435539245605469,0.869386 +600,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.394854582323811,7.870548110777217,-17.498743363524373,0.004435539245605469,0.941553 +620,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.360408122735632,7.801849933723111,-16.900148820132806,0.004435539245605469,1.016153 +640,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.332182524169608,7.745335706289596,-16.312002846243146,0.004435539245605469,1.093294 +660,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.286484086266954,7.672164501241343,-15.864310422998045,0.004435539245605469,1.1728 +680,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.240017672508232,7.591734569529257,-15.773518040276521,0.004435539245605469,1.259908 +700,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.203631741702394,7.526058935068808,-15.917522479350382,0.004435539245605469,1.347915 +720,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.1985518333986755,7.481861117849272,-16.086729414362967,0.004435539245605469,1.436748 +740,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.200051628353664,7.4433149034441595,-15.900950117878587,0.004435539245605469,1.526395 +760,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.146415466772512,7.367313347205083,-15.736951087672441,0.004435539245605469,1.61685 +780,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.164438314106662,7.352756459959702,-15.745558187055657,0.004435539245605469,1.708114 +800,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.199091748701669,7.381485816300255,-16.029304780133675,0.004435539245605469,1.8001699999999998 +820,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.184244405270293,7.343677512392477,-16.040406471643664,0.004435539245605469,1.892982 +840,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.162940711797175,7.2951968772545595,-15.972283354719572,0.004435539245605469,1.986541 +860,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.146772746928229,7.251973114148485,-15.742866229030533,0.004435539245605469,2.080851 +880,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.141562534384022,7.225910341165371,-15.54014218136778,0.004435539245605469,2.175912 +900,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.113671043317916,7.181653170625269,-15.40700562565575,0.004435539245605469,2.271722 +920,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.0827257569327715,7.134180367835326,-15.456838882747107,0.004435539245605469,2.368328 +940,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.049460198345376,7.092641752853287,-15.403746251323405,0.004435539245605469,2.491683 +960,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.012955702794688,7.041188655025779,-15.335641983730405,0.004435539245605469,2.616037 +980,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.992587411597517,7.002009756347646,-15.468099715303381,0.004435539245605469,2.7412199999999998 +1000,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.986581819477306,6.97972894589718,-15.638912943338369,0.004435539245605469,2.867218 +1001,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.984033991902679,6.9766666383395455,-15.63541499061877,0.004435539245605469,2.993378 +11,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,39.19936706045659,55.118879370280126,-3909.733983269086,0.0034055709838867188,0.001533 +22,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.495026158423794,43.23165104261441,-1978.3965328342838,0.0034055709838867188,0.004589 +33,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,30.680053698816124,39.985066603327745,-1109.3949268723327,0.0034055709838867188,0.008788 +44,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,29.375885022911746,37.29886968855784,-1094.3128086885838,0.0034055709838867188,0.014141 +55,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.707444751978134,40.753235251415205,-323.21264874535376,0.0034055709838867188,0.020635 +66,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.96097441162184,40.14945868859866,-134.64726490280094,0.0034055709838867188,0.028271 +77,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.25989567011213,39.82501544894248,-88.45229320906665,0.0034055709838867188,0.041195 +88,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.76307262878121,39.802536485586,-80.01195436020778,0.0034055709838867188,0.054526000000000005 +99,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.66411513705659,39.325402336106926,-65.1420916497486,0.0034055709838867188,0.06822700000000001 +110,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.19940912800194,40.704130728492046,-48.48362457590105,0.0034055709838867188,0.082293 +121,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.629161705635866,40.92880988729008,-37.532194399087835,0.0034055709838867188,0.096719 +132,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,35.29035427006805,41.59178542812187,-31.520750879588867,0.0034055709838867188,0.111503 +143,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.23638449140802,42.62018794050648,-26.659453761649477,0.0034055709838867188,0.126644 +154,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.725010132899186,42.91835139661213,-22.836775105518452,0.0034055709838867188,0.14214100000000002 +165,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.731745662210095,42.91744223234227,-18.16084111691443,0.0034055709838867188,0.15799600000000003 +176,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.94402632003076,44.39720610255875,-15.533183531813599,0.0034055709838867188,0.17420800000000003 +187,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,38.698580833397834,45.06856008203835,-12.949305609255877,0.0034055709838867188,0.19077600000000003 +198,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.18624064352699,46.68267333461602,-10.905017439998023,0.0034055709838867188,0.20770000000000002 +209,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.854323276826534,47.463811090322665,-9.145320047375163,0.0034055709838867188,0.224978 +220,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,41.36451127701117,48.41262940233051,-8.240888884363313,0.0034055709838867188,0.24261000000000002 +231,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,42.17342712408468,49.46918668675267,-7.253093192412523,0.0034055709838867188,0.260594 +242,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,43.81461612103895,51.73551020684679,-6.2632609796369465,0.0034055709838867188,0.27893 +253,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,44.90819615603068,53.07334253773936,-5.6387075541588505,0.0034055709838867188,0.297618 +264,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,46.45334973048907,55.82244674340302,-5.710187070138379,0.0034589767456054688,0.31665899999999997 +275,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,48.05643802527038,58.804796990371536,-5.552357606253864,0.0034589767456054688,0.33605399999999996 +286,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,49.41721923566732,60.72765972830183,-5.052332799264802,0.0034589767456054688,0.35580199999999995 +297,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,51.23299901747073,63.29154255446438,-4.701716347098143,0.0034589767456054688,0.37590499999999993 +308,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,52.82583967659276,65.36972550348784,-4.417008197806662,0.0034589767456054688,0.39636799999999994 +319,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,54.851023886215806,70.45860717413167,-4.71089374971376,0.0034589767456054688,0.41718399999999994 +330,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,56.58220488738844,72.62689780553444,-4.192702597603254,0.0034589767456054688,0.43835299999999994 +341,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,58.456862484765374,75.26810540469758,-3.994165343488624,0.0034589767456054688,0.45987999999999996 +352,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,59.98229295122657,76.97767263775137,-3.748486775975887,0.0034589767456054688,0.48176199999999997 +363,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,61.989108820835376,80.62951920841103,-4.0588898392459045,0.0034589767456054688,0.503996 +374,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,63.93796840595574,84.48840832488506,-4.106322034628877,0.0034589767456054688,0.526584 +385,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,65.15236861414519,85.79755918852514,-3.6588935912158114,0.0034589767456054688,0.551564 +396,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,66.90365663892747,87.9249329113371,-3.562003118430529,0.0034589767456054688,0.5777180000000001 +407,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,68.17917622540308,89.58756462611774,-3.402382103640547,0.0034589767456054688,0.6050150000000001 +418,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,70.80702754948452,94.96753809429286,-3.643808228470034,0.0034589767456054688,0.6334650000000001 +429,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,72.44730173566225,97.09455233033468,-3.313534410167211,0.0034589767456054688,0.663057 +440,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,74.29167351363806,99.40774027870643,-3.2014833153265316,0.0034589767456054688,0.693783 +451,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,75.83174494284101,101.77506329990584,-3.216760347648722,0.0034589767456054688,0.725638 +462,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,78.5111288104629,106.99570126481906,-3.3774383617967887,0.0034589767456054688,0.758621 +473,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,81.63741116996734,112.58139375423264,-3.2793958269429844,0.0034589767456054688,0.79391 +484,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,82.66628549198501,113.50934761838603,-3.1118432523868984,0.0034589767456054688,0.829617 +495,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,84.40016304476833,116.34990208847,-3.0639935553557365,0.0034589767456054688,0.865723 +506,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,86.52132256561038,120.30772815943004,-3.2188880650982723,0.0034589767456054688,0.902224 +517,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,87.79244029751037,121.63869088166206,-3.0698668478095374,0.0034589767456054688,0.9391200000000001 +528,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,90.43735682351402,126.62066541565774,-2.9650251625135784,0.0034589767456054688,0.9763980000000001 +539,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,91.59763342322412,127.62959496409519,-2.862114672867245,0.0034589767456054688,1.014034 +550,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,93.80067010965053,131.39026699356185,-2.9669210878459156,0.0034589767456054688,1.0520230000000002 +561,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,96.52355815418714,136.33091173427522,-3.0840934586689466,0.0034589767456054688,1.0903630000000002 +572,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,99.60515399822415,141.80943605664237,-3.0875177525301325,0.0034589767456054688,1.1290550000000001 +578,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,100.62422612381133,143.06646930774232,-3.0591132110693486,0.0034589767456054688,1.167982 +20,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,48.24517612267716,65.52170729560882,-10068.892101934754,0.004302024841308594,0.0014 +40,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,41.96170708962665,54.398737007050464,-1188.7151382109587,0.004302024841308594,0.0037089999999999996 +60,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,37.75687919715097,48.78450375470138,-1288.953469480389,0.004302024841308594,0.0068 +80,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,34.906129137913965,44.99379649673769,-1099.675197364534,0.004302024841308594,0.010662 +100,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,33.91700787894482,42.88559259598606,-626.4029768570122,0.004302024841308594,0.015342999999999999 +120,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,33.25318798467783,41.41783833748641,-495.44216046034904,0.004302024841308594,0.020797999999999997 +140,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.454169303664,40.065346416262614,-479.0547686921885,0.004302024841308594,0.027020999999999996 +160,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.456143135335843,38.757475924320815,-395.16052146995384,0.004302024841308594,0.034013999999999996 +180,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.609503890456164,37.605439707525925,-326.55474603918685,0.004302024841308594,0.04177499999999999 +200,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.18524212396377,36.915721425331306,-315.5882175367347,0.004302024841308594,0.05032999999999999 +220,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.065472528043387,36.44442035805252,-331.8421153382835,0.004302024841308594,0.05964399999999999 +240,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.78865598017145,35.91292614465666,-324.6366197799479,0.004302024841308594,0.06971499999999999 +260,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.781114432924582,35.79236523689611,-326.81251307944893,0.004435539245605469,0.08530999999999998 +280,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.533238425747374,35.35890478021234,-333.95306350592915,0.004435539245605469,0.10330299999999998 +300,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.457831453745214,35.09485674586195,-323.50634592736793,0.004435539245605469,0.12379499999999997 +320,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.587592426740265,34.99099947403571,-337.56135797788454,0.004435539245605469,0.14665799999999998 +340,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.592186767063264,34.82748458593961,-353.4405109424598,0.004435539245605469,0.178415 +360,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.811871336212132,34.87255971663822,-357.27671195740294,0.004435539245605469,0.21101599999999998 +380,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.96085998978186,34.8837386376585,-369.9082961036221,0.004435539245605469,0.24444299999999997 +400,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.10861760053803,34.89146879370085,-380.5600928496674,0.004435539245605469,0.27875099999999997 +420,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.237056214581205,34.87113676993804,-392.72834237954817,0.004435539245605469,0.31389799999999995 +440,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.396870134836657,34.919939008119975,-386.68686883790514,0.004435539245605469,0.34987699999999994 +460,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.514209015244397,34.936230377424984,-366.98596965456716,0.004435539245605469,0.38662699999999994 +480,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.60304766371323,34.90556469597589,-357.88920799289923,0.004435539245605469,0.42413599999999996 +500,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.723498435612367,34.929338036322235,-350.83863344189655,0.004435539245605469,0.462404 +520,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.811640107301315,34.91688659850233,-351.20164208687333,0.004435539245605469,0.5014689999999999 +540,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.90684609870959,34.93305250730057,-350.23597283973027,0.004435539245605469,0.541291 +560,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.83222834631613,34.80844611918478,-356.0442181025925,0.004435539245605469,0.58187 +580,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.81214247339674,34.7464796172207,-363.5733105101423,0.004435539245605469,0.629234 +600,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.96266506693229,34.82326914665847,-361.13570824853826,0.004435539245605469,0.6774979999999999 +620,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.066025409450507,34.86865150113252,-356.54586556020155,0.004435539245605469,0.743837 +640,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.17687176552783,34.929241693507976,-351.0830657253844,0.004435539245605469,0.810979 +660,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.17965741293356,34.892251240403844,-347.8114699445998,0.004435539245605469,0.87889 +680,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.2564554130016,34.924087575336145,-353.970503405387,0.004435539245605469,0.947564 +700,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.205643809070587,34.8991435368638,-362.77351000506667,0.004435539245605469,1.017054 +720,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.176512353694502,34.84497410939031,-369.61240318757564,0.004435539245605469,1.087302 +740,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.10578554229227,34.74995813099662,-367.37224871607793,0.004435539245605469,1.158309 +760,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.047274834607858,34.691812272848594,-370.11801689051305,0.004435539245605469,1.23401 +780,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.10380007346799,34.703384372728905,-372.0292841604823,0.004435539245605469,1.319073 +800,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.08555480002386,34.670374973731704,-374.68721566223496,0.004435539245605469,1.406663 +820,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.19885148971359,34.750222550469864,-380.56447731408525,0.004435539245605469,1.496625 +840,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.254637155655836,34.7657556157579,-384.4513633760299,0.004435539245605469,1.5889890000000002 +860,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.316317159155098,34.79186578621207,-384.3655387384781,0.004435539245605469,1.693075 +880,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.241979864666273,34.706915019978055,-380.5804591262153,0.004435539245605469,2.065505 +900,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.224226006229813,34.673719949901624,-381.45588348920205,0.004435539245605469,2.440347 +920,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.14134426690263,34.58836614994504,-385.8283921715467,0.004435539245605469,2.817664 +940,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.997921544748234,34.45657797481166,-386.1428842704396,0.004435539245605469,3.197344 +960,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.060400411885407,34.566740304864304,-392.69608794190344,0.004435539245605469,3.57811 +980,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.96911325529305,34.515642414657464,-399.1566023636026,0.004435539245605469,3.959721 +1000,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.09609109084082,34.63142513356851,-408.62698817771025,0.004435539245605469,4.342153 +1001,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.093334457100017,34.62570772928248,-408.7651443035993,0.004435539245605469,4.724752 +11,Regression,k-Nearest Neighbors,ChickWeights,4.6439393939393945,12.708027567111456,-206.8805289598106,0.0208587646484375,0.001745 +22,Regression,k-Nearest Neighbors,ChickWeights,2.7674242424242426,9.021574170013263,-85.19732920009746,0.030094146728515625,0.005817 +33,Regression,k-Nearest Neighbors,ChickWeights,2.3601010101010105,7.4346315168437105,-37.38846247874159,0.0395355224609375,0.012524 +44,Regression,k-Nearest Neighbors,ChickWeights,1.9882575757575767,6.459864921032004,-31.8544119108943,0.0488433837890625,0.023287000000000002 +55,Regression,k-Nearest Neighbors,ChickWeights,2.201515151515152,6.079045396219125,-6.214006750846093,0.05837249755859375,0.035467 +66,Regression,k-Nearest Neighbors,ChickWeights,2.2709595959595963,5.693634951086079,-1.7279153546475992,0.06856918334960938,0.049542 +77,Regression,k-Nearest Neighbors,ChickWeights,2.6114718614718617,5.706903555891601,-0.8368793810695487,0.0782623291015625,0.06880900000000001 +88,Regression,k-Nearest Neighbors,ChickWeights,2.5236742424242427,5.412016943708686,-0.4977726858852578,0.08795547485351562,0.09929800000000001 +99,Regression,k-Nearest Neighbors,ChickWeights,2.4695286195286204,5.169211114529652,-0.1428260058474422,0.09764862060546875,0.13234700000000002 +110,Regression,k-Nearest Neighbors,ChickWeights,2.7553030303030313,5.269495069058163,0.1706792355598563,0.10734176635742188,0.16795900000000002 +121,Regression,k-Nearest Neighbors,ChickWeights,3.1511019283746564,5.580125306741311,0.2837685080447375,0.117034912109375,0.20637100000000003 +132,Regression,k-Nearest Neighbors,ChickWeights,3.3157828282828294,5.649452649212155,0.3999904226030885,0.12723159790039062,0.24803200000000003 +143,Regression,k-Nearest Neighbors,ChickWeights,3.6019813519813537,5.868270501527574,0.47563568627460706,0.13692474365234375,0.31342200000000003 +154,Regression,k-Nearest Neighbors,ChickWeights,3.7459956709956725,5.964828521670115,0.5395766265984425,0.14661788940429688,0.409887 +165,Regression,k-Nearest Neighbors,ChickWeights,4.050202020202021,6.4542180762994805,0.5666546129487657,0.15631103515625,0.575073 +176,Regression,k-Nearest Neighbors,ChickWeights,4.420928030303032,6.954884488253524,0.5942812793055753,0.16600418090820312,0.746583 +187,Regression,k-Nearest Neighbors,ChickWeights,4.757664884135474,7.278917476631412,0.6361362300357987,0.17569732666015625,0.9226570000000001 +198,Regression,k-Nearest Neighbors,ChickWeights,5.192340067340069,7.767087259749381,0.6704396407154757,0.18589401245117188,1.103506 +209,Regression,k-Nearest Neighbors,ChickWeights,5.571690590111645,8.414476478500024,0.6811438926382001,0.195587158203125,1.2899880000000001 +220,Regression,k-Nearest Neighbors,ChickWeights,6.017651515151518,9.535434778453542,0.641509702161033,0.20528030395507812,1.4817580000000001 +231,Regression,k-Nearest Neighbors,ChickWeights,6.514646464646468,10.15268578355149,0.652376522878304,0.21497344970703125,1.688886 +242,Regression,k-Nearest Neighbors,ChickWeights,7.006955922865016,10.883499074839365,0.6785664047839641,0.22466659545898438,1.9058080000000002 +253,Regression,k-Nearest Neighbors,ChickWeights,7.401119894598158,11.259257694820905,0.7012209269570091,0.2343597412109375,2.129833 +264,Regression,k-Nearest Neighbors,ChickWeights,7.873800505050509,12.237701558545494,0.6775097363055258,0.24460983276367188,2.3598440000000003 +275,Regression,k-Nearest Neighbors,ChickWeights,8.501393939393942,13.456617650281162,0.6568816796501455,0.254302978515625,2.605392 +286,Regression,k-Nearest Neighbors,ChickWeights,8.999592074592076,14.081405883193678,0.6745818706784585,0.2639961242675781,2.8696780000000004 +297,Regression,k-Nearest Neighbors,ChickWeights,9.403647586980924,14.487230370517851,0.7012657763253116,0.27368927001953125,3.1592620000000005 +308,Regression,k-Nearest Neighbors,ChickWeights,9.825595238095241,15.247017337775036,0.7053028346163965,0.2833824157714844,3.4714450000000006 +319,Regression,k-Nearest Neighbors,ChickWeights,10.570794148380358,17.082267622288043,0.6643188025566307,0.2930755615234375,3.8957660000000005 +330,Regression,k-Nearest Neighbors,ChickWeights,11.342676767676771,18.20491056057454,0.6737311884314376,0.3032722473144531,4.333389 +341,Regression,k-Nearest Neighbors,ChickWeights,11.756256109481921,18.5968301788559,0.6951271166039881,0.31296539306640625,4.790776 +352,Regression,k-Nearest Neighbors,ChickWeights,12.16955492424243,18.94133239132977,0.7124941202708752,0.3226585388183594,5.260338 +363,Regression,k-Nearest Neighbors,ChickWeights,12.609595959595964,19.7022738973151,0.6979354313341102,0.3323516845703125,5.750168 +374,Regression,k-Nearest Neighbors,ChickWeights,13.251024955436726,20.7851367099449,0.6909564285254863,0.3420448303222656,6.251716 +385,Regression,k-Nearest Neighbors,ChickWeights,13.78255411255412,21.481025974379733,0.7079595790244884,0.35224151611328125,6.769761 +396,Regression,k-Nearest Neighbors,ChickWeights,14.010311447811455,21.53574862211497,0.7263147242326703,0.3619346618652344,7.297159 +407,Regression,k-Nearest Neighbors,ChickWeights,14.576126126126132,22.56379182999173,0.720735043690873,0.3716278076171875,7.841892 +418,Regression,k-Nearest Neighbors,ChickWeights,15.256658692185015,23.708044463333223,0.710588766956741,0.38134765625,8.4256 +429,Regression,k-Nearest Neighbors,ChickWeights,15.863597513597522,24.650993900023582,0.7219567169230845,0.3910675048828125,9.118775999999999 +440,Regression,k-Nearest Neighbors,ChickWeights,16.15655303030304,24.89490243600041,0.7364984966983625,0.4007606506347656,9.834439999999999 +451,Regression,k-Nearest Neighbors,ChickWeights,16.474242424242437,25.235361878916873,0.7407521096740679,0.41095733642578125,10.564262999999999 +462,Regression,k-Nearest Neighbors,ChickWeights,17.206240981241,26.51959634874256,0.731081178462164,0.42067718505859375,11.311639 +473,Regression,k-Nearest Neighbors,ChickWeights,18.061486962649766,27.919441407022266,0.7368140706560946,0.430450439453125,12.077289 +484,Regression,k-Nearest Neighbors,ChickWeights,18.444800275482105,28.396609389438456,0.742660608098584,0.4401702880859375,12.86575 +495,Regression,k-Nearest Neighbors,ChickWeights,18.85067340067341,28.917019336286593,0.7489686179689856,0.4499168395996094,13.665871000000001 +506,Regression,k-Nearest Neighbors,ChickWeights,19.397397891963116,29.705616030262235,0.7427898649120724,2.5872955322265625,16.820609 +517,Regression,k-Nearest Neighbors,ChickWeights,20.115441650548043,30.735303248634356,0.7401565757784102,2.6296463012695312,20.027458000000003 +528,Regression,k-Nearest Neighbors,ChickWeights,20.836142676767683,31.986233829047414,0.7469752640852343,2.6746597290039062,23.267231000000002 +539,Regression,k-Nearest Neighbors,ChickWeights,21.017594310451457,32.125858524254696,0.7553011842320496,2.717792510986328,26.555547000000004 +550,Regression,k-Nearest Neighbors,ChickWeights,21.677242424242426,32.83678407493398,0.7522301799631583,2.769092559814453,29.885669000000004 +561,Regression,k-Nearest Neighbors,ChickWeights,22.80977421271539,35.198755082788004,0.727753941720713,2.8112449645996094,33.249092000000005 +572,Regression,k-Nearest Neighbors,ChickWeights,24.195600233100233,38.25560047694445,0.7025325582791198,2.857513427734375,36.653026000000004 +578,Regression,k-Nearest Neighbors,ChickWeights,24.840628604382932,39.201635479156685,0.6952358931227007,2.8852157592773438,40.087818000000006 +20,Regression,k-Nearest Neighbors,TrumpApproval,2.554585433333335,9.794739803036965,-224.02989290855143,0.033588409423828125,0.001545 +40,Regression,k-Nearest Neighbors,TrumpApproval,1.7993247666666672,6.973235588114817,-18.54942689237887,0.055484771728515625,0.0054 +60,Regression,k-Nearest Neighbors,TrumpApproval,1.366773144444445,5.705236645726316,-16.642396889136542,0.07735443115234375,0.012332 +80,Regression,k-Nearest Neighbors,TrumpApproval,1.1277757833333335,4.947712433075743,-12.30953248968821,0.09975433349609375,0.028826 +100,Regression,k-Nearest Neighbors,TrumpApproval,1.046201766666667,4.4398629296748915,-5.724544452799038,0.12165069580078125,0.050318 +120,Regression,k-Nearest Neighbors,TrumpApproval,1.000865705555556,4.0744555355418335,-3.804331488196434,0.14354705810546875,0.086896 +140,Regression,k-Nearest Neighbors,TrumpApproval,0.9447764619047624,3.7809361134406263,-3.275153002458012,0.16594696044921875,0.149837 +160,Regression,k-Nearest Neighbors,TrumpApproval,0.9352969166666671,3.5531790499707645,-2.3296179824080356,0.18784332275390625,0.23044599999999998 +180,Regression,k-Nearest Neighbors,TrumpApproval,0.9445764925925928,3.380979243961517,-1.647692611170827,0.20973968505859375,0.432465 +200,Regression,k-Nearest Neighbors,TrumpApproval,0.9456943733333335,3.2327893391999836,-1.427877878808435,0.23213958740234375,0.648003 +220,Regression,k-Nearest Neighbors,TrumpApproval,0.9124697575757575,3.0919339165015143,-1.3957229068060464,0.25403594970703125,0.888162 +240,Regression,k-Nearest Neighbors,TrumpApproval,0.9329223611111109,2.985727855147271,-1.2507750530936188,0.27593231201171875,1.171554 +260,Regression,k-Nearest Neighbors,TrumpApproval,0.9025974717948716,2.873740673763463,-1.11319648675526,0.2984657287597656,1.484213 +280,Regression,k-Nearest Neighbors,TrumpApproval,0.8654126523809523,2.773524640439575,-1.0608690746642817,0.3203620910644531,1.81098 +300,Regression,k-Nearest Neighbors,TrumpApproval,0.8525042622222223,2.688069339615046,-0.9037818439458585,0.3422584533691406,2.170241 +320,Regression,k-Nearest Neighbors,TrumpApproval,0.8265282395833334,2.6077957497476296,-0.880493509713772,0.3646583557128906,2.5581009999999997 +340,Regression,k-Nearest Neighbors,TrumpApproval,0.8137511019607846,2.539210136300266,-0.8840673465916704,0.3865547180175781,3.1375499999999996 +360,Regression,k-Nearest Neighbors,TrumpApproval,0.7887328240740744,2.4696835584739105,-0.7969398815662787,0.4084510803222656,3.7440789999999997 +380,Regression,k-Nearest Neighbors,TrumpApproval,0.7710879228070179,2.4087271831437693,-0.7684619785143365,0.4303474426269531,4.380375 +400,Regression,k-Nearest Neighbors,TrumpApproval,0.756179386666667,2.351105641867075,-0.7324819925835522,0.4527473449707031,5.04744 +420,Regression,k-Nearest Neighbors,TrumpApproval,0.7300392539682541,2.295700426816902,-0.7064552265553199,0.4746437072753906,5.735437 +440,Regression,k-Nearest Neighbors,TrumpApproval,0.7180258560606063,2.24592493832078,-0.6037054809307543,0.4965400695800781,6.669957 +460,Regression,k-Nearest Neighbors,TrumpApproval,0.7103659666666668,2.200554873752302,-0.45996881871915263,0.5189399719238281,7.633919000000001 +480,Regression,k-Nearest Neighbors,TrumpApproval,0.6905233472222223,2.1551860359584523,-0.36817166319202155,0.5408363342285156,8.635162000000001 +500,Regression,k-Nearest Neighbors,TrumpApproval,0.6835753693333335,2.1161668272230596,-0.2914054626850582,2.7066993713378906,12.111573000000002 +520,Regression,k-Nearest Neighbors,TrumpApproval,0.6741869282051286,2.0775236231845557,-0.24684449790743135,2.7946739196777344,15.640183000000002 +540,Regression,k-Nearest Neighbors,TrumpApproval,0.6635047197530868,2.0412653603832833,-0.19929175315985925,2.8836631774902344,19.224183000000004 +560,Regression,k-Nearest Neighbors,TrumpApproval,0.6666769047619049,2.01181749557566,-0.19269635778937388,2.973125457763672,22.863484000000003 +580,Regression,k-Nearest Neighbors,TrumpApproval,0.662313208045977,1.9804661409620818,-0.18439917312598686,3.0619163513183594,26.560120000000005 +600,Regression,k-Nearest Neighbors,TrumpApproval,0.6595208444444446,1.9515625148224913,-0.13735805248393262,3.158283233642578,30.314082000000006 +620,Regression,k-Nearest Neighbors,TrumpApproval,0.6603871010752689,1.924909501402362,-0.08963762017358134,3.248737335205078,34.12595900000001 +640,Regression,k-Nearest Neighbors,TrumpApproval,0.6518434010416667,1.8967107462711992,-0.038171332083393406,3.341320037841797,37.99311800000001 +660,Regression,k-Nearest Neighbors,TrumpApproval,0.6481796161616163,1.873162681009878,-0.005272423030620033,3.435527801513672,41.91854100000001 +680,Regression,k-Nearest Neighbors,TrumpApproval,0.6594073715686274,1.8574009428793898,-0.004045635504021261,3.5269508361816406,45.90599500000001 +700,Regression,k-Nearest Neighbors,TrumpApproval,0.6619153695238096,1.8376987056605067,-0.008672432190871993,3.615283966064453,49.957909000000015 +720,Regression,k-Nearest Neighbors,TrumpApproval,0.6538050537037038,1.8142062090777376,-0.004645759004504146,3.7059364318847656,54.07172000000001 +740,Regression,k-Nearest Neighbors,TrumpApproval,0.6437102684684685,1.7904191974020043,0.02211499739805689,3.8005104064941406,58.24412800000001 +760,Regression,k-Nearest Neighbors,TrumpApproval,0.6465423666666668,1.7722456151874884,0.03148604083873929,3.8912620544433594,62.47501600000001 +780,Regression,k-Nearest Neighbors,TrumpApproval,0.6423591829059828,1.752432393946061,0.0487781645727875,3.987659454345703,66.76475200000002 +800,Regression,k-Nearest Neighbors,TrumpApproval,0.6415445258333332,1.7335108155585357,0.060790557140155244,4.087154388427734,71.11932700000001 +820,Regression,k-Nearest Neighbors,TrumpApproval,0.641812437398374,1.7198679523833968,0.06536301290969171,4.179523468017578,75.53533700000001 +840,Regression,k-Nearest Neighbors,TrumpApproval,0.6391550126984127,1.7023246638821516,0.07583179507597027,4.276576995849609,80.015814 +860,Regression,k-Nearest Neighbors,TrumpApproval,0.6397551612403103,1.6865214638981003,0.0944734629735503,4.372867584228516,84.56990800000001 +880,Regression,k-Nearest Neighbors,TrumpApproval,0.6401663234848486,1.6719359262678322,0.11449022182092672,4.465221405029297,89.18993400000001 +900,Regression,k-Nearest Neighbors,TrumpApproval,0.6373928251851855,1.6559913256631793,0.1276383063389357,4.558887481689453,93.87754600000001 +920,Regression,k-Nearest Neighbors,TrumpApproval,0.6333341724637681,1.6410816825275085,0.12919955333528133,4.652858734130859,98.62624600000001 +940,Regression,k-Nearest Neighbors,TrumpApproval,0.637460545390071,1.6307722122541641,0.13281132177791266,4.746517181396484,103.437785 +960,Regression,k-Nearest Neighbors,TrumpApproval,0.6446958777777775,1.6213030711335545,0.13389079092896516,4.844425201416016,108.312072 +980,Regression,k-Nearest Neighbors,TrumpApproval,0.643768610068027,1.6085965270907718,0.1308548353743899,4.935100555419922,113.251739 +1000,Regression,k-Nearest Neighbors,TrumpApproval,0.6420156240666665,1.59493855356346,0.13116812210504825,5.030651092529297,118.255967 +1001,Regression,k-Nearest Neighbors,TrumpApproval,0.6416785025641023,1.5941707450098015,0.1314249186277071,5.032634735107422,123.301096 +11,Regression,Hoeffding Tree,ChickWeights,8.042756132756132,17.336048579080593,-385.86349170941764,0.016208648681640625,0.002632 +22,Regression,Hoeffding Tree,ChickWeights,4.456785613727984,12.282422261556867,-158.770726389092,0.017787933349609375,0.007319 +33,Regression,Hoeffding Tree,ChickWeights,3.4353973358733074,10.070376517434479,-69.4325218162971,0.023052215576171875,0.013907 +44,Regression,Hoeffding Tree,ChickWeights,2.736909422894262,8.732393473100391,-59.03623058514604,0.024105072021484375,0.021700999999999998 +55,Regression,Hoeffding Tree,ChickWeights,2.788577579622257,8.074088551816661,-11.726025456653014,0.030948638916015625,0.030334999999999997 +66,Regression,Hoeffding Tree,ChickWeights,3.3958800855981375,7.878422021930021,-4.223121571879303,0.040424346923828125,0.040093 +77,Regression,Hoeffding Tree,ChickWeights,3.8895265016210883,7.800910386370324,-2.432180745921895,0.046741485595703125,0.05116999999999999 +88,Regression,Hoeffding Tree,ChickWeights,4.072650698433535,7.572197783925699,-1.9320509270116553,0.052532196044921875,0.06356099999999999 +99,Regression,Hoeffding Tree,ChickWeights,4.410984939713907,7.55185413515251,-1.439151418709002,0.053585052490234375,0.07724199999999999 +110,Regression,Hoeffding Tree,ChickWeights,4.370948473977548,7.327634340090197,-0.6036593212329582,0.055164337158203125,0.09210199999999999 +121,Regression,Hoeffding Tree,ChickWeights,4.401973824893138,7.197046558152955,-0.19144536988389782,0.055164337158203125,0.10816699999999999 +132,Regression,Hoeffding Tree,ChickWeights,4.283071400630936,6.979735895990854,0.08415196835499827,0.055164337158203125,0.13239599999999999 +143,Regression,Hoeffding Tree,ChickWeights,4.169649051526778,6.77851615807502,0.3003478880703081,0.055690765380859375,0.16024499999999997 +154,Regression,Hoeffding Tree,ChickWeights,4.107721988217097,6.620782354691122,0.4327427443050297,0.055690765380859375,0.19148199999999999 +165,Regression,Hoeffding Tree,ChickWeights,4.3861341291386235,6.8739888422895685,0.5084535624523276,0.055690765380859375,0.23199899999999998 +176,Regression,Hoeffding Tree,ChickWeights,4.592324836010107,7.0395287886899816,0.5843455987500039,0.056217193603515625,0.273788 +187,Regression,Hoeffding Tree,ChickWeights,4.658423416973056,7.057579140031887,0.6579286220132116,0.056217193603515625,0.316974 +198,Regression,Hoeffding Tree,ChickWeights,4.6782517314261085,7.042640058036562,0.7290497323677609,0.056217193603515625,0.361531 +209,Regression,Hoeffding Tree,ChickWeights,4.8966529592561265,7.410861778989444,0.7526693351807108,0.02174663543701172,0.409543 +220,Regression,Hoeffding Tree,ChickWeights,5.507880191409123,8.546476599974424,0.7120144996082314,0.02806377410888672,0.458317 +231,Regression,Hoeffding Tree,ChickWeights,5.703958017872014,8.760797449465004,0.7411581545051223,0.03332805633544922,0.507954 +242,Regression,Hoeffding Tree,ChickWeights,5.934527728379076,9.145062262320872,0.7730513990797492,0.03806591033935547,0.576578 +253,Regression,Hoeffding Tree,ChickWeights,6.025889093973978,9.259481324724224,0.7979290061199974,0.04175090789794922,0.647861 +264,Regression,Hoeffding Tree,ChickWeights,6.701040765258382,10.569442782845146,0.7594412957229723,0.041831016540527344,0.7217790000000001 +275,Regression,Hoeffding Tree,ChickWeights,7.201977905163474,11.695812678726385,0.740801257827299,0.041831016540527344,0.7983520000000001 +286,Regression,Hoeffding Tree,ChickWeights,7.4760897436283305,12.176082777300051,0.7566872347890514,0.042357444763183594,0.889757 +297,Regression,Hoeffding Tree,ChickWeights,7.495029117947843,12.186858586615225,0.7886035011133373,0.042357444763183594,0.982264 +308,Regression,Hoeffding Tree,ChickWeights,8.05089484284177,13.06419009031293,0.7836428997387894,0.042357444763183594,1.075782 +319,Regression,Hoeffding Tree,ChickWeights,9.171875092169309,15.802620207207104,0.7127274179827436,0.042357444763183594,1.1703270000000001 +330,Regression,Hoeffding Tree,ChickWeights,9.626867556328977,16.443718231711543,0.7338058453397931,0.042357444763183594,1.26591 +341,Regression,Hoeffding Tree,ChickWeights,9.854283538219805,16.574189924013226,0.7578382368534643,0.042357444763183594,1.362543 +352,Regression,Hoeffding Tree,ChickWeights,10.034558550660114,16.72149964752778,0.7759339138910493,0.042357444763183594,1.460131 +363,Regression,Hoeffding Tree,ChickWeights,10.942839439265006,18.18973374364872,0.7425340708967089,0.042357444763183594,1.65219 +374,Regression,Hoeffding Tree,ChickWeights,11.480189522121245,19.36955258798825,0.7316181626186655,0.042357444763183594,1.847066 +385,Regression,Hoeffding Tree,ChickWeights,11.884428250077962,20.018801475409063,0.7463650656532205,0.042357444763183594,2.044712 +396,Regression,Hoeffding Tree,ChickWeights,12.037067702603977,20.025071614924446,0.7633646392298079,0.042357444763183594,2.245044 +407,Regression,Hoeffding Tree,ChickWeights,12.938689395183468,21.571547182252875,0.7447563988620904,0.039313316345214844,2.459951 +418,Regression,Hoeffding Tree,ChickWeights,13.737065020554605,23.070023559587742,0.7259561921053947,0.039839744567871094,2.675857 +429,Regression,Hoeffding Tree,ChickWeights,14.305628841534727,24.020997573013894,0.7359868139097058,0.040892601013183594,2.892761 +440,Regression,Hoeffding Tree,ChickWeights,14.503019064271445,24.118168317988548,0.7526847575357923,0.041419029235839844,3.110678 +451,Regression,Hoeffding Tree,ChickWeights,15.042001004765993,24.757154413851225,0.7504844548860922,0.042998313903808594,3.329579 +462,Regression,Hoeffding Tree,ChickWeights,16.165694044127083,26.934291479182736,0.7226050873941003,0.043524742126464844,3.549505 +473,Regression,Hoeffding Tree,ChickWeights,16.958578383564387,28.26726815061745,0.7302155620528221,0.043524742126464844,3.77047 +484,Regression,Hoeffding Tree,ChickWeights,17.309589456804158,28.5754148947933,0.7394096166099926,0.043524742126464844,4.010874 +495,Regression,Hoeffding Tree,ChickWeights,17.77955786237919,29.119281838039548,0.7454446166142166,0.043524742126464844,4.254034 +506,Regression,Hoeffding Tree,ChickWeights,18.687135400012505,30.600738447390604,0.7270552375925041,0.043524742126464844,4.499866 +517,Regression,Hoeffding Tree,ChickWeights,19.426270300418786,31.613839238226678,0.7250895764829616,0.043524742126464844,4.748399 +528,Regression,Hoeffding Tree,ChickWeights,20.230319490239392,32.829508990096734,0.7334580691909136,0.043524742126464844,5.00325 +539,Regression,Hoeffding Tree,ChickWeights,20.415951878027045,32.83473210597698,0.7443832332812113,0.043524742126464844,5.259172 +550,Regression,Hoeffding Tree,ChickWeights,21.41946931942451,34.477948502753435,0.726844465494657,0.043524742126464844,5.516121 +561,Regression,Hoeffding Tree,ChickWeights,22.135259536350134,35.412182207518484,0.7244424125617825,0.043524742126464844,5.774111 +572,Regression,Hoeffding Tree,ChickWeights,22.998428764364284,36.61317436816486,0.7275265693889857,0.044051170349121094,6.033148000000001 +578,Regression,Hoeffding Tree,ChickWeights,23.16185046142029,36.73359474841229,0.7324023432169282,0.044051170349121094,6.293050000000001 +20,Regression,Hoeffding Tree,TrumpApproval,4.834704431652337,13.708514217962266,-439.7934984576362,0.05008697509765625,0.001817 +40,Regression,Hoeffding Tree,TrumpApproval,3.4692310697037447,9.813795721313518,-37.72035957928713,0.07324981689453125,0.005553000000000001 +60,Regression,Hoeffding Tree,TrumpApproval,2.530247618203559,8.024836796214231,-33.90460110966681,0.08588409423828125,0.011369 +80,Regression,Hoeffding Tree,TrumpApproval,2.1398752670733447,6.982837000856316,-25.510487239912003,0.09588623046875,0.023421 +100,Regression,Hoeffding Tree,TrumpApproval,2.2521629689485394,6.362737158647257,-12.810573390910955,0.1053619384765625,0.037893 +120,Regression,Hoeffding Tree,TrumpApproval,2.2753311831165886,5.895687482983747,-9.059182991303912,0.1095733642578125,0.054815 +140,Regression,Hoeffding Tree,TrumpApproval,2.181766409647037,5.493495699082884,-8.025069637302263,0.1116790771484375,0.07421900000000001 +160,Regression,Hoeffding Tree,TrumpApproval,2.0635226048812747,5.165876255053421,-6.037983110569301,0.1158905029296875,0.09851900000000001 +180,Regression,Hoeffding Tree,TrumpApproval,1.9951428730766114,4.906287161641783,-4.575559841528811,0.1179962158203125,0.13018800000000003 +200,Regression,Hoeffding Tree,TrumpApproval,1.8700446037321659,4.662539866408188,-4.050299616280768,0.015085220336914062,0.16948700000000003 +220,Regression,Hoeffding Tree,TrumpApproval,1.7830718267282506,4.458344141345012,-3.981078161152351,0.031249046325683594,0.21017900000000003 +240,Regression,Hoeffding Tree,TrumpApproval,1.714887283408722,4.280191261764102,-3.6254927572925757,0.037039756774902344,0.27354600000000007 +260,Regression,Hoeffding Tree,TrumpApproval,1.6268995152596541,4.116599014627653,-3.336325373761703,0.044569969177246094,0.33844300000000005 +280,Regression,Hoeffding Tree,TrumpApproval,1.6037708656255951,3.992199218884993,-3.269831686495559,0.05755901336669922,0.40512500000000007 +300,Regression,Hoeffding Tree,TrumpApproval,1.5808413297038584,3.882244388071726,-2.9710192082752114,0.06756114959716797,0.4768960000000001 +320,Regression,Hoeffding Tree,TrumpApproval,1.5112246352788372,3.7620340381312185,-2.9135432145577016,0.07545757293701172,0.5560110000000001 +340,Regression,Hoeffding Tree,TrumpApproval,1.464954049061847,3.6574443601858126,-2.9089002921657214,0.08072185516357422,0.6372390000000001 +360,Regression,Hoeffding Tree,TrumpApproval,1.4845626481571885,3.5832345434246853,-2.782695640732784,0.08861827850341797,0.7206760000000001 +380,Regression,Hoeffding Tree,TrumpApproval,1.4519403327978173,3.4965427251184518,-2.72647470962537,0.09388256072998047,0.8063740000000001 +400,Regression,Hoeffding Tree,TrumpApproval,1.4093274160891025,3.4133346926199284,-2.6515915354000197,0.10125255584716797,0.8943350000000001 +420,Regression,Hoeffding Tree,TrumpApproval,1.3677964737960675,3.3343173536823296,-2.5997996751089016,0.10546398162841797,1.079576 +440,Regression,Hoeffding Tree,TrumpApproval,1.3357172246731819,3.2621145597551164,-2.3832380441779537,0.11125469207763672,1.2716070000000002 +460,Regression,Hoeffding Tree,TrumpApproval,1.3223220949397412,3.20054856097613,-2.088360697350681,0.11967754364013672,1.466366 +480,Regression,Hoeffding Tree,TrumpApproval,1.2961820395725512,3.1370925842333546,-1.8988499404168713,0.12757396697998047,1.663894 +500,Regression,Hoeffding Tree,TrumpApproval,1.2652762767168435,3.076750388249757,-1.7299037995212605,0.13231182098388672,1.864298 +520,Regression,Hoeffding Tree,TrumpApproval,1.2471740635308572,3.0222901376128295,-1.6387160551274738,0.13757610321044922,2.0709020000000002 +540,Regression,Hoeffding Tree,TrumpApproval,1.222508472081129,2.9683885282447466,-1.5361060189709668,0.13968181610107422,2.286544 +560,Regression,Hoeffding Tree,TrumpApproval,1.2073384071706728,2.920065266046622,-1.5126838513129575,0.14441967010498047,2.5053300000000003 +580,Regression,Hoeffding Tree,TrumpApproval,1.1845779132924192,2.8723790540044147,-1.4914188956527816,0.14705181121826172,2.7271330000000003 +600,Regression,Hoeffding Tree,TrumpApproval,1.1745692976588702,2.8296294830278073,-1.3910651808346999,0.14148998260498047,2.96944 +620,Regression,Hoeffding Tree,TrumpApproval,1.1708259630571383,2.7920061348512903,-1.2924200227078337,0.14412212371826172,3.214893 +640,Regression,Hoeffding Tree,TrumpApproval,1.1599967464968943,2.7528504813508814,-1.186915838733254,0.14622783660888672,3.481443 +660,Regression,Hoeffding Tree,TrumpApproval,1.1455993461288598,2.715465758170179,-1.112620243595547,0.09333324432373047,3.768351 +680,Regression,Hoeffding Tree,TrumpApproval,1.1331386715536063,2.679518493749607,-1.0895638535289454,0.10280895233154297,4.057625 +700,Regression,Hoeffding Tree,TrumpApproval,1.1287919059851137,2.648832972736431,-1.0956110522943683,0.11070537567138672,4.349483 +720,Regression,Hoeffding Tree,TrumpApproval,1.1090542602054634,2.6130484736329,-1.0841769561048746,0.11702251434326172,4.655905000000001 +740,Regression,Hoeffding Tree,TrumpApproval,1.0919225542546631,2.579731998640208,-1.0301471378292058,0.12070751190185547,4.969391000000001 +760,Regression,Hoeffding Tree,TrumpApproval,1.0729346607841277,2.546521266569091,-0.9996439724530697,0.12386608123779297,5.409378000000001 +780,Regression,Hoeffding Tree,TrumpApproval,1.0548522699101792,2.514796200212546,-0.958866579835745,0.13018321990966797,5.856313000000001 +800,Regression,Hoeffding Tree,TrumpApproval,1.0458975693179249,2.4863814517835756,-0.9321678603320387,0.14051342010498047,6.306401000000001 +820,Regression,Hoeffding Tree,TrumpApproval,1.042667475968943,2.463395040447954,-0.9174360179218257,0.14683055877685547,6.759550000000001 +840,Regression,Hoeffding Tree,TrumpApproval,1.0338402028724885,2.4371652901742165,-0.8942452584110789,0.15051555633544922,7.215791000000001 +860,Regression,Hoeffding Tree,TrumpApproval,1.0182769822752689,2.409744604248102,-0.8486703239118398,0.15209484100341797,7.680462000000001 +880,Regression,Hoeffding Tree,TrumpApproval,1.0072949101764561,2.3841216724611445,-0.8005738256179296,0.15525341033935547,8.149683000000001 +900,Regression,Hoeffding Tree,TrumpApproval,0.9984699415812968,2.359722022526475,-0.7713409518355698,0.15735912322998047,8.622145000000002 +920,Regression,Hoeffding Tree,TrumpApproval,0.9848390746890626,2.3349754381173082,-0.7628805257854674,0.12545299530029297,9.108636 +940,Regression,Hoeffding Tree,TrumpApproval,0.9804934467335737,2.3136297350671566,-0.7454793227879806,0.13442516326904297,9.599086 +960,Regression,Hoeffding Tree,TrumpApproval,0.9715993160407668,2.291923159938466,-0.7307898991199615,0.14021587371826172,10.092417 +980,Regression,Hoeffding Tree,TrumpApproval,0.96479276321034,2.271398262551761,-0.7329444574748756,0.14442729949951172,10.588738 +1000,Regression,Hoeffding Tree,TrumpApproval,0.9567764270416781,2.250974677037298,-0.7305695321170174,0.14863872528076172,11.174487 +1001,Regression,Hoeffding Tree,TrumpApproval,0.9561028857812052,2.249867758958838,-0.7300222157865335,0.14863872528076172,11.765557999999999 +11,Regression,Hoeffding Adaptive Tree,ChickWeights,8.051220648038832,17.336198122120386,-385.87016600913427,0.022922515869140625,0.002862 +22,Regression,Hoeffding Adaptive Tree,ChickWeights,4.498502947359929,12.285286375364281,-158.84524831763767,0.024562835693359375,0.008031 +33,Regression,Hoeffding Adaptive Tree,ChickWeights,3.4668695042339137,10.074636808082968,-69.49212762837747,0.029827117919921875,0.0152 +44,Regression,Hoeffding Adaptive Tree,ChickWeights,2.7637805804889553,8.735764655686483,-59.08259408516962,0.030941009521484375,0.027573 +55,Regression,Hoeffding Adaptive Tree,ChickWeights,2.814517498310432,8.074396776941786,-11.726997097138026,0.037784576416015625,0.040817 +66,Regression,Hoeffding Adaptive Tree,ChickWeights,3.396900059747575,7.862006773633152,-4.201378762014764,0.047260284423828125,0.055065 +77,Regression,Hoeffding Adaptive Tree,ChickWeights,3.8844336568547537,7.782255505653143,-2.415785129732385,0.053638458251953125,0.07050300000000001 +88,Regression,Hoeffding Adaptive Tree,ChickWeights,4.068768385552718,7.555909217267645,-1.9194502155140074,0.059429168701171875,0.08723500000000001 +99,Regression,Hoeffding Adaptive Tree,ChickWeights,4.319029347030655,7.489629607912237,-1.3991215781815165,0.060482025146484375,0.105314 +110,Regression,Hoeffding Adaptive Tree,ChickWeights,4.231978704025333,7.230698639905546,-0.5615110336669555,0.062061309814453125,0.124657 +121,Regression,Hoeffding Adaptive Tree,ChickWeights,4.279767976439616,7.114292598648662,-0.1642036472993016,0.062061309814453125,0.145348 +132,Regression,Hoeffding Adaptive Tree,ChickWeights,4.161677712403324,6.8979209349412445,0.1054968774084013,0.062061309814453125,0.183683 +143,Regression,Hoeffding Adaptive Tree,ChickWeights,4.036201943040193,6.686446116179646,0.3192250351622916,0.024164199829101562,0.23334700000000003 +154,Regression,Hoeffding Adaptive Tree,ChickWeights,4.002163310161137,6.555243218534794,0.4439177197734564,0.034926414489746094,0.28395200000000004 +165,Regression,Hoeffding Adaptive Tree,ChickWeights,4.269310553181931,6.794169336453219,0.5198027804498322,0.041365623474121094,0.33552800000000005 +176,Regression,Hoeffding Adaptive Tree,ChickWeights,4.394431170074558,6.916563516446891,0.5987399306940604,0.047156333923339844,0.38817000000000007 +187,Regression,Hoeffding Adaptive Tree,ChickWeights,4.429782113532627,6.896434310822903,0.6733712331422652,0.052016258239746094,0.44195900000000005 +198,Regression,Hoeffding Adaptive Tree,ChickWeights,4.448580123995543,6.86078369215091,0.7428621234581485,0.054648399353027344,0.49692500000000006 +209,Regression,Hoeffding Adaptive Tree,ChickWeights,4.634718338792146,7.17917659207716,0.7678921596594357,0.054648399353027344,0.5531490000000001 +220,Regression,Hoeffding Adaptive Tree,ChickWeights,5.229854791420841,8.435313620968111,0.7194573631198581,0.055296897888183594,0.6106500000000001 +231,Regression,Hoeffding Adaptive Tree,ChickWeights,5.4006373247873825,8.615072190659467,0.7496975788166091,0.055296897888183594,0.6850710000000001 +242,Regression,Hoeffding Adaptive Tree,ChickWeights,5.6226073005416035,8.982158345389516,0.781064800957145,0.055296897888183594,0.7630730000000001 +253,Regression,Hoeffding Adaptive Tree,ChickWeights,5.728895576419993,9.10264619767678,0.8047163053551843,0.055296897888183594,0.8439190000000001 +264,Regression,Hoeffding Adaptive Tree,ChickWeights,6.468790531655633,10.532848432020362,0.7611041743489119,0.05537700653076172,0.926058 +275,Regression,Hoeffding Adaptive Tree,ChickWeights,6.961259791220884,11.725202267966395,0.7394969764024641,0.05537700653076172,1.0095260000000001 +286,Regression,Hoeffding Adaptive Tree,ChickWeights,7.243017687832032,12.175095097400797,0.7567267064951951,0.05391216278076172,1.109836 +297,Regression,Hoeffding Adaptive Tree,ChickWeights,7.333189926829036,12.221129948725446,0.7874128689691341,0.05443859100341797,1.3004930000000001 +308,Regression,Hoeffding Adaptive Tree,ChickWeights,7.907494608974745,13.13418786953933,0.7813182108747583,0.05456066131591797,1.4945650000000001 +319,Regression,Hoeffding Adaptive Tree,ChickWeights,9.086203691627809,16.084282058543664,0.7023956098414756,0.05613994598388672,1.6919570000000002 +330,Regression,Hoeffding Adaptive Tree,ChickWeights,9.398286710797228,16.38837159928856,0.7355947540985646,0.05613994598388672,1.900794 +341,Regression,Hoeffding Adaptive Tree,ChickWeights,9.688169379844998,16.65705092991554,0.7554108572015372,0.05613994598388672,2.110987 +352,Regression,Hoeffding Adaptive Tree,ChickWeights,9.856066264187849,16.815734957180027,0.7734013139584004,0.05613994598388672,2.322457 +363,Regression,Hoeffding Adaptive Tree,ChickWeights,10.788654210226415,18.368645129880047,0.7374443731514406,0.05613994598388672,2.535213 +374,Regression,Hoeffding Adaptive Tree,ChickWeights,11.535989444086796,20.177763325541775,0.7087539856658172,0.0658864974975586,2.749718 +385,Regression,Hoeffding Adaptive Tree,ChickWeights,11.949331836981814,20.800028245688587,0.7261827687212361,0.0713338851928711,2.965855 +396,Regression,Hoeffding Adaptive Tree,ChickWeights,11.958714190964645,20.660643879084812,0.748105206776327,0.07817745208740234,3.1836569999999997 +407,Regression,Hoeffding Adaptive Tree,ChickWeights,12.807531574997112,22.01468171576837,0.7341619793955468,0.08257198333740234,3.4189649999999996 +418,Regression,Hoeffding Adaptive Tree,ChickWeights,13.71794187476778,23.73901232910809,0.7098322050491193,0.08467769622802734,3.6591389999999997 +429,Regression,Hoeffding Adaptive Tree,ChickWeights,14.269314924317156,24.652748132937095,0.7219171428567855,0.06568050384521484,3.9122609999999995 +440,Regression,Hoeffding Adaptive Tree,ChickWeights,14.511771919641937,24.834167752766053,0.7377826277560943,0.0706624984741211,4.16702 +451,Regression,Hoeffding Adaptive Tree,ChickWeights,15.00667707818897,25.401748915029017,0.7373221851710817,0.07874202728271484,4.423509 +462,Regression,Hoeffding Adaptive Tree,ChickWeights,16.106263610815663,27.4394567629727,0.7121021651653525,0.0857076644897461,4.681795 +473,Regression,Hoeffding Adaptive Tree,ChickWeights,16.950411373417108,28.951900473786843,0.7169889638801871,0.0888662338256836,4.941903 +484,Regression,Hoeffding Adaptive Tree,ChickWeights,17.321905164714362,29.29627092175635,0.7260962478080234,0.0889272689819336,5.203768 +495,Regression,Hoeffding Adaptive Tree,ChickWeights,17.829552469069228,29.855361574147427,0.732412614196017,0.0889272689819336,5.467412 +506,Regression,Hoeffding Adaptive Tree,ChickWeights,18.715769054600834,31.21095148117224,0.7160610523989874,0.08951473236083984,5.743327000000001 +517,Regression,Hoeffding Adaptive Tree,ChickWeights,19.54236471467993,32.39367117342827,0.7113596352744775,0.0743856430053711,6.026441000000001 +528,Regression,Hoeffding Adaptive Tree,ChickWeights,20.379374275832948,33.670378810622296,0.7196292071862618,0.0787191390991211,6.311317000000001 +539,Regression,Hoeffding Adaptive Tree,ChickWeights,20.522458105265056,33.639909372937744,0.7316929916628531,0.0872030258178711,6.597982000000001 +550,Regression,Hoeffding Adaptive Tree,ChickWeights,21.5114661084191,35.24478084224406,0.714558707096332,0.0935201644897461,6.886526000000001 +561,Regression,Hoeffding Adaptive Tree,ChickWeights,22.293418976341684,36.29050935662323,0.7106036021726428,0.09343624114990234,7.177067000000001 +572,Regression,Hoeffding Adaptive Tree,ChickWeights,23.158877831353536,37.47206255417766,0.7145930209145848,0.09461116790771484,7.581349000000001 +578,Regression,Hoeffding Adaptive Tree,ChickWeights,23.373902189510932,37.6579284312523,0.7187656938003131,0.09473323822021484,7.990285000000001 +20,Regression,Hoeffding Adaptive Tree,TrumpApproval,4.828377634536296,13.70786256219322,-439.7515918302183,0.05686187744140625,0.005477 +40,Regression,Hoeffding Adaptive Tree,TrumpApproval,3.453811275213839,9.811073218407973,-37.69887927291551,0.08008575439453125,0.014203 +60,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.5116544078850294,8.021960641037959,-33.879585508404254,0.09272003173828125,0.025216000000000002 +80,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.1224425015381523,6.9797990571526345,-25.487425023640153,0.102783203125,0.038581000000000004 +100,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.246653919301699,6.363694444016854,-12.814729355257526,0.1122589111328125,0.054574000000000004 +120,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.270681160376927,5.896666779393501,-9.062525006956841,0.1164703369140625,0.096737 +140,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.162967815650222,5.491011289549727,-8.016908386196121,0.1185760498046875,0.14519300000000002 +160,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.9648637778298337,5.1475477542568076,-5.988130255135697,0.048110008239746094,0.20161800000000002 +180,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.8665278782891501,4.8758843309507505,-4.506673701927233,0.06455135345458984,0.259848 +200,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.773434994745299,4.638841370319518,-3.9990913279754245,0.07511425018310547,0.334681 +220,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.6594682627798778,4.42936028038101,-3.916524330360767,0.0809926986694336,0.415547 +240,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.5811297097344512,4.24689633509078,-3.553810703437006,0.0831594467163086,0.499019 +260,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4918706813368772,4.083314206963185,-3.2664860479391056,0.08697795867919922,0.584777 +280,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4582505621214346,3.950619643811522,-3.181352514384196,0.09651470184326172,0.672987 +300,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4293807431017047,3.836527362327468,-2.8780450161882043,0.10505962371826172,0.763791 +320,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3766835460490845,3.7183907131031066,-2.8232679475596494,0.11137676239013672,0.862886 +340,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3285707966483495,3.6114631285578054,-2.8112330604866624,0.09692668914794922,1.077289 +360,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3305028688272291,3.538102571280229,-2.6880072396238157,0.10482311248779297,1.294249 +380,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3086678355415842,3.4529556765760527,-2.6341471363086995,0.11014842987060547,1.513868 +400,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.256053624567095,3.3666460142322228,-2.552379472359031,0.11751842498779297,1.736306 +420,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2254239545780012,3.2887455105144454,-2.5020714662192383,0.12172985076904297,1.9788649999999999 +440,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.204020924712129,3.2198773978896,-2.2961943419959137,0.12752056121826172,2.244474 +460,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1975328241312166,3.1601130927415366,-2.010817456858815,0.13547801971435547,2.513147 +480,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.186148143661266,3.1001176815841753,-1.8309188655239268,0.14337444305419922,2.784897 +500,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1667856894749518,3.0429667282148514,-1.6702825792738007,0.13623332977294922,3.07197 +520,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.153194144927427,2.98944402729251,-1.5816728306403074,0.14155864715576172,3.362254 +540,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1356058423088553,2.9370365647466374,-1.4828164968540292,0.14366436004638672,3.6556480000000002 +560,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.125648357086568,2.890393580385493,-1.4618789770567937,0.14840221405029297,3.952243 +580,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1072323197222282,2.84377722554966,-1.4420491211959612,0.15103435516357422,4.252035 +600,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0962221602561253,2.8010574809052513,-1.343021715112041,0.15471935272216797,4.557891000000001 +620,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0955492072151647,2.7650292224496735,-1.2483344123018605,0.15787792205810547,4.883158000000001 +640,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.085957414095071,2.726589883354214,-1.1453910301968575,0.15998363494873047,5.354946000000001 +660,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0751762466913892,2.6908702968299423,-1.074523242859362,0.16366863250732422,5.834119000000001 +680,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0667684392102676,2.656475453821568,-1.0537791659469917,0.16630077362060547,6.316591000000001 +700,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0667188907522647,2.6278494556992995,-1.0625405514881172,0.15471935272216797,6.8062700000000005 +720,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0487756272096453,2.5923957614440996,-1.0513617944147002,0.15945720672607422,7.299083 +740,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0336933816342644,2.5596915816453274,-0.9987276211091367,0.16320323944091797,7.795134000000001 +760,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0143808347523189,2.5263993770636084,-0.9681675843117681,0.16478252410888672,8.298925 +780,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0004245938094416,2.495691505058861,-0.9292169429583497,0.16958141326904297,8.809149000000001 +800,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9976736219043986,2.469777786083391,-0.9064485942635294,0.16168498992919922,9.32665 +820,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0020392091388555,2.450590646975973,-0.8975546778436754,0.16431713104248047,9.853038000000002 +840,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9936292081382507,2.424886643827349,-0.8752066007627983,0.16800212860107422,10.484318000000002 +860,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9794930742877991,2.3980423354299125,-0.8307587924463844,0.17010784149169922,11.125036000000001 +880,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9694853941742789,2.372794343098121,-0.7835048635250907,0.17273998260498047,11.769143000000001 +900,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9594920424525858,2.348266033222206,-0.7541836724323567,0.11153697967529297,12.432199 +920,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9482726802907966,2.324135545417226,-0.7465505219679065,0.11639690399169922,13.105410000000001 +940,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9455376055826031,2.30345366329758,-0.7301587545146957,0.12230968475341797,13.781537 +960,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9379298129457146,2.2821811442731286,-0.7161074287562055,0.12967967987060547,14.460614 +980,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.930996996530802,2.261860474984104,-0.7184214614837348,0.13494396209716797,15.148323 +1000,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9214575102921838,2.2404008018877137,-0.714349138962711,0.13822460174560547,15.950631 +1001,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9213134079227226,2.239416179559339,-0.7139861919037982,0.13822460174560547,16.757639 +11,Regression,Stochastic Gradient Tree,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.009614944458007812,0.001328 +22,Regression,Stochastic Gradient Tree,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012609481811523438,0.003944 +33,Regression,Stochastic Gradient Tree,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.015787124633789062,0.007623 +44,Regression,Stochastic Gradient Tree,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.018873214721679688,0.012489 +55,Regression,Stochastic Gradient Tree,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.021825790405273438,0.019505 +66,Regression,Stochastic Gradient Tree,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.024618148803710938,0.027128 +77,Regression,Stochastic Gradient Tree,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.027502059936523438,0.035372 +88,Regression,Stochastic Gradient Tree,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.030019760131835938,0.047911 +99,Regression,Stochastic Gradient Tree,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.03290367126464844,0.072727 +110,Regression,Stochastic Gradient Tree,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.26967811584472656,0.103163 +121,Regression,Stochastic Gradient Tree,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.26967811584472656,0.146595 +132,Regression,Stochastic Gradient Tree,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.26967811584472656,0.196283 +143,Regression,Stochastic Gradient Tree,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.26967811584472656,0.25852200000000003 +154,Regression,Stochastic Gradient Tree,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.26967811584472656,0.329566 +165,Regression,Stochastic Gradient Tree,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.26967811584472656,0.40393 +176,Regression,Stochastic Gradient Tree,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.26967811584472656,0.481694 +187,Regression,Stochastic Gradient Tree,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.26967811584472656,0.681251 +198,Regression,Stochastic Gradient Tree,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.26967811584472656,0.884966 +209,Regression,Stochastic Gradient Tree,ChickWeights,55.16354936929098,57.0482200725598,-13.656313160472004,0.6838865280151367,1.1316950000000001 +220,Regression,Stochastic Gradient Tree,ChickWeights,53.62203856749311,56.03531795068661,-11.37998411824978,0.6869077682495117,1.3969520000000002 +231,Regression,Stochastic Gradient Tree,ChickWeights,52.77279286370195,55.29408706815337,-9.311090357596036,0.6899290084838867,1.6754760000000002 +242,Regression,Stochastic Gradient Tree,ChickWeights,52.49661908339594,55.007104536867395,-7.210918602421254,0.6929502487182617,1.9600240000000002 +253,Regression,Stochastic Gradient Tree,ChickWeights,52.25631812193077,54.713446605156875,-6.055353919833875,0.6947126388549805,2.2702780000000002 +264,Regression,Stochastic Gradient Tree,ChickWeights,51.62511478420569,54.312843786153664,-5.352168023774992,0.6947126388549805,2.586688 +275,Regression,Stochastic Gradient Tree,ChickWeights,51.4425344352617,54.29364548356293,-4.585603291722447,0.6947126388549805,2.915419 +286,Regression,Stochastic Gradient Tree,ChickWeights,51.75651621106165,54.635705044608144,-3.8989478253777694,0.6947126388549805,3.266148 +297,Regression,Stochastic Gradient Tree,ChickWeights,52.373839404142416,55.25476711535166,-3.3456400671942,0.6947126388549805,3.622985 +308,Regression,Stochastic Gradient Tree,ChickWeights,52.87239275875638,55.86677247417265,-2.9565197175813713,0.6947126388549805,3.98691 +319,Regression,Stochastic Gradient Tree,ChickWeights,52.69554478958866,56.2770501442128,-2.6433309475704183,0.6947126388549805,4.356941 +330,Regression,Stochastic Gradient Tree,ChickWeights,53.85316804407712,57.75044402630399,-2.2832890424968193,0.6947126388549805,4.733992 +341,Regression,Stochastic Gradient Tree,ChickWeights,54.90678041411178,59.01114057562677,-2.0697921090482247,0.6947126388549805,5.128946 +352,Regression,Stochastic Gradient Tree,ChickWeights,56.00533746556472,60.302245208561004,-1.9140207825503284,0.6947126388549805,5.54848 +363,Regression,Stochastic Gradient Tree,ChickWeights,55.99599298772852,60.54917173074773,-1.852879941931207,0.6947126388549805,6.1724879999999995 +374,Regression,Stochastic Gradient Tree,ChickWeights,56.87222492302705,61.81275171085535,-1.7331917323651345,0.6947126388549805,6.808446 +385,Regression,Stochastic Gradient Tree,ChickWeights,58.41786698150333,63.95254893573906,-1.5885028214279253,0.6947126388549805,7.450193 +396,Regression,Stochastic Gradient Tree,ChickWeights,59.7033976124885,65.46926983257002,-1.5293357430909813,0.6947126388549805,8.100657 +407,Regression,Stochastic Gradient Tree,ChickWeights,60.057805647389294,66.17359973042984,-1.4019380007417155,1.1097631454467773,8.796904 +418,Regression,Stochastic Gradient Tree,ChickWeights,59.7070864579051,66.11592086962122,-1.2507954049688483,1.1127843856811523,9.50192 +429,Regression,Stochastic Gradient Tree,ChickWeights,60.122823673891816,66.73609937588846,-1.0378169857688957,1.1158056259155273,10.222461000000001 +440,Regression,Stochastic Gradient Tree,ChickWeights,60.39504675635191,66.96100690444877,-0.906365593827489,1.1188268661499023,10.951743 +451,Regression,Stochastic Gradient Tree,ChickWeights,60.27126048587789,66.93502892662679,-0.8239085862185902,1.120589256286621,11.696828 +462,Regression,Stochastic Gradient Tree,ChickWeights,60.340686610373176,67.43825007380137,-0.7390015352251049,1.120589256286621,12.465469 +473,Regression,Stochastic Gradient Tree,ChickWeights,61.40703262301831,69.11306667757516,-0.6127592621572406,1.120589256286621,13.248766 +484,Regression,Stochastic Gradient Tree,ChickWeights,61.95796621360106,69.71422620021941,-0.5510154280248158,1.120589256286621,14.047315 +495,Regression,Stochastic Gradient Tree,ChickWeights,62.59018166487368,70.55352405729404,-0.4943708535906215,1.120589256286621,14.854826 +506,Regression,Stochastic Gradient Tree,ChickWeights,62.49664579133251,70.88193125644693,-0.46447524520130457,1.120589256286621,15.674674999999999 +517,Regression,Stochastic Gradient Tree,ChickWeights,63.25224079915844,71.92080214464903,-0.4228062717918979,1.120589256286621,16.51129 +528,Regression,Stochastic Gradient Tree,ChickWeights,64.80783657170488,74.3681944005728,-0.36776422230083305,1.120589256286621,17.364023 +539,Regression,Stochastic Gradient Tree,ChickWeights,65.59959781369417,75.30113885843834,-0.3443906138479853,1.120589256286621,18.342071999999998 +550,Regression,Stochastic Gradient Tree,ChickWeights,65.79684627343133,76.01328745307667,-0.32771909731089166,1.120589256286621,19.334775999999998 +561,Regression,Stochastic Gradient Tree,ChickWeights,66.6512855136148,77.20436469287773,-0.30975691666695093,1.120589256286621,20.336346 +572,Regression,Stochastic Gradient Tree,ChickWeights,68.11975592628174,79.56492566870935,-0.2867456678376987,1.120589256286621,21.353617 +578,Regression,Stochastic Gradient Tree,ChickWeights,68.75877313437184,80.35800679505147,-0.2806007657015741,1.120589256286621,22.38029 +20,Regression,Stochastic Gradient Tree,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.019941329956054688,0.002168 +40,Regression,Stochastic Gradient Tree,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.03173637390136719,0.006794 +60,Regression,Stochastic Gradient Tree,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.04389762878417969,0.018434 +80,Regression,Stochastic Gradient Tree,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.05624198913574219,0.031286 +100,Regression,Stochastic Gradient Tree,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.5381031036376953,0.048039 +120,Regression,Stochastic Gradient Tree,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.5386066436767578,0.080711 +140,Regression,Stochastic Gradient Tree,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.5391101837158203,0.16679100000000002 +160,Regression,Stochastic Gradient Tree,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.5393619537353516,0.262676 +180,Regression,Stochastic Gradient Tree,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.5396137237548828,0.43318 +200,Regression,Stochastic Gradient Tree,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.5077581405639648,0.638958 +220,Regression,Stochastic Gradient Tree,TrumpApproval,36.497516001377406,38.019453444701035,-361.2329206514933,1.3602590560913086,0.9135530000000001 +240,Regression,Stochastic Gradient Tree,TrumpApproval,33.64243104419191,36.40668421494773,-333.65237138497804,1.360762596130371,1.221179 +260,Regression,Stochastic Gradient Tree,TrumpApproval,31.222114965034955,34.98371838354962,-312.16748668977897,1.3610143661499023,1.570709 +280,Regression,Stochastic Gradient Tree,TrumpApproval,29.182059468614717,33.71869814960704,-303.5986275675674,1.361769676208496,1.939253 +300,Regression,Stochastic Gradient Tree,TrumpApproval,27.34275770505051,32.57805191350732,-278.63174197976707,1.3620214462280273,2.3245549999999997 +320,Regression,Stochastic Gradient Tree,TrumpApproval,25.81388747443183,31.5521424826706,-274.2849072221064,1.3630285263061523,2.8771169999999997 +340,Regression,Stochastic Gradient Tree,TrumpApproval,24.51835124153299,30.62414457186519,-273.0482727941538,1.3640356063842773,3.4469999999999996 +360,Regression,Stochastic Gradient Tree,TrumpApproval,23.451930423400693,29.787924926455332,-260.4155562259403,1.3660497665405273,4.029196 +380,Regression,Stochastic Gradient Tree,TrumpApproval,22.468440533492842,29.014219480552867,-255.59151052979877,1.3665533065795898,4.629964999999999 +400,Regression,Stochastic Gradient Tree,TrumpApproval,21.594907007575774,28.301677882839346,-250.0434007116766,0.510127067565918,5.253793 +420,Regression,Stochastic Gradient Tree,TrumpApproval,20.62268781294523,27.62086591367872,-246.0239415518119,1.3623762130737305,5.968102 +440,Regression,Stochastic Gradient Tree,TrumpApproval,19.786863931462925,26.990398924900393,-230.60756767519214,1.3643903732299805,6.700306 +460,Regression,Stochastic Gradient Tree,TrumpApproval,19.05732899619648,26.404670160589287,-209.2038511633616,1.3666563034057617,7.451319000000001 +480,Regression,Stochastic Gradient Tree,TrumpApproval,18.376512097202227,25.854792215140314,-195.90337768575387,1.3701810836791992,8.221931000000001 +500,Regression,Stochastic Gradient Tree,TrumpApproval,17.755044410127518,25.338820973360427,-184.15507530651482,1.3716917037963867,9.124580000000002 +520,Regression,Stochastic Gradient Tree,TrumpApproval,17.16611419898163,24.851444862058347,-177.4118263333629,1.3737058639526367,10.044684000000002 +540,Regression,Stochastic Gradient Tree,TrumpApproval,16.628565596068775,24.392285078947275,-170.25012213753183,1.3747129440307617,10.981068000000002 +560,Regression,Stochastic Gradient Tree,TrumpApproval,16.091244232649693,23.955027361350904,-168.10096043791202,1.3752164840698242,11.990243000000003 +580,Regression,Stochastic Gradient Tree,TrumpApproval,15.590768135673304,23.54051091957351,-166.33817208986073,1.3764753341674805,13.016881000000003 +600,Regression,Stochastic Gradient Tree,TrumpApproval,15.168708628495342,23.15108754841241,-159.05714501634571,0.5124959945678711,14.090212000000003 +620,Regression,Stochastic Gradient Tree,TrumpApproval,14.742446374247313,22.779539618023726,-151.59887848495535,3.0642080307006836,15.285325000000004 +640,Regression,Stochastic Gradient Tree,TrumpApproval,14.319364852585176,22.42187566882095,-144.08105420081068,3.0679845809936523,16.529242000000004 +660,Regression,Stochastic Gradient Tree,TrumpApproval,13.916412195872256,22.080274918425697,-138.68241285181185,3.0712575912475586,17.842975000000003 +680,Regression,Stochastic Gradient Tree,TrumpApproval,13.515604789075645,21.753254558457893,-136.71797028279042,3.074782371520996,19.280557 +700,Regression,Stochastic Gradient Tree,TrumpApproval,13.16391092204058,21.44141764506316,-136.3120101768532,3.0773000717163086,20.753146 +720,Regression,Stochastic Gradient Tree,TrumpApproval,12.828283113852926,21.142484202016185,-135.44313416922282,3.078558921813965,22.284495 +740,Regression,Stochastic Gradient Tree,TrumpApproval,12.504466467012781,20.855361315179096,-131.6825380828392,3.0800695419311523,23.930702 +760,Regression,Stochastic Gradient Tree,TrumpApproval,12.187542748969033,20.57929219886472,-129.592708960364,3.0813283920288086,25.608717 +780,Regression,Stochastic Gradient Tree,TrumpApproval,11.899403743710545,20.31464229706916,-126.82553676745258,3.08359432220459,27.347365999999997 +800,Regression,Stochastic Gradient Tree,TrumpApproval,11.634366305883285,20.06137952581079,-124.7856004590591,3.084601402282715,29.130084999999998 +820,Regression,Stochastic Gradient Tree,TrumpApproval,11.363415331478278,19.815492221289514,-123.0687724200615,3.08560848236084,30.98707 +840,Regression,Stochastic Gradient Tree,TrumpApproval,11.106640469158773,19.57848368678801,-121.24309788996561,3.086615562438965,32.880055 +860,Regression,Stochastic Gradient Tree,TrumpApproval,10.873909665943762,19.350226189127362,-118.20364312373843,3.0871191024780273,34.808534 +880,Regression,Stochastic Gradient Tree,TrumpApproval,10.65545006969969,19.130035299019603,-114.92727947355435,3.0873708724975586,36.791638 +900,Regression,Stochastic Gradient Tree,TrumpApproval,10.439309697188909,18.916827199314994,-112.83532852765143,3.08762264251709,38.832751 +920,Regression,Stochastic Gradient Tree,TrumpApproval,10.21789524284777,18.710158789526105,-112.19133803320567,3.087874412536621,40.951802 +940,Regression,Stochastic Gradient Tree,TrumpApproval,10.012578535125467,18.510293787577226,-110.72583714230211,3.077906608581543,43.146806 +960,Regression,Stochastic Gradient Tree,TrumpApproval,9.811853150109151,18.316579311485903,-109.54344305213982,3.0804243087768555,45.38444 +980,Regression,Stochastic Gradient Tree,TrumpApproval,9.61909067795052,18.12881604876013,-109.39183420714343,3.080927848815918,47.662490999999996 +1000,Regression,Stochastic Gradient Tree,TrumpApproval,9.438738635632271,17.946847607318464,-109.00797869183796,3.0824384689331055,50.039238 +1001,Regression,Stochastic Gradient Tree,TrumpApproval,9.429746533156267,17.937886241411594,-108.97151968967047,3.0824384689331055,52.450717 +11,Regression,Adaptive Random Forest,ChickWeights,7.837563210503649,16.830121687224917,-363.61289911513376,0.1506052017211914,0.01357 +22,Regression,Adaptive Random Forest,ChickWeights,4.3557641651310055,11.925612892987612,-149.62275175212707,0.1761331558227539,0.033876 +33,Regression,Adaptive Random Forest,ChickWeights,3.3711466349197527,9.780434627556833,-65.4351822307151,0.21426868438720703,0.06570500000000001 +44,Regression,Adaptive Random Forest,ChickWeights,2.6922077728217833,8.482083592242564,-55.643739991610765,0.23125934600830078,0.11059100000000002 +55,Regression,Adaptive Random Forest,ChickWeights,2.74736475641488,7.825318026963682,-10.953904022002215,0.28695011138916016,0.16601600000000002 +66,Regression,Adaptive Random Forest,ChickWeights,2.8724679940162905,7.312536888278379,-3.4997438549991955,0.33327198028564453,0.24397800000000003 +77,Regression,Adaptive Random Forest,ChickWeights,3.0470429271529937,7.064245743713448,-1.8145642692685127,0.3119173049926758,0.346862 +88,Regression,Adaptive Random Forest,ChickWeights,2.9741223361578246,6.690154558226259,-1.288758200280824,0.3463144302368164,0.647937 +99,Regression,Adaptive Random Forest,ChickWeights,3.5306191185317584,6.892431773474538,-1.031779280787943,0.3914194107055664,0.960086 +110,Regression,Adaptive Random Forest,ChickWeights,3.8799314967396747,6.981555605673833,-0.45575716788658527,0.4218912124633789,1.292146 +121,Regression,Adaptive Random Forest,ChickWeights,4.113667008668635,7.033914104811044,-0.13804551272932075,0.4422159194946289,1.662194 +132,Regression,Adaptive Random Forest,ChickWeights,4.34164975929163,7.0584702899254435,0.06337311670854429,0.46953678131103516,2.044753 +143,Regression,Adaptive Random Forest,ChickWeights,4.57586761829926,7.15786747745719,0.2198462773680444,0.5039682388305664,2.459644 +154,Regression,Adaptive Random Forest,ChickWeights,4.72768375743327,7.245199860946492,0.3206991207112422,0.5465364456176758,2.897301 +165,Regression,Adaptive Random Forest,ChickWeights,5.104360720447454,7.731417459148682,0.3781793296599212,0.5543031692504883,3.478625 +176,Regression,Adaptive Random Forest,ChickWeights,5.614563299993537,8.384781892618234,0.41030323544665537,0.577855110168457,4.083596 +187,Regression,Adaptive Random Forest,ChickWeights,6.030281219875818,8.796345271037008,0.46861442712072365,0.5973634719848633,4.712472 +198,Regression,Adaptive Random Forest,ChickWeights,6.128233569544692,8.84845009665535,0.572286448100142,0.6156282424926758,5.362321 +209,Regression,Adaptive Random Forest,ChickWeights,6.65587905711115,9.652732357425101,0.5803946009681837,0.637272834777832,6.0687109999999995 +220,Regression,Adaptive Random Forest,ChickWeights,7.106977341119842,10.677274056234571,0.550512947323095,0.6516351699829102,6.792503 +231,Regression,Adaptive Random Forest,ChickWeights,7.51605472684967,11.121858780588035,0.582840670024279,0.6455926895141602,7.540065 +242,Regression,Adaptive Random Forest,ChickWeights,7.8763674823035235,11.54794620868086,0.6381207504866175,0.654301643371582,8.314509000000001 +253,Regression,Adaptive Random Forest,ChickWeights,8.048654689630025,11.785882981718466,0.6726179175042853,0.6542215347290039,9.228486 +264,Regression,Adaptive Random Forest,ChickWeights,8.558470564817128,12.694815113306078,0.6529678969258632,0.7006998062133789,10.165772 +275,Regression,Adaptive Random Forest,ChickWeights,9.011287699636803,13.865710758190522,0.6357023625954032,0.7181978225708008,11.12726 +286,Regression,Adaptive Random Forest,ChickWeights,9.454493871269733,14.39909947248495,0.6597325750664246,0.7397470474243164,12.112744 +297,Regression,Adaptive Random Forest,ChickWeights,9.455634964453314,14.370566123736594,0.7060577585099084,0.6961946487426758,13.208544 +308,Regression,Adaptive Random Forest,ChickWeights,9.98259297559382,15.278989711680778,0.7040656028742478,0.7122316360473633,14.327932 +319,Regression,Adaptive Random Forest,ChickWeights,10.896304985778038,17.680267148091307,0.6404050215106214,0.7230386734008789,15.472301 +330,Regression,Adaptive Random Forest,ChickWeights,11.34830207391465,18.238325787402868,0.6725323523293205,0.7481813430786133,16.699404 +341,Regression,Adaptive Random Forest,ChickWeights,11.700671911575691,18.698639858183288,0.6917798823884449,0.750828742980957,17.953966 +352,Regression,Adaptive Random Forest,ChickWeights,12.012928806619971,19.028065448277466,0.7098550919958697,0.779423713684082,19.24314 +363,Regression,Adaptive Random Forest,ChickWeights,12.590729727774809,20.061815233276363,0.6868102538385266,0.8219194412231445,20.584825000000002 +374,Regression,Adaptive Random Forest,ChickWeights,13.29572445199132,21.688967498502105,0.6634948622954009,0.8368387222290039,21.949532 +385,Regression,Adaptive Random Forest,ChickWeights,13.850252347511734,22.377982941031114,0.6830616430184708,0.8398981094360352,23.337733 +396,Regression,Adaptive Random Forest,ChickWeights,13.995508749414423,22.434927630401365,0.7029833246789492,0.8451242446899414,24.808931 +407,Regression,Adaptive Random Forest,ChickWeights,14.855647843034443,23.972462409994428,0.6847772413527866,0.8440675735473633,26.305221 +418,Regression,Adaptive Random Forest,ChickWeights,15.648428200057216,25.832735423225586,0.6563908585574095,0.8621377944946289,27.821711999999998 +429,Regression,Adaptive Random Forest,ChickWeights,16.477960681723363,27.016517310630082,0.6660339910533338,0.8826723098754883,29.421276999999996 +440,Regression,Adaptive Random Forest,ChickWeights,16.794784005292485,27.277386650758192,0.6836500576952018,0.8854074478149414,31.044845999999996 +451,Regression,Adaptive Random Forest,ChickWeights,17.2443539228967,27.815314781786782,0.6850336806962379,0.915654182434082,32.692344999999996 +462,Regression,Adaptive Random Forest,ChickWeights,18.21783864235053,29.965283642676138,0.6566601868655235,0.9476785659790039,34.453267999999994 +473,Regression,Adaptive Random Forest,ChickWeights,19.154558799374207,31.279498058996012,0.6696542166515442,0.9631280899047852,36.23863899999999 +484,Regression,Adaptive Random Forest,ChickWeights,19.653022199172934,31.710924929172922,0.6790841892096586,0.979741096496582,38.04845199999999 +495,Regression,Adaptive Random Forest,ChickWeights,20.17748759588543,32.35841629000369,0.6856630158751376,1.0035409927368164,39.89288999999999 +506,Regression,Adaptive Random Forest,ChickWeights,20.994447812000203,33.88452895368057,0.6653322556738073,1.0402307510375977,41.77183299999999 +517,Regression,Adaptive Random Forest,ChickWeights,21.74940325928189,34.92971521251369,0.6643962418834424,1.0591440200805664,43.682228999999985 +528,Regression,Adaptive Random Forest,ChickWeights,22.718068194641532,36.272080231437364,0.6746268651566016,1.0802621841430664,45.622796999999984 +539,Regression,Adaptive Random Forest,ChickWeights,22.976084812890594,36.32299861842887,0.6871862958215178,1.1105661392211914,47.62064799999998 +550,Regression,Adaptive Random Forest,ChickWeights,23.812560792713985,37.68037385984369,0.6737446986071818,1.1564149856567383,49.63871399999998 +561,Regression,Adaptive Random Forest,ChickWeights,24.744158926088524,38.956389615090316,0.6665241448790927,1.171940803527832,51.68634399999998 +572,Regression,Adaptive Random Forest,ChickWeights,25.965548256363952,40.779089345824126,0.6619939776632806,1.1861085891723633,53.83172099999997 +578,Regression,Adaptive Random Forest,ChickWeights,26.10164191353107,40.80941552099692,0.669724624616493,1.1904268264770508,56.00600499999997 +20,Regression,Adaptive Random Forest,TrumpApproval,4.656196028844478,13.301506400077992,-414.0076115498352,0.20158100128173828,0.057323 +40,Regression,Adaptive Random Forest,TrumpApproval,3.307191630717303,9.514843640593101,-35.39725790498291,0.28950977325439453,0.159522 +60,Regression,Adaptive Random Forest,TrumpApproval,2.3916587233350866,7.783560456255013,-31.83725667748105,0.32280826568603516,0.43784 +80,Regression,Adaptive Random Forest,TrumpApproval,2.0172424359013847,6.770328731809264,-23.921456088954436,0.36927127838134766,0.749169 +100,Regression,Adaptive Random Forest,TrumpApproval,2.069330341220504,6.141775226189047,-11.868015650386665,0.4076700210571289,1.082433 +120,Regression,Adaptive Random Forest,TrumpApproval,2.013474643057227,5.653544639730099,-8.249866206703038,0.4241609573364258,1.485703 +140,Regression,Adaptive Random Forest,TrumpApproval,1.8943659201342373,5.255534318342925,-7.260127227254786,0.44395923614501953,2.052392 +160,Regression,Adaptive Random Forest,TrumpApproval,1.9423634360618716,4.987168106592344,-5.559462264629689,0.45576953887939453,2.6505280000000004 +180,Regression,Adaptive Random Forest,TrumpApproval,1.9639788846395134,4.758402061618727,-4.244508869717663,0.47258472442626953,3.3297560000000006 +200,Regression,Adaptive Random Forest,TrumpApproval,1.9045329443413326,4.539431452034987,-3.787127034775958,0.5018167495727539,4.037089000000001 +220,Regression,Adaptive Random Forest,TrumpApproval,1.7801675790175082,4.332908187325825,-3.7047348470369075,0.539036750793457,4.871381000000001 +240,Regression,Adaptive Random Forest,TrumpApproval,1.7262455165213564,4.162317120423255,-3.3742337174670682,0.5583086013793945,5.726343000000002 +260,Regression,Adaptive Random Forest,TrumpApproval,1.6726006855046047,4.010034080286883,-3.1147254000977194,0.586766242980957,6.608691000000002 +280,Regression,Adaptive Random Forest,TrumpApproval,1.6001254820213158,3.8693841240389197,-3.01116046378164,0.6034517288208008,7.537396000000002 +300,Regression,Adaptive Random Forest,TrumpApproval,1.5903246290151523,3.758572865099384,-2.72204992570884,0.6344270706176758,8.563678000000001 +320,Regression,Adaptive Random Forest,TrumpApproval,1.5306703522535514,3.644833568467773,-2.673500446315358,0.6524057388305664,9.653495000000001 +340,Regression,Adaptive Random Forest,TrumpApproval,1.462120415173825,3.538151879462345,-2.658070572544154,0.6771516799926758,10.778313 +360,Regression,Adaptive Random Forest,TrumpApproval,1.4104873891633294,3.442715407420023,-2.491830651593505,0.712040901184082,12.006044000000001 +380,Regression,Adaptive Random Forest,TrumpApproval,1.3577274631021345,3.3535534396577877,-2.4279222429470106,0.7612085342407227,13.264975000000002 +400,Regression,Adaptive Random Forest,TrumpApproval,1.328889471148693,3.2750276755937473,-2.3616646758926834,0.7830896377563477,14.608014 +420,Regression,Adaptive Random Forest,TrumpApproval,1.2856838141339133,3.198005596242657,-2.3114858734875385,0.8153314590454102,15.987435000000001 +440,Regression,Adaptive Random Forest,TrumpApproval,1.2502461578606217,3.1277634460074983,-2.1102975482726696,0.8549776077270508,17.476653000000002 +460,Regression,Adaptive Random Forest,TrumpApproval,1.2118787702501406,3.0607885313580625,-1.8245276191275441,0.8641138076782227,19.005275 +480,Regression,Adaptive Random Forest,TrumpApproval,1.1755519926992437,2.997482691409013,-1.6465763687209671,0.904881477355957,20.582478000000002 +500,Regression,Adaptive Random Forest,TrumpApproval,1.1542746800420942,2.9412002898427465,-1.494663742459657,0.9429025650024414,22.213321 +520,Regression,Adaptive Random Forest,TrumpApproval,1.1232655769227813,2.8856256311301474,-1.4054721071787495,0.9187402725219727,23.93785 +540,Regression,Adaptive Random Forest,TrumpApproval,1.0927628011224122,2.8324064719208977,-1.3090698562992058,0.9784936904907227,25.72016 +560,Regression,Adaptive Random Forest,TrumpApproval,1.0798076211233283,2.7860066009246953,-1.2872677886963872,0.8415918350219727,27.559893 +580,Regression,Adaptive Random Forest,TrumpApproval,1.0533259806656756,2.7386650773118006,-1.2648586320750757,0.926945686340332,29.430927 +600,Regression,Adaptive Random Forest,TrumpApproval,1.0370277841126194,2.695306817676886,-1.1694452334238137,1.0152063369750977,31.394822 +620,Regression,Adaptive Random Forest,TrumpApproval,1.0220360797787769,2.6548714349996483,-1.0727572654712625,0.9687509536743164,33.420245 +640,Regression,Adaptive Random Forest,TrumpApproval,1.006223169156282,2.6150891537993277,-0.9735122270872276,0.8030519485473633,35.538976 +660,Regression,Adaptive Random Forest,TrumpApproval,0.9862189251721106,2.576116595691222,-0.901357605879683,0.7759256362915039,37.763356 +680,Regression,Adaptive Random Forest,TrumpApproval,0.9658028732124053,2.5385905860617046,-0.8755448750460146,0.8428354263305664,40.028227 +700,Regression,Adaptive Random Forest,TrumpApproval,0.9580702867531661,2.506070409170758,-0.8758066430098239,0.9465646743774414,42.353807 +720,Regression,Adaptive Random Forest,TrumpApproval,0.9436099236768006,2.472715624364642,-0.8663281360281503,1.0379304885864258,44.723793 +740,Regression,Adaptive Random Forest,TrumpApproval,0.9279645732871133,2.4402852992549255,-0.8166009677654511,1.1119890213012695,47.149898 +760,Regression,Adaptive Random Forest,TrumpApproval,0.9159099470417099,2.410261116608071,-0.7913739428902633,1.1737489700317383,49.62861 +780,Regression,Adaptive Random Forest,TrumpApproval,0.8968362370347621,2.379411736746614,-0.7536320120768303,1.261582374572754,52.179919 +800,Regression,Adaptive Random Forest,TrumpApproval,0.8878342141912964,2.3513921402434717,-0.7280625702741705,1.3552255630493164,54.784302 +820,Regression,Adaptive Random Forest,TrumpApproval,0.8775558321142263,2.3237456817971016,-0.7062000372474271,1.4321069717407227,57.452517 +840,Regression,Adaptive Random Forest,TrumpApproval,0.8672496542857573,2.297532908897418,-0.6834092983276716,1.4874773025512695,60.173386 +860,Regression,Adaptive Random Forest,TrumpApproval,0.8593706057522699,2.272389423762812,-0.6439286158863597,1.5595178604125977,62.976048 +880,Regression,Adaptive Random Forest,TrumpApproval,0.8551106332542915,2.2487703297155224,-0.6019328469057044,1.619084358215332,65.856537 +900,Regression,Adaptive Random Forest,TrumpApproval,0.8437512715146732,2.224375873084905,-0.5739713521606553,1.1261072158813477,68.798174 +920,Regression,Adaptive Random Forest,TrumpApproval,0.8344220404851989,2.2010168562801016,-0.5664083310843888,1.167832374572754,71.779904 +940,Regression,Adaptive Random Forest,TrumpApproval,0.825939320609599,2.179009982884269,-0.5482654902802699,1.1199464797973633,74.829165 +960,Regression,Adaptive Random Forest,TrumpApproval,0.8156984309758435,2.1571048007400404,-0.5331573747015668,1.1733713150024414,77.929016 +980,Regression,Adaptive Random Forest,TrumpApproval,0.806477335746804,2.1360065895495888,-0.5325097322303367,1.2283296585083008,81.05698100000001 +1000,Regression,Adaptive Random Forest,TrumpApproval,0.8008625237630099,2.1159877488140326,-0.5292346593649373,1.2836008071899414,84.241983 +1001,Regression,Adaptive Random Forest,TrumpApproval,0.800378499538596,2.1149541843634605,-0.5287610996295022,1.2846193313598633,87.445729 +11,Regression,Aggregated Mondrian Forest,ChickWeights,1.0878895070954884,1.3778002085324723,-1.2599207317049026,0.17917156219482422,0.003809 +22,Regression,Aggregated Mondrian Forest,ChickWeights,1.15171477394762,1.5218208011368886,-1.3974856828423898,0.33136653900146484,0.017797 +33,Regression,Aggregated Mondrian Forest,ChickWeights,1.2596040860169628,1.630698561429495,-0.8214033882315572,0.48356151580810547,0.056943 +44,Regression,Aggregated Mondrian Forest,ChickWeights,1.1470025325021567,1.5136945038262,-0.7860708992998826,0.6357030868530273,0.120895 +55,Regression,Aggregated Mondrian Forest,ChickWeights,1.7448650745312246,2.8901942810902064,-0.6023944619968462,0.795161247253418,0.34128800000000004 +66,Regression,Aggregated Mondrian Forest,ChickWeights,1.9741736434582027,3.1122799656868354,0.1967701507194095,0.949946403503418,0.602886 +77,Regression,Aggregated Mondrian Forest,ChickWeights,2.3465039451978784,3.868783481489585,0.16539043694478717,1.1044378280639648,0.885897 +88,Regression,Aggregated Mondrian Forest,ChickWeights,2.3152944739841907,3.751470845606434,0.286453015670338,1.2595434188842773,1.213122 +99,Regression,Aggregated Mondrian Forest,ChickWeights,2.4851266884813286,3.8753788781661265,0.3615628965518305,1.4176397323608398,1.708637 +110,Regression,Aggregated Mondrian Forest,ChickWeights,2.679180085056696,4.098463178184459,0.5005082908479199,1.580409049987793,2.233256 +121,Regression,Aggregated Mondrian Forest,ChickWeights,2.993112128155013,4.501608187312601,0.5353065115430311,1.7385053634643555,2.789253 +132,Regression,Aggregated Mondrian Forest,ChickWeights,3.049130101089184,4.474860576824222,0.624267329970883,1.8972959518432617,3.530617 +143,Regression,Aggregated Mondrian Forest,ChickWeights,3.129389359320645,4.535626207267123,0.6870855629914132,2.0540571212768555,4.307795 +154,Regression,Aggregated Mondrian Forest,ChickWeights,3.2350921629171503,4.614317779917637,0.7245583098520811,2.21335506439209,5.238077 +165,Regression,Aggregated Mondrian Forest,ChickWeights,3.615407192454655,5.434402308521257,0.6928112980835472,2.370730400085449,6.2153469999999995 +176,Regression,Aggregated Mondrian Forest,ChickWeights,3.842899644735678,5.8926781106586255,0.7087106044563829,2.5251951217651367,7.308114 +187,Regression,Aggregated Mondrian Forest,ChickWeights,3.939333513046091,5.936527515565436,0.7578865873655871,2.680434226989746,8.444739 +198,Regression,Aggregated Mondrian Forest,ChickWeights,4.1526220464224926,6.160116941975886,0.7926170753106898,2.8339643478393555,9.747256 +209,Regression,Aggregated Mondrian Forest,ChickWeights,4.486090256229248,6.857164593682279,0.7881489392686998,2.9901113510131836,11.107311 +220,Regression,Aggregated Mondrian Forest,ChickWeights,5.095083445923365,8.268326900050806,0.7303274183124314,3.147219657897949,12.601650999999999 +231,Regression,Aggregated Mondrian Forest,ChickWeights,5.345901760482457,8.651953805757511,0.7474084291359289,3.301150321960449,14.172197999999998 +242,Regression,Aggregated Mondrian Forest,ChickWeights,5.775936882313693,9.234098241635358,0.7685060952534608,3.4575910568237305,15.876551999999998 +253,Regression,Aggregated Mondrian Forest,ChickWeights,6.050411841877211,9.480574702158652,0.7880472652798773,3.6158742904663086,17.675130999999997 +264,Regression,Aggregated Mondrian Forest,ChickWeights,6.7396819662512994,10.861908099063555,0.7457953067175733,3.77274227142334,19.599012999999996 +275,Regression,Aggregated Mondrian Forest,ChickWeights,7.418933110619537,12.596893007879746,0.699156722346497,3.926619529724121,21.625010999999997 +286,Regression,Aggregated Mondrian Forest,ChickWeights,7.830180870941,13.02165358749325,0.7215679622698357,4.082179069519043,23.771257999999996 +297,Regression,Aggregated Mondrian Forest,ChickWeights,8.059624975297776,13.201631143135529,0.7517949935656911,4.237311363220215,26.095146999999997 +308,Regression,Aggregated Mondrian Forest,ChickWeights,8.517266870602596,14.029786197157003,0.750336177123377,4.390841484069824,28.501656999999998 +319,Regression,Aggregated Mondrian Forest,ChickWeights,9.872910663629112,17.67011178426297,0.6406578335650022,4.544772148132324,31.026265 +330,Regression,Aggregated Mondrian Forest,ChickWeights,10.355957081475973,18.251720539867826,0.671924837978655,4.698409080505371,33.677707999999996 +341,Regression,Aggregated Mondrian Forest,ChickWeights,10.779061369126929,18.644503325392748,0.6934349036095702,4.852313041687012,36.483968999999995 +352,Regression,Aggregated Mondrian Forest,ChickWeights,10.97013178962945,18.690294927177735,0.7199342471488321,5.009421348571777,39.412921 +363,Regression,Aggregated Mondrian Forest,ChickWeights,11.836385670325258,20.411474322578705,0.6756306209292051,5.165541648864746,42.477176 +374,Regression,Aggregated Mondrian Forest,ChickWeights,12.650208752532226,22.152599191433616,0.6487731216482631,5.323611259460449,45.685202 +385,Regression,Aggregated Mondrian Forest,ChickWeights,13.26433375884341,22.74111870549559,0.672536034672935,5.478930473327637,49.011466999999996 +396,Regression,Aggregated Mondrian Forest,ChickWeights,13.285084454056173,22.62858691877232,0.6976709876564118,5.63400936126709,52.447627 +407,Regression,Aggregated Mondrian Forest,ChickWeights,14.297859574888522,24.603705237609702,0.6677818184200794,5.789168357849121,55.986709 +418,Regression,Aggregated Mondrian Forest,ChickWeights,15.277775247208368,26.91758918665374,0.6267299649165277,5.945448875427246,59.629264 +429,Regression,Aggregated Mondrian Forest,ChickWeights,16.148002577856595,27.91235298687263,0.643353503146777,6.099112510681152,63.379205999999996 +440,Regression,Aggregated Mondrian Forest,ChickWeights,16.450833155107055,28.053185003016473,0.6652347923635655,6.252856254577637,67.23493599999999 +451,Regression,Aggregated Mondrian Forest,ChickWeights,16.938736394119786,28.680885446607185,0.6649461832952053,6.4079084396362305,71.205463 +462,Regression,Aggregated Mondrian Forest,ChickWeights,18.465286457846624,32.222162406640614,0.6027938253530374,6.562827110290527,75.286683 +473,Regression,Aggregated Mondrian Forest,ChickWeights,19.368786292726078,33.403615991184594,0.6231055060350865,6.717398643493652,79.474485 +484,Regression,Aggregated Mondrian Forest,ChickWeights,19.88015130963188,33.764210229402664,0.6360141173956066,6.872824668884277,83.769797 +495,Regression,Aggregated Mondrian Forest,ChickWeights,20.57744796303998,34.830627929035586,0.6356250399764956,7.029131889343262,88.176271 +506,Regression,Aggregated Mondrian Forest,ChickWeights,21.43571571603741,36.40788480688662,0.6134430891768862,7.184878349304199,92.694929 +517,Regression,Aggregated Mondrian Forest,ChickWeights,22.34914238062968,37.807266067412606,0.6066317565796657,7.340197563171387,97.332285 +528,Regression,Aggregated Mondrian Forest,ChickWeights,23.191315994328228,38.81894260965106,0.6271697641288401,7.495863914489746,102.073572 +539,Regression,Aggregated Mondrian Forest,ChickWeights,23.34075784343543,38.827434948624926,0.6423969913213963,7.652411460876465,106.92036399999999 +550,Regression,Aggregated Mondrian Forest,ChickWeights,24.125457325549842,39.99965605849559,0.6321733437483394,7.811335563659668,111.876093 +561,Regression,Aggregated Mondrian Forest,ChickWeights,24.859407485948264,40.9180834433101,0.6319179521646502,7.9698591232299805,116.942977 +572,Regression,Aggregated Mondrian Forest,ChickWeights,25.582967433016186,41.65667948828452,0.6471310448579161,8.12806224822998,122.12293 +578,Regression,Aggregated Mondrian Forest,ChickWeights,25.674172622955844,41.71227980537356,0.65479005999511,8.21412181854248,127.41509599999999 +20,Regression,Aggregated Mondrian Forest,TrumpApproval,0.38127899900663437,0.4856864156914124,0.4734504440676397,0.3661947250366211,0.012803 +40,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3903932807396207,0.4802129236445582,0.908098150441852,0.7077703475952148,0.066318 +60,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3553562094560649,0.44754485397583466,0.8908737885344612,1.0465993881225586,0.161186 +80,Regression,Aggregated Mondrian Forest,TrumpApproval,0.37850782280668976,0.48181042919820394,0.8725877843301283,1.386988639831543,0.310355 +100,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3456451525369771,0.45047631187257403,0.9301027529077078,1.7262754440307617,0.520575 +120,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3369671927041528,0.4421642502671299,0.9427860880043346,2.0684385299682617,0.828496 +140,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3170957614007029,0.42172730009164255,0.9461011323760549,2.404311180114746,1.349971 +160,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3307037984070857,0.4315519243898653,0.9502341257934384,2.7412595748901367,1.938101 +180,Regression,Aggregated Mondrian Forest,TrumpApproval,0.32391175568062514,0.4198186275021798,0.9586532343987925,3.0777502059936523,2.6707590000000003 +200,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3216990324082377,0.4152317221827294,0.959406710806771,3.4146718978881836,3.5252660000000002 +220,Regression,Aggregated Mondrian Forest,TrumpApproval,0.31954266191014236,0.40981538467826617,0.9573113010689299,3.7566747665405273,4.592894 +240,Regression,Aggregated Mondrian Forest,TrumpApproval,0.32058338930030683,0.40973424053171104,0.9569909127297425,4.096526145935059,5.7511 +260,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30963502752669864,0.3977121378847216,0.958918388812615,4.436213493347168,7.098098 +280,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3021514917324106,0.38814569807877647,0.9590118642619369,4.772730827331543,8.655558000000001 +300,Regression,Aggregated Mondrian Forest,TrumpApproval,0.302378432925199,0.38793022611752936,0.9597410141114792,5.1073408126831055,10.324296 +320,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30426355112773323,0.38808206022274244,0.9577019017103428,5.440821647644043,12.208908000000001 +340,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30611168054734034,0.391104192749731,0.9546026616524738,5.773791313171387,14.314788 +360,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3092553456162419,0.39535170942611675,0.9532980843409116,6.1096906661987305,16.588556 +380,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3076082401740039,0.39602056260851237,0.9515479192781826,6.445483207702637,19.05639 +400,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3014609513967445,0.3890658925747077,0.951945723428325,6.780200004577637,21.697867000000002 +420,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29711382936926317,0.3834166944817816,0.9518088379060109,7.1165571212768555,24.511965000000004 +440,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29823263394549426,0.3845174635941775,0.952475485177767,7.451247215270996,27.506667000000004 +460,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2946274143925286,0.38012238021570716,0.9560358463571957,7.7864484786987305,30.697063000000004 +480,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29339680191338824,0.3767220961039539,0.9578595845961764,8.120524406433105,34.045785 +500,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2879691623817681,0.37098658104945786,0.9600287871527097,8.45413875579834,37.543006000000005 +520,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2858512762877924,0.36803361403114826,0.9606162857565064,8.791060447692871,41.194255000000005 +540,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28337361966731006,0.3643610952909248,0.9615619008486129,9.128493309020996,45.008334000000005 +560,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28829960115327347,0.3724151060029248,0.9588932716362207,9.463343620300293,48.989560000000004 +580,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28931551719826604,0.37295833877986023,0.957758997678865,9.801314353942871,53.13082800000001 +600,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28804453093670845,0.3714720231862385,0.9585805866298193,10.13992977142334,57.44083900000001 +620,Regression,Aggregated Mondrian Forest,TrumpApproval,0.285913070387579,0.3694806072400069,0.9596689430521099,10.477011680603027,61.917696000000014 +640,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2840899599351511,0.36696425721615267,0.9609793991220156,10.80936336517334,66.56801000000002 +660,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2804371592513609,0.3629082548929102,0.9621252180674722,11.1486234664917,71.37699000000002 +680,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2832721366095781,0.36464822523085283,0.9611623224331389,11.485033988952637,76.34970200000002 +700,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28537572628090535,0.3664704272728199,0.959741096022156,11.82703685760498,81.48889300000002 +720,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2846995678315971,0.3663721647933076,0.9588788359612517,12.1649808883667,86.79821800000002 +740,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28438969208914106,0.36562346066591106,0.9590812376103318,12.5026273727417,92.27027700000002 +760,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2812524955317089,0.36227858565351356,0.9593969665626949,12.84118938446045,97.89633500000002 +780,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2784736419799919,0.35904953945649953,0.9599459478528628,13.181014060974121,103.67983800000002 +800,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28122680710979,0.3614117991183927,0.9590552347518728,13.522452354431152,109.62063100000002 +820,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2798414038154103,0.35991057058708614,0.9589530251045719,13.858756065368652,115.71824100000002 +840,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2792299366421054,0.358810295818463,0.9588293518961978,14.199065208435059,121.97670500000002 +860,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2757234931419036,0.35572944295397174,0.9596100235239657,14.53821849822998,128.39866500000002 +880,Regression,Aggregated Mondrian Forest,TrumpApproval,0.27250879183678145,0.3526976163964828,0.9604999212537026,14.877989768981934,134.987331 +900,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2707423985398595,0.35053221584455113,0.9608236269181287,15.214400291442871,141.746125 +920,Regression,Aggregated Mondrian Forest,TrumpApproval,0.27149682778681117,0.35071953709177356,0.960138783514385,15.551268577575684,148.677278 +940,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2704179121014184,0.350599255928843,0.9598313134304426,15.892088890075684,155.784091 +960,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2707082259086565,0.3516525290937312,0.9591696518431827,16.229090690612793,163.071856 +980,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2709225398475326,0.3517696828828596,0.9583487676398438,16.574454307556152,170.541967 +1000,Regression,Aggregated Mondrian Forest,TrumpApproval,0.26869541998932756,0.349579763566054,0.9581740736545136,16.91306972503662,178.198313 +1001,Regression,Aggregated Mondrian Forest,TrumpApproval,0.268533139095725,0.34942113435685235,0.9581842100200093,16.932257652282715,186.03359 +11,Regression,Adaptive Model Rules,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.019614219665527344,0.000715 +22,Regression,Adaptive Model Rules,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.021178245544433594,0.002248 +33,Regression,Adaptive Model Rules,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.02634716033935547,0.0045000000000000005 +44,Regression,Adaptive Model Rules,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.027434349060058594,0.007522000000000001 +55,Regression,Adaptive Model Rules,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.034033775329589844,0.011341 +66,Regression,Adaptive Model Rules,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.043257713317871094,0.016081 +77,Regression,Adaptive Model Rules,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.04948711395263672,0.021988 +88,Regression,Adaptive Model Rules,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.05513286590576172,0.050945000000000004 +99,Regression,Adaptive Model Rules,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.05624675750732422,0.083616 +110,Regression,Adaptive Model Rules,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.057623863220214844,0.119642 +121,Regression,Adaptive Model Rules,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.05775737762451172,0.157993 +132,Regression,Adaptive Model Rules,ChickWeights,5.216037157212015,7.551012267266295,-0.0719037453282565,0.05781078338623047,0.197604 +143,Regression,Adaptive Model Rules,ChickWeights,5.030848211447775,7.321940337412501,0.18367095381254994,0.058394432067871094,0.238591 +154,Regression,Adaptive Model Rules,ChickWeights,4.907406922429448,7.137924382310331,0.3406662269828342,0.058447837829589844,0.28091 +165,Regression,Adaptive Model Rules,ChickWeights,5.132506734403487,7.341156657504303,0.439370571581684,0.058447837829589844,0.32452 +176,Regression,Adaptive Model Rules,ChickWeights,5.292049153445915,7.468652514259996,0.5321251372519638,0.059058189392089844,0.369448 +187,Regression,Adaptive Model Rules,ChickWeights,5.31698748044205,7.461166418014795,0.6176873420824156,0.059111595153808594,0.415771 +198,Regression,Adaptive Model Rules,ChickWeights,5.300228902480157,7.425148329077998,0.6988181014109027,0.059058189392089844,0.474994 +209,Regression,Adaptive Model Rules,ChickWeights,5.830499581707958,9.648698249017793,0.5807452540036802,0.02527141571044922,0.54427 +220,Regression,Adaptive Model Rules,ChickWeights,6.400718854692065,10.45569246424029,0.5689754490886993,0.03142070770263672,0.614353 +231,Regression,Adaptive Model Rules,ChickWeights,6.611665150046439,10.617456980307361,0.6198209084753062,0.036536216735839844,0.6853290000000001 +242,Regression,Adaptive Model Rules,ChickWeights,7.029624246247838,11.197269958950692,0.6597654020329642,0.041068077087402344,0.7572760000000001 +253,Regression,Adaptive Model Rules,ChickWeights,7.254490759785878,11.350610231674398,0.6963529412438163,0.044592857360839844,0.830219 +264,Regression,Adaptive Model Rules,ChickWeights,7.784750145903498,12.258358647532567,0.6764200982742594,0.044699668884277344,0.9041760000000001 +275,Regression,Adaptive Model Rules,ChickWeights,8.342804112650073,13.247943494163705,0.6674407623884211,0.044699668884277344,0.986871 +286,Regression,Adaptive Model Rules,ChickWeights,8.88061114203256,14.075280539927816,0.6748649197186086,0.045203208923339844,1.0705630000000002 +297,Regression,Adaptive Model Rules,ChickWeights,9.500784996803779,14.855892526018593,0.6858683144490312,0.04522991180419922,1.1552540000000002 +308,Regression,Adaptive Model Rules,ChickWeights,10.070788242104461,15.77018489177999,0.6847321098344714,0.045256614685058594,1.2409280000000003 +319,Regression,Adaptive Model Rules,ChickWeights,10.988840488902909,17.80174938329892,0.6354464447499208,0.045256614685058594,1.3276340000000002 +330,Regression,Adaptive Model Rules,ChickWeights,11.635092222175304,18.61329763011445,0.6589287557789436,0.04528331756591797,1.4153970000000002 +341,Regression,Adaptive Model Rules,ChickWeights,11.7817306308102,18.651657721342477,0.6933268021215234,0.04528331756591797,1.5042310000000003 +352,Regression,Adaptive Model Rules,ChickWeights,11.878812775671825,18.699040402285984,0.7198024587207095,0.04528331756591797,1.5941090000000002 +363,Regression,Adaptive Model Rules,ChickWeights,12.712200605470676,19.934033697107445,0.690787203614232,0.045310020446777344,1.6850070000000001 +374,Regression,Adaptive Model Rules,ChickWeights,13.202927457133043,20.9603625224819,0.6857237785454591,0.04533672332763672,1.7769460000000001 +385,Regression,Adaptive Model Rules,ChickWeights,13.5542070698499,21.51079994203591,0.707149447507495,0.04533672332763672,1.8699420000000002 +396,Regression,Adaptive Model Rules,ChickWeights,13.642433072457155,21.454130101613703,0.7283852775805406,0.04533672332763672,1.9639870000000001 +407,Regression,Adaptive Model Rules,ChickWeights,14.50232093628697,22.86556238504221,0.7132152539462153,0.045676231384277344,2.060696 +418,Regression,Adaptive Model Rules,ChickWeights,15.245933470432924,24.220098655355127,0.6979521608965717,0.045729637145996094,2.158386 +429,Regression,Adaptive Model Rules,ChickWeights,15.766409258920858,25.08619072251902,0.7120527209837302,0.045729637145996094,2.2570650000000003 +440,Regression,Adaptive Model Rules,ChickWeights,15.931210335947624,25.166941851240068,0.7307081986676882,0.04564952850341797,2.4439120000000005 +451,Regression,Adaptive Model Rules,ChickWeights,16.418312975299003,25.736739737917958,0.7303482652633313,0.046179771423339844,2.6336030000000004 +462,Regression,Adaptive Model Rules,ChickWeights,17.4982370763817,27.78944281741256,0.7047111429028308,0.04692363739013672,2.8262370000000003 +473,Regression,Adaptive Model Rules,ChickWeights,18.254684132762545,29.056725346353634,0.7149358826261665,0.04697704315185547,3.0200670000000005 +484,Regression,Adaptive Model Rules,ChickWeights,18.58513038702809,29.354635254956722,0.7250038129485413,0.046950340270996094,3.2149970000000003 +495,Regression,Adaptive Model Rules,ChickWeights,19.01404260598322,29.86038018890717,0.7323226450377984,0.046896934509277344,3.430483 +506,Regression,Adaptive Model Rules,ChickWeights,19.88342353555136,31.26600741511644,0.7150584356224581,0.04697704315185547,3.648791 +517,Regression,Adaptive Model Rules,ChickWeights,20.595063111922975,32.24616798680886,0.713982273554131,0.047003746032714844,3.869932 +528,Regression,Adaptive Model Rules,ChickWeights,21.380474467010046,33.43504054753495,0.7235347994633756,0.047003746032714844,4.098743 +539,Regression,Adaptive Model Rules,ChickWeights,21.53249764026729,33.42135235584957,0.735168024057878,0.047003746032714844,4.328606 +550,Regression,Adaptive Model Rules,ChickWeights,22.499187843294454,35.002118414433774,0.7184757368310433,0.04703044891357422,4.559521999999999 +561,Regression,Adaptive Model Rules,ChickWeights,23.191634121895575,35.912468657285935,0.7166015220750928,0.047003746032714844,4.791517 +572,Regression,Adaptive Model Rules,ChickWeights,24.040656821383894,37.100860859043735,0.7202195492645626,0.046950340270996094,5.02459 +578,Regression,Adaptive Model Rules,ChickWeights,24.194319129377014,37.21658551958108,0.7253190778127725,0.04697704315185547,5.258551 +20,Regression,Adaptive Model Rules,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.053809165954589844,0.004347 +40,Regression,Adaptive Model Rules,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.07615184783935547,0.011776 +60,Regression,Adaptive Model Rules,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.08839702606201172,0.021496 +80,Regression,Adaptive Model Rules,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.09804439544677734,0.033628000000000005 +100,Regression,Adaptive Model Rules,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.10713481903076172,0.04833900000000001 +120,Regression,Adaptive Model Rules,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.11132335662841797,0.06604700000000001 +140,Regression,Adaptive Model Rules,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.11333751678466797,0.086317 +160,Regression,Adaptive Model Rules,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.1174459457397461,0.109348 +180,Regression,Adaptive Model Rules,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.1194601058959961,0.13519 +200,Regression,Adaptive Model Rules,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.017618179321289062,0.169486 +220,Regression,Adaptive Model Rules,TrumpApproval,2.0738811328897206,4.417664564856108,-3.890594467356201,0.03579998016357422,0.205189 +240,Regression,Adaptive Model Rules,TrumpApproval,1.9726100065438286,4.2375242409752385,-3.5337340888030546,0.04149913787841797,0.24247600000000002 +260,Regression,Adaptive Model Rules,TrumpApproval,1.8594315384151245,4.074751007989252,-3.248610147038553,0.048842430114746094,0.28140600000000004 +280,Regression,Adaptive Model Rules,TrumpApproval,1.7773205119132678,3.936654153117972,-3.1518424972300867,0.06378841400146484,0.322149 +300,Regression,Adaptive Model Rules,TrumpApproval,1.8265705896173514,3.8591002097544127,-2.923813511442849,0.07349681854248047,0.364943 +320,Regression,Adaptive Model Rules,TrumpApproval,1.7442837931334845,3.739506488697679,-2.866813933026025,0.08107662200927734,0.40978200000000004 +340,Regression,Adaptive Model Rules,TrumpApproval,1.6994316865849048,3.6380049904847285,-2.8674589929341425,0.08619213104248047,0.45684600000000003 +360,Regression,Adaptive Model Rules,TrumpApproval,1.6868885299887,3.5545855692388106,-2.7224500036418355,0.0937795639038086,0.506202 +380,Regression,Adaptive Model Rules,TrumpApproval,1.637461983479605,3.464628975063406,-2.658760364179245,0.09889507293701172,0.560423 +400,Regression,Adaptive Model Rules,TrumpApproval,1.622197889515682,3.392154183911459,-2.6064142473473755,0.10611248016357422,0.624493 +420,Regression,Adaptive Model Rules,TrumpApproval,1.6252883623828789,3.3313119696358306,-2.5933132470830746,0.11011409759521484,0.691125 +440,Regression,Adaptive Model Rules,TrumpApproval,1.663593439145693,3.2993129689970107,-2.4608371725208844,0.11575984954833984,0.760369 +460,Regression,Adaptive Model Rules,TrumpApproval,1.6928806013876205,3.26900202016339,-2.221881423949668,0.12384319305419922,0.832438 +480,Regression,Adaptive Model Rules,TrumpApproval,1.6463369530072471,3.2036213976345094,-2.0231064080329655,0.13150310516357422,0.907505 +500,Regression,Adaptive Model Rules,TrumpApproval,1.6312675040418116,3.1569789450171624,-1.8741285299844175,0.07843685150146484,0.99002 +520,Regression,Adaptive Model Rules,TrumpApproval,1.6486177246548734,3.1232792518100463,-1.81800645719813,0.08357906341552734,1.082168 +540,Regression,Adaptive Model Rules,TrumpApproval,1.664948820150162,3.091452157271598,-1.7507490735781142,0.0873861312866211,1.179534 +560,Regression,Adaptive Model Rules,TrumpApproval,1.6361907885919602,3.043459997537018,-1.7295303491345493,0.08855342864990234,1.466782 +580,Regression,Adaptive Model Rules,TrumpApproval,1.6082012495575047,2.9965453347231947,-1.7114709556760634,0.08905696868896484,1.757406 +600,Regression,Adaptive Model Rules,TrumpApproval,1.622569336171024,2.97009213510141,-1.6343417506962359,0.09091472625732422,2.050417 +620,Regression,Adaptive Model Rules,TrumpApproval,1.6368903964872519,2.946158197159977,-1.5525460315178896,0.0923452377319336,2.345765 +640,Regression,Adaptive Model Rules,TrumpApproval,1.652159107256621,2.9245287804119107,-1.4681901897894076,0.09443950653076172,2.643466 +660,Regression,Adaptive Model Rules,TrumpApproval,1.6570267761004454,2.8972896524900835,-1.4050084478390592,0.09603023529052734,2.943569 +680,Regression,Adaptive Model Rules,TrumpApproval,1.6362052297782712,2.859601997032609,-1.379870428705038,0.09812450408935547,3.2460560000000003 +700,Regression,Adaptive Model Rules,TrumpApproval,1.608205636538717,2.8213269237454877,-1.377433396876134,0.10156917572021484,3.5543920000000004 +720,Regression,Adaptive Model Rules,TrumpApproval,1.5855230254631891,2.785659545407005,-1.3686218528413674,0.1027364730834961,3.8755710000000003 +740,Regression,Adaptive Model Rules,TrumpApproval,1.583695771004626,2.7597111871599203,-1.3233016566851918,0.1038503646850586,4.202987 +760,Regression,Adaptive Model Rules,TrumpApproval,1.5704020318609786,2.7290361106702816,-1.2965538228485634,0.1038503646850586,4.532954 +780,Regression,Adaptive Model Rules,TrumpApproval,1.5638796853366008,2.702190403614744,-1.2616800152467116,0.10642147064208984,4.876142 +800,Regression,Adaptive Model Rules,TrumpApproval,1.5494799828615766,2.674411214594314,-1.2354538504080876,0.10703182220458984,5.2219169999999995 +820,Regression,Adaptive Model Rules,TrumpApproval,1.533437809889996,2.6465115200139584,-1.2130964074464639,0.1085958480834961,5.570086999999999 +840,Regression,Adaptive Model Rules,TrumpApproval,1.5202839319169328,2.6201051582792827,-1.1892919715417847,0.11015987396240234,5.920738999999999 +860,Regression,Adaptive Model Rules,TrumpApproval,1.5178574341866524,2.5988091386120904,-1.1501373691585313,0.11077022552490234,6.280333 +880,Regression,Adaptive Model Rules,TrumpApproval,1.4962844530295305,2.571223801389781,-1.0942757338776041,0.11124706268310547,6.652339 +900,Regression,Adaptive Model Rules,TrumpApproval,1.4724252749133646,2.5436398469986066,-1.0582196084183888,0.11164379119873047,7.031402 +920,Regression,Adaptive Model Rules,TrumpApproval,1.4596881679466962,2.5220256913044325,-1.056635177134157,0.11167049407958984,7.413564 +940,Regression,Adaptive Model Rules,TrumpApproval,1.452139596196528,2.5028075284250018,-1.0425932823285438,0.11270427703857422,7.802334 +960,Regression,Adaptive Model Rules,TrumpApproval,1.4364147887122178,2.481230554777158,-1.0285162299402342,0.11320781707763672,8.193766 +980,Regression,Adaptive Model Rules,TrumpApproval,1.4186260884044517,2.45780687839372,-1.0290538610685447,0.11381816864013672,8.587828 +1000,Regression,Adaptive Model Rules,TrumpApproval,1.3997779646996387,2.434572696055838,-1.024386017127401,0.11442852020263672,8.984547 +1001,Regression,Adaptive Model Rules,TrumpApproval,1.3984653255896196,2.433357833975862,-1.0237164038272608,0.11442852020263672,9.38293 +11,Regression,Streaming Random Patches,ChickWeights,4.674710287324511,12.709622005759085,-206.93269654300337,0.14386653900146484,0.053261 +22,Regression,Streaming Random Patches,ChickWeights,2.741934273684416,9.017856101646904,-85.12629469646626,0.16807842254638672,0.14276 +33,Regression,Streaming Random Patches,ChickWeights,2.3543476002974106,7.430504888974863,-37.34585890537725,0.20960521697998047,0.266148 +44,Regression,Streaming Random Patches,ChickWeights,1.9327820011330463,6.452362261246447,-31.77814024428305,0.24174785614013672,0.646479 +55,Regression,Streaming Random Patches,ChickWeights,2.2606373648191784,6.1461368420669364,-6.374120366305681,0.30608272552490234,1.057843 +66,Regression,Streaming Random Patches,ChickWeights,2.3521495161457713,5.750947689984691,-1.7831107407377038,0.35672664642333984,1.521792 +77,Regression,Streaming Random Patches,ChickWeights,2.707478618787897,5.832856917221716,-0.9188552689556648,0.3732900619506836,2.259814 +88,Regression,Streaming Random Patches,ChickWeights,2.60389034076398,5.525482549715508,-0.5612341217350767,0.41286373138427734,3.03269 +99,Regression,Streaming Random Patches,ChickWeights,2.7646559934763433,5.466320467144536,-0.27797322073999386,0.4623746871948242,3.8541800000000004 +110,Regression,Streaming Random Patches,ChickWeights,2.880719733615897,5.407041915578862,0.12681954319141486,0.5318593978881836,4.717509000000001 +121,Regression,Streaming Random Patches,ChickWeights,3.0896780011355176,5.466874267225462,0.31254594053869156,0.5604543685913086,5.631386000000001 +132,Regression,Streaming Random Patches,ChickWeights,3.270943191870578,5.7618521847151385,0.37587775495273845,0.1946859359741211,6.649151000000001 +143,Regression,Streaming Random Patches,ChickWeights,3.2470159770350198,5.633009027852055,0.5168368848346436,0.22883892059326172,7.703019000000001 +154,Regression,Streaming Random Patches,ChickWeights,3.2192007860807728,5.520141144427338,0.6056681999848637,0.2577199935913086,8.869892000000002 +165,Regression,Streaming Random Patches,ChickWeights,3.5165819956804767,5.874797514643079,0.6409683688760216,0.26273250579833984,10.065309000000003 +176,Regression,Streaming Random Patches,ChickWeights,3.700430602978386,6.068185859760413,0.6911390854727877,0.29411983489990234,11.324773000000002 +187,Regression,Streaming Random Patches,ChickWeights,3.8037309027428843,6.1218084380222,0.7426259865968339,0.3320951461791992,12.629055000000003 +198,Regression,Streaming Random Patches,ChickWeights,4.006649900662983,6.578339511639692,0.7635979888713487,0.25274181365966797,13.981910000000003 +209,Regression,Streaming Random Patches,ChickWeights,4.229383118423565,6.982583803939909,0.780430075854037,0.32021617889404297,15.391493000000002 +220,Regression,Streaming Random Patches,ChickWeights,4.825249759558611,8.423350501384354,0.720252540483964,0.35108089447021484,16.880127 +231,Regression,Streaming Random Patches,ChickWeights,5.088028806665401,8.669832171958772,0.7465054715218886,0.32001399993896484,18.399834000000002 +242,Regression,Streaming Random Patches,ChickWeights,5.462442991686406,9.175585237136575,0.7715339230013022,0.3710927963256836,20.020863000000002 +253,Regression,Streaming Random Patches,ChickWeights,5.5636194675564115,9.260101660572655,0.797901929856006,0.41926097869873047,21.677784000000003 +264,Regression,Streaming Random Patches,ChickWeights,6.261116867150435,10.599777327157994,0.7580584961853923,0.3416013717651367,23.387217000000003 +275,Regression,Streaming Random Patches,ChickWeights,6.742618468073929,11.802240597789721,0.7360625543191792,0.3569021224975586,25.127202000000004 +286,Regression,Streaming Random Patches,ChickWeights,7.039594962415952,12.249488193444416,0.7537446936710837,0.39655208587646484,26.911059000000005 +297,Regression,Streaming Random Patches,ChickWeights,7.148800229885712,12.311677740983953,0.7842510327710687,0.4258260726928711,28.739538000000007 +308,Regression,Streaming Random Patches,ChickWeights,7.753683144422786,13.244190950829555,0.7776398094561477,0.45011425018310547,30.593856000000006 +319,Regression,Streaming Random Patches,ChickWeights,8.773143666519827,15.939753659782179,0.7077199430319765,0.4718656539916992,32.508295000000004 +330,Regression,Streaming Random Patches,ChickWeights,9.312574124937234,16.796832021919023,0.7222505421616592,0.3236379623413086,34.514388000000004 +341,Regression,Streaming Random Patches,ChickWeights,9.544591695703465,16.94083213248977,0.7470058810264122,0.3676939010620117,36.550995 +352,Regression,Streaming Random Patches,ChickWeights,9.680039805071173,17.006622056031052,0.7682275557667291,0.36295223236083984,38.648436000000004 +363,Regression,Streaming Random Patches,ChickWeights,10.417098847501563,18.381838377902795,0.7370670774420947,0.3571195602416992,40.771216 +374,Regression,Streaming Random Patches,ChickWeights,11.080869197293334,19.965812195666537,0.7148404591067161,0.39987850189208984,42.937715000000004 +385,Regression,Streaming Random Patches,ChickWeights,11.60940210623338,20.687378926969966,0.7291406299077132,0.3288450241088867,45.213373000000004 +396,Regression,Streaming Random Patches,ChickWeights,11.737814918904208,20.678726756260627,0.7476640805491588,0.4024953842163086,47.506899000000004 +407,Regression,Streaming Random Patches,ChickWeights,12.682108791666492,22.361726245252385,0.7257144517605874,0.4475545883178711,49.84079500000001 +418,Regression,Streaming Random Patches,ChickWeights,13.622708961705229,24.146094170569185,0.6997951546356598,0.4968290328979492,52.212878 +429,Regression,Streaming Random Patches,ChickWeights,14.165217959113354,24.88032134675199,0.7167593971826183,0.5376424789428711,54.630802 +440,Regression,Streaming Random Patches,ChickWeights,14.411006646174876,24.97835315387625,0.7347289581181171,0.5657072067260742,57.077112 +451,Regression,Streaming Random Patches,ChickWeights,14.766578325445964,25.376772271610328,0.7378384948653763,0.25832653045654297,59.570857 +462,Regression,Streaming Random Patches,ChickWeights,16.09445226127713,28.12961105819035,0.6974376845026461,0.3047628402709961,62.101037999999996 +473,Regression,Streaming Random Patches,ChickWeights,16.916275460891086,29.341089843915018,0.7093290035354769,0.3544912338256836,64.67061 +484,Regression,Streaming Random Patches,ChickWeights,17.222566694739786,29.549549967606488,0.7213397403469026,0.39832592010498047,67.26974899999999 +495,Regression,Streaming Random Patches,ChickWeights,17.854950072386483,30.34354672604944,0.7235900637963901,0.43242549896240234,69.89740799999998 +506,Regression,Streaming Random Patches,ChickWeights,18.84874733203415,31.79966974813451,0.7052484004379906,0.46784114837646484,72.66954099999998 +517,Regression,Streaming Random Patches,ChickWeights,19.785853660032195,33.20181112471305,0.6967783021275082,0.49755001068115234,75.49558399999998 +528,Regression,Streaming Random Patches,ChickWeights,20.52664258005787,34.100310164439925,0.7124234805234935,0.5351285934448242,78.36005299999998 +539,Regression,Streaming Random Patches,ChickWeights,20.766026265849113,34.21097619783695,0.7225061795517687,0.576685905456543,81.26779099999997 +550,Regression,Streaming Random Patches,ChickWeights,21.840503815170695,36.158966072689324,0.6995590139862528,0.4753904342651367,84.25568299999998 +561,Regression,Streaming Random Patches,ChickWeights,22.596906243133255,37.108967777985264,0.6974029137241997,0.5189352035522461,87.27382099999997 +572,Regression,Streaming Random Patches,ChickWeights,23.534320250737128,38.28067851879141,0.7021424273708647,0.5585355758666992,90.32555299999997 +578,Regression,Streaming Random Patches,ChickWeights,23.709683411591413,38.441629018276466,0.7069383356385298,0.3551816940307617,93.40141099999997 +20,Regression,Streaming Random Patches,TrumpApproval,2.677140920600926,9.804891856735377,-224.4966127051096,0.2373647689819336,0.078317 +40,Regression,Streaming Random Patches,TrumpApproval,2.42676306487335,7.1506632844470275,-19.556918338481843,0.3270711898803711,0.26006399999999996 +60,Regression,Streaming Random Patches,TrumpApproval,1.8116457742622056,5.852230884873156,-17.563213740222555,0.34931087493896484,0.628767 +80,Regression,Streaming Random Patches,TrumpApproval,1.5261658032230543,5.084894428469453,-13.057813649460385,0.3968191146850586,1.163603 +100,Regression,Streaming Random Patches,TrumpApproval,1.4048851039170587,4.580518627305071,-6.157363117938322,0.41078853607177734,1.7740749999999998 +120,Regression,Streaming Random Patches,TrumpApproval,1.2872329076385731,4.198963935277897,-4.1024421406573515,0.4562673568725586,2.4559889999999998 +140,Regression,Streaming Random Patches,TrumpApproval,1.4191481295394186,4.9019146331166,-6.185954638838571,0.20268917083740234,3.2282499999999996 +160,Regression,Streaming Random Patches,TrumpApproval,1.3292908695512107,4.594852312450113,-4.568052693162012,0.32157421112060547,4.121231 +180,Regression,Streaming Random Patches,TrumpApproval,1.2559271503392595,4.341680890984575,-3.3661470093978725,0.4017667770385742,5.147797 +200,Regression,Streaming Random Patches,TrumpApproval,1.169313410896163,4.12134361195162,-2.94593273053925,0.47729015350341797,6.3380719999999995 +220,Regression,Streaming Random Patches,TrumpApproval,1.1066399346497042,3.9344660517514094,-2.8792501333638807,0.5200605392456055,7.615136 +240,Regression,Streaming Random Patches,TrumpApproval,1.0535228972416337,3.7704097053754206,-2.5892915995637975,0.590418815612793,8.98983 +260,Regression,Streaming Random Patches,TrumpApproval,1.0002808672832586,3.6243313765079748,-2.3612477765650133,0.677699089050293,10.49221 +280,Regression,Streaming Random Patches,TrumpApproval,0.9484528900796008,3.4935839886686573,-2.269856713922535,0.7459287643432617,12.09809 +300,Regression,Streaming Random Patches,TrumpApproval,0.9319658343242508,3.3810597344709987,-2.011909605217061,0.8300580978393555,13.828035999999999 +320,Regression,Streaming Random Patches,TrumpApproval,0.9015525068993323,3.2761126415748776,-1.9678527090537403,0.8134641647338867,15.697273999999998 +340,Regression,Streaming Random Patches,TrumpApproval,0.9086073156973856,3.2065165500712434,-2.0044577988421626,0.7938528060913086,17.678026 +360,Regression,Streaming Random Patches,TrumpApproval,0.9209686764414103,3.130698586248577,-1.8875761151685357,0.8660383224487305,19.789752 +380,Regression,Streaming Random Patches,TrumpApproval,0.9054594388814018,3.0518145886207013,-1.8388130234405762,0.9304952621459961,22.025823 +400,Regression,Streaming Random Patches,TrumpApproval,0.9021459083449618,2.9892737243691805,-1.8006304820861794,0.9046812057495117,24.377516 +420,Regression,Streaming Random Patches,TrumpApproval,0.9000900027115483,2.937103674242639,-1.7932063526136273,0.2984609603881836,26.835964 +440,Regression,Streaming Random Patches,TrumpApproval,0.884833385913356,2.873027664171451,-1.6243016536608597,0.3453207015991211,29.357596 +460,Regression,Streaming Random Patches,TrumpApproval,0.8690754265879537,2.8131168800056585,-1.3859136859847618,0.3807516098022461,31.984181 +480,Regression,Streaming Random Patches,TrumpApproval,0.8473225380080763,2.7551095271995596,-1.235881587238783,0.44824886322021484,34.68911 +500,Regression,Streaming Random Patches,TrumpApproval,0.8286186581223807,2.701061117260836,-1.1039316995995572,0.49751949310302734,37.472375 +520,Regression,Streaming Random Patches,TrumpApproval,0.8308331247066605,2.6760879938297655,-1.0688124961584973,0.37538814544677734,40.349897 +540,Regression,Streaming Random Patches,TrumpApproval,0.8063786739892863,2.6263308571208617,-0.9852938090834253,0.40636730194091797,43.316687 +560,Regression,Streaming Random Patches,TrumpApproval,0.7997929461413226,2.582727501713279,-0.9656668153374994,0.43744945526123047,46.3679 +580,Regression,Streaming Random Patches,TrumpApproval,0.7908850979728871,2.5414571653673215,-0.950423138286411,0.49677181243896484,49.479346 +600,Regression,Streaming Random Patches,TrumpApproval,0.7789627943481009,2.500361162030882,-0.8669718089652279,0.569575309753418,52.654549 +620,Regression,Streaming Random Patches,TrumpApproval,0.7682254429218135,2.4616773328611172,-0.7820656947310429,0.6584272384643555,55.885269 +640,Regression,Streaming Random Patches,TrumpApproval,0.756836908225871,2.4246570785119217,-0.6965531574296129,0.7120962142944336,59.181412 +660,Regression,Streaming Random Patches,TrumpApproval,0.7406340846119412,2.388088765643962,-0.6339309775627988,0.8089780807495117,62.54643 +680,Regression,Streaming Random Patches,TrumpApproval,0.7257657440750075,2.3532857176647086,-0.6117268738432657,0.8398160934448242,65.979515 +700,Regression,Streaming Random Patches,TrumpApproval,0.7284794894639326,2.3250297797266612,-0.6145762958857721,0.9275884628295898,69.48963400000001 +720,Regression,Streaming Random Patches,TrumpApproval,0.7231955460113827,2.297270435922827,-0.6108826519065647,0.9087285995483398,73.071921 +740,Regression,Streaming Random Patches,TrumpApproval,0.7217944839950929,2.2699024953355416,-0.5717835473178023,0.8719320297241211,76.723943 +760,Regression,Streaming Random Patches,TrumpApproval,0.7121024512438853,2.241058668519108,-0.5486900768629306,0.9295186996459961,80.452493 +780,Regression,Streaming Random Patches,TrumpApproval,0.7019422114012909,2.213016497735788,-0.5169405784918113,1.0446271896362305,84.252351 +800,Regression,Streaming Random Patches,TrumpApproval,0.7005931807120314,2.1884283322336215,-0.49683523063919743,1.1031560897827148,88.13395700000001 +820,Regression,Streaming Random Patches,TrumpApproval,0.6997484436891046,2.169363820936814,-0.48702250637871436,1.0328702926635742,92.08914500000002 +840,Regression,Streaming Random Patches,TrumpApproval,0.6949195885567419,2.14957176369946,-0.4735678496288336,0.7475957870483398,96.10836100000002 +860,Regression,Streaming Random Patches,TrumpApproval,0.6920590805093112,2.1264870362465933,-0.439603572136809,0.8119535446166992,100.18224400000001 +880,Regression,Streaming Random Patches,TrumpApproval,0.6882027439938415,2.1038322601427426,-0.40209138862407245,0.8655576705932617,104.313167 +900,Regression,Streaming Random Patches,TrumpApproval,0.6818594219129391,2.085410616994055,-0.38345052454273,0.8467855453491211,108.50343900000001 +920,Regression,Streaming Random Patches,TrumpApproval,0.6756248333192205,2.0637081851469703,-0.37706620583821104,0.9400205612182617,112.75318600000001 +940,Regression,Streaming Random Patches,TrumpApproval,0.6689624136970388,2.0428592411141833,-0.3608300191067024,0.8168668746948242,117.06153900000001 +960,Regression,Streaming Random Patches,TrumpApproval,0.6627773066160889,2.022520414368002,-0.3478143420231987,0.9120321273803711,121.43268700000002 +980,Regression,Streaming Random Patches,TrumpApproval,0.6600305544016135,2.003593726688123,-0.34839580077130505,0.9782476425170898,125.86239700000002 +1000,Regression,Streaming Random Patches,TrumpApproval,0.657029407021691,1.9853014454830338,-0.3461726175729922,1.0593442916870117,130.37428000000003 +1001,Regression,Streaming Random Patches,TrumpApproval,0.6566965628025029,1.9843359394661053,-0.34576147632314735,1.0646085739135742,134.90266400000002 +11,Regression,Bagging,ChickWeights,10.955590565999659,17.7409835250609,-404.147256051216,0.1553668975830078,0.005094 +22,Regression,Bagging,ChickWeights,5.88626580700965,12.566688603347808,-166.25182631838038,0.1681652069091797,0.018278 +33,Regression,Bagging,ChickWeights,4.383857039198176,10.299865918219764,-72.67921052893462,0.20520591735839844,0.039075 +44,Regression,Bagging,ChickWeights,3.446496162870555,8.931116231999566,-61.79980671874969,0.2209186553955078,0.065167 +55,Regression,Bagging,ChickWeights,3.3513349782155033,8.247717183177938,-12.279242202465667,0.2687969207763672,0.09656600000000001 +66,Regression,Bagging,ChickWeights,3.889627188952696,8.0458642201752,-4.4474976461238604,0.3383464813232422,0.144605 +77,Regression,Bagging,ChickWeights,4.337751636727128,7.9681159743419645,-2.5808890563388096,0.3940753936767578,0.201306 +88,Regression,Bagging,ChickWeights,4.489908334389532,7.740787033322287,-2.0640641214272355,0.4405231475830078,0.274421 +99,Regression,Bagging,ChickWeights,4.7831270806190425,7.705843596650206,-1.5396388125269618,0.4615802764892578,0.37284300000000004 +110,Regression,Bagging,ChickWeights,4.73395080514245,7.47334250555501,-0.6680701376440403,0.4910602569580078,0.5845670000000001 +121,Regression,Bagging,ChickWeights,4.733710015085173,7.331306378435282,-0.23631246502535208,0.5042209625244141,0.806575 +132,Regression,Bagging,ChickWeights,4.565752852065114,7.0976416640915465,0.05294854472396571,0.5126438140869141,1.039517 +143,Regression,Bagging,ChickWeights,4.439022558662509,6.895745596080793,0.2759386934515202,0.5216274261474609,1.284562 +154,Regression,Bagging,ChickWeights,4.362170284876481,6.736533340066285,0.4127346685162743,0.5262050628662109,1.543379 +165,Regression,Bagging,ChickWeights,4.647894929983432,7.008861526290804,0.48897532974091285,0.5290126800537109,1.82575 +176,Regression,Bagging,ChickWeights,4.817744211127824,7.136288548419971,0.5728405597677722,0.5066156387329102,2.145522 +187,Regression,Bagging,ChickWeights,4.867036081233358,7.152118590173611,0.6487028411390494,0.4790925979614258,2.491984 +198,Regression,Bagging,ChickWeights,4.887061675652274,7.15502256260352,0.720333392468735,0.3514680862426758,2.944156 +209,Regression,Bagging,ChickWeights,5.070260383978678,7.484020932149266,0.7477619935177958,0.3277406692504883,3.412471 +220,Regression,Bagging,ChickWeights,5.671227628293087,8.602358503958763,0.7082361492582977,0.3353548049926758,3.897275 +231,Regression,Bagging,ChickWeights,5.872335352103937,8.83175934179542,0.7369479677711775,0.37228870391845703,4.389179 +242,Regression,Bagging,ChickWeights,6.107145463120707,9.222510821361377,0.769191114739663,0.3991250991821289,4.889463 +253,Regression,Bagging,ChickWeights,6.19844823305091,9.33633324769997,0.7945607849348244,0.42070865631103516,5.506109 +264,Regression,Bagging,ChickWeights,6.823605404288741,10.586090935492885,0.758682880699561,0.43335819244384766,6.131382 +275,Regression,Bagging,ChickWeights,7.289576170484155,11.670233638164651,0.7419337665758028,0.4418039321899414,6.777342 +286,Regression,Bagging,ChickWeights,7.579012857443305,12.145524073459754,0.75790700185782,0.45081424713134766,7.439278 +297,Regression,Bagging,ChickWeights,7.564986803201262,12.135208564512551,0.7903915740986345,0.45409488677978516,8.115354 +308,Regression,Bagging,ChickWeights,8.103353916061925,13.02855032884451,0.7848217554522041,0.4577798843383789,8.913327 +319,Regression,Bagging,ChickWeights,9.2182891996096,15.75502466975724,0.7144552709875674,0.4609994888305664,9.721339 +330,Regression,Bagging,ChickWeights,9.685083231372472,16.400765556025647,0.7351946818510116,0.4719209671020508,10.546217 +341,Regression,Bagging,ChickWeights,9.903299441393282,16.527032528363478,0.759214288686034,0.47708606719970703,11.387382 +352,Regression,Bagging,ChickWeights,10.047801743751695,16.62843798311862,0.778421004008311,0.48488330841064453,12.279877 +363,Regression,Bagging,ChickWeights,10.963674059851892,18.110346084278056,0.7447765461541069,0.48735523223876953,13.191450000000001 +374,Regression,Bagging,ChickWeights,11.492835005144466,19.28081121430548,0.7340717056357994,0.4739046096801758,14.120497000000002 +385,Regression,Bagging,ChickWeights,11.898657720927194,19.943213385356135,0.7482768269892894,0.4488801956176758,15.069160000000002 +396,Regression,Bagging,ChickWeights,12.0617729851989,19.965773188137263,0.7647640178574115,0.4161386489868164,16.114715 +407,Regression,Bagging,ChickWeights,12.97304553348899,21.57136186484404,0.7447607843499935,0.40607547760009766,17.182106 +418,Regression,Bagging,ChickWeights,13.747411939847145,23.06575414024212,0.7260576137332548,0.42958927154541016,18.259955 +429,Regression,Bagging,ChickWeights,14.305030376306712,23.986335540211613,0.7367482003695625,0.4628152847290039,19.347052 +440,Regression,Bagging,ChickWeights,14.526268288674354,24.074522605416067,0.7535790611891788,0.4899911880493164,20.478651000000003 +451,Regression,Bagging,ChickWeights,15.027594657922048,24.671918598232004,0.7521995995009789,0.5175485610961914,21.623166 +462,Regression,Bagging,ChickWeights,16.208274238827823,27.03842360672758,0.7204560380476568,0.5713090896606445,22.784126 +473,Regression,Bagging,ChickWeights,16.99589357541462,28.364120175265192,0.7283636722963454,0.5903940200805664,23.973479 +484,Regression,Bagging,ChickWeights,17.304815327407063,28.547476213614512,0.739918935022724,0.6084756851196289,25.225522 +495,Regression,Bagging,ChickWeights,17.747173803776352,29.064129392830434,0.7464079684248853,0.6325922012329102,26.517497000000002 +506,Regression,Bagging,ChickWeights,18.655435380807354,30.57773482627066,0.7274654471662664,0.6401453018188477,27.826703000000002 +517,Regression,Bagging,ChickWeights,19.38212758204995,31.566942752755278,0.7259045847606268,0.5900964736938477,29.161388000000002 +528,Regression,Bagging,ChickWeights,20.162555380752366,32.73116726334994,0.7350525453098611,0.6013956069946289,30.577640000000002 +539,Regression,Bagging,ChickWeights,20.34377517847412,32.75647044736101,0.7456003073902285,0.6148271560668945,32.006392000000005 +550,Regression,Bagging,ChickWeights,21.397093652240404,34.43808497807088,0.7274757471078548,0.6217546463012695,33.45442800000001 +561,Regression,Bagging,ChickWeights,22.130535392790673,35.39551310036421,0.7247017706467436,0.6181917190551758,34.916661000000005 +572,Regression,Bagging,ChickWeights,22.976096797270415,36.539264510866154,0.7286255260499503,0.6243486404418945,36.463466000000004 +578,Regression,Bagging,ChickWeights,23.114298050830314,36.631109645590286,0.7338934315030725,0.6280336380004883,38.020312000000004 +20,Regression,Bagging,TrumpApproval,6.57361785669815,13.877675781396096,-450.73930630825186,0.3875865936279297,0.030628 +40,Regression,Bagging,TrumpApproval,4.357601810962072,9.93598927447802,-38.690592530050864,0.5688495635986328,0.09352200000000001 +60,Regression,Bagging,TrumpApproval,3.120546196671925,8.124382016407804,-34.775930157070896,0.6946392059326172,0.180013 +80,Regression,Bagging,TrumpApproval,2.5823668216656817,7.068571931029129,-26.165472568815836,0.7988948822021484,0.365225 +100,Regression,Bagging,TrumpApproval,2.6103510398716643,6.439797187103485,-13.147122820254193,0.9021625518798828,0.589181 +120,Regression,Bagging,TrumpApproval,2.5653436103516496,5.96335184363353,-9.29140495411716,0.9421710968017578,0.842127 +140,Regression,Bagging,TrumpApproval,2.4314692166818666,5.556159680491977,-8.232140838080387,0.9627094268798828,1.43468 +160,Regression,Bagging,TrumpApproval,2.270493582871441,5.217534738727647,-6.179445803611509,1.0016803741455078,2.055929 +180,Regression,Bagging,TrumpApproval,2.1841879014169865,4.9594120506005,-4.6969569828406526,0.9622507095336914,2.757735 +200,Regression,Bagging,TrumpApproval,2.030794616399332,4.7110231793054895,-4.155876544063708,0.5397500991821289,3.551711 +220,Regression,Bagging,TrumpApproval,1.922882727301643,4.50300441964265,-4.081371242371108,0.37749576568603516,4.407055 +240,Regression,Bagging,TrumpApproval,1.8390508968191754,4.321014818317665,-3.7141474735663893,0.43126392364501953,5.281937999999999 +260,Regression,Bagging,TrumpApproval,1.7379678526387645,4.155226166492631,-3.4180849750109363,0.4941263198852539,6.197258999999999 +280,Regression,Bagging,TrumpApproval,1.7042826877160742,4.0269186303191065,-3.3444224917120184,0.5997896194458008,7.197467999999999 +300,Regression,Bagging,TrumpApproval,1.6796571065333832,3.9174008876388,-3.043265693703045,0.6906900405883789,8.219126999999999 +320,Regression,Bagging,TrumpApproval,1.5891460162001483,3.7936804881645676,-2.979662055693274,0.757817268371582,9.288978999999998 +340,Regression,Bagging,TrumpApproval,1.5335884019062007,3.685003453386448,-2.968029890458662,0.7969903945922852,10.383406999999998 +360,Regression,Bagging,TrumpApproval,1.54418408246079,3.606838798974545,-2.832696164635261,0.8751077651977539,11.515104999999998 +380,Regression,Bagging,TrumpApproval,1.5054402320411853,3.5177984273762375,-2.771919367898169,0.9264421463012695,12.700466999999998 +400,Regression,Bagging,TrumpApproval,1.4723491084260332,3.435525460733128,-2.699225318590951,0.9911317825317383,13.924277999999997 +420,Regression,Bagging,TrumpApproval,1.429579158986208,3.3562143901072865,-2.6472359354971333,0.9703760147094727,15.190464999999998 +440,Regression,Bagging,TrumpApproval,1.3992424504019558,3.2853835752695946,-2.4316761923151153,1.0316247940063477,16.527614999999997 +460,Regression,Bagging,TrumpApproval,1.365864594828797,3.217129802398014,-2.120443637805541,1.1097803115844727,17.906522 +480,Regression,Bagging,TrumpApproval,1.3301487592579586,3.151695763852486,-1.9259010739386908,1.1814966201782227,19.341224 +500,Regression,Bagging,TrumpApproval,1.2968821746115176,3.0904885141585767,-1.7543370405557224,1.2276010513305664,20.84359 +520,Regression,Bagging,TrumpApproval,1.2678501702074907,3.0331483120333496,-1.6577103317872361,1.2783823013305664,22.421222999999998 +540,Regression,Bagging,TrumpApproval,1.2343399552126226,2.9775564396478966,-1.551795811786203,1.2796869277954102,24.086657999999996 +560,Regression,Bagging,TrumpApproval,1.220715255826944,2.9300367331798807,-1.5298738258017646,1.2170305252075195,25.790580999999996 +580,Regression,Bagging,TrumpApproval,1.1924146311245054,2.8809984439523286,-1.5063937515255619,1.0756006240844727,27.649708999999994 +600,Regression,Bagging,TrumpApproval,1.1793821598427467,2.837096711415006,-1.4037015974174163,0.9519128799438477,29.574726999999996 +620,Regression,Bagging,TrumpApproval,1.1645342598465298,2.7961228756221597,-1.2991852345603885,0.9679117202758789,31.579397999999998 +640,Regression,Bagging,TrumpApproval,1.1398425529628198,2.753175690936408,-1.1874325743915652,0.936314582824707,33.628169 +660,Regression,Bagging,TrumpApproval,1.1198821044801988,2.7133180185933967,-1.1092797015680484,0.9601030349731445,35.724068 +680,Regression,Bagging,TrumpApproval,1.103543375947734,2.675681585437618,-1.0835838801311364,1.0132951736450195,37.850435000000004 +700,Regression,Bagging,TrumpApproval,1.0951788603095205,2.6436744635360436,-1.0874567422761272,1.0955934524536133,40.041881000000004 +720,Regression,Bagging,TrumpApproval,1.073603439060177,2.607328103868497,-1.0750617689055861,1.1506471633911133,42.267731000000005 +740,Regression,Bagging,TrumpApproval,1.052216298294681,2.5725220480150233,-1.0188151031858665,1.1900300979614258,44.534549000000005 +760,Regression,Bagging,TrumpApproval,1.0329729065757678,2.539265324444459,-0.9882648176410449,1.2228517532348633,46.85075200000001 +780,Regression,Bagging,TrumpApproval,1.0152010354431573,2.5074934380267417,-0.9475063222432678,1.2825212478637695,49.22632000000001 +800,Regression,Bagging,TrumpApproval,1.007374170791078,2.4793027589713232,-0.9211818120686948,1.3126497268676758,51.66223500000001 +820,Regression,Bagging,TrumpApproval,1.002386830132377,2.4547936379778226,-0.9040692252875042,1.3594255447387695,54.13772100000001 +840,Regression,Bagging,TrumpApproval,0.9929186057762197,2.428247138481691,-0.8804076589484491,1.397130012512207,56.66204300000001 +860,Regression,Bagging,TrumpApproval,0.9756374748391698,2.400488599154532,-0.8344958433267429,1.4288606643676758,59.23948900000001 +880,Regression,Bagging,TrumpApproval,0.964181960731443,2.37450147105602,-0.7860721035239808,1.4496355056762695,61.87688900000001 +900,Regression,Bagging,TrumpApproval,0.9549728240782616,2.3497625798451978,-0.7564202624419067,1.3498811721801758,64.59788000000002 +920,Regression,Bagging,TrumpApproval,0.9412862327577896,2.3247867434211136,-0.747529388757789,1.1945161819458008,67.39612200000002 +940,Regression,Bagging,TrumpApproval,0.934469347520636,2.302477656767798,-0.7286928787152502,1.2202577590942383,70.24630900000002 +960,Regression,Bagging,TrumpApproval,0.9259837543593707,2.280476047701142,-0.7135440601163805,1.2635469436645508,73.13750900000002 +980,Regression,Bagging,TrumpApproval,0.9196316545824527,2.2597103614150886,-0.715155968132227,1.2794008255004883,76.07904100000002 +1000,Regression,Bagging,TrumpApproval,0.9087747756519627,2.2382775608394114,-0.7111012811273396,1.3157854080200195,79.06112300000002 +1001,Regression,Bagging,TrumpApproval,0.9082029688272106,2.2371845268363217,-0.710571805505718,1.3157854080200195,82.06893700000002 +11,Regression,Exponentially Weighted Average,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.06525707244873047,0.004749 +22,Regression,Exponentially Weighted Average,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.07764339447021484,0.02229 +33,Regression,Exponentially Weighted Average,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.09733104705810547,0.042479 +44,Regression,Exponentially Weighted Average,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.10875988006591797,0.065964 +55,Regression,Exponentially Weighted Average,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.13167858123779297,0.11433299999999999 +66,Regression,Exponentially Weighted Average,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.1604146957397461,0.17057299999999997 +77,Regression,Exponentially Weighted Average,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.18252849578857422,0.23188899999999998 +88,Regression,Exponentially Weighted Average,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.2035512924194336,0.308411 +99,Regression,Exponentially Weighted Average,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.21530437469482422,0.393152 +110,Regression,Exponentially Weighted Average,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.2280874252319336,0.484559 +121,Regression,Exponentially Weighted Average,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.23778057098388672,0.583103 +132,Regression,Exponentially Weighted Average,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.24797725677490234,0.792791 +143,Regression,Exponentially Weighted Average,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.22072410583496094,1.018133 +154,Regression,Exponentially Weighted Average,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.2406787872314453,1.2542229999999999 +165,Regression,Exponentially Weighted Average,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.25681114196777344,1.5160939999999998 +176,Regression,Exponentially Weighted Average,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.2727985382080078,1.7876089999999998 +187,Regression,Exponentially Weighted Average,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.2873516082763672,2.0795459999999997 +198,Regression,Exponentially Weighted Average,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.30018043518066406,2.3857599999999994 +209,Regression,Exponentially Weighted Average,ChickWeights,57.5645933014354,59.46206220864915,-14.922837840066967,0.27625083923339844,2.7086299999999994 +220,Regression,Exponentially Weighted Average,ChickWeights,58.69090909090908,60.81327606250582,-13.581197962556498,0.2926349639892578,3.0480419999999993 +231,Regression,Exponentially Weighted Average,ChickWeights,60.25541125541125,62.66764529032318,-12.244451024360147,0.30736351013183594,3.437715999999999 +242,Regression,Exponentially Weighted Average,ChickWeights,62.17355371900826,65.06963847478845,-10.489760184397113,0.32158851623535156,3.8493299999999993 +253,Regression,Exponentially Weighted Average,ChickWeights,63.936758893280626,67.17295239601157,-9.634560128382748,0.3348064422607422,4.393024 +264,Regression,Exponentially Weighted Average,ChickWeights,65.10606060606062,68.57980310513724,-9.127665748505592,0.3451099395751953,4.977281 +275,Regression,Exponentially Weighted Average,ChickWeights,66.61454545454548,70.46451073219248,-8.408339126213217,0.35480308532714844,5.586244 +286,Regression,Exponentially Weighted Average,ChickWeights,68.48951048951052,72.8020594498525,-7.6983532427125105,0.3655261993408203,6.228505 +297,Regression,Exponentially Weighted Average,ChickWeights,70.55218855218858,75.3669362796119,-7.08492451355157,0.3711223602294922,6.899176000000001 +308,Regression,Exponentially Weighted Average,ChickWeights,72.39285714285718,77.65033596401675,-6.643510181414674,0.3809375762939453,7.597348 +319,Regression,Exponentially Weighted Average,ChickWeights,73.45454545454551,79.15086186624424,-6.206879640065647,0.39273643493652344,8.345726 +330,Regression,Exponentially Weighted Average,ChickWeights,75.77878787878792,82.20832738177494,-5.653192449779911,0.40398597717285156,9.225203 +341,Regression,Exponentially Weighted Average,ChickWeights,77.92375366568919,84.89106353805269,-5.352795814687307,0.4136791229248047,10.124705 +352,Regression,Exponentially Weighted Average,ChickWeights,80.04545454545458,87.49376601169416,-5.134510311668016,0.4233722686767578,11.048248000000001 +363,Regression,Exponentially Weighted Average,ChickWeights,80.99724517906337,88.57562798692558,-5.105139086016474,0.43306541442871094,11.989622 +374,Regression,Exponentially Weighted Average,ChickWeights,82.77807486631018,90.83029071422122,-4.901675845817959,0.45247840881347656,12.967698 +385,Regression,Exponentially Weighted Average,ChickWeights,85.1766233766234,93.99517810235533,-4.591702735915359,0.4681224822998047,14.032748 +396,Regression,Exponentially Weighted Average,ChickWeights,87.26767676767678,96.48964983485283,-4.494054297851511,0.48465919494628906,15.12168 +407,Regression,Exponentially Weighted Average,ChickWeights,89.00737100737103,98.71879502607636,-4.345544683073043,0.4991130828857422,16.236767999999998 +418,Regression,Exponentially Weighted Average,ChickWeights,90.57416267942587,100.72635724110245,-4.224084264201084,0.5109386444091797,17.416859999999996 +429,Regression,Exponentially Weighted Average,ChickWeights,93.12121212121215,104.19735398794236,-3.9677178403495805,0.5206584930419922,18.643557999999995 +440,Regression,Exponentially Weighted Average,ChickWeights,95.41818181818185,107.03565676064125,-3.8710119659250104,0.5303516387939453,19.910713999999995 +451,Regression,Exponentially Weighted Average,ChickWeights,97.16629711751665,109.07665280092142,-3.843505105397095,0.5280742645263672,21.225085999999994 +462,Regression,Exponentially Weighted Average,ChickWeights,98.71645021645023,111.17636431671961,-3.72620239405422,0.5443019866943359,22.607732999999993 +473,Regression,Exponentially Weighted Average,ChickWeights,101.54122621564484,115.20584573786859,-3.4812404756668593,0.5577869415283203,24.015635999999994 +484,Regression,Exponentially Weighted Average,ChickWeights,103.77066115702482,117.90601559037043,-3.4365483842712585,0.5712184906005859,25.474227999999993 +495,Regression,Exponentially Weighted Average,ChickWeights,106.02424242424244,120.71525892518193,-3.37467008920777,0.5825443267822266,26.96779499999999 +506,Regression,Exponentially Weighted Average,ChickWeights,107.31620553359684,122.26004165941237,-3.356924458603192,2.7805843353271484,30.43480299999999 +517,Regression,Exponentially Weighted Average,ChickWeights,109.39651837524178,124.91233289427785,-3.291877964737682,2.825040817260742,33.93932499999999 +528,Regression,Exponentially Weighted Average,ChickWeights,112.36553030303028,129.1106745698386,-3.1225038051323804,2.876035690307617,37.48648499999999 +539,Regression,Exponentially Weighted Average,ChickWeights,114.52504638218922,131.65752925403248,-3.109734667916423,2.927671432495117,41.07985699999999 +550,Regression,Exponentially Weighted Average,ChickWeights,115.89999999999996,133.35909826820617,-3.0866973064470367,2.9773387908935547,44.71417799999999 +561,Regression,Exponentially Weighted Average,ChickWeights,117.86452762923346,135.8046463151548,-3.0526234314410727,3.0208263397216797,48.39640499999999 +572,Regression,Exponentially Weighted Average,ChickWeights,120.54020979020974,139.4624607986965,-2.953338846956928,3.065652847290039,52.120071999999986 +578,Regression,Exponentially Weighted Average,ChickWeights,121.81833910034597,141.00422703423635,-2.942935834251463,3.092409133911133,55.885136999999986 +20,Regression,Exponentially Weighted Average,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.1445150375366211,0.017697 +40,Regression,Exponentially Weighted Average,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.21173763275146484,0.059669 +60,Regression,Exponentially Weighted Average,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.25832653045654297,0.116093 +80,Regression,Exponentially Weighted Average,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.3003568649291992,0.19454100000000002 +100,Regression,Exponentially Weighted Average,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.3407926559448242,0.29552300000000004 +120,Regression,Exponentially Weighted Average,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.3709287643432617,0.510712 +140,Regression,Exponentially Weighted Average,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.3974485397338867,0.760252 +160,Regression,Exponentially Weighted Average,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.3402233123779297,1.044132 +180,Regression,Exponentially Weighted Average,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.3811016082763672,1.367502 +200,Regression,Exponentially Weighted Average,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.3127880096435547,1.794637 +220,Regression,Exponentially Weighted Average,TrumpApproval,39.727043136363626,39.77723500009918,-395.50198072931875,0.3578624725341797,2.266575 +240,Regression,Exponentially Weighted Average,TrumpApproval,39.56323079166665,39.61325406766278,-395.19837684116754,0.3875255584716797,2.775551 +260,Regression,Exponentially Weighted Average,TrumpApproval,39.42014538461535,39.46968290441584,-397.63185900832246,0.42084693908691406,3.358614 +280,Regression,Exponentially Weighted Average,TrumpApproval,39.33200189285712,39.37942345737111,-414.45601593500356,0.47014808654785156,4.097303 +300,Regression,Exponentially Weighted Average,TrumpApproval,39.18435719999999,39.23275803924839,-404.5402138221895,0.5117359161376953,4.882924 +320,Regression,Exponentially Weighted Average,TrumpApproval,39.13568690624999,39.1818628962716,-423.5167725219512,0.5480670928955078,5.740529 +340,Regression,Exponentially Weighted Average,TrumpApproval,39.14620944117645,39.18989510023786,-447.7943063391533,0.5615406036376953,6.72918 +360,Regression,Exponentially Weighted Average,TrumpApproval,39.24072974999997,39.28395553300239,-453.6543473793619,0.5990085601806641,7.76074 +380,Regression,Exponentially Weighted Average,TrumpApproval,39.29597665789471,39.337699215460226,-470.6701690846498,0.6312580108642578,8.924786000000001 +400,Regression,Exponentially Weighted Average,TrumpApproval,39.35730624999997,39.39781946688104,-485.4842825426507,0.6682605743408203,10.154856 +420,Regression,Exponentially Weighted Average,TrumpApproval,39.40549083333331,39.44465897881697,-502.77995042269276,0.6983966827392578,11.469727 +440,Regression,Exponentially Weighted Average,TrumpApproval,39.49730674999998,39.53710368662846,-495.9856416828035,0.7316226959228516,12.901862000000001 +460,Regression,Exponentially Weighted Average,TrumpApproval,39.61474728260867,39.65658853240579,-473.14358309219216,0.7705020904541016,14.448849000000001 +480,Regression,Exponentially Weighted Average,TrumpApproval,39.71032456249997,39.753047582709755,-464.4916761787406,0.8079357147216797,16.094285 +500,Regression,Exponentially Weighted Average,TrumpApproval,39.80313951999997,39.84667590965187,-456.87508245086684,2.9298267364501953,19.825027 +520,Regression,Exponentially Weighted Average,TrumpApproval,39.873547134615364,39.916931033645376,-459.2932847271911,3.0076160430908203,23.629967 +540,Regression,Exponentially Weighted Average,TrumpApproval,39.94649651851849,39.98996046818772,-459.28610565666287,3.1059207916259766,27.509295 +560,Regression,Exponentially Weighted Average,TrumpApproval,39.976066142857114,40.018487723609816,-470.92618770667195,3.203706741333008,31.453258 +580,Regression,Exponentially Weighted Average,TrumpApproval,40.00338510344825,40.044755101652726,-483.2331705341176,3.296670913696289,35.460995000000004 +600,Regression,Exponentially Weighted Average,TrumpApproval,40.07393431666663,40.11569326301364,-479.5746686678817,3.391347885131836,39.544454 +620,Regression,Exponentially Weighted Average,TrumpApproval,40.1459417741935,40.18827077358568,-473.96334667177865,3.4906063079833984,43.697410000000005 +640,Regression,Exponentially Weighted Average,TrumpApproval,40.219438156249964,40.26249426545423,-466.8085709746123,3.5870800018310547,47.924640000000004 +660,Regression,Exponentially Weighted Average,TrumpApproval,40.28296777272724,40.32626722721455,-464.9172853497744,3.6864986419677734,52.218165000000006 +680,Regression,Exponentially Weighted Average,TrumpApproval,40.31998279411761,40.36256991107017,-473.1325264408024,3.782560348510742,56.58359200000001 +700,Regression,Exponentially Weighted Average,TrumpApproval,40.31359012857138,40.35509446667054,-485.40526703956544,3.816682815551758,61.02307200000001 +720,Regression,Exponentially Weighted Average,TrumpApproval,40.31730695833329,40.357915759594896,-496.1610725544049,3.917215347290039,65.52718500000002 +740,Regression,Exponentially Weighted Average,TrumpApproval,40.36653568918915,40.407119416424955,-497.07428037101636,4.021100997924805,70.10250300000001 +760,Regression,Exponentially Weighted Average,TrumpApproval,40.403143671052604,40.443256311482514,-503.3712175162706,4.113973617553711,74.743136 +780,Regression,Exponentially Weighted Average,TrumpApproval,40.44545064102563,40.48534274444009,-506.6856716110208,4.221334457397461,79.45160200000001 +800,Regression,Exponentially Weighted Average,TrumpApproval,40.478548249999996,40.518050685964006,-512.1052117095793,4.33137321472168,84.22367000000001 +820,Regression,Exponentially Weighted Average,TrumpApproval,40.50894034146341,40.5479845946661,-518.5068774177179,4.393171310424805,89.06994900000001 +840,Regression,Exponentially Weighted Average,TrumpApproval,40.5406558690476,40.579310897365986,-524.140575335229,4.501546859741211,93.98653900000001 +860,Regression,Exponentially Weighted Average,TrumpApproval,40.58371181395347,40.62239247493601,-524.3496319016275,4.600507736206055,98.97766000000001 +880,Regression,Exponentially Weighted Average,TrumpApproval,40.62855514772725,40.66738601007716,-522.897851512946,4.697576522827148,104.03937600000002 +900,Regression,Exponentially Weighted Average,TrumpApproval,40.664104233333326,40.702738445808535,-526.020768835918,4.774164199829102,109.17863200000002 +920,Regression,Exponentially Weighted Average,TrumpApproval,40.68274704347825,40.72073961991632,-535.1540147256861,4.872934341430664,114.38942200000002 +940,Regression,Exponentially Weighted Average,TrumpApproval,40.70972619148935,40.74737437775791,-540.4099749760601,4.975519180297852,119.67171900000002 +960,Regression,Exponentially Weighted Average,TrumpApproval,40.734006364583315,40.771242977826994,-546.7118652484228,5.07771110534668,125.02115000000002 +980,Regression,Exponentially Weighted Average,TrumpApproval,40.74031829795916,40.776840159239676,-557.5026042066913,5.174932479858398,130.44017000000002 +1000,Regression,Exponentially Weighted Average,TrumpApproval,40.75359492299998,40.78950075300399,-567.2567645513548,5.274145126342773,135.92720000000003 +1001,Regression,Exponentially Weighted Average,TrumpApproval,40.75458054545452,40.7904615623717,-567.6629514867817,5.276128768920898,141.45243100000002 +11,Regression,River MLP,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.012152671813964844,0.004659 +22,Regression,River MLP,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012152671813964844,0.035685 +33,Regression,River MLP,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.012152671813964844,0.08499899999999999 +44,Regression,River MLP,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.012152671813964844,0.154054 +55,Regression,River MLP,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.012152671813964844,0.236038 +66,Regression,River MLP,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.012152671813964844,0.334153 +77,Regression,River MLP,ChickWeights,43.46421300395698,43.662828265715675,-106.52347713504811,0.012152671813964844,0.44537099999999996 +88,Regression,River MLP,ChickWeights,43.359772412267546,43.583709810639945,-96.13505707304522,0.012152671813964844,0.810611 +99,Regression,River MLP,ChickWeights,39.34760833403674,41.28110871337288,-71.88434940071843,0.012152671813964844,1.1795 +110,Regression,River MLP,ChickWeights,36.27694842893514,39.43109665219568,-45.43679588251114,0.012152671813964844,1.5519370000000001 +121,Regression,River MLP,ChickWeights,33.384495302116036,37.629851771248454,-31.570957412576163,0.012152671813964844,1.9276810000000002 +132,Regression,River MLP,ChickWeights,30.760956861305427,36.03410144813446,-23.410283906032372,0.012152671813964844,2.306782 +143,Regression,River MLP,ChickWeights,28.636527077105512,34.63559483719005,-17.266619380249022,0.012152671813964844,2.6893580000000004 +154,Regression,River MLP,ChickWeights,26.848366395937333,33.39569685051843,-13.432529594168455,0.012152671813964844,3.0876050000000004 +165,Regression,River MLP,ChickWeights,25.68994399106157,32.40192925941153,-9.921659843170621,0.012152671813964844,3.5625080000000002 +176,Regression,River MLP,ChickWeights,24.410830110997512,31.436328147747602,-7.289127728255313,0.012152671813964844,4.041374 +187,Regression,River MLP,ChickWeights,23.27797268834062,30.533192270092133,-5.402500905628177,0.012152671813964844,4.523515000000001 +198,Regression,River MLP,ChickWeights,22.21718064008457,29.702466677215767,-3.8195183008370286,0.012152671813964844,5.01074 +209,Regression,River MLP,ChickWeights,21.553196218218126,29.080353233595055,-2.8083766468986493,0.012152671813964844,5.506907 +220,Regression,River MLP,ChickWeights,21.477175147376318,28.875533005215566,-2.287429329651187,0.012152671813964844,6.006767 +231,Regression,River MLP,ChickWeights,20.952511346795177,28.337840419816686,-1.7081998467380504,0.012152671813964844,6.546588 +242,Regression,River MLP,ChickWeights,20.676711476832995,27.952207916118613,-1.120246781438643,0.012152671813964844,7.091828 +253,Regression,River MLP,ChickWeights,20.0995501155626,27.40917062551413,-0.77060809797316,0.012152671813964844,7.64039 +264,Regression,River MLP,ChickWeights,20.480542447806847,27.75611161124588,-0.6589532289622051,0.012312889099121094,8.194426 +275,Regression,River MLP,ChickWeights,20.614496901205563,28.041519628611827,-0.48996193124700227,0.012312889099121094,8.751968 +286,Regression,River MLP,ChickWeights,20.565278129073285,27.847512109279503,-0.27268965368266684,0.012312889099121094,9.312993 +297,Regression,River MLP,ChickWeights,20.274958424672484,27.625687239183637,-0.08627660996898667,0.012312889099121094,9.883614000000001 +308,Regression,River MLP,ChickWeights,20.45327995927562,27.912230969749725,0.012367732509979579,0.012312889099121094,10.459328000000001 +319,Regression,River MLP,ChickWeights,21.510251079645446,30.009813508826145,-0.03600670421086383,0.012312889099121094,11.038549000000001 +330,Regression,River MLP,ChickWeights,21.665590739767385,30.044848619277115,0.11133412773326878,0.012312889099121094,11.621241000000001 +341,Regression,River MLP,ChickWeights,21.89048226594194,30.334415208142868,0.18882940014056426,0.012312889099121094,12.207835000000001 +352,Regression,River MLP,ChickWeights,21.893952955210157,30.339570241246864,0.2623598804373043,0.012312889099121094,12.797939000000001 +363,Regression,River MLP,ChickWeights,22.90533819708786,32.054250800509514,0.20046341020600145,0.012312889099121094,13.393067000000002 +374,Regression,River MLP,ChickWeights,23.763742364378043,33.85198840163,0.1802483296948254,0.012312889099121094,13.997035000000002 +385,Regression,River MLP,ChickWeights,24.571542788077334,34.76894700117602,0.23490387687324887,0.012312889099121094,14.642537000000003 +396,Regression,River MLP,ChickWeights,24.36284577450351,34.48838301267373,0.2980969129506975,0.012312889099121094,15.292230000000002 +407,Regression,River MLP,ChickWeights,25.734101326034633,37.062399123466015,0.24654151138402014,0.012312889099121094,15.945417000000003 +418,Regression,River MLP,ChickWeights,27.11119186006235,39.928390080533305,0.17910517682254135,0.012312889099121094,16.606412000000002 +429,Regression,River MLP,ChickWeights,28.3590406143643,42.09339312084405,0.18927902325132573,0.012312889099121094,17.270793 +440,Regression,River MLP,ChickWeights,29.357663164641018,43.68705243766196,0.18853885804982917,0.012312889099121094,17.938627 +451,Regression,River MLP,ChickWeights,30.691095011752385,45.674890437608916,0.15071943546696187,0.012312889099121094,18.61722 +462,Regression,River MLP,ChickWeights,32.58469239048663,49.95891321104316,0.04563754616882043,0.012312889099121094,19.304175 +473,Regression,River MLP,ChickWeights,34.457204466549335,53.836031572602984,0.02142234380208996,0.012312889099121094,19.9947 +484,Regression,River MLP,ChickWeights,37.61656772325176,59.47905507802133,-0.1290194303344141,0.012312889099121094,20.688692000000003 +495,Regression,River MLP,ChickWeights,39.835053793820734,63.30264151550531,-0.20299724250356888,0.012312889099121094,21.389042000000003 +506,Regression,River MLP,ChickWeights,40.84375246428709,64.75828138125749,-0.2223668969879733,0.012312889099121094,22.096762000000002 +517,Regression,River MLP,ChickWeights,42.3259867290128,66.7403629812066,-0.22521937114525392,0.012312889099121094,22.959028000000004 +528,Regression,River MLP,ChickWeights,43.903763311459905,69.27444073484561,-0.18681443911135398,0.012312889099121094,23.825387000000003 +539,Regression,River MLP,ChickWeights,45.11725117254523,70.97054614693485,-0.19420442814828975,0.012312889099121094,24.695185000000002 +550,Regression,River MLP,ChickWeights,46.19146939493793,72.84904016514736,-0.2194804408254527,0.012312889099121094,25.570022 +561,Regression,River MLP,ChickWeights,48.0255382358928,75.55118081102547,-0.25426558573377567,0.012312889099121094,26.453531 +572,Regression,River MLP,ChickWeights,49.60861685612801,77.95895414232838,-0.23532548305844236,0.012312889099121094,27.340524000000002 +578,Regression,River MLP,ChickWeights,51.40782550111089,80.92025038917566,-0.298583502299673,0.012312889099121094,28.229463000000003 +20,Regression,River MLP,TrumpApproval,28.203089584036217,31.678254793976468,-2352.839799462937,0.013110160827636719,0.018592 +40,Regression,River MLP,TrumpApproval,17.631407237579232,23.536801219235826,-221.7205207554288,0.013110160827636719,0.053933999999999996 +60,Regression,River MLP,TrumpApproval,13.441671937224772,19.739075566761823,-210.18539534147195,0.013110160827636719,0.098078 +80,Regression,River MLP,TrumpApproval,11.196749290061339,17.292913087737123,-161.5886474703317,0.013110160827636719,0.159515 +100,Regression,River MLP,TrumpApproval,9.529407951935296,15.54264880746251,-81.40884208187767,0.013110160827636719,0.228076 +120,Regression,River MLP,TrumpApproval,8.478754286735066,14.272499783288554,-57.95136830581733,0.013110160827636719,0.332328 +140,Regression,River MLP,TrumpApproval,7.525552058981039,13.242333407520347,-51.44233495767236,0.013110160827636719,0.527456 +160,Regression,River MLP,TrumpApproval,6.729532853932534,12.401843141618142,-39.56324503441056,0.013110160827636719,0.7296090000000001 +180,Regression,River MLP,TrumpApproval,6.20494414148211,11.727398222866162,-30.855608065765253,0.013110160827636719,0.938725 +200,Regression,River MLP,TrumpApproval,5.707613016041334,11.135875265707485,-27.808506433676282,0.013110160827636719,1.173453 +220,Regression,River MLP,TrumpApproval,5.35235544657082,10.636236352263047,-27.34993958822678,0.013110160827636719,1.423387 +240,Regression,River MLP,TrumpApproval,4.997211310189409,10.191758203807838,-25.225868327846438,0.013110160827636719,1.696442 +260,Regression,River MLP,TrumpApproval,4.698339965696975,9.799142308635478,-23.570888426665658,0.013350486755371094,2.044166 +280,Regression,River MLP,TrumpApproval,4.429952698677103,9.448184269747657,-22.91569767610472,0.013350486755371094,2.398587 +300,Regression,River MLP,TrumpApproval,4.185436867704573,9.131292683908228,-20.968518634865898,0.013350486755371094,2.759636 +320,Regression,River MLP,TrumpApproval,3.989857840855361,8.848493522992882,-20.65027251080777,0.013350486755371094,3.127609 +340,Regression,River MLP,TrumpApproval,3.793510888989401,8.58729864958044,-20.548263158423392,0.013350486755371094,3.5072970000000003 +360,Regression,River MLP,TrumpApproval,3.624008920532304,8.350732680595982,-19.544713512132038,0.013350486755371094,3.994133 +380,Regression,River MLP,TrumpApproval,3.4723941591948395,8.130732570333006,-19.15022282865096,0.013350486755371094,4.488831 +400,Regression,River MLP,TrumpApproval,3.327129028584169,7.926491124989248,-18.691844486765735,0.013350486755371094,4.996594 +420,Regression,River MLP,TrumpApproval,3.1988914312464622,7.737168953530663,-18.383340677896445,0.013350486755371094,5.512642 +440,Regression,River MLP,TrumpApproval,3.0905418652204037,7.562941397323993,-17.185096454486196,0.013350486755371094,6.041213 +460,Regression,River MLP,TrumpApproval,2.9841087219087,7.398658950448711,-15.50384711789857,0.013350486755371094,6.671723 +480,Regression,River MLP,TrumpApproval,2.878027938067315,7.243616567021465,-14.455461195017083,0.013350486755371094,7.311147 +500,Regression,River MLP,TrumpApproval,2.790174040420949,7.099353406661882,-13.534510391092628,0.013350486755371094,7.962598 +520,Regression,River MLP,TrumpApproval,2.702735665583147,6.962851553400364,-13.005371203657658,0.013350486755371094,8.621042 +540,Regression,River MLP,TrumpApproval,2.619923274637493,6.8334980874356335,-12.440396167539378,0.013350486755371094,9.286045999999999 +560,Regression,River MLP,TrumpApproval,2.556848015725479,6.714676061099242,-12.286263553450382,0.013350486755371094,9.968405999999998 +580,Regression,River MLP,TrumpApproval,2.4920466556079988,6.599727625727584,-12.1527109643015,0.013350486755371094,10.662936999999998 +600,Regression,River MLP,TrumpApproval,2.4260558633236777,6.490118235625791,-11.578750092830703,0.013350486755371094,11.381531999999998 +620,Regression,River MLP,TrumpApproval,2.3708694907142864,6.387338726311831,-10.997792654674662,0.013350486755371094,12.131809999999998 +640,Regression,River MLP,TrumpApproval,2.309077397643504,6.287531812954472,-10.40846497658843,0.013350486755371094,12.902857999999998 +660,Regression,River MLP,TrumpApproval,2.253417256192923,6.192441467899733,-9.986430076809746,0.013350486755371094,13.765244 +680,Regression,River MLP,TrumpApproval,2.1933714736526424,6.100884478116631,-9.832475924452435,0.013350486755371094,14.638727 +700,Regression,River MLP,TrumpApproval,2.1444840100167193,6.014053532220149,-9.80279442231031,0.013350486755371094,15.530555 +720,Regression,River MLP,TrumpApproval,2.0889149793500317,5.930058413028094,-9.733901359629304,0.013350486755371094,16.434104 +740,Regression,River MLP,TrumpApproval,2.038014375475162,5.849577408393744,-9.43824097776182,0.013350486755371094,17.347853 +760,Regression,River MLP,TrumpApproval,1.990139846596363,5.772270602035659,-9.274280741061778,0.013350486755371094,18.296023 +780,Regression,River MLP,TrumpApproval,1.946515411702069,5.69815370877383,-9.05697999731458,0.013350486755371094,19.263123 +800,Regression,River MLP,TrumpApproval,1.9088971171085878,5.627293726045093,-8.897112526821198,0.013350486755371094,20.241821 +820,Regression,River MLP,TrumpApproval,1.8732261968689674,5.559226632327132,-8.765208356441784,0.013350486755371094,21.227083 +840,Regression,River MLP,TrumpApproval,1.8347271749400444,5.492864392938861,-8.621969919695442,0.013350486755371094,22.223141000000002 +860,Regression,River MLP,TrumpApproval,1.8001515928803729,5.4291765082898245,-8.383942958793503,0.013350486755371094,23.230427000000002 +880,Regression,River MLP,TrumpApproval,1.762610565031098,5.367211780988228,-8.125392758933815,0.013350486755371094,24.244373000000003 +900,Regression,River MLP,TrumpApproval,1.7278213800286457,5.307357454879676,-7.9606012045493255,0.013350486755371094,25.346781000000004 +920,Regression,River MLP,TrumpApproval,1.6959197142820022,5.249600105314111,-7.910676780558388,0.013350486755371094,26.455778000000002 +940,Regression,River MLP,TrumpApproval,1.6672680101890094,5.193857129216991,-7.796440957593809,0.013350486755371094,27.578615000000003 +960,Regression,River MLP,TrumpApproval,1.6372087427382507,5.139738169534271,-7.704147396017831,0.013350486755371094,28.708325000000002 +980,Regression,River MLP,TrumpApproval,1.6082702309133736,5.087224346398139,-7.692803163516414,0.013350486755371094,29.852185000000002 +1000,Regression,River MLP,TrumpApproval,1.582128560540168,5.036439630005545,-7.663534315499042,0.013350486755371094,31.047385000000002 +1001,Regression,River MLP,TrumpApproval,1.5805783932319006,5.033923389051291,-7.660658200179739,0.013350486755371094,32.243154000000004 +11,Regression,[baseline] Mean predictor,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.0004901885986328125,0.000258 +22,Regression,[baseline] Mean predictor,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.0004901885986328125,0.000737 +33,Regression,[baseline] Mean predictor,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.0004901885986328125,0.00134 +44,Regression,[baseline] Mean predictor,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.0004901885986328125,0.002066 +55,Regression,[baseline] Mean predictor,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.0004901885986328125,0.0029100000000000003 +66,Regression,[baseline] Mean predictor,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.0004901885986328125,0.0038720000000000004 +77,Regression,[baseline] Mean predictor,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.0004901885986328125,0.004952000000000001 +88,Regression,[baseline] Mean predictor,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.0004901885986328125,0.006149000000000001 +99,Regression,[baseline] Mean predictor,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.0004901885986328125,0.007464000000000001 +110,Regression,[baseline] Mean predictor,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.0004901885986328125,0.008896000000000001 +121,Regression,[baseline] Mean predictor,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.0004901885986328125,0.010446 +132,Regression,[baseline] Mean predictor,ChickWeights,5.875758254207378,8.194624755054596,-0.2624191661321591,0.0004901885986328125,0.012113 +143,Regression,[baseline] Mean predictor,ChickWeights,6.530760796045927,8.870097879563003,-0.19803554240449484,0.0004901885986328125,0.013898 +154,Regression,[baseline] Mean predictor,ChickWeights,7.121466111912466,9.458403141043558,-0.15770278521517955,0.0004901885986328125,0.015801 +165,Regression,[baseline] Mean predictor,ChickWeights,7.772438504082036,10.375670403553157,-0.11989999304508925,0.0004901885986328125,0.017821999999999998 +176,Regression,[baseline] Mean predictor,ChickWeights,8.565827130563894,11.410434180005831,-0.09206765686265328,0.0004901885986328125,0.019960999999999996 +187,Regression,[baseline] Mean predictor,ChickWeights,9.429958588641576,12.495061319237752,-0.07221531716282037,0.0004901885986328125,0.022216999999999997 +198,Regression,[baseline] Mean predictor,ChickWeights,10.47731537859646,13.900491647656429,-0.05555027037575888,0.0004901885986328125,0.024589999999999997 +209,Regression,[baseline] Mean predictor,ChickWeights,11.43172675762076,15.229123619635446,-0.04445651287163721,0.0004901885986328125,0.027079 +220,Regression,[baseline] Mean predictor,ChickWeights,11.974320980081139,16.22368260926648,-0.03775608698471111,0.0004901885986328125,0.029685 +231,Regression,[baseline] Mean predictor,ChickWeights,12.9382196746461,17.489503190785292,-0.03157819728271183,0.0004901885986328125,0.032406 +242,Regression,[baseline] Mean predictor,ChickWeights,14.229204186206863,19.43725798629848,-0.02523677186741935,0.0004901885986328125,0.035243 +253,Regression,[baseline] Mean predictor,ChickWeights,15.339413196393396,20.820238312545918,-0.021649789303838762,0.0004901885986328125,0.041904 +264,Regression,[baseline] Mean predictor,ChickWeights,15.948617107030818,21.75817315507082,-0.019440185124094622,0.0004901885986328125,0.048726 +275,Regression,[baseline] Mean predictor,ChickWeights,16.794155127707494,23.16724301729152,-0.016999619323781356,0.0004901885986328125,0.055688 +286,Regression,[baseline] Mean predictor,ChickWeights,17.990009992534457,24.865985915258104,-0.014754713395529917,0.0004901885986328125,0.062787 +297,Regression,[baseline] Mean predictor,ChickWeights,19.34919450213405,26.676209297603677,-0.012890456560007202,0.0004901885986328125,0.070018 +308,Regression,[baseline] Mean predictor,ChickWeights,20.46881241431745,28.248013022827834,-0.011537481517321035,0.0004901885986328125,0.077383 +319,Regression,[baseline] Mean predictor,ChickWeights,20.993702124162965,29.638141143499492,-0.010503673119392376,0.0004901885986328125,0.08488399999999999 +330,Regression,[baseline] Mean predictor,ChickWeights,22.586872779548433,32.01796640002603,-0.009220237952050514,0.0004901885986328125,0.09251699999999999 +341,Regression,[baseline] Mean predictor,ChickWeights,23.973458872107372,33.821533603903084,-0.008387701903732392,0.0004901885986328125,0.10028199999999998 +352,Regression,[baseline] Mean predictor,ChickWeights,25.315991788770976,35.461698606860665,-0.007731302158646702,0.0004901885986328125,0.10817799999999998 +363,Regression,[baseline] Mean predictor,ChickWeights,25.615062978866305,35.981300981590465,-0.007443749031205149,0.0004901885986328125,0.11620599999999998 +374,Regression,[baseline] Mean predictor,ChickWeights,26.673321526932543,37.51836715700961,-0.006935846124255907,0.0004901885986328125,0.12436199999999997 +385,Regression,[baseline] Mean predictor,ChickWeights,28.27694482780972,39.8753298933956,-0.006332510983879436,0.0004901885986328125,0.13263999999999998 +396,Regression,[baseline] Mean predictor,ChickWeights,29.55612496209691,41.288487059450155,-0.005980181891907188,0.0004901885986328125,0.14104099999999997 +407,Regression,[baseline] Mean predictor,ChickWeights,30.561677112682855,42.81802042618151,-0.005646723150046551,0.0004901885986328125,0.14956599999999998 +418,Regression,[baseline] Mean predictor,ChickWeights,31.39346669137945,44.18765357092498,-0.0053697143301307815,0.0004901885986328125,0.15821399999999997 +429,Regression,[baseline] Mean predictor,ChickWeights,33.10612890637694,46.865579751152914,-0.004966366070605188,0.0004901885986328125,0.16698499999999997 +440,Regression,[baseline] Mean predictor,ChickWeights,34.54914638861108,48.61167278858254,-0.004716123854972665,0.0004901885986328125,0.17588299999999996 +451,Regression,[baseline] Mean predictor,ChickWeights,35.43263419295921,49.67507127970072,-0.004553693807187953,0.0004901885986328125,0.18490599999999996 +462,Regression,[baseline] Mean predictor,ChickWeights,36.308550382896186,51.2507761435036,-0.004357377489546899,0.0004901885986328125,0.19405499999999995 +473,Regression,[baseline] Mean predictor,ChickWeights,38.26330298063241,54.532250497281034,-0.004051661204895529,0.0004901885986328125,0.20332799999999995 +484,Regression,[baseline] Mean predictor,ChickWeights,39.598662348008276,56.08659790201894,-0.0039023944795495424,0.0004901885986328125,0.21272499999999994 +495,Regression,[baseline] Mean predictor,ChickWeights,40.94697327298068,57.823326559810994,-0.0037535911132069444,0.0004901885986328125,0.22224499999999994 +506,Regression,[baseline] Mean predictor,ChickWeights,41.42384714758024,58.679845942015916,-0.003665234721119459,0.0004901885986328125,0.23188899999999996 +517,Regression,[baseline] Mean predictor,ChickWeights,42.72663002099646,60.40151056768402,-0.0035345422299792872,0.0004901885986328125,0.24165999999999996 +528,Regression,[baseline] Mean predictor,ChickWeights,44.77321528369677,63.69509749878913,-0.0033415055563215112,0.0004901885986328125,0.25155399999999994 +539,Regression,[baseline] Mean predictor,ChickWeights,45.99579764939489,65.0494992510053,-0.0032526095626370655,0.0004901885986328125,0.26157099999999994 +550,Regression,[baseline] Mean predictor,ChickWeights,46.57020777663759,66.07332710234044,-0.0031815200825582313,0.0004901885986328125,0.2717109999999999 +561,Regression,[baseline] Mean predictor,ChickWeights,47.758257606406204,67.5643396193493,-0.0030950009187136196,0.0004901885986328125,0.28197199999999994 +572,Regression,[baseline] Mean predictor,ChickWeights,49.49138874897682,70.24569214117749,-0.002971942406188699,0.0004901885986328125,0.29235599999999995 +578,Regression,[baseline] Mean predictor,ChickWeights,50.250899455914585,71.11438743304103,-0.002929468639104371,0.0004901885986328125,0.30283499999999997 +20,Regression,[baseline] Mean predictor,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.0004901885986328125,0.001338 +40,Regression,[baseline] Mean predictor,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.0004901885986328125,0.0038250000000000003 +60,Regression,[baseline] Mean predictor,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.0004901885986328125,0.00717 +80,Regression,[baseline] Mean predictor,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.0004901885986328125,0.011356999999999999 +100,Regression,[baseline] Mean predictor,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.0004901885986328125,0.020929 +120,Regression,[baseline] Mean predictor,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.0004901885986328125,0.030834 +140,Regression,[baseline] Mean predictor,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.0004901885986328125,0.041039 +160,Regression,[baseline] Mean predictor,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.0004901885986328125,0.051538 +180,Regression,[baseline] Mean predictor,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.0004901885986328125,0.062312 +200,Regression,[baseline] Mean predictor,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.0004901885986328125,0.073408 +220,Regression,[baseline] Mean predictor,TrumpApproval,1.9409519346841397,3.56902990398404,-2.19210047340805,0.0004901885986328125,0.084777 +240,Regression,[baseline] Mean predictor,TrumpApproval,1.9366756524315063,3.4612902974772624,-2.024876884626847,0.0004901885986328125,0.096419 +260,Regression,[baseline] Mean predictor,TrumpApproval,1.9250039777458068,3.363327951159923,-1.8945640461454523,0.0004901885986328125,0.108333 +280,Regression,[baseline] Mean predictor,TrumpApproval,1.8726934920539138,3.257010428159885,-1.8420037280027222,0.0004901885986328125,0.120517 +300,Regression,[baseline] Mean predictor,TrumpApproval,1.8907476896224937,3.1958821895815714,-1.6910252267675165,0.0004901885986328125,0.133002 +320,Regression,[baseline] Mean predictor,TrumpApproval,1.819623890420079,3.103812605138666,-1.663886258690169,0.0004901885986328125,0.145758 +340,Regression,[baseline] Mean predictor,TrumpApproval,1.7396293145937214,3.014220627768389,-1.654906383755708,0.0004901885986328125,0.158784 +360,Regression,[baseline] Mean predictor,TrumpApproval,1.7350691203787965,2.9569384317632506,-1.5759385016835008,0.0004901885986328125,0.172076 +380,Regression,[baseline] Mean predictor,TrumpApproval,1.6987131960417108,2.8893997308323693,-1.5446951110541192,0.0004901885986328125,0.185636 +400,Regression,[baseline] Mean predictor,TrumpApproval,1.673610627740774,2.82935583501861,-1.5089937655143242,0.0004901885986328125,0.199488 +420,Regression,[baseline] Mean predictor,TrumpApproval,1.6410137122925974,2.7701802079251965,-1.484737486096575,0.0004901885986328125,0.213608 +440,Regression,[baseline] Mean predictor,TrumpApproval,1.6565972573555454,2.7427790467379385,-1.391750010744973,0.0004901885986328125,0.227993 +460,Regression,[baseline] Mean predictor,TrumpApproval,1.699464840115161,2.7394674040138396,-1.2626191030939884,0.0004901885986328125,0.242643 +480,Regression,[baseline] Mean predictor,TrumpApproval,1.7224824441896143,2.7219018737730583,-1.182307732575659,0.0004901885986328125,0.25756 +500,Regression,[baseline] Mean predictor,TrumpApproval,1.7446092142173422,2.7058035442295596,-1.1113262021905803,0.0004901885986328125,0.272747 +520,Regression,[baseline] Mean predictor,TrumpApproval,1.7464998751860934,2.677192702589883,-1.0705208906620065,0.0004901885986328125,0.288233 +540,Regression,[baseline] Mean predictor,TrumpApproval,1.7535492786865425,2.653885630983747,-1.0271707062792519,0.0004901885986328125,0.303987 +560,Regression,[baseline] Mean predictor,TrumpApproval,1.7201019899937544,2.614359234374483,-1.0141103337708768,0.0004901885986328125,0.320009 +580,Regression,[baseline] Mean predictor,TrumpApproval,1.6887559504032665,2.5757257291728384,-1.0033760803823184,0.0004901885986328125,0.336298 +600,Regression,[baseline] Mean predictor,TrumpApproval,1.701917368353294,2.5614247637328695,-0.9592753712060649,0.0004901885986328125,0.35287999999999997 +620,Regression,[baseline] Mean predictor,TrumpApproval,1.7178157166185173,2.5513468959681562,-0.9142580419512063,0.0004901885986328125,0.369731 +640,Regression,[baseline] Mean predictor,TrumpApproval,1.7365901196485038,2.545046385321895,-0.8692105635365064,0.0004901885986328125,0.386852 +660,Regression,[baseline] Mean predictor,TrumpApproval,1.7465677425181807,2.532051562790666,-0.8368676529707118,0.0004901885986328125,0.40424 +680,Regression,[baseline] Mean predictor,TrumpApproval,1.731617734826669,2.5042261861708606,-0.8251107974736909,0.0004901885986328125,0.42189499999999996 +700,Regression,[baseline] Mean predictor,TrumpApproval,1.6973720107412233,2.4702678919797196,-0.8225927549994396,0.0004901885986328125,0.439849 +720,Regression,[baseline] Mean predictor,TrumpApproval,1.6698372433333928,2.4400355004771073,-0.81732226470892,0.0004901885986328125,0.458072 +740,Regression,[baseline] Mean predictor,TrumpApproval,1.6732482399922957,2.425592833263792,-0.7947920429290933,0.0004901885986328125,0.47656299999999996 +760,Regression,[baseline] Mean predictor,TrumpApproval,1.6653913599894004,2.404136439714782,-0.7822814452716051,0.0004901885986328125,0.49532099999999996 +780,Regression,[baseline] Mean predictor,TrumpApproval,1.6644612180457288,2.387561393188575,-0.7656652158374817,0.0004901885986328125,0.514347 +800,Regression,[baseline] Mean predictor,TrumpApproval,1.6556359332933146,2.368497267913513,-0.7532954885990883,0.0004901885986328125,0.533661 +820,Regression,[baseline] Mean predictor,TrumpApproval,1.6452077788467467,2.348678653798561,-0.7430103139622937,0.0004901885986328125,0.5532450000000001 +840,Regression,[baseline] Mean predictor,TrumpApproval,1.6374623223784903,2.3305035344735936,-0.7320713255917544,0.0004901885986328125,0.5730930000000001 +860,Regression,[baseline] Mean predictor,TrumpApproval,1.6419505315856449,2.3202080137162757,-0.7138439732116804,0.0004901885986328125,0.6284980000000001 +880,Regression,[baseline] Mean predictor,TrumpApproval,1.6490002164922652,2.3126155324510744,-0.6941855677649247,0.0004901885986328125,0.6842080000000001 +900,Regression,[baseline] Mean predictor,TrumpApproval,1.6474991175923384,2.299197536504521,-0.6816400531907807,0.0004901885986328125,0.7401880000000002 +920,Regression,[baseline] Mean predictor,TrumpApproval,1.6301006788336792,2.2779225390149764,-0.6777843948800273,0.0004901885986328125,0.7964830000000002 +940,Regression,[baseline] Mean predictor,TrumpApproval,1.6221876471839873,2.2623787372500574,-0.6690049120995847,0.0004901885986328125,0.8530460000000002 +960,Regression,[baseline] Mean predictor,TrumpApproval,1.6124120493571745,2.245866476718547,-0.6619276404267609,0.0004901885986328125,0.9098760000000002 +980,Regression,[baseline] Mean predictor,TrumpApproval,1.5867001120604314,2.223758235975506,-0.661013659831075,0.0004901885986328125,0.9669740000000002 +1000,Regression,[baseline] Mean predictor,TrumpApproval,1.5681359363812417,2.2037391763141216,-0.6587014308970958,0.0004901885986328125,1.0243380000000002 +1001,Regression,[baseline] Mean predictor,TrumpApproval,1.567554989468773,2.202858861923226,-0.6584830635688459,0.0004901885986328125,1.081765 diff --git a/benchmarks/render.py b/benchmarks/render.py index 9e4bea987c..0a3b2143b2 100644 --- a/benchmarks/render.py +++ b/benchmarks/render.py @@ -1,8 +1,9 @@ +from __future__ import annotations + import json import shutil import textwrap from pathlib import Path -from typing import List import pandas as pd from dominate.tags import pre @@ -77,7 +78,9 @@ def render_df(df_path: Path) -> dict: track_dir = Path(f"../docs/benchmarks/{track_name}") track_dir.mkdir(exist_ok=True) with open(f"../docs/benchmarks/{track_name}/index.md", "w", encoding="utf-8") as f: - print_ = lambda x: print(x, file=f, end="\n\n") + + def print_(x): + return print(x, file=f, end="\n\n") print_(f"# {track_name}") @@ -119,14 +122,14 @@ def render_df(df_path: Path) -> dict: for dataset_name, dataset_details in track_details["Dataset"].items(): print_(f'???- abstract "{dataset_name}"') print_(textwrap.indent(dataset_details, " ")) - print_(f"") + print_("") print_("## Models") for model_name, model_details in track_details["Model"].items(): print_(f'???- example "{model_name}"') print_( f" 

{textwrap.indent(model_details, '    ').replace('    ', '', 1)}
" ) - print_(f"") + print_("") print_("## Environment") print_( diff --git a/benchmarks/run.py b/benchmarks/run.py index c4df0be893..0d3eca7a53 100644 --- a/benchmarks/run.py +++ b/benchmarks/run.py @@ -1,19 +1,19 @@ +from __future__ import annotations + import copy import itertools import json import logging import multiprocessing -from datetime import timedelta -from typing import List import pandas as pd from config import MODELS, N_CHECKPOINTS, TRACKS +from tqdm import tqdm from river import metrics logging.basicConfig(level=logging.WARN) logger = logging.getLogger(__name__) -from tqdm import tqdm def run_dataset(model_str, no_dataset, no_track): @@ -48,7 +48,7 @@ def run_dataset(model_str, no_dataset, no_track): return results -def run_track(models: List[str], no_track: int, n_workers: int = 50): +def run_track(models: list[str], no_track: int, n_workers: int = 50): pool = multiprocessing.Pool(processes=n_workers) track = TRACKS[no_track] runs = list(itertools.product(models, range(len(track.datasets)), [no_track])) @@ -61,7 +61,6 @@ def run_track(models: List[str], no_track: int, n_workers: int = 50): if __name__ == "__main__": - MODELS["Binary classification"].update(MODELS["Multiclass classification"]) details = {} diff --git a/build.py b/build.py new file mode 100644 index 0000000000..6511455d42 --- /dev/null +++ b/build.py @@ -0,0 +1,72 @@ +import platform +from distutils.command.build_ext import build_ext +from distutils.errors import CCompilerError, DistutilsExecError, DistutilsPlatformError +import setuptools +from setuptools_rust import Binding, RustExtension + +try: + from numpy import __version__ as numpy_version + from numpy import get_include +except ImportError: + subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"]) + from numpy import __version__ as numpy_version + from numpy import get_include + +try: + from Cython.Build import cythonize +except ImportError: + subprocess.check_call([sys.executable, "-m", "pip", "install", "Cython"]) + from Cython.Build import cythonize # type: ignore + + +ext_modules = cythonize( + module_list=[ + setuptools.Extension( + "*", + sources=["**/*.pyx"], + include_dirs=[get_include()], + libraries=[] if platform.system() == "Windows" else ["m"], + define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")], + ) + ], + compiler_directives={ + "language_level": 3, + "binding": True, + "embedsignature": True, + }, +) + +rust_extensions = [RustExtension("river.stats._rust_stats", binding=Binding.PyO3)] + + +class BuildFailed(Exception): + pass + + +class ExtBuilder(build_ext): + def run(self): + try: + build_ext.run(self) + except (DistutilsPlatformError, FileNotFoundError): + raise BuildFailed("File not found. Could not compile C extension.") + + def build_extension(self, ext): + try: + build_ext.build_extension(self, ext) + except (CCompilerError, DistutilsExecError, DistutilsPlatformError, ValueError): + raise BuildFailed("Could not compile C extension.") + + +def build(setup_kwargs): + """ + This function is mandatory in order to build the extensions. + """ + setup_kwargs.update( + { + "ext_modules": ext_modules, + "cmdclass": {"build_ext": ExtBuilder}, + "rust_extensions": rust_extensions, + "zip_safe": False, + "include_package_data": True, + } + ) diff --git a/docs/.pages b/docs/.pages index 9f7b7e5360..c72bb6ee96 100644 --- a/docs/.pages +++ b/docs/.pages @@ -6,3 +6,4 @@ nav: - faq - releases - benchmarks + - license diff --git a/docs/benchmarks/Binary classification/binary_classification.csv b/docs/benchmarks/Binary classification/binary_classification.csv new file mode 100644 index 0000000000..4b38617bef --- /dev/null +++ b/docs/benchmarks/Binary classification/binary_classification.csv @@ -0,0 +1,3637 @@ +step,track,model,dataset,Accuracy,F1,Memory in Mb,Time in s +106,Binary classification,Logistic regression,Bananas,0.49056603773584906,0.3414634146341463,0.004187583923339844,0.00989 +212,Binary classification,Logistic regression,Bananas,0.5141509433962265,0.3832335329341317,0.004187583923339844,0.123413 +318,Binary classification,Logistic regression,Bananas,0.5220125786163522,0.42424242424242425,0.004240989685058594,0.315017 +424,Binary classification,Logistic regression,Bananas,0.5165094339622641,0.40233236151603496,0.004240989685058594,0.5849610000000001 +530,Binary classification,Logistic regression,Bananas,0.5320754716981132,0.36410256410256414,0.004240989685058594,0.9372130000000001 +636,Binary classification,Logistic regression,Bananas,0.5377358490566038,0.32876712328767127,0.004240989685058594,1.342505 +742,Binary classification,Logistic regression,Bananas,0.5525606469002695,0.3054393305439331,0.004240989685058594,1.8950680000000002 +848,Binary classification,Logistic regression,Bananas,0.5530660377358491,0.28083491461100574,0.004240989685058594,2.518365 +954,Binary classification,Logistic regression,Bananas,0.5555555555555556,0.25874125874125875,0.004240989685058594,3.1930270000000003 +1060,Binary classification,Logistic regression,Bananas,0.5622641509433962,0.2418300653594771,0.004240989685058594,3.938137 +1166,Binary classification,Logistic regression,Bananas,0.5608919382504288,0.22424242424242424,0.004240989685058594,4.7351090000000005 +1272,Binary classification,Logistic regression,Bananas,0.5613207547169812,0.2206703910614525,0.004240989685058594,5.600857 +1378,Binary classification,Logistic regression,Bananas,0.5645863570391872,0.20844327176781002,0.004240989685058594,6.476079 +1484,Binary classification,Logistic regression,Bananas,0.5646900269541779,0.19651741293532338,0.004240989685058594,7.428853 +1590,Binary classification,Logistic regression,Bananas,0.5647798742138365,0.18588235294117644,0.004240989685058594,8.473991 +1696,Binary classification,Logistic regression,Bananas,0.5660377358490566,0.17857142857142858,0.004240989685058594,9.59319 +1802,Binary classification,Logistic regression,Bananas,0.562708102108768,0.17052631578947366,0.004240989685058594,10.745503 +1908,Binary classification,Logistic regression,Bananas,0.5587002096436059,0.16798418972332016,0.004240989685058594,11.962335 +2014,Binary classification,Logistic regression,Bananas,0.5516385302879842,0.16620498614958448,0.004240989685058594,13.252336 +2120,Binary classification,Logistic regression,Bananas,0.5495283018867925,0.1688424717145344,0.004240989685058594,14.603624 +2226,Binary classification,Logistic regression,Bananas,0.5485175202156334,0.18092909535452323,0.004240989685058594,15.981958 +2332,Binary classification,Logistic regression,Bananas,0.5484562607204116,0.19679633867276888,0.004240989685058594,17.395643 +2438,Binary classification,Logistic regression,Bananas,0.5471698113207547,0.19999999999999998,0.004240989685058594,18.850781 +2544,Binary classification,Logistic regression,Bananas,0.5479559748427673,0.21662125340599456,0.004240989685058594,20.422045 +2650,Binary classification,Logistic regression,Bananas,0.5452830188679245,0.2260757867694284,0.004240989685058594,22.049363 +2756,Binary classification,Logistic regression,Bananas,0.5395500725689405,0.22857142857142854,0.004240989685058594,23.763248 +2862,Binary classification,Logistic regression,Bananas,0.5391334730957372,0.230005837711617,0.004240989685058594,25.51638 +2968,Binary classification,Logistic regression,Bananas,0.5411051212938005,0.22613636363636364,0.004240989685058594,27.316788000000003 +3074,Binary classification,Logistic regression,Bananas,0.5403383214053351,0.22148760330578512,0.004240989685058594,29.124189 +3180,Binary classification,Logistic regression,Bananas,0.5437106918238994,0.22031166039763567,0.004240989685058594,31.016333000000003 +3286,Binary classification,Logistic regression,Bananas,0.5450395617772368,0.21604614577871,0.004240989685058594,32.984057 +3392,Binary classification,Logistic regression,Bananas,0.5439268867924528,0.21272264631043258,0.004240989685058594,35.003757 +3498,Binary classification,Logistic regression,Bananas,0.5457404230989137,0.20827105132037868,0.004240989685058594,37.068178 +3604,Binary classification,Logistic regression,Bananas,0.5480022197558269,0.2042012701514411,0.004240989685058594,39.232173 +3710,Binary classification,Logistic regression,Bananas,0.546900269541779,0.19914244878513576,0.004240989685058594,41.450117000000006 +3816,Binary classification,Logistic regression,Bananas,0.5463836477987422,0.19450907398790138,0.004240989685058594,43.72876300000001 +3922,Binary classification,Logistic regression,Bananas,0.5474247832738399,0.1906064751481988,0.004240989685058594,46.072390000000006 +4028,Binary classification,Logistic regression,Bananas,0.547914597815293,0.1866904868244752,0.004240989685058594,48.42327300000001 +4134,Binary classification,Logistic regression,Bananas,0.548137397194001,0.18285214348206474,0.004240989685058594,50.870554000000006 +4240,Binary classification,Logistic regression,Bananas,0.5474056603773585,0.17886178861788615,0.004240989685058594,53.39424700000001 +4346,Binary classification,Logistic regression,Bananas,0.5476300046019328,0.17671691792294805,0.004240989685058594,55.939767 +4452,Binary classification,Logistic regression,Bananas,0.5498652291105122,0.1820408163265306,0.004240989685058594,58.584779000000005 +4558,Binary classification,Logistic regression,Bananas,0.5467310223782361,0.1814580031695721,0.004240989685058594,61.26661800000001 +4664,Binary classification,Logistic regression,Bananas,0.5465265866209262,0.18809980806142035,0.004240989685058594,64.04445700000001 +4770,Binary classification,Logistic regression,Bananas,0.5467505241090147,0.19086826347305388,0.004240989685058594,66.91140200000001 +4876,Binary classification,Logistic regression,Bananas,0.5469647251845775,0.19113877700476017,0.004240989685058594,69.84398600000002 +4982,Binary classification,Logistic regression,Bananas,0.5469690887193898,0.19765375044436545,0.004240989685058594,72.84582100000002 +5088,Binary classification,Logistic regression,Bananas,0.5448113207547169,0.19583333333333333,0.004240989685058594,75.85667200000002 +5194,Binary classification,Logistic regression,Bananas,0.5429341547939931,0.19416157501697218,0.004240989685058594,78.94956300000001 +5300,Binary classification,Logistic regression,Bananas,0.5432075471698113,0.1970149253731343,0.004240989685058594,82.06889500000001 +906,Binary classification,Logistic regression,Elec2,0.7980132450331126,0.7834319526627219,0.0053730010986328125,0.687155 +1812,Binary classification,Logistic regression,Elec2,0.8134657836644592,0.7488855869242199,0.0053730010986328125,2.092465 +2718,Binary classification,Logistic regression,Elec2,0.8024282560706402,0.7300150829562596,0.0053730010986328125,4.064074 +3624,Binary classification,Logistic regression,Elec2,0.8192604856512141,0.7598093142647598,0.0053730010986328125,6.824807 +4530,Binary classification,Logistic regression,Elec2,0.8289183222958058,0.7613181398213735,0.0053730010986328125,10.234028 +5436,Binary classification,Logistic regression,Elec2,0.8226637233259749,0.7528205128205128,0.0053730010986328125,14.344314 +6342,Binary classification,Logistic regression,Elec2,0.8229265216020183,0.7589611504614724,0.0053730010986328125,19.167838 +7248,Binary classification,Logistic regression,Elec2,0.8261589403973509,0.7617246596066566,0.0053730010986328125,24.744494 +8154,Binary classification,Logistic regression,Elec2,0.8318616629874908,0.7833096254148886,0.0053730010986328125,31.081721 +9060,Binary classification,Logistic regression,Elec2,0.8375275938189846,0.7975797579757975,0.0053730010986328125,38.163875000000004 +9966,Binary classification,Logistic regression,Elec2,0.8377483443708609,0.802008081302804,0.0053730010986328125,45.915004 +10872,Binary classification,Logistic regression,Elec2,0.8400478292862399,0.8089220964729151,0.0053730010986328125,54.352834 +11778,Binary classification,Logistic regression,Elec2,0.8432671081677704,0.8127789046653143,0.0053730010986328125,63.489549000000004 +12684,Binary classification,Logistic regression,Elec2,0.8419268369599495,0.8117547648108159,0.0053730010986328125,73.399178 +13590,Binary classification,Logistic regression,Elec2,0.8437821927888153,0.8167141500474834,0.0053730010986328125,84.03825400000001 +14496,Binary classification,Logistic regression,Elec2,0.8447157836644592,0.8189204408334004,0.0053730010986328125,95.41495900000001 +15402,Binary classification,Logistic regression,Elec2,0.8464485131801065,0.8201110519510155,0.0053730010986328125,107.55183300000002 +16308,Binary classification,Logistic regression,Elec2,0.8411822418444935,0.812780106982796,0.0053730010986328125,120.38661500000002 +17214,Binary classification,Logistic regression,Elec2,0.8397234808876496,0.8069954529555788,0.0053730010986328125,133.99787400000002 +18120,Binary classification,Logistic regression,Elec2,0.8419426048565122,0.80987785448752,0.0053730010986328125,148.356557 +19026,Binary classification,Logistic regression,Elec2,0.8451066961000736,0.8115849370244869,0.0053730010986328125,163.518734 +19932,Binary classification,Logistic regression,Elec2,0.8428155729480232,0.8097637986520129,0.0053730010986328125,179.395561 +20838,Binary classification,Logistic regression,Elec2,0.8393799788847298,0.805689404934688,0.0053730010986328125,196.009478 +21744,Binary classification,Logistic regression,Elec2,0.8402777777777778,0.8036632935722765,0.0053730010986328125,213.342445 +22650,Binary classification,Logistic regression,Elec2,0.8394701986754967,0.8009198423127463,0.0053730010986328125,231.348647 +23556,Binary classification,Logistic regression,Elec2,0.8357106469689252,0.7954545454545454,0.0053730010986328125,250.064916 +24462,Binary classification,Logistic regression,Elec2,0.8330471752105306,0.791441119395363,0.0053730010986328125,269.489469 +25368,Binary classification,Logistic regression,Elec2,0.8298249763481551,0.7872875092387287,0.0053730010986328125,289.628629 +26274,Binary classification,Logistic regression,Elec2,0.8304407398949532,0.787745962170661,0.0053730010986328125,310.508458 +27180,Binary classification,Logistic regression,Elec2,0.8308682855040471,0.7889638709085066,0.0053730010986328125,332.12320800000003 +28086,Binary classification,Logistic regression,Elec2,0.8277077547532579,0.7843678980437593,0.0053730010986328125,354.49968 +28992,Binary classification,Logistic regression,Elec2,0.8270212472406181,0.7820039121930016,0.0053730010986328125,377.655941 +29898,Binary classification,Logistic regression,Elec2,0.8260418757107498,0.780872129766168,0.0053730010986328125,401.520333 +30804,Binary classification,Logistic regression,Elec2,0.8258992338657317,0.7797807251673304,0.0053730010986328125,426.09085 +31710,Binary classification,Logistic regression,Elec2,0.821286660359508,0.7731294287201249,0.0053730010986328125,451.387139 +32616,Binary classification,Logistic regression,Elec2,0.8188619082658818,0.7700093428838368,0.0053730010986328125,477.46355 +33522,Binary classification,Logistic regression,Elec2,0.8168963665652408,0.7682024169184289,0.0053730010986328125,504.189667 +34428,Binary classification,Logistic regression,Elec2,0.8143952596723597,0.7647795037915042,0.0053730010986328125,531.635688 +35334,Binary classification,Logistic regression,Elec2,0.8142016188373804,0.7627822944896115,0.0053730010986328125,559.807066 +36240,Binary classification,Logistic regression,Elec2,0.8154801324503311,0.7629984051036682,0.0053730010986328125,588.709839 +37146,Binary classification,Logistic regression,Elec2,0.815161794002046,0.7614481273017858,0.0053730010986328125,618.344034 +38052,Binary classification,Logistic regression,Elec2,0.8151476926311363,0.7609596955073744,0.0053730010986328125,648.6306599999999 +38958,Binary classification,Logistic regression,Elec2,0.8162379998973254,0.7631274195149389,0.0053730010986328125,679.6980239999999 +39864,Binary classification,Logistic regression,Elec2,0.8169275536825206,0.7661946562439931,0.0053730010986328125,711.4719719999999 +40770,Binary classification,Logistic regression,Elec2,0.8186656855531028,0.7707241432780277,0.0053730010986328125,743.966698 +41676,Binary classification,Logistic regression,Elec2,0.8201602840963624,0.7745390006918749,0.0053730010986328125,777.181242 +42582,Binary classification,Logistic regression,Elec2,0.8211920529801324,0.7763613934089174,0.0053730010986328125,811.063029 +43488,Binary classification,Logistic regression,Elec2,0.8216979396615158,0.7772863051470587,0.0053730010986328125,845.685827 +44394,Binary classification,Logistic regression,Elec2,0.8211695274136145,0.7754109027129481,0.0053730010986328125,881.122534 +45300,Binary classification,Logistic regression,Elec2,0.8221412803532009,0.7771292633675417,0.0053730010986328125,917.330239 +45312,Binary classification,Logistic regression,Elec2,0.8221442443502824,0.7770862722319033,0.0053730010986328125,953.539999 +25,Binary classification,Logistic regression,Phishing,0.6,0.6428571428571429,0.005324363708496094,0.005087 +50,Binary classification,Logistic regression,Phishing,0.76,0.7499999999999999,0.005324363708496094,0.014273000000000001 +75,Binary classification,Logistic regression,Phishing,0.8,0.8,0.005324363708496094,0.080154 +100,Binary classification,Logistic regression,Phishing,0.81,0.8041237113402061,0.005324363708496094,0.160529 +125,Binary classification,Logistic regression,Phishing,0.8,0.7933884297520661,0.005324363708496094,0.244823 +150,Binary classification,Logistic regression,Phishing,0.8066666666666666,0.8079470198675497,0.005324363708496094,0.373717 +175,Binary classification,Logistic regression,Phishing,0.8171428571428572,0.8072289156626506,0.005324363708496094,0.564558 +200,Binary classification,Logistic regression,Phishing,0.815,0.8042328042328043,0.005324363708496094,0.765703 +225,Binary classification,Logistic regression,Phishing,0.8133333333333334,0.7980769230769231,0.005324363708496094,0.969796 +250,Binary classification,Logistic regression,Phishing,0.82,0.8068669527896996,0.005324363708496094,1.176844 +275,Binary classification,Logistic regression,Phishing,0.8218181818181818,0.8078431372549019,0.005564689636230469,1.38745 +300,Binary classification,Logistic regression,Phishing,0.8333333333333334,0.8161764705882353,0.005564689636230469,1.6264800000000001 +325,Binary classification,Logistic regression,Phishing,0.84,0.8181818181818181,0.005564689636230469,1.9406150000000002 +350,Binary classification,Logistic regression,Phishing,0.8514285714285714,0.8278145695364238,0.005564689636230469,2.281543 +375,Binary classification,Logistic regression,Phishing,0.848,0.8213166144200628,0.005564689636230469,2.625835 +400,Binary classification,Logistic regression,Phishing,0.85,0.8214285714285715,0.005564689636230469,2.973623 +425,Binary classification,Logistic regression,Phishing,0.8564705882352941,0.825214899713467,0.005564689636230469,3.3575019999999998 +450,Binary classification,Logistic regression,Phishing,0.86,0.8273972602739726,0.005564689636230469,3.744344 +475,Binary classification,Logistic regression,Phishing,0.8568421052631578,0.8247422680412371,0.005564689636230469,4.182096 +500,Binary classification,Logistic regression,Phishing,0.858,0.8297362110311751,0.005564689636230469,4.631479 +525,Binary classification,Logistic regression,Phishing,0.8571428571428571,0.8251748251748252,0.005564689636230469,5.084116 +550,Binary classification,Logistic regression,Phishing,0.8581818181818182,0.827433628318584,0.005564689636230469,5.539997 +575,Binary classification,Logistic regression,Phishing,0.8608695652173913,0.8305084745762712,0.005564689636230469,6.065522 +600,Binary classification,Logistic regression,Phishing,0.865,0.8329896907216495,0.005564689636230469,6.5948839999999995 +625,Binary classification,Logistic regression,Phishing,0.8672,0.8323232323232322,0.005564689636230469,7.192367999999999 +650,Binary classification,Logistic regression,Phishing,0.8707692307692307,0.8390804597701149,0.005564689636230469,7.814115999999999 +675,Binary classification,Logistic regression,Phishing,0.8711111111111111,0.8426763110307414,0.005564689636230469,8.439065999999999 +700,Binary classification,Logistic regression,Phishing,0.8757142857142857,0.8465608465608465,0.005564689636230469,9.067184 +725,Binary classification,Logistic regression,Phishing,0.8772413793103448,0.8514190317195326,0.005564689636230469,9.744983999999999 +750,Binary classification,Logistic regression,Phishing,0.8786666666666667,0.8539325842696629,0.005564689636230469,10.426390999999999 +775,Binary classification,Logistic regression,Phishing,0.88,0.8549141965678626,0.005564689636230469,11.153806 +800,Binary classification,Logistic regression,Phishing,0.88,0.8567164179104476,0.005564689636230469,11.884597 +825,Binary classification,Logistic regression,Phishing,0.88,0.8579626972740315,0.005564689636230469,12.619003 +850,Binary classification,Logistic regression,Phishing,0.8811764705882353,0.8587412587412586,0.005564689636230469,13.411055999999999 +875,Binary classification,Logistic regression,Phishing,0.8845714285714286,0.8622100954979536,0.005564689636230469,14.234523999999999 +900,Binary classification,Logistic regression,Phishing,0.8844444444444445,0.8617021276595744,0.005564689636230469,15.105192999999998 +925,Binary classification,Logistic regression,Phishing,0.8864864864864865,0.8655569782330347,0.005564689636230469,15.990264999999997 +950,Binary classification,Logistic regression,Phishing,0.8852631578947369,0.8655980271270037,0.005564689636230469,16.878196999999997 +975,Binary classification,Logistic regression,Phishing,0.8861538461538462,0.8664259927797834,0.005564689636230469,17.769031 +1000,Binary classification,Logistic regression,Phishing,0.887,0.8675263774912075,0.005564689636230469,18.72316 +1025,Binary classification,Logistic regression,Phishing,0.8868292682926829,0.8678815489749431,0.005564689636230469,19.680949 +1050,Binary classification,Logistic regression,Phishing,0.8885714285714286,0.8704318936877077,0.005564689636230469,20.642059 +1075,Binary classification,Logistic regression,Phishing,0.8874418604651163,0.8703108252947481,0.005564689636230469,21.642509 +1100,Binary classification,Logistic regression,Phishing,0.889090909090909,0.8723849372384936,0.005564689636230469,22.64645 +1125,Binary classification,Logistic regression,Phishing,0.8897777777777778,0.8742393509127788,0.005564689636230469,23.715816 +1150,Binary classification,Logistic regression,Phishing,0.8895652173913043,0.8738828202581926,0.005564689636230469,24.78868 +1175,Binary classification,Logistic regression,Phishing,0.8885106382978724,0.872444011684518,0.005564689636230469,25.864657 +1200,Binary classification,Logistic regression,Phishing,0.8891666666666667,0.8729703915950333,0.005564689636230469,26.968066 +1225,Binary classification,Logistic regression,Phishing,0.889795918367347,0.8737137511693172,0.005564689636230469,28.075126 +1250,Binary classification,Logistic regression,Phishing,0.8872,0.8712328767123287,0.005564689636230469,29.206647 +1903,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,1.174944 +3806,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,3.465965 +5709,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,6.937403 +7612,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,11.610183 +9515,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,17.462392 +11418,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,24.519273000000002 +13321,Binary classification,Logistic regression,SMTP,1.0,0.0,0.004383087158203125,32.784706 +15224,Binary classification,Logistic regression,SMTP,0.9996715712033631,0.7058823529411764,0.004383087158203125,42.234241 +17127,Binary classification,Logistic regression,SMTP,0.9997080632918783,0.761904761904762,0.004383087158203125,52.882453 +19030,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.761904761904762,0.004383087158203125,64.622668 +20933,Binary classification,Logistic regression,SMTP,0.999761142693355,0.761904761904762,0.004383087158203125,77.568109 +22836,Binary classification,Logistic regression,SMTP,0.9997810474689087,0.761904761904762,0.004383087158203125,91.771967 +24739,Binary classification,Logistic regression,SMTP,0.9997978899713004,0.761904761904762,0.004383087158203125,107.109486 +26642,Binary classification,Logistic regression,SMTP,0.9997747916823061,0.7272727272727273,0.004383087158203125,123.68183400000001 +28545,Binary classification,Logistic regression,SMTP,0.9997898055701524,0.7272727272727273,0.004383087158203125,141.369945 +30448,Binary classification,Logistic regression,SMTP,0.9998029427220179,0.7272727272727273,0.004383087158203125,160.23044 +32351,Binary classification,Logistic regression,SMTP,0.999814534326605,0.7272727272727273,0.004383087158203125,180.23963199999997 +34254,Binary classification,Logistic regression,SMTP,0.999824837975127,0.7272727272727273,0.004383087158203125,201.31894799999998 +36157,Binary classification,Logistic regression,SMTP,0.9998340570290677,0.7272727272727273,0.004383087158203125,223.51927299999997 +38060,Binary classification,Logistic regression,SMTP,0.9998423541776142,0.7272727272727273,0.004383087158203125,246.97671399999996 +39963,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.7272727272727273,0.004383087158203125,271.56812399999995 +41866,Binary classification,Logistic regression,SMTP,0.999856685616013,0.7272727272727273,0.004383087158203125,297.29584399999993 +43769,Binary classification,Logistic regression,SMTP,0.9998629166761863,0.7272727272727273,0.004383087158203125,324.2115329999999 +45672,Binary classification,Logistic regression,SMTP,0.9998686284813453,0.7272727272727273,0.004383087158203125,352.27523699999995 +47575,Binary classification,Logistic regression,SMTP,0.9998738833420915,0.7272727272727273,0.004383087158203125,381.59710399999994 +49478,Binary classification,Logistic regression,SMTP,0.9998787339827803,0.7272727272727273,0.004383087158203125,412.11662699999994 +51381,Binary classification,Logistic regression,SMTP,0.9998443004223351,0.6666666666666666,0.004383087158203125,443.86742899999996 +53284,Binary classification,Logistic regression,SMTP,0.9998498611215374,0.6666666666666666,0.004383087158203125,476.83879799999994 +55187,Binary classification,Logistic regression,SMTP,0.999855038324243,0.6666666666666666,0.004383087158203125,510.9819989999999 +57090,Binary classification,Logistic regression,SMTP,0.9997022245577158,0.48484848484848486,0.004383087158203125,546.274013 +58993,Binary classification,Logistic regression,SMTP,0.9997118302171444,0.48484848484848486,0.004383087158203125,582.6678519999999 +60896,Binary classification,Logistic regression,SMTP,0.9997208355228586,0.48484848484848486,0.004383087158203125,620.2082039999999 +62799,Binary classification,Logistic regression,SMTP,0.999697447411583,0.45714285714285713,0.004383087158203125,658.8625569999999 +64702,Binary classification,Logistic regression,SMTP,0.9997063460171247,0.45714285714285713,0.004383087158203125,698.5852799999999 +66605,Binary classification,Logistic regression,SMTP,0.9997147361309211,0.45714285714285713,0.004383087158203125,739.3620329999999 +68508,Binary classification,Logistic regression,SMTP,0.9996934664564723,0.4324324324324324,0.004383087158203125,781.2563779999999 +70411,Binary classification,Logistic regression,SMTP,0.9997017511468379,0.4324324324324324,0.004383087158203125,824.198222 +72314,Binary classification,Logistic regression,SMTP,0.9997095998008685,0.4324324324324324,0.004383087158203125,868.202086 +74217,Binary classification,Logistic regression,SMTP,0.9997170459598205,0.4324324324324324,0.004383087158203125,913.268811 +76120,Binary classification,Logistic regression,SMTP,0.999724119810825,0.4324324324324324,0.004383087158203125,959.4161730000001 +78023,Binary classification,Logistic regression,SMTP,0.9997308485959269,0.4324324324324324,0.004383087158203125,1006.608919 +79926,Binary classification,Logistic regression,SMTP,0.9997372569626904,0.4324324324324324,0.004383087158203125,1054.8516300000001 +81829,Binary classification,Logistic regression,SMTP,0.9997433672658838,0.4324324324324324,0.004383087158203125,1104.06085 +83732,Binary classification,Logistic regression,SMTP,0.9997491998280228,0.4324324324324324,0.004383087158203125,1154.258062 +85635,Binary classification,Logistic regression,SMTP,0.9997547731651778,0.4324324324324324,0.004383087158203125,1205.3715320000001 +87538,Binary classification,Logistic regression,SMTP,0.9997601041833261,0.4324324324324324,0.004383087158203125,1257.4462130000002 +89441,Binary classification,Logistic regression,SMTP,0.9997540277948592,0.4210526315789474,0.004383087158203125,1310.5048250000002 +91344,Binary classification,Logistic regression,SMTP,0.9997591522157996,0.4210526315789474,0.004383087158203125,1364.5437910000003 +93247,Binary classification,Logistic regression,SMTP,0.9997640674767017,0.4210526315789474,0.004383087158203125,1419.4942320000002 +95150,Binary classification,Logistic regression,SMTP,0.9997687861271676,0.4210526315789474,0.004383087158203125,1475.4318390000003 +95156,Binary classification,Logistic regression,SMTP,0.9997688007062088,0.4210526315789474,0.004383087158203125,1531.3705140000004 +106,Binary classification,Aggregated Mondrian Forest,Bananas,0.7047619047619048,0.6990291262135924,0.8133068084716797,0.833499 +212,Binary classification,Aggregated Mondrian Forest,Bananas,0.7867298578199052,0.7668393782383419,1.3378009796142578,2.8663 +318,Binary classification,Aggregated Mondrian Forest,Bananas,0.8233438485804416,0.806896551724138,1.855398178100586,6.250927 +424,Binary classification,Aggregated Mondrian Forest,Bananas,0.8392434988179669,0.8229166666666667,2.3226680755615234,11.143336 +530,Binary classification,Aggregated Mondrian Forest,Bananas,0.8412098298676749,0.8181818181818182,2.776212692260742,17.797124 +636,Binary classification,Aggregated Mondrian Forest,Bananas,0.8488188976377953,0.8267148014440434,3.173288345336914,26.396562 +742,Binary classification,Aggregated Mondrian Forest,Bananas,0.8596491228070176,0.8359621451104102,3.5500621795654297,36.969223 +848,Binary classification,Aggregated Mondrian Forest,Bananas,0.8677685950413223,0.8461538461538461,3.917997360229492,49.692848 +954,Binary classification,Aggregated Mondrian Forest,Bananas,0.8730325288562435,0.8515337423312884,4.238534927368164,64.631677 +1060,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772426817752597,0.8549107142857144,4.491437911987305,81.765253 +1166,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772532188841202,0.8557013118062564,4.809717178344727,101.295253 +1272,Binary classification,Aggregated Mondrian Forest,Bananas,0.8772619984264359,0.8566176470588236,5.171953201293945,123.161687 +1378,Binary classification,Aggregated Mondrian Forest,Bananas,0.8779956427015251,0.8561643835616438,5.501619338989258,147.513883 +1484,Binary classification,Aggregated Mondrian Forest,Bananas,0.8813216453135536,0.860759493670886,5.80189323425293,174.53874199999998 +1590,Binary classification,Aggregated Mondrian Forest,Bananas,0.8785399622404028,0.8579838116261957,6.17225456237793,204.250002 +1696,Binary classification,Aggregated Mondrian Forest,Bananas,0.8790560471976401,0.8585231193926847,6.45002555847168,237.091398 +1802,Binary classification,Aggregated Mondrian Forest,Bananas,0.8806218767351471,0.8613797549967763,6.703157424926758,272.83416 +1908,Binary classification,Aggregated Mondrian Forest,Bananas,0.8783429470372313,0.8602409638554217,7.075212478637695,311.419605 +2014,Binary classification,Aggregated Mondrian Forest,Bananas,0.8777943368107303,0.8607021517553795,7.409914016723633,352.79492 +2120,Binary classification,Aggregated Mondrian Forest,Bananas,0.8791882963662104,0.8636847710330138,7.730207443237305,397.065386 +2226,Binary classification,Aggregated Mondrian Forest,Bananas,0.8782022471910113,0.8626457171819564,8.068941116333008,444.302777 +2332,Binary classification,Aggregated Mondrian Forest,Bananas,0.8777348777348777,0.8621190130624092,8.392999649047852,494.454577 +2438,Binary classification,Aggregated Mondrian Forest,Bananas,0.8781288469429627,0.8624363131079205,8.738908767700195,547.433225 +2544,Binary classification,Aggregated Mondrian Forest,Bananas,0.8784899724734565,0.8635761589403974,9.069158554077148,603.367304 +2650,Binary classification,Aggregated Mondrian Forest,Bananas,0.8799546998867497,0.8654822335025381,9.380228042602539,661.971994 +2756,Binary classification,Aggregated Mondrian Forest,Bananas,0.8820326678765881,0.8676171079429736,9.675683975219727,723.088894 +2862,Binary classification,Aggregated Mondrian Forest,Bananas,0.8836071303739951,0.86905230043256,10.005556106567383,786.780009 +2968,Binary classification,Aggregated Mondrian Forest,Bananas,0.8840579710144928,0.8691019786910198,10.283010482788086,853.0146269999999 +3074,Binary classification,Aggregated Mondrian Forest,Bananas,0.8831760494630654,0.8683535020168683,10.632661819458008,921.6671329999999 +3180,Binary classification,Aggregated Mondrian Forest,Bananas,0.8858131487889274,0.8707725169099323,10.90281867980957,992.810764 +3286,Binary classification,Aggregated Mondrian Forest,Bananas,0.8852359208523592,0.8696854476322157,11.200468063354492,1066.389204 +3392,Binary classification,Aggregated Mondrian Forest,Bananas,0.8849896785608965,0.87017310252996,11.512235641479492,1142.442462 +3498,Binary classification,Aggregated Mondrian Forest,Bananas,0.8864741206748642,0.8712293220888745,11.797895431518555,1221.036812 +3604,Binary classification,Aggregated Mondrian Forest,Bananas,0.8878712184290869,0.8721518987341771,12.102933883666992,1302.125963 +3710,Binary classification,Aggregated Mondrian Forest,Bananas,0.8878403882448099,0.8725490196078431,12.41331672668457,1385.838182 +3816,Binary classification,Aggregated Mondrian Forest,Bananas,0.889646133682831,0.8746650788925276,12.665735244750977,1472.135343 +3922,Binary classification,Aggregated Mondrian Forest,Bananas,0.8885488395817394,0.8730758059831543,13.002767562866211,1561.047711 +4028,Binary classification,Aggregated Mondrian Forest,Bananas,0.8872609883287808,0.8714609286523215,13.407987594604492,1652.580672 +4134,Binary classification,Aggregated Mondrian Forest,Bananas,0.8874909266876361,0.8717241379310345,13.751871109008789,1746.8148660000002 +4240,Binary classification,Aggregated Mondrian Forest,Bananas,0.8886529841943854,0.8731864588930682,13.96497917175293,1843.750561 +4346,Binary classification,Aggregated Mondrian Forest,Bananas,0.8895281933256617,0.8742138364779874,14.240518569946289,1943.4032140000002 +4452,Binary classification,Aggregated Mondrian Forest,Bananas,0.8890137047854415,0.8735926305015352,14.605810165405273,2045.776976 +4558,Binary classification,Aggregated Mondrian Forest,Bananas,0.8894009216589862,0.874439461883408,14.917993545532227,2150.6554650000003 +4664,Binary classification,Aggregated Mondrian Forest,Bananas,0.8893416255629423,0.8748180494905387,15.239774703979492,2258.064088 +4770,Binary classification,Aggregated Mondrian Forest,Bananas,0.8880268400083875,0.8729176582579724,15.676980972290039,2367.913167 +4876,Binary classification,Aggregated Mondrian Forest,Bananas,0.8888205128205128,0.8733644859813083,15.964864730834961,2480.267593 +4982,Binary classification,Aggregated Mondrian Forest,Bananas,0.889580405541056,0.8746010031919745,16.210702896118164,2595.134509 +5088,Binary classification,Aggregated Mondrian Forest,Bananas,0.8891291527422842,0.8740509155873157,16.543100357055664,2712.434229 +5194,Binary classification,Aggregated Mondrian Forest,Bananas,0.8894665896398999,0.8743982494529539,16.87101936340332,2832.294496 +5300,Binary classification,Aggregated Mondrian Forest,Bananas,0.889413096810719,0.8742489270386266,17.23769187927246,2954.746773 +906,Binary classification,Aggregated Mondrian Forest,Elec2,0.8662983425414365,0.8638920134983127,5.093213081359863,9.961559 +1812,Binary classification,Aggregated Mondrian Forest,Elec2,0.8895637769188294,0.863013698630137,9.274415016174316,34.997891 +2718,Binary classification,Aggregated Mondrian Forest,Elec2,0.8737578211262422,0.8433074463225217,14.81954288482666,77.180768 +3624,Binary classification,Aggregated Mondrian Forest,Elec2,0.8746894838531604,0.8451568894952252,20.35789203643799,135.799753 +4530,Binary classification,Aggregated Mondrian Forest,Elec2,0.869728416869066,0.8295782784517621,25.320820808410645,209.04868100000002 +5436,Binary classification,Aggregated Mondrian Forest,Elec2,0.8658693652253909,0.8254728273880776,30.942105293273926,297.509476 +6342,Binary classification,Aggregated Mondrian Forest,Elec2,0.8613783314934553,0.8220287507592631,36.922226905822754,401.254404 +7248,Binary classification,Aggregated Mondrian Forest,Elec2,0.8563543535255967,0.8144715736945286,42.8322229385376,518.853069 +8154,Binary classification,Aggregated Mondrian Forest,Elec2,0.8547773825585674,0.8211480362537765,49.13461780548096,650.61595 +9060,Binary classification,Aggregated Mondrian Forest,Elec2,0.8564963020200905,0.8276776246023331,54.274807929992676,797.031608 +9966,Binary classification,Aggregated Mondrian Forest,Elec2,0.8559959859508279,0.830478440637921,59.58850955963135,957.298151 +10872,Binary classification,Aggregated Mondrian Forest,Elec2,0.858522675006899,0.8360690684289065,64.43849277496338,1132.655012 +11778,Binary classification,Aggregated Mondrian Forest,Elec2,0.8588774730406725,0.8365138697619515,69.77676105499268,1321.3849659999998 +12684,Binary classification,Aggregated Mondrian Forest,Elec2,0.8572892848695104,0.8352148579752368,75.08023929595947,1522.7749099999999 +13590,Binary classification,Aggregated Mondrian Forest,Elec2,0.8577525940098609,0.8380665158750105,79.94311618804932,1737.8701859999999 +14496,Binary classification,Aggregated Mondrian Forest,Elec2,0.8584339427388755,0.8393863494051347,84.43613529205322,1968.1055499999998 +15402,Binary classification,Aggregated Mondrian Forest,Elec2,0.8584507499513019,0.8387335404645658,89.24470615386963,2211.4324739999997 +16308,Binary classification,Aggregated Mondrian Forest,Elec2,0.8561354019746121,0.8352296670880741,95.65516376495361,2468.910492 +17214,Binary classification,Aggregated Mondrian Forest,Elec2,0.8563295183872655,0.8333445649976414,100.85075855255127,2740.76049 +18120,Binary classification,Aggregated Mondrian Forest,Elec2,0.8570009382416248,0.834176,106.8406229019165,3026.823297 +19026,Binary classification,Aggregated Mondrian Forest,Elec2,0.858712220762155,0.8348082595870207,111.74584293365479,3325.548438 +19932,Binary classification,Aggregated Mondrian Forest,Elec2,0.8587125583262255,0.8363361618040218,117.02025699615479,3636.553219 +20838,Binary classification,Aggregated Mondrian Forest,Elec2,0.8564572635216202,0.8339348176114596,123.37252902984619,3960.554229 +21744,Binary classification,Aggregated Mondrian Forest,Elec2,0.8540219840868325,0.8286917098445596,130.42929553985596,4298.210438 +22650,Binary classification,Aggregated Mondrian Forest,Elec2,0.8531944015188309,0.8264160793526494,136.64212131500244,4650.500753 +23556,Binary classification,Aggregated Mondrian Forest,Elec2,0.8528550201655699,0.8255134917438581,142.6701021194458,5016.675492 +24462,Binary classification,Aggregated Mondrian Forest,Elec2,0.8532766444544376,0.8247130647130647,148.4442949295044,5397.142957 +25368,Binary classification,Aggregated Mondrian Forest,Elec2,0.8514605589939686,0.8225487425826504,154.72937488555908,5792.939295 +26274,Binary classification,Aggregated Mondrian Forest,Elec2,0.8521676245575306,0.8231490756761678,160.280930519104,6204.791143 +27180,Binary classification,Aggregated Mondrian Forest,Elec2,0.8530851024688179,0.8247069669432372,165.12001132965088,6630.671498000001 +28086,Binary classification,Aggregated Mondrian Forest,Elec2,0.8528752002848495,0.8239904583404327,171.1938066482544,7068.974646000001 +28992,Binary classification,Aggregated Mondrian Forest,Elec2,0.8532303128557138,0.8236415633937083,176.66365909576416,7519.88705 +29898,Binary classification,Aggregated Mondrian Forest,Elec2,0.8538649362812323,0.8241355713883187,181.78493976593018,7981.8746790000005 +30804,Binary classification,Aggregated Mondrian Forest,Elec2,0.8542349771126189,0.8238110186783865,187.08849048614502,8454.454599 +31710,Binary classification,Aggregated Mondrian Forest,Elec2,0.8525655176763695,0.8216125462662648,193.5201120376587,8938.242097 +32616,Binary classification,Aggregated Mondrian Forest,Elec2,0.852245899126169,0.821432541594101,199.6366205215454,9433.534304 +33522,Binary classification,Aggregated Mondrian Forest,Elec2,0.852003221860923,0.8214247147330909,205.81115818023682,9940.639789 +34428,Binary classification,Aggregated Mondrian Forest,Elec2,0.851715223516426,0.8209965286300361,212.10033893585205,10459.964952 +35334,Binary classification,Aggregated Mondrian Forest,Elec2,0.8513287861206238,0.8197137660019906,218.64550113677979,10993.026606 +36240,Binary classification,Aggregated Mondrian Forest,Elec2,0.8508788873865173,0.8179735920237133,225.19258975982666,11538.003928999999 +37146,Binary classification,Aggregated Mondrian Forest,Elec2,0.8496432898102032,0.8159741671883752,232.33557987213135,12096.169426999999 +38052,Binary classification,Aggregated Mondrian Forest,Elec2,0.8497279966360937,0.8155126798735239,238.56606006622314,12664.877691 +38958,Binary classification,Aggregated Mondrian Forest,Elec2,0.8494493929203994,0.8154906093686098,244.89648151397705,13243.508414 +39864,Binary classification,Aggregated Mondrian Forest,Elec2,0.8492336251661942,0.8164773421277635,251.12543201446533,13830.859859 +40770,Binary classification,Aggregated Mondrian Forest,Elec2,0.8486104638328141,0.8170174918470204,257.83575916290283,14427.278119 +41676,Binary classification,Aggregated Mondrian Forest,Elec2,0.8490941811637672,0.8186928820595613,264.1331262588501,15032.883602 +42582,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493929217256523,0.8194385787087872,270.1314744949341,15648.679676 +43488,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493802745648125,0.8194995590828924,276.04683017730713,16273.986894 +44394,Binary classification,Aggregated Mondrian Forest,Elec2,0.8493681436262474,0.8189620164063134,282.1419038772583,16909.074578 +45300,Binary classification,Aggregated Mondrian Forest,Elec2,0.8499083864985982,0.8197651300267741,287.208477973938,17554.066457 +45312,Binary classification,Aggregated Mondrian Forest,Elec2,0.8499039968219637,0.8197312269727252,287.3145227432251,18206.640571 +25,Binary classification,Aggregated Mondrian Forest,Phishing,0.6666666666666666,0.6923076923076924,0.2663440704345703,0.180038 +50,Binary classification,Aggregated Mondrian Forest,Phishing,0.7755102040816326,0.7555555555555555,0.40291404724121094,0.591649 +75,Binary classification,Aggregated Mondrian Forest,Phishing,0.7972972972972973,0.7945205479452055,0.5196552276611328,1.2897159999999999 +100,Binary classification,Aggregated Mondrian Forest,Phishing,0.8181818181818182,0.8125,0.6383838653564453,2.331468 +125,Binary classification,Aggregated Mondrian Forest,Phishing,0.8225806451612904,0.819672131147541,0.7669887542724609,3.7241540000000004 +150,Binary classification,Aggregated Mondrian Forest,Phishing,0.8456375838926175,0.847682119205298,0.9175167083740234,5.520175 +175,Binary classification,Aggregated Mondrian Forest,Phishing,0.867816091954023,0.8606060606060606,1.0086803436279297,7.7498439999999995 +200,Binary classification,Aggregated Mondrian Forest,Phishing,0.864321608040201,0.8571428571428572,1.1245098114013672,10.53336 +225,Binary classification,Aggregated Mondrian Forest,Phishing,0.8660714285714286,0.8557692307692308,1.2114391326904297,13.795268 +250,Binary classification,Aggregated Mondrian Forest,Phishing,0.8554216867469879,0.8448275862068965,1.322244644165039,17.57486 +275,Binary classification,Aggregated Mondrian Forest,Phishing,0.8540145985401459,0.84251968503937,1.3987751007080078,21.876977 +300,Binary classification,Aggregated Mondrian Forest,Phishing,0.8561872909698997,0.8413284132841329,1.489828109741211,26.743447 +325,Binary classification,Aggregated Mondrian Forest,Phishing,0.8672839506172839,0.8501742160278746,1.5769939422607422,32.2729 +350,Binary classification,Aggregated Mondrian Forest,Phishing,0.8681948424068768,0.8486842105263156,1.638784408569336,38.477964 +375,Binary classification,Aggregated Mondrian Forest,Phishing,0.8689839572192514,0.8482972136222912,1.7178211212158203,45.357054 +400,Binary classification,Aggregated Mondrian Forest,Phishing,0.8671679197994987,0.8436578171091446,1.7941875457763672,52.888585 +425,Binary classification,Aggregated Mondrian Forest,Phishing,0.8702830188679245,0.8433048433048433,1.8353633880615234,61.095765 +450,Binary classification,Aggregated Mondrian Forest,Phishing,0.8730512249443207,0.8455284552845528,1.9096240997314453,70.024579 +475,Binary classification,Aggregated Mondrian Forest,Phishing,0.8755274261603375,0.8506329113924052,1.988790512084961,79.720297 +500,Binary classification,Aggregated Mondrian Forest,Phishing,0.875751503006012,0.8530805687203792,2.063833236694336,90.07863400000001 +525,Binary classification,Aggregated Mondrian Forest,Phishing,0.8778625954198473,0.8525345622119817,2.144712448120117,101.25810100000001 +550,Binary classification,Aggregated Mondrian Forest,Phishing,0.8779599271402551,0.8533916849015317,2.1996402740478516,113.25181900000001 +575,Binary classification,Aggregated Mondrian Forest,Phishing,0.8780487804878049,0.8535564853556484,2.2528209686279297,125.93584100000001 +600,Binary classification,Aggregated Mondrian Forest,Phishing,0.8797996661101837,0.8536585365853657,2.283121109008789,139.44840100000002 +625,Binary classification,Aggregated Mondrian Forest,Phishing,0.8814102564102564,0.852589641434263,2.343900680541992,153.77905700000002 +650,Binary classification,Aggregated Mondrian Forest,Phishing,0.884437596302003,0.8587570621468926,2.418844223022461,168.92061400000003 +675,Binary classification,Aggregated Mondrian Forest,Phishing,0.884272997032641,0.8617021276595745,2.468423843383789,184.94000100000002 +700,Binary classification,Aggregated Mondrian Forest,Phishing,0.8884120171673819,0.8650519031141869,2.478273391723633,201.76583000000002 +725,Binary classification,Aggregated Mondrian Forest,Phishing,0.8895027624309392,0.8684210526315789,2.5243663787841797,219.457713 +750,Binary classification,Aggregated Mondrian Forest,Phishing,0.8918558077436582,0.8716323296354993,2.5813236236572266,238.014124 +775,Binary classification,Aggregated Mondrian Forest,Phishing,0.8914728682170543,0.8707692307692307,2.6200389862060547,257.461391 +800,Binary classification,Aggregated Mondrian Forest,Phishing,0.8898623279098874,0.8702064896755163,2.657014846801758,277.779634 +825,Binary classification,Aggregated Mondrian Forest,Phishing,0.8907766990291263,0.872159090909091,2.706361770629883,298.980548 +850,Binary classification,Aggregated Mondrian Forest,Phishing,0.8928150765606596,0.8741355463347164,2.730466842651367,321.097396 +875,Binary classification,Aggregated Mondrian Forest,Phishing,0.8958810068649885,0.8771929824561403,2.7533512115478516,344.186724 +900,Binary classification,Aggregated Mondrian Forest,Phishing,0.8976640711902113,0.8786279683377309,2.807779312133789,368.101507 +925,Binary classification,Aggregated Mondrian Forest,Phishing,0.9004329004329005,0.8829516539440204,2.8523120880126953,392.98062400000003 +950,Binary classification,Aggregated Mondrian Forest,Phishing,0.9009483667017913,0.8850855745721271,2.913583755493164,418.83123200000006 +975,Binary classification,Aggregated Mondrian Forest,Phishing,0.9024640657084189,0.8867699642431467,2.943540573120117,445.63277700000003 +1000,Binary classification,Aggregated Mondrian Forest,Phishing,0.9009009009009009,0.8850174216027874,2.9903697967529297,473.39902800000004 +1025,Binary classification,Aggregated Mondrian Forest,Phishing,0.8994140625,0.8836158192090395,3.035707473754883,502.22467600000004 +1050,Binary classification,Aggregated Mondrian Forest,Phishing,0.9008579599618685,0.8857142857142858,3.069150924682617,532.049603 +1075,Binary classification,Aggregated Mondrian Forest,Phishing,0.9013035381750466,0.8869936034115138,3.114839553833008,562.838704 +1100,Binary classification,Aggregated Mondrian Forest,Phishing,0.9035486806187443,0.8898128898128899,3.132375717163086,594.67778 +1125,Binary classification,Aggregated Mondrian Forest,Phishing,0.905693950177936,0.8933601609657947,3.1889095306396484,627.518257 +1150,Binary classification,Aggregated Mondrian Forest,Phishing,0.9060052219321149,0.893491124260355,3.220029830932617,661.4048929999999 +1175,Binary classification,Aggregated Mondrian Forest,Phishing,0.9045996592844975,0.8916827852998066,3.270620346069336,696.4079739999999 +1200,Binary classification,Aggregated Mondrian Forest,Phishing,0.9040867389491243,0.8909952606635072,3.311410903930664,732.4743999999998 +1225,Binary classification,Aggregated Mondrian Forest,Phishing,0.9044117647058824,0.8911627906976743,3.344022750854492,769.4892029999999 +1250,Binary classification,Aggregated Mondrian Forest,Phishing,0.9047237790232185,0.8921124206708976,3.391061782836914,807.5726659999999 +1903,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,2.745403 +3806,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,8.183125 +5709,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,16.539666 +7612,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,27.755785000000003 +9515,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,41.777067 +11418,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,58.637769000000006 +13321,Binary classification,Aggregated Mondrian Forest,SMTP,1.0,0.0,0.04407501220703125,78.268206 +15224,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998686198515404,0.9090909090909091,0.09231853485107422,101.443914 +17127,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998832184981898,0.9230769230769231,0.09723186492919922,131.805417 +19030,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998948972620737,0.9230769230769231,0.09728145599365234,169.246217 +20933,Binary classification,Aggregated Mondrian Forest,SMTP,0.999904452512899,0.9230769230769231,0.09728145599365234,213.148727 +22836,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999124151521787,0.9230769230769231,0.09728145599365234,263.357684 +24739,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999191527205109,0.9230769230769231,0.09730815887451172,319.49775 +26642,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998873916144289,0.888888888888889,0.10914134979248047,381.401703 +28545,Binary classification,Aggregated Mondrian Forest,SMTP,0.999894899103139,0.888888888888889,0.10916423797607422,448.60874 +30448,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999014681249384,0.888888888888889,0.10916423797607422,520.91477 +32351,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999072642967543,0.888888888888889,0.10966777801513672,598.09858 +34254,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999124164306776,0.888888888888889,0.11131954193115234,680.064697 +36157,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999170262197146,0.888888888888889,0.11127376556396484,766.82968 +38060,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999211750177356,0.888888888888889,0.11127376556396484,858.2478070000001 +39963,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999249286822481,0.888888888888889,0.11127376556396484,954.233503 +41866,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999283410963812,0.888888888888889,0.11127376556396484,1054.7914 +43769,Binary classification,Aggregated Mondrian Forest,SMTP,0.999931456772071,0.888888888888889,0.11127376556396484,1159.6703400000001 +45672,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999343128024348,0.888888888888889,0.11127376556396484,1268.4432900000002 +47575,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999369403455669,0.888888888888889,0.1298818588256836,1381.2685860000001 +49478,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999393657659115,0.888888888888889,0.1299276351928711,1498.4984390000002 +51381,Binary classification,Aggregated Mondrian Forest,SMTP,0.999941611521993,0.9032258064516129,0.14348888397216797,1620.0599740000002 +53284,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999436968639153,0.9032258064516129,0.14348888397216797,1745.8256190000002 +55187,Binary classification,Aggregated Mondrian Forest,SMTP,0.9999456383865473,0.9032258064516129,0.14403820037841797,1875.6542580000003 +57090,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998248349068998,0.7619047619047621,0.1476888656616211,2010.2723030000002 +58993,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998304854895579,0.7619047619047621,0.15108394622802734,2149.2802330000004 +60896,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998357829050004,0.7619047619047621,0.1510610580444336,2292.3763450000006 +62799,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998089111118188,0.7272727272727272,0.15114116668701172,2439.4913830000005 +64702,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998145314601011,0.7272727272727272,0.15341472625732422,2590.5360980000005 +66605,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998198306408024,0.7272727272727272,0.1576833724975586,2745.6113380000006 +68508,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998248354182784,0.75,0.1762075424194336,2905.2725530000007 +70411,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998295696634001,0.75,0.1762075424194336,3069.514522000001 +72314,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998340547342801,0.75,0.1762075424194336,3238.428366000001 +74217,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998383097984263,0.75,0.17613887786865234,3411.935267000001 +76120,Binary classification,Aggregated Mondrian Forest,SMTP,0.99984235210657,0.75,0.1760702133178711,3590.1136440000014 +78023,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998461972264233,0.75,0.1782979965209961,3773.0849660000013 +79926,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998498592430404,0.75,0.1782979965209961,3960.4867140000015 +81829,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998533509312216,0.75,0.1782979965209961,4152.338698000001 +83732,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998566839044082,0.75,0.17832088470458984,4348.642178000001 +85635,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998598687437232,0.75,0.17832088470458984,4549.410423000001 +87538,Binary classification,Aggregated Mondrian Forest,SMTP,0.999862915110182,0.75,0.17832088470458984,4754.622131000001 +89441,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998546511627907,0.7346938775510204,0.17834758758544922,4964.315109000001 +91344,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998576792967168,0.7346938775510204,0.19727230072021484,5178.776489000001 +93247,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998605838320143,0.7346938775510204,0.21181774139404297,5398.511461000001 +95150,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998633721846788,0.7346938775510204,0.21174907684326172,5623.669278000001 +95156,Binary classification,Aggregated Mondrian Forest,SMTP,0.9998633807997478,0.7346938775510204,0.21174907684326172,5848.865968000001 +106,Binary classification,ALMA,Bananas,0.5377358490566038,0.5242718446601942,0.0028944015502929688,0.039715 +212,Binary classification,ALMA,Bananas,0.5330188679245284,0.5217391304347825,0.0028944015502929688,0.180531 +318,Binary classification,ALMA,Bananas,0.5188679245283019,0.5173501577287066,0.0029211044311523438,0.33386499999999997 +424,Binary classification,ALMA,Bananas,0.5330188679245284,0.5330188679245282,0.0029211044311523438,0.49377399999999994 +530,Binary classification,ALMA,Bananas,0.5207547169811321,0.5115384615384615,0.0029211044311523438,0.7446539999999999 +636,Binary classification,ALMA,Bananas,0.5377358490566038,0.5303514376996804,0.0029211044311523438,1.03169 +742,Binary classification,ALMA,Bananas,0.522911051212938,0.512396694214876,0.0029211044311523438,1.379859 +848,Binary classification,ALMA,Bananas,0.5235849056603774,0.5061124694376529,0.0029211044311523438,1.737155 +954,Binary classification,ALMA,Bananas,0.5157232704402516,0.5,0.0029211044311523438,2.173505 +1060,Binary classification,ALMA,Bananas,0.5160377358490567,0.4975514201762978,0.0029211044311523438,2.70321 +1166,Binary classification,ALMA,Bananas,0.5154373927958834,0.49598572702943805,0.0029211044311523438,3.270309 +1272,Binary classification,ALMA,Bananas,0.5165094339622641,0.4979591836734694,0.0029211044311523438,3.844268 +1378,Binary classification,ALMA,Bananas,0.5195936139332366,0.4977238239757208,0.0029211044311523438,4.501151 +1484,Binary classification,ALMA,Bananas,0.5195417789757413,0.4968242766407903,0.0029211044311523438,5.229491 +1590,Binary classification,ALMA,Bananas,0.5226415094339623,0.4983476536682089,0.0029211044311523438,6.030342 +1696,Binary classification,ALMA,Bananas,0.5194575471698113,0.49473031618102914,0.0029211044311523438,6.8837410000000006 +1802,Binary classification,ALMA,Bananas,0.5205327413984462,0.4965034965034965,0.0029211044311523438,7.813207 +1908,Binary classification,ALMA,Bananas,0.5193920335429769,0.4964305326743548,0.0029211044311523438,8.751116 +2014,Binary classification,ALMA,Bananas,0.519364448857994,0.4989648033126293,0.0029211044311523438,9.762632 +2120,Binary classification,ALMA,Bananas,0.5174528301886793,0.4997555012224939,0.0029211044311523438,10.806008 +2226,Binary classification,ALMA,Bananas,0.5197663971248877,0.5002337540906966,0.0029211044311523438,11.968014 +2332,Binary classification,ALMA,Bananas,0.5175814751286449,0.4975435462259938,0.0029211044311523438,13.16512 +2438,Binary classification,ALMA,Bananas,0.5176374077112387,0.4957118353344769,0.0029211044311523438,14.408045 +2544,Binary classification,ALMA,Bananas,0.5196540880503144,0.5008169934640523,0.0029211044311523438,15.661105 +2650,Binary classification,ALMA,Bananas,0.520377358490566,0.5037094884810621,0.0029211044311523438,17.014893999999998 +2756,Binary classification,ALMA,Bananas,0.521044992743106,0.5041322314049587,0.0029211044311523438,18.454389 +2862,Binary classification,ALMA,Bananas,0.5213137665967854,0.5032632342277013,0.0029211044311523438,19.942263 +2968,Binary classification,ALMA,Bananas,0.5175202156334232,0.49859943977591037,0.0029211044311523438,21.473074 +3074,Binary classification,ALMA,Bananas,0.5152895250487963,0.49696151249155973,0.0029211044311523438,23.106855 +3180,Binary classification,ALMA,Bananas,0.5132075471698113,0.4931237721021611,0.0029211044311523438,24.747764 +3286,Binary classification,ALMA,Bananas,0.5130858186244674,0.4927076727964489,0.0029211044311523438,26.464385 +3392,Binary classification,ALMA,Bananas,0.5103183962264151,0.49095923996322405,0.0029211044311523438,28.215584 +3498,Binary classification,ALMA,Bananas,0.5091480846197828,0.48914013686402846,0.0029211044311523438,30.068049 +3604,Binary classification,ALMA,Bananas,0.5097114317425083,0.4876775877065816,0.0029211044311523438,31.96012 +3710,Binary classification,ALMA,Bananas,0.5118598382749326,0.49086308687095864,0.0029211044311523438,33.864206 +3816,Binary classification,ALMA,Bananas,0.510482180293501,0.4893384363039912,0.0029211044311523438,35.803291 +3922,Binary classification,ALMA,Bananas,0.50790413054564,0.48588172615876407,0.0029211044311523438,37.844614 +4028,Binary classification,ALMA,Bananas,0.506454816285998,0.48443983402489627,0.0029211044311523438,39.968017 +4134,Binary classification,ALMA,Bananas,0.5050798258345428,0.48281092012133464,0.0029211044311523438,42.128298 +4240,Binary classification,ALMA,Bananas,0.5068396226415094,0.48484848484848486,0.0029211044311523438,44.30306 +4346,Binary classification,ALMA,Bananas,0.5080533824206167,0.4858104858104858,0.0029211044311523438,46.485881 +4452,Binary classification,ALMA,Bananas,0.5080862533692723,0.4847058823529412,0.0029211044311523438,48.746465 +4558,Binary classification,ALMA,Bananas,0.5063624396665204,0.48370812299219823,0.0029211044311523438,51.058035000000004 +4664,Binary classification,ALMA,Bananas,0.5051457975986278,0.4829749103942652,0.0029211044311523438,53.475871000000005 +4770,Binary classification,ALMA,Bananas,0.5048218029350104,0.48201754385964907,0.0029211044311523438,55.900409 +4876,Binary classification,ALMA,Bananas,0.5036915504511895,0.4802405498281787,0.0029211044311523438,58.409701000000005 +4982,Binary classification,ALMA,Bananas,0.5038137294259334,0.4811083123425693,0.0029211044311523438,60.970617000000004 +5088,Binary classification,ALMA,Bananas,0.5029481132075472,0.47995064774830354,0.0029211044311523438,63.61249900000001 +5194,Binary classification,ALMA,Bananas,0.5040431266846361,0.4810636583400483,0.0029211044311523438,66.28843800000001 +5300,Binary classification,ALMA,Bananas,0.5064150943396226,0.4825949367088608,0.0029211044311523438,68.97313600000001 +906,Binary classification,ALMA,Elec2,0.9072847682119205,0.9056179775280899,0.0043582916259765625,0.679052 +1812,Binary classification,ALMA,Elec2,0.9166666666666666,0.8967874231032126,0.0043582916259765625,1.978643 +2718,Binary classification,ALMA,Elec2,0.9175864606328182,0.898458748866727,0.0043582916259765625,3.929769 +3624,Binary classification,ALMA,Elec2,0.9268763796909493,0.9098945936756205,0.0043582916259765625,6.478699 +4530,Binary classification,ALMA,Elec2,0.9271523178807947,0.9076664801343034,0.0043582916259765625,9.702945 +5436,Binary classification,ALMA,Elec2,0.9269683590875644,0.9074376311494521,0.0043582916259765625,13.508006 +6342,Binary classification,ALMA,Elec2,0.9274676758120467,0.9089108910891088,0.0043582916259765625,17.915655 +7248,Binary classification,ALMA,Elec2,0.9254966887417219,0.9066390041493776,0.0043582916259765625,22.910275000000002 +8154,Binary classification,ALMA,Elec2,0.9251900907530046,0.9100294985250738,0.0043582916259765625,28.53571 +9060,Binary classification,ALMA,Elec2,0.9266004415011038,0.9135128105085185,0.0043582916259765625,34.833569000000004 +9966,Binary classification,ALMA,Elec2,0.9293598233995585,0.9182535996284256,0.0043582916259765625,41.779447000000005 +10872,Binary classification,ALMA,Elec2,0.931383370125092,0.9217208814270723,0.0043582916259765625,49.40882500000001 +11778,Binary classification,ALMA,Elec2,0.9313975208014943,0.9218568665377176,0.0043582916259765625,57.61408300000001 +12684,Binary classification,ALMA,Elec2,0.9290444654683065,0.9191665169750315,0.0043582916259765625,66.44160400000001 +13590,Binary classification,ALMA,Elec2,0.9298013245033112,0.9209872453205235,0.0043582916259765625,75.85703300000002 +14496,Binary classification,ALMA,Elec2,0.9305325607064018,0.9222213640225535,0.0043582916259765625,85.90938600000001 +15402,Binary classification,ALMA,Elec2,0.9308531359563693,0.922279792746114,0.0043582916259765625,96.62160000000002 +16308,Binary classification,ALMA,Elec2,0.9293598233995585,0.9203319502074688,0.0043582916259765625,107.96559300000001 +17214,Binary classification,ALMA,Elec2,0.9279656093877077,0.9176298658163943,0.0043582916259765625,119.98412300000001 +18120,Binary classification,ALMA,Elec2,0.9266004415011038,0.9160141449861076,0.0043582916259765625,132.63841200000002 +19026,Binary classification,ALMA,Elec2,0.9265741616734994,0.915143048047136,0.0043582916259765625,146.00166900000002 +19932,Binary classification,ALMA,Elec2,0.9262994180212724,0.9155892662184681,0.0043582916259765625,159.938555 +20838,Binary classification,ALMA,Elec2,0.9232171993473461,0.9122710823555213,0.0043582916259765625,174.54734100000002 +21744,Binary classification,ALMA,Elec2,0.9225073583517293,0.9101955977189149,0.0043582916259765625,189.73533200000003 +22650,Binary classification,ALMA,Elec2,0.9217218543046357,0.9087540528022233,0.0043582916259765625,205.61954500000002 +23556,Binary classification,ALMA,Elec2,0.9186619120393955,0.9050639183430779,0.0043582916259765625,222.16184900000002 +24462,Binary classification,ALMA,Elec2,0.9173003025100155,0.902885123133791,0.0043582916259765625,239.39901500000002 +25368,Binary classification,ALMA,Elec2,0.9144985808893094,0.8997643144322751,0.0043582916259765625,257.26822000000004 +26274,Binary classification,ALMA,Elec2,0.9142498287280201,0.8992622401073105,0.0043582916259765625,275.765482 +27180,Binary classification,ALMA,Elec2,0.9138337012509198,0.89909521757863,0.0043582916259765625,294.848784 +28086,Binary classification,ALMA,Elec2,0.9110232856227302,0.8955049132343716,0.0043582916259765625,314.617946 +28992,Binary classification,ALMA,Elec2,0.9101476269315674,0.8940927755417328,0.0043582916259765625,335.076303 +29898,Binary classification,ALMA,Elec2,0.9094922737306843,0.8931701539676272,0.0043582916259765625,356.063392 +30804,Binary classification,ALMA,Elec2,0.9083235943383976,0.8913093680240166,0.0043582916259765625,377.56682800000004 +31710,Binary classification,ALMA,Elec2,0.9062125512456638,0.8888722815933038,0.0043582916259765625,399.67833300000007 +32616,Binary classification,ALMA,Elec2,0.9052918812852587,0.8879294706671989,0.0043582916259765625,422.4669390000001 +33522,Binary classification,ALMA,Elec2,0.9050474315374978,0.8877050626212737,0.0043582916259765625,445.7904010000001 +34428,Binary classification,ALMA,Elec2,0.9050772626931567,0.8877901387172092,0.0043582916259765625,469.7481780000001 +35334,Binary classification,ALMA,Elec2,0.9045395369898681,0.8866104144955793,0.0043582916259765625,494.21097500000013 +36240,Binary classification,ALMA,Elec2,0.9048013245033113,0.8860483551327785,0.0043582916259765625,519.3392310000002 +37146,Binary classification,ALMA,Elec2,0.9045119259139611,0.8854217139903738,0.0043582916259765625,545.0643520000001 +38052,Binary classification,ALMA,Elec2,0.9042625880374224,0.8846092933388237,0.0043582916259765625,571.4397520000001 +38958,Binary classification,ALMA,Elec2,0.904409877303763,0.88502624266749,0.0043582916259765625,598.3711440000001 +39864,Binary classification,ALMA,Elec2,0.904926750953241,0.8863636363636365,0.0043582916259765625,626.0397580000001 +40770,Binary classification,ALMA,Elec2,0.9055187637969095,0.8878667908709827,0.0043582916259765625,654.3634580000002 +41676,Binary classification,ALMA,Elec2,0.9061090315769268,0.8892285916489738,0.0043582916259765625,683.2521370000002 +42582,Binary classification,ALMA,Elec2,0.9063923723639097,0.889810361032786,0.0043582916259765625,712.7682970000002 +43488,Binary classification,ALMA,Elec2,0.9067098969830758,0.8902534693104661,0.0043582916259765625,742.9022130000002 +44394,Binary classification,ALMA,Elec2,0.9062711177186106,0.8894732648019762,0.0043582916259765625,773.7300850000001 +45300,Binary classification,ALMA,Elec2,0.9064238410596026,0.8897844569823977,0.0043582916259765625,805.1132940000001 +45312,Binary classification,ALMA,Elec2,0.9064265536723164,0.8897670549084858,0.0043582916259765625,836.4982200000001 +25,Binary classification,ALMA,Phishing,0.56,0.5217391304347826,0.004366874694824219,0.003459 +50,Binary classification,ALMA,Phishing,0.7,0.6341463414634146,0.004366874694824219,0.050212 +75,Binary classification,ALMA,Phishing,0.7066666666666667,0.676470588235294,0.004366874694824219,0.100001 +100,Binary classification,ALMA,Phishing,0.72,0.702127659574468,0.004366874694824219,0.15312800000000001 +125,Binary classification,ALMA,Phishing,0.72,0.7058823529411765,0.004366874694824219,0.22806300000000002 +150,Binary classification,ALMA,Phishing,0.7133333333333334,0.7189542483660132,0.004366874694824219,0.334445 +175,Binary classification,ALMA,Phishing,0.7314285714285714,0.718562874251497,0.004366874694824219,0.511774 +200,Binary classification,ALMA,Phishing,0.735,0.7225130890052356,0.004366874694824219,0.692607 +225,Binary classification,ALMA,Phishing,0.7244444444444444,0.701923076923077,0.004366874694824219,0.876779 +250,Binary classification,ALMA,Phishing,0.724,0.7038626609442059,0.004366874694824219,1.156005 +275,Binary classification,ALMA,Phishing,0.7345454545454545,0.7137254901960783,0.004580497741699219,1.438242 +300,Binary classification,ALMA,Phishing,0.7366666666666667,0.7127272727272725,0.004580497741699219,1.723391 +325,Binary classification,ALMA,Phishing,0.7476923076923077,0.7172413793103447,0.004580497741699219,2.078902 +350,Binary classification,ALMA,Phishing,0.7542857142857143,0.7225806451612904,0.004580497741699219,2.4379969999999997 +375,Binary classification,ALMA,Phishing,0.7573333333333333,0.723404255319149,0.004580497741699219,2.8003189999999996 +400,Binary classification,ALMA,Phishing,0.76,0.7257142857142856,0.004580497741699219,3.1655669999999994 +425,Binary classification,ALMA,Phishing,0.76,0.7197802197802199,0.004580497741699219,3.585508999999999 +450,Binary classification,ALMA,Phishing,0.7622222222222222,0.7206266318537858,0.004580497741699219,4.009149999999999 +475,Binary classification,ALMA,Phishing,0.7663157894736842,0.7272727272727272,0.004580497741699219,4.435539999999999 +500,Binary classification,ALMA,Phishing,0.768,0.7327188940092165,0.004580497741699219,4.959426999999999 +525,Binary classification,ALMA,Phishing,0.7714285714285715,0.7321428571428573,0.004580497741699219,5.485955999999999 +550,Binary classification,ALMA,Phishing,0.7709090909090909,0.7341772151898734,0.004580497741699219,6.028689999999999 +575,Binary classification,ALMA,Phishing,0.7739130434782608,0.7379032258064516,0.004580497741699219,6.595545999999999 +600,Binary classification,ALMA,Phishing,0.78,0.7401574803149605,0.004580497741699219,7.165257999999999 +625,Binary classification,ALMA,Phishing,0.7744,0.7314285714285715,0.004580497741699219,7.741693999999999 +650,Binary classification,ALMA,Phishing,0.7815384615384615,0.7427536231884059,0.004580497741699219,8.363988999999998 +675,Binary classification,ALMA,Phishing,0.7837037037037037,0.75,0.004580497741699219,9.010548999999997 +700,Binary classification,ALMA,Phishing,0.79,0.7545909849749582,0.004580497741699219,9.660288999999997 +725,Binary classification,ALMA,Phishing,0.7917241379310345,0.7606973058637084,0.004580497741699219,10.349524999999996 +750,Binary classification,ALMA,Phishing,0.792,0.7621951219512195,0.004580497741699219,11.041959999999996 +775,Binary classification,ALMA,Phishing,0.792258064516129,0.7614814814814814,0.004580497741699219,11.737615999999996 +800,Binary classification,ALMA,Phishing,0.795,0.7670454545454546,0.004580497741699219,12.526707999999996 +825,Binary classification,ALMA,Phishing,0.793939393939394,0.7671232876712327,0.004580497741699219,13.319008999999996 +850,Binary classification,ALMA,Phishing,0.7976470588235294,0.7706666666666667,0.004580497741699219,14.117527999999997 +875,Binary classification,ALMA,Phishing,0.8022857142857143,0.7744458930899608,0.004580497741699219,14.959668999999996 +900,Binary classification,ALMA,Phishing,0.8011111111111111,0.7737041719342603,0.004580497741699219,15.804425999999996 +925,Binary classification,ALMA,Phishing,0.8054054054054054,0.7804878048780488,0.004580497741699219,16.651646999999997 +950,Binary classification,ALMA,Phishing,0.8073684210526316,0.7849588719153936,0.004580497741699219,17.502014999999997 +975,Binary classification,ALMA,Phishing,0.8102564102564103,0.7880870561282932,0.004580497741699219,18.418237999999995 +1000,Binary classification,ALMA,Phishing,0.811,0.7892976588628764,0.004580497741699219,19.337672999999995 +1025,Binary classification,ALMA,Phishing,0.8146341463414634,0.7943722943722944,0.004580497741699219,20.260238999999995 +1050,Binary classification,ALMA,Phishing,0.8161904761904762,0.7970557308096741,0.004580497741699219,21.242111999999995 +1075,Binary classification,ALMA,Phishing,0.815813953488372,0.7983706720977597,0.004580497741699219,22.243638999999995 +1100,Binary classification,ALMA,Phishing,0.8190909090909091,0.8023833167825224,0.004580497741699219,23.247954999999994 +1125,Binary classification,ALMA,Phishing,0.8213333333333334,0.8061716489874637,0.004580497741699219,24.273946999999993 +1150,Binary classification,ALMA,Phishing,0.8226086956521739,0.8071833648393195,0.004580497741699219,25.351544999999994 +1175,Binary classification,ALMA,Phishing,0.8212765957446808,0.8059149722735675,0.004580497741699219,26.431981999999994 +1200,Binary classification,ALMA,Phishing,0.8233333333333334,0.8076225045372051,0.004580497741699219,27.515220999999993 +1225,Binary classification,ALMA,Phishing,0.8244897959183674,0.8088888888888888,0.004580497741699219,28.636604999999992 +1250,Binary classification,ALMA,Phishing,0.8256,0.810763888888889,0.004580497741699219,29.761263999999994 +1903,Binary classification,ALMA,SMTP,0.720966894377299,0.0,0.003093719482421875,1.027868 +3806,Binary classification,ALMA,SMTP,0.7769311613242249,0.0,0.003093719482421875,3.106358 +5709,Binary classification,ALMA,SMTP,0.7509196006305833,0.0,0.003093719482421875,6.233245 +7612,Binary classification,ALMA,SMTP,0.7900683131897005,0.0,0.003093719482421875,10.302873 +9515,Binary classification,ALMA,SMTP,0.7826589595375723,0.0,0.003093719482421875,15.393504 +11418,Binary classification,ALMA,SMTP,0.7699246803293046,0.0,0.003093719482421875,21.578682 +13321,Binary classification,ALMA,SMTP,0.7722393213722694,0.0,0.003093719482421875,28.779608 +15224,Binary classification,ALMA,SMTP,0.7791644771413557,0.004146919431279621,0.003093719482421875,37.113003 +17127,Binary classification,ALMA,SMTP,0.783207800548841,0.004824443848834093,0.003093719482421875,46.3898 +19030,Binary classification,ALMA,SMTP,0.7891224382553862,0.004465393202679235,0.003093719482421875,56.715322 +20933,Binary classification,ALMA,SMTP,0.7832131084889887,0.003950834064969272,0.003093719482421875,68.217717 +22836,Binary classification,ALMA,SMTP,0.7821422315641969,0.0036050470658922497,0.003093719482421875,80.77427399999999 +24739,Binary classification,ALMA,SMTP,0.7877440478596548,0.0034162080091098878,0.003093719482421875,94.43257899999999 +26642,Binary classification,ALMA,SMTP,0.78188574431349,0.003429943405933802,0.003093719482421875,109.06303 +28545,Binary classification,ALMA,SMTP,0.7857418111753371,0.003259452411994785,0.003093719482421875,124.719605 +30448,Binary classification,ALMA,SMTP,0.7871452968996322,0.0030764497769573914,0.003093719482421875,141.369597 +32351,Binary classification,ALMA,SMTP,0.7866835646502427,0.0028897558156335793,0.003093719482421875,159.093537 +34254,Binary classification,ALMA,SMTP,0.7860979739592456,0.0027221995372260785,0.003093719482421875,177.829964 +36157,Binary classification,ALMA,SMTP,0.7771939043615345,0.0024764735017335313,0.003093719482421875,197.576045 +38060,Binary classification,ALMA,SMTP,0.7831581713084603,0.00241750271969056,0.003093719482421875,218.31617 +39963,Binary classification,ALMA,SMTP,0.779496033831294,0.002264492753623189,0.003093719482421875,240.067789 +41866,Binary classification,ALMA,SMTP,0.7831175655663307,0.0021978021978021974,0.003093719482421875,262.83473100000003 +43769,Binary classification,ALMA,SMTP,0.7791130708949257,0.002064409578860446,0.003093719482421875,286.65613700000006 +45672,Binary classification,ALMA,SMTP,0.7808066211245402,0.001993819160602133,0.003093719482421875,311.45044200000007 +47575,Binary classification,ALMA,SMTP,0.7799684708355229,0.001906941266209001,0.003093719482421875,337.28748800000005 +49478,Binary classification,ALMA,SMTP,0.7778810784591131,0.00181653042688465,0.003093719482421875,364.14950600000003 +51381,Binary classification,ALMA,SMTP,0.7807944570950351,0.0021263400372109505,0.003093719482421875,392.065506 +53284,Binary classification,ALMA,SMTP,0.7777193904361535,0.0020222446916076846,0.003093719482421875,421.04388200000005 +55187,Binary classification,ALMA,SMTP,0.7785891604906953,0.0019603038470963,0.003093719482421875,451.02283200000005 +57090,Binary classification,ALMA,SMTP,0.7758801891749869,0.002650245537454205,0.003093719482421875,482.06238400000007 +58993,Binary classification,ALMA,SMTP,0.774159646059702,0.002545481769858501,0.003093719482421875,514.138052 +60896,Binary classification,ALMA,SMTP,0.7746157383079348,0.002471109819027546,0.003093719482421875,547.227498 +62799,Binary classification,ALMA,SMTP,0.7704899759550311,0.0023534297778085413,0.003093719482421875,581.3499069999999 +64702,Binary classification,ALMA,SMTP,0.771274458285679,0.0022921863412660956,0.003093719482421875,616.580127 +66605,Binary classification,ALMA,SMTP,0.7721942797087306,0.002235812454790557,0.003093719482421875,652.800029 +68508,Binary classification,ALMA,SMTP,0.7705085537455479,0.0024111675126903555,0.003093719482421875,690.043858 +70411,Binary classification,ALMA,SMTP,0.7685872945988553,0.0023267205486162137,0.003093719482421875,728.276014 +72314,Binary classification,ALMA,SMTP,0.7687999557485411,0.002267709017127171,0.003093719482421875,767.526171 +74217,Binary classification,ALMA,SMTP,0.7657140547313958,0.0021806496040399402,0.003093719482421875,807.759105 +76120,Binary classification,ALMA,SMTP,0.7665002627430373,0.0021333932180552435,0.003093719482421875,848.897716 +78023,Binary classification,ALMA,SMTP,0.7657101111210797,0.002074462277541216,0.003093719482421875,890.953712 +79926,Binary classification,ALMA,SMTP,0.7636313590070816,0.002007395668251453,0.003093719482421875,933.942273 +81829,Binary classification,ALMA,SMTP,0.7647777682728617,0.001970341180130665,0.003093719482421875,977.888636 +83732,Binary classification,ALMA,SMTP,0.7652868676252806,0.0019298156518206286,0.003093719482421875,1022.739679 +85635,Binary classification,ALMA,SMTP,0.7642552694575816,0.0018787699001285476,0.003093719482421875,1068.5586680000001 +87538,Binary classification,ALMA,SMTP,0.7644680024674998,0.0018396591789310612,0.003093719482421875,1115.337297 +89441,Binary classification,ALMA,SMTP,0.7635312664214399,0.0018876828692779614,0.003093719482421875,1162.988807 +91344,Binary classification,ALMA,SMTP,0.7650091960063058,0.0018600325505696352,0.003093719482421875,1211.563326 +93247,Binary classification,ALMA,SMTP,0.7647859984771628,0.0018204159650480134,0.003093719482421875,1260.984755 +95150,Binary classification,ALMA,SMTP,0.7649710982658959,0.0017854751595768425,0.003093719482421875,1311.295723 +95156,Binary classification,ALMA,SMTP,0.7649859178611963,0.0017854751595768425,0.003093719482421875,1361.607838 +106,Binary classification,sklearn SGDClassifier,Bananas,0.5283018867924528,0.4680851063829788,0.005551338195800781,0.50714 +212,Binary classification,sklearn SGDClassifier,Bananas,0.5377358490566038,0.4673913043478261,0.005551338195800781,1.5449950000000001 +318,Binary classification,sklearn SGDClassifier,Bananas,0.5345911949685535,0.4861111111111111,0.005578041076660156,3.028568 +424,Binary classification,sklearn SGDClassifier,Bananas,0.5188679245283019,0.46596858638743455,0.005578041076660156,4.996646 +530,Binary classification,sklearn SGDClassifier,Bananas,0.5264150943396226,0.42562929061784893,0.005578041076660156,7.439068000000001 +636,Binary classification,sklearn SGDClassifier,Bananas,0.5235849056603774,0.3878787878787879,0.005578041076660156,10.329429000000001 +742,Binary classification,sklearn SGDClassifier,Bananas,0.5363881401617251,0.36296296296296293,0.005578041076660156,13.751562000000002 +848,Binary classification,sklearn SGDClassifier,Bananas,0.5400943396226415,0.33898305084745767,0.005578041076660156,17.710995 +954,Binary classification,sklearn SGDClassifier,Bananas,0.5440251572327044,0.31496062992125984,0.005578041076660156,22.189814 +1060,Binary classification,sklearn SGDClassifier,Bananas,0.5518867924528302,0.2962962962962963,0.005578041076660156,27.154881999999997 +1166,Binary classification,sklearn SGDClassifier,Bananas,0.5523156089193825,0.27900552486187846,0.005578041076660156,32.629664 +1272,Binary classification,sklearn SGDClassifier,Bananas,0.5542452830188679,0.27586206896551724,0.005578041076660156,38.64774 +1378,Binary classification,sklearn SGDClassifier,Bananas,0.5566037735849056,0.2611850060459492,0.005578041076660156,45.182197 +1484,Binary classification,sklearn SGDClassifier,Bananas,0.557277628032345,0.24742268041237112,0.005578041076660156,52.237503000000004 +1590,Binary classification,sklearn SGDClassifier,Bananas,0.5578616352201258,0.23503808487486397,0.005578041076660156,59.74126700000001 +1696,Binary classification,sklearn SGDClassifier,Bananas,0.5595518867924528,0.22590673575129533,0.005578041076660156,67.72058500000001 +1802,Binary classification,sklearn SGDClassifier,Bananas,0.5566037735849056,0.21589793915603536,0.005578041076660156,76.17010700000002 +1908,Binary classification,sklearn SGDClassifier,Bananas,0.5545073375262054,0.21150278293135436,0.005578041076660156,85.08376800000002 +2014,Binary classification,sklearn SGDClassifier,Bananas,0.5496524329692155,0.20088105726872246,0.005578041076660156,94.42289500000003 +2120,Binary classification,sklearn SGDClassifier,Bananas,0.5466981132075471,0.19446772841575857,0.005578041076660156,104.22299500000003 +2226,Binary classification,sklearn SGDClassifier,Bananas,0.550314465408805,0.2036595067621321,0.005578041076660156,114.52672400000003 +2332,Binary classification,sklearn SGDClassifier,Bananas,0.5493138936535163,0.21036814425244177,0.005578041076660156,125.29025300000004 +2438,Binary classification,sklearn SGDClassifier,Bananas,0.5479901558654635,0.21173104434907009,0.005578041076660156,136.49837800000003 +2544,Binary classification,sklearn SGDClassifier,Bananas,0.5483490566037735,0.22626262626262625,0.005578041076660156,148.22051100000004 +2650,Binary classification,sklearn SGDClassifier,Bananas,0.5460377358490566,0.2322910019144863,0.005578041076660156,160.35879600000004 +2756,Binary classification,sklearn SGDClassifier,Bananas,0.5395500725689405,0.23044269254093389,0.005578041076660156,173.00042100000005 +2862,Binary classification,sklearn SGDClassifier,Bananas,0.5394828791055206,0.2310385064177363,0.005578041076660156,186.18470300000004 +2968,Binary classification,sklearn SGDClassifier,Bananas,0.5411051212938005,0.23050847457627116,0.005578041076660156,199.85251500000004 +3074,Binary classification,sklearn SGDClassifier,Bananas,0.5396877033181522,0.227198252321136,0.005578041076660156,214.04388100000003 +3180,Binary classification,sklearn SGDClassifier,Bananas,0.5430817610062894,0.22835900159320233,0.005578041076660156,228.72034700000003 +3286,Binary classification,sklearn SGDClassifier,Bananas,0.5444309190505173,0.22475401346452614,0.005578041076660156,243.83191300000004 +3392,Binary classification,sklearn SGDClassifier,Bananas,0.5445165094339622,0.22478675363773204,0.005578041076660156,259.480064 +3498,Binary classification,sklearn SGDClassifier,Bananas,0.5463121783876501,0.22014742014742014,0.005578041076660156,275.54602900000003 +3604,Binary classification,sklearn SGDClassifier,Bananas,0.548834628190899,0.21676300578034682,0.005578041076660156,292.10262700000004 +3710,Binary classification,sklearn SGDClassifier,Bananas,0.547978436657682,0.21230624706434945,0.005578041076660156,309.14156900000006 +3816,Binary classification,sklearn SGDClassifier,Bananas,0.5474318658280922,0.20743460302891234,0.005578041076660156,326.6693460000001 +3922,Binary classification,sklearn SGDClassifier,Bananas,0.5484446710861806,0.20332883490778228,0.005578041076660156,344.5959390000001 +4028,Binary classification,sklearn SGDClassifier,Bananas,0.5489076464746773,0.1992066989863376,0.005578041076660156,363.0622570000001 +4134,Binary classification,sklearn SGDClassifier,Bananas,0.5491049830672472,0.19516407599309155,0.005578041076660156,382.0101650000001 +4240,Binary classification,sklearn SGDClassifier,Bananas,0.5483490566037735,0.19095901985635824,0.005578041076660156,401.4423870000001 +4346,Binary classification,sklearn SGDClassifier,Bananas,0.548550391164289,0.18858560794044665,0.005578041076660156,421.3360060000001 +4452,Binary classification,sklearn SGDClassifier,Bananas,0.550763701707098,0.1935483870967742,0.005578041076660156,441.6814400000001 +4558,Binary classification,sklearn SGDClassifier,Bananas,0.5482667836770513,0.19349784567175873,0.005578041076660156,462.3969810000001 +4664,Binary classification,sklearn SGDClassifier,Bananas,0.5490994854202401,0.19763449065242272,0.005578041076660156,483.6474930000001 +4770,Binary classification,sklearn SGDClassifier,Bananas,0.550104821802935,0.19985085756897839,0.005578041076660156,505.4107300000001 +4876,Binary classification,sklearn SGDClassifier,Bananas,0.5504511894995898,0.2,0.005578041076660156,527.6413840000001 +4982,Binary classification,sklearn SGDClassifier,Bananas,0.5503813729425934,0.2062367115520907,0.005578041076660156,550.3366250000001 +5088,Binary classification,sklearn SGDClassifier,Bananas,0.5479559748427673,0.20415224913494812,0.005578041076660156,573.5279610000001 +5194,Binary classification,sklearn SGDClassifier,Bananas,0.5462071621101271,0.20236886632825718,0.005578041076660156,597.2511250000001 +5300,Binary classification,sklearn SGDClassifier,Bananas,0.5464150943396227,0.205026455026455,0.005578041076660156,621.4261850000001 +906,Binary classification,sklearn SGDClassifier,Elec2,0.8002207505518764,0.7868080094228505,0.006801605224609375,4.395754 +1812,Binary classification,sklearn SGDClassifier,Elec2,0.8140176600441501,0.7501853224610822,0.006801605224609375,13.314942 +2718,Binary classification,sklearn SGDClassifier,Elec2,0.8005886681383371,0.7262626262626262,0.006801605224609375,26.594138 +3624,Binary classification,sklearn SGDClassifier,Elec2,0.8189845474613686,0.7586460632818247,0.006801605224609375,44.068779 +4530,Binary classification,sklearn SGDClassifier,Elec2,0.8278145695364238,0.7588126159554731,0.006801605224609375,65.924464 +5436,Binary classification,sklearn SGDClassifier,Elec2,0.8211920529801324,0.7498713329902212,0.006801605224609375,92.07692 +6342,Binary classification,sklearn SGDClassifier,Elec2,0.8222958057395143,0.7575822757582275,0.006801605224609375,122.546282 +7248,Binary classification,sklearn SGDClassifier,Elec2,0.8253311258278145,0.7598634294385433,0.006801605224609375,157.279906 +8154,Binary classification,sklearn SGDClassifier,Elec2,0.8303899926416483,0.780789348549691,0.006801605224609375,196.124663 +9060,Binary classification,sklearn SGDClassifier,Elec2,0.8364238410596027,0.7958677685950413,0.006801605224609375,238.938569 +9966,Binary classification,sklearn SGDClassifier,Elec2,0.8371462974111981,0.8011273128293102,0.006801605224609375,285.711115 +10872,Binary classification,sklearn SGDClassifier,Elec2,0.8393119941133186,0.8079586676926458,0.006801605224609375,336.134661 +11778,Binary classification,sklearn SGDClassifier,Elec2,0.8422482594668025,0.8114088509947219,0.006801605224609375,390.124895 +12684,Binary classification,sklearn SGDClassifier,Elec2,0.8409019236833807,0.810445237647943,0.006801605224609375,447.475874 +13590,Binary classification,sklearn SGDClassifier,Elec2,0.8427520235467255,0.8154098643862832,0.006801605224609375,507.886031 +14496,Binary classification,sklearn SGDClassifier,Elec2,0.8438189845474614,0.8177720540888602,0.006801605224609375,571.200551 +15402,Binary classification,sklearn SGDClassifier,Elec2,0.845214907154915,0.8184587267742918,0.006801605224609375,637.3575030000001 +16308,Binary classification,sklearn SGDClassifier,Elec2,0.8397105714986509,0.8108264582428716,0.006801605224609375,706.2352450000001 +17214,Binary classification,sklearn SGDClassifier,Elec2,0.8384454513767864,0.8053202660133008,0.006801605224609375,777.6642180000001 +18120,Binary classification,sklearn SGDClassifier,Elec2,0.840728476821192,0.8082646824342281,0.006801605224609375,851.7523750000001 +19026,Binary classification,sklearn SGDClassifier,Elec2,0.843950383685483,0.8100326316462987,0.006801605224609375,928.4036970000002 +19932,Binary classification,sklearn SGDClassifier,Elec2,0.8412101143889223,0.8075636894266431,0.006801605224609375,1007.5500400000002 +20838,Binary classification,sklearn SGDClassifier,Elec2,0.8373644303675977,0.8028848950154133,0.006801605224609375,1089.2694250000002 +21744,Binary classification,sklearn SGDClassifier,Elec2,0.8382542310522443,0.8008155405788072,0.006801605224609375,1173.5296240000002 +22650,Binary classification,sklearn SGDClassifier,Elec2,0.8376600441501104,0.7982441700960219,0.006801605224609375,1260.4177460000003 +23556,Binary classification,sklearn SGDClassifier,Elec2,0.8337578536254033,0.7924748277689453,0.006801605224609375,1349.7468710000003 +24462,Binary classification,sklearn SGDClassifier,Elec2,0.8313302264737144,0.7887569117345894,0.006801605224609375,1441.5955620000002 +25368,Binary classification,sklearn SGDClassifier,Elec2,0.8278539892778304,0.7842711060613546,0.006801605224609375,1535.8662290000002 +26274,Binary classification,sklearn SGDClassifier,Elec2,0.8282712948161681,0.784486052732136,0.006801605224609375,1632.5910050000002 +27180,Binary classification,sklearn SGDClassifier,Elec2,0.8285504047093452,0.785431439359057,0.006801605224609375,1731.7068200000003 +28086,Binary classification,sklearn SGDClassifier,Elec2,0.825357829523606,0.7809192013935414,0.006801605224609375,1833.2277320000003 +28992,Binary classification,sklearn SGDClassifier,Elec2,0.8246412803532008,0.7785135488368041,0.006801605224609375,1936.9708820000003 +29898,Binary classification,sklearn SGDClassifier,Elec2,0.8228644056458626,0.7766343315056937,0.006801605224609375,2042.9068730000004 +30804,Binary classification,sklearn SGDClassifier,Elec2,0.8227827554863005,0.775599128540305,0.006801605224609375,2150.889688 +31710,Binary classification,sklearn SGDClassifier,Elec2,0.8180384736676127,0.7686632988533396,0.006801605224609375,2260.9522580000003 +32616,Binary classification,sklearn SGDClassifier,Elec2,0.8156119695854795,0.765426320305796,0.006801605224609375,2373.012802 +33522,Binary classification,sklearn SGDClassifier,Elec2,0.8136746017540719,0.7636955205811137,0.006801605224609375,2487.1515040000004 +34428,Binary classification,sklearn SGDClassifier,Elec2,0.8108516323922389,0.7597934341571375,0.006801605224609375,2603.2748090000005 +35334,Binary classification,sklearn SGDClassifier,Elec2,0.811031867323258,0.7582811425261557,0.006801605224609375,2721.3856460000006 +36240,Binary classification,sklearn SGDClassifier,Elec2,0.8123344370860928,0.7585643792821898,0.006801605224609375,2841.498866000001 +37146,Binary classification,sklearn SGDClassifier,Elec2,0.8119312981209282,0.7567887480852249,0.006801605224609375,2963.634415000001 +38052,Binary classification,sklearn SGDClassifier,Elec2,0.8118364343529907,0.7562636165577343,0.006801605224609375,3087.755988000001 +38958,Binary classification,sklearn SGDClassifier,Elec2,0.8128497356127111,0.7583601232890332,0.006801605224609375,3213.7594090000007 +39864,Binary classification,sklearn SGDClassifier,Elec2,0.8136162954043749,0.7616297722168751,0.006801605224609375,3341.6530200000007 +40770,Binary classification,sklearn SGDClassifier,Elec2,0.8154034829531518,0.7662732919254659,0.006801605224609375,3471.3422760000008 +41676,Binary classification,sklearn SGDClassifier,Elec2,0.8169929935694404,0.7702641645832705,0.006801605224609375,3602.9648230000007 +42582,Binary classification,sklearn SGDClassifier,Elec2,0.8180216993095675,0.7720681236579698,0.006801605224609375,3737.0799900000006 +43488,Binary classification,sklearn SGDClassifier,Elec2,0.8185936350257542,0.7730894238789657,0.006801605224609375,3873.4157740000005 +44394,Binary classification,sklearn SGDClassifier,Elec2,0.8179708969680587,0.7710051290770494,0.006801605224609375,4011.6127160000005 +45300,Binary classification,sklearn SGDClassifier,Elec2,0.8190949227373069,0.7729350807680585,0.006801605224609375,4151.679177000001 +45312,Binary classification,sklearn SGDClassifier,Elec2,0.8190986935028248,0.7728922505749037,0.006801605224609375,4291.771713000001 +25,Binary classification,sklearn SGDClassifier,Phishing,0.68,0.6923076923076923,0.006802558898925781,0.149754 +50,Binary classification,sklearn SGDClassifier,Phishing,0.8,0.782608695652174,0.006802558898925781,0.457736 +75,Binary classification,sklearn SGDClassifier,Phishing,0.8266666666666667,0.8219178082191781,0.006802558898925781,0.892069 +100,Binary classification,sklearn SGDClassifier,Phishing,0.83,0.8210526315789473,0.006802558898925781,1.4190939999999999 +125,Binary classification,sklearn SGDClassifier,Phishing,0.816,0.8067226890756303,0.006802558898925781,2.091236 +150,Binary classification,sklearn SGDClassifier,Phishing,0.82,0.8187919463087249,0.006802558898925781,2.916232 +175,Binary classification,sklearn SGDClassifier,Phishing,0.8285714285714286,0.8170731707317075,0.006802558898925781,3.840025 +200,Binary classification,sklearn SGDClassifier,Phishing,0.825,0.8128342245989306,0.006802558898925781,4.9100459999999995 +225,Binary classification,sklearn SGDClassifier,Phishing,0.8222222222222222,0.8058252427184465,0.006802558898925781,6.121922 +250,Binary classification,sklearn SGDClassifier,Phishing,0.824,0.8103448275862069,0.006802558898925781,7.4794909999999994 +275,Binary classification,sklearn SGDClassifier,Phishing,0.8254545454545454,0.8110236220472441,0.007016181945800781,8.920382 +300,Binary classification,sklearn SGDClassifier,Phishing,0.8366666666666667,0.8191881918819188,0.007016181945800781,10.509974 +325,Binary classification,sklearn SGDClassifier,Phishing,0.8461538461538461,0.8251748251748252,0.007016181945800781,12.191811999999999 +350,Binary classification,sklearn SGDClassifier,Phishing,0.8514285714285714,0.8289473684210525,0.007016181945800781,13.999137 +375,Binary classification,sklearn SGDClassifier,Phishing,0.8506666666666667,0.8271604938271606,0.007016181945800781,15.959285 +400,Binary classification,sklearn SGDClassifier,Phishing,0.8525,0.8269794721407624,0.007016181945800781,18.058664 +425,Binary classification,sklearn SGDClassifier,Phishing,0.8564705882352941,0.828169014084507,0.007016181945800781,20.312993 +450,Binary classification,sklearn SGDClassifier,Phishing,0.86,0.8301886792452831,0.007016181945800781,22.675489 +475,Binary classification,sklearn SGDClassifier,Phishing,0.8589473684210527,0.830379746835443,0.007016181945800781,25.19503 +500,Binary classification,sklearn SGDClassifier,Phishing,0.858,0.8329411764705883,0.007016181945800781,27.784240999999998 +525,Binary classification,sklearn SGDClassifier,Phishing,0.8571428571428571,0.8283752860411898,0.007016181945800781,30.514065 +550,Binary classification,sklearn SGDClassifier,Phishing,0.8618181818181818,0.8354978354978354,0.007016181945800781,33.400870999999995 +575,Binary classification,sklearn SGDClassifier,Phishing,0.8626086956521739,0.8364389233954452,0.007016181945800781,36.397645999999995 +600,Binary classification,sklearn SGDClassifier,Phishing,0.8666666666666667,0.8387096774193549,0.007016181945800781,39.57675499999999 +625,Binary classification,sklearn SGDClassifier,Phishing,0.8672,0.8362919132149901,0.007016181945800781,42.828695999999994 +650,Binary classification,sklearn SGDClassifier,Phishing,0.8707692307692307,0.8432835820895522,0.007016181945800781,46.253181999999995 +675,Binary classification,sklearn SGDClassifier,Phishing,0.8725925925925926,0.8485915492957746,0.007016181945800781,49.816151 +700,Binary classification,sklearn SGDClassifier,Phishing,0.8771428571428571,0.8522336769759451,0.007016181945800781,53.516545 +725,Binary classification,sklearn SGDClassifier,Phishing,0.8786206896551724,0.8566775244299674,0.007016181945800781,57.358180000000004 +750,Binary classification,sklearn SGDClassifier,Phishing,0.88,0.8589341692789968,0.007016181945800781,61.281034000000005 +775,Binary classification,sklearn SGDClassifier,Phishing,0.8812903225806452,0.8597560975609757,0.007016181945800781,65.347537 +800,Binary classification,sklearn SGDClassifier,Phishing,0.88125,0.8613138686131386,0.007016181945800781,69.566336 +825,Binary classification,sklearn SGDClassifier,Phishing,0.8812121212121212,0.8623595505617978,0.007016181945800781,73.91498000000001 +850,Binary classification,sklearn SGDClassifier,Phishing,0.8823529411764706,0.8630136986301369,0.007016181945800781,78.39968800000001 +875,Binary classification,sklearn SGDClassifier,Phishing,0.8857142857142857,0.8663101604278075,0.007016181945800781,83.02084100000002 +900,Binary classification,sklearn SGDClassifier,Phishing,0.8844444444444445,0.8645833333333334,0.007016181945800781,87.71921500000002 +925,Binary classification,sklearn SGDClassifier,Phishing,0.8864864864864865,0.8682559598494354,0.007016181945800781,92.55798800000002 +950,Binary classification,sklearn SGDClassifier,Phishing,0.8863157894736842,0.8695652173913043,0.007016181945800781,97.51738800000003 +975,Binary classification,sklearn SGDClassifier,Phishing,0.8871794871794871,0.8702830188679245,0.007016181945800781,102.59954100000003 +1000,Binary classification,sklearn SGDClassifier,Phishing,0.888,0.871264367816092,0.007016181945800781,107.87282600000003 +1025,Binary classification,sklearn SGDClassifier,Phishing,0.8878048780487805,0.8715083798882682,0.007016181945800781,113.28564700000003 +1050,Binary classification,sklearn SGDClassifier,Phishing,0.8895238095238095,0.8739130434782609,0.007016181945800781,118.79277100000003 +1075,Binary classification,sklearn SGDClassifier,Phishing,0.8883720930232558,0.8736842105263158,0.007016181945800781,124.46348200000003 +1100,Binary classification,sklearn SGDClassifier,Phishing,0.89,0.8756423432682425,0.007016181945800781,130.26843700000003 +1125,Binary classification,sklearn SGDClassifier,Phishing,0.8915555555555555,0.8784860557768924,0.007016181945800781,136.21796400000002 +1150,Binary classification,sklearn SGDClassifier,Phishing,0.8913043478260869,0.878048780487805,0.007016181945800781,142.31432400000003 +1175,Binary classification,sklearn SGDClassifier,Phishing,0.8902127659574468,0.876555023923445,0.007016181945800781,148.52290000000002 +1200,Binary classification,sklearn SGDClassifier,Phishing,0.8908333333333334,0.8769953051643193,0.007016181945800781,154.887447 +1225,Binary classification,sklearn SGDClassifier,Phishing,0.8914285714285715,0.8776448942042319,0.007016181945800781,161.410896 +1250,Binary classification,sklearn SGDClassifier,Phishing,0.8896,0.8761220825852785,0.007016181945800781,167.984219 +1903,Binary classification,sklearn SGDClassifier,SMTP,0.9968470835522859,0.0,0.0057430267333984375,9.012274 +3806,Binary classification,sklearn SGDClassifier,SMTP,0.9984235417761429,0.0,0.0057430267333984375,26.992092 +5709,Binary classification,sklearn SGDClassifier,SMTP,0.998949027850762,0.0,0.0057430267333984375,53.749217 +7612,Binary classification,sklearn SGDClassifier,SMTP,0.9992117708880714,0.0,0.0057430267333984375,89.545782 +9515,Binary classification,sklearn SGDClassifier,SMTP,0.9993694167104572,0.0,0.0057430267333984375,133.365466 +11418,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.0,0.0057430267333984375,185.06742500000001 +13321,Binary classification,sklearn SGDClassifier,SMTP,0.9995495833646123,0.0,0.0057430267333984375,243.739666 +15224,Binary classification,sklearn SGDClassifier,SMTP,0.9992774566473989,0.5217391304347826,0.0057430267333984375,308.57406100000003 +17127,Binary classification,sklearn SGDClassifier,SMTP,0.9993577392421323,0.5925925925925927,0.0057430267333984375,378.971453 +19030,Binary classification,sklearn SGDClassifier,SMTP,0.999421965317919,0.5925925925925927,0.0057430267333984375,454.798992 +20933,Binary classification,sklearn SGDClassifier,SMTP,0.999474513925381,0.5925925925925927,0.0057430267333984375,535.937031 +22836,Binary classification,sklearn SGDClassifier,SMTP,0.9995183044315993,0.5925925925925927,0.0057430267333984375,622.51799 +24739,Binary classification,sklearn SGDClassifier,SMTP,0.9995553579368608,0.5925925925925927,0.0057430267333984375,714.221122 +26642,Binary classification,sklearn SGDClassifier,SMTP,0.9995495833646123,0.5714285714285714,0.0057430267333984375,811.098386 +28545,Binary classification,sklearn SGDClassifier,SMTP,0.9995796111403048,0.5714285714285714,0.0057430267333984375,912.878884 +30448,Binary classification,sklearn SGDClassifier,SMTP,0.9996058854440357,0.5714285714285714,0.0057430267333984375,1019.269091 +32351,Binary classification,sklearn SGDClassifier,SMTP,0.9996290686532101,0.5714285714285714,0.0057430267333984375,1129.962426 +34254,Binary classification,sklearn SGDClassifier,SMTP,0.999649675950254,0.5714285714285714,0.0057430267333984375,1244.872652 +36157,Binary classification,sklearn SGDClassifier,SMTP,0.9996681140581354,0.5714285714285714,0.0057430267333984375,1363.9176400000001 +38060,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.5714285714285714,0.0057430267333984375,1487.072194 +39963,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.5714285714285714,0.0057430267333984375,1614.171257 +41866,Binary classification,sklearn SGDClassifier,SMTP,0.999713371232026,0.5714285714285714,0.0057430267333984375,1745.093316 +43769,Binary classification,sklearn SGDClassifier,SMTP,0.9997258333523726,0.5714285714285714,0.0057430267333984375,1880.714485 +45672,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.5714285714285714,0.0057430267333984375,2020.289668 +47575,Binary classification,sklearn SGDClassifier,SMTP,0.9997477666841829,0.5714285714285714,0.0057430267333984375,2163.67936 +49478,Binary classification,sklearn SGDClassifier,SMTP,0.9997574679655604,0.5714285714285714,0.0057430267333984375,2310.817497 +51381,Binary classification,sklearn SGDClassifier,SMTP,0.9997275257390864,0.5333333333333333,0.0057430267333984375,2461.472155 +53284,Binary classification,sklearn SGDClassifier,SMTP,0.9997372569626904,0.5333333333333333,0.0057430267333984375,2615.677493 +55187,Binary classification,sklearn SGDClassifier,SMTP,0.9997463170674253,0.5333333333333333,0.0057430267333984375,2773.449537 +57090,Binary classification,sklearn SGDClassifier,SMTP,0.999597127342792,0.41025641025641024,0.0057430267333984375,2934.793173 +58993,Binary classification,sklearn SGDClassifier,SMTP,0.9996101232349601,0.41025641025641024,0.0057430267333984375,3099.6490870000002 +60896,Binary classification,sklearn SGDClassifier,SMTP,0.9996223068838676,0.41025641025641024,0.0057430267333984375,3268.0999800000004 +62799,Binary classification,sklearn SGDClassifier,SMTP,0.9996019044889249,0.3902439024390244,0.0057430267333984375,3440.0722140000003 +64702,Binary classification,sklearn SGDClassifier,SMTP,0.9996136131804272,0.3902439024390244,0.0057430267333984375,3615.5032180000003 +66605,Binary classification,sklearn SGDClassifier,SMTP,0.9996246528038436,0.3902439024390244,0.0057430267333984375,3794.4378890000003 +68508,Binary classification,sklearn SGDClassifier,SMTP,0.9996058854440357,0.37209302325581395,0.0057430267333984375,3977.0945010000005 +70411,Binary classification,sklearn SGDClassifier,SMTP,0.9996165371887915,0.37209302325581395,0.0057430267333984375,4163.174128000001 +72314,Binary classification,sklearn SGDClassifier,SMTP,0.9996266283154023,0.37209302325581395,0.0057430267333984375,4352.606393000001 +74217,Binary classification,sklearn SGDClassifier,SMTP,0.9996362019483407,0.37209302325581395,0.0057430267333984375,4545.416162000001 +76120,Binary classification,sklearn SGDClassifier,SMTP,0.9996452968996321,0.37209302325581395,0.0057430267333984375,4741.562007000001 +78023,Binary classification,sklearn SGDClassifier,SMTP,0.999653948194763,0.37209302325581395,0.0057430267333984375,4941.079189000001 +79926,Binary classification,sklearn SGDClassifier,SMTP,0.9996621875234591,0.37209302325581395,0.0057430267333984375,5143.951781000001 +81829,Binary classification,sklearn SGDClassifier,SMTP,0.9996700436275648,0.37209302325581395,0.0057430267333984375,5350.231536 +83732,Binary classification,sklearn SGDClassifier,SMTP,0.9996775426360293,0.37209302325581395,0.0057430267333984375,5559.929647 +85635,Binary classification,sklearn SGDClassifier,SMTP,0.9996847083552286,0.37209302325581395,0.0057430267333984375,5773.003953 +87538,Binary classification,sklearn SGDClassifier,SMTP,0.9996915625214192,0.37209302325581395,0.0057430267333984375,5989.467769000001 +89441,Binary classification,sklearn SGDClassifier,SMTP,0.9996869444661844,0.36363636363636365,0.0057430267333984375,6209.264779000001 +91344,Binary classification,sklearn SGDClassifier,SMTP,0.9996934664564723,0.36363636363636365,0.0057430267333984375,6432.452666000001 +93247,Binary classification,sklearn SGDClassifier,SMTP,0.9996997222430748,0.36363636363636365,0.0057430267333984375,6658.918178000001 +95150,Binary classification,sklearn SGDClassifier,SMTP,0.9997057277982133,0.36363636363636365,0.0057430267333984375,6888.546542000001 +95156,Binary classification,sklearn SGDClassifier,SMTP,0.9997057463533566,0.36363636363636365,0.0057430267333984375,7118.179378000001 +106,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5,0.0,0.0006465911865234375,0.16057 +212,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5283018867924528,0.0,0.0006465911865234375,0.37729 +318,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5314465408805031,0.0,0.0006465911865234375,0.7064710000000001 +424,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5400943396226415,0.0,0.0006465911865234375,1.0774430000000002 +530,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5547169811320755,0.0,0.0006465911865234375,1.4923790000000001 +636,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5550314465408805,0.0,0.0006465911865234375,1.9966470000000003 +742,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5660377358490566,0.0,0.0006465911865234375,2.539797 +848,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5636792452830188,0.0,0.0006465911865234375,3.1757850000000003 +954,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5649895178197065,0.0,0.0006465911865234375,3.8551140000000004 +1060,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5707547169811321,0.0,0.0006465911865234375,4.635951 +1166,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5686106346483705,0.0,0.0006465911865234375,5.458947 +1272,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5644654088050315,0.0,0.0006465911865234375,6.34328 +1378,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5682148040638607,0.0,0.0006465911865234375,7.308669 +1484,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5680592991913747,0.0,0.0006465911865234375,8.359952 +1590,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5679245283018868,0.0,0.0006465911865234375,9.451883 +1696,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5683962264150944,0.0,0.0006465911865234375,10.590847 +1802,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5643729189789123,0.0,0.0006465911865234375,11.83715 +1908,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.560272536687631,0.0,0.0006465911865234375,13.126961999999999 +2014,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5551142005958292,0.0,0.0006465911865234375,14.497203999999998 +2120,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,15.938437999999998 +2226,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5512129380053908,0.0,0.0006465911865234375,17.424999 +2332,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5506003430531733,0.0,0.0006465911865234375,19.022886 +2438,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.551681706316653,0.0,0.0006465911865234375,20.666828 +2544,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487421383647799,0.0,0.0006465911865234375,22.355415999999998 +2650,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5467924528301886,0.0,0.0006465911865234375,24.051772 +2756,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5471698113207547,0.0,0.0006465911865234375,25.858309 +2862,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5489168413696716,0.0,0.0006465911865234375,27.751458999999997 +2968,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505390835579514,0.0,0.0006465911865234375,29.665551999999998 +3074,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5487963565387117,0.0,0.0006465911865234375,31.686176 +3180,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509433962264151,0.0,0.0006465911865234375,33.740652 +3286,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5517346317711503,0.0,0.0006465911865234375,35.89104 +3392,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5498231132075472,0.0,0.0006465911865234375,38.079414 +3498,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514579759862779,0.0,0.0006465911865234375,40.353903 +3604,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5535516093229744,0.0,0.0006465911865234375,42.668922 +3710,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5522911051212938,0.0,0.0006465911865234375,45.086801 +3816,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5516247379454927,0.0,0.0006465911865234375,47.540759 +3922,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5525242223355431,0.0,0.0006465911865234375,50.094246 +4028,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5528798411122146,0.0,0.0006465911865234375,52.697210999999996 +4134,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5529753265602322,0.0,0.0006465911865234375,55.369586999999996 +4240,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5523584905660377,0.0,0.0006465911865234375,58.109435999999995 +4346,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5526921306948919,0.0,0.0006465911865234375,60.894093 +4452,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5530098831985625,0.0,0.0006465911865234375,63.717346 +4558,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5508995173321632,0.0,0.0006465911865234375,66.643891 +4664,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5497427101200686,0.0,0.0006465911865234375,69.6601 +4770,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5505241090146751,0.0,0.0006465911865234375,72.725555 +4876,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5518867924528302,0.0,0.0006465911865234375,75.798736 +4982,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5509835407466881,0.0,0.0006465911865234375,78.970205 +5088,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5511006289308176,0.0,0.0006465911865234375,82.150165 +5194,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5514054678475163,0.0,0.0006465911865234375,85.416127 +5300,Binary classification,Vowpal Wabbit logistic regression,Bananas,0.5513207547169812,0.0,0.0006465911865234375,88.72481 +906,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6799116997792495,0.5482866043613708,0.0006465911865234375,0.820242 +1812,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7190949227373068,0.4904904904904904,0.0006465911865234375,2.329863 +2718,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.43243243243243246,0.0006465911865234375,4.585071 +3624,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7047461368653422,0.4478844169246646,0.0006465911865234375,7.424633 +4530,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7024282560706402,0.4118673647469459,0.0006465911865234375,10.992865 +5436,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.7041942604856513,0.4165457184325108,0.0006465911865234375,15.263433 +6342,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6986754966887417,0.40485829959514175,0.0006465911865234375,20.287067999999998 +7248,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695364238410596,0.3953997809419496,0.0006465911865234375,26.004013999999998 +8154,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6873926907039489,0.4084474355999072,0.0006465911865234375,32.433811999999996 +9060,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6864238410596026,0.42408270829110073,0.0006465911865234375,39.59982599999999 +9966,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.687537627934979,0.4433321415802646,0.0006465911865234375,47.447314999999996 +10872,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6938925680647535,0.4717460317460317,0.0006465911865234375,55.964904999999995 +11778,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6932416369502462,0.47155185022670765,0.0006465911865234375,65.217817 +12684,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944970040996531,0.47557179591284343,0.0006465911865234375,75.258293 +13590,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6942604856512141,0.48429936701005344,0.0006465911865234375,85.993354 +14496,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6935016556291391,0.48606130711393875,0.0006465911865234375,97.389046 +15402,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6929619529931178,0.48095708484249805,0.0006465911865234375,109.49806 +16308,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6904586705911209,0.47130289065772935,0.0006465911865234375,122.273049 +17214,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6921691646334379,0.4645852278468223,0.0006465911865234375,135.723897 +18120,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.694205298013245,0.46859115757168884,0.0006465911865234375,150.008391 +19026,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6967307894460212,0.467515688445921,0.0006465911865234375,164.94785199999998 +19932,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6958157736303432,0.4737435986459509,0.0006465911865234375,180.578389 +20838,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.4696604963891426,0.0006465911865234375,196.972492 +21744,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6968359087564385,0.4670978172999191,0.0006465911865234375,214.03759399999998 +22650,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6977041942604857,0.4643667370726746,0.0006465911865234375,231.758696 +23556,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952368823229751,0.4573285962657797,0.0006465911865234375,250.24763199999998 +24462,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6978170223203336,0.4597281099254495,0.0006465911865234375,269.425119 +25368,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6976505834121728,0.46122506322000567,0.0006465911865234375,289.286378 +26274,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6983329527289336,0.4614757439869547,0.0006465911865234375,309.833587 +27180,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6959896983075791,0.4576304561864129,0.0006465911865234375,331.075152 +28086,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.695649077832372,0.4559572301425662,0.0006465911865234375,353.085242 +28992,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6952262693156733,0.4515207945375543,0.0006465911865234375,375.713328 +29898,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6939260151180681,0.4465678863017841,0.0006465911865234375,399.011341 +30804,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6941630957018569,0.44330201500915917,0.0006465911865234375,423.007885 +31710,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6917376222011984,0.4368266405484819,0.0006465911865234375,447.82068599999997 +32616,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6893549178317391,0.4316805025802109,0.0006465911865234375,473.25918699999994 +33522,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.688353916830738,0.42909448603748845,0.0006465911865234375,499.4094079999999 +34428,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6863599395840595,0.4245363461948412,0.0006465911865234375,526.1781749999999 +35334,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6869304352748061,0.4212013394725827,0.0006465911865234375,553.6489789999999 +36240,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6911147902869758,0.4267718148299877,0.0006465911865234375,581.8684239999999 +37146,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6919722177354224,0.42698317307692313,0.0006465911865234375,610.7708879999999 +38052,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6944181646168401,0.43117111828588206,0.0006465911865234375,640.2659799999999 +38958,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6937727809435803,0.43082061068702293,0.0006465911865234375,670.4759659999999 +39864,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6930814770218744,0.4344288818009522,0.0006465911865234375,701.3646249999998 +40770,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6924208977189109,0.4391771019677997,0.0006465911865234375,733.0001779999998 +41676,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6933966791438718,0.44722270288977334,0.0006465911865234375,765.3741459999998 +42582,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6956225635244939,0.45507672903090185,0.0006465911865234375,798.4329459999998 +43488,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6962150478292862,0.4576097220511558,0.0006465911865234375,832.1587539999998 +44394,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6963103122043519,0.45575649927337314,0.0006465911865234375,866.6068249999998 +45300,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.697439293598234,0.4596278189560006,0.0006465911865234375,901.8081399999999 +45312,Binary classification,Vowpal Wabbit logistic regression,Elec2,0.6974752824858758,0.45959157927935035,0.0006465911865234375,937.0113409999999 +25,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.52,0.33333333333333337,0.0006465911865234375,0.00395 +50,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.56,0.21428571428571427,0.0006465911865234375,0.07842199999999999 +75,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.5866666666666667,0.3404255319148936,0.0006465911865234375,0.15624899999999997 +100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6,0.375,0.0006465911865234375,0.23731799999999997 +125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.64,0.4705882352941176,0.0006465911865234375,0.41813199999999995 +150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.62,0.44660194174757284,0.0006465911865234375,0.6021909999999999 +175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6342857142857142,0.41818181818181815,0.0006465911865234375,0.7890869999999999 +200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.63,0.4126984126984127,0.0006465911865234375,1.0120959999999999 +225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6488888888888888,0.4316546762589928,0.0006465911865234375,1.2378889999999998 +250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.648,0.4358974358974359,0.0006465911865234375,1.4692909999999997 +275,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6618181818181819,0.4561403508771929,0.0006465911865234375,1.7531039999999996 +300,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6733333333333333,0.46153846153846156,0.0006465911865234375,2.0408739999999996 +325,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.683076923076923,0.46632124352331616,0.0006465911865234375,2.33165 +350,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.6942857142857143,0.47804878048780486,0.0006465911865234375,2.715045 +375,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7013333333333334,0.4909090909090909,0.0006465911865234375,3.1015189999999997 +400,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.705,0.4913793103448276,0.0006465911865234375,3.4910129999999997 +425,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7105882352941176,0.4896265560165975,0.0006465911865234375,3.9231499999999997 +450,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7222222222222222,0.5098039215686275,0.0006465911865234375,4.358171 +475,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7157894736842105,0.5054945054945055,0.0006465911865234375,4.7960329999999995 +500,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.718,0.5252525252525252,0.0006465911865234375,5.248043999999999 +525,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7257142857142858,0.5294117647058824,0.0006465911865234375,5.702586999999999 +550,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7218181818181818,0.5233644859813085,0.0006465911865234375,6.1599829999999995 +575,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7217391304347827,0.5209580838323353,0.0006465911865234375,6.620335 +600,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7283333333333334,0.5275362318840581,0.0006465911865234375,7.1135079999999995 +625,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7376,0.5340909090909091,0.0006465911865234375,7.613357 +650,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7369230769230769,0.5415549597855228,0.0006465911865234375,8.116107 +675,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7333333333333333,0.5477386934673367,0.0006465911865234375,8.713363 +700,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74,0.5560975609756097,0.0006465911865234375,9.313647 +725,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.743448275862069,0.5753424657534246,0.0006465911865234375,9.917033 +750,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7453333333333333,0.5820568927789934,0.0006465911865234375,10.613639 +775,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7470967741935484,0.5847457627118644,0.0006465911865234375,11.313362999999999 +800,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.74625,0.5915492957746479,0.0006465911865234375,12.015877 +825,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7490909090909091,0.602687140115163,0.0006465911865234375,12.766805 +850,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7541176470588236,0.6122448979591837,0.0006465911865234375,13.520246 +875,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7554285714285714,0.6123188405797102,0.0006465911865234375,14.2887 +900,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7566666666666667,0.6123893805309735,0.0006465911865234375,15.059985000000001 +925,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.76,0.6237288135593221,0.0006465911865234375,15.877357000000002 +950,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7589473684210526,0.6288492706645057,0.0006465911865234375,16.697524 +975,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7610256410256411,0.631911532385466,0.0006465911865234375,17.562951 +1000,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.761,0.6328725038402457,0.0006465911865234375,18.43148 +1025,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7609756097560976,0.635958395245171,0.0006465911865234375,19.325245 +1050,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7638095238095238,0.6436781609195402,0.0006465911865234375,20.222005 +1075,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7665116279069767,0.651872399445215,0.0006465911865234375,21.121639 +1100,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.77,0.6594885598923284,0.0006465911865234375,22.071389 +1125,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.768,0.6597131681877444,0.0006465911865234375,23.024294 +1150,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7695652173913043,0.6615581098339719,0.0006465911865234375,23.980116000000002 +1175,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7702127659574468,0.6633416458852868,0.0006465911865234375,24.939110000000003 +1200,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7741666666666667,0.6691086691086692,0.0006465911865234375,25.901192 +1225,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7771428571428571,0.6746126340882003,0.0006465911865234375,26.865956 +1250,Binary classification,Vowpal Wabbit logistic regression,Phishing,0.7736,0.6697782963827306,0.0006465911865234375,27.833412 +1903,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,1.287853 +3806,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,3.7805989999999996 +5709,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,7.576109 +7612,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,12.534125 +9515,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,18.771881 +11418,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,26.322128 +13321,Binary classification,Vowpal Wabbit logistic regression,SMTP,1.0,0.0,0.0006465911865234375,35.214625 +15224,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9992774566473989,0.0,0.0006465911865234375,45.441497999999996 +17127,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999299351900508,0.14285714285714288,0.0006465911865234375,56.927386 +19030,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9993694167104572,0.14285714285714288,0.0006465911865234375,69.74153799999999 +20933,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9994267424640519,0.14285714285714288,0.0006465911865234375,83.765543 +22836,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999474513925381,0.14285714285714288,0.0006465911865234375,99.06549199999999 +24739,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999514935931121,0.14285714285714288,0.0006465911865234375,115.581943 +26642,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995120486449967,0.13333333333333333,0.0006465911865234375,133.361343 +28545,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995445787353302,0.13333333333333333,0.0006465911865234375,152.36548100000002 +30448,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995730425643721,0.13333333333333333,0.0006465911865234375,172.57866 +32351,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,193.96982500000001 +34254,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996204822794418,0.13333333333333333,0.0006465911865234375,216.600052 +36157,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996404568963133,0.13333333333333333,0.0006465911865234375,240.511883 +38060,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996584340514977,0.13333333333333333,0.0006465911865234375,265.709607 +39963,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996746990966644,0.13333333333333333,0.0006465911865234375,292.142637 +41866,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996894855013615,0.13333333333333333,0.0006465911865234375,319.87834699999996 +43769,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999702986131737,0.13333333333333333,0.0006465911865234375,348.79444399999994 +45672,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997153617095814,0.13333333333333333,0.0006465911865234375,378.9697039999999 +47575,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997267472411981,0.13333333333333333,0.0006465911865234375,410.4053809999999 +49478,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997372569626904,0.13333333333333333,0.0006465911865234375,443.05761399999994 +51381,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997080632918783,0.11764705882352941,0.0006465911865234375,477.02532299999996 +53284,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997184896028827,0.11764705882352941,0.0006465911865234375,512.247669 +55187,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9997281968579557,0.11764705882352941,0.0006465911865234375,548.612896 +57090,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995796111403048,0.14285714285714285,0.0006465911865234375,586.233337 +58993,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995931720712626,0.14285714285714285,0.0006465911865234375,625.057298 +60896,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996058854440357,0.14285714285714285,0.0006465911865234375,665.0820319999999 +62799,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999585980668482,0.13333333333333333,0.0006465911865234375,706.1803269999999 +64702,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995981577076443,0.13333333333333333,0.0006465911865234375,748.3509649999999 +66605,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996096389159973,0.13333333333333333,0.0006465911865234375,791.6670189999999 +68508,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9995912886086297,0.125,0.0006465911865234375,836.0877649999999 +70411,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996023348624504,0.125,0.0006465911865234375,881.7116259999999 +72314,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996127997344912,0.125,0.0006465911865234375,928.538605 +74217,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996227279464274,0.125,0.0006465911865234375,976.4092069999999 +76120,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996321597477666,0.125,0.0006465911865234375,1025.3623619999998 +78023,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996411314612358,0.125,0.0006465911865234375,1075.4142359999998 +79926,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999649675950254,0.125,0.0006465911865234375,1126.58116 +81829,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996578230211783,0.125,0.0006465911865234375,1178.778759 +83732,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999665599770697,0.125,0.0006465911865234375,1231.992839 +85635,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996730308869037,0.125,0.0006465911865234375,1286.303482 +87538,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996801389111014,0.125,0.0006465911865234375,1341.617815 +89441,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996757639114053,0.1212121212121212,0.0006465911865234375,1397.839199 +91344,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996825188299177,0.1212121212121212,0.0006465911865234375,1455.075933 +93247,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996889980374704,0.1212121212121212,0.0006465911865234375,1513.261041 +95150,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.999695218076721,0.1212121212121212,0.0006465911865234375,1572.312523 +95156,Binary classification,Vowpal Wabbit logistic regression,SMTP,0.9996952372945479,0.1212121212121212,0.0006465911865234375,1631.3703540000001 +106,Binary classification,Naive Bayes,Bananas,0.5333333333333333,0.46153846153846156,0.014024734497070312,0.089081 +212,Binary classification,Naive Bayes,Bananas,0.5592417061611374,0.5026737967914437,0.014024734497070312,0.244558 +318,Binary classification,Naive Bayes,Bananas,0.555205047318612,0.5154639175257733,0.014024734497070312,0.45393700000000003 +424,Binary classification,Naive Bayes,Bananas,0.5626477541371159,0.5066666666666667,0.014024734497070312,0.76271 +530,Binary classification,Naive Bayes,Bananas,0.5689981096408318,0.48181818181818187,0.014024734497070312,1.109688 +636,Binary classification,Naive Bayes,Bananas,0.5716535433070866,0.4645669291338582,0.014024734497070312,1.6198730000000001 +742,Binary classification,Naive Bayes,Bananas,0.5870445344129555,0.4555160142348755,0.014024734497070312,2.197835 +848,Binary classification,Naive Bayes,Bananas,0.5962219598583235,0.4554140127388535,0.014024734497070312,2.834188 +954,Binary classification,Naive Bayes,Bananas,0.6002098635886673,0.4454148471615721,0.014024734497070312,3.5547570000000004 +1060,Binary classification,Naive Bayes,Bananas,0.6090651558073654,0.44054054054054054,0.014024734497070312,4.339157 +1166,Binary classification,Naive Bayes,Bananas,0.6068669527896996,0.42606516290726815,0.014024734497070312,5.220598 +1272,Binary classification,Naive Bayes,Bananas,0.6136900078678206,0.433679354094579,0.014024734497070312,6.1398969999999995 +1378,Binary classification,Naive Bayes,Bananas,0.6143790849673203,0.419672131147541,0.014024734497070312,7.157157999999999 +1484,Binary classification,Naive Bayes,Bananas,0.6142953472690492,0.4127310061601643,0.014024734497070312,8.301379999999998 +1590,Binary classification,Naive Bayes,Bananas,0.6135934550031467,0.40618955512572535,0.014024734497070312,9.487101 +1696,Binary classification,Naive Bayes,Bananas,0.6141592920353982,0.4010989010989011,0.014024734497070312,10.730798 +1802,Binary classification,Naive Bayes,Bananas,0.614658523042754,0.40378006872852235,0.014024734497070312,12.063669 +1908,Binary classification,Naive Bayes,Bananas,0.6151022548505506,0.4080645161290322,0.014024734497070312,13.448557000000001 +2014,Binary classification,Naive Bayes,Bananas,0.6100347739692003,0.40485216072782415,0.014024734497070312,14.939018 +2120,Binary classification,Naive Bayes,Bananas,0.608305804624823,0.4071428571428571,0.014024734497070312,16.439687 +2226,Binary classification,Naive Bayes,Bananas,0.6089887640449438,0.4089673913043478,0.014024734497070312,18.077419 +2332,Binary classification,Naive Bayes,Bananas,0.6096096096096096,0.4098573281452659,0.014024734497070312,19.752885 +2438,Binary classification,Naive Bayes,Bananas,0.6101764464505539,0.40846824408468246,0.014024734497070312,21.52049 +2544,Binary classification,Naive Bayes,Bananas,0.6114825009830909,0.41538461538461535,0.014024734497070312,23.348549 +2650,Binary classification,Naive Bayes,Bananas,0.6100415251038127,0.41273450824332003,0.014024734497070312,25.279207 +2756,Binary classification,Naive Bayes,Bananas,0.6076225045372051,0.4070213933077345,0.014024734497070312,27.31295 +2862,Binary classification,Naive Bayes,Bananas,0.6085284865431667,0.4092827004219409,0.014024734497070312,29.37924 +2968,Binary classification,Naive Bayes,Bananas,0.6083586113919784,0.4065372829417773,0.014024734497070312,31.545651 +3074,Binary classification,Naive Bayes,Bananas,0.60624796615685,0.40628066732090284,0.014024734497070312,33.733230999999996 +3180,Binary classification,Naive Bayes,Bananas,0.6071091538219566,0.4077761972498815,0.014024734497070312,36.007059 +3286,Binary classification,Naive Bayes,Bananas,0.6063926940639269,0.4049700874367234,0.014024734497070312,38.312687 +3392,Binary classification,Naive Bayes,Bananas,0.6048363314656443,0.40602836879432624,0.014024734497070312,40.720333999999994 +3498,Binary classification,Naive Bayes,Bananas,0.6065198741778668,0.40535868625756266,0.014024734497070312,43.213055999999995 +3604,Binary classification,Naive Bayes,Bananas,0.6086594504579517,0.40905280804694044,0.014024734497070312,45.745915999999994 +3710,Binary classification,Naive Bayes,Bananas,0.6085198166621731,0.4078303425774878,0.014024734497070312,48.41046399999999 +3816,Binary classification,Naive Bayes,Bananas,0.6070773263433814,0.40492258832870187,0.014024734497070312,51.18183799999999 +3922,Binary classification,Naive Bayes,Bananas,0.6067329762815609,0.4027885360185902,0.014024734497070312,54.01538599999999 +4028,Binary classification,Naive Bayes,Bananas,0.6088899925502855,0.405436013590034,0.014024734497070312,56.94241999999999 +4134,Binary classification,Naive Bayes,Bananas,0.6106944108395839,0.40780272359219727,0.014024734497070312,59.88324999999999 +4240,Binary classification,Naive Bayes,Bananas,0.611936777541873,0.41186986056489094,0.014024734497070312,62.92792699999999 +4346,Binary classification,Naive Bayes,Bananas,0.6131185270425776,0.4128536500174642,0.014024734497070312,66.00451999999999 +4452,Binary classification,Naive Bayes,Bananas,0.6137946528869916,0.413510747185261,0.014024734497070312,69.16846599999998 +4558,Binary classification,Naive Bayes,Bananas,0.6122448979591837,0.4115884115884116,0.014024734497070312,72.34300699999999 +4664,Binary classification,Naive Bayes,Bananas,0.6126956894702981,0.41249186727391024,0.014024734497070312,75.66876099999999 +4770,Binary classification,Naive Bayes,Bananas,0.6143845669951772,0.41302266198531756,0.014024734497070312,79.08375899999999 +4876,Binary classification,Naive Bayes,Bananas,0.6153846153846154,0.4131455399061033,0.014024734497070312,82.54849799999998 +4982,Binary classification,Naive Bayes,Bananas,0.6163420999799237,0.41684467500762895,0.014024734497070312,86.09394899999998 +5088,Binary classification,Naive Bayes,Bananas,0.6150973068606251,0.41412327947336924,0.014024734497070312,89.70897599999998 +5194,Binary classification,Naive Bayes,Bananas,0.6146735990756788,0.4133685136323659,0.014024734497070312,93.40231399999998 +5300,Binary classification,Naive Bayes,Bananas,0.6152104170598226,0.4139120436907157,0.014024734497070312,97.15397799999998 +906,Binary classification,Naive Bayes,Elec2,0.8187845303867404,0.8284518828451883,0.05103778839111328,0.90253 +1812,Binary classification,Naive Bayes,Elec2,0.8023191606847045,0.7475317348377998,0.05103778839111328,2.6687279999999998 +2718,Binary classification,Naive Bayes,Elec2,0.784688995215311,0.706177800100452,0.05103778839111328,5.38565 +3624,Binary classification,Naive Bayes,Elec2,0.8032017664918576,0.7356321839080461,0.05103778839111328,8.965856 +4530,Binary classification,Naive Bayes,Elec2,0.7979686465003312,0.7073872721458268,0.05103778839111328,13.460125000000001 +5436,Binary classification,Naive Bayes,Elec2,0.7937442502299908,0.6972724817715366,0.05103778839111328,18.947959 +6342,Binary classification,Naive Bayes,Elec2,0.7982967986122063,0.7065840789171829,0.05103778839111328,25.368016 +7248,Binary classification,Naive Bayes,Elec2,0.790396025941769,0.6875128574367414,0.05103778839111328,32.74734 +8154,Binary classification,Naive Bayes,Elec2,0.7841285416411137,0.6888260254596887,0.05103778839111328,41.092102 +9060,Binary classification,Naive Bayes,Elec2,0.7897118887294403,0.7086710506193606,0.05103778839111328,50.330248999999995 +9966,Binary classification,Naive Bayes,Elec2,0.793176116407426,0.7240594457089301,0.05103778839111328,60.487047999999994 +10872,Binary classification,Naive Bayes,Elec2,0.7960629196946003,0.7361656551231703,0.05103778839111328,71.57583 +11778,Binary classification,Naive Bayes,Elec2,0.792137216608644,0.7295027624309391,0.05103778839111328,83.57396899999999 +12684,Binary classification,Naive Bayes,Elec2,0.7820704880548766,0.7260111022997621,0.05103778839111328,96.50585899999999 +13590,Binary classification,Naive Bayes,Elec2,0.7858562072264331,0.7383564107174968,0.05103778839111328,110.37856099999999 +14496,Binary classification,Naive Bayes,Elec2,0.7866850638151086,0.7435727317963178,0.05103778839111328,125.16282799999999 +15402,Binary classification,Naive Bayes,Elec2,0.785728199467567,0.738593155893536,0.05103778839111328,140.864825 +16308,Binary classification,Naive Bayes,Elec2,0.7806463481940271,0.7274666666666666,0.05103778839111328,157.49451299999998 +17214,Binary classification,Naive Bayes,Elec2,0.7788880497298554,0.7181158346911569,0.05103778839111328,175.04662 +18120,Binary classification,Naive Bayes,Elec2,0.7728903361112645,0.7138983522213725,0.05103778839111328,193.53438899999998 +19026,Binary classification,Naive Bayes,Elec2,0.7701445466491459,0.7094931242941608,0.05103778839111328,212.92156899999998 +19932,Binary classification,Naive Bayes,Elec2,0.7628317696051378,0.702236220472441,0.05103778839111328,233.287368 +20838,Binary classification,Naive Bayes,Elec2,0.7537553390603254,0.6903626817934946,0.05103778839111328,254.638983 +21744,Binary classification,Naive Bayes,Elec2,0.7508163546888654,0.6836389115964032,0.05103778839111328,276.932282 +22650,Binary classification,Naive Bayes,Elec2,0.7509823833281822,0.6798001589644601,0.05103778839111328,300.18967299999997 +23556,Binary classification,Naive Bayes,Elec2,0.7457015495648482,0.668217569513681,0.05103778839111328,324.33763999999996 +24462,Binary classification,Naive Bayes,Elec2,0.7466170638976329,0.665839982747466,0.05103778839111328,349.45202499999994 +25368,Binary classification,Naive Bayes,Elec2,0.7447865336854969,0.6611180904522613,0.05103778839111328,375.51598899999993 +26274,Binary classification,Naive Bayes,Elec2,0.7448711605069843,0.6581322996888865,0.05103778839111328,402.57675099999994 +27180,Binary classification,Naive Bayes,Elec2,0.741123661650539,0.650402464473815,0.05103778839111328,430.58128099999993 +28086,Binary classification,Naive Bayes,Elec2,0.7390065871461634,0.6440019426906265,0.05103778839111328,459.5402439999999 +28992,Binary classification,Naive Bayes,Elec2,0.7358145631402849,0.6343280019097637,0.05103778839111328,489.4018749999999 +29898,Binary classification,Naive Bayes,Elec2,0.7320466936481921,0.6243023964732918,0.05103778839111328,520.249024 +30804,Binary classification,Naive Bayes,Elec2,0.7297990455475116,0.6158319870759289,0.05103778839111328,552.052505 +31710,Binary classification,Naive Bayes,Elec2,0.7256930209088902,0.6059617649723658,0.05103778839111328,584.838155 +32616,Binary classification,Naive Bayes,Elec2,0.7215391690939752,0.596427301813011,0.05103778839111328,618.5052350000001 +33522,Binary classification,Naive Bayes,Elec2,0.7176695205990274,0.5867248908296943,0.05103778839111328,653.1230730000001 +34428,Binary classification,Naive Bayes,Elec2,0.7142359194818021,0.5779493779493778,0.05103778839111328,688.6949970000001 +35334,Binary classification,Naive Bayes,Elec2,0.7138369229898395,0.5724554949469323,0.05103778839111328,725.19325 +36240,Binary classification,Naive Bayes,Elec2,0.7174866856149452,0.5752924583091347,0.05103778839111328,762.649856 +37146,Binary classification,Naive Bayes,Elec2,0.7169740207295733,0.5716148486206756,0.05103778839111328,801.028112 +38052,Binary classification,Naive Bayes,Elec2,0.7183516858952459,0.573859795618116,0.05103778839111328,840.263393 +38958,Binary classification,Naive Bayes,Elec2,0.7206407064198989,0.5799529121154812,0.05103778839111328,880.2889349999999 +39864,Binary classification,Naive Bayes,Elec2,0.7217720693374808,0.5866964784795975,0.05103778839111328,921.221106 +40770,Binary classification,Naive Bayes,Elec2,0.7228776766660944,0.5923065819861432,0.05103778839111328,962.947955 +41676,Binary classification,Naive Bayes,Elec2,0.724127174565087,0.5973170817134251,0.05103778839111328,1005.542302 +42582,Binary classification,Naive Bayes,Elec2,0.7260280406754186,0.6013259517462921,0.05103778839111328,1049.006993 +43488,Binary classification,Naive Bayes,Elec2,0.7277117299422816,0.6045222270465248,0.05103778839111328,1093.33419 +44394,Binary classification,Naive Bayes,Elec2,0.7273894532921857,0.6015933631814591,0.05103778839111328,1138.520645 +45300,Binary classification,Naive Bayes,Elec2,0.7287136581381487,0.6038234630387828,0.05103778839111328,1184.586595 +45312,Binary classification,Naive Bayes,Elec2,0.7287413652314008,0.6037845330582509,0.05103778839111328,1230.65543 +25,Binary classification,Naive Bayes,Phishing,0.5833333333333334,0.7058823529411764,0.05722999572753906,0.005899 +50,Binary classification,Naive Bayes,Phishing,0.7346938775510204,0.7636363636363637,0.05722999572753906,0.034194 +75,Binary classification,Naive Bayes,Phishing,0.7837837837837838,0.8048780487804877,0.05722999572753906,0.070237 +100,Binary classification,Naive Bayes,Phishing,0.8080808080808081,0.819047619047619,0.05722999572753906,0.11917699999999999 +125,Binary classification,Naive Bayes,Phishing,0.8145161290322581,0.8217054263565893,0.05722999572753906,0.172458 +150,Binary classification,Naive Bayes,Phishing,0.8187919463087249,0.830188679245283,0.05722999572753906,0.294112 +175,Binary classification,Naive Bayes,Phishing,0.8333333333333334,0.8323699421965318,0.05722999572753906,0.432822 +200,Binary classification,Naive Bayes,Phishing,0.8341708542713567,0.83248730964467,0.05722999572753906,0.620751 +225,Binary classification,Naive Bayes,Phishing,0.8303571428571429,0.8240740740740741,0.05722999572753906,0.8126760000000001 +250,Binary classification,Naive Bayes,Phishing,0.8313253012048193,0.825,0.05722999572753906,1.097418 +275,Binary classification,Naive Bayes,Phishing,0.8321167883211679,0.8244274809160306,0.05722999572753906,1.3867479999999999 +300,Binary classification,Naive Bayes,Phishing,0.8394648829431438,0.8285714285714285,0.05722999572753906,1.7081089999999999 +325,Binary classification,Naive Bayes,Phishing,0.845679012345679,0.8299319727891157,0.05722999572753906,2.0343679999999997 +350,Binary classification,Naive Bayes,Phishing,0.8510028653295129,0.8322580645161292,0.05722999572753906,2.472974 +375,Binary classification,Naive Bayes,Phishing,0.8529411764705882,0.8318042813455658,0.05722999572753906,2.916035 +400,Binary classification,Naive Bayes,Phishing,0.8546365914786967,0.8313953488372093,0.05722999572753906,3.458949 +425,Binary classification,Naive Bayes,Phishing,0.8561320754716981,0.8291316526610645,0.05722999572753906,4.00711 +450,Binary classification,Naive Bayes,Phishing,0.8596881959910914,0.8310991957104559,0.05722999572753906,4.560221 +475,Binary classification,Naive Bayes,Phishing,0.8565400843881856,0.8291457286432161,0.05722999572753906,5.117465 +500,Binary classification,Naive Bayes,Phishing,0.8577154308617234,0.8337236533957845,0.05722999572753906,5.6788810000000005 +525,Binary classification,Naive Bayes,Phishing,0.8587786259541985,0.8310502283105022,0.05722999572753906,6.3168120000000005 +550,Binary classification,Naive Bayes,Phishing,0.8579234972677595,0.8311688311688311,0.05722999572753906,6.9590250000000005 +575,Binary classification,Naive Bayes,Phishing,0.8606271777003485,0.8340248962655602,0.05722999572753906,7.6702010000000005 +600,Binary classification,Naive Bayes,Phishing,0.8647746243739566,0.8363636363636363,0.05722999572753906,8.386169 +625,Binary classification,Naive Bayes,Phishing,0.8669871794871795,0.8356435643564357,0.05722999572753906,9.138945000000001 +650,Binary classification,Naive Bayes,Phishing,0.8705701078582434,0.8426966292134833,0.05722999572753906,9.901064000000002 +675,Binary classification,Naive Bayes,Phishing,0.870919881305638,0.8465608465608465,0.05722999572753906,10.713223000000001 +700,Binary classification,Naive Bayes,Phishing,0.8755364806866953,0.8502581755593803,0.05722999572753906,11.569231 +725,Binary classification,Naive Bayes,Phishing,0.8784530386740331,0.8562091503267973,0.05722999572753906,12.458796 +750,Binary classification,Naive Bayes,Phishing,0.8798397863818425,0.8584905660377359,0.05722999572753906,13.352328 +775,Binary classification,Naive Bayes,Phishing,0.8798449612403101,0.8580152671755725,0.05722999572753906,14.337352 +800,Binary classification,Naive Bayes,Phishing,0.8798498122653317,0.8596491228070174,0.05722999572753906,15.326948 +825,Binary classification,Naive Bayes,Phishing,0.8798543689320388,0.860759493670886,0.05722999572753906,16.325159 +850,Binary classification,Naive Bayes,Phishing,0.8798586572438163,0.8602739726027396,0.05722999572753906,17.375421 +875,Binary classification,Naive Bayes,Phishing,0.8832951945080092,0.8636363636363635,0.05722999572753906,18.429913 +900,Binary classification,Naive Bayes,Phishing,0.8809788654060067,0.8608582574772432,0.05722999572753906,19.528876999999998 +925,Binary classification,Naive Bayes,Phishing,0.8820346320346321,0.8635794743429286,0.05722999572753906,20.632713999999996 +950,Binary classification,Naive Bayes,Phishing,0.8819810326659642,0.8650602409638554,0.05722999572753906,21.817704999999997 +975,Binary classification,Naive Bayes,Phishing,0.8829568788501027,0.8661971830985915,0.05722999572753906,23.016962999999997 +1000,Binary classification,Naive Bayes,Phishing,0.8808808808808809,0.8643101482326111,0.05722999572753906,24.246232999999997 +1025,Binary classification,Naive Bayes,Phishing,0.880859375,0.8647450110864746,0.05722999572753906,25.480750999999998 +1050,Binary classification,Naive Bayes,Phishing,0.882745471877979,0.8673139158576052,0.05722999572753906,26.819710999999998 +1075,Binary classification,Naive Bayes,Phishing,0.8817504655493482,0.8672936259143157,0.05722999572753906,28.162913999999997 +1100,Binary classification,Naive Bayes,Phishing,0.8835304822565969,0.8693877551020409,0.05722999572753906,29.538843999999997 +1125,Binary classification,Naive Bayes,Phishing,0.8861209964412812,0.8735177865612648,0.05722999572753906,30.932978999999996 +1150,Binary classification,Naive Bayes,Phishing,0.8859878154917319,0.8731848983543079,0.05722999572753906,32.425236999999996 +1175,Binary classification,Naive Bayes,Phishing,0.8850085178875639,0.8717948717948718,0.05722999572753906,33.921729 +1200,Binary classification,Naive Bayes,Phishing,0.8865721434528774,0.8731343283582089,0.05722999572753906,35.452877 +1225,Binary classification,Naive Bayes,Phishing,0.886437908496732,0.8728270814272644,0.05722999572753906,36.988201000000004 +1250,Binary classification,Naive Bayes,Phishing,0.8847077662129704,0.8714285714285714,0.05722999572753906,38.528021 +1903,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,1.286863 +3806,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,3.863138 +5709,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,7.731956 +7612,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,13.024672 +9515,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,19.659339000000003 +11418,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,27.654251000000002 +13321,Binary classification,Naive Bayes,SMTP,1.0,0.0,0.010775566101074219,36.976608 +15224,Binary classification,Naive Bayes,SMTP,0.9997372397030808,0.7777777777777778,0.020140647888183594,47.719054 +17127,Binary classification,Naive Bayes,SMTP,0.9997664369963798,0.8181818181818181,0.020140647888183594,59.951688 +19030,Binary classification,Naive Bayes,SMTP,0.9997897945241474,0.8181818181818181,0.020140647888183594,73.68853899999999 +20933,Binary classification,Naive Bayes,SMTP,0.9998089050257978,0.8181818181818181,0.020140647888183594,88.86509 +22836,Binary classification,Naive Bayes,SMTP,0.9998248303043573,0.8181818181818181,0.020140647888183594,105.535247 +24739,Binary classification,Naive Bayes,SMTP,0.9998383054410219,0.8181818181818181,0.020140647888183594,123.661746 +26642,Binary classification,Naive Bayes,SMTP,0.9998498554859052,0.8333333333333333,0.020140647888183594,143.18039199999998 +28545,Binary classification,Naive Bayes,SMTP,0.999859865470852,0.8333333333333333,0.020140647888183594,164.12799199999998 +30448,Binary classification,Naive Bayes,SMTP,0.9998686241665845,0.8333333333333333,0.020140647888183594,186.62736299999997 +32351,Binary classification,Naive Bayes,SMTP,0.9998763523956723,0.8333333333333333,0.020140647888183594,210.51749199999998 +34254,Binary classification,Naive Bayes,SMTP,0.9998832219075702,0.8333333333333333,0.020140647888183594,235.83223599999997 +36157,Binary classification,Naive Bayes,SMTP,0.9998893682929527,0.8333333333333333,0.020140647888183594,262.657063 +38060,Binary classification,Naive Bayes,SMTP,0.9998949000236474,0.8333333333333333,0.020140647888183594,290.942762 +39963,Binary classification,Naive Bayes,SMTP,0.9998999049096642,0.8333333333333333,0.020140647888183594,320.716469 +41866,Binary classification,Naive Bayes,SMTP,0.999904454795175,0.8333333333333333,0.020140647888183594,352.027934 +43769,Binary classification,Naive Bayes,SMTP,0.9999086090294279,0.8333333333333333,0.020140647888183594,384.764181 +45672,Binary classification,Naive Bayes,SMTP,0.9999124170699131,0.8333333333333333,0.020140647888183594,419.010574 +47575,Binary classification,Naive Bayes,SMTP,0.9999159204607558,0.8333333333333333,0.020140647888183594,454.738206 +49478,Binary classification,Naive Bayes,SMTP,0.9999191543545486,0.8333333333333333,0.020140647888183594,491.833824 +51381,Binary classification,Naive Bayes,SMTP,0.9999026858699883,0.8275862068965517,0.020140647888183594,530.367488 +53284,Binary classification,Naive Bayes,SMTP,0.9999061614398589,0.8275862068965517,0.020140647888183594,570.267553 +55187,Binary classification,Naive Bayes,SMTP,0.9998912767730946,0.7999999999999999,0.020140647888183594,611.572363 +57090,Binary classification,Naive Bayes,SMTP,0.9993869221741492,0.4444444444444444,0.020140647888183594,654.167087 +58993,Binary classification,Naive Bayes,SMTP,0.9988473013289938,0.29166666666666663,0.020140647888183594,698.17064 +60896,Binary classification,Naive Bayes,SMTP,0.9986369981115034,0.2522522522522523,0.020140647888183594,743.490298 +62799,Binary classification,Naive Bayes,SMTP,0.9979139463040224,0.1761006289308176,0.020140647888183594,790.115891 +64702,Binary classification,Naive Bayes,SMTP,0.9979443903494536,0.17391304347826086,0.020140647888183594,838.025404 +66605,Binary classification,Naive Bayes,SMTP,0.9977478830100295,0.15730337078651685,0.020140647888183594,887.160342 +68508,Binary classification,Naive Bayes,SMTP,0.9967302611411972,0.12500000000000003,0.020140647888183594,937.511304 +70411,Binary classification,Naive Bayes,SMTP,0.9964777730436017,0.1142857142857143,0.020140647888183594,989.136144 +72314,Binary classification,Naive Bayes,SMTP,0.9964045192427364,0.10958904109589042,0.020140647888183594,1041.952402 +74217,Binary classification,Naive Bayes,SMTP,0.9958230031260106,0.0935672514619883,0.020140647888183594,1095.894331 +76120,Binary classification,Naive Bayes,SMTP,0.9956515456062218,0.08815426997245178,0.020140647888183594,1151.054816 +78023,Binary classification,Naive Bayes,SMTP,0.9951936633257287,0.07862407862407862,0.020140647888183594,1207.299045 +79926,Binary classification,Naive Bayes,SMTP,0.9946700031279324,0.06986899563318777,0.020140647888183594,1264.68116 +81829,Binary classification,Naive Bayes,SMTP,0.9945862052109302,0.06736842105263158,0.020140647888183594,1323.182614 +83732,Binary classification,Naive Bayes,SMTP,0.9945539883675102,0.06557377049180328,0.020140647888183594,1382.690018 +85635,Binary classification,Naive Bayes,SMTP,0.9939860335847911,0.05850091407678244,0.020140647888183594,1443.2513940000001 +87538,Binary classification,Naive Bayes,SMTP,0.9938540274398254,0.05614035087719298,0.020140647888183594,1504.7859910000002 +89441,Binary classification,Naive Bayes,SMTP,0.9938618067978533,0.05507745266781411,0.020140647888183594,1567.3680330000002 +91344,Binary classification,Naive Bayes,SMTP,0.9939677917300723,0.0548885077186964,0.020140647888183594,1630.9013820000002 +93247,Binary classification,Naive Bayes,SMTP,0.993543958990198,0.050473186119873815,0.020140647888183594,1695.4283070000001 +95150,Binary classification,Naive Bayes,SMTP,0.993483904192372,0.049079754601226995,0.020140647888183594,1760.949483 +95156,Binary classification,Naive Bayes,SMTP,0.9934843150648941,0.049079754601226995,0.020140647888183594,1826.472109 +106,Binary classification,Hoeffding Tree,Bananas,0.49523809523809526,0.208955223880597,0.019225120544433594,0.143993 +212,Binary classification,Hoeffding Tree,Bananas,0.5213270142180095,0.3129251700680272,0.019248008728027344,0.331364 +318,Binary classification,Hoeffding Tree,Bananas,0.5299684542586751,0.40637450199203184,0.019248008728027344,0.6339969999999999 +424,Binary classification,Hoeffding Tree,Bananas,0.5437352245862884,0.42388059701492536,0.019248008728027344,1.026482 +530,Binary classification,Hoeffding Tree,Bananas,0.553875236294896,0.4099999999999999,0.019248008728027344,1.502748 +636,Binary classification,Hoeffding Tree,Bananas,0.5590551181102362,0.4017094017094017,0.019248008728027344,2.038539 +742,Binary classification,Hoeffding Tree,Bananas,0.5762483130904184,0.3984674329501916,0.019248008728027344,2.585217 +848,Binary classification,Hoeffding Tree,Bananas,0.5867768595041323,0.40476190476190477,0.019248008728027344,3.2443470000000003 +954,Binary classification,Hoeffding Tree,Bananas,0.5918153200419727,0.3987635239567234,0.019248008728027344,4.029044000000001 +1060,Binary classification,Hoeffding Tree,Bananas,0.6015108593012276,0.39714285714285713,0.019248008728027344,4.857172 +1166,Binary classification,Hoeffding Tree,Bananas,0.6,0.38522427440633245,0.019248008728027344,5.757943 +1272,Binary classification,Hoeffding Tree,Bananas,0.6073957513768686,0.3966142684401451,0.019248008728027344,6.7312840000000005 +1378,Binary classification,Hoeffding Tree,Bananas,0.6085693536673928,0.384,0.019248008728027344,7.793338 +1484,Binary classification,Hoeffding Tree,Bananas,0.6089008766014835,0.3790149892933619,0.019248008728027344,8.86628 +1590,Binary classification,Hoeffding Tree,Bananas,0.6085588420390182,0.37424547283702214,0.019248008728027344,10.05345 +1696,Binary classification,Hoeffding Tree,Bananas,0.6094395280235988,0.37072243346007605,0.019248008728027344,11.283728 +1802,Binary classification,Hoeffding Tree,Bananas,0.6102165463631316,0.37544483985765126,0.019248008728027344,12.570083 +1908,Binary classification,Hoeffding Tree,Bananas,0.610907184058731,0.3816666666666667,0.019248008728027344,13.875162 +2014,Binary classification,Hoeffding Tree,Bananas,0.6060606060606061,0.3799843627834245,0.019248008728027344,15.24589 +2120,Binary classification,Hoeffding Tree,Bananas,0.6045304388862671,0.38382352941176473,0.019248008728027344,16.723857 +2226,Binary classification,Hoeffding Tree,Bananas,0.6053932584269663,0.38687150837988826,0.019248008728027344,18.213202 +2332,Binary classification,Hoeffding Tree,Bananas,0.6061776061776062,0.38881491344873503,0.019248008728027344,19.838043 +2438,Binary classification,Hoeffding Tree,Bananas,0.606893721789085,0.388250319284802,0.019248008728027344,21.528239 +2544,Binary classification,Hoeffding Tree,Bananas,0.608336610302792,0.39636363636363636,0.019248008728027344,23.295102 +2650,Binary classification,Hoeffding Tree,Bananas,0.6070215175537939,0.3944153577661431,0.019248008728027344,25.108421 +2756,Binary classification,Hoeffding Tree,Bananas,0.6047186932849364,0.3892316320807628,0.019248008728027344,26.99552 +2862,Binary classification,Hoeffding Tree,Bananas,0.6057322614470465,0.3922413793103448,0.019248008728027344,28.904989 +2968,Binary classification,Hoeffding Tree,Bananas,0.6056622851365016,0.3899895724713243,0.019248008728027344,30.87684 +3074,Binary classification,Hoeffding Tree,Bananas,0.6036446469248291,0.3903903903903904,0.019248008728027344,32.946938 +3180,Binary classification,Hoeffding Tree,Bananas,0.6045926391947153,0.3924601256645723,0.019248008728027344,35.112766 +3286,Binary classification,Hoeffding Tree,Bananas,0.6039573820395738,0.39006094702297234,0.019248008728027344,37.308874 +3392,Binary classification,Hoeffding Tree,Bananas,0.6024771453848422,0.39169675090252704,0.019248008728027344,39.588614 +3498,Binary classification,Hoeffding Tree,Bananas,0.6030883614526737,0.39335664335664333,0.03483390808105469,41.900558 +3604,Binary classification,Hoeffding Tree,Bananas,0.6069941715237303,0.40353833192923344,0.03483390808105469,44.304379999999995 +3710,Binary classification,Hoeffding Tree,Bananas,0.6079805877595039,0.40798045602605865,0.03483390808105469,46.776579 +3816,Binary classification,Hoeffding Tree,Bananas,0.6107470511140236,0.4146629877808436,0.03483390808105469,49.299973 +3922,Binary classification,Hoeffding Tree,Bananas,0.6123437898495282,0.4180704441041348,0.04409217834472656,51.9923 +4028,Binary classification,Hoeffding Tree,Bananas,0.6143531164638689,0.4246017043349389,0.05025672912597656,54.748055 +4134,Binary classification,Hoeffding Tree,Bananas,0.617227195741592,0.43216080402010054,0.05025672912597656,57.554127 +4240,Binary classification,Hoeffding Tree,Bananas,0.6218447747110167,0.4439819632327437,0.05025672912597656,60.441345 +4346,Binary classification,Hoeffding Tree,Bananas,0.6239355581127733,0.45130960376091334,0.05025672912597656,63.387968 +4452,Binary classification,Hoeffding Tree,Bananas,0.6259267580319029,0.45676998368678623,0.05025672912597656,66.368103 +4558,Binary classification,Hoeffding Tree,Bananas,0.6276058810621022,0.46382306477093216,0.05025672912597656,69.45485500000001 +4664,Binary classification,Hoeffding Tree,Bananas,0.6283508470941453,0.4695439240893787,0.05025672912597656,72.676145 +4770,Binary classification,Hoeffding Tree,Bananas,0.6288530090165653,0.47164179104477605,0.05941963195800781,75.94439200000001 +4876,Binary classification,Hoeffding Tree,Bananas,0.6311794871794871,0.47580174927113705,0.05946540832519531,79.31100500000001 +4982,Binary classification,Hoeffding Tree,Bananas,0.6336077092953222,0.484026010743568,0.05946540832519531,82.69585900000001 +5088,Binary classification,Hoeffding Tree,Bananas,0.6361313151169649,0.49050371593724196,0.05946540832519531,86.19871000000002 +5194,Binary classification,Hoeffding Tree,Bananas,0.6383593298671288,0.495703544575725,0.05946540832519531,89.82165300000003 +5300,Binary classification,Hoeffding Tree,Bananas,0.6421966408756369,0.5034049240440022,0.05946540832519531,93.53024900000003 +906,Binary classification,Hoeffding Tree,Elec2,0.8530386740331491,0.8513966480446927,0.1757516860961914,1.081486 +1812,Binary classification,Hoeffding Tree,Elec2,0.8663721700717836,0.8393094289508632,0.2084512710571289,3.2087309999999998 +2718,Binary classification,Hoeffding Tree,Elec2,0.8365844681634156,0.809278350515464,0.23302173614501953,6.394793 +3624,Binary classification,Hoeffding Tree,Elec2,0.8459839911675407,0.8210391276459269,0.23302173614501953,10.694791 +4530,Binary classification,Hoeffding Tree,Elec2,0.8511812762199161,0.8157463094587206,0.23296833038330078,16.02834 +5436,Binary classification,Hoeffding Tree,Elec2,0.8404783808647655,0.8020095912308747,0.23296833038330078,22.571918 +6342,Binary classification,Hoeffding Tree,Elec2,0.8334647531935025,0.7966884867154409,0.23296833038330078,30.238204 +7248,Binary classification,Hoeffding Tree,Elec2,0.8330343590451221,0.7912353347135956,0.23296833038330078,38.961308 +8154,Binary classification,Hoeffding Tree,Elec2,0.8344167790997179,0.8013537374926426,0.23296833038330078,48.732242 +9060,Binary classification,Hoeffding Tree,Elec2,0.8403797328623468,0.8129849974133472,0.2980508804321289,59.636444 +9966,Binary classification,Hoeffding Tree,Elec2,0.8398394380331159,0.8171402383134739,0.29816532135009766,71.55266999999999 +10872,Binary classification,Hoeffding Tree,Elec2,0.840493054916751,0.8200124558854057,0.29816532135009766,84.613385 +11778,Binary classification,Hoeffding Tree,Elec2,0.8404517279442982,0.8184014690248381,0.3811311721801758,98.771271 +12684,Binary classification,Hoeffding Tree,Elec2,0.8397066939998423,0.8184983483617534,0.3811311721801758,114.088656 +13590,Binary classification,Hoeffding Tree,Elec2,0.8422253293104717,0.8228684732319893,0.3811311721801758,130.484857 +14496,Binary classification,Hoeffding Tree,Elec2,0.8440841669541221,0.8259128023417038,0.38237476348876953,148.034702 +15402,Binary classification,Hoeffding Tree,Elec2,0.8445555483410169,0.8246153846153847,0.3824014663696289,166.630313 +16308,Binary classification,Hoeffding Tree,Elec2,0.8382903047770895,0.8146221441124781,0.40816211700439453,186.33286 +17214,Binary classification,Hoeffding Tree,Elec2,0.8345436588624876,0.8052516411378555,0.40816211700439453,207.14980100000002 +18120,Binary classification,Hoeffding Tree,Elec2,0.8332689442022186,0.8030253635000325,0.40884876251220703,229.10312700000003 +19026,Binary classification,Hoeffding Tree,Elec2,0.8340604467805519,0.8008327550312283,0.4101419448852539,252.19556200000002 +19932,Binary classification,Hoeffding Tree,Elec2,0.8288595655009784,0.7951228302000121,0.4740419387817383,276.349221 +20838,Binary classification,Hoeffding Tree,Elec2,0.8238230071507414,0.787570163763671,0.4986543655395508,301.796373 +21744,Binary classification,Hoeffding Tree,Elec2,0.8251391252357081,0.7858028169014086,0.49881458282470703,328.42960500000004 +22650,Binary classification,Hoeffding Tree,Elec2,0.8245838668373879,0.7828843106180666,0.4754457473754883,356.18046400000003 +23556,Binary classification,Hoeffding Tree,Elec2,0.81761834005519,0.7712703652433182,0.5000581741333008,385.033694 +24462,Binary classification,Hoeffding Tree,Elec2,0.8151342954090184,0.7656509121061359,0.5002222061157227,415.059878 +25368,Binary classification,Hoeffding Tree,Elec2,0.8133401663578665,0.7649540828989824,0.5574884414672852,446.38466700000004 +26274,Binary classification,Hoeffding Tree,Elec2,0.8142199215925094,0.7659329592864336,0.5574884414672852,478.875266 +27180,Binary classification,Hoeffding Tree,Elec2,0.8130909893667906,0.7650758416574177,0.5574884414672852,512.642228 +28086,Binary classification,Hoeffding Tree,Elec2,0.810646252447926,0.7611605137878379,0.5575571060180664,547.6069200000001 +28992,Binary classification,Hoeffding Tree,Elec2,0.8084233037839329,0.755846667838931,0.5575571060180664,583.7938770000001 +29898,Binary classification,Hoeffding Tree,Elec2,0.8039602635715958,0.7488322262695523,0.5575571060180664,621.109455 +30804,Binary classification,Hoeffding Tree,Elec2,0.8052787066194851,0.7498540328634582,0.6720895767211914,659.6567610000001 +31710,Binary classification,Hoeffding Tree,Elec2,0.802863540319783,0.7460285215130216,0.6720895767211914,699.5069490000001 +32616,Binary classification,Hoeffding Tree,Elec2,0.8010731258623333,0.7451889089623752,0.6838197708129883,740.492735 +33522,Binary classification,Hoeffding Tree,Elec2,0.8010500880045345,0.7469934367768125,0.7644319534301758,782.6537000000001 +34428,Binary classification,Hoeffding Tree,Elec2,0.799663055160194,0.7444893120438633,0.7656755447387695,825.979857 +35334,Binary classification,Hoeffding Tree,Elec2,0.7997056576005434,0.7438746335637508,0.796971321105957,870.4262610000001 +36240,Binary classification,Hoeffding Tree,Elec2,0.798283617097602,0.7418420680887131,0.8215837478637695,916.0524170000001 +37146,Binary classification,Hoeffding Tree,Elec2,0.7980347287656482,0.741577678263865,0.8528566360473633,962.7298460000001 +38052,Binary classification,Hoeffding Tree,Elec2,0.7942761031247536,0.7384913476314559,0.8296480178833008,1010.431184 +38958,Binary classification,Hoeffding Tree,Elec2,0.791975768154632,0.7385131646876614,0.8296480178833008,1059.228611 +39864,Binary classification,Hoeffding Tree,Elec2,0.7917617841105787,0.7414904549842734,0.8308916091918945,1109.092208 +40770,Binary classification,Hoeffding Tree,Elec2,0.7937158134857367,0.7465187775031646,0.8308916091918945,1159.9359670000001 +41676,Binary classification,Hoeffding Tree,Elec2,0.7945770845830834,0.749744219357479,0.8553438186645508,1211.819823 +42582,Binary classification,Hoeffding Tree,Elec2,0.7952373124163359,0.7509355271802782,0.8799333572387695,1264.739744 +43488,Binary classification,Hoeffding Tree,Elec2,0.7953871271874353,0.7515912897822445,0.881199836730957,1318.691004 +44394,Binary classification,Hoeffding Tree,Elec2,0.7949676750839096,0.7499038303016982,0.881199836730957,1373.745004 +45300,Binary classification,Hoeffding Tree,Elec2,0.7956246274752202,0.7508745492707605,0.9384660720825195,1429.8385990000002 +45312,Binary classification,Hoeffding Tree,Elec2,0.7956346141113637,0.7508341405661393,0.9384660720825195,1485.976427 +25,Binary classification,Hoeffding Tree,Phishing,0.5833333333333334,0.6428571428571429,0.06842708587646484,0.007366 +50,Binary classification,Hoeffding Tree,Phishing,0.7346938775510204,0.7346938775510203,0.06842708587646484,0.021904 +75,Binary classification,Hoeffding Tree,Phishing,0.7837837837837838,0.7894736842105262,0.06842708587646484,0.108104 +100,Binary classification,Hoeffding Tree,Phishing,0.8080808080808081,0.8080808080808081,0.06842708587646484,0.26246400000000003 +125,Binary classification,Hoeffding Tree,Phishing,0.8145161290322581,0.8130081300813008,0.06842708587646484,0.42699600000000004 +150,Binary classification,Hoeffding Tree,Phishing,0.8187919463087249,0.8235294117647058,0.06842708587646484,0.670297 +175,Binary classification,Hoeffding Tree,Phishing,0.8333333333333334,0.8263473053892215,0.06842708587646484,0.944361 +200,Binary classification,Hoeffding Tree,Phishing,0.8341708542713567,0.8272251308900525,0.0684499740600586,1.225091 +225,Binary classification,Hoeffding Tree,Phishing,0.8303571428571429,0.8190476190476189,0.0684499740600586,1.620606 +250,Binary classification,Hoeffding Tree,Phishing,0.8313253012048193,0.8205128205128206,0.0684499740600586,2.072395 +275,Binary classification,Hoeffding Tree,Phishing,0.8321167883211679,0.8203125000000001,0.0684499740600586,2.536963 +300,Binary classification,Hoeffding Tree,Phishing,0.8394648829431438,0.8248175182481753,0.0684499740600586,3.035956 +325,Binary classification,Hoeffding Tree,Phishing,0.845679012345679,0.8263888888888888,0.0684499740600586,3.5418380000000003 +350,Binary classification,Hoeffding Tree,Phishing,0.8510028653295129,0.8289473684210527,0.0684499740600586,4.130076000000001 +375,Binary classification,Hoeffding Tree,Phishing,0.8529411764705882,0.8286604361370716,0.0684499740600586,4.770647 +400,Binary classification,Hoeffding Tree,Phishing,0.8546365914786967,0.8284023668639053,0.0684499740600586,5.418701 +425,Binary classification,Hoeffding Tree,Phishing,0.8561320754716981,0.8262108262108262,0.0684499740600586,6.103302 +450,Binary classification,Hoeffding Tree,Phishing,0.8596881959910914,0.8283378746594006,0.0684499740600586,6.878701 +475,Binary classification,Hoeffding Tree,Phishing,0.8565400843881856,0.826530612244898,0.0684499740600586,7.659851000000001 +500,Binary classification,Hoeffding Tree,Phishing,0.8577154308617234,0.8313539192399049,0.0684499740600586,8.471725000000001 +525,Binary classification,Hoeffding Tree,Phishing,0.8587786259541985,0.8287037037037036,0.0684499740600586,9.290161000000001 +550,Binary classification,Hoeffding Tree,Phishing,0.8579234972677595,0.8289473684210527,0.0684499740600586,10.200081 +575,Binary classification,Hoeffding Tree,Phishing,0.8606271777003485,0.8319327731092437,0.0684499740600586,11.144835 +600,Binary classification,Hoeffding Tree,Phishing,0.8647746243739566,0.834355828220859,0.0684499740600586,12.149797 +625,Binary classification,Hoeffding Tree,Phishing,0.8669871794871795,0.8336673346693387,0.0684499740600586,13.191129 +650,Binary classification,Hoeffding Tree,Phishing,0.8705701078582434,0.8409090909090909,0.0684499740600586,14.297317 +675,Binary classification,Hoeffding Tree,Phishing,0.870919881305638,0.8449197860962566,0.0684499740600586,15.408972 +700,Binary classification,Hoeffding Tree,Phishing,0.8755364806866953,0.8486956521739131,0.0684499740600586,16.598807 +725,Binary classification,Hoeffding Tree,Phishing,0.8784530386740331,0.8547854785478548,0.0684499740600586,17.82593 +750,Binary classification,Hoeffding Tree,Phishing,0.8798397863818425,0.8571428571428571,0.0684499740600586,19.123649 +775,Binary classification,Hoeffding Tree,Phishing,0.8798449612403101,0.8567026194144837,0.0684499740600586,20.439518 +800,Binary classification,Hoeffding Tree,Phishing,0.8798498122653317,0.8584070796460177,0.006070137023925781,21.870793 +825,Binary classification,Hoeffding Tree,Phishing,0.8786407766990292,0.8575498575498576,0.1326732635498047,23.308925 +850,Binary classification,Hoeffding Tree,Phishing,0.8798586572438163,0.8579387186629527,0.13269615173339844,24.793982 +875,Binary classification,Hoeffding Tree,Phishing,0.8810068649885584,0.8583106267029972,0.13269615173339844,26.327422 +900,Binary classification,Hoeffding Tree,Phishing,0.882091212458287,0.8590425531914893,0.1327190399169922,27.867454 +925,Binary classification,Hoeffding Tree,Phishing,0.8831168831168831,0.8611825192802056,0.1327190399169922,29.49699 +950,Binary classification,Hoeffding Tree,Phishing,0.880927291886196,0.8599752168525404,0.1327190399169922,31.132602 +975,Binary classification,Hoeffding Tree,Phishing,0.8819301848049281,0.8609431680773881,0.1327190399169922,32.858381 +1000,Binary classification,Hoeffding Tree,Phishing,0.8828828828828829,0.8621908127208481,0.1327190399169922,34.600804000000004 +1025,Binary classification,Hoeffding Tree,Phishing,0.8818359375,0.8613974799541809,0.1327190399169922,36.37479200000001 +1050,Binary classification,Hoeffding Tree,Phishing,0.8836987607244995,0.8641425389755011,0.1327190399169922,38.236126000000006 +1075,Binary classification,Hoeffding Tree,Phishing,0.8845437616387337,0.8658008658008659,0.1327190399169922,40.114172 +1100,Binary classification,Hoeffding Tree,Phishing,0.8844404003639672,0.8656084656084656,0.1327190399169922,41.998405000000005 +1125,Binary classification,Hoeffding Tree,Phishing,0.8816725978647687,0.8630278063851698,0.1327190399169922,43.96255500000001 +1150,Binary classification,Hoeffding Tree,Phishing,0.8807658833768495,0.8614762386248735,0.1327190399169922,45.93298000000001 +1175,Binary classification,Hoeffding Tree,Phishing,0.879045996592845,0.8594059405940594,0.1327190399169922,47.92952100000001 +1200,Binary classification,Hoeffding Tree,Phishing,0.8807339449541285,0.8610301263362489,0.1327190399169922,50.02063300000001 +1225,Binary classification,Hoeffding Tree,Phishing,0.880718954248366,0.8609523809523809,0.1327190399169922,52.11693600000001 +1250,Binary classification,Hoeffding Tree,Phishing,0.8799039231385108,0.8605947955390334,0.1327190399169922,54.27575100000001 +1903,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,1.086226 +3806,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,3.167363 +5709,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,6.335451 +7612,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,10.587829 +9515,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,15.968691 +11418,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,22.515101 +13321,Binary classification,Hoeffding Tree,SMTP,1.0,0.0,0.01702117919921875,30.177580000000003 +15224,Binary classification,Hoeffding Tree,SMTP,0.9992774091834724,0.0,0.02622222900390625,38.907943 +17127,Binary classification,Hoeffding Tree,SMTP,0.9992409202382343,0.0,0.0170440673828125,48.927066 +19030,Binary classification,Hoeffding Tree,SMTP,0.9993168322034789,0.0,0.0170440673828125,60.172540000000005 +20933,Binary classification,Hoeffding Tree,SMTP,0.999378941333843,0.0,0.0170440673828125,72.66801500000001 +22836,Binary classification,Hoeffding Tree,SMTP,0.9994306984891613,0.0,0.0170440673828125,86.48422800000002 +24739,Binary classification,Hoeffding Tree,SMTP,0.9994744926833212,0.0,0.0170440673828125,101.51872100000001 +26642,Binary classification,Hoeffding Tree,SMTP,0.9994744942006681,0.0,0.0170440673828125,117.73184500000002 +28545,Binary classification,Hoeffding Tree,SMTP,0.9995095291479821,0.0,0.0170440673828125,135.114956 +30448,Binary classification,Hoeffding Tree,SMTP,0.9995401845830459,0.0,0.0170440673828125,153.67141 +32351,Binary classification,Hoeffding Tree,SMTP,0.9995672333848532,0.0,0.0170440673828125,173.49434000000002 +34254,Binary classification,Hoeffding Tree,SMTP,0.9995912766764955,0.0,0.0170440673828125,194.59387200000003 +36157,Binary classification,Hoeffding Tree,SMTP,0.9996127890253347,0.0,0.0170440673828125,216.90021000000004 +38060,Binary classification,Hoeffding Tree,SMTP,0.9996321500827662,0.0,0.0170440673828125,240.42103000000003 +39963,Binary classification,Hoeffding Tree,SMTP,0.9996496671838246,0.0,0.0170440673828125,265.208452 +41866,Binary classification,Hoeffding Tree,SMTP,0.9996655917831124,0.0,0.0170440673828125,291.209442 +43769,Binary classification,Hoeffding Tree,SMTP,0.9996801316029976,0.0,0.0170440673828125,318.43843100000004 +45672,Binary classification,Hoeffding Tree,SMTP,0.9996934597446958,0.0,0.0170440673828125,346.89099000000004 +47575,Binary classification,Hoeffding Tree,SMTP,0.9997057216126456,0.0,0.0170440673828125,376.52940800000005 +49478,Binary classification,Hoeffding Tree,SMTP,0.99971704024092,0.0,0.0170440673828125,407.49804200000005 +51381,Binary classification,Hoeffding Tree,SMTP,0.9996885947839627,0.0,0.0170440673828125,439.6062170000001 +53284,Binary classification,Hoeffding Tree,SMTP,0.9996997166075484,0.0,0.0170440673828125,472.95335100000005 +55187,Binary classification,Hoeffding Tree,SMTP,0.999710071394919,0.0,0.0170440673828125,507.47402400000004 +57090,Binary classification,Hoeffding Tree,SMTP,0.9995620872672494,0.0,0.0170440673828125,543.2100710000001 +58993,Binary classification,Hoeffding Tree,SMTP,0.9995762137238947,0.0,0.0170440673828125,580.098547 +60896,Binary classification,Hoeffding Tree,SMTP,0.999589457262501,0.0,0.0170440673828125,618.1800870000001 +62799,Binary classification,Hoeffding Tree,SMTP,0.9995700500015924,0.0,0.0170440673828125,657.2504170000001 +64702,Binary classification,Hoeffding Tree,SMTP,0.9995826957852274,0.0,0.0170440673828125,697.447209 +66605,Binary classification,Hoeffding Tree,SMTP,0.9995946189418053,0.0,0.0170440673828125,738.7766780000001 +68508,Binary classification,Hoeffding Tree,SMTP,0.9995766855941729,0.0,0.0170440673828125,781.207926 +70411,Binary classification,Hoeffding Tree,SMTP,0.9995881266865502,0.0,0.0170440673828125,824.7260210000001 +72314,Binary classification,Hoeffding Tree,SMTP,0.9995989656078437,0.0,0.0170440673828125,869.3947840000001 +74217,Binary classification,Hoeffding Tree,SMTP,0.99960924867953,0.0,0.0170440673828125,915.176721 +76120,Binary classification,Hoeffding Tree,SMTP,0.9996190175908775,0.0,0.0170440673828125,961.985028 +78023,Binary classification,Hoeffding Tree,SMTP,0.9996283099638563,0.0,0.0170440673828125,1009.890756 +79926,Binary classification,Hoeffding Tree,SMTP,0.9996371598373475,0.0,0.0170440673828125,1058.848202 +81829,Binary classification,Hoeffding Tree,SMTP,0.9996455980837855,0.0,0.0170440673828125,1108.7919539999998 +83732,Binary classification,Hoeffding Tree,SMTP,0.9996536527689864,0.0,0.0170440673828125,1159.7893379999998 +85635,Binary classification,Hoeffding Tree,SMTP,0.999661349463998,0.0,0.0170440673828125,1211.840415 +87538,Binary classification,Hoeffding Tree,SMTP,0.9996687115162731,0.0,0.0170440673828125,1264.8087919999998 +89441,Binary classification,Hoeffding Tree,SMTP,0.9996645796064401,0.0,0.0170440673828125,1318.8158339999998 +91344,Binary classification,Hoeffding Tree,SMTP,0.999671567607808,0.0,0.0170440673828125,1373.8298589999997 +93247,Binary classification,Hoeffding Tree,SMTP,0.9996782703815713,0.0,0.0170440673828125,1429.7685149999998 +95150,Binary classification,Hoeffding Tree,SMTP,0.9996847050415664,0.0,0.0170440673828125,1486.6476859999998 +95156,Binary classification,Hoeffding Tree,SMTP,0.9996847249224948,0.0,0.0170440673828125,1543.5553739999998 +106,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5714285714285714,0.628099173553719,0.025684356689453125,0.216494 +212,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5592417061611374,0.5903083700440529,0.025768280029296875,0.463954 +318,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5615141955835962,0.5947521865889213,0.025829315185546875,0.862573 +424,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5555555555555556,0.5822222222222222,0.025829315185546875,1.3898329999999999 +530,Binary classification,Hoeffding Adaptive Tree,Bananas,0.555765595463138,0.5506692160611854,0.025829315185546875,1.9641119999999999 +636,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5543307086614173,0.5291181364392679,0.025890350341796875,2.6939569999999997 +742,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5708502024291497,0.5167173252279634,0.025890350341796875,3.4801279999999997 +848,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5761511216056671,0.510231923601637,0.025890350341796875,4.453125999999999 +954,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5844700944386149,0.505,0.025890350341796875,5.580188 +1060,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5920679886685553,0.49532710280373826,0.025890350341796875,6.755147 +1166,Binary classification,Hoeffding Adaptive Tree,Bananas,0.590557939914163,0.478688524590164,0.025890350341796875,8.08575 +1272,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971675845790716,0.48073022312373226,0.025890350341796875,9.558451000000002 +1378,Binary classification,Hoeffding Adaptive Tree,Bananas,0.599128540305011,0.4661508704061895,0.025951385498046875,11.087295000000001 +1484,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5994605529332434,0.458029197080292,0.025951385498046875,12.740385000000002 +1590,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5997482693517936,0.4517241379310345,0.025951385498046875,14.490633000000003 +1696,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6011799410029498,0.4459016393442623,0.025951385498046875,16.383274000000004 +1802,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6018878400888396,0.44547563805104406,0.025951385498046875,18.381747000000004 +1908,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6030414263240692,0.44704163623082543,0.025951385498046875,20.420329000000002 +2014,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5986090412319921,0.44352617079889806,0.025951385498046875,22.615668000000003 +2120,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5960358659745163,0.4427083333333333,0.025951385498046875,24.891681000000002 +2226,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5968539325842697,0.4425108763206961,0.025951385498046875,27.295309000000003 +2332,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5975975975975976,0.44233055885850175,0.025951385498046875,29.792211 +2438,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5982765695527288,0.4396107613050944,0.025951385498046875,32.414577 +2544,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5973259929217459,0.4398249452954048,0.03029155731201172,35.17394 +2650,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5956964892412231,0.44363636363636366,0.056708335876464844,38.067898 +2756,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5985480943738657,0.44975124378109455,0.056952476501464844,41.158639 +2862,Binary classification,Hoeffding Adaptive Tree,Bananas,0.600139811254806,0.4536771728748806,0.057196617126464844,44.452629 +2968,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5979103471520054,0.45250114731528224,0.057303428649902344,47.888765 +3074,Binary classification,Hoeffding Adaptive Tree,Bananas,0.5971363488447771,0.4497777777777778,0.057425498962402344,51.476908 +3180,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6008178672538534,0.44993498049414826,0.057486534118652344,55.178717 +3286,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6024353120243531,0.4470787468247248,0.057608604431152344,59.035765 +3392,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6012975523444412,0.444991789819376,0.057669639587402344,63.084021 +3498,Binary classification,Hoeffding Adaptive Tree,Bananas,0.603946239633972,0.44310414153598715,0.057730674743652344,67.283017 +3604,Binary classification,Hoeffding Adaptive Tree,Bananas,0.607826810990841,0.4452296819787986,0.057730674743652344,71.628079 +3710,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6071717444055001,0.441976254308694,0.057730674743652344,76.17092 +3816,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6062909567496724,0.43787425149700593,0.057791709899902344,80.84267399999999 +3922,Binary classification,Hoeffding Adaptive Tree,Bananas,0.606988013261923,0.4353242946134115,0.057852745056152344,85.696272 +4028,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6088899925502855,0.4360902255639098,0.057852745056152344,90.65857299999999 +4134,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6082748608758771,0.4341139461726669,0.057913780212402344,95.89501499999999 +4240,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6105213493748526,0.4370951244459598,0.057913780212402344,101.21739399999998 +4346,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6119677790563867,0.43724966622162886,0.057974815368652344,106.75825799999998 +4452,Binary classification,Hoeffding Adaptive Tree,Bananas,0.614243990114581,0.4387054593004249,0.057974815368652344,112.41943399999998 +4558,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6126837831906956,0.4355612408058842,0.057974815368652344,118.26167899999999 +4664,Binary classification,Hoeffding Adaptive Tree,Bananas,0.613339052112374,0.4360337816703159,0.057974815368652344,124.26691199999999 +4770,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6148039421262319,0.4352905010759299,0.0641164779663086,130.389994 +4876,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6157948717948718,0.4332829046898639,0.0641164779663086,136.677955 +4982,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6167436257779563,0.43470535978679303,0.0641164779663086,143.134937 +5088,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6158836249262827,0.4313154831199068,0.0641775131225586,149.736273 +5194,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6160215674947044,0.42963386727688785,0.0642385482788086,156.525598 +5300,Binary classification,Hoeffding Adaptive Tree,Bananas,0.6165314210228345,0.42824985931344967,0.06184673309326172,163.516222 +906,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8386740331491712,0.8370535714285713,0.15532493591308594,2.212895 +1812,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8823854224185533,0.857334226389819,0.2904033660888672,6.521798 +2718,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8715495031284505,0.8438478747203579,0.1283740997314453,13.845606 +3624,Binary classification,Hoeffding Adaptive Tree,Elec2,0.875241512558653,0.8472972972972973,0.2500133514404297,22.913432999999998 +4530,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8737028041510267,0.8396860986547084,0.3712940216064453,34.075607999999995 +5436,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8656853725850966,0.8300744878957169,0.4407672882080078,47.709683999999996 +6342,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8646901119697209,0.8296943231441047,0.26204872131347656,63.50523799999999 +7248,Binary classification,Hoeffding Adaptive Tree,Elec2,0.864771629639851,0.8289703315881326,0.2866535186767578,81.25362299999999 +8154,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8572304673126456,0.8312064965197217,0.28668785095214844,101.144105 +9060,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8580417264598742,0.8370501773948302,0.2865924835205078,123.033084 +9966,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8544907175112895,0.8369320737741789,0.3109416961669922,147.021637 +10872,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8583386992916935,0.8434322895485971,0.37159156799316406,172.950216 +11778,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8529336842999066,0.8357982555934774,0.46280479431152344,201.490822 +12684,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8533469999211543,0.8362099330750264,0.1895275115966797,232.642586 +13590,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8551769813820002,0.8395303326810176,0.19393348693847656,265.763258 +14496,Binary classification,Hoeffding Adaptive Tree,Elec2,0.855122456019317,0.8397435897435896,0.1697406768798828,300.834382 +15402,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8537757288487761,0.8365984617617181,0.1694965362548828,338.213883 +16308,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8506776231066413,0.8316626339440029,0.16408348083496094,377.804328 +17214,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8495904258409341,0.8278704873346187,0.1691112518310547,419.462025 +18120,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8498261493459904,0.827752104830031,0.19867897033691406,463.148728 +19026,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8522996057818659,0.8287211995611362,0.25722312927246094,508.959572 +19932,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8470222266820531,0.8238895627563102,0.31537818908691406,557.675148 +20838,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8434035609732687,0.8200121352529097,0.32396507263183594,609.8923100000001 +21744,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8450535804626776,0.8196563353139554,0.3222179412841797,664.4541280000001 +22650,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8452911828336792,0.8184455958549223,0.4409503936767578,721.910691 +23556,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8424962852897474,0.8143700590413289,0.44409751892089844,782.4177400000001 +24462,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8403990024937655,0.8111089607122121,0.5018138885498047,845.9758730000001 +25368,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8367169945204399,0.8072053621299572,0.5608501434326172,913.0025360000001 +26274,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8375137974346287,0.8080226649278229,0.31543540954589844,983.5552650000001 +27180,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8378527539644579,0.8096081565645656,0.31577491760253906,1056.640488 +28086,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8349652839594089,0.8051456678017405,0.18702125549316406,1131.521461 +28992,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8328791693973991,0.8007566722868775,0.2230243682861328,1208.700577 +29898,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8303843194969395,0.7968267959453503,0.10404396057128906,1288.2399990000001 +30804,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8300490211992338,0.7958984755740965,0.22612571716308594,1369.046902 +31710,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8277460657857391,0.7934815486993346,0.37830543518066406,1451.951614 +32616,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8227502682814656,0.7867025790502896,0.1292285919189453,1536.962592 +33522,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8198442767220548,0.7850354180756772,0.1289234161376953,1623.2849270000002 +34428,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8166264850262875,0.7809127190699289,0.19405555725097656,1710.9311020000002 +35334,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8170265757224124,0.7804530172852923,0.34708213806152344,1800.2335220000002 +36240,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8175446342338365,0.7795559111822364,0.4078502655029297,1890.9592170000003 +37146,Binary classification,Hoeffding Adaptive Tree,Elec2,0.816610580158837,0.7771817349208426,0.4166545867919922,1983.2562510000002 +38052,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8155107618722242,0.7753456221198157,0.1291065216064453,2077.01254 +38958,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8146674538593834,0.7751479289940829,0.20058250427246094,2172.116472 +39864,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8157188370167825,0.7778516995282448,0.16962623596191406,2268.811937 +40770,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8171650028207706,0.7812151452891106,0.2508678436279297,2366.696648 +41676,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8187402519496101,0.7846022241231821,0.2554492950439453,2465.8461540000003 +42582,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8196848359597003,0.7859015113490603,0.3113727569580078,2566.2285070000003 +43488,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8203141168625107,0.7864093592827466,0.2909717559814453,2667.8448940000003 +44394,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8199265649989863,0.7853959731543624,0.43657493591308594,2771.03508 +45300,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8212543323252169,0.7873854475750336,0.43532752990722656,2875.842657 +45312,Binary classification,Hoeffding Adaptive Tree,Elec2,0.8212575312837942,0.7873440987265327,0.43532752990722656,2980.68937 +25,Binary classification,Hoeffding Adaptive Tree,Phishing,0.5833333333333334,0.6428571428571429,0.07476425170898438,0.008848 +50,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7346938775510204,0.7346938775510203,0.07482528686523438,0.123836 +75,Binary classification,Hoeffding Adaptive Tree,Phishing,0.7837837837837838,0.7894736842105262,0.07482528686523438,0.332733 +100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8080808080808081,0.8080808080808081,0.07488632202148438,0.575353 +125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8225806451612904,0.819672131147541,0.07488632202148438,0.909775 +150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.825503355704698,0.8289473684210527,0.07490921020507812,1.284417 +175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8333333333333334,0.8242424242424242,0.07497024536132812,1.765942 +200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8291457286432161,0.8191489361702128,0.07497024536132812,2.25515 +225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8303571428571429,0.8155339805825242,0.07497024536132812,2.805996 +250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8313253012048193,0.817391304347826,0.07497024536132812,3.45663 +275,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8321167883211679,0.8174603174603176,0.07497024536132812,4.129749 +300,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8361204013377926,0.8178438661710038,0.07497024536132812,4.871246 +325,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8425925925925926,0.8197879858657244,0.07503128051757812,5.6743250000000005 +350,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8481375358166189,0.822742474916388,0.07503128051757812,6.528728000000001 +375,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8502673796791443,0.8227848101265823,0.07503128051757812,7.4517880000000005 +400,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8521303258145363,0.8228228228228228,0.07503128051757812,8.382915 +425,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8537735849056604,0.8208092485549133,0.07503128051757812,9.397859 +450,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8574610244988864,0.8232044198895027,0.07503128051757812,10.451359 +475,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8565400843881856,0.8238341968911918,0.07503128051757812,11.619284 +500,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8557114228456913,0.8260869565217391,0.07503128051757812,12.854081 +525,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8568702290076335,0.823529411764706,0.07503128051757812,14.155744 +550,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8561020036429873,0.8240534521158129,0.07503128051757812,15.508673 +575,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8554006968641115,0.8230277185501066,0.11409282684326172,16.956902 +600,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8547579298831386,0.8176100628930818,0.14198589324951172,18.498414999999998 +625,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8573717948717948,0.8172484599589321,0.14222240447998047,20.046891 +650,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8597842835130971,0.8233009708737864,0.14239025115966797,21.659211 +675,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8590504451038575,0.8263254113345521,0.14245128631591797,23.289464 +700,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8640915593705293,0.8306595365418894,0.14251232147216797,25.025232 +725,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8646408839779005,0.8344594594594595,0.14257335662841797,26.77059 +750,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8664886515353805,0.8371335504885993,0.14263439178466797,28.602532 +775,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8643410852713178,0.8330683624801273,0.14265727996826172,30.506622 +800,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8635794743429287,0.8340943683409437,0.14265727996826172,32.425334 +825,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8628640776699029,0.8345534407027819,0.14265727996826172,34.352118 +850,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8645465253239105,0.8364153627311521,0.14271831512451172,36.371837 +875,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8672768878718535,0.838888888888889,0.14271831512451172,38.48177 +900,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8665183537263627,0.8378378378378378,0.14277935028076172,40.637242 +925,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8668831168831169,0.8400520156046815,0.14277935028076172,42.878637 +950,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8661749209694415,0.8410513141426783,0.14284038543701172,45.126805999999995 +975,Binary classification,Hoeffding Adaptive Tree,Phishing,0.86652977412731,0.8414634146341464,0.14284038543701172,47.480371 +1000,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8638638638638638,0.8392434988179669,0.14284038543701172,49.860963999999996 +1025,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8623046875,0.8377445339470656,0.14284038543701172,52.359728 +1050,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8636796949475691,0.8402234636871508,0.14284038543701172,54.917308999999996 +1075,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8649906890130353,0.8429035752979415,0.14284038543701172,57.530035 +1100,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8671519563239308,0.8456659619450316,0.14284038543701172,60.237019 +1125,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8701067615658363,0.8507157464212679,0.14284038543701172,62.977514 +1150,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8720626631853786,0.852852852852853,0.14290142059326172,65.816825 +1175,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8713798977853492,0.8521057786483839,0.14290142059326172,68.70086099999999 +1200,Binary classification,Hoeffding Adaptive Tree,Phishing,0.872393661384487,0.8530259365994236,0.14290142059326172,71.69850399999999 +1225,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8733660130718954,0.8541862652869238,0.14296245574951172,74.74610599999998 +1250,Binary classification,Hoeffding Adaptive Tree,Phishing,0.8742994395516414,0.8560953253895509,0.14296245574951172,77.86495099999998 +1903,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02372455596923828,1.538486 +3806,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02378559112548828,4.672632 +5709,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,9.280899 +7612,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,15.518128 +9515,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02384662628173828,23.359252 +11418,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02390766143798828,32.724381 +13321,Binary classification,Hoeffding Adaptive Tree,SMTP,1.0,0.0,0.02390766143798828,43.566998 +15224,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992774091834724,0.0,0.03315448760986328,56.059492 +17127,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9992409202382343,0.0,0.02393054962158203,70.315874 +19030,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9993168322034789,0.0,0.02393054962158203,86.215201 +20933,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999378941333843,0.0,0.02399158477783203,103.810178 +22836,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994306984891613,0.0,0.02399158477783203,123.100461 +24739,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744926833212,0.0,0.02399158477783203,144.232391 +26642,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9994744942006681,0.0,0.02399158477783203,167.059054 +28545,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995095291479821,0.0,0.02399158477783203,191.625058 +30448,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995401845830459,0.0,0.02399158477783203,217.971038 +32351,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995672333848532,0.0,0.02399158477783203,246.1385 +34254,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995912766764955,0.0,0.02399158477783203,276.028821 +36157,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996127890253347,0.0,0.02399158477783203,307.727155 +38060,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996321500827662,0.0,0.02399158477783203,341.14046099999996 +39963,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996496671838246,0.0,0.02399158477783203,376.24706999999995 +41866,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996655917831124,0.0,0.02405261993408203,412.98223499999995 +43769,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996801316029976,0.0,0.02405261993408203,451.37683599999997 +45672,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996934597446958,0.0,0.02405261993408203,491.34241199999997 +47575,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9997057216126456,0.0,0.02405261993408203,532.9409959999999 +49478,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99971704024092,0.0,0.02405261993408203,576.05932 +51381,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996885947839627,0.0,0.02405261993408203,620.875381 +53284,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996997166075484,0.0,0.02405261993408203,667.2134759999999 +55187,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999710071394919,0.0,0.02405261993408203,715.0678459999999 +57090,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995620872672494,0.0,0.02405261993408203,764.41064 +58993,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995762137238947,0.0,0.02405261993408203,815.214443 +60896,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999589457262501,0.0,0.02405261993408203,867.5634849999999 +62799,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995700500015924,0.0,0.02405261993408203,921.3075979999999 +64702,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995826957852274,0.0,0.02405261993408203,976.5012499999999 +66605,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995946189418053,0.0,0.02405261993408203,1033.066349 +68508,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995766855941729,0.0,0.02405261993408203,1090.838953 +70411,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995881266865502,0.0,0.02405261993408203,1149.858677 +72314,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9995989656078437,0.0,0.02405261993408203,1210.0750699999999 +74217,Binary classification,Hoeffding Adaptive Tree,SMTP,0.99960924867953,0.0,0.02405261993408203,1271.4412419999999 +76120,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996190175908775,0.0,0.02405261993408203,1333.994434 +78023,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996283099638563,0.0,0.02405261993408203,1397.762471 +79926,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996371598373475,0.0,0.02405261993408203,1462.67416 +81829,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996455980837855,0.0,0.02405261993408203,1528.680001 +83732,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996536527689864,0.0,0.02411365509033203,1595.853878 +85635,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999661349463998,0.0,0.02411365509033203,1664.0432529999998 +87538,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996687115162731,0.0,0.02411365509033203,1733.3243249999998 +89441,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996645796064401,0.0,0.02411365509033203,1803.716354 +91344,Binary classification,Hoeffding Adaptive Tree,SMTP,0.999671567607808,0.0,0.02411365509033203,1875.203937 +93247,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996782703815713,0.0,0.02411365509033203,1947.740223 +95150,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996847050415664,0.0,0.02411365509033203,2021.343945 +95156,Binary classification,Hoeffding Adaptive Tree,SMTP,0.9996847249224948,0.0,0.02411365509033203,2094.94949 +106,Binary classification,Adaptive Random Forest,Bananas,0.638095238095238,0.5777777777777778,0.6023197174072266,1.25138 +212,Binary classification,Adaptive Random Forest,Bananas,0.7535545023696683,0.711111111111111,1.0872974395751953,3.920593 +318,Binary classification,Adaptive Random Forest,Bananas,0.7760252365930599,0.7380073800738007,1.471883773803711,8.005582 +424,Binary classification,Adaptive Random Forest,Bananas,0.8085106382978723,0.7768595041322315,1.8271961212158203,13.704046000000002 +530,Binary classification,Adaptive Random Forest,Bananas,0.8204158790170132,0.7845804988662132,2.2761096954345703,21.017212 +636,Binary classification,Adaptive Random Forest,Bananas,0.8362204724409449,0.8052434456928838,2.6539440155029297,30.07026 +742,Binary classification,Adaptive Random Forest,Bananas,0.8434547908232118,0.8110749185667754,3.0667247772216797,40.88011 +848,Binary classification,Adaptive Random Forest,Bananas,0.8512396694214877,0.8220338983050847,3.4897289276123047,53.580909000000005 +954,Binary classification,Adaptive Random Forest,Bananas,0.8583420776495279,0.8301886792452831,3.9347667694091797,68.252368 +1060,Binary classification,Adaptive Random Forest,Bananas,0.8659112370160529,0.8378995433789953,4.283300399780273,84.83204500000001 +1166,Binary classification,Adaptive Random Forest,Bananas,0.8695278969957082,0.8429752066115702,4.800313949584961,103.574646 +1272,Binary classification,Adaptive Random Forest,Bananas,0.8693941778127459,0.8442776735459662,5.391313552856445,124.497156 +1378,Binary classification,Adaptive Random Forest,Bananas,0.8714596949891068,0.8454148471615721,5.846994400024414,147.830368 +1484,Binary classification,Adaptive Random Forest,Bananas,0.8759271746459879,0.8518518518518519,6.193078994750977,173.44391299999998 +1590,Binary classification,Adaptive Random Forest,Bananas,0.8753933291378225,0.8520179372197308,6.296388626098633,201.55742899999998 +1696,Binary classification,Adaptive Random Forest,Bananas,0.8755162241887906,0.8523442967109867,6.211141586303711,232.060182 +1802,Binary classification,Adaptive Random Forest,Bananas,0.8767351471404775,0.8550913838120104,6.65928840637207,264.912691 +1908,Binary classification,Adaptive Random Forest,Bananas,0.8730991085474568,0.8522588522588523,6.686662673950195,300.275827 +2014,Binary classification,Adaptive Random Forest,Bananas,0.8708395429706905,0.8507462686567164,7.210599899291992,338.470819 +2120,Binary classification,Adaptive Random Forest,Bananas,0.8725814063237376,0.8540540540540541,7.48176383972168,378.991608 +2226,Binary classification,Adaptive Random Forest,Bananas,0.8723595505617977,0.8539094650205761,7.915548324584961,421.86917 +2332,Binary classification,Adaptive Random Forest,Bananas,0.8751608751608752,0.8574228319451249,8.423246383666992,467.302026 +2438,Binary classification,Adaptive Random Forest,Bananas,0.8740254411161263,0.8560712611345522,8.870996475219727,515.200386 +2544,Binary classification,Adaptive Random Forest,Bananas,0.874557609123083,0.857779759251003,9.376256942749023,565.505493 +2650,Binary classification,Adaptive Random Forest,Bananas,0.8761796904492262,0.8600682593856656,9.769472122192383,618.233402 +2756,Binary classification,Adaptive Random Forest,Bananas,0.8780399274047187,0.8621821164889254,10.359186172485352,673.368122 +2862,Binary classification,Adaptive Random Forest,Bananas,0.8797623208668298,0.8638163103721298,10.741575241088867,730.824809 +2968,Binary classification,Adaptive Random Forest,Bananas,0.8800134816312774,0.8637059724349159,11.09235954284668,790.5343869999999 +3074,Binary classification,Adaptive Random Forest,Bananas,0.8805727302310445,0.8648250460405157,11.562868118286133,852.458144 +3180,Binary classification,Adaptive Random Forest,Bananas,0.8826675055048757,0.8667381207574134,10.152639389038086,916.4431709999999 +3286,Binary classification,Adaptive Random Forest,Bananas,0.882496194824962,0.8663434903047091,10.670488357543945,982.3914759999999 +3392,Binary classification,Adaptive Random Forest,Bananas,0.8826304924800944,0.867244829886591,11.057397842407227,1050.1851049999998 +3498,Binary classification,Adaptive Random Forest,Bananas,0.8839004861309694,0.8680961663417803,10.334085464477539,1119.7824649999998 +3604,Binary classification,Adaptive Random Forest,Bananas,0.8850957535387177,0.8689043698543382,10.692270278930664,1191.1920959999998 +3710,Binary classification,Adaptive Random Forest,Bananas,0.8846050148287948,0.8687116564417178,11.112970352172852,1264.4750769999998 +3816,Binary classification,Adaptive Random Forest,Bananas,0.8859764089121888,0.870420017873101,11.59941291809082,1339.6069449999998 +3922,Binary classification,Adaptive Random Forest,Bananas,0.884723284876307,0.8687572590011614,12.03856086730957,1416.6409169999997 +4028,Binary classification,Adaptive Random Forest,Bananas,0.8840327787434815,0.867892503536068,12.434591293334961,1495.7064749999997 +4134,Binary classification,Adaptive Random Forest,Bananas,0.884587466731188,0.868558831634059,12.796384811401367,1576.7285749999996 +4240,Binary classification,Adaptive Random Forest,Bananas,0.8858221278603444,0.8701019860440149,13.120096206665039,1659.7498909999997 +4346,Binary classification,Adaptive Random Forest,Bananas,0.8872266973532796,0.8716605552645365,13.362188339233398,1744.8641249999996 +4452,Binary classification,Adaptive Random Forest,Bananas,0.8869916872612896,0.8713225888974162,13.906320571899414,1831.9815919999996 +4558,Binary classification,Adaptive Random Forest,Bananas,0.8872064955014264,0.8719481813652217,14.321008682250977,1921.0718599999996 +4664,Binary classification,Adaptive Random Forest,Bananas,0.8876259918507399,0.8728155339805825,14.677774429321289,2012.2265639999996 +4770,Binary classification,Adaptive Random Forest,Bananas,0.8867687146152233,0.8716119828815977,15.041936874389648,2105.5104569999994 +4876,Binary classification,Adaptive Random Forest,Bananas,0.886974358974359,0.8715318256003731,15.36302375793457,2200.9404059999993 +4982,Binary classification,Adaptive Random Forest,Bananas,0.8877735394499097,0.8726941471191073,14.241693496704102,2298.464636999999 +5088,Binary classification,Adaptive Random Forest,Bananas,0.886966778061726,0.8716804284757868,14.559698104858398,2397.961828999999 +5194,Binary classification,Adaptive Random Forest,Bananas,0.8869632197188523,0.8716939890710382,15.019205093383789,2499.520506999999 +5300,Binary classification,Adaptive Random Forest,Bananas,0.886959803736554,0.8717070036410367,15.355104446411133,2603.0162549999986 +906,Binary classification,Adaptive Random Forest,Elec2,0.8674033149171271,0.8669623059866962,3.022599220275879,14.706798 +1812,Binary classification,Adaptive Random Forest,Elec2,0.8956377691882937,0.8737474949899798,3.453568458557129,43.639849999999996 +2718,Binary classification,Adaptive Random Forest,Elec2,0.889216047110784,0.8638625056535504,5.134407997131348,89.85880599999999 +3624,Binary classification,Adaptive Random Forest,Elec2,0.8901462876069556,0.8665325285043594,5.045891761779785,149.36791399999998 +4530,Binary classification,Adaptive Random Forest,Elec2,0.8924707440936189,0.8628555336524922,6.377499580383301,220.195834 +5436,Binary classification,Adaptive Random Forest,Elec2,0.8870285188592456,0.8556652562294312,8.556572914123535,302.53910099999996 +6342,Binary classification,Adaptive Random Forest,Elec2,0.884245387162908,0.8540175019888624,10.355942726135254,396.16016899999994 +7248,Binary classification,Adaptive Random Forest,Elec2,0.8835380157306472,0.8516174402250353,10.061070442199707,501.0892999999999 +8154,Binary classification,Adaptive Random Forest,Elec2,0.8847050165583221,0.8605341246290802,12.516213417053223,615.7123809999999 +9060,Binary classification,Adaptive Random Forest,Elec2,0.8869632409758251,0.8668400520156047,14.3945894241333,740.068354 +9966,Binary classification,Adaptive Random Forest,Elec2,0.8839939789262419,0.8666974169741698,15.028592109680176,874.678214 +10872,Binary classification,Adaptive Random Forest,Elec2,0.886119032287738,0.8712830110210023,18.58602237701416,1018.7273250000001 +11778,Binary classification,Adaptive Random Forest,Elec2,0.8851150547677676,0.869464544138929,18.284192085266113,1172.329585 +12684,Binary classification,Adaptive Random Forest,Elec2,0.8825987542379563,0.8672550592850139,18.562626838684082,1335.5183499999998 +13590,Binary classification,Adaptive Random Forest,Elec2,0.8835087202884686,0.8699794661190965,22.36763858795166,1507.9316749999998 +14496,Binary classification,Adaptive Random Forest,Elec2,0.883270093135564,0.870085995085995,23.97218418121338,1690.157865 +15402,Binary classification,Adaptive Random Forest,Elec2,0.8826050256476852,0.8679713743245216,24.89116382598877,1881.8741799999998 +16308,Binary classification,Adaptive Random Forest,Elec2,0.8806034218433801,0.8649885583524027,9.630642890930176,2082.7138459999996 +17214,Binary classification,Adaptive Random Forest,Elec2,0.880787776680416,0.862742474916388,9.825531959533691,2290.8714669999995 +18120,Binary classification,Adaptive Random Forest,Elec2,0.881505601854407,0.8635178946030132,13.432568550109863,2506.1422499999994 +19026,Binary classification,Adaptive Random Forest,Elec2,0.8835742444152431,0.864335150364427,11.236374855041504,2728.1698299999994 +19932,Binary classification,Adaptive Random Forest,Elec2,0.8847022226682053,0.8666744024135531,10.915810585021973,2956.9763619999994 +20838,Binary classification,Adaptive Random Forest,Elec2,0.8845803138647598,0.866618601297765,6.771607398986816,3192.2184919999995 +21744,Binary classification,Adaptive Random Forest,Elec2,0.8842845973416732,0.8643665768194071,9.905537605285645,3433.7445519999997 +22650,Binary classification,Adaptive Random Forest,Elec2,0.8832178021104684,0.8619303648796786,11.609391212463379,3682.1047309999994 +23556,Binary classification,Adaptive Random Forest,Elec2,0.8819783485459562,0.8599637316139432,7.878331184387207,3937.9632889999993 +24462,Binary classification,Adaptive Random Forest,Elec2,0.8805854216916724,0.8573800107416631,10.653840065002441,4201.066475999999 +25368,Binary classification,Adaptive Random Forest,Elec2,0.8791343083533725,0.8556497175141242,11.591797828674316,4470.752341999999 +26274,Binary classification,Adaptive Random Forest,Elec2,0.8801431127012522,0.8566616596112704,13.86082935333252,4746.937765999999 +27180,Binary classification,Adaptive Random Forest,Elec2,0.881195040288458,0.8584082438061829,14.463074684143066,5029.888654999999 +28086,Binary classification,Adaptive Random Forest,Elec2,0.8798646964571836,0.8561807331628303,15.367924690246582,5319.9108369999985 +28992,Binary classification,Adaptive Random Forest,Elec2,0.8796523058880342,0.8551019560612982,16.377129554748535,5616.458601999999 +29898,Binary classification,Adaptive Random Forest,Elec2,0.879285547044854,0.8544934080554772,16.05477237701416,5919.470834999999 +30804,Binary classification,Adaptive Random Forest,Elec2,0.8791351491737818,0.8535347574648885,17.57622241973877,6228.255318999999 +31710,Binary classification,Adaptive Random Forest,Elec2,0.8775111167176511,0.851267519338286,17.546963691711426,6543.393541999999 +32616,Binary classification,Adaptive Random Forest,Elec2,0.8771117583933773,0.8510701545778836,17.15481662750244,6864.899302999999 +33522,Binary classification,Adaptive Random Forest,Elec2,0.8770621401509502,0.8512864927285193,13.577618598937988,7192.851994 +34428,Binary classification,Adaptive Random Forest,Elec2,0.8757080198681267,0.849643346568748,12.363858222961426,7526.9490559999995 +35334,Binary classification,Adaptive Random Forest,Elec2,0.8754139189992358,0.848624484181568,12.552750587463379,7866.609101999999 +36240,Binary classification,Adaptive Random Forest,Elec2,0.875134523579569,0.8474427699672971,12.88097858428955,8211.574848999999 +37146,Binary classification,Adaptive Random Forest,Elec2,0.8743034055727554,0.8459838363846282,15.634392738342285,8562.247513999999 +38052,Binary classification,Adaptive Random Forest,Elec2,0.8741163175737826,0.8451642099818981,17.75814151763916,8918.936239999999 +38958,Binary classification,Adaptive Random Forest,Elec2,0.8743743101368175,0.8458873913591133,18.55082416534424,9281.578228999999 +39864,Binary classification,Adaptive Random Forest,Elec2,0.8744951458746206,0.847381104908331,20.39273166656494,9650.394165999998 +40770,Binary classification,Adaptive Random Forest,Elec2,0.8750521229365449,0.8493434283686265,20.04684543609619,10025.208226999997 +41676,Binary classification,Adaptive Random Forest,Elec2,0.8757768446310737,0.8512911843276937,22.40410327911377,10405.883388999997 +42582,Binary classification,Adaptive Random Forest,Elec2,0.8760010333247223,0.8517603458925264,17.905674934387207,10792.424187999997 +43488,Binary classification,Adaptive Random Forest,Elec2,0.8758249591832041,0.8516320474777449,17.979458808898926,11185.171687999997 +44394,Binary classification,Adaptive Random Forest,Elec2,0.8758362804946725,0.8511557571829769,19.483532905578613,11584.749531999996 +45300,Binary classification,Adaptive Random Forest,Elec2,0.8765977173889048,0.8524053440354862,22.381768226623535,11990.855977999996 +45312,Binary classification,Adaptive Random Forest,Elec2,0.8766083291033082,0.8523906328378699,22.39494037628174,12397.578789999996 +25,Binary classification,Adaptive Random Forest,Phishing,0.625,0.7096774193548387,0.41788291931152344,0.504078 +50,Binary classification,Adaptive Random Forest,Phishing,0.7346938775510204,0.7450980392156864,0.6195468902587891,1.506996 +75,Binary classification,Adaptive Random Forest,Phishing,0.7837837837837838,0.7999999999999999,0.8261966705322266,2.945496 +100,Binary classification,Adaptive Random Forest,Phishing,0.797979797979798,0.8039215686274509,0.9074077606201172,4.810448 +125,Binary classification,Adaptive Random Forest,Phishing,0.7903225806451613,0.7968749999999999,1.0524044036865234,7.208508 +150,Binary classification,Adaptive Random Forest,Phishing,0.8120805369127517,0.8227848101265823,1.155344009399414,10.051324000000001 +175,Binary classification,Adaptive Random Forest,Phishing,0.8390804597701149,0.8372093023255814,1.2272701263427734,13.451327000000001 +200,Binary classification,Adaptive Random Forest,Phishing,0.8442211055276382,0.8426395939086295,1.3437442779541016,17.363237 +225,Binary classification,Adaptive Random Forest,Phishing,0.8526785714285714,0.8465116279069769,1.4417095184326172,21.777532 +250,Binary classification,Adaptive Random Forest,Phishing,0.8554216867469879,0.85,1.652822494506836,26.610844 +275,Binary classification,Adaptive Random Forest,Phishing,0.8540145985401459,0.8473282442748092,1.7137775421142578,32.103705 +300,Binary classification,Adaptive Random Forest,Phishing,0.8595317725752508,0.85,1.6836071014404297,38.177642 +325,Binary classification,Adaptive Random Forest,Phishing,0.8672839506172839,0.8542372881355932,1.8154468536376953,44.724647 +350,Binary classification,Adaptive Random Forest,Phishing,0.8681948424068768,0.8525641025641026,1.9355945587158203,51.783981999999995 +375,Binary classification,Adaptive Random Forest,Phishing,0.8663101604278075,0.8484848484848485,2.1126270294189453,59.453596999999995 +400,Binary classification,Adaptive Random Forest,Phishing,0.8696741854636592,0.8505747126436781,2.2513599395751953,67.701448 +425,Binary classification,Adaptive Random Forest,Phishing,0.8702830188679245,0.8467966573816157,2.4080867767333984,76.525873 +450,Binary classification,Adaptive Random Forest,Phishing,0.8775055679287305,0.8533333333333333,2.413846969604492,85.91210000000001 +475,Binary classification,Adaptive Random Forest,Phishing,0.879746835443038,0.85785536159601,2.540945053100586,95.91125100000001 +500,Binary classification,Adaptive Random Forest,Phishing,0.8817635270541082,0.8624708624708626,2.727457046508789,106.551347 +525,Binary classification,Adaptive Random Forest,Phishing,0.8835877862595419,0.8623024830699774,2.780088424682617,117.81370000000001 +550,Binary classification,Adaptive Random Forest,Phishing,0.8816029143897997,0.8602150537634409,2.8441905975341797,129.668358 +575,Binary classification,Adaptive Random Forest,Phishing,0.8832752613240418,0.8618556701030927,2.9667911529541016,142.149346 +600,Binary classification,Adaptive Random Forest,Phishing,0.8864774624373957,0.8634538152610441,2.9313793182373047,155.146824 +625,Binary classification,Adaptive Random Forest,Phishing,0.8878205128205128,0.8622047244094488,3.1180286407470703,168.863765 +650,Binary classification,Adaptive Random Forest,Phishing,0.8906009244992296,0.8672897196261682,3.1772937774658203,183.176428 +675,Binary classification,Adaptive Random Forest,Phishing,0.8931750741839762,0.8732394366197184,3.270914077758789,198.116157 +700,Binary classification,Adaptive Random Forest,Phishing,0.8969957081545065,0.8762886597938143,3.2819652557373047,213.702702 +725,Binary classification,Adaptive Random Forest,Phishing,0.8950276243093923,0.8762214983713356,3.465627670288086,229.894704 +750,Binary classification,Adaptive Random Forest,Phishing,0.897196261682243,0.8791208791208791,3.637697219848633,246.71919699999998 +775,Binary classification,Adaptive Random Forest,Phishing,0.8979328165374677,0.8793893129770992,3.6838626861572266,264.27439799999996 +800,Binary classification,Adaptive Random Forest,Phishing,0.8961201501877347,0.8784773060029282,3.7807750701904297,282.50010699999996 +825,Binary classification,Adaptive Random Forest,Phishing,0.8968446601941747,0.8801128349788435,3.913633346557617,301.46455399999996 +850,Binary classification,Adaptive Random Forest,Phishing,0.8987043580683156,0.8818681318681318,4.011789321899414,321.06457099999994 +875,Binary classification,Adaptive Random Forest,Phishing,0.9016018306636155,0.8847184986595175,4.15968132019043,341.44966299999993 +900,Binary classification,Adaptive Random Forest,Phishing,0.9010011123470523,0.8836601307189543,3.946676254272461,362.64137199999993 +925,Binary classification,Adaptive Random Forest,Phishing,0.9036796536796536,0.8877679697351829,4.049928665161133,384.6765929999999 +950,Binary classification,Adaptive Random Forest,Phishing,0.9030558482613277,0.8883495145631068,3.6841602325439453,407.5276099999999 +975,Binary classification,Adaptive Random Forest,Phishing,0.9045174537987679,0.8899408284023669,3.787748336791992,431.1052179999999 +1000,Binary classification,Adaptive Random Forest,Phishing,0.9049049049049049,0.8904267589388698,4.052656173706055,455.43358799999993 +1025,Binary classification,Adaptive Random Forest,Phishing,0.9033203125,0.888888888888889,4.062379837036133,480.5827999999999 +1050,Binary classification,Adaptive Random Forest,Phishing,0.9046711153479504,0.8908296943231442,4.190084457397461,506.4991779999999 +1075,Binary classification,Adaptive Random Forest,Phishing,0.9059590316573557,0.8928950159066809,4.285711288452148,533.2628339999999 +1100,Binary classification,Adaptive Random Forest,Phishing,0.9062784349408554,0.8934850051706308,4.370790481567383,560.8485399999998 +1125,Binary classification,Adaptive Random Forest,Phishing,0.9065836298932385,0.8948948948948948,3.9348621368408203,589.2233909999999 +1150,Binary classification,Adaptive Random Forest,Phishing,0.9077458659704091,0.896078431372549,4.19316291809082,618.4066789999998 +1175,Binary classification,Adaptive Random Forest,Phishing,0.9063032367972743,0.8942307692307692,4.349401473999023,648.4320089999999 +1200,Binary classification,Adaptive Random Forest,Phishing,0.9065888240200167,0.8943396226415095,4.34752082824707,679.2709969999999 +1225,Binary classification,Adaptive Random Forest,Phishing,0.9068627450980392,0.8944444444444444,4.031515121459961,710.9105619999998 +1250,Binary classification,Adaptive Random Forest,Phishing,0.9079263410728583,0.896115627822945,4.102910995483398,743.3769359999998 +1903,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17035293579101562,12.381202 +3806,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17157363891601562,37.073822 +5709,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,74.106824 +7612,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,122.30955 +9515,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17279434204101562,180.539768 +11418,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17401504516601562,247.17494200000002 +13321,Binary classification,Adaptive Random Forest,SMTP,1.0,0.0,0.17401504516601562,321.611977 +15224,Binary classification,Adaptive Random Forest,SMTP,0.9992774091834724,0.0,0.23138427734375,404.612477 +17127,Binary classification,Adaptive Random Forest,SMTP,0.9992409202382343,0.0,0.17718124389648438,498.19936 +19030,Binary classification,Adaptive Random Forest,SMTP,0.9993168322034789,0.0,0.16917037963867188,601.949625 +20933,Binary classification,Adaptive Random Forest,SMTP,0.999378941333843,0.0,0.17043685913085938,714.510104 +22836,Binary classification,Adaptive Random Forest,SMTP,0.9994306984891613,0.0,0.17826461791992188,835.601872 +24739,Binary classification,Adaptive Random Forest,SMTP,0.9994744926833212,0.0,0.16260147094726562,964.8451829999999 +26642,Binary classification,Adaptive Random Forest,SMTP,0.9994744942006681,0.0,0.170440673828125,1103.1275799999999 +28545,Binary classification,Adaptive Random Forest,SMTP,0.9995095291479821,0.0,0.17826461791992188,1249.157139 +30448,Binary classification,Adaptive Random Forest,SMTP,0.9995401845830459,0.0,0.17816925048828125,1402.52966 +32351,Binary classification,Adaptive Random Forest,SMTP,0.9995672333848532,0.0,0.16262054443359375,1563.348194 +34254,Binary classification,Adaptive Random Forest,SMTP,0.9995912766764955,0.0,0.17043685913085938,1731.617157 +36157,Binary classification,Adaptive Random Forest,SMTP,0.9996127890253347,0.0,0.17043304443359375,1907.1505949999998 +38060,Binary classification,Adaptive Random Forest,SMTP,0.9996321500827662,0.0,0.1781158447265625,2090.2143509999996 +39963,Binary classification,Adaptive Random Forest,SMTP,0.9996496671838246,0.0,0.1704559326171875,2280.326256 +41866,Binary classification,Adaptive Random Forest,SMTP,0.9996655917831124,0.0,0.163818359375,2477.410916 +43769,Binary classification,Adaptive Random Forest,SMTP,0.9996801316029976,0.0,0.17158126831054688,2681.481315 +45672,Binary classification,Adaptive Random Forest,SMTP,0.9996934597446958,0.0,0.171630859375,2892.603814 +47575,Binary classification,Adaptive Random Forest,SMTP,0.9997057216126456,0.0,0.17947769165039062,3110.724932 +49478,Binary classification,Adaptive Random Forest,SMTP,0.99971704024092,0.0,0.32259368896484375,3336.815763 +51381,Binary classification,Adaptive Random Forest,SMTP,0.9996885947839627,0.0,0.3238716125488281,3571.848368 +53284,Binary classification,Adaptive Random Forest,SMTP,0.9996997166075484,0.0,0.2926750183105469,3815.616702 +55187,Binary classification,Adaptive Random Forest,SMTP,0.999710071394919,0.0,0.3244895935058594,4068.2038989999996 +57090,Binary classification,Adaptive Random Forest,SMTP,0.9995620872672494,0.0,0.2933502197265625,4329.847041999999 +58993,Binary classification,Adaptive Random Forest,SMTP,0.9995762137238947,0.0,0.27820587158203125,4599.769264 +60896,Binary classification,Adaptive Random Forest,SMTP,0.999589457262501,0.0,0.3094024658203125,4878.106527 +62799,Binary classification,Adaptive Random Forest,SMTP,0.9995700500015924,0.0,0.30953216552734375,5164.770925 +64702,Binary classification,Adaptive Random Forest,SMTP,0.9995826957852274,0.0,0.29401397705078125,5459.39874 +66605,Binary classification,Adaptive Random Forest,SMTP,0.9995946189418053,0.0,0.293975830078125,5761.313045999999 +68508,Binary classification,Adaptive Random Forest,SMTP,0.9995766855941729,0.0,0.32515716552734375,6070.487373999999 +70411,Binary classification,Adaptive Random Forest,SMTP,0.9995881266865502,0.0,0.3101234436035156,6386.879354 +72314,Binary classification,Adaptive Random Forest,SMTP,0.9995989656078437,0.0,0.3101387023925781,6710.520715 +74217,Binary classification,Adaptive Random Forest,SMTP,0.99960924867953,0.0,0.3101615905761719,7041.446151 +76120,Binary classification,Adaptive Random Forest,SMTP,0.9996190175908775,0.0,0.3101768493652344,7379.414547 +78023,Binary classification,Adaptive Random Forest,SMTP,0.9996283099638563,0.0,0.3100128173828125,7724.224697000001 +79926,Binary classification,Adaptive Random Forest,SMTP,0.9996371598373475,0.0,0.31014251708984375,8075.760258 +81829,Binary classification,Adaptive Random Forest,SMTP,0.9996455980837855,0.0,0.3100852966308594,8434.038373 +83732,Binary classification,Adaptive Random Forest,SMTP,0.9996536527689864,0.0,0.31072235107421875,8799.103777999999 +85635,Binary classification,Adaptive Random Forest,SMTP,0.999661349463998,0.0,0.310821533203125,9170.869249 +87538,Binary classification,Adaptive Random Forest,SMTP,0.9996687115162731,0.0,0.2950706481933594,9549.506615999999 +89441,Binary classification,Adaptive Random Forest,SMTP,0.9996645796064401,0.0,0.2951812744140625,9935.081315999998 +91344,Binary classification,Adaptive Random Forest,SMTP,0.999671567607808,0.0,0.2957954406738281,10327.647623999997 +93247,Binary classification,Adaptive Random Forest,SMTP,0.9996782703815713,0.0,0.3115119934082031,10728.119291999998 +95150,Binary classification,Adaptive Random Forest,SMTP,0.9996847050415664,0.0,0.3115577697753906,11135.737891999997 +95156,Binary classification,Adaptive Random Forest,SMTP,0.9996847249224948,0.0,0.32709503173828125,11543.384409999997 +106,Binary classification,Streaming Random Patches,Bananas,0.5428571428571428,0.4,0.2255392074584961,2.569769 +212,Binary classification,Streaming Random Patches,Bananas,0.5592417061611374,0.4685714285714286,0.6304416656494141,8.011061999999999 +318,Binary classification,Streaming Random Patches,Bananas,0.637223974763407,0.5724907063197027,0.9710559844970703,16.523663 +424,Binary classification,Streaming Random Patches,Bananas,0.6926713947990544,0.6448087431693988,1.2628002166748047,28.175313 +530,Binary classification,Streaming Random Patches,Bananas,0.7145557655954632,0.6621923937360179,1.5703105926513672,43.218913 +636,Binary classification,Streaming Random Patches,Bananas,0.7448818897637796,0.7000000000000001,1.467294692993164,61.573557 +742,Binary classification,Streaming Random Patches,Bananas,0.7624831309041835,0.7170418006430868,1.877767562866211,83.22394800000001 +848,Binary classification,Streaming Random Patches,Bananas,0.7827626918536009,0.7430167597765364,2.3253536224365234,108.413447 +954,Binary classification,Streaming Random Patches,Bananas,0.7964323189926548,0.7599009900990098,1.7426891326904297,137.183902 +1060,Binary classification,Streaming Random Patches,Bananas,0.8054768649669499,0.7674943566591422,1.7942829132080078,169.50541299999998 +1166,Binary classification,Streaming Random Patches,Bananas,0.8103004291845494,0.7747196738022425,1.8575687408447266,205.46256099999997 +1272,Binary classification,Streaming Random Patches,Bananas,0.8151062155782848,0.7822057460611677,1.917165756225586,244.58779499999997 +1378,Binary classification,Streaming Random Patches,Bananas,0.8191721132897604,0.7851596203623814,2.1873340606689453,286.663235 +1484,Binary classification,Streaming Random Patches,Bananas,0.8240053944706676,0.7916999201915402,2.2810306549072266,331.700902 +1590,Binary classification,Streaming Random Patches,Bananas,0.8231592196349906,0.7916975537435137,2.585817337036133,379.560946 +1696,Binary classification,Streaming Random Patches,Bananas,0.8271386430678466,0.7961029923451634,2.8855953216552734,430.279509 +1802,Binary classification,Streaming Random Patches,Bananas,0.8334258745141588,0.8046875,2.8240184783935547,483.724395 +1908,Binary classification,Streaming Random Patches,Bananas,0.8332459360251704,0.8060975609756097,3.138376235961914,539.732848 +2014,Binary classification,Streaming Random Patches,Bananas,0.8340784898161947,0.8084862385321101,3.5751514434814453,598.378334 +2120,Binary classification,Streaming Random Patches,Bananas,0.8367154318074563,0.813778256189451,3.890401840209961,659.469374 +2226,Binary classification,Streaming Random Patches,Bananas,0.8382022471910112,0.8157625383828044,4.414094924926758,723.240852 +2332,Binary classification,Streaming Random Patches,Bananas,0.8404118404118404,0.8185365853658537,4.828973770141602,789.4489060000001 +2438,Binary classification,Streaming Random Patches,Bananas,0.8432498974148543,0.8216619981325864,4.724649429321289,858.0992190000001 +2544,Binary classification,Streaming Random Patches,Bananas,0.8450648839952811,0.8247330960854093,4.20762825012207,929.1630060000001 +2650,Binary classification,Streaming Random Patches,Bananas,0.846734616836542,0.8270868824531515,4.517709732055664,1002.5522950000001 +2756,Binary classification,Streaming Random Patches,Bananas,0.8500907441016334,0.8306683066830667,4.757001876831055,1078.240465 +2862,Binary classification,Streaming Random Patches,Bananas,0.8521495980426425,0.8324752475247525,4.690572738647461,1156.1945970000002 +2968,Binary classification,Streaming Random Patches,Bananas,0.854061341422312,0.8339087073264287,4.873067855834961,1236.185934 +3074,Binary classification,Streaming Random Patches,Bananas,0.8538887081028311,0.8340110905730129,5.244169235229492,1318.3478 +3180,Binary classification,Streaming Random Patches,Bananas,0.8565586662472475,0.836441893830703,5.473237991333008,1402.707418 +3286,Binary classification,Streaming Random Patches,Bananas,0.8575342465753425,0.8371607515657619,5.716192245483398,1489.296647 +3392,Binary classification,Streaming Random Patches,Bananas,0.8593335299321734,0.8400938652363393,6.05610466003418,1578.3688909999998 +3498,Binary classification,Streaming Random Patches,Bananas,0.8615956534172148,0.8419333768778575,6.433168411254883,1669.799251 +3604,Binary classification,Streaming Random Patches,Bananas,0.8631695809048016,0.8430436166825852,6.670698165893555,1763.5519829999998 +3710,Binary classification,Streaming Random Patches,Bananas,0.8638447020760313,0.8444718201416692,7.050989151000977,1859.6268179999997 +3816,Binary classification,Streaming Random Patches,Bananas,0.8657929226736566,0.84688995215311,7.316404342651367,1958.0125029999997 +3922,Binary classification,Streaming Random Patches,Bananas,0.8653404743687835,0.846064139941691,7.61528205871582,2058.6755279999998 +4028,Binary classification,Streaming Random Patches,Bananas,0.8644151974174323,0.8449744463373083,7.967977523803711,2161.5792619999997 +4134,Binary classification,Streaming Random Patches,Bananas,0.8654730220179047,0.8462389380530975,7.394952774047852,2266.5958729999998 +4240,Binary classification,Streaming Random Patches,Bananas,0.8674215616890776,0.8486806677436726,7.571531295776367,2373.458397 +4346,Binary classification,Streaming Random Patches,Bananas,0.8688147295742232,0.8503937007874016,7.877435684204102,2482.305033 +4452,Binary classification,Streaming Random Patches,Bananas,0.8683441923163334,0.8496664956387892,8.180627822875977,2593.165336 +4558,Binary classification,Streaming Random Patches,Bananas,0.8689927583936801,0.8508618536097925,8.39448356628418,2705.945127 +4664,Binary classification,Streaming Random Patches,Bananas,0.8691829294445635,0.8516536964980544,8.710580825805664,2820.674173 +4770,Binary classification,Streaming Random Patches,Bananas,0.8689452715453974,0.8510131108462455,9.014997482299805,2937.453009 +4876,Binary classification,Streaming Random Patches,Bananas,0.8703589743589744,0.8523364485981308,9.167715072631836,3056.177406 +4982,Binary classification,Streaming Random Patches,Bananas,0.8713109817305762,0.8538864827900615,9.482858657836914,3176.936292 +5088,Binary classification,Streaming Random Patches,Bananas,0.8714369962649892,0.8539526574363555,9.87147331237793,3299.754349 +5194,Binary classification,Streaming Random Patches,Bananas,0.8717504332755632,0.8543944031482291,10.204122543334961,3424.594329 +5300,Binary classification,Streaming Random Patches,Bananas,0.8716739007359879,0.8542648949849978,10.538087844848633,3551.414099 +906,Binary classification,Streaming Random Patches,Elec2,0.8828729281767956,0.8811659192825113,5.258722305297852,37.408806 +1812,Binary classification,Streaming Random Patches,Elec2,0.9039204859193816,0.8804945054945055,8.443174362182617,104.985856 +2718,Binary classification,Streaming Random Patches,Elec2,0.8873757821126242,0.8602739726027397,12.445928573608398,198.51440200000002 +3624,Binary classification,Streaming Random Patches,Elec2,0.884902014904775,0.8576305906452714,16.533422470092773,314.268209 +4530,Binary classification,Streaming Random Patches,Elec2,0.8812099801280636,0.8452243958573072,19.266294479370117,451.77035 +5436,Binary classification,Streaming Random Patches,Elec2,0.8756209751609936,0.8372652864708715,24.12981605529785,609.66041 +6342,Binary classification,Streaming Random Patches,Elec2,0.8719444882510645,0.8340825500612996,28.348302841186523,788.9253299999999 +7248,Binary classification,Streaming Random Patches,Elec2,0.8691872498965089,0.8308351177730193,31.664392471313477,988.709629 +8154,Binary classification,Streaming Random Patches,Elec2,0.8690052741322213,0.8387681159420289,35.27585411071777,1209.125777 +9060,Binary classification,Streaming Random Patches,Elec2,0.869742797218236,0.844162704701532,38.39363670349121,1448.169528 +9966,Binary classification,Streaming Random Patches,Elec2,0.8681384846964375,0.8455934195064629,42.49019813537598,1706.33653 +10872,Binary classification,Streaming Random Patches,Elec2,0.8687333272008095,0.849265870920038,46.91076469421387,1983.228319 +11778,Binary classification,Streaming Random Patches,Elec2,0.8694064702386006,0.8495402073958129,41.518564224243164,2278.772125 +12684,Binary classification,Streaming Random Patches,Elec2,0.8672238429393676,0.8479320931912588,46.98099327087402,2591.81768 +13590,Binary classification,Streaming Random Patches,Elec2,0.8682758113179778,0.8513289036544851,50.757638931274414,2922.748705 +14496,Binary classification,Streaming Random Patches,Elec2,0.8687823387374957,0.8527863777089782,43.74130058288574,3269.777293 +15402,Binary classification,Streaming Random Patches,Elec2,0.8686448931887539,0.8518708354689903,49.06788444519043,3632.343799 +16308,Binary classification,Streaming Random Patches,Elec2,0.8649659655362728,0.8467427616926504,54.357858657836914,4011.129855 +17214,Binary classification,Streaming Random Patches,Elec2,0.865392435949573,0.8447987139125192,52.38222694396973,4407.893822 +18120,Binary classification,Streaming Random Patches,Elec2,0.8652795408135107,0.8448286822198208,59.36540412902832,4822.718697 +19026,Binary classification,Streaming Random Patches,Elec2,0.867700394218134,0.8459136822773186,57.10729789733887,5251.994155 +19932,Binary classification,Streaming Random Patches,Elec2,0.8692489087351363,0.84882236918436,51.34463310241699,5694.600867 +20838,Binary classification,Streaming Random Patches,Elec2,0.8691750251955656,0.8490085299656586,53.35045051574707,6149.875029 +21744,Binary classification,Streaming Random Patches,Elec2,0.8700271351699398,0.8478682170542635,59.89077186584473,6616.7506189999995 +22650,Binary classification,Streaming Random Patches,Elec2,0.8694865115457636,0.8459614382490881,65.61615180969238,7094.9355989999995 +23556,Binary classification,Streaming Random Patches,Elec2,0.868860114625345,0.8444690599667689,72.22853660583496,7585.744803 +24462,Binary classification,Streaming Random Patches,Elec2,0.8682392379706472,0.8428648042513772,80.47726249694824,8090.441156999999 +25368,Binary classification,Streaming Random Patches,Elec2,0.8672290771474751,0.8417888012025554,73.03231239318848,8608.413273 +26274,Binary classification,Streaming Random Patches,Elec2,0.8684581128915617,0.8430802760624773,79.16955757141113,9138.877569 +27180,Binary classification,Streaming Random Patches,Elec2,0.8698259685786821,0.8451234459814394,84.2670955657959,9681.232951 +28086,Binary classification,Streaming Random Patches,Elec2,0.8689335944454335,0.8434616202423985,93.53372383117676,10236.036265 +28992,Binary classification,Streaming Random Patches,Elec2,0.8688558518160808,0.8423322551215061,92.75358009338379,10801.143387 +29898,Binary classification,Streaming Random Patches,Elec2,0.8689166137070609,0.8421603769785332,90.79977607727051,11375.205452 +30804,Binary classification,Streaming Random Patches,Elec2,0.8684868356978216,0.8408063818917749,97.95510292053223,11957.401901000001 +31710,Binary classification,Streaming Random Patches,Elec2,0.8668832192752847,0.8384553561177235,105.25788688659668,12547.475367000001 +32616,Binary classification,Streaming Random Patches,Elec2,0.8664724819868159,0.8381943154374885,94.53887367248535,13145.300695000002 +33522,Binary classification,Streaming Random Patches,Elec2,0.8661436114674383,0.8380553650701988,102.01883506774902,13750.733881000002 +34428,Binary classification,Streaming Random Patches,Elec2,0.8648444534812793,0.8364441632394811,100.42782783508301,14363.910035000003 +35334,Binary classification,Streaming Random Patches,Elec2,0.8647723091727281,0.8357849876271651,107.87262153625488,14984.863075000003 +36240,Binary classification,Streaming Random Patches,Elec2,0.8642346643119292,0.83420946219167,112.83228874206543,15613.594311000003 +37146,Binary classification,Streaming Random Patches,Elec2,0.8632655808318751,0.8326689289361843,120.66686058044434,16250.422859000002 +38052,Binary classification,Streaming Random Patches,Elec2,0.8627631336889964,0.8316135689410551,126.15458106994629,16895.166705000003 +38958,Binary classification,Streaming Random Patches,Elec2,0.8634391765279668,0.8328620797989319,107.22049903869629,17548.303432000004 +39864,Binary classification,Streaming Random Patches,Elec2,0.8644356922459423,0.8353041570157259,103.5422191619873,18208.490072000004 +40770,Binary classification,Streaming Random Patches,Elec2,0.865436974171552,0.8378745788758201,97.78764533996582,18874.504490000003 +41676,Binary classification,Streaming Random Patches,Elec2,0.8666586682663467,0.8403940603728063,102.76390266418457,19545.753018000003 +42582,Binary classification,Streaming Random Patches,Elec2,0.8673821657546793,0.8415055151702265,107.51249122619629,20221.607860000004 +43488,Binary classification,Streaming Random Patches,Elec2,0.8677075907742544,0.841885392332005,102.9560489654541,20902.251232000002 +44394,Binary classification,Streaming Random Patches,Elec2,0.8679071024711104,0.8415905775568644,103.86480903625488,21587.715975000003 +45300,Binary classification,Streaming Random Patches,Elec2,0.8688933530541513,0.843053830501308,107.29028511047363,22278.011723000003 +45312,Binary classification,Streaming Random Patches,Elec2,0.8688839354682086,0.8430092751631743,107.32242012023926,22968.976341 +25,Binary classification,Streaming Random Patches,Phishing,0.8333333333333334,0.8333333333333334,0.7029104232788086,1.141902 +50,Binary classification,Streaming Random Patches,Phishing,0.8571428571428571,0.8372093023255814,0.9397382736206055,3.355867 +75,Binary classification,Streaming Random Patches,Phishing,0.8783783783783784,0.8695652173913043,0.9708013534545898,6.532426 +100,Binary classification,Streaming Random Patches,Phishing,0.8888888888888888,0.8817204301075269,1.056624412536621,10.815831 +125,Binary classification,Streaming Random Patches,Phishing,0.8790322580645161,0.8739495798319329,1.3782567977905273,16.293882 +150,Binary classification,Streaming Random Patches,Phishing,0.8791946308724832,0.8783783783783784,1.379134178161621,22.890072 +175,Binary classification,Streaming Random Patches,Phishing,0.896551724137931,0.888888888888889,1.4786596298217773,30.523139999999998 +200,Binary classification,Streaming Random Patches,Phishing,0.8944723618090452,0.8864864864864866,1.6607275009155273,39.247513 +225,Binary classification,Streaming Random Patches,Phishing,0.8973214285714286,0.8866995073891626,1.686568260192871,49.014512999999994 +250,Binary classification,Streaming Random Patches,Phishing,0.891566265060241,0.88,1.9668035507202148,59.910523 +275,Binary classification,Streaming Random Patches,Phishing,0.8905109489051095,0.8780487804878049,2.071291923522949,71.88595 +300,Binary classification,Streaming Random Patches,Phishing,0.8896321070234113,0.8754716981132077,2.2423620223999023,85.00905599999999 +325,Binary classification,Streaming Random Patches,Phishing,0.8888888888888888,0.8723404255319148,2.4750547409057617,99.14632499999999 +350,Binary classification,Streaming Random Patches,Phishing,0.8853868194842407,0.8666666666666667,2.5328550338745117,114.35437499999999 +375,Binary classification,Streaming Random Patches,Phishing,0.8850267379679144,0.8652037617554859,2.8150205612182617,130.79065599999998 +400,Binary classification,Streaming Random Patches,Phishing,0.8822055137844611,0.8613569321533923,2.795191764831543,148.41625799999997 +425,Binary classification,Streaming Random Patches,Phishing,0.8844339622641509,0.8611898016997167,2.962000846862793,167.06847699999997 +450,Binary classification,Streaming Random Patches,Phishing,0.888641425389755,0.8648648648648649,3.03415584564209,186.853211 +475,Binary classification,Streaming Random Patches,Phishing,0.890295358649789,0.8686868686868687,3.071761131286621,207.82899999999998 +500,Binary classification,Streaming Random Patches,Phishing,0.8917835671342685,0.8726415094339622,3.1551198959350586,229.951047 +525,Binary classification,Streaming Random Patches,Phishing,0.8950381679389313,0.8741418764302059,3.1928510665893555,253.214946 +550,Binary classification,Streaming Random Patches,Phishing,0.8943533697632058,0.8739130434782608,3.2878904342651367,277.566695 +575,Binary classification,Streaming Random Patches,Phishing,0.8937282229965157,0.8726513569937369,3.4417715072631836,303.140017 +600,Binary classification,Streaming Random Patches,Phishing,0.8964941569282137,0.8739837398373984,3.515273094177246,329.715755 +625,Binary classification,Streaming Random Patches,Phishing,0.8958333333333334,0.8707753479125249,3.5807180404663086,357.461609 +650,Binary classification,Streaming Random Patches,Phishing,0.8983050847457628,0.8754716981132076,3.695376396179199,386.398038 +675,Binary classification,Streaming Random Patches,Phishing,0.8961424332344213,0.8754448398576512,3.7550153732299805,416.46849299999997 +700,Binary classification,Streaming Random Patches,Phishing,0.899856938483548,0.8784722222222222,3.7909955978393555,447.643877 +725,Binary classification,Streaming Random Patches,Phishing,0.899171270718232,0.8797364085667215,3.9393529891967773,479.929216 +750,Binary classification,Streaming Random Patches,Phishing,0.9012016021361816,0.8825396825396825,3.942519187927246,513.493128 +775,Binary classification,Streaming Random Patches,Phishing,0.9018087855297158,0.8827160493827161,4.2751874923706055,548.2965389999999 +800,Binary classification,Streaming Random Patches,Phishing,0.899874843554443,0.8816568047337278,4.513812065124512,584.3583229999999 +825,Binary classification,Streaming Random Patches,Phishing,0.8992718446601942,0.8819345661450925,4.773520469665527,621.611368 +850,Binary classification,Streaming Random Patches,Phishing,0.901060070671378,0.8836565096952909,4.8153791427612305,660.1796979999999 +875,Binary classification,Streaming Random Patches,Phishing,0.902745995423341,0.884979702300406,4.980830192565918,699.8371419999999 +900,Binary classification,Streaming Random Patches,Phishing,0.9043381535038932,0.8862433862433862,5.134486198425293,740.7850809999999 +925,Binary classification,Streaming Random Patches,Phishing,0.9069264069264069,0.8903061224489796,5.209948539733887,782.8443949999998 +950,Binary classification,Streaming Random Patches,Phishing,0.9083245521601686,0.8932515337423312,5.338950157165527,826.1813729999999 +975,Binary classification,Streaming Random Patches,Phishing,0.9106776180698152,0.895808383233533,5.382990837097168,870.7373769999999 +1000,Binary classification,Streaming Random Patches,Phishing,0.9109109109109109,0.896149358226371,5.44773006439209,916.477949 +1025,Binary classification,Streaming Random Patches,Phishing,0.9111328125,0.896942242355606,5.5915327072143555,963.445117 +1050,Binary classification,Streaming Random Patches,Phishing,0.9113441372735939,0.8976897689768977,5.678961753845215,1011.481541 +1075,Binary classification,Streaming Random Patches,Phishing,0.9115456238361266,0.8986125933831376,5.788058280944824,1060.652982 +1100,Binary classification,Streaming Random Patches,Phishing,0.9117379435850773,0.8990634755463061,5.880267143249512,1110.965738 +1125,Binary classification,Streaming Random Patches,Phishing,0.9119217081850534,0.9003021148036253,6.120665550231934,1162.442095 +1150,Binary classification,Streaming Random Patches,Phishing,0.9129677980852916,0.9013806706114399,6.185591697692871,1215.0055750000001 +1175,Binary classification,Streaming Random Patches,Phishing,0.9114139693356048,0.8996138996138997,6.431841850280762,1268.8167280000002 +1200,Binary classification,Streaming Random Patches,Phishing,0.9124270225187656,0.9004739336492891,6.484606742858887,1323.7082160000002 +1225,Binary classification,Streaming Random Patches,Phishing,0.9133986928104575,0.9014869888475836,6.481654167175293,1379.6419000000003 +1250,Binary classification,Streaming Random Patches,Phishing,0.9135308246597278,0.9019963702359347,6.595587730407715,1436.6903440000003 +1903,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1670236587524414,31.246172 +3806,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1682443618774414,90.057064 +5709,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,168.92668600000002 +7612,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,266.339332 +9515,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1694650650024414,379.70068100000003 +11418,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1706857681274414,507.50093200000003 +13321,Binary classification,Streaming Random Patches,SMTP,1.0,0.0,0.1706857681274414,650.046105 +15224,Binary classification,Streaming Random Patches,SMTP,0.9992774091834724,0.0,0.2171335220336914,806.74928 +17127,Binary classification,Streaming Random Patches,SMTP,0.9992409202382343,0.0,0.1745767593383789,979.905317 +19030,Binary classification,Streaming Random Patches,SMTP,0.9993168322034789,0.0,0.1744394302368164,1169.37771 +20933,Binary classification,Streaming Random Patches,SMTP,0.999378941333843,0.0,0.17577457427978516,1374.513378 +22836,Binary classification,Streaming Random Patches,SMTP,0.9994306984891613,0.0,0.17572879791259766,1595.365052 +24739,Binary classification,Streaming Random Patches,SMTP,0.9994744926833212,0.0,0.1757516860961914,1830.5281120000002 +26642,Binary classification,Streaming Random Patches,SMTP,0.9994744942006681,0.0,0.17572879791259766,2079.072293 +28545,Binary classification,Streaming Random Patches,SMTP,0.9995095291479821,0.0,0.17572879791259766,2341.3308700000002 +30448,Binary classification,Streaming Random Patches,SMTP,0.9995401845830459,0.0,0.17563724517822266,2616.3107910000003 +32351,Binary classification,Streaming Random Patches,SMTP,0.9995672333848532,0.0,0.17577457427978516,2903.8369350000003 +34254,Binary classification,Streaming Random Patches,SMTP,0.9995912766764955,0.0,0.1756601333618164,3203.0985050000004 +36157,Binary classification,Streaming Random Patches,SMTP,0.9996127890253347,0.0,0.17568302154541016,3513.3936680000006 +38060,Binary classification,Streaming Random Patches,SMTP,0.9996321500827662,0.0,0.1757059097290039,3834.7595300000007 +39963,Binary classification,Streaming Random Patches,SMTP,0.9996496671838246,0.0,0.17577457427978516,4167.168481000001 +41866,Binary classification,Streaming Random Patches,SMTP,0.9996655917831124,0.0,0.1769266128540039,4510.027218000001 +43769,Binary classification,Streaming Random Patches,SMTP,0.9996801316029976,0.0,0.1769266128540039,4863.234695000001 +45672,Binary classification,Streaming Random Patches,SMTP,0.9996934597446958,0.0,0.1769266128540039,5226.731618000001 +47575,Binary classification,Streaming Random Patches,SMTP,0.9997057216126456,0.0,0.1770639419555664,5600.511358000001 +49478,Binary classification,Streaming Random Patches,SMTP,0.99971704024092,0.0,0.17690372467041016,5984.651066 +51381,Binary classification,Streaming Random Patches,SMTP,0.9996885947839627,0.0,0.16916751861572266,6379.477192 +53284,Binary classification,Streaming Random Patches,SMTP,0.9996997166075484,0.0,0.1770639419555664,6785.903036000001 +55187,Binary classification,Streaming Random Patches,SMTP,0.999710071394919,0.0,0.17704105377197266,7201.6518080000005 +57090,Binary classification,Streaming Random Patches,SMTP,0.9995620872672494,0.0,0.1769266128540039,7626.427031 +58993,Binary classification,Streaming Random Patches,SMTP,0.9995762137238947,0.0,0.1769266128540039,8059.133738 +60896,Binary classification,Streaming Random Patches,SMTP,0.999589457262501,0.0,0.1769723892211914,8499.283325 +62799,Binary classification,Streaming Random Patches,SMTP,0.9995700500015924,0.0,0.1769266128540039,8946.957028 +64702,Binary classification,Streaming Random Patches,SMTP,0.9995826957852274,0.0,0.17699527740478516,9401.959764000001 +66605,Binary classification,Streaming Random Patches,SMTP,0.9995946189418053,0.0,0.1769723892211914,9864.111715000001 +68508,Binary classification,Streaming Random Patches,SMTP,0.9995766855941729,0.0,0.1691446304321289,10332.853073 +70411,Binary classification,Streaming Random Patches,SMTP,0.9995881266865502,0.0,0.17690372467041016,10808.168746 +72314,Binary classification,Streaming Random Patches,SMTP,0.9995989656078437,0.0,0.16912174224853516,11290.14581 +74217,Binary classification,Streaming Random Patches,SMTP,0.99960924867953,0.0,0.1769723892211914,11778.656001 +76120,Binary classification,Streaming Random Patches,SMTP,0.9996190175908775,0.0,0.1769723892211914,12273.787996 +78023,Binary classification,Streaming Random Patches,SMTP,0.9996283099638563,0.0,0.17699527740478516,12775.472063 +79926,Binary classification,Streaming Random Patches,SMTP,0.9996371598373475,0.0,0.1769723892211914,13283.764207999999 +81829,Binary classification,Streaming Random Patches,SMTP,0.9996455980837855,0.0,0.1770181655883789,13798.661938 +83732,Binary classification,Streaming Random Patches,SMTP,0.9996536527689864,0.0,0.17826175689697266,14320.191281 +85635,Binary classification,Streaming Random Patches,SMTP,0.999661349463998,0.0,0.1703653335571289,14848.294436 +87538,Binary classification,Streaming Random Patches,SMTP,0.9996687115162731,0.0,0.17029666900634766,15383.005183 +89441,Binary classification,Streaming Random Patches,SMTP,0.9996645796064401,0.0,0.1781930923461914,15923.647685 +91344,Binary classification,Streaming Random Patches,SMTP,0.999671567607808,0.0,0.1781473159790039,16470.415157 +93247,Binary classification,Streaming Random Patches,SMTP,0.9996782703815713,0.0,0.17821598052978516,17023.687732 +95150,Binary classification,Streaming Random Patches,SMTP,0.9996847050415664,0.0,0.17821598052978516,17582.958979 +95156,Binary classification,Streaming Random Patches,SMTP,0.9996847249224948,0.0,0.17817020416259766,18142.251024999998 +106,Binary classification,k-Nearest Neighbors,Bananas,0.7238095238095238,0.6881720430107527,0.10328006744384766,0.213787 +212,Binary classification,k-Nearest Neighbors,Bananas,0.8056872037914692,0.7807486631016043,0.1952676773071289,0.888466 +318,Binary classification,k-Nearest Neighbors,Bananas,0.807570977917981,0.7859649122807018,0.28677845001220703,2.29757 +424,Binary classification,k-Nearest Neighbors,Bananas,0.8297872340425532,0.8115183246073298,0.3787660598754883,4.640547 +530,Binary classification,k-Nearest Neighbors,Bananas,0.831758034026465,0.8061002178649236,2.6361207962036133,29.527472000000003 +636,Binary classification,k-Nearest Neighbors,Bananas,0.8472440944881889,0.8245931283905967,3.060887336730957,56.29478 +742,Binary classification,k-Nearest Neighbors,Bananas,0.8529014844804319,0.8278041074249604,3.5180253982543945,85.033958 +848,Binary classification,k-Nearest Neighbors,Bananas,0.8559622195985832,0.8328767123287671,3.9749040603637695,116.00953899999999 +954,Binary classification,k-Nearest Neighbors,Bananas,0.8604407135362014,0.8372093023255813,4.4283952713012695,149.38786199999998 +1060,Binary classification,k-Nearest Neighbors,Bananas,0.8706326723323891,0.8476084538375974,4.5923662185668945,185.28018899999998 +1166,Binary classification,k-Nearest Neighbors,Bananas,0.871244635193133,0.8484848484848485,4.394963264465332,223.46140799999998 +1272,Binary classification,k-Nearest Neighbors,Bananas,0.8693941778127459,0.8477064220183486,4.242337226867676,263.59538599999996 +1378,Binary classification,k-Nearest Neighbors,Bananas,0.8714596949891068,0.8488471391972673,4.1376237869262695,305.59304199999997 +1484,Binary classification,k-Nearest Neighbors,Bananas,0.8759271746459879,0.8548895899053628,4.233838081359863,349.62285599999996 +1590,Binary classification,k-Nearest Neighbors,Bananas,0.8735053492762744,0.8527472527472527,4.485638618469238,396.123591 +1696,Binary classification,k-Nearest Neighbors,Bananas,0.8755162241887906,0.854982817869416,4.566784858703613,444.689218 +1802,Binary classification,k-Nearest Neighbors,Bananas,0.8778456413103831,0.858611825192802,4.580937385559082,495.109217 +1908,Binary classification,k-Nearest Neighbors,Bananas,0.8778185631882538,0.8598917618761276,4.5537919998168945,547.400313 +2014,Binary classification,k-Nearest Neighbors,Bananas,0.877297565822156,0.8605307735742519,4.4779558181762695,601.303554 +2120,Binary classification,k-Nearest Neighbors,Bananas,0.8787163756488909,0.8635156664896441,4.453892707824707,656.840699 +2226,Binary classification,k-Nearest Neighbors,Bananas,0.8782022471910113,0.8630621526023244,4.4562273025512695,714.0315049999999 +2332,Binary classification,k-Nearest Neighbors,Bananas,0.8777348777348777,0.862782859894078,4.439526557922363,772.745195 +2438,Binary classification,k-Nearest Neighbors,Bananas,0.8785391875256463,0.8635944700460828,4.450131416320801,833.024947 +2544,Binary classification,k-Nearest Neighbors,Bananas,0.8788832088084939,0.864793678665496,4.448788642883301,894.808032 +2650,Binary classification,k-Nearest Neighbors,Bananas,0.8784446961117403,0.8647058823529411,4.491581916809082,958.170481 +2756,Binary classification,k-Nearest Neighbors,Bananas,0.879491833030853,0.8659127625201939,4.482541084289551,1022.970177 +2862,Binary classification,k-Nearest Neighbors,Bananas,0.8808109052778749,0.867056530214425,4.4542436599731445,1089.131713 +2968,Binary classification,k-Nearest Neighbors,Bananas,0.8813616447590158,0.8673700075357951,4.489590644836426,1156.687087 +3074,Binary classification,k-Nearest Neighbors,Bananas,0.8805727302310445,0.8665939658306071,4.4426774978637695,1225.526022 +3180,Binary classification,k-Nearest Neighbors,Bananas,0.8820383768480654,0.8677248677248678,4.4409685134887695,1295.666774 +3286,Binary classification,k-Nearest Neighbors,Bananas,0.882496194824962,0.8678082191780822,4.441540718078613,1367.284144 +3392,Binary classification,k-Nearest Neighbors,Bananas,0.8832202890002949,0.8693931398416888,4.4570817947387695,1440.244405 +3498,Binary classification,k-Nearest Neighbors,Bananas,0.8850443237060337,0.8709055876685934,4.465977668762207,1514.504066 +3604,Binary classification,k-Nearest Neighbors,Bananas,0.8856508465167916,0.8710888610763454,4.4596757888793945,1590.040367 +3710,Binary classification,k-Nearest Neighbors,Bananas,0.8864923159881369,0.8724628900333233,4.477154731750488,1666.899992 +3816,Binary classification,k-Nearest Neighbors,Bananas,0.8875491480996068,0.8737120989108037,4.4705095291137695,1745.0344980000002 +3922,Binary classification,k-Nearest Neighbors,Bananas,0.8867635807192042,0.8724870763928776,4.4566545486450195,1824.4605280000003 +4028,Binary classification,k-Nearest Neighbors,Bananas,0.8852743978147505,0.8706606942889138,4.454602241516113,1905.1708120000003 +4134,Binary classification,k-Nearest Neighbors,Bananas,0.8857972417130414,0.8712493180578287,4.461682319641113,1987.1975480000003 +4240,Binary classification,k-Nearest Neighbors,Bananas,0.886765746638358,0.8724760892667376,4.4584245681762695,2070.530861 +4346,Binary classification,k-Nearest Neighbors,Bananas,0.8876869965477561,0.8735751295336789,4.494175910949707,2155.1880220000003 +4452,Binary classification,k-Nearest Neighbors,Bananas,0.8869916872612896,0.8725614390676463,4.517621040344238,2241.198533 +4558,Binary classification,k-Nearest Neighbors,Bananas,0.8869870528856704,0.8729336294103133,4.495129585266113,2328.452784 +4664,Binary classification,k-Nearest Neighbors,Bananas,0.886982629208664,0.873286847799952,4.453595161437988,2416.918556 +4770,Binary classification,k-Nearest Neighbors,Bananas,0.8857202767875865,0.8715531463587085,4.469174385070801,2506.628209 +4876,Binary classification,k-Nearest Neighbors,Bananas,0.8861538461538462,0.871616932685635,4.478787422180176,2597.661861 +4982,Binary classification,k-Nearest Neighbors,Bananas,0.8869704878538446,0.8728832693610296,4.4154863357543945,2689.8978660000002 +5088,Binary classification,k-Nearest Neighbors,Bananas,0.885983880479654,0.8716814159292035,4.439602851867676,2783.355406 +5194,Binary classification,k-Nearest Neighbors,Bananas,0.885422684382823,0.8711842390127733,4.5029191970825195,2878.2182510000002 +5300,Binary classification,k-Nearest Neighbors,Bananas,0.8850726552179656,0.8708377518557794,4.509961128234863,2974.330637 +906,Binary classification,k-Nearest Neighbors,Elec2,0.8784530386740331,0.8711943793911008,4.434150695800781,37.114054 +1812,Binary classification,k-Nearest Neighbors,Elec2,0.8801766979569299,0.8453314326443336,4.643096923828125,93.709907 +2718,Binary classification,k-Nearest Neighbors,Elec2,0.8568273831431726,0.8160756501182034,4.6672821044921875,164.56349699999998 +3624,Binary classification,k-Nearest Neighbors,Elec2,0.8746894838531604,0.8411476557032889,4.594398498535156,248.37817199999998 +4530,Binary classification,k-Nearest Neighbors,Elec2,0.8783395893133142,0.8399651466744118,4.710762023925781,344.82085099999995 +5436,Binary classification,k-Nearest Neighbors,Elec2,0.8745170193192272,0.8360576923076923,4.698677062988281,452.38148599999994 +6342,Binary classification,k-Nearest Neighbors,Elec2,0.8747831572307208,0.8384865744507731,4.6694183349609375,569.8523869999999 +7248,Binary classification,k-Nearest Neighbors,Elec2,0.8723609769559818,0.8348509194786646,4.666007995605469,697.1091419999999 +8154,Binary classification,k-Nearest Neighbors,Elec2,0.8718263215994113,0.8430695299594534,4.7265625,834.9178869999998 +9060,Binary classification,k-Nearest Neighbors,Elec2,0.8738271332376643,0.8493475682087781,4.708610534667969,981.8103579999998 +9966,Binary classification,k-Nearest Neighbors,Elec2,0.8720521826392373,0.8501234277653698,4.6251678466796875,1137.002296 +10872,Binary classification,k-Nearest Neighbors,Elec2,0.8740686229417717,0.8545628386274301,4.637184143066406,1300.798705 +11778,Binary classification,k-Nearest Neighbors,Elec2,0.8742464124989386,0.8546756942400157,4.6933135986328125,1473.412993 +12684,Binary classification,k-Nearest Neighbors,Elec2,0.872664196168099,0.8527937289217027,4.810676574707031,1655.5819629999999 +13590,Binary classification,k-Nearest Neighbors,Elec2,0.8748252262859666,0.8573824096587573,4.703468322753906,1846.5010799999998 +14496,Binary classification,k-Nearest Neighbors,Elec2,0.8750603656433252,0.85826093762229,4.7199859619140625,2046.3736259999998 +15402,Binary classification,k-Nearest Neighbors,Elec2,0.8755924939938965,0.8581371242410781,4.7149505615234375,2254.7741159999996 +16308,Binary classification,k-Nearest Neighbors,Elec2,0.872079475072055,0.8535112359550563,4.6830902099609375,2471.4717159999996 +17214,Binary classification,k-Nearest Neighbors,Elec2,0.8723058153721025,0.8517669274345832,4.657257080078125,2696.3467009999995 +18120,Binary classification,k-Nearest Neighbors,Elec2,0.87234394834152,0.8515118443859535,4.7351837158203125,2929.4346569999993 +19026,Binary classification,k-Nearest Neighbors,Elec2,0.8734822601839685,0.8509505232522138,4.8458709716796875,3171.2751989999992 +19932,Binary classification,k-Nearest Neighbors,Elec2,0.8722091214690683,0.8505193966782089,4.8552703857421875,3421.524358999999 +20838,Binary classification,k-Nearest Neighbors,Elec2,0.8678312616979411,0.8451765234989881,4.8942718505859375,3679.924087999999 +21744,Binary classification,k-Nearest Neighbors,Elec2,0.8677735363105368,0.8427672955974842,4.7196807861328125,3945.793201999999 +22650,Binary classification,k-Nearest Neighbors,Elec2,0.8669256920835356,0.840444679724722,4.8090057373046875,4218.904371999999 +23556,Binary classification,k-Nearest Neighbors,Elec2,0.8647845468053492,0.8373257061136934,4.794342041015625,4499.001777999999 +24462,Binary classification,k-Nearest Neighbors,Elec2,0.8644372674870201,0.8359715077166601,4.7589569091796875,4786.067247999999 +25368,Binary classification,k-Nearest Neighbors,Elec2,0.8619860448614342,0.8330710914032329,4.846771240234375,5079.937268999999 +26274,Binary classification,k-Nearest Neighbors,Elec2,0.8623301488219846,0.8333410127632125,4.699310302734375,5380.101847999999 +27180,Binary classification,k-Nearest Neighbors,Elec2,0.8632767945840538,0.8350350705851016,4.794769287109375,5686.6129089999995 +28086,Binary classification,k-Nearest Neighbors,Elec2,0.862061598718177,0.8333620096352374,4.6817474365234375,5999.306036 +28992,Binary classification,k-Nearest Neighbors,Elec2,0.8618191852643924,0.8323989624299222,4.8116455078125,6318.1198079999995 +29898,Binary classification,k-Nearest Neighbors,Elec2,0.8607218115529987,0.8308417289567761,4.769432067871094,6643.155825 +30804,Binary classification,k-Nearest Neighbors,Elec2,0.8599162419244879,0.8291562735083342,4.83782958984375,6975.21929 +31710,Binary classification,k-Nearest Neighbors,Elec2,0.8578006244283958,0.8263832736513803,4.7655487060546875,7313.109579 +32616,Binary classification,k-Nearest Neighbors,Elec2,0.8558332055802544,0.8246307623452186,4.726959228515625,7656.831982 +33522,Binary classification,k-Nearest Neighbors,Elec2,0.8543897855075923,0.8232354325861008,4.798057556152344,8006.181245 +34428,Binary classification,k-Nearest Neighbors,Elec2,0.8533128068086095,0.8218066337332393,4.773887634277344,8361.39601 +35334,Binary classification,k-Nearest Neighbors,Elec2,0.8518099227351201,0.8192737815822173,4.808341979980469,8722.632877 +36240,Binary classification,k-Nearest Neighbors,Elec2,0.8522310218273131,0.8186651315566692,4.722572326660156,9089.688296 +37146,Binary classification,k-Nearest Neighbors,Elec2,0.8505586216179836,0.8161859664227292,4.720252990722656,9462.293988 +38052,Binary classification,k-Nearest Neighbors,Elec2,0.8507792173661665,0.81590039556449,4.766929626464844,9840.72504 +38958,Binary classification,k-Nearest Neighbors,Elec2,0.8507841979618553,0.8163523204751524,4.769111633300781,10225.058019 +39864,Binary classification,k-Nearest Neighbors,Elec2,0.850889295838246,0.8178809976101478,4.736076354980469,10615.291715 +40770,Binary classification,k-Nearest Neighbors,Elec2,0.8509161372611543,0.8193329766363474,4.725471496582031,11011.540551999999 +41676,Binary classification,k-Nearest Neighbors,Elec2,0.8518536292741452,0.8217770336585647,4.700096130371094,11414.574327999999 +42582,Binary classification,k-Nearest Neighbors,Elec2,0.8529156196425636,0.8235028885444553,4.746559143066406,11823.240958999999 +43488,Binary classification,k-Nearest Neighbors,Elec2,0.8525536367190195,0.8231074817920989,4.826316833496094,12236.954316 +44394,Binary classification,k-Nearest Neighbors,Elec2,0.8525217939765278,0.8226754421602882,4.775764465332031,12655.735001 +45300,Binary classification,k-Nearest Neighbors,Elec2,0.853131415704541,0.8236541468974475,4.7673492431640625,13079.486139999999 +45312,Binary classification,k-Nearest Neighbors,Elec2,0.8531482421487057,0.8236416644579911,4.7660369873046875,13503.439196 +25,Binary classification,k-Nearest Neighbors,Phishing,0.5833333333333334,0.7058823529411764,0.041108131408691406,0.04635 +50,Binary classification,k-Nearest Neighbors,Phishing,0.7551020408163265,0.7777777777777778,0.0695962905883789,0.16308 +75,Binary classification,k-Nearest Neighbors,Phishing,0.7972972972972973,0.8235294117647058,0.09861469268798828,0.336872 +100,Binary classification,k-Nearest Neighbors,Phishing,0.797979797979798,0.8148148148148148,0.12712955474853516,0.6777850000000001 +125,Binary classification,k-Nearest Neighbors,Phishing,0.8064516129032258,0.8208955223880596,0.15564441680908203,1.226658 +150,Binary classification,k-Nearest Neighbors,Phishing,0.8187919463087249,0.834355828220859,0.1846628189086914,1.947513 +175,Binary classification,k-Nearest Neighbors,Phishing,0.8390804597701149,0.8426966292134832,0.21317768096923828,2.9471350000000003 +200,Binary classification,k-Nearest Neighbors,Phishing,0.8391959798994975,0.8415841584158417,0.24219608306884766,4.26255 +225,Binary classification,k-Nearest Neighbors,Phishing,0.8392857142857143,0.8363636363636364,0.27071094512939453,5.830504 +250,Binary classification,k-Nearest Neighbors,Phishing,0.8232931726907631,0.8225806451612903,0.2992258071899414,7.819846 +275,Binary classification,k-Nearest Neighbors,Phishing,0.8248175182481752,0.8208955223880596,0.3284578323364258,10.199689 +300,Binary classification,k-Nearest Neighbors,Phishing,0.8260869565217391,0.8181818181818181,0.35697269439697266,12.972731999999999 +325,Binary classification,k-Nearest Neighbors,Phishing,0.8364197530864198,0.8250825082508251,0.38599109649658203,16.225203999999998 +350,Binary classification,k-Nearest Neighbors,Phishing,0.8452722063037249,0.83125,0.4145059585571289,19.962148 +375,Binary classification,k-Nearest Neighbors,Phishing,0.839572192513369,0.8235294117647058,0.4430208206176758,24.257676 +400,Binary classification,k-Nearest Neighbors,Phishing,0.8421052631578947,0.8225352112676055,0.47203922271728516,29.175886 +425,Binary classification,k-Nearest Neighbors,Phishing,0.8443396226415094,0.819672131147541,0.500554084777832,34.714831 +450,Binary classification,k-Nearest Neighbors,Phishing,0.8463251670378619,0.8198433420365536,0.5295724868774414,40.875927999999995 +475,Binary classification,k-Nearest Neighbors,Phishing,0.8438818565400844,0.8177339901477833,0.5580873489379883,47.66810099999999 +500,Binary classification,k-Nearest Neighbors,Phishing,0.845691382765531,0.8229885057471266,2.6757898330688477,79.03492 +525,Binary classification,k-Nearest Neighbors,Phishing,0.8454198473282443,0.8187919463087249,2.7769289016723633,111.488727 +550,Binary classification,k-Nearest Neighbors,Phishing,0.848816029143898,0.8237791932059448,2.8829355239868164,145.039549 +575,Binary classification,k-Nearest Neighbors,Phishing,0.8519163763066202,0.8268839103869654,2.989964485168457,179.782132 +600,Binary classification,k-Nearest Neighbors,Phishing,0.8514190317195326,0.8230616302186878,3.098984718322754,215.642079 +625,Binary classification,k-Nearest Neighbors,Phishing,0.8525641025641025,0.8210116731517509,3.2059221267700195,252.521109 +650,Binary classification,k-Nearest Neighbors,Phishing,0.8582434514637904,0.8302583025830258,3.3169260025024414,290.529559 +675,Binary classification,k-Nearest Neighbors,Phishing,0.8620178041543026,0.8382608695652174,3.4291276931762695,329.62297 +700,Binary classification,k-Nearest Neighbors,Phishing,0.8669527896995708,0.8421052631578948,3.5458459854125977,369.665809 +725,Binary classification,k-Nearest Neighbors,Phishing,0.8674033149171271,0.8456591639871384,3.6591615676879883,410.97711300000003 +750,Binary classification,k-Nearest Neighbors,Phishing,0.8678237650200267,0.8465116279069768,3.769242286682129,453.55010200000004 +775,Binary classification,k-Nearest Neighbors,Phishing,0.8669250645994832,0.8446455505279035,3.881718635559082,497.265773 +800,Binary classification,k-Nearest Neighbors,Phishing,0.8648310387984981,0.8434782608695652,3.9942636489868164,542.189116 +825,Binary classification,k-Nearest Neighbors,Phishing,0.8628640776699029,0.8423988842398884,4.110844612121582,588.325742 +850,Binary classification,k-Nearest Neighbors,Phishing,0.8657243816254417,0.8451086956521738,4.225159645080566,635.696811 +875,Binary classification,k-Nearest Neighbors,Phishing,0.868421052631579,0.847277556440903,4.342709541320801,684.216091 +900,Binary classification,k-Nearest Neighbors,Phishing,0.8698553948832035,0.8482490272373541,4.455658912658691,733.952375 +925,Binary classification,k-Nearest Neighbors,Phishing,0.8712121212121212,0.8514357053682896,4.573666572570801,785.013527 +950,Binary classification,k-Nearest Neighbors,Phishing,0.8735511064278187,0.8561151079136691,4.697152137756348,837.27657 +975,Binary classification,k-Nearest Neighbors,Phishing,0.8757700205338809,0.8581477139507622,4.820996284484863,890.836474 +1000,Binary classification,k-Nearest Neighbors,Phishing,0.8758758758758759,0.858447488584475,4.93715763092041,945.663339 +1025,Binary classification,k-Nearest Neighbors,Phishing,0.8759765625,0.8590455049944505,4.905686378479004,1001.6659109999999 +1050,Binary classification,k-Nearest Neighbors,Phishing,0.8779790276453765,0.8617710583153347,4.881028175354004,1058.8496129999999 +1075,Binary classification,k-Nearest Neighbors,Phishing,0.8780260707635009,0.86282722513089,4.857575416564941,1117.1299479999998 +1100,Binary classification,k-Nearest Neighbors,Phishing,0.8789808917197452,0.8641470888661901,4.821175575256348,1176.4409139999998 +1125,Binary classification,k-Nearest Neighbors,Phishing,0.8798932384341637,0.8662041625371655,4.749619483947754,1236.7626339999997 +1150,Binary classification,k-Nearest Neighbors,Phishing,0.8807658833768495,0.8668610301263362,4.722535133361816,1298.0588499999997 +1175,Binary classification,k-Nearest Neighbors,Phishing,0.879045996592845,0.8645038167938931,4.706612586975098,1360.3228199999996 +1200,Binary classification,k-Nearest Neighbors,Phishing,0.8807339449541285,0.865979381443299,4.686341285705566,1423.5043029999997 +1225,Binary classification,k-Nearest Neighbors,Phishing,0.8815359477124183,0.8666053357865686,4.653275489807129,1487.6393369999996 +1250,Binary classification,k-Nearest Neighbors,Phishing,0.8815052041633307,0.867145421903052,4.596428871154785,1552.6498929999996 +1903,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.559709548950195,49.463009 +3806,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.594751358032227,126.299444 +5709,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.435243606567383,223.803561 +7612,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.493677139282227,340.763146 +9515,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.534708023071289,475.19475 +11418,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.455095291137695,625.35715 +13321,Binary classification,k-Nearest Neighbors,SMTP,1.0,0.0,4.479013442993164,790.6914730000001 +15224,Binary classification,k-Nearest Neighbors,SMTP,0.9998686198515404,0.9,4.445444107055664,971.1285220000001 +17127,Binary classification,k-Nearest Neighbors,SMTP,0.9998832184981898,0.9166666666666666,4.544534683227539,1166.447296 +19030,Binary classification,k-Nearest Neighbors,SMTP,0.9998948972620737,0.9166666666666666,4.52708625793457,1376.022994 +20933,Binary classification,k-Nearest Neighbors,SMTP,0.999904452512899,0.9166666666666666,4.493997573852539,1599.513782 +22836,Binary classification,k-Nearest Neighbors,SMTP,0.9999124151521787,0.9166666666666666,4.490983963012695,1835.5325109999999 +24739,Binary classification,k-Nearest Neighbors,SMTP,0.9999191527205109,0.9166666666666666,4.531465530395508,2083.498651 +26642,Binary classification,k-Nearest Neighbors,SMTP,0.9998873916144289,0.88,4.54191780090332,2343.113276 +28545,Binary classification,k-Nearest Neighbors,SMTP,0.999894899103139,0.88,4.488824844360352,2613.833594 +30448,Binary classification,k-Nearest Neighbors,SMTP,0.9999014681249384,0.88,4.459695816040039,2894.8346570000003 +32351,Binary classification,k-Nearest Neighbors,SMTP,0.9999072642967543,0.88,4.475152969360352,3186.5040240000003 +34254,Binary classification,k-Nearest Neighbors,SMTP,0.9999124164306776,0.88,4.543954849243164,3487.9588300000005 +36157,Binary classification,k-Nearest Neighbors,SMTP,0.9999170262197146,0.88,4.482622146606445,3798.7831540000006 +38060,Binary classification,k-Nearest Neighbors,SMTP,0.9999211750177356,0.88,4.496248245239258,4119.269013000001 +39963,Binary classification,k-Nearest Neighbors,SMTP,0.9999249286822481,0.88,4.471353530883789,4448.958874000001 +41866,Binary classification,k-Nearest Neighbors,SMTP,0.9999283410963812,0.88,4.53770637512207,4788.142489000001 +43769,Binary classification,k-Nearest Neighbors,SMTP,0.999931456772071,0.88,4.51286506652832,5135.940338 +45672,Binary classification,k-Nearest Neighbors,SMTP,0.9999343128024348,0.88,4.49894905090332,5492.9415070000005 +47575,Binary classification,k-Nearest Neighbors,SMTP,0.9999369403455669,0.88,4.555765151977539,5859.118968000001 +49478,Binary classification,k-Nearest Neighbors,SMTP,0.9999393657659115,0.88,4.430139541625977,6234.600581000001 +51381,Binary classification,k-Nearest Neighbors,SMTP,0.9999221486959906,0.8571428571428571,4.466188430786133,6619.789592000001 +53284,Binary classification,k-Nearest Neighbors,SMTP,0.9999249291518871,0.8571428571428571,4.526651382446289,7013.956607000001 +55187,Binary classification,k-Nearest Neighbors,SMTP,0.9999275178487298,0.8571428571428571,4.485139846801758,7418.775951000001 +57090,Binary classification,k-Nearest Neighbors,SMTP,0.9997898018882797,0.7391304347826089,4.452577590942383,7831.9045620000015 +58993,Binary classification,k-Nearest Neighbors,SMTP,0.9997965825874695,0.7391304347826089,4.485406875610352,8252.946705000002 +60896,Binary classification,k-Nearest Neighbors,SMTP,0.9998029394860005,0.7391304347826089,4.502649307250977,8681.511861000003 +62799,Binary classification,k-Nearest Neighbors,SMTP,0.9997770629637887,0.7083333333333334,4.495584487915039,9116.499033000002 +64702,Binary classification,k-Nearest Neighbors,SMTP,0.9997836200367846,0.7083333333333334,4.500345230102539,9557.922914000002 +66605,Binary classification,k-Nearest Neighbors,SMTP,0.9997898024142694,0.7083333333333334,4.572656631469727,10005.892137000003 +68508,Binary classification,k-Nearest Neighbors,SMTP,0.9997664472243712,0.68,4.537866592407227,10460.064213000003 +70411,Binary classification,k-Nearest Neighbors,SMTP,0.9997727595512002,0.68,4.469621658325195,10920.643886000003 +72314,Binary classification,k-Nearest Neighbors,SMTP,0.9997787396457068,0.68,4.537904739379883,11387.046035000003 +74217,Binary classification,k-Nearest Neighbors,SMTP,0.9997844130645683,0.68,4.493074417114258,11859.048710000003 +76120,Binary classification,k-Nearest Neighbors,SMTP,0.99978980280876,0.68,4.520692825317383,12336.641580000003 +78023,Binary classification,k-Nearest Neighbors,SMTP,0.9997949296352311,0.68,4.566102981567383,12820.170836000003 +79926,Binary classification,k-Nearest Neighbors,SMTP,0.9997998123240538,0.68,4.500688552856445,13309.287446000002 +81829,Binary classification,k-Nearest Neighbors,SMTP,0.9998044679082955,0.68,4.506959915161133,13804.219559000003 +83732,Binary classification,k-Nearest Neighbors,SMTP,0.9998089118725442,0.68,4.503435134887695,14304.793113000003 +85635,Binary classification,k-Nearest Neighbors,SMTP,0.9998131583249644,0.68,4.498682022094727,14810.923265000003 +87538,Binary classification,k-Nearest Neighbors,SMTP,0.9998172201469093,0.68,4.491861343383789,15322.783751000003 +89441,Binary classification,k-Nearest Neighbors,SMTP,0.9998099284436494,0.6666666666666666,4.496858596801758,15840.351229000004 +91344,Binary classification,k-Nearest Neighbors,SMTP,0.9998138883110912,0.6666666666666666,4.480546951293945,16363.581709000004 +93247,Binary classification,k-Nearest Neighbors,SMTP,0.999817686549557,0.6666666666666666,4.533571243286133,16892.331870000005 +95150,Binary classification,k-Nearest Neighbors,SMTP,0.9998213328568876,0.6666666666666666,4.517786026000977,17426.665113000006 +95156,Binary classification,k-Nearest Neighbors,SMTP,0.9998213441227471,0.6666666666666666,4.518220901489258,17961.110841000005 +106,Binary classification,ADWIN Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.1797952651977539,0.693272 +212,Binary classification,ADWIN Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.1805887222290039,2.027128 +318,Binary classification,ADWIN Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.18126773834228516,4.089008 +424,Binary classification,ADWIN Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.18131351470947266,6.9179189999999995 +530,Binary classification,ADWIN Bagging,Bananas,0.55765595463138,0.45581395348837206,0.1813364028930664,10.429995 +636,Binary classification,ADWIN Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.1819925308227539,14.687229 +742,Binary classification,ADWIN Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.1820383071899414,19.647457 +848,Binary classification,ADWIN Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.18196964263916016,25.366910999999998 +954,Binary classification,ADWIN Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.1819467544555664,31.800241999999997 +1060,Binary classification,ADWIN Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.1819467544555664,39.029576 +1166,Binary classification,ADWIN Bagging,Bananas,0.590557939914163,0.4015056461731493,0.18192386627197266,46.984262 +1272,Binary classification,ADWIN Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.18192386627197266,55.672123 +1378,Binary classification,ADWIN Bagging,Bananas,0.599128540305011,0.3973799126637554,0.18253421783447266,65.02100899999999 +1484,Binary classification,ADWIN Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.18248844146728516,75.177128 +1590,Binary classification,ADWIN Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.1824655532836914,86.053176 +1696,Binary classification,ADWIN Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.1824655532836914,97.727606 +1802,Binary classification,ADWIN Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.18248844146728516,110.067211 +1908,Binary classification,ADWIN Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.18248844146728516,123.213825 +2014,Binary classification,ADWIN Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.18248844146728516,137.051202 +2120,Binary classification,ADWIN Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.18248844146728516,151.568436 +2226,Binary classification,ADWIN Bagging,Bananas,0.597752808988764,0.40133779264214053,0.18244266510009766,166.814875 +2332,Binary classification,ADWIN Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.18244266510009766,182.823981 +2438,Binary classification,ADWIN Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.1824655532836914,199.616425 +2544,Binary classification,ADWIN Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.1824655532836914,217.084375 +2650,Binary classification,ADWIN Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.18309879302978516,235.279245 +2756,Binary classification,ADWIN Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.18309879302978516,254.250965 +2862,Binary classification,ADWIN Bagging,Bananas,0.599790283117791,0.4051948051948052,0.18309879302978516,273.857236 +2968,Binary classification,ADWIN Bagging,Bananas,0.599932591843613,0.40261701056869653,0.1831216812133789,294.204326 +3074,Binary classification,ADWIN Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.1831216812133789,315.311898 +3180,Binary classification,ADWIN Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.1831216812133789,337.189575 +3286,Binary classification,ADWIN Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.17859649658203125,359.75124 +3392,Binary classification,ADWIN Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.2364349365234375,383.144231 +3498,Binary classification,ADWIN Bagging,Bananas,0.597369173577352,0.40237691001697795,0.2806434631347656,407.32428699999997 +3604,Binary classification,ADWIN Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3000526428222656,432.314941 +3710,Binary classification,ADWIN Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.3464546203613281,458.107983 +3816,Binary classification,ADWIN Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.3760719299316406,484.645149 +3922,Binary classification,ADWIN Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.4113121032714844,512.014837 +4028,Binary classification,ADWIN Bagging,Bananas,0.608393344921778,0.4195804195804196,0.4392280578613281,540.239956 +4134,Binary classification,ADWIN Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.4532661437988281,569.3761920000001 +4240,Binary classification,ADWIN Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.4546051025390625,599.333749 +4346,Binary classification,ADWIN Bagging,Bananas,0.6186421173762946,0.4384954252795662,0.4373931884765625,630.119 +4452,Binary classification,ADWIN Bagging,Bananas,0.6212087171422153,0.44209133024487096,0.43770599365234375,661.732786 +4558,Binary classification,ADWIN Bagging,Bananas,0.6214614878209348,0.44372782973234437,0.42758941650390625,694.0925080000001 +4664,Binary classification,ADWIN Bagging,Bananas,0.6219172206733863,0.44542308902170497,0.3975372314453125,727.2725200000001 +4770,Binary classification,ADWIN Bagging,Bananas,0.6227720696162717,0.4449244060475162,0.42584228515625,761.2761580000001 +4876,Binary classification,ADWIN Bagging,Bananas,0.6235897435897436,0.4444444444444444,0.393829345703125,796.1318860000001 +4982,Binary classification,ADWIN Bagging,Bananas,0.6251756675366392,0.44910002950722927,0.39398193359375,831.6856570000001 +5088,Binary classification,ADWIN Bagging,Bananas,0.624139964615687,0.44675925925925924,0.39410400390625,867.9313910000001 +5194,Binary classification,ADWIN Bagging,Bananas,0.6248796456768727,0.44690516751845544,0.394500732421875,904.9575060000001 +5300,Binary classification,ADWIN Bagging,Bananas,0.6259671636157765,0.44821826280623617,0.40065765380859375,942.730038 +906,Binary classification,ADWIN Bagging,Elec2,0.8651933701657458,0.8685344827586208,1.5650663375854492,11.845084 +1812,Binary classification,ADWIN Bagging,Elec2,0.8895637769188294,0.8684210526315789,1.8734617233276367,34.886970000000005 +2718,Binary classification,ADWIN Bagging,Elec2,0.8778064041221936,0.8547681539807523,1.7035398483276367,70.08374500000001 +3624,Binary classification,ADWIN Bagging,Elec2,0.8835219431410434,0.8607260726072606,1.6641263961791992,115.953507 +4530,Binary classification,ADWIN Bagging,Elec2,0.8878339589313314,0.8599007170435742,2.0698423385620117,171.720075 +5436,Binary classification,ADWIN Bagging,Elec2,0.886292548298068,0.8580615525953147,2.326838493347168,235.88433300000003 +6342,Binary classification,ADWIN Bagging,Elec2,0.8845607948273143,0.8556782334384859,1.8882322311401367,307.801578 +7248,Binary classification,ADWIN Bagging,Elec2,0.8835380157306472,0.8526021655606008,1.7675046920776367,386.842642 +8154,Binary classification,ADWIN Bagging,Elec2,0.8854409419845456,0.8617115783239561,1.8750486373901367,473.251889 +9060,Binary classification,ADWIN Bagging,Elec2,0.8863009162159179,0.8664765361680064,1.8668012619018555,566.6143930000001 +9966,Binary classification,ADWIN Bagging,Elec2,0.883492222779729,0.8657337805019083,1.624751091003418,666.8526760000001 +10872,Binary classification,ADWIN Bagging,Elec2,0.8851071658541072,0.8689539397754694,1.8364439010620117,773.0944300000001 +11778,Binary classification,ADWIN Bagging,Elec2,0.8819733378619343,0.8645224171539961,1.461909294128418,884.893217 +12684,Binary classification,ADWIN Bagging,Elec2,0.8788930063865016,0.8610709117221419,1.412806510925293,1002.145707 +13590,Binary classification,ADWIN Bagging,Elec2,0.880197218338362,0.863970588235294,1.521845817565918,1125.032122 +14496,Binary classification,ADWIN Bagging,Elec2,0.8799586064160055,0.8644437519476471,1.922499656677246,1253.0070850000002 +15402,Binary classification,ADWIN Bagging,Elec2,0.8805272384910071,0.8643667993513195,1.924330711364746,1386.9590420000002 +16308,Binary classification,ADWIN Bagging,Elec2,0.8794382780401054,0.8622670589883704,1.6739492416381836,1526.6043270000002 +17214,Binary classification,ADWIN Bagging,Elec2,0.8781153779120432,0.8583389601620527,2.050276756286621,1671.5850450000003 +18120,Binary classification,ADWIN Bagging,Elec2,0.8772559192008389,0.8570510348373829,2.0607213973999023,1821.6195730000002 +19026,Binary classification,ADWIN Bagging,Elec2,0.8782128777923784,0.8564702967230379,1.4914274215698242,1976.6826170000002 +19932,Binary classification,ADWIN Bagging,Elec2,0.8703025437760273,0.8482357776081723,0.7451009750366211,2137.4066430000003 +20838,Binary classification,ADWIN Bagging,Elec2,0.8626001823679033,0.8387314820030418,0.7786626815795898,2303.79277 +21744,Binary classification,ADWIN Bagging,Elec2,0.8638642321666743,0.8378259916721456,0.8927946090698242,2475.479733 +22650,Binary classification,ADWIN Bagging,Elec2,0.8620689655172413,0.8337590464027246,1.0149259567260742,2652.48421 +23556,Binary classification,ADWIN Bagging,Elec2,0.8552748885586924,0.8239789332369494,0.7698392868041992,2835.224884 +24462,Binary classification,ADWIN Bagging,Elec2,0.8513143371080496,0.8171902488062327,0.8274068832397461,3023.118045 +25368,Binary classification,ADWIN Bagging,Elec2,0.8471242165017543,0.8121670057153928,0.9264287948608398,3216.225907 +26274,Binary classification,ADWIN Bagging,Elec2,0.8469912077037263,0.8116213683223992,1.0318632125854492,3414.151818 +27180,Binary classification,ADWIN Bagging,Elec2,0.8476397218440708,0.8134600657687283,0.828364372253418,3617.194246 +28086,Binary classification,ADWIN Bagging,Elec2,0.8442585009791703,0.8083260297984224,0.9404935836791992,3825.52125 +28992,Binary classification,ADWIN Bagging,Elec2,0.8416405091235211,0.8035935828877006,0.932948112487793,4039.2676589999996 +29898,Binary classification,ADWIN Bagging,Elec2,0.8389804997156906,0.7997670742866649,0.8770322799682617,4258.3083369999995 +30804,Binary classification,ADWIN Bagging,Elec2,0.8374508976398403,0.7964054812344976,1.024897575378418,4482.681616999999 +31710,Binary classification,ADWIN Bagging,Elec2,0.8326973414488,0.7888725275599952,0.9494352340698242,4711.951208999999 +32616,Binary classification,ADWIN Bagging,Elec2,0.827318718381113,0.7808219178082191,0.861109733581543,4945.765051999999 +33522,Binary classification,ADWIN Bagging,Elec2,0.8271531278899794,0.7806964420893262,1.093031883239746,5184.141968999998 +34428,Binary classification,ADWIN Bagging,Elec2,0.8255439044935661,0.7779174678302028,1.133570671081543,5427.007607999998 +35334,Binary classification,ADWIN Bagging,Elec2,0.8253191067840263,0.7766196163590301,1.1872129440307617,5674.462564999998 +36240,Binary classification,ADWIN Bagging,Elec2,0.8264576837109192,0.7764070110569915,1.431788444519043,5926.318084999998 +37146,Binary classification,ADWIN Bagging,Elec2,0.8245255081437609,0.7721616331096196,1.357222557067871,6182.753490999998 +38052,Binary classification,ADWIN Bagging,Elec2,0.8241833328953247,0.7704974271012006,1.1449995040893555,6443.434381999998 +38958,Binary classification,ADWIN Bagging,Elec2,0.8233950252842878,0.7698534823041413,1.147334098815918,6708.424011999998 +39864,Binary classification,ADWIN Bagging,Elec2,0.8222160901086221,0.7699250073044834,0.7468709945678711,6977.570913999998 +40770,Binary classification,ADWIN Bagging,Elec2,0.8227329588658049,0.7725140860587366,0.9336042404174805,7251.011506999998 +41676,Binary classification,ADWIN Bagging,Elec2,0.8235872825434913,0.7755388654820785,1.097620964050293,7528.4976689999985 +42582,Binary classification,ADWIN Bagging,Elec2,0.8245696437378174,0.7776785714285713,1.5453977584838867,7809.843107999998 +43488,Binary classification,ADWIN Bagging,Elec2,0.8251431462276082,0.7787605469886529,0.8941831588745117,8094.859004999998 +44394,Binary classification,ADWIN Bagging,Elec2,0.8243867276372401,0.7766701042740918,0.744959831237793,8383.886548999999 +45300,Binary classification,ADWIN Bagging,Elec2,0.823770944170953,0.7766305716444221,0.5983161926269531,8676.996437 +45312,Binary classification,ADWIN Bagging,Elec2,0.8237734766392267,0.7765871128395959,0.5984382629394531,8970.151376 +25,Binary classification,ADWIN Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.6633157730102539,0.427424 +50,Binary classification,ADWIN Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.6639947891235352,1.324595 +75,Binary classification,ADWIN Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.6639490127563477,2.554164 +100,Binary classification,ADWIN Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.6645593643188477,4.28613 +125,Binary classification,ADWIN Bagging,Phishing,0.8548387096774194,0.85,0.6645593643188477,6.454494 +150,Binary classification,ADWIN Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.6645593643188477,8.992416 +175,Binary classification,ADWIN Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.6651926040649414,11.944220000000001 +200,Binary classification,ADWIN Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.6653299331665039,15.477702 +225,Binary classification,ADWIN Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7024993896484375,19.458772 +250,Binary classification,ADWIN Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.730194091796875,23.970287 +275,Binary classification,ADWIN Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7302398681640625,28.914006 +300,Binary classification,ADWIN Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7302398681640625,34.304573 +325,Binary classification,ADWIN Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7308502197265625,40.225778999999996 +350,Binary classification,ADWIN Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7308731079101562,46.580822 +375,Binary classification,ADWIN Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.7674179077148438,53.398646 +400,Binary classification,ADWIN Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.804534912109375,60.683253 +425,Binary classification,ADWIN Bagging,Phishing,0.8679245283018868,0.839080459770115,0.8596954345703125,68.459501 +450,Binary classification,ADWIN Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.8597640991210938,76.689807 +475,Binary classification,ADWIN Bagging,Phishing,0.869198312236287,0.8402061855670103,0.859832763671875,85.340536 +500,Binary classification,ADWIN Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.8598556518554688,94.431883 +525,Binary classification,ADWIN Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.8598556518554688,103.968058 +550,Binary classification,ADWIN Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.8599014282226562,113.947374 +575,Binary classification,ADWIN Bagging,Phishing,0.867595818815331,0.8382978723404255,0.8599014282226562,124.33795699999999 +600,Binary classification,ADWIN Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.8599014282226562,135.24069899999998 +625,Binary classification,ADWIN Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.8966064453125,146.56138699999997 +650,Binary classification,ADWIN Bagging,Phishing,0.8767334360554699,0.846153846153846,0.897308349609375,158.32837399999997 +675,Binary classification,ADWIN Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.92486572265625,170.52005599999995 +700,Binary classification,ADWIN Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.8633918762207031,183.10001399999996 +725,Binary classification,ADWIN Bagging,Phishing,0.8825966850828729,0.8576214405360134,0.9612770080566406,196.14419299999997 +750,Binary classification,ADWIN Bagging,Phishing,0.8865153538050734,0.8631239935587761,0.9975471496582031,209.59830599999998 +775,Binary classification,ADWIN Bagging,Phishing,0.8875968992248062,0.863849765258216,1.0525703430175781,223.56014999999996 +800,Binary classification,ADWIN Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.1531257629394531,237.94997899999996 +825,Binary classification,ADWIN Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.1537437438964844,252.78040299999995 +850,Binary classification,ADWIN Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.1632118225097656,267.983484 +875,Binary classification,ADWIN Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.1906776428222656,283.60495299999997 +900,Binary classification,ADWIN Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.2457008361816406,299.65263899999997 +925,Binary classification,ADWIN Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.2457923889160156,316.05876399999994 +950,Binary classification,ADWIN Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.2458381652832031,332.97263699999996 +975,Binary classification,ADWIN Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.2458839416503906,350.27117 +1000,Binary classification,ADWIN Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.2458610534667969,368.008026 +1025,Binary classification,ADWIN Bagging,Phishing,0.8876953125,0.8700564971751412,1.2459068298339844,386.172169 +1050,Binary classification,ADWIN Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.2458839416503906,404.74234 +1075,Binary classification,ADWIN Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.2458839416503906,423.71995200000003 +1100,Binary classification,ADWIN Bagging,Phishing,0.89171974522293,0.8761706555671176,1.2458839416503906,443.13918 +1125,Binary classification,ADWIN Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.2458839416503906,462.95518100000004 +1150,Binary classification,ADWIN Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.2458839416503906,483.090208 +1175,Binary classification,ADWIN Bagging,Phishing,0.8926746166950597,0.877906976744186,1.2458839416503906,503.74833900000004 +1200,Binary classification,ADWIN Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.2550315856933594,524.8299360000001 +1225,Binary classification,ADWIN Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3099861145019531,546.3067460000001 +1250,Binary classification,ADWIN Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3100776672363281,568.2182720000001 +1903,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.15993690490722656,9.565839 +3806,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16054725646972656,28.660555 +5709,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.1610889434814453,57.169533 +7612,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16111183166503906,95.02592100000001 +9515,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16111183166503906,141.315828 +11418,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.16172218322753906,195.517174 +13321,Binary classification,ADWIN Bagging,SMTP,1.0,0.0,0.1617450714111328,256.578558 +15224,Binary classification,ADWIN Bagging,SMTP,0.9992774091834724,0.0,0.2173633575439453,324.084886 +17127,Binary classification,ADWIN Bagging,SMTP,0.9992409202382343,0.0,0.16245460510253906,398.342181 +19030,Binary classification,ADWIN Bagging,SMTP,0.9993168322034789,0.0,0.16227149963378906,479.30544 +20933,Binary classification,ADWIN Bagging,SMTP,0.999378941333843,0.0,0.1629047393798828,566.848605 +22836,Binary classification,ADWIN Bagging,SMTP,0.9994306984891613,0.0,0.1629962921142578,660.867353 +24739,Binary classification,ADWIN Bagging,SMTP,0.9994744926833212,0.0,0.1631336212158203,761.190417 +26642,Binary classification,ADWIN Bagging,SMTP,0.9994744942006681,0.0,0.1628131866455078,867.1613560000001 +28545,Binary classification,ADWIN Bagging,SMTP,0.9995095291479821,0.0,0.1630420684814453,978.3809960000001 +30448,Binary classification,ADWIN Bagging,SMTP,0.9995401845830459,0.0,0.16292762756347656,1094.7710920000002 +32351,Binary classification,ADWIN Bagging,SMTP,0.9995672333848532,0.0,0.16306495666503906,1216.3368180000002 +34254,Binary classification,ADWIN Bagging,SMTP,0.9995912766764955,0.0,0.16297340393066406,1342.8439390000003 +36157,Binary classification,ADWIN Bagging,SMTP,0.9996127890253347,0.0,0.16297340393066406,1474.4175280000004 +38060,Binary classification,ADWIN Bagging,SMTP,0.9996321500827662,0.0,0.1629962921142578,1611.5765880000004 +39963,Binary classification,ADWIN Bagging,SMTP,0.9996496671838246,0.0,0.1628589630126953,1753.4950250000004 +41866,Binary classification,ADWIN Bagging,SMTP,0.9996655917831124,0.0,0.1635608673095703,1900.0594340000005 +43769,Binary classification,ADWIN Bagging,SMTP,0.9996801316029976,0.0,0.1636524200439453,2051.0940990000004 +45672,Binary classification,ADWIN Bagging,SMTP,0.9996934597446958,0.0,0.16362953186035156,2206.5822620000004 +47575,Binary classification,ADWIN Bagging,SMTP,0.9997057216126456,0.0,0.1635608673095703,2366.582792 +49478,Binary classification,ADWIN Bagging,SMTP,0.99971704024092,0.0,0.1516590118408203,2531.0740060000003 +51381,Binary classification,ADWIN Bagging,SMTP,0.9996885947839627,0.0,0.16358375549316406,2700.0024150000004 +53284,Binary classification,ADWIN Bagging,SMTP,0.9996997166075484,0.0,0.16358375549316406,2873.4051700000005 +55187,Binary classification,ADWIN Bagging,SMTP,0.999710071394919,0.0,0.16353797912597656,3051.2280980000005 +57090,Binary classification,ADWIN Bagging,SMTP,0.9995620872672494,0.0,0.1635608673095703,3233.6982610000005 +58993,Binary classification,ADWIN Bagging,SMTP,0.9995762137238947,0.0,0.1634693145751953,3420.4241430000006 +60896,Binary classification,ADWIN Bagging,SMTP,0.999589457262501,0.0,0.16340065002441406,3611.3532100000007 +62799,Binary classification,ADWIN Bagging,SMTP,0.9995700500015924,0.0,0.1635608673095703,3806.5756970000007 +64702,Binary classification,ADWIN Bagging,SMTP,0.9995826957852274,0.0,0.1636524200439453,4005.990633000001 +66605,Binary classification,ADWIN Bagging,SMTP,0.9995946189418053,0.0,0.1635608673095703,4209.692892000001 +68508,Binary classification,ADWIN Bagging,SMTP,0.9995766855941729,0.0,0.1636066436767578,4417.671552000001 +70411,Binary classification,ADWIN Bagging,SMTP,0.9995881266865502,0.0,0.16344642639160156,4629.924287000001 +72314,Binary classification,ADWIN Bagging,SMTP,0.9995989656078437,0.0,0.1636066436767578,4846.389066000001 +74217,Binary classification,ADWIN Bagging,SMTP,0.99960924867953,0.0,0.1636066436767578,5067.145737000001 +76120,Binary classification,ADWIN Bagging,SMTP,0.9996190175908775,0.0,0.1636524200439453,5292.119512000001 +78023,Binary classification,ADWIN Bagging,SMTP,0.9996283099638563,0.0,0.1636524200439453,5520.9977020000015 +79926,Binary classification,ADWIN Bagging,SMTP,0.9996371598373475,0.0,0.1633319854736328,5753.467598000001 +81829,Binary classification,ADWIN Bagging,SMTP,0.9996455980837855,0.0,0.1635608673095703,5989.673013000001 +83732,Binary classification,ADWIN Bagging,SMTP,0.9996536527689864,0.0,0.1642627716064453,6229.466851000001 +85635,Binary classification,ADWIN Bagging,SMTP,0.999661349463998,0.0,0.16428565979003906,6472.919364000001 +87538,Binary classification,ADWIN Bagging,SMTP,0.9996687115162731,0.0,0.16410255432128906,6719.889077000002 +89441,Binary classification,ADWIN Bagging,SMTP,0.9996645796064401,0.0,0.16414833068847656,6970.567347000002 +91344,Binary classification,ADWIN Bagging,SMTP,0.999671567607808,0.0,0.16405677795410156,7224.925889000002 +93247,Binary classification,ADWIN Bagging,SMTP,0.9996782703815713,0.0,0.1523609161376953,7483.091298000002 +95150,Binary classification,ADWIN Bagging,SMTP,0.9996847050415664,0.0,0.16419410705566406,7744.930013000002 +95156,Binary classification,ADWIN Bagging,SMTP,0.9996847249224948,0.0,0.1642169952392578,8006.777295000002 +106,Binary classification,AdaBoost,Bananas,0.5523809523809524,0.5252525252525252,0.16639232635498047,0.661448 +212,Binary classification,AdaBoost,Bananas,0.5829383886255924,0.5555555555555555,0.16659832000732422,2.064295 +318,Binary classification,AdaBoost,Bananas,0.6025236593059937,0.5827814569536425,0.16664409637451172,4.087538 +424,Binary classification,AdaBoost,Bananas,0.6099290780141844,0.5758354755784061,0.16664409637451172,6.767182 +530,Binary classification,AdaBoost,Bananas,0.5841209829867675,0.5089285714285714,0.16659832000732422,10.090322 +636,Binary classification,AdaBoost,Bananas,0.5748031496062992,0.4981412639405205,0.16664409637451172,14.036758 +742,Binary classification,AdaBoost,Bananas,0.582995951417004,0.48925619834710743,0.16657543182373047,18.750104 +848,Binary classification,AdaBoost,Bananas,0.5749704840613932,0.4812680115273775,0.16652965545654297,24.116907 +954,Binary classification,AdaBoost,Bananas,0.5760755508919203,0.482051282051282,0.16652965545654297,30.255333 +1060,Binary classification,AdaBoost,Bananas,0.5873465533522191,0.48284023668639053,0.16652965545654297,37.077733 +1166,Binary classification,AdaBoost,Bananas,0.5931330472103005,0.49250535331905776,0.16657543182373047,44.554975 +1272,Binary classification,AdaBoost,Bananas,0.5979543666404405,0.5034013605442177,0.16657543182373047,52.671883 +1378,Binary classification,AdaBoost,Bananas,0.6005809731299927,0.4990892531876139,0.16657543182373047,61.596702 +1484,Binary classification,AdaBoost,Bananas,0.6089008766014835,0.5117845117845117,0.16657543182373047,71.17423 +1590,Binary classification,AdaBoost,Bananas,0.6091881686595343,0.5121759622937941,0.16657543182373047,81.390284 +1696,Binary classification,AdaBoost,Bananas,0.6135693215339233,0.5194424064563462,0.16657543182373047,92.36515499999999 +1802,Binary classification,AdaBoost,Bananas,0.6185452526374237,0.5354969574036511,0.16657543182373047,104.03539999999998 +1908,Binary classification,AdaBoost,Bananas,0.6208704771893025,0.5467084639498432,0.16659832000732422,116.33010199999998 +2014,Binary classification,AdaBoost,Bananas,0.620963735717834,0.5561372891215823,0.16662120819091797,129.40978099999998 +2120,Binary classification,AdaBoost,Bananas,0.6252949504483247,0.56941431670282,0.16662120819091797,143.16628799999998 +2226,Binary classification,AdaBoost,Bananas,0.6242696629213483,0.5721596724667348,0.16664409637451172,157.57341499999998 +2332,Binary classification,AdaBoost,Bananas,0.6229086229086229,0.5763855421686748,0.16664409637451172,172.619666 +2438,Binary classification,AdaBoost,Bananas,0.62330734509643,0.5796703296703297,0.16664409637451172,188.342899 +2544,Binary classification,AdaBoost,Bananas,0.6244593000393236,0.5860424794104898,0.16664409637451172,204.77139699999998 +2650,Binary classification,AdaBoost,Bananas,0.6266515666289165,0.591828312009905,0.16668987274169922,221.90157299999998 +2756,Binary classification,AdaBoost,Bananas,0.6250453720508167,0.5921831819976313,0.16668987274169922,239.67413799999997 +2862,Binary classification,AdaBoost,Bananas,0.6249563089828731,0.5927893738140417,0.16668987274169922,258.142834 +2968,Binary classification,AdaBoost,Bananas,0.6248736097067745,0.5924569754668619,0.16668987274169922,277.299077 +3074,Binary classification,AdaBoost,Bananas,0.6260982753010088,0.5958494548012664,0.16668987274169922,297.096451 +3180,Binary classification,AdaBoost,Bananas,0.62378106322743,0.5934738273283481,0.15410423278808594,317.58276 +3286,Binary classification,AdaBoost,Bananas,0.6246575342465753,0.5937397034596376,0.1971263885498047,338.83455200000003 +3392,Binary classification,AdaBoost,Bananas,0.6234149218519611,0.5931825422108953,0.2317180633544922,360.725819 +3498,Binary classification,AdaBoost,Bananas,0.6211038032599371,0.5894019212891229,0.2662029266357422,383.343908 +3604,Binary classification,AdaBoost,Bananas,0.6194837635303914,0.5866747060596926,0.31232261657714844,406.679113 +3710,Binary classification,AdaBoost,Bananas,0.6238878403882449,0.5915080527086384,0.32015037536621094,430.74398699999995 +3816,Binary classification,AdaBoost,Bananas,0.6277850589777195,0.5970488081725313,0.32617759704589844,455.49479399999996 +3922,Binary classification,AdaBoost,Bananas,0.6322366743177761,0.6009961261759823,0.35390281677246094,480.941019 +4028,Binary classification,AdaBoost,Bananas,0.6354606406754407,0.6034575904916262,0.3679637908935547,507.154231 +4134,Binary classification,AdaBoost,Bananas,0.6399709654004355,0.6073878627968339,0.3758831024169922,534.089651 +4240,Binary classification,AdaBoost,Bananas,0.644963434772352,0.6130110568269478,0.37590599060058594,561.67777 +4346,Binary classification,AdaBoost,Bananas,0.6508630609896433,0.6185567010309279,0.3759288787841797,589.949243 +4452,Binary classification,AdaBoost,Bananas,0.6535609975286453,0.620384047267356,0.3758831024169922,618.987429 +4558,Binary classification,AdaBoost,Bananas,0.6570111915734036,0.6243691420331651,0.3760662078857422,648.691271 +4664,Binary classification,AdaBoost,Bananas,0.6607334334119666,0.6288127639605818,0.3761119842529297,679.16585 +4770,Binary classification,AdaBoost,Bananas,0.6630320821975257,0.6303197607545433,0.43157005310058594,710.3587739999999 +4876,Binary classification,AdaBoost,Bananas,0.6670769230769231,0.6330544879041374,0.43948936462402344,742.192598 +4982,Binary classification,AdaBoost,Bananas,0.6707488456133307,0.6378091872791519,0.44574546813964844,774.746403 +5088,Binary classification,AdaBoost,Bananas,0.6734814232356988,0.6407094959982694,0.45186424255371094,807.9427459999999 +5194,Binary classification,AdaBoost,Bananas,0.674369343346813,0.6412051771695311,0.4518413543701172,841.965614 +5300,Binary classification,AdaBoost,Bananas,0.6778637478769579,0.64504054897068,0.4531536102294922,876.7139659999999 +906,Binary classification,AdaBoost,Elec2,0.9337016574585635,0.933184855233853,1.423478126525879,13.145088 +1812,Binary classification,AdaBoost,Elec2,0.9491993373826615,0.9378378378378379,2.051041603088379,36.742593 +2718,Binary classification,AdaBoost,Elec2,0.9385351490614648,0.9243316719528772,2.3655481338500977,75.07794200000001 +3624,Binary classification,AdaBoost,Elec2,0.9359646701628485,0.9209809264305179,2.6522607803344727,124.64144900000001 +4530,Binary classification,AdaBoost,Elec2,0.9361890041951866,0.9185226952354102,3.339066505432129,184.39942100000002 +5436,Binary classification,AdaBoost,Elec2,0.9332106715731371,0.9144876325088339,3.582810401916504,253.628197 +6342,Binary classification,AdaBoost,Elec2,0.9309257214950323,0.9124350259896042,3.74349308013916,332.298993 +7248,Binary classification,AdaBoost,Elec2,0.9232785980405686,0.9024903542616626,3.9959611892700195,420.603134 +8154,Binary classification,AdaBoost,Elec2,0.9207653624432725,0.9042962962962964,4.062603950500488,517.241068 +9060,Binary classification,AdaBoost,Elec2,0.9214041284910034,0.9072191816523326,4.2443437576293945,621.271616 +9966,Binary classification,AdaBoost,Elec2,0.9173105870546914,0.9037158214536105,4.387467384338379,732.039898 +10872,Binary classification,AdaBoost,Elec2,0.916842976727072,0.9044195390145908,4.416756629943848,849.200167 +11778,Binary classification,AdaBoost,Elec2,0.9150887322747728,0.9024580569644947,4.712822914123535,973.870605 +12684,Binary classification,AdaBoost,Elec2,0.9128755026413309,0.9002077124537162,5.243111610412598,1105.403187 +13590,Binary classification,AdaBoost,Elec2,0.9123555817205092,0.900890405259216,5.419106483459473,1243.898383 +14496,Binary classification,AdaBoost,Elec2,0.9112107623318386,0.9002402914502752,5.619416236877441,1388.7625189999999 +15402,Binary classification,AdaBoost,Elec2,0.9125381468735796,0.9014414282578475,5.888123512268066,1539.2398159999998 +16308,Binary classification,AdaBoost,Elec2,0.9096093702091127,0.8977808599167822,6.072480201721191,1695.9498239999998 +17214,Binary classification,AdaBoost,Elec2,0.9093708243769244,0.8958611481975968,6.119706153869629,1858.6477019999998 +18120,Binary classification,AdaBoost,Elec2,0.9071140791434406,0.892972972972973,6.420571327209473,2027.8856049999997 +19026,Binary classification,AdaBoost,Elec2,0.907910643889619,0.8927784577723377,6.732544898986816,2202.4394989999996 +19932,Binary classification,AdaBoost,Elec2,0.9079323666649942,0.8936540133294696,6.836274147033691,2383.1617499999998 +20838,Binary classification,AdaBoost,Elec2,0.9073283102174018,0.8931673582295988,7.145352363586426,2570.030805 +21744,Binary classification,AdaBoost,Elec2,0.9069585613760751,0.8912424063222407,7.368103981018066,2762.4680289999997 +22650,Binary classification,AdaBoost,Elec2,0.9053379840169544,0.8884611382790553,7.513260841369629,2961.0147009999996 +23556,Binary classification,AdaBoost,Elec2,0.9031203566121843,0.885441767068273,7.7879228591918945,3165.9004179999997 +24462,Binary classification,AdaBoost,Elec2,0.9015984628592453,0.8830361047669955,7.954785346984863,3377.5787039999996 +25368,Binary classification,AdaBoost,Elec2,0.8990026412267907,0.8799775133514476,8.00295352935791,3596.2655339999997 +26274,Binary classification,AdaBoost,Elec2,0.8993263045712329,0.8800942925789926,8.124005317687988,3821.215918 +27180,Binary classification,AdaBoost,Elec2,0.8986717686449097,0.8798324461122262,8.133870124816895,4052.049016 +28086,Binary classification,AdaBoost,Elec2,0.8958874844222895,0.8761436801084379,8.60555362701416,4289.013778 +28992,Binary classification,AdaBoost,Elec2,0.8951398709944466,0.8747011787981206,8.944867134094238,4531.544758 +29898,Binary classification,AdaBoost,Elec2,0.8927986085560424,0.8719485396939551,9.235833168029785,4780.469894 +30804,Binary classification,AdaBoost,Elec2,0.8921533616855502,0.8705882352941176,9.317421913146973,5034.96976 +31710,Binary classification,AdaBoost,Elec2,0.8903465892964143,0.8684499262229957,9.565300941467285,5295.565511 +32616,Binary classification,AdaBoost,Elec2,0.8890387858347386,0.867226767435888,9.898663520812988,5561.886477999999 +33522,Binary classification,AdaBoost,Elec2,0.8882789892902956,0.8666547979348406,10.141366004943848,5833.648399 +34428,Binary classification,AdaBoost,Elec2,0.8878496528887211,0.8660444783679699,10.462204933166504,6110.893153 +35334,Binary classification,AdaBoost,Elec2,0.8864800611326522,0.8639185750636134,10.841256141662598,6393.894783 +36240,Binary classification,AdaBoost,Elec2,0.8857584370429648,0.8622387861040862,11.138581275939941,6682.129445 +37146,Binary classification,AdaBoost,Elec2,0.8846412706959214,0.8604643589827087,11.587563514709473,6975.5213029999995 +38052,Binary classification,AdaBoost,Elec2,0.883682426217445,0.8588377878420617,12.028901100158691,7273.546291 +38958,Binary classification,AdaBoost,Elec2,0.8819210924865878,0.8569117830036083,12.1774263381958,7576.403824 +39864,Binary classification,AdaBoost,Elec2,0.880741539773725,0.8567122792211707,12.330445289611816,7883.760394 +40770,Binary classification,AdaBoost,Elec2,0.880423851455763,0.8574603081781235,12.583298683166504,8195.49812 +41676,Binary classification,AdaBoost,Elec2,0.8811517696460708,0.8591977712710009,12.884881019592285,8511.394385 +42582,Binary classification,AdaBoost,Elec2,0.8815199267278834,0.8597403319525146,13.200516700744629,8831.520838 +43488,Binary classification,AdaBoost,Elec2,0.8809069377055212,0.8591399896646449,13.322876930236816,9156.403803000001 +44394,Binary classification,AdaBoost,Elec2,0.880476651724371,0.8583404527979496,13.499638557434082,9485.877502000001 +45300,Binary classification,AdaBoost,Elec2,0.8805713150400671,0.8587024655244463,13.542492866516113,9819.626928000001 +45312,Binary classification,AdaBoost,Elec2,0.8805808744013595,0.8586874200203704,13.542401313781738,10153.705154000001 +25,Binary classification,AdaBoost,Phishing,0.6666666666666666,0.7142857142857143,0.6517477035522461,0.344782 +50,Binary classification,AdaBoost,Phishing,0.7551020408163265,0.7391304347826088,0.6519079208374023,1.052047 +75,Binary classification,AdaBoost,Phishing,0.7972972972972973,0.7945205479452055,0.6519308090209961,2.04852 +100,Binary classification,AdaBoost,Phishing,0.8080808080808081,0.7999999999999999,0.6519536972045898,3.481699 +125,Binary classification,AdaBoost,Phishing,0.8064516129032258,0.8000000000000002,0.6519804000854492,5.345952 +150,Binary classification,AdaBoost,Phishing,0.8187919463087249,0.8211920529801323,0.6519804000854492,7.607799 +175,Binary classification,AdaBoost,Phishing,0.8390804597701149,0.8313253012048192,0.6519804000854492,10.226605 +200,Binary classification,AdaBoost,Phishing,0.8341708542713567,0.8253968253968254,0.6889629364013672,13.384675 +225,Binary classification,AdaBoost,Phishing,0.8303571428571429,0.8173076923076923,0.6891918182373047,16.995274 +250,Binary classification,AdaBoost,Phishing,0.8273092369477911,0.8154506437768241,0.6892147064208984,21.029961999999998 +275,Binary classification,AdaBoost,Phishing,0.8321167883211679,0.8188976377952757,0.6892833709716797,25.500590999999996 +300,Binary classification,AdaBoost,Phishing,0.8394648829431438,0.823529411764706,0.6893062591552734,30.459704999999996 +325,Binary classification,AdaBoost,Phishing,0.845679012345679,0.8263888888888888,0.6893062591552734,35.865097 +350,Binary classification,AdaBoost,Phishing,0.8510028653295129,0.8289473684210527,0.6893062591552734,41.618204999999996 +375,Binary classification,AdaBoost,Phishing,0.8502673796791443,0.8260869565217391,0.6892795562744141,47.877466 +400,Binary classification,AdaBoost,Phishing,0.849624060150376,0.8235294117647061,0.6893062591552734,54.483198 +425,Binary classification,AdaBoost,Phishing,0.8561320754716981,0.8271954674220963,0.6893062591552734,61.544972 +450,Binary classification,AdaBoost,Phishing,0.8530066815144766,0.8225806451612903,0.6893062591552734,69.002264 +475,Binary classification,AdaBoost,Phishing,0.8523206751054853,0.8241206030150755,0.6893062591552734,77.051638 +500,Binary classification,AdaBoost,Phishing,0.8557114228456913,0.8317757009345793,0.6893062591552734,85.50066 +525,Binary classification,AdaBoost,Phishing,0.8530534351145038,0.8253968253968255,0.6893062591552734,94.362743 +550,Binary classification,AdaBoost,Phishing,0.8579234972677595,0.832618025751073,0.6893062591552734,103.688841 +575,Binary classification,AdaBoost,Phishing,0.8588850174216028,0.8336755646817249,0.6893062591552734,113.60832099999999 +600,Binary classification,AdaBoost,Phishing,0.8631051752921536,0.8360000000000001,0.6893062591552734,123.90541499999999 +625,Binary classification,AdaBoost,Phishing,0.8621794871794872,0.83203125,0.6893062591552734,134.725616 +650,Binary classification,AdaBoost,Phishing,0.8659476117103235,0.8391866913123845,0.6893291473388672,146.008333 +675,Binary classification,AdaBoost,Phishing,0.8679525222551929,0.8446771378708552,0.6893291473388672,157.728693 +700,Binary classification,AdaBoost,Phishing,0.8726752503576538,0.848381601362862,0.6893291473388672,169.816185 +725,Binary classification,AdaBoost,Phishing,0.8756906077348067,0.8543689320388349,0.6893291473388672,182.22931499999999 +750,Binary classification,AdaBoost,Phishing,0.87716955941255,0.8566978193146417,0.6893291473388672,195.113196 +775,Binary classification,AdaBoost,Phishing,0.8785529715762274,0.8575757575757577,0.6893291473388672,208.50176599999998 +800,Binary classification,AdaBoost,Phishing,0.8785982478097623,0.8592162554426704,0.7275295257568359,222.49654599999997 +825,Binary classification,AdaBoost,Phishing,0.8798543689320388,0.8619246861924686,0.7627391815185547,236.94804099999996 +850,Binary classification,AdaBoost,Phishing,0.8798586572438163,0.8614130434782608,0.7627849578857422,251.81301299999996 +875,Binary classification,AdaBoost,Phishing,0.8787185354691075,0.8594164456233422,0.7628536224365234,267.105354 +900,Binary classification,AdaBoost,Phishing,0.8787541713014461,0.8589909443725743,0.7628765106201172,282.96574799999996 +925,Binary classification,AdaBoost,Phishing,0.8809523809523809,0.8628428927680798,0.7628765106201172,299.16754399999996 +950,Binary classification,AdaBoost,Phishing,0.8798735511064278,0.8629807692307693,0.7628765106201172,315.934336 +975,Binary classification,AdaBoost,Phishing,0.8819301848049281,0.8651817116060961,0.7628765106201172,333.021735 +1000,Binary classification,AdaBoost,Phishing,0.8828828828828829,0.8662857142857143,0.7645549774169922,350.749086 +1025,Binary classification,AdaBoost,Phishing,0.8828125,0.8666666666666666,0.8362636566162109,368.847305 +1050,Binary classification,AdaBoost,Phishing,0.8846520495710201,0.8691891891891892,0.8363094329833984,387.397742 +1075,Binary classification,AdaBoost,Phishing,0.8836126629422719,0.8691099476439791,0.8378963470458984,406.476047 +1100,Binary classification,AdaBoost,Phishing,0.8844404003639672,0.8702757916241062,0.8380107879638672,425.926155 +1125,Binary classification,AdaBoost,Phishing,0.8861209964412812,0.8732673267326733,0.8731288909912109,445.763528 +1150,Binary classification,AdaBoost,Phishing,0.8842471714534378,0.8707482993197277,0.8731517791748047,466.112685 +1175,Binary classification,AdaBoost,Phishing,0.8816013628620102,0.8677450047573739,0.8732662200927734,487.01292 +1200,Binary classification,AdaBoost,Phishing,0.8798999165971643,0.8654205607476635,0.8732662200927734,508.389566 +1225,Binary classification,AdaBoost,Phishing,0.880718954248366,0.8660550458715598,0.8732891082763672,530.180451 +1250,Binary classification,AdaBoost,Phishing,0.8783026421136909,0.8635547576301617,0.8733119964599609,552.608585 +1903,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14459228515625,4.671696 +3806,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14466094970703125,14.150102 +5709,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14461517333984375,28.360088 +7612,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1446380615234375,47.155736000000005 +9515,Binary classification,AdaBoost,SMTP,1.0,0.0,0.1446380615234375,70.60316700000001 +11418,Binary classification,AdaBoost,SMTP,1.0,0.0,0.14461517333984375,98.66041500000001 +13321,Binary classification,AdaBoost,SMTP,1.0,0.0,0.144683837890625,131.464682 +15224,Binary classification,AdaBoost,SMTP,0.9996715496288511,0.761904761904762,0.3174581527709961,185.699411 +17127,Binary classification,AdaBoost,SMTP,0.9997080462454747,0.8,0.3083944320678711,248.358611 +19030,Binary classification,AdaBoost,SMTP,0.9997372431551842,0.8,0.3005514144897461,315.58373 +20933,Binary classification,AdaBoost,SMTP,0.9997611312822473,0.8,0.29268550872802734,387.343573 +22836,Binary classification,AdaBoost,SMTP,0.9997810378804467,0.8,0.29268550872802734,463.52644699999996 +24739,Binary classification,AdaBoost,SMTP,0.9997978818012774,0.8,0.29268550872802734,544.0157879999999 +26642,Binary classification,AdaBoost,SMTP,0.9998123193573815,0.8148148148148148,0.35791683197021484,629.1407149999999 +28545,Binary classification,AdaBoost,SMTP,0.9998248318385651,0.8148148148148148,0.35791683197021484,718.5008859999999 +30448,Binary classification,AdaBoost,SMTP,0.9998357802082307,0.8148148148148148,0.35791683197021484,812.0251739999999 +32351,Binary classification,AdaBoost,SMTP,0.9998454404945905,0.8148148148148148,0.35796260833740234,909.6873759999999 +34254,Binary classification,AdaBoost,SMTP,0.9998540273844627,0.8148148148148148,0.3579854965209961,1011.4173349999999 +36157,Binary classification,AdaBoost,SMTP,0.999861710366191,0.8148148148148148,0.35800838470458984,1116.7962549999997 +38060,Binary classification,AdaBoost,SMTP,0.9998686250295594,0.8148148148148148,0.35800838470458984,1225.6397989999998 +39963,Binary classification,AdaBoost,SMTP,0.9998748811370802,0.8148148148148148,0.35800838470458984,1337.9609139999998 +41866,Binary classification,AdaBoost,SMTP,0.9998805684939687,0.8148148148148148,0.35800838470458984,1453.6581889999998 +43769,Binary classification,AdaBoost,SMTP,0.9998857612867849,0.8148148148148148,0.35800838470458984,1572.7472669999997 +45672,Binary classification,AdaBoost,SMTP,0.9998905213373913,0.8148148148148148,0.35800838470458984,1695.2891979999997 +47575,Binary classification,AdaBoost,SMTP,0.9998738806911338,0.7857142857142857,0.39035701751708984,1822.1171059999997 +49478,Binary classification,AdaBoost,SMTP,0.9998787315318228,0.7857142857142857,0.39284420013427734,1952.4393319999997 +51381,Binary classification,AdaBoost,SMTP,0.9998637602179836,0.787878787878788,0.48020076751708984,2088.5562569999997 +53284,Binary classification,AdaBoost,SMTP,0.9998686260158024,0.787878787878788,0.48024654388427734,2228.050331 +55187,Binary classification,AdaBoost,SMTP,0.9998550356974595,0.7647058823529411,0.5258626937866211,2371.062704 +57090,Binary classification,AdaBoost,SMTP,0.999281823118289,0.4383561643835616,0.8453359603881836,2524.551865 +58993,Binary classification,AdaBoost,SMTP,0.9993049905071875,0.4383561643835616,0.8887395858764648,2681.9732639999997 +60896,Binary classification,AdaBoost,SMTP,0.9993267099105017,0.4383561643835616,0.8967199325561523,2843.1145869999996 +62799,Binary classification,AdaBoost,SMTP,0.9993152648173509,0.4266666666666667,1.0689306259155273,3009.1891499999997 +64702,Binary classification,AdaBoost,SMTP,0.9993354043986955,0.4266666666666667,1.0783147811889648,3178.9568209999998 +66605,Binary classification,AdaBoost,SMTP,0.9993393790162753,0.42105263157894735,1.0915288925170898,3352.4236079999996 +68508,Binary classification,AdaBoost,SMTP,0.9993577298670209,0.45000000000000007,1.0735387802124023,3530.7159619999998 +70411,Binary classification,AdaBoost,SMTP,0.9993750887658003,0.45000000000000007,1.0788640975952148,3712.739099 +72314,Binary classification,AdaBoost,SMTP,0.9993915340256938,0.45000000000000007,1.0906057357788086,3898.148799 +74217,Binary classification,AdaBoost,SMTP,0.9994071359275628,0.45000000000000007,1.0906057357788086,4086.957815 +76120,Binary classification,AdaBoost,SMTP,0.9994219577240899,0.45000000000000007,1.090651512145996,4279.139143 +78023,Binary classification,AdaBoost,SMTP,0.9994232395990874,0.4444444444444444,1.1481237411499023,4474.636732 +79926,Binary classification,AdaBoost,SMTP,0.9994369721614013,0.4444444444444444,1.1493444442749023,4673.509203 +81829,Binary classification,AdaBoost,SMTP,0.999450065992081,0.4444444444444444,1.1612462997436523,4875.758715 +83732,Binary classification,AdaBoost,SMTP,0.9994625646415306,0.4444444444444444,1.161269187927246,5081.435613 +85635,Binary classification,AdaBoost,SMTP,0.9994745077889623,0.4444444444444444,1.1612234115600586,5290.523743 +87538,Binary classification,AdaBoost,SMTP,0.9994859316631824,0.4444444444444444,1.1584348678588867,5502.994331999999 +89441,Binary classification,AdaBoost,SMTP,0.9994633273703041,0.42857142857142855,1.2966947555541992,5719.6431489999995 +91344,Binary classification,AdaBoost,SMTP,0.9994745081724927,0.42857142857142855,1.3124494552612305,5939.715090999999 +93247,Binary classification,AdaBoost,SMTP,0.9994316110074427,0.40449438202247195,1.3362340927124023,6163.415711999999 +95150,Binary classification,AdaBoost,SMTP,0.9994429789067673,0.40449438202247195,1.3363256454467773,6390.433574999999 +95156,Binary classification,AdaBoost,SMTP,0.9994430140297409,0.40449438202247195,1.3363256454467773,6617.502892999999 +106,Binary classification,Bagging,Bananas,0.4857142857142857,0.45999999999999996,0.22373199462890625,0.813651 +212,Binary classification,Bagging,Bananas,0.5165876777251185,0.45744680851063835,0.22452545166015625,2.392298 +318,Binary classification,Bagging,Bananas,0.5205047318611987,0.4722222222222222,0.2251434326171875,4.879886 +424,Binary classification,Bagging,Bananas,0.5460992907801419,0.4838709677419355,0.225250244140625,8.257922 +530,Binary classification,Bagging,Bananas,0.55765595463138,0.45581395348837206,0.22527313232421875,12.416081000000002 +636,Binary classification,Bagging,Bananas,0.5543307086614173,0.42596348884381346,0.22574615478515625,17.551695000000002 +742,Binary classification,Bagging,Bananas,0.5748987854251012,0.4220183486238532,0.22597503662109375,23.418389 +848,Binary classification,Bagging,Bananas,0.5785123966942148,0.42326332794830374,0.2259063720703125,30.181971 +954,Binary classification,Bagging,Bananas,0.5844700944386149,0.41935483870967744,0.22588348388671875,37.806045 +1060,Binary classification,Bagging,Bananas,0.5920679886685553,0.4146341463414634,0.22563934326171875,46.336236 +1166,Binary classification,Bagging,Bananas,0.590557939914163,0.4015056461731493,0.225738525390625,55.794626 +1272,Binary classification,Bagging,Bananas,0.5971675845790716,0.41013824884792627,0.226043701171875,66.093431 +1378,Binary classification,Bagging,Bananas,0.599128540305011,0.3973799126637554,0.226348876953125,77.304266 +1484,Binary classification,Bagging,Bananas,0.5994605529332434,0.39263803680981596,0.2263031005859375,89.41731899999999 +1590,Binary classification,Bagging,Bananas,0.5997482693517936,0.38963531669865636,0.22628021240234375,102.38846199999999 +1696,Binary classification,Bagging,Bananas,0.6011799410029498,0.38768115942028986,0.22634124755859375,116.26624899999999 +1802,Binary classification,Bagging,Bananas,0.6013325930038868,0.39049235993208825,0.2263641357421875,130.90050499999998 +1908,Binary classification,Bagging,Bananas,0.6030414263240692,0.39681274900398406,0.2263641357421875,146.406164 +2014,Binary classification,Bagging,Bananas,0.5986090412319921,0.39611360239162924,0.2263641357421875,162.81699799999998 +2120,Binary classification,Bagging,Bananas,0.5969797074091553,0.39943741209563993,0.2263641357421875,180.02605599999998 +2226,Binary classification,Bagging,Bananas,0.597752808988764,0.40133779264214053,0.226318359375,198.114263 +2332,Binary classification,Bagging,Bananas,0.5988845988845989,0.40331844288449265,0.22637939453125,217.043548 +2438,Binary classification,Bagging,Bananas,0.5995075913007797,0.4019607843137255,0.22640228271484375,236.778245 +2544,Binary classification,Bagging,Bananas,0.6008651199370821,0.40885264997087944,0.22676849365234375,257.426783 +2650,Binary classification,Bagging,Bananas,0.6002265005662514,0.4073866815892558,0.2269744873046875,278.871455 +2756,Binary classification,Bagging,Bananas,0.5985480943738657,0.40280777537796975,0.2269744873046875,301.18545300000005 +2862,Binary classification,Bagging,Bananas,0.599790283117791,0.4051948051948052,0.2269744873046875,324.33878000000004 +2968,Binary classification,Bagging,Bananas,0.599932591843613,0.40261701056869653,0.22699737548828125,348.42370100000005 +3074,Binary classification,Bagging,Bananas,0.5977871786527823,0.40232108317214693,0.22699737548828125,373.34600700000004 +3180,Binary classification,Bagging,Bananas,0.5986159169550173,0.40429505135387495,0.22699737548828125,399.17600200000004 +3286,Binary classification,Bagging,Bananas,0.5981735159817352,0.40217391304347827,0.22489070892333984,425.805579 +3392,Binary classification,Bagging,Bananas,0.5959893836626364,0.40226876090750435,0.2988729476928711,453.430877 +3498,Binary classification,Bagging,Bananas,0.597369173577352,0.40237691001697795,0.3531064987182617,482.040674 +3604,Binary classification,Bagging,Bananas,0.6008881487649181,0.4087171052631579,0.3826017379760742,511.80088500000005 +3710,Binary classification,Bagging,Bananas,0.6012402264761392,0.40863654538184724,0.4367246627807617,542.835536 +3816,Binary classification,Bagging,Bananas,0.6023591087811271,0.4104158569762923,0.4704160690307617,575.071901 +3922,Binary classification,Bagging,Bananas,0.6052027543993879,0.4145234493192133,0.5176496505737305,608.725741 +4028,Binary classification,Bagging,Bananas,0.608393344921778,0.4195804195804196,0.5480222702026367,643.745138 +4134,Binary classification,Bagging,Bananas,0.6121461408178079,0.4260651629072682,0.5632429122924805,680.30634 +4240,Binary classification,Bagging,Bananas,0.6157112526539278,0.4329968673860076,0.5676107406616211,718.216367 +4346,Binary classification,Bagging,Bananas,0.6193325661680092,0.438560760353021,0.5822668075561523,757.397991 +4452,Binary classification,Bagging,Bananas,0.6218827229835991,0.4421610871726881,0.5884695053100586,797.943115 +4558,Binary classification,Bagging,Bananas,0.6219003730524468,0.44293566117038474,0.6275625228881836,839.803567 +4664,Binary classification,Bagging,Bananas,0.623203945957538,0.4455664247396655,0.6328649520874023,883.024854 +4770,Binary classification,Bagging,Bananas,0.6250786328370728,0.446096654275093,0.6821584701538086,927.682473 +4876,Binary classification,Bagging,Bananas,0.6266666666666667,0.44680851063829785,0.6950826644897461,973.7206689999999 +4982,Binary classification,Bagging,Bananas,0.629592451314997,0.4530091906314853,0.7119512557983398,1021.0804549999999 +5088,Binary classification,Bagging,Bananas,0.6298407705917043,0.4527753560011624,0.6960439682006836,1069.7402539999998 +5194,Binary classification,Bagging,Bananas,0.6321971885230118,0.456459874786568,0.6964941024780273,1119.6924109999998 +5300,Binary classification,Bagging,Bananas,0.6340819022457067,0.4594368553108447,0.7031240463256836,1170.8531239999998 +906,Binary classification,Bagging,Elec2,0.8629834254143647,0.8663793103448276,1.7490100860595703,16.056337 +1812,Binary classification,Bagging,Elec2,0.8890115958034235,0.8680236375574525,2.496591567993164,50.652304 +2718,Binary classification,Bagging,Elec2,0.87523003312477,0.8521587440034889,1.8562908172607422,106.697102 +3624,Binary classification,Bagging,Elec2,0.8868341153739995,0.8653972422849641,2.5584278106689453,176.150463 +4530,Binary classification,Bagging,Elec2,0.8880547582247736,0.8593619972260749,3.1707210540771484,258.677392 +5436,Binary classification,Bagging,Elec2,0.8829806807727691,0.8518863530507685,2.113290786743164,353.604927 +6342,Binary classification,Bagging,Elec2,0.8814067181832519,0.8497802636835796,2.4726314544677734,459.993548 +7248,Binary classification,Bagging,Elec2,0.883262039464606,0.8516310066643283,2.354246139526367,576.649537 +8154,Binary classification,Bagging,Elec2,0.8828652029927634,0.8585394756332394,2.1453304290771484,702.348431 +9060,Binary classification,Bagging,Elec2,0.8839827795562424,0.8639129871811472,2.1982364654541016,836.637311 +9966,Binary classification,Bagging,Elec2,0.880983442047165,0.8635840809753854,2.4484920501708984,979.832141 +10872,Binary classification,Bagging,Elec2,0.881151687977187,0.8654446990210373,2.578580856323242,1131.438926 +11778,Binary classification,Bagging,Elec2,0.8799354674365288,0.8634344214796214,2.730459213256836,1291.261447 +12684,Binary classification,Bagging,Elec2,0.8768430182133564,0.8601361031518624,2.090116500854492,1459.3451100000002 +13590,Binary classification,Bagging,Elec2,0.8789462064905438,0.8639483913654784,1.8772754669189453,1635.065473 +14496,Binary classification,Bagging,Elec2,0.878854777509486,0.86444341516134,2.105062484741211,1818.351758 +15402,Binary classification,Bagging,Elec2,0.8775404194532822,0.86187197890728,2.440736770629883,2009.388651 +16308,Binary classification,Bagging,Elec2,0.8765560802109523,0.8599262403451395,2.627225875854492,2209.910977 +17214,Binary classification,Bagging,Elec2,0.8758496485214663,0.8567214213878646,2.5119991302490234,2419.733571 +18120,Binary classification,Bagging,Elec2,0.8760969148407749,0.8567600331780769,2.7164859771728516,2638.279319 +19026,Binary classification,Bagging,Elec2,0.8772141918528252,0.8562284588872477,3.019651412963867,2865.7383750000004 +19932,Binary classification,Bagging,Elec2,0.8739651798705534,0.8535106134826219,2.721925735473633,3103.6766270000003 +20838,Binary classification,Bagging,Elec2,0.8716225944233815,0.8503663925714606,2.4018421173095703,3351.3122810000004 +21744,Binary classification,Bagging,Elec2,0.872556684910086,0.8492300995701616,2.248655319213867,3607.2754260000006 +22650,Binary classification,Bagging,Elec2,0.870722769217184,0.845275840202917,2.6111698150634766,3871.072358000001 +23556,Binary classification,Bagging,Elec2,0.8645722776480578,0.8365611230658879,1.8957767486572266,4144.250301000001 +24462,Binary classification,Bagging,Elec2,0.8614120436613385,0.8315276811450154,1.5607776641845703,4424.237452000001 +25368,Binary classification,Bagging,Elec2,0.8560334292584855,0.8249113050148624,1.3715801239013672,4711.1951020000015 +26274,Binary classification,Bagging,Elec2,0.8558596277547292,0.824277295717136,1.6112499237060547,5004.571280000002 +27180,Binary classification,Bagging,Elec2,0.8564332756907906,0.8258035714285713,2.025979995727539,5304.465246000002 +28086,Binary classification,Bagging,Elec2,0.8535517179989318,0.8215385950449082,1.8488483428955078,5611.580754000001 +28992,Binary classification,Bagging,Elec2,0.8515746266082578,0.8178624338624338,2.0671520233154297,5927.3319900000015 +29898,Binary classification,Bagging,Elec2,0.849048399504967,0.8140885684860969,1.3224430084228516,6250.652578000001 +30804,Binary classification,Bagging,Elec2,0.8473849949680226,0.8106344410876132,1.549489974975586,6580.126329000001 +31710,Binary classification,Bagging,Elec2,0.8429783342268756,0.8039377830281552,1.5209712982177734,6916.182134000001 +32616,Binary classification,Bagging,Elec2,0.8411773723746743,0.8020785572367416,1.9952220916748047,7258.669481000001 +33522,Binary classification,Bagging,Elec2,0.8415023418155783,0.8033751526590429,1.8286800384521484,7608.320925000001 +34428,Binary classification,Bagging,Elec2,0.839689778371627,0.8006357692446627,2.2426509857177734,7966.087012000001 +35334,Binary classification,Bagging,Elec2,0.8395550901423598,0.7993487417265422,2.1107349395751953,8331.986249000001 +36240,Binary classification,Bagging,Elec2,0.8400618118601507,0.7984280447937677,1.8943347930908203,8703.460619000001 +37146,Binary classification,Bagging,Elec2,0.839278503163279,0.796356938190749,1.3389415740966797,9080.992051000001 +38052,Binary classification,Bagging,Elec2,0.8389267036345956,0.7946940006029546,1.6071338653564453,9463.837927 +38958,Binary classification,Bagging,Elec2,0.8382832353620658,0.7942655607079877,1.8687000274658203,9853.704286 +39864,Binary classification,Bagging,Elec2,0.8387477109098663,0.7967495098969203,1.466756820678711,10249.501094 +40770,Binary classification,Bagging,Elec2,0.8400009811376291,0.8001225677953119,2.0175647735595703,10651.197686 +41676,Binary classification,Bagging,Elec2,0.8407918416316736,0.8026413635146792,2.1117191314697266,11058.632461 +42582,Binary classification,Bagging,Elec2,0.8411732932528593,0.8035781708344224,2.033967971801758,11470.822586999999 +43488,Binary classification,Bagging,Elec2,0.8416538275806563,0.804308286915994,1.7070560455322266,11887.700533 +44394,Binary classification,Bagging,Elec2,0.8406280269411844,0.8019483246087955,2.2816905975341797,12309.209906 +45300,Binary classification,Bagging,Elec2,0.8404379787633282,0.802124397722295,2.2888126373291016,12736.77001 +45312,Binary classification,Bagging,Elec2,0.8404360971949416,0.8020804817957842,2.2889575958251953,13164.474026 +25,Binary classification,Bagging,Phishing,0.7083333333333334,0.7407407407407408,0.7072525024414062,0.45657 +50,Binary classification,Bagging,Phishing,0.8163265306122449,0.8085106382978724,0.7079315185546875,1.426682 +75,Binary classification,Bagging,Phishing,0.8513513513513513,0.8493150684931507,0.708251953125,2.8732379999999997 +100,Binary classification,Bagging,Phishing,0.8585858585858586,0.8541666666666666,0.70849609375,4.790442 +125,Binary classification,Bagging,Phishing,0.8548387096774194,0.85,0.70849609375,7.239611999999999 +150,Binary classification,Bagging,Phishing,0.8523489932885906,0.8533333333333335,0.708740234375,10.202642999999998 +175,Binary classification,Bagging,Phishing,0.8620689655172413,0.8536585365853658,0.7091293334960938,13.595279999999999 +200,Binary classification,Bagging,Phishing,0.8592964824120602,0.8510638297872339,0.7092666625976562,17.527801 +225,Binary classification,Bagging,Phishing,0.8526785714285714,0.8405797101449276,0.7491827011108398,22.029145 +250,Binary classification,Bagging,Phishing,0.8473895582329317,0.8347826086956521,0.7771825790405273,27.026806999999998 +275,Binary classification,Bagging,Phishing,0.8467153284671532,0.8333333333333335,0.7774114608764648,32.501577 +300,Binary classification,Bagging,Phishing,0.8528428093645485,0.837037037037037,0.7775945663452148,38.42215899999999 +325,Binary classification,Bagging,Phishing,0.8611111111111112,0.8421052631578947,0.7779607772827148,44.92146699999999 +350,Binary classification,Bagging,Phishing,0.8653295128939829,0.8438538205980067,0.7781057357788086,51.897794999999995 +375,Binary classification,Bagging,Phishing,0.8663101604278075,0.8427672955974843,0.8172750473022461,59.363139999999994 +400,Binary classification,Bagging,Phishing,0.8671679197994987,0.8417910447761194,0.8571996688842773,67.416022 +425,Binary classification,Bagging,Phishing,0.8679245283018868,0.839080459770115,0.9128484725952148,76.017673 +450,Binary classification,Bagging,Phishing,0.8708240534521158,0.8406593406593408,0.9131002426147461,85.092057 +475,Binary classification,Bagging,Phishing,0.869198312236287,0.8402061855670103,0.9133520126342773,94.603797 +500,Binary classification,Bagging,Phishing,0.8677354709418837,0.8413461538461539,0.9135580062866211,104.609638 +525,Binary classification,Bagging,Phishing,0.8683206106870229,0.8384074941451991,0.9136190414428711,115.080656 +550,Binary classification,Bagging,Phishing,0.8670309653916212,0.8381374722838136,0.9137258529663086,126.050962 +575,Binary classification,Bagging,Phishing,0.867595818815331,0.8382978723404255,0.9137868881225586,137.397676 +600,Binary classification,Bagging,Phishing,0.8697829716193656,0.8381742738589212,0.9139089584350586,149.31562 +625,Binary classification,Bagging,Phishing,0.8717948717948718,0.8373983739837398,0.9536046981811523,161.695664 +650,Binary classification,Bagging,Phishing,0.8767334360554699,0.846153846153846,0.9540624618530273,174.565593 +675,Binary classification,Bagging,Phishing,0.8753709198813057,0.8478260869565216,0.9818639755249023,187.898512 +700,Binary classification,Bagging,Phishing,0.8798283261802575,0.8515901060070671,0.9230222702026367,201.73587500000002 +725,Binary classification,Bagging,Phishing,0.8825966850828729,0.8576214405360134,1.021204948425293,216.08538800000002 +750,Binary classification,Bagging,Phishing,0.8865153538050734,0.8631239935587761,1.0604047775268555,230.90612000000002 +775,Binary classification,Bagging,Phishing,0.8875968992248062,0.863849765258216,1.1157331466674805,246.307883 +800,Binary classification,Bagging,Phishing,0.8873591989987485,0.8652694610778443,1.2215375900268555,262.241262 +825,Binary classification,Bagging,Phishing,0.8871359223300971,0.8661870503597122,1.2229490280151367,278.553882 +850,Binary classification,Bagging,Phishing,0.8881036513545347,0.8671328671328671,1.235407829284668,295.406724 +875,Binary classification,Bagging,Phishing,0.8901601830663616,0.8688524590163934,1.263422966003418,312.749904 +900,Binary classification,Bagging,Phishing,0.8887652947719689,0.8670212765957446,1.318751335144043,330.632511 +925,Binary classification,Bagging,Phishing,0.8896103896103896,0.8695652173913043,1.318964958190918,348.945693 +950,Binary classification,Bagging,Phishing,0.8893572181243414,0.8708487084870848,1.3194990158081055,367.70198800000003 +975,Binary classification,Bagging,Phishing,0.8901437371663244,0.8718562874251498,1.319605827331543,386.96933700000005 +1000,Binary classification,Bagging,Phishing,0.8878878878878879,0.8697674418604652,1.3197660446166992,406.75034200000005 +1025,Binary classification,Bagging,Phishing,0.8876953125,0.8700564971751412,1.3200559616088867,426.99481600000007 +1050,Binary classification,Bagging,Phishing,0.8894184938036225,0.8725274725274725,1.320155143737793,447.8295280000001 +1075,Binary classification,Bagging,Phishing,0.8901303538175046,0.8742004264392325,1.320277214050293,469.1965740000001 +1100,Binary classification,Bagging,Phishing,0.89171974522293,0.8761706555671176,1.320643424987793,491.1150510000001 +1125,Binary classification,Bagging,Phishing,0.8932384341637011,0.8790322580645162,1.320704460144043,513.5552500000001 +1150,Binary classification,Bagging,Phishing,0.8938207136640557,0.8794466403162056,1.320704460144043,536.4428780000001 +1175,Binary classification,Bagging,Phishing,0.8926746166950597,0.877906976744186,1.320765495300293,559.8515450000001 +1200,Binary classification,Bagging,Phishing,0.8932443703085905,0.8783269961977186,1.3328428268432617,583.8125340000001 +1225,Binary classification,Bagging,Phishing,0.8929738562091504,0.8779123951537745,1.3880414962768555,608.2234330000001 +1250,Binary classification,Bagging,Phishing,0.8935148118494796,0.8792007266121706,1.3882551193237305,633.1359570000001 +1903,Binary classification,Bagging,SMTP,1.0,0.0,0.2038736343383789,10.878823 +3806,Binary classification,Bagging,SMTP,1.0,0.0,0.2044839859008789,32.501535000000004 +5709,Binary classification,Bagging,SMTP,1.0,0.0,0.20502567291259766,64.818606 +7612,Binary classification,Bagging,SMTP,1.0,0.0,0.2050485610961914,107.076722 +9515,Binary classification,Bagging,SMTP,1.0,0.0,0.2050485610961914,158.807432 +11418,Binary classification,Bagging,SMTP,1.0,0.0,0.2056589126586914,218.4459 +13321,Binary classification,Bagging,SMTP,1.0,0.0,0.20568180084228516,285.19327499999997 +15224,Binary classification,Bagging,SMTP,0.9992774091834724,0.0,0.26130008697509766,359.03964299999996 +17127,Binary classification,Bagging,SMTP,0.9992409202382343,0.0,0.2063913345336914,440.61769699999996 +19030,Binary classification,Bagging,SMTP,0.9993168322034789,0.0,0.2062082290649414,529.79805 +20933,Binary classification,Bagging,SMTP,0.999378941333843,0.0,0.20684146881103516,626.4074929999999 +22836,Binary classification,Bagging,SMTP,0.9994306984891613,0.0,0.20693302154541016,729.8228539999999 +24739,Binary classification,Bagging,SMTP,0.9994744926833212,0.0,0.20707035064697266,839.3276679999999 +26642,Binary classification,Bagging,SMTP,0.9994744942006681,0.0,0.20674991607666016,954.8656409999999 +28545,Binary classification,Bagging,SMTP,0.9995095291479821,0.0,0.20697879791259766,1076.4115539999998 +30448,Binary classification,Bagging,SMTP,0.9995401845830459,0.0,0.2068643569946289,1203.4939569999997 +32351,Binary classification,Bagging,SMTP,0.9995672333848532,0.0,0.2070016860961914,1337.1471509999997 +34254,Binary classification,Bagging,SMTP,0.9995912766764955,0.0,0.2069101333618164,1476.3596139999997 +36157,Binary classification,Bagging,SMTP,0.9996127890253347,0.0,0.2069101333618164,1621.0863639999998 +38060,Binary classification,Bagging,SMTP,0.9996321500827662,0.0,0.20693302154541016,1771.0710339999998 +39963,Binary classification,Bagging,SMTP,0.9996496671838246,0.0,0.20679569244384766,1926.3060879999998 +41866,Binary classification,Bagging,SMTP,0.9996655917831124,0.0,0.20749759674072266,2086.761849 +43769,Binary classification,Bagging,SMTP,0.9996801316029976,0.0,0.20697879791259766,2252.424258 +45672,Binary classification,Bagging,SMTP,0.9996934597446958,0.0,0.2072610855102539,2423.176177 +47575,Binary classification,Bagging,SMTP,0.9997057216126456,0.0,0.20725345611572266,2599.130792 +49478,Binary classification,Bagging,SMTP,0.99971704024092,0.0,0.19541263580322266,2780.435567 +51381,Binary classification,Bagging,SMTP,0.9996885947839627,0.0,0.2073373794555664,2966.651736 +53284,Binary classification,Bagging,SMTP,0.9996997166075484,0.0,0.2073373794555664,3157.874809 +55187,Binary classification,Bagging,SMTP,0.999710071394919,0.0,0.2073526382446289,3353.943675 +57090,Binary classification,Bagging,SMTP,0.9995620872672494,0.0,0.20707035064697266,3555.07983 +58993,Binary classification,Bagging,SMTP,0.9995762137238947,0.0,0.20703983306884766,3761.219357 +60896,Binary classification,Bagging,SMTP,0.999589457262501,0.0,0.2070322036743164,3972.252634 +62799,Binary classification,Bagging,SMTP,0.9995700500015924,0.0,0.20719242095947266,4188.261246 +64702,Binary classification,Bagging,SMTP,0.9995826957852274,0.0,0.20734500885009766,4409.191759 +66605,Binary classification,Bagging,SMTP,0.9995946189418053,0.0,0.20731449127197266,4634.878548000001 +68508,Binary classification,Bagging,SMTP,0.9995766855941729,0.0,0.20736026763916016,4864.934707 +70411,Binary classification,Bagging,SMTP,0.9995881266865502,0.0,0.2072000503540039,5099.2875650000005 +72314,Binary classification,Bagging,SMTP,0.9995989656078437,0.0,0.20736026763916016,5337.886074000001 +74217,Binary classification,Bagging,SMTP,0.99960924867953,0.0,0.20736026763916016,5580.709613000001 +76120,Binary classification,Bagging,SMTP,0.9996190175908775,0.0,0.20746707916259766,5827.7179830000005 +78023,Binary classification,Bagging,SMTP,0.9996283099638563,0.0,0.20746707916259766,6079.015463000001 +79926,Binary classification,Bagging,SMTP,0.9996371598373475,0.0,0.20714664459228516,6334.716663000001 +81829,Binary classification,Bagging,SMTP,0.9996455980837855,0.0,0.20755863189697266,6594.692589000001 +83732,Binary classification,Bagging,SMTP,0.9996536527689864,0.0,0.20795536041259766,6858.666542000001 +85635,Binary classification,Bagging,SMTP,0.999661349463998,0.0,0.2080392837524414,7126.604303000001 +87538,Binary classification,Bagging,SMTP,0.9996687115162731,0.0,0.2078561782836914,7398.595214000001 +89441,Binary classification,Bagging,SMTP,0.9996645796064401,0.0,0.2079019546508789,7674.616155000001 +91344,Binary classification,Bagging,SMTP,0.999671567607808,0.0,0.2078104019165039,7954.656032000001 +93247,Binary classification,Bagging,SMTP,0.9996782703815713,0.0,0.19611454010009766,8238.690655 +95150,Binary classification,Bagging,SMTP,0.9996847050415664,0.0,0.2079477310180664,8526.751857000001 +95156,Binary classification,Bagging,SMTP,0.9996847249224948,0.0,0.20797061920166016,8814.843001000001 +106,Binary classification,Leveraging Bagging,Bananas,0.5142857142857142,0.45161290322580644,0.1802501678466797,1.958268 +212,Binary classification,Leveraging Bagging,Bananas,0.5402843601895735,0.4756756756756757,0.1808605194091797,6.1304110000000005 +318,Binary classification,Leveraging Bagging,Bananas,0.5394321766561514,0.4930555555555555,0.18149375915527344,12.559627 +424,Binary classification,Leveraging Bagging,Bananas,0.5531914893617021,0.4932975871313673,0.1814708709716797,21.158430000000003 +530,Binary classification,Leveraging Bagging,Bananas,0.5614366729678639,0.4703196347031963,0.1814708709716797,31.954501 +636,Binary classification,Leveraging Bagging,Bananas,0.5763779527559055,0.4836852207293666,0.41109561920166016,45.100937 +742,Binary classification,Leveraging Bagging,Bananas,0.5991902834008097,0.4940374787052811,0.5197267532348633,60.647662000000004 +848,Binary classification,Leveraging Bagging,Bananas,0.6210153482880756,0.5201793721973094,0.6145830154418945,78.646382 +954,Binary classification,Leveraging Bagging,Bananas,0.6411332633788038,0.5464190981432361,0.681065559387207,99.167462 +1060,Binary classification,Leveraging Bagging,Bananas,0.6515580736543909,0.555956678700361,0.7228097915649414,122.156435 +1166,Binary classification,Leveraging Bagging,Bananas,0.6626609442060086,0.5732899022801302,0.8111352920532227,147.677601 +1272,Binary classification,Leveraging Bagging,Bananas,0.6766325727773407,0.5958702064896755,0.8519144058227539,175.66982000000002 +1378,Binary classification,Leveraging Bagging,Bananas,0.6877269426289034,0.6062271062271062,0.9361848831176758,206.11220300000002 +1484,Binary classification,Leveraging Bagging,Bananas,0.6999325691166555,0.6238377007607777,0.978398323059082,239.08437400000003 +1590,Binary classification,Leveraging Bagging,Bananas,0.7073631214600378,0.6375681995323461,1.0816278457641602,274.51056400000004 +1696,Binary classification,Leveraging Bagging,Bananas,0.7162241887905605,0.6496722505462491,1.146012306213379,312.20930000000004 +1802,Binary classification,Leveraging Bagging,Bananas,0.7262631871182677,0.6662153012863914,1.231095314025879,352.32891700000005 +1908,Binary classification,Leveraging Bagging,Bananas,0.7320398531725223,0.677602523659306,1.3021745681762695,394.808348 +2014,Binary classification,Leveraging Bagging,Bananas,0.7391952309985097,0.6902654867256638,1.3571443557739258,439.684188 +2120,Binary classification,Leveraging Bagging,Bananas,0.7456347333647947,0.7020453289110005,1.439896583557129,486.825513 +2226,Binary classification,Leveraging Bagging,Bananas,0.750561797752809,0.7080483955812729,1.4615755081176758,536.150148 +2332,Binary classification,Leveraging Bagging,Bananas,0.7554697554697555,0.715,1.4801912307739258,587.578093 +2438,Binary classification,Leveraging Bagging,Bananas,0.7599507591300779,0.7202295552367289,1.5264062881469727,641.095023 +2544,Binary classification,Leveraging Bagging,Bananas,0.7624852536374361,0.7257039055404179,1.5866899490356445,696.665992 +2650,Binary classification,Leveraging Bagging,Bananas,0.7678369195922989,0.7331887201735358,1.6338167190551758,754.18395 +2756,Binary classification,Leveraging Bagging,Bananas,0.7731397459165155,0.7396917950853811,1.718327522277832,813.521765 +2862,Binary classification,Leveraging Bagging,Bananas,0.777350576721426,0.7440739252711932,1.7761125564575195,874.768076 +2968,Binary classification,Leveraging Bagging,Bananas,0.7812605325244355,0.7479611650485437,1.876938819885254,937.8386529999999 +3074,Binary classification,Leveraging Bagging,Bananas,0.7845753335502766,0.7526158445440957,1.974156379699707,1002.618853 +3180,Binary classification,Leveraging Bagging,Bananas,0.7892418999685435,0.7572463768115942,2.0079431533813477,1069.033932 +3286,Binary classification,Leveraging Bagging,Bananas,0.7923896499238965,0.7605337078651686,2.0704050064086914,1137.092928 +3392,Binary classification,Leveraging Bagging,Bananas,0.7938661161899144,0.7636117686844774,2.141594886779785,1206.880705 +3498,Binary classification,Leveraging Bagging,Bananas,0.7966828710323134,0.7657331136738056,2.2472352981567383,1278.374701 +3604,Binary classification,Leveraging Bagging,Bananas,0.7998889814043852,0.7685393258426965,2.2915468215942383,1351.505796 +3710,Binary classification,Leveraging Bagging,Bananas,0.8021029927204099,0.7717661691542288,2.3504953384399414,1426.3168569999998 +3816,Binary classification,Leveraging Bagging,Bananas,0.8055045871559633,0.7761013880506941,2.3972253799438477,1502.8248519999997 +3922,Binary classification,Leveraging Bagging,Bananas,0.8071920428462127,0.7776470588235294,2.4447336196899414,1580.9903339999996 +4028,Binary classification,Leveraging Bagging,Bananas,0.8085423392103303,0.7788930312589618,2.513848304748535,1660.8236829999996 +4134,Binary classification,Leveraging Bagging,Bananas,0.8107911928381321,0.7816862088218872,2.6076173782348633,1742.3313809999995 +4240,Binary classification,Leveraging Bagging,Bananas,0.8136352913422977,0.7852093529091897,2.653599739074707,1825.5059619999995 +4346,Binary classification,Leveraging Bagging,Bananas,0.8161104718066743,0.7881198621055423,2.7111101150512695,1910.3837179999996 +4452,Binary classification,Leveraging Bagging,Bananas,0.8173444169849472,0.7894327894327894,2.7411813735961914,1996.8929369999996 +4558,Binary classification,Leveraging Bagging,Bananas,0.8183015141540487,0.7910146390711761,2.7629594802856445,2085.0686209999994 +4664,Binary classification,Leveraging Bagging,Bananas,0.8205018228608192,0.7941971969510695,2.818455696105957,2174.8872669999996 +4770,Binary classification,Leveraging Bagging,Bananas,0.8209268190396309,0.7942168674698795,2.852097511291504,2266.3018959999995 +4876,Binary classification,Leveraging Bagging,Bananas,0.822974358974359,0.795932844644124,2.940415382385254,2359.3812719999996 +4982,Binary classification,Leveraging Bagging,Bananas,0.825135514956836,0.7990772779700116,2.986912727355957,2454.1200449999997 +5088,Binary classification,Leveraging Bagging,Bananas,0.825437389424022,0.7995485327313769,3.072648048400879,2550.4404699999996 +5194,Binary classification,Leveraging Bagging,Bananas,0.8266897746967071,0.8008849557522125,3.1882104873657227,2648.3615419999996 +5300,Binary classification,Leveraging Bagging,Bananas,0.8282694848084544,0.8026886383347789,3.2357072830200195,2747.9506659999997 +906,Binary classification,Leveraging Bagging,Elec2,0.8895027624309392,0.8873873873873873,2.5379486083984375,35.61841 +1812,Binary classification,Leveraging Bagging,Elec2,0.9127553837658752,0.8941018766756033,3.2672500610351562,98.66906499999999 +2718,Binary classification,Leveraging Bagging,Elec2,0.9013617960986382,0.8815207780725023,2.908538818359375,185.051304 +3624,Binary classification,Leveraging Bagging,Elec2,0.905051062655258,0.8859416445623343,4.239933013916016,291.140366 +4530,Binary classification,Leveraging Bagging,Elec2,0.9059395009935968,0.8829026937877955,4.5028228759765625,414.05054599999994 +5436,Binary classification,Leveraging Bagging,Elec2,0.904691812327507,0.8806451612903227,5.411556243896484,555.0527619999999 +6342,Binary classification,Leveraging Bagging,Elec2,0.904746885349314,0.8810086682427108,3.64324951171875,712.2276919999999 +7248,Binary classification,Leveraging Bagging,Elec2,0.9038222712846695,0.8793908980792524,4.176555633544922,885.4512659999999 +8154,Binary classification,Leveraging Bagging,Elec2,0.9062921623942107,0.8879107981220656,4.873016357421875,1073.942042 +9060,Binary classification,Leveraging Bagging,Elec2,0.9073849210729661,0.8915600361897376,6.068294525146484,1277.3056459999998 +9966,Binary classification,Leveraging Bagging,Elec2,0.9066733567486202,0.8924855491329481,5.883171081542969,1496.0059789999998 +10872,Binary classification,Leveraging Bagging,Elec2,0.9090240088308343,0.8966238110170377,7.123630523681641,1728.5474849999998 +11778,Binary classification,Leveraging Bagging,Elec2,0.9088052984631061,0.8963720571208027,4.904956817626953,1974.076492 +12684,Binary classification,Leveraging Bagging,Elec2,0.9071197666167311,0.8948214285714287,4.745685577392578,2233.08693 +13590,Binary classification,Leveraging Bagging,Elec2,0.908234601515932,0.8972732515034187,5.919612884521484,2504.250556 +14496,Binary classification,Leveraging Bagging,Elec2,0.9082442221455674,0.8976293103448276,4.272552490234375,2787.365554 +15402,Binary classification,Leveraging Bagging,Elec2,0.9089669501980391,0.8979027090008739,4.651363372802734,3081.637323 +16308,Binary classification,Leveraging Bagging,Elec2,0.9085668731219722,0.8971653217463273,5.967304229736328,3386.795808 +17214,Binary classification,Leveraging Bagging,Elec2,0.9075698599895428,0.8943488943488943,5.553913116455078,3703.0592819999997 +18120,Binary classification,Leveraging Bagging,Elec2,0.9077211766653789,0.8943911066195048,7.001399993896484,4030.5410249999995 +19026,Binary classification,Leveraging Bagging,Elec2,0.9078580814717477,0.8932854446947099,7.953182220458984,4368.505934999999 +19932,Binary classification,Leveraging Bagging,Elec2,0.9081832321509207,0.8944636678200691,8.54180908203125,4717.761576999999 +20838,Binary classification,Leveraging Bagging,Elec2,0.9064644622546432,0.8926111631494847,7.284095764160156,5078.966551999999 +21744,Binary classification,Leveraging Bagging,Elec2,0.9064066596145886,0.8909840895698291,8.78485107421875,5451.1446879999985 +22650,Binary classification,Leveraging Bagging,Elec2,0.9054704401960352,0.8890386110391294,9.895774841308594,5834.8071089999985 +23556,Binary classification,Leveraging Bagging,Elec2,0.9032052642751008,0.8860113988601139,9.921958923339844,6231.782156999999 +24462,Binary classification,Leveraging Bagging,Elec2,0.9009443604104493,0.8825098191339766,6.414276123046875,6640.471452999998 +25368,Binary classification,Leveraging Bagging,Elec2,0.8975046320022076,0.87847059923343,7.025360107421875,7059.615553999998 +26274,Binary classification,Leveraging Bagging,Elec2,0.8978418909146272,0.878705712219812,8.249675750732422,7487.941528999998 +27180,Binary classification,Leveraging Bagging,Elec2,0.8983038375216159,0.879888753693725,7.590415954589844,7924.652190999998 +28086,Binary classification,Leveraging Bagging,Elec2,0.8965640021363718,0.8772448763997466,7.862815856933594,8369.790676999999 +28992,Binary classification,Leveraging Bagging,Elec2,0.8963126487530613,0.8762962962962964,9.08489990234375,8823.391086 +29898,Binary classification,Leveraging Bagging,Elec2,0.8952403251162324,0.8748901493968203,2.6490402221679688,9284.744376999999 +30804,Binary classification,Leveraging Bagging,Elec2,0.8947505113138331,0.8736357966947302,3.2276954650878906,9752.904185 +31710,Binary classification,Leveraging Bagging,Elec2,0.8935948784256835,0.8721291594027135,3.8703384399414062,10228.075712 +32616,Binary classification,Leveraging Bagging,Elec2,0.8929940211559099,0.8717194736455194,4.073085784912109,10710.354293 +33522,Binary classification,Leveraging Bagging,Elec2,0.8932311088571343,0.872338148742643,4.776435852050781,11199.826533 +34428,Binary classification,Leveraging Bagging,Elec2,0.8924390739826299,0.8711865585974188,4.868198394775391,11696.46579 +35334,Binary classification,Leveraging Bagging,Elec2,0.8922537005066086,0.8703735231025913,5.445720672607422,12200.310087 +36240,Binary classification,Leveraging Bagging,Elec2,0.8919948122188802,0.8690619563762879,4.9837493896484375,12711.187581 +37146,Binary classification,Leveraging Bagging,Elec2,0.8920985327769552,0.8688996467355751,5.313899993896484,13229.101200000001 +38052,Binary classification,Leveraging Bagging,Elec2,0.8916979842842501,0.8676919125437441,5.129566192626953,13754.199791000001 +38958,Binary classification,Leveraging Bagging,Elec2,0.8912647277767796,0.8674924924924926,5.3661651611328125,14286.524658 +39864,Binary classification,Leveraging Bagging,Elec2,0.8915535709806086,0.8687813021702837,5.776020050048828,14825.184636 +40770,Binary classification,Leveraging Bagging,Elec2,0.8920012754789178,0.8703512852978417,6.964508056640625,15370.438083000001 +41676,Binary classification,Leveraging Bagging,Elec2,0.89250149970006,0.8717728547713092,8.029548645019531,15922.831901000001 +42582,Binary classification,Leveraging Bagging,Elec2,0.8927925600619995,0.8723184068469779,8.723072052001953,16481.133162000002 +43488,Binary classification,Leveraging Bagging,Elec2,0.8927495573389749,0.8722821622213702,8.793426513671875,17045.039295000002 +44394,Binary classification,Leveraging Bagging,Elec2,0.8920775797986169,0.8710467526175545,6.634971618652344,17614.470389000002 +45300,Binary classification,Leveraging Bagging,Elec2,0.8926687123336056,0.8720122143834896,7.5638427734375,18188.843118 +45312,Binary classification,Leveraging Bagging,Elec2,0.8926529981682152,0.8719663069228745,7.565349578857422,18763.342135 +25,Binary classification,Leveraging Bagging,Phishing,0.75,0.75,0.6626491546630859,1.23946 +50,Binary classification,Leveraging Bagging,Phishing,0.8163265306122449,0.8,0.6635112762451172,3.9379920000000004 +75,Binary classification,Leveraging Bagging,Phishing,0.8378378378378378,0.8333333333333334,0.6635112762451172,8.007437 +100,Binary classification,Leveraging Bagging,Phishing,0.8484848484848485,0.8421052631578947,0.6476030349731445,13.398751 +125,Binary classification,Leveraging Bagging,Phishing,0.8467741935483871,0.8403361344537815,0.9203081130981445,19.997869 +150,Binary classification,Leveraging Bagging,Phishing,0.8456375838926175,0.8456375838926175,0.9203310012817383,27.874049 +175,Binary classification,Leveraging Bagging,Phishing,0.867816091954023,0.8588957055214724,1.0861825942993164,37.283539 +200,Binary classification,Leveraging Bagging,Phishing,0.8693467336683417,0.8617021276595744,1.2813997268676758,47.998757 +225,Binary classification,Leveraging Bagging,Phishing,0.8660714285714286,0.8557692307692308,1.3089113235473633,59.952352999999995 +250,Binary classification,Leveraging Bagging,Phishing,0.8554216867469879,0.8434782608695653,1.3089799880981445,73.11976999999999 +275,Binary classification,Leveraging Bagging,Phishing,0.8576642335766423,0.844621513944223,1.2476167678833008,87.72028699999998 +300,Binary classification,Leveraging Bagging,Phishing,0.862876254180602,0.8464419475655431,1.4594087600708008,103.60422699999998 +325,Binary classification,Leveraging Bagging,Phishing,0.8703703703703703,0.851063829787234,1.4950456619262695,120.70292199999997 +350,Binary classification,Leveraging Bagging,Phishing,0.8710601719197708,0.8494983277591974,1.5330171585083008,138.98074599999998 +375,Binary classification,Leveraging Bagging,Phishing,0.8716577540106952,0.8481012658227849,1.809849739074707,158.64485 +400,Binary classification,Leveraging Bagging,Phishing,0.8696741854636592,0.8433734939759037,2.068051338195801,179.64681099999999 +425,Binary classification,Leveraging Bagging,Phishing,0.8702830188679245,0.8405797101449276,2.104710578918457,201.85095299999998 +450,Binary classification,Leveraging Bagging,Phishing,0.8752783964365256,0.845303867403315,2.104527473449707,225.23996699999998 +475,Binary classification,Leveraging Bagging,Phishing,0.8776371308016878,0.8505154639175259,2.132199287414551,249.91758899999996 +500,Binary classification,Leveraging Bagging,Phishing,0.875751503006012,0.8502415458937198,2.1503801345825195,275.75512899999995 +525,Binary classification,Leveraging Bagging,Phishing,0.8778625954198473,0.8497652582159624,2.187130928039551,302.922289 +550,Binary classification,Leveraging Bagging,Phishing,0.8743169398907104,0.8463251670378619,2.2971315383911133,331.41425 +575,Binary classification,Leveraging Bagging,Phishing,0.8763066202090593,0.8479657387580299,2.4067888259887695,361.219435 +600,Binary classification,Leveraging Bagging,Phishing,0.8764607679465777,0.8451882845188285,2.406834602355957,392.26266999999996 +625,Binary classification,Leveraging Bagging,Phishing,0.8782051282051282,0.8442622950819672,2.352017402648926,424.555734 +650,Binary classification,Leveraging Bagging,Phishing,0.8813559322033898,0.850485436893204,2.279099464416504,458.24235899999996 +675,Binary classification,Leveraging Bagging,Phishing,0.8798219584569733,0.8513761467889909,2.54854679107666,493.20967599999994 +700,Binary classification,Leveraging Bagging,Phishing,0.8841201716738197,0.8550983899821109,2.565995216369629,529.352808 +725,Binary classification,Leveraging Bagging,Phishing,0.8812154696132597,0.8537414965986394,2.870518684387207,566.738792 +750,Binary classification,Leveraging Bagging,Phishing,0.8825100133511349,0.8557377049180328,2.941006660461426,605.3090109999999 +775,Binary classification,Leveraging Bagging,Phishing,0.8837209302325582,0.856687898089172,3.0508241653442383,645.053635 +800,Binary classification,Leveraging Bagging,Phishing,0.8836045056320401,0.8584474885844748,3.1606874465942383,686.031259 +825,Binary classification,Leveraging Bagging,Phishing,0.8810679611650486,0.8567251461988304,3.270321846008301,728.1933819999999 +850,Binary classification,Leveraging Bagging,Phishing,0.8833922261484098,0.8591749644381224,3.2882280349731445,771.57935 +875,Binary classification,Leveraging Bagging,Phishing,0.8844393592677345,0.8595271210013908,3.2632036209106445,816.1995999999999 +900,Binary classification,Leveraging Bagging,Phishing,0.8832035595105673,0.8575305291723202,3.380833625793457,861.997768 +925,Binary classification,Leveraging Bagging,Phishing,0.8841991341991342,0.8597640891218873,3.4813432693481445,908.9730649999999 +950,Binary classification,Leveraging Bagging,Phishing,0.8851422550052687,0.8628930817610063,3.5117311477661133,957.1207809999999 +975,Binary classification,Leveraging Bagging,Phishing,0.8870636550308009,0.8651960784313726,3.5666399002075195,1006.3541349999998 +1000,Binary classification,Leveraging Bagging,Phishing,0.8878878878878879,0.8663484486873507,3.645543098449707,1056.728559 +1025,Binary classification,Leveraging Bagging,Phishing,0.8876953125,0.8667439165701043,3.735753059387207,1108.282035 +1050,Binary classification,Leveraging Bagging,Phishing,0.8894184938036225,0.8693693693693694,3.808384895324707,1160.9493009999999 +1075,Binary classification,Leveraging Bagging,Phishing,0.8901303538175046,0.87117903930131,3.9453020095825195,1214.7027389999998 +1100,Binary classification,Leveraging Bagging,Phishing,0.89171974522293,0.873269435569755,3.945347785949707,1269.5439849999998 +1125,Binary classification,Leveraging Bagging,Phishing,0.8905693950177936,0.8730650154798762,3.972836494445801,1325.4243339999998 +1150,Binary classification,Leveraging Bagging,Phishing,0.8920800696257616,0.8747474747474747,3.945645332336426,1382.2374609999997 +1175,Binary classification,Leveraging Bagging,Phishing,0.8909710391822828,0.8732673267326733,3.9732484817504883,1440.0925429999998 +1200,Binary classification,Leveraging Bagging,Phishing,0.8924103419516264,0.8746355685131195,3.9472780227661133,1498.9929549999997 +1225,Binary classification,Leveraging Bagging,Phishing,0.8937908496732027,0.8761904761904762,3.982327461242676,1558.8210809999996 +1250,Binary classification,Leveraging Bagging,Phishing,0.8943154523618895,0.8773234200743495,4.0114030838012695,1619.6535709999996 +1903,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16020870208740234,31.58816 +3806,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16081905364990234,89.620857 +5709,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,167.42750999999998 +7612,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,263.688726 +9515,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16142940521240234,375.65509199999997 +11418,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16203975677490234,502.19041899999996 +13321,Binary classification,Leveraging Bagging,SMTP,1.0,0.0,0.16203975677490234,643.105063 +15224,Binary classification,Leveraging Bagging,SMTP,0.9992117191092426,0.0,0.2446889877319336,797.8947989999999 +17127,Binary classification,Leveraging Bagging,SMTP,0.9991825294873292,0.0,0.1627492904663086,968.1431969999999 +19030,Binary classification,Leveraging Bagging,SMTP,0.9992642808345158,0.0,0.16258907318115234,1153.4864309999998 +20933,Binary classification,Leveraging Bagging,SMTP,0.9993311675902924,0.0,0.16315364837646484,1353.5382149999998 +22836,Binary classification,Leveraging Bagging,SMTP,0.9993869060652507,0.0,0.1632680892944336,1568.3644279999999 +24739,Binary classification,Leveraging Bagging,SMTP,0.9994340690435767,0.0,0.16329097747802734,1796.9469139999999 +26642,Binary classification,Leveraging Bagging,SMTP,0.9994369580721444,0.0,0.1630849838256836,2038.09448 +28545,Binary classification,Leveraging Bagging,SMTP,0.9994744955156951,0.0,0.1630849838256836,2291.954796 +30448,Binary classification,Leveraging Bagging,SMTP,0.999507340624692,0.0,0.16315364837646484,2557.874515 +32351,Binary classification,Leveraging Bagging,SMTP,0.9995363214837713,0.0,0.1631765365600586,2835.65121 +34254,Binary classification,Leveraging Bagging,SMTP,0.999562082153388,0.0,0.16315364837646484,3124.961925 +36157,Binary classification,Leveraging Bagging,SMTP,0.9995851310985728,0.0,0.16310787200927734,3424.787068 +38060,Binary classification,Leveraging Bagging,SMTP,0.9996058750886782,0.0,0.1631307601928711,3734.997034 +39963,Binary classification,Leveraging Bagging,SMTP,0.9996246434112407,0.0,0.16319942474365234,4055.587677 +41866,Binary classification,Leveraging Bagging,SMTP,0.9996417054819061,0.0,0.1638784408569336,4386.458848 +43769,Binary classification,Leveraging Bagging,SMTP,0.9996572838603546,0.0,0.1636495590209961,4726.906731 +45672,Binary classification,Leveraging Bagging,SMTP,0.999671564012174,0.0,0.16385555267333984,5077.03597 +47575,Binary classification,Leveraging Bagging,SMTP,0.9996847017278345,0.0,0.16394710540771484,5436.8723119999995 +49478,Binary classification,Leveraging Bagging,SMTP,0.9996968288295571,0.0,0.16371822357177734,5806.3009919999995 +51381,Binary classification,Leveraging Bagging,SMTP,0.9996691319579603,0.0,0.16394710540771484,6185.544045999999 +53284,Binary classification,Leveraging Bagging,SMTP,0.9996809488955202,0.0,0.1641073226928711,6574.848143999999 +55187,Binary classification,Leveraging Bagging,SMTP,0.9996919508571014,0.0,0.16376399993896484,6975.121680999999 +57090,Binary classification,Leveraging Bagging,SMTP,0.9995445707579393,0.0,0.1638784408569336,7384.584095999999 +58993,Binary classification,Leveraging Bagging,SMTP,0.9995592622728505,0.0,0.16390132904052734,7802.5953629999985 +60896,Binary classification,Leveraging Bagging,SMTP,0.999573035553001,0.0,0.16371822357177734,8228.594131999998 +62799,Binary classification,Leveraging Bagging,SMTP,0.9995541259275773,0.0,0.16390132904052734,8661.618187999999 +64702,Binary classification,Leveraging Bagging,SMTP,0.9995672400735692,0.0,0.16399288177490234,9101.660232999999 +66605,Binary classification,Leveraging Bagging,SMTP,0.9995796048285388,0.0,0.1638326644897461,9548.697329999999 +68508,Binary classification,Leveraging Bagging,SMTP,0.9995620885456961,0.0,0.16380977630615234,10002.655235999999 +70411,Binary classification,Leveraging Bagging,SMTP,0.9995739241585002,0.0,0.16371822357177734,10463.105480999999 +72314,Binary classification,Leveraging Bagging,SMTP,0.9995851368357004,0.0,0.16376399993896484,10929.695224 +74217,Binary classification,Leveraging Bagging,SMTP,0.9995957744960655,0.0,0.16380977630615234,11402.447204 +76120,Binary classification,Leveraging Bagging,SMTP,0.9996058802664249,0.0,0.1638784408569336,11881.476782 +78023,Binary classification,Leveraging Bagging,SMTP,0.9996154930660582,0.0,0.1637411117553711,12366.666901 +79926,Binary classification,Leveraging Bagging,SMTP,0.9996246481076009,0.0,0.16371822357177734,12858.057531 +81829,Binary classification,Leveraging Bagging,SMTP,0.999633377328054,0.0,0.1521596908569336,13355.582794 +83732,Binary classification,Leveraging Bagging,SMTP,0.9996417097610204,0.0,0.15288448333740234,13859.323941 +85635,Binary classification,Leveraging Bagging,SMTP,0.9996496718593082,0.0,0.16432857513427734,14369.189455 +87538,Binary classification,Leveraging Bagging,SMTP,0.9996572877754549,0.0,0.16460323333740234,14885.224126 +89441,Binary classification,Leveraging Bagging,SMTP,0.9996533989266547,0.0,0.16451168060302734,15407.418989999998 +91344,Binary classification,Leveraging Bagging,SMTP,0.9996606198614015,0.0,0.16432857513427734,15935.791255999999 +93247,Binary classification,Leveraging Bagging,SMTP,0.9996675460609571,0.0,0.16451168060302734,16470.041814999997 +95150,Binary classification,Leveraging Bagging,SMTP,0.9996741952096186,0.0,0.1645345687866211,17009.813748999997 +95156,Binary classification,Leveraging Bagging,SMTP,0.9996742157532447,0.0,0.16460323333740234,17549.605714999998 +106,Binary classification,Stacking,Bananas,0.6095238095238096,0.577319587628866,0.7777948379516602,2.119535 +212,Binary classification,Stacking,Bananas,0.7109004739336493,0.6702702702702703,1.3802881240844727,6.931057 +318,Binary classification,Stacking,Bananas,0.7602523659305994,0.7361111111111112,1.8119163513183594,15.160032000000001 +424,Binary classification,Stacking,Bananas,0.7943262411347518,0.772845953002611,2.401026725769043,27.407145 +530,Binary classification,Stacking,Bananas,0.8052930056710775,0.7775377969762419,5.0262651443481445,65.121823 +636,Binary classification,Stacking,Bananas,0.8236220472440945,0.7992831541218638,5.88111686706543,107.288216 +742,Binary classification,Stacking,Bananas,0.8299595141700404,0.8025078369905957,6.734616279602051,153.798119 +848,Binary classification,Stacking,Bananas,0.8347107438016529,0.8087431693989071,7.555168151855469,204.76644700000003 +954,Binary classification,Stacking,Bananas,0.8426023084994754,0.8166259168704157,8.384669303894043,260.019764 +1060,Binary classification,Stacking,Bananas,0.8536355051935789,0.8275862068965517,8.926264762878418,319.31365700000003 +1166,Binary classification,Stacking,Bananas,0.8532188841201717,0.8274470232088799,9.188977241516113,382.58132300000005 +1272,Binary classification,Stacking,Bananas,0.8536585365853658,0.8290441176470588,9.45701789855957,449.39877900000005 +1378,Binary classification,Stacking,Bananas,0.8576615831517792,0.8321917808219177,9.84501838684082,519.6596460000001 +1484,Binary classification,Stacking,Bananas,0.8590694538098449,0.8345209817893903,10.364198684692383,593.3590610000001 +1590,Binary classification,Stacking,Bananas,0.8565135305223411,0.8321060382916053,10.468925476074219,670.9077340000001 +1696,Binary classification,Stacking,Bananas,0.8595870206489675,0.8354080221300139,10.966409683227539,752.0270200000001 +1802,Binary classification,Stacking,Bananas,0.8634092171016102,0.8410852713178295,10.118447303771973,836.636461 +1908,Binary classification,Stacking,Bananas,0.8626114315679078,0.8417874396135265,10.3862943649292,924.5557000000001 +2014,Binary classification,Stacking,Bananas,0.8614008941877794,0.8415672913117546,10.646858215332031,1015.8872450000001 +2120,Binary classification,Stacking,Bananas,0.8640868334119868,0.8459893048128343,10.9229736328125,1110.420791 +2226,Binary classification,Stacking,Bananas,0.8642696629213483,0.8462321792260691,11.325839042663574,1208.24484 +2332,Binary classification,Stacking,Bananas,0.864006864006864,0.8461911693352742,11.659860610961914,1308.9666100000002 +2438,Binary classification,Stacking,Bananas,0.8641772671317193,0.8465461288827074,11.19693660736084,1412.476276 +2544,Binary classification,Stacking,Bananas,0.8651199370821864,0.8484312859036677,11.452000617980957,1518.7765900000002 +2650,Binary classification,Stacking,Bananas,0.864477161192903,0.8480744815911976,11.787381172180176,1627.8671150000002 +2756,Binary classification,Stacking,Bananas,0.8653357531760436,0.849125660837739,12.11353874206543,1739.6750610000001 +2862,Binary classification,Stacking,Bananas,0.8678783642083188,0.8515318146111548,12.359809875488281,1854.1318460000002 +2968,Binary classification,Stacking,Bananas,0.8695652173913043,0.8529076396807297,12.722334861755371,1971.3857770000002 +3074,Binary classification,Stacking,Bananas,0.8682069638789457,0.8515939904727006,13.0479736328125,2091.3191060000004 +3180,Binary classification,Stacking,Bananas,0.8700849323686694,0.8530771967271433,13.308364868164062,2213.9114950000003 +3286,Binary classification,Stacking,Bananas,0.8700152207001522,0.8523002421307506,13.608009338378906,2339.1031470000003 +3392,Binary classification,Stacking,Bananas,0.871129460336184,0.8544788544788545,13.707662582397461,2466.937686 +3498,Binary classification,Stacking,Bananas,0.872748069774092,0.8557536466774717,14.051713943481445,2598.213051 +3604,Binary classification,Stacking,Bananas,0.8742714404662781,0.856872037914692,14.268294334411621,2732.2090540000004 +3710,Binary classification,Stacking,Bananas,0.8751685090320841,0.8583664729275007,14.518733978271484,2868.9613940000004 +3816,Binary classification,Stacking,Bananas,0.8762778505897771,0.8596908442330559,14.742842674255371,3008.2640120000005 +3922,Binary classification,Stacking,Bananas,0.8747768426421831,0.8578048074138431,15.053866386413574,3150.2484420000005 +4028,Binary classification,Stacking,Bananas,0.8733548547305686,0.8560135516657256,15.453622817993164,3294.8873180000005 +4134,Binary classification,Stacking,Bananas,0.874183401887249,0.8569069895432032,15.755488395690918,3442.0965550000005 +4240,Binary classification,Stacking,Bananas,0.8749705119131871,0.8579849946409432,15.976973533630371,3591.8798750000005 +4346,Binary classification,Stacking,Bananas,0.8759493670886076,0.8590849673202615,16.313834190368652,3744.3343260000006 +4452,Binary classification,Stacking,Bananas,0.8755335879577623,0.8585291113381002,16.729196548461914,3899.4355060000007 +4558,Binary classification,Stacking,Bananas,0.8757954794821154,0.8592039800995025,17.032727241516113,4057.1530940000007 +4664,Binary classification,Stacking,Bananas,0.8756165558653227,0.8594961240310078,17.45319175720215,4217.5274930000005 +4770,Binary classification,Stacking,Bananas,0.8754455860767456,0.8590412909349787,17.6948184967041,4380.594611 +4876,Binary classification,Stacking,Bananas,0.8756923076923077,0.8588070829450141,17.917430877685547,4546.383704000001 +4982,Binary classification,Stacking,Bananas,0.8761292913069665,0.8596770525358198,18.09793186187744,4714.847995000001 +5088,Binary classification,Stacking,Bananas,0.8757617456261058,0.8591800356506238,18.51348114013672,4886.014522000001 +5194,Binary classification,Stacking,Bananas,0.876372039283651,0.8598865124399825,18.892748832702637,5059.990176000001 +5300,Binary classification,Stacking,Bananas,0.8762030571806001,0.8596491228070176,19.194592475891113,5236.837813000001 +906,Binary classification,Stacking,Elec2,0.9116022099447514,0.908256880733945,7.041282653808594,59.400144 +1812,Binary classification,Stacking,Elec2,0.906129210381005,0.8855989232839839,9.07800579071045,148.928403 +2718,Binary classification,Stacking,Elec2,0.9002576370997424,0.8772088808337108,9.477606773376465,264.671315 +3624,Binary classification,Stacking,Elec2,0.9064311344189898,0.8849677638276212,10.383838653564453,404.188666 +4530,Binary classification,Stacking,Elec2,0.90527710311327,0.8788477831121152,11.437847137451172,565.129871 +5436,Binary classification,Stacking,Elec2,0.9000919963201472,0.8723854289071681,14.209432601928711,747.817107 +6342,Binary classification,Stacking,Elec2,0.897019397571361,0.8704622098789923,15.688876152038574,951.5612199999999 +7248,Binary classification,Stacking,Elec2,0.8965089002345799,0.8694744169857292,19.837779998779297,1176.633371 +8154,Binary classification,Stacking,Elec2,0.8980743284680486,0.8778839088905216,22.41482448577881,1420.714573 +9060,Binary classification,Stacking,Elec2,0.9000993487139861,0.8828478964401294,25.97023296356201,1683.800751 +9966,Binary classification,Stacking,Elec2,0.8982438534872053,0.8827203331020125,28.783666610717773,1965.440637 +10872,Binary classification,Stacking,Elec2,0.9006531137889798,0.8870765370138016,30.882869720458984,2263.780185 +11778,Binary classification,Stacking,Elec2,0.9021822195805383,0.8888030888030888,33.277831077575684,2578.868179 +12684,Binary classification,Stacking,Elec2,0.9012851848931641,0.8879541793449078,35.50911808013916,2911.085292 +13590,Binary classification,Stacking,Elec2,0.901905953344617,0.8899529431189631,38.6168327331543,3259.512573 +14496,Binary classification,Stacking,Elec2,0.9030010348395998,0.8917128773875539,41.26064682006836,3624.142031 +15402,Binary classification,Stacking,Elec2,0.9039023440036361,0.8922382408620941,43.65532207489014,4004.812957 +16308,Binary classification,Stacking,Elec2,0.9002882197829153,0.8879239040529363,45.1539192199707,4403.507418 +17214,Binary classification,Stacking,Elec2,0.8997850461860222,0.8856176646111001,37.54582214355469,4816.829749 +18120,Binary classification,Stacking,Elec2,0.9001048622992439,0.8858908082209053,41.9152717590332,5243.603799 +19026,Binary classification,Stacking,Elec2,0.9014454664914586,0.886177381169186,44.45838737487793,5683.033974000001 +19932,Binary classification,Stacking,Elec2,0.9011088254477949,0.8867826986041704,48.213175773620605,6135.158415000001 +20838,Binary classification,Stacking,Elec2,0.8992657292316553,0.8848411696933121,47.78572177886963,6598.876461000001 +21744,Binary classification,Stacking,Elec2,0.8986800349537782,0.8824376967821121,49.356743812561035,7073.276106000001 +22650,Binary classification,Stacking,Elec2,0.898361958585368,0.8812912541254125,47.91073036193848,7558.681153000001 +23556,Binary classification,Stacking,Elec2,0.8961579282530249,0.8784656663022956,53.37149906158447,8055.088432000001 +24462,Binary classification,Stacking,Elec2,0.8945259801316381,0.8760211436809228,52.06687641143799,8562.621137000002 +25368,Binary classification,Stacking,Elec2,0.8922221784207829,0.8734610756271406,20.63737678527832,9080.309208000002 +26274,Binary classification,Stacking,Elec2,0.8928177216153466,0.873801201039706,17.83812713623047,9607.290172000003 +27180,Binary classification,Stacking,Elec2,0.8932263880201626,0.874632797649905,19.305461883544922,10142.799192000002 +28086,Binary classification,Stacking,Elec2,0.8915435285739719,0.8721564677243349,18.23539447784424,10686.882271000002 +28992,Binary classification,Stacking,Elec2,0.891345590010693,0.8712498978173793,21.229859352111816,11240.330868000003 +29898,Binary classification,Stacking,Elec2,0.8905575810281968,0.8700246285850481,25.24380111694336,11801.274420000003 +30804,Binary classification,Stacking,Elec2,0.890335356945752,0.8690697674418605,28.836254119873047,12369.623812000003 +31710,Binary classification,Stacking,Elec2,0.8895266328171813,0.8679458664756663,27.00519371032715,12944.308752000003 +32616,Binary classification,Stacking,Elec2,0.8879963207113292,0.86634224872855,30.17805576324463,13524.938061000003 +33522,Binary classification,Stacking,Elec2,0.8870260433757943,0.8654563541407612,32.107930183410645,14111.446119000002 +34428,Binary classification,Stacking,Elec2,0.8857582711244082,0.8638770636486346,32.29031944274902,14703.865141000002 +35334,Binary classification,Stacking,Elec2,0.8850083491353692,0.8622665175090681,36.271653175354004,15302.291934000003 +36240,Binary classification,Stacking,Elec2,0.8850133833715058,0.8612988050461006,35.55355262756348,15906.592759000003 +37146,Binary classification,Stacking,Elec2,0.8836182527931081,0.859061715515274,38.756625175476074,16517.021041000004 +38052,Binary classification,Stacking,Elec2,0.8830516937794013,0.8577547628180539,41.063425064086914,17133.624997000003 +38958,Binary classification,Stacking,Elec2,0.8831788895448828,0.8582198822393221,42.255154609680176,17756.348401000003 +39864,Binary classification,Stacking,Elec2,0.8836765923287259,0.8598797328740216,43.789937019348145,18385.013164000004 +40770,Binary classification,Stacking,Elec2,0.8840295322426354,0.8614708467623791,40.85312080383301,19019.639174000004 +41676,Binary classification,Stacking,Elec2,0.8847030593881223,0.8632106356933413,39.29591369628906,19660.085711000003 +42582,Binary classification,Stacking,Elec2,0.8851130786031328,0.8639675212724542,42.33915042877197,20306.976876000004 +43488,Binary classification,Stacking,Elec2,0.8848391473313864,0.8636091290375292,42.20731544494629,20960.039327000006 +44394,Binary classification,Stacking,Elec2,0.8848467100669024,0.8632350580555408,44.13865566253662,21618.107174000004 +45300,Binary classification,Stacking,Elec2,0.8854720854765006,0.8642027012878233,40.63082981109619,22281.084676000006 +45312,Binary classification,Stacking,Elec2,0.8854582772395224,0.8641574621787154,40.75471591949463,22944.429270000004 +25,Binary classification,Stacking,Phishing,0.6666666666666666,0.7142857142857143,0.6122617721557617,0.640752 +50,Binary classification,Stacking,Phishing,0.7755102040816326,0.7659574468085107,0.7524843215942383,1.992597 +75,Binary classification,Stacking,Phishing,0.8243243243243243,0.8266666666666667,0.9228668212890625,4.151733 +100,Binary classification,Stacking,Phishing,0.8282828282828283,0.8282828282828283,1.193608283996582,7.194986 +125,Binary classification,Stacking,Phishing,0.8306451612903226,0.8292682926829269,1.3295679092407227,11.208746999999999 +150,Binary classification,Stacking,Phishing,0.8389261744966443,0.8441558441558442,1.3798675537109375,16.195196 +175,Binary classification,Stacking,Phishing,0.8563218390804598,0.8520710059171597,1.4546594619750977,22.422242999999998 +200,Binary classification,Stacking,Phishing,0.8542713567839196,0.8497409326424871,1.6083984375,29.888727999999997 +225,Binary classification,Stacking,Phishing,0.8526785714285714,0.8436018957345972,1.7997064590454102,38.482186 +250,Binary classification,Stacking,Phishing,0.8433734939759037,0.8354430379746836,1.9343080520629883,48.311454 +275,Binary classification,Stacking,Phishing,0.8467153284671532,0.8372093023255813,2.053934097290039,59.483297 +300,Binary classification,Stacking,Phishing,0.8461538461538461,0.8333333333333334,2.12460994720459,72.133375 +325,Binary classification,Stacking,Phishing,0.8518518518518519,0.8356164383561644,2.201033592224121,86.30699200000001 +350,Binary classification,Stacking,Phishing,0.8595988538681948,0.8414239482200646,2.2356014251708984,102.05020300000001 +375,Binary classification,Stacking,Phishing,0.8556149732620321,0.8353658536585366,2.328523635864258,119.41383300000001 +400,Binary classification,Stacking,Phishing,0.8571428571428571,0.8347826086956521,2.316814422607422,138.615081 +425,Binary classification,Stacking,Phishing,0.8608490566037735,0.8356545961002786,2.353947639465332,159.65345200000002 +450,Binary classification,Stacking,Phishing,0.8619153674832962,0.8351063829787234,2.4291276931762695,182.47163200000003 +475,Binary classification,Stacking,Phishing,0.8649789029535865,0.8407960199004976,2.5799179077148438,207.23725000000002 +500,Binary classification,Stacking,Phishing,0.8637274549098196,0.841860465116279,4.818408012390137,255.437855 +525,Binary classification,Stacking,Phishing,0.8645038167938931,0.8397291196388261,5.000759124755859,305.327991 +550,Binary classification,Stacking,Phishing,0.8652094717668488,0.8418803418803419,5.177936553955078,356.788916 +575,Binary classification,Stacking,Phishing,0.8658536585365854,0.8425357873210634,5.324765205383301,409.72389599999997 +600,Binary classification,Stacking,Phishing,0.8697829716193656,0.8446215139442231,5.557343482971191,464.11454999999995 +625,Binary classification,Stacking,Phishing,0.8685897435897436,0.8404669260700389,5.701066970825195,520.0238599999999 +650,Binary classification,Stacking,Phishing,0.8721109399075501,0.847145488029466,5.875107765197754,577.5076929999999 +675,Binary classification,Stacking,Phishing,0.8753709198813057,0.8541666666666667,5.993474006652832,636.4901669999999 +700,Binary classification,Stacking,Phishing,0.8798283261802575,0.8576271186440678,6.097118377685547,696.8595059999999 +725,Binary classification,Stacking,Phishing,0.8798342541436464,0.8603531300160514,6.2616376876831055,758.6517739999999 +750,Binary classification,Stacking,Phishing,0.8811748998664887,0.8624420401854715,6.510566711425781,822.0041369999999 +775,Binary classification,Stacking,Phishing,0.8824289405684754,0.8631578947368422,6.659415245056152,886.863173 +800,Binary classification,Stacking,Phishing,0.8823529411764706,0.8645533141210374,6.793304443359375,953.2293709999999 +825,Binary classification,Stacking,Phishing,0.8822815533980582,0.8654646324549237,7.087222099304199,1021.1070179999999 +850,Binary classification,Stacking,Phishing,0.8833922261484098,0.8660351826792964,7.324291229248047,1090.559446 +875,Binary classification,Stacking,Phishing,0.88558352402746,0.8677248677248677,7.470724105834961,1161.497406 +900,Binary classification,Stacking,Phishing,0.8854282536151279,0.8670967741935484,7.810632705688477,1233.983859 +925,Binary classification,Stacking,Phishing,0.8874458874458875,0.8706467661691542,7.971014976501465,1307.96073 +950,Binary classification,Stacking,Phishing,0.8872497365648051,0.8718562874251498,8.080266952514648,1383.548543 +975,Binary classification,Stacking,Phishing,0.8891170431211499,0.8738317757009346,8.205357551574707,1460.7186840000002 +1000,Binary classification,Stacking,Phishing,0.8888888888888888,0.8737201365187712,8.39128303527832,1539.5016600000001 +1025,Binary classification,Stacking,Phishing,0.888671875,0.8738938053097345,8.406519889831543,1619.9124450000002 +1050,Binary classification,Stacking,Phishing,0.8903717826501429,0.8762109795479011,8.400672912597656,1701.87919 +1075,Binary classification,Stacking,Phishing,0.8910614525139665,0.877742946708464,8.453279495239258,1785.406003 +1100,Binary classification,Stacking,Phishing,0.8926296633303002,0.8795918367346939,8.421560287475586,1870.444315 +1125,Binary classification,Stacking,Phishing,0.8932384341637011,0.8811881188118813,8.383057594299316,1956.980231 +1150,Binary classification,Stacking,Phishing,0.8929503916449086,0.8806983511154219,8.433841705322266,2045.031177 +1175,Binary classification,Stacking,Phishing,0.8909710391822828,0.8783269961977185,8.58321475982666,2134.506504 +1200,Binary classification,Stacking,Phishing,0.8932443703085905,0.8803738317757008,8.605175971984863,2225.421876 +1225,Binary classification,Stacking,Phishing,0.8946078431372549,0.8817598533455545,8.70528507232666,2317.752916 +1250,Binary classification,Stacking,Phishing,0.8951160928742994,0.88272157564906,8.721240997314453,2411.4111159999998 +1903,Binary classification,Stacking,SMTP,1.0,0.0,4.7766571044921875,62.937451 +3806,Binary classification,Stacking,SMTP,1.0,0.0,4.703582763671875,162.906939 +5709,Binary classification,Stacking,SMTP,1.0,0.0,4.683967590332031,291.99956099999997 +7612,Binary classification,Stacking,SMTP,1.0,0.0,4.6548919677734375,449.905783 +9515,Binary classification,Stacking,SMTP,1.0,0.0,4.674896240234375,634.264648 +11418,Binary classification,Stacking,SMTP,1.0,0.0,4.68939208984375,844.6132379999999 +13321,Binary classification,Stacking,SMTP,1.0,0.0,4.6727142333984375,1077.972428 +15224,Binary classification,Stacking,SMTP,0.9992774091834724,0.0,4.755153656005859,1334.293209 +17127,Binary classification,Stacking,SMTP,0.9992409202382343,0.0,4.668712615966797,1613.416001 +19030,Binary classification,Stacking,SMTP,0.9993168322034789,0.0,4.707424163818359,1913.210948 +20933,Binary classification,Stacking,SMTP,0.999378941333843,0.0,4.680248260498047,2233.020054 +22836,Binary classification,Stacking,SMTP,0.9994306984891613,0.0,4.695384979248047,2572.124593 +24739,Binary classification,Stacking,SMTP,0.9994744926833212,0.0,4.721019744873047,2930.571035 +26642,Binary classification,Stacking,SMTP,0.9994744942006681,0.0,4.747562408447266,3309.032834 +28545,Binary classification,Stacking,SMTP,0.9995095291479821,0.0,4.741054534912109,3705.221814 +30448,Binary classification,Stacking,SMTP,0.9995401845830459,0.0,4.678241729736328,4115.910057 +32351,Binary classification,Stacking,SMTP,0.9995672333848532,0.0,4.619670867919922,4539.977119 +34254,Binary classification,Stacking,SMTP,0.9995912766764955,0.0,4.749675750732422,4977.188868 +36157,Binary classification,Stacking,SMTP,0.9996127890253347,0.0,4.678524017333984,5426.058059 +38060,Binary classification,Stacking,SMTP,0.9996321500827662,0.0,4.705173492431641,5886.859448 +39963,Binary classification,Stacking,SMTP,0.9996496671838246,0.0,4.729236602783203,6359.258672 +41866,Binary classification,Stacking,SMTP,0.9996655917831124,0.0,4.729305267333984,6843.735511 +43769,Binary classification,Stacking,SMTP,0.9996801316029976,0.0,4.741458892822266,7339.76837 +45672,Binary classification,Stacking,SMTP,0.9996934597446958,0.0,4.677211761474609,7847.750223999999 +47575,Binary classification,Stacking,SMTP,0.9997057216126456,0.0,4.833148956298828,8367.044639 +49478,Binary classification,Stacking,SMTP,0.99971704024092,0.0,4.807292938232422,8898.416265 +51381,Binary classification,Stacking,SMTP,0.9996885947839627,0.0,4.893611907958984,9441.161399999999 +53284,Binary classification,Stacking,SMTP,0.9996997166075484,0.0,4.877178192138672,9993.506038 +55187,Binary classification,Stacking,SMTP,0.999710071394919,0.0,4.888896942138672,10554.524537 +57090,Binary classification,Stacking,SMTP,0.9995620872672494,0.0,4.783634185791016,11123.611551 +58993,Binary classification,Stacking,SMTP,0.9995762137238947,0.0,4.831531524658203,11701.029088 +60896,Binary classification,Stacking,SMTP,0.999589457262501,0.0,4.854015350341797,12286.755545999999 +62799,Binary classification,Stacking,SMTP,0.9995700500015924,0.0,4.858226776123047,12880.441842999999 +64702,Binary classification,Stacking,SMTP,0.9995826957852274,0.0,4.846561431884766,13482.274685999999 +66605,Binary classification,Stacking,SMTP,0.9995946189418053,0.0,4.872089385986328,14092.292626999999 +68508,Binary classification,Stacking,SMTP,0.9995766855941729,0.0,4.843868255615234,14710.448755 +70411,Binary classification,Stacking,SMTP,0.9995881266865502,0.0,4.835132598876953,15336.827121999999 +72314,Binary classification,Stacking,SMTP,0.9995989656078437,0.0,4.892154693603516,15971.964918999998 +74217,Binary classification,Stacking,SMTP,0.99960924867953,0.0,4.812671661376953,16613.982513 +76120,Binary classification,Stacking,SMTP,0.9996190175908775,0.0,4.880641937255859,17262.391145999998 +78023,Binary classification,Stacking,SMTP,0.9996283099638563,0.0,4.831180572509766,17916.095854 +79926,Binary classification,Stacking,SMTP,0.9996371598373475,0.0,4.851375579833984,18574.918078 +81829,Binary classification,Stacking,SMTP,0.9996455980837855,0.0,4.851016998291016,19239.025055 +83732,Binary classification,Stacking,SMTP,0.9996536527689864,0.0,4.869503021240234,19908.351771999998 +85635,Binary classification,Stacking,SMTP,0.999661349463998,0.0,4.886287689208984,20582.843625999998 +87538,Binary classification,Stacking,SMTP,0.9996687115162731,0.0,4.888690948486328,21262.535161 +89441,Binary classification,Stacking,SMTP,0.9996645796064401,0.0,4.888484954833984,21947.343421999998 +91344,Binary classification,Stacking,SMTP,0.999671567607808,0.0,4.876293182373047,22637.362347 +93247,Binary classification,Stacking,SMTP,0.9996782703815713,0.0,4.905620574951172,23332.514825 +95150,Binary classification,Stacking,SMTP,0.9996847050415664,0.0,4.880191802978516,24032.848442 +95156,Binary classification,Stacking,SMTP,0.9996847249224948,0.0,4.888683319091797,24733.238040999997 +106,Binary classification,Voting,Bananas,0.6761904761904762,0.6136363636363638,0.14342212677001953,0.374142 +212,Binary classification,Voting,Bananas,0.7772511848341233,0.7374301675977653,0.23540592193603516,1.3677169999999998 +318,Binary classification,Voting,Bananas,0.7886435331230284,0.7527675276752769,0.3270235061645508,3.238746 +424,Binary classification,Voting,Bananas,0.7990543735224587,0.7658402203856748,0.41901111602783203,6.2520690000000005 +530,Binary classification,Voting,Bananas,0.8015122873345936,0.7575057736720554,2.719620704650879,30.609493999999998 +636,Binary classification,Voting,Bananas,0.8173228346456692,0.7777777777777779,3.159085273742676,56.745796 +742,Binary classification,Voting,Bananas,0.8259109311740891,0.7839195979899498,3.6036806106567383,84.9251 +848,Binary classification,Voting,Bananas,0.8299881936245572,0.7913043478260869,4.0666093826293945,115.11768599999999 +954,Binary classification,Voting,Bananas,0.8352570828961176,0.7963683527885861,4.521588325500488,147.5017 +1060,Binary classification,Voting,Bananas,0.8470254957507082,0.8094117647058824,4.660099983215332,182.008963 +1166,Binary classification,Voting,Bananas,0.8497854077253219,0.8132337246531482,4.474972724914551,218.357961 +1272,Binary classification,Voting,Bananas,0.8489378442171518,0.8135922330097087,4.3258256912231445,256.411001 +1378,Binary classification,Voting,Bananas,0.8482207697893972,0.8112014453477868,4.1847429275512695,296.011707 +1484,Binary classification,Voting,Bananas,0.8530006743088334,0.8180300500834724,4.276310920715332,337.212047 +1590,Binary classification,Voting,Bananas,0.8539962240402769,0.8198757763975156,4.522702217102051,380.36472399999997 +1696,Binary classification,Voting,Bananas,0.8584070796460177,0.8250728862973761,4.5992326736450195,425.08911199999994 +1802,Binary classification,Voting,Bananas,0.8622987229317046,0.8315217391304348,4.6097002029418945,471.42038399999996 +1908,Binary classification,Voting,Bananas,0.8610382800209754,0.8319594166138238,4.583279609680176,519.2335119999999 +2014,Binary classification,Voting,Bananas,0.8584202682563339,0.8302561048243002,4.509037971496582,568.4848139999999 +2120,Binary classification,Voting,Bananas,0.8612553091080698,0.8355704697986577,4.487088203430176,619.150273 +2226,Binary classification,Voting,Bananas,0.8624719101123596,0.8370607028753994,4.479489326477051,671.27983 +2332,Binary classification,Voting,Bananas,0.8614328614328615,0.8357905439755974,4.476758003234863,724.8282939999999 +2438,Binary classification,Voting,Bananas,0.8621255642183012,0.8364167478091528,4.495999336242676,779.843051 +2544,Binary classification,Voting,Bananas,0.8623672827369249,0.8375116063138347,4.492741584777832,836.3752579999999 +2650,Binary classification,Voting,Bananas,0.8618346545866364,0.8374777975133214,4.535428047180176,894.373804 +2756,Binary classification,Voting,Bananas,0.8627949183303085,0.8384615384615384,4.529454231262207,953.727688 +2862,Binary classification,Voting,Bananas,0.8661307235232436,0.842061855670103,4.489285469055176,1014.5232589999999 +2968,Binary classification,Voting,Bananas,0.8678800134816312,0.8437001594896333,4.522076606750488,1076.832167 +3074,Binary classification,Voting,Bananas,0.8662544744549301,0.8419838523644751,4.4861345291137695,1140.498025 +3180,Binary classification,Voting,Bananas,0.8678829820698333,0.8432835820895522,4.490513801574707,1205.597432 +3286,Binary classification,Voting,Bananas,0.8684931506849315,0.8433647570703406,4.5036516189575195,1272.097546 +3392,Binary classification,Voting,Bananas,0.8690651725154822,0.8449720670391062,4.519848823547363,1340.030829 +3498,Binary classification,Voting,Bananas,0.8687446382613668,0.8439306358381503,4.534294128417969,1409.423343 +3604,Binary classification,Voting,Bananas,0.8701082431307244,0.8451356717405691,4.515525817871094,1480.1764939999998 +3710,Binary classification,Voting,Bananas,0.8705850633593961,0.8462524023062139,4.521697998046875,1552.3097799999998 +3816,Binary classification,Voting,Bananas,0.8718217562254259,0.847900466562986,4.52362060546875,1625.8426229999998 +3922,Binary classification,Voting,Bananas,0.8704412139760265,0.845873786407767,4.511474609375,1700.7302089999998 +4028,Binary classification,Voting,Bananas,0.8698783213310156,0.8450620934358367,4.530387878417969,1777.041184 +4134,Binary classification,Voting,Bananas,0.8707960319380595,0.8461981566820277,4.540306091308594,1854.755673 +4240,Binary classification,Voting,Bananas,0.8723755602736495,0.8485018202184262,4.5452880859375,1933.8281619999998 +4346,Binary classification,Voting,Bananas,0.8734177215189873,0.8498088476242489,4.5819854736328125,2014.1792419999997 +4452,Binary classification,Voting,Bananas,0.8732869018198157,0.8494394020288306,4.5782928466796875,2095.8385169999997 +4558,Binary classification,Voting,Bananas,0.8720649550142637,0.8482166102577455,4.539161682128906,2178.6750019999995 +4664,Binary classification,Voting,Bananas,0.8719708342268926,0.8485156051763512,4.509727478027344,2262.6244259999994 +4770,Binary classification,Voting,Bananas,0.8712518347661984,0.8472636815920398,4.5496673583984375,2347.8035639999994 +4876,Binary classification,Voting,Bananas,0.8717948717948718,0.8474493531852575,4.560760498046875,2434.1199849999994 +4982,Binary classification,Voting,Bananas,0.8725155591246737,0.8487735175041676,4.513671875,2521.5491019999995 +5088,Binary classification,Voting,Bananas,0.8718301552978179,0.8480186480186479,4.541267395019531,2610.1395069999994 +5194,Binary classification,Voting,Bananas,0.8725207009435779,0.848927430397079,4.5822906494140625,2699.9579359999993 +5300,Binary classification,Voting,Bananas,0.8726174749952821,0.8491620111731844,4.5840301513671875,2790.9651129999993 +906,Binary classification,Voting,Elec2,0.8795580110497238,0.880351262349067,4.715929985046387,35.551681 +1812,Binary classification,Voting,Elec2,0.8807288790723358,0.8536585365853658,4.9170331954956055,87.613259 +2718,Binary classification,Voting,Elec2,0.8689731321310269,0.8344186046511628,4.988085746765137,154.923184 +3624,Binary classification,Voting,Elec2,0.8793817278498481,0.8493622888659084,4.879870414733887,235.92708 +4530,Binary classification,Voting,Elec2,0.8792227864870833,0.8405712620227338,5.017077445983887,328.79801 +5436,Binary classification,Voting,Elec2,0.8689972401103956,0.8260869565217391,4.985064506530762,432.967134 +6342,Binary classification,Voting,Elec2,0.8680018924459865,0.8269588587967748,4.949084281921387,548.642546 +7248,Binary classification,Voting,Elec2,0.8643576652407893,0.8194010655888295,4.962946891784668,674.769885 +8154,Binary classification,Voting,Elec2,0.8671654605666625,0.8317016317016317,5.020190238952637,811.067026 +9060,Binary classification,Voting,Elec2,0.8711778341980351,0.8417627118644068,5.0752363204956055,957.595858 +9966,Binary classification,Voting,Elec2,0.8706472654290015,0.845165165165165,4.979113578796387,1113.746382 +10872,Binary classification,Voting,Elec2,0.8737006715113605,0.8516156922079325,4.9885969161987305,1279.290866 +11778,Binary classification,Voting,Elec2,0.8733972998216863,0.8507059176930009,5.106616020202637,1454.661274 +12684,Binary classification,Voting,Elec2,0.873294961759836,0.8513551012857274,5.2120466232299805,1640.606202 +13590,Binary classification,Voting,Elec2,0.8755611156082125,0.8560973534167304,5.144991874694824,1836.8358979999998 +14496,Binary classification,Voting,Elec2,0.8765781303897896,0.8583643416989946,5.178118705749512,2042.985996 +15402,Binary classification,Voting,Elec2,0.8766963184208818,0.8575500712624708,5.108157157897949,2257.366998 +16308,Binary classification,Voting,Elec2,0.8711596247010487,0.8493366798135532,5.1558027267456055,2480.0447249999997 +17214,Binary classification,Voting,Elec2,0.8687038865973392,0.8429683157309616,5.140070915222168,2710.925816 +18120,Binary classification,Voting,Elec2,0.8689773166289531,0.8433623647400369,5.172907829284668,2950.660411 +19026,Binary classification,Voting,Elec2,0.8696977660972405,0.8421320766732471,5.328249931335449,3199.829749 +19932,Binary classification,Voting,Elec2,0.8659876574180925,0.8380132209351688,5.355593681335449,3457.907085 +20838,Binary classification,Voting,Elec2,0.8617843259586313,0.8322851153039832,5.442904472351074,3724.5720149999997 +21744,Binary classification,Voting,Elec2,0.862668445016787,0.8307064293003741,5.347712516784668,3998.8413929999997 +22650,Binary classification,Voting,Elec2,0.8610093160845953,0.8268045774647886,5.363558769226074,4280.436632 +23556,Binary classification,Voting,Elec2,0.85434090426661,0.8163571160948456,5.3763532638549805,4569.013267 +24462,Binary classification,Voting,Elec2,0.8534401700666366,0.8138145936120489,5.330439567565918,4864.708005 +25368,Binary classification,Voting,Elec2,0.8518941932431899,0.8121030257564391,5.456484794616699,5167.517117 +26274,Binary classification,Voting,Elec2,0.8530811098846725,0.8130931628897928,5.321070671081543,5477.642376000001 +27180,Binary classification,Voting,Elec2,0.8524964126715479,0.8125672074430782,5.426630973815918,5794.594214000001 +28086,Binary classification,Voting,Elec2,0.8496350364963504,0.8078795323233702,5.3666486740112305,6118.251751000001 +28992,Binary classification,Voting,Elec2,0.8475043979165948,0.8032575319300432,5.4148359298706055,6448.591708000001 +29898,Binary classification,Voting,Elec2,0.8460046158477439,0.8002429711905589,5.3892927169799805,6785.693940000001 +30804,Binary classification,Voting,Elec2,0.8462162776352953,0.7992881657556884,5.532000541687012,7130.317230000001 +31710,Binary classification,Voting,Elec2,0.8429783342268756,0.7938387644403958,5.507189750671387,7481.445887000001 +32616,Binary classification,Voting,Elec2,0.8419745515866932,0.7926122646064703,5.485476493835449,7839.281994000001 +33522,Binary classification,Voting,Elec2,0.8421884788639957,0.7931492922499414,5.629275321960449,8203.623919000001 +34428,Binary classification,Voting,Elec2,0.8400092950300636,0.7894656371837016,5.587969779968262,8574.868737 +35334,Binary classification,Voting,Elec2,0.8398947159878867,0.7879287722586691,5.669405937194824,8954.118864 +36240,Binary classification,Voting,Elec2,0.8408896492728828,0.7879523389232127,5.650286674499512,9340.118817 +37146,Binary classification,Voting,Elec2,0.8397092475434109,0.7854569040069184,5.6525373458862305,9731.872155000001 +38052,Binary classification,Voting,Elec2,0.8398202412551575,0.7854402083993381,5.638819694519043,10129.488521000001 +38958,Binary classification,Voting,Elec2,0.8406704828400544,0.787685992816829,5.6699628829956055,10532.813227 +39864,Binary classification,Voting,Elec2,0.841381732433585,0.7910235647949235,5.623560905456543,10941.028498 +40770,Binary classification,Voting,Elec2,0.8422085408030612,0.7943480067772769,5.627467155456543,11353.937417 +41676,Binary classification,Voting,Elec2,0.8431673665266947,0.7973835947671896,5.641619682312012,11771.547133999999 +42582,Binary classification,Voting,Elec2,0.8438505436697118,0.7987529888919157,5.640711784362793,12193.871152999998 +43488,Binary classification,Voting,Elec2,0.843999356129418,0.7991592160577892,5.725451469421387,12620.815871999997 +44394,Binary classification,Voting,Elec2,0.8432635775910616,0.7972256221950225,5.7456769943237305,13052.460535999997 +45300,Binary classification,Voting,Elec2,0.8436830835117773,0.7980031379261162,5.7547407150268555,13488.904282999996 +45312,Binary classification,Voting,Elec2,0.8436803425216834,0.7979576118892089,5.757502555847168,13925.545040999996 +25,Binary classification,Voting,Phishing,0.5833333333333334,0.7058823529411764,0.17400836944580078,0.162813 +50,Binary classification,Voting,Phishing,0.7346938775510204,0.7636363636363637,0.20249652862548828,0.520257 +75,Binary classification,Voting,Phishing,0.7837837837837838,0.8048780487804877,0.23151493072509766,1.0500919999999998 +100,Binary classification,Voting,Phishing,0.8080808080808081,0.819047619047619,0.26002979278564453,1.7634529999999997 +125,Binary classification,Voting,Phishing,0.8145161290322581,0.8217054263565893,0.2885446548461914,2.7650449999999998 +150,Binary classification,Voting,Phishing,0.8187919463087249,0.830188679245283,0.3175630569458008,4.083864999999999 +175,Binary classification,Voting,Phishing,0.8390804597701149,0.8390804597701148,0.34607791900634766,5.803779 +200,Binary classification,Voting,Phishing,0.8391959798994975,0.8383838383838383,0.3750925064086914,7.866028 +225,Binary classification,Voting,Phishing,0.8348214285714286,0.8294930875576038,0.4036073684692383,10.317012 +250,Binary classification,Voting,Phishing,0.8313253012048193,0.8264462809917356,0.43212223052978516,13.231482 +275,Binary classification,Voting,Phishing,0.8357664233576643,0.8288973384030419,0.46164798736572266,16.680039999999998 +300,Binary classification,Voting,Phishing,0.842809364548495,0.8327402135231317,0.49016284942626953,20.639260999999998 +325,Binary classification,Voting,Phishing,0.8549382716049383,0.8417508417508418,0.5191812515258789,25.174847 +350,Binary classification,Voting,Phishing,0.8624641833810889,0.8471337579617835,0.5476961135864258,30.349829 +375,Binary classification,Voting,Phishing,0.8609625668449198,0.8433734939759037,0.5762109756469727,36.11991 +400,Binary classification,Voting,Phishing,0.8621553884711779,0.8424068767908309,0.605229377746582,42.598397999999996 +425,Binary classification,Voting,Phishing,0.8632075471698113,0.839779005524862,0.6337442398071289,49.849208 +450,Binary classification,Voting,Phishing,0.8663697104677061,0.8412698412698413,0.6627893447875977,57.823257999999996 +475,Binary classification,Voting,Phishing,0.8649789029535865,0.8407960199004976,0.6913042068481445,66.560459 +500,Binary classification,Voting,Phishing,0.8657314629258517,0.8445475638051043,2.87209415435791,96.81871699999999 +525,Binary classification,Voting,Phishing,0.8683206106870229,0.8442437923250564,2.980504035949707,127.96343099999999 +550,Binary classification,Voting,Phishing,0.8688524590163934,0.8461538461538463,3.08364200592041,159.97764899999999 +575,Binary classification,Voting,Phishing,0.8710801393728222,0.848360655737705,3.1883134841918945,192.90880399999998 +600,Binary classification,Voting,Phishing,0.8747913188647746,0.8502994011976048,3.300492286682129,226.71997 +625,Binary classification,Voting,Phishing,0.8733974358974359,0.8460038986354775,3.412938117980957,261.370264 +650,Binary classification,Voting,Phishing,0.8767334360554699,0.8523985239852399,3.522160530090332,296.927054 +675,Binary classification,Voting,Phishing,0.8783382789317508,0.8571428571428572,3.6335840225219727,333.391974 +700,Binary classification,Voting,Phishing,0.882689556509299,0.8605442176870748,3.7482118606567383,370.807982 +725,Binary classification,Voting,Phishing,0.8839779005524862,0.864516129032258,3.8615503311157227,409.129822 +750,Binary classification,Voting,Phishing,0.8851802403204272,0.8664596273291927,3.975522041320801,448.34317699999997 +775,Binary classification,Voting,Phishing,0.8863049095607235,0.8670694864048338,4.095002174377441,488.481904 +800,Binary classification,Voting,Phishing,0.886107634543179,0.8683068017366136,4.146827697753906,529.573688 +825,Binary classification,Voting,Phishing,0.8859223300970874,0.8690807799442897,4.390903472900391,571.6173799999999 +850,Binary classification,Voting,Phishing,0.8869257950530035,0.8695652173913044,4.504707336425781,614.6492939999999 +875,Binary classification,Voting,Phishing,0.8890160183066361,0.8711819389110226,4.624469757080078,658.6172529999999 +900,Binary classification,Voting,Phishing,0.8876529477196885,0.869340232858991,4.741554260253906,703.5317429999999 +925,Binary classification,Voting,Phishing,0.8896103896103896,0.8728179551122195,4.862430572509766,749.5245539999999 +950,Binary classification,Voting,Phishing,0.8904109589041096,0.8752997601918464,4.984291076660156,796.5006999999998 +975,Binary classification,Voting,Phishing,0.8921971252566735,0.8771929824561404,5.102375030517578,844.5149469999998 +1000,Binary classification,Voting,Phishing,0.8928928928928929,0.8779931584948689,5.219093322753906,893.5084249999998 +1025,Binary classification,Voting,Phishing,0.892578125,0.8780487804878048,5.178688049316406,943.5715689999997 +1050,Binary classification,Voting,Phishing,0.894184938036225,0.8802588996763754,5.151969909667969,994.6247309999998 +1075,Binary classification,Voting,Phishing,0.8929236499068901,0.8798328108672936,5.117225646972656,1046.6512989999997 +1100,Binary classification,Voting,Phishing,0.8944494995450409,0.8816326530612245,5.075950622558594,1099.6701919999996 +1125,Binary classification,Voting,Phishing,0.8959074733096085,0.884272997032641,5.007194519042969,1153.6019869999996 +1150,Binary classification,Voting,Phishing,0.896431679721497,0.8845780795344327,4.982025146484375,1208.4750359999996 +1175,Binary classification,Voting,Phishing,0.8952299829642248,0.8829686013320648,4.96966552734375,1264.1689839999997 +1200,Binary classification,Voting,Phishing,0.896580483736447,0.8841121495327102,4.9371490478515625,1320.7795169999997 +1225,Binary classification,Voting,Phishing,0.8970588235294118,0.8844036697247706,4.8813018798828125,1378.3046599999998 +1250,Binary classification,Voting,Phishing,0.8967173738991193,0.8845120859444942,4.820304870605469,1436.7224909999998 +1903,Binary classification,Voting,SMTP,1.0,0.0,4.661611557006836,43.527964 +3806,Binary classification,Voting,SMTP,1.0,0.0,4.557134628295898,113.03314999999999 +5709,Binary classification,Voting,SMTP,1.0,0.0,4.496244430541992,201.747221 +7612,Binary classification,Voting,SMTP,1.0,0.0,4.508665084838867,310.754596 +9515,Binary classification,Voting,SMTP,1.0,0.0,4.565656661987305,436.825284 +11418,Binary classification,Voting,SMTP,1.0,0.0,4.554738998413086,579.964386 +13321,Binary classification,Voting,SMTP,1.0,0.0,4.492513656616211,739.485002 +15224,Binary classification,Voting,SMTP,0.9997372397030808,0.7777777777777778,4.532373428344727,915.25293 +17127,Binary classification,Voting,SMTP,0.9997664369963798,0.8181818181818181,4.528841018676758,1107.42481 +19030,Binary classification,Voting,SMTP,0.9997897945241474,0.8181818181818181,4.520586013793945,1314.036215 +20933,Binary classification,Voting,SMTP,0.9998089050257978,0.8181818181818181,4.519166946411133,1534.1862760000001 +22836,Binary classification,Voting,SMTP,0.9998248303043573,0.8181818181818181,4.512857437133789,1768.1243410000002 +24739,Binary classification,Voting,SMTP,0.9998383054410219,0.8181818181818181,4.568696975708008,2014.7470960000003 +26642,Binary classification,Voting,SMTP,0.9998123193573815,0.782608695652174,4.55253791809082,2273.6909760000003 +28545,Binary classification,Voting,SMTP,0.9998248318385651,0.782608695652174,4.554193496704102,2543.9053730000005 +30448,Binary classification,Voting,SMTP,0.9998357802082307,0.782608695652174,4.487833023071289,2824.6447140000005 +32351,Binary classification,Voting,SMTP,0.9998454404945905,0.782608695652174,4.52525520324707,3116.2234200000003 +34254,Binary classification,Voting,SMTP,0.9998540273844627,0.782608695652174,4.608850479125977,3418.6303260000004 +36157,Binary classification,Voting,SMTP,0.999861710366191,0.782608695652174,4.462549209594727,3731.2161630000005 +38060,Binary classification,Voting,SMTP,0.9998686250295594,0.782608695652174,4.517663955688477,4053.8854360000005 +39963,Binary classification,Voting,SMTP,0.9998748811370802,0.782608695652174,4.596040725708008,4386.480402 +41866,Binary classification,Voting,SMTP,0.9998805684939687,0.782608695652174,4.581964492797852,4729.507439 +43769,Binary classification,Voting,SMTP,0.9998857612867849,0.782608695652174,4.56077766418457,5082.623411 +45672,Binary classification,Voting,SMTP,0.9998905213373913,0.782608695652174,4.554697036743164,5446.283888999999 +47575,Binary classification,Voting,SMTP,0.9998949005759449,0.782608695652174,4.589784622192383,5821.612630999999 +49478,Binary classification,Voting,SMTP,0.9998989429431857,0.782608695652174,4.514947891235352,6206.523862999999 +51381,Binary classification,Voting,SMTP,0.9998637602179836,0.72,4.571069717407227,6600.877267999999 +53284,Binary classification,Voting,SMTP,0.9998686260158024,0.72,4.544900894165039,7002.824473 +55187,Binary classification,Voting,SMTP,0.9998731562352771,0.72,4.490015029907227,7412.266105 +57090,Binary classification,Voting,SMTP,0.9997197358510396,0.5294117647058824,4.520219802856445,7829.0322129999995 +58993,Binary classification,Voting,SMTP,0.9997287767832926,0.5294117647058824,4.567926406860352,8253.169596 +60896,Binary classification,Voting,SMTP,0.9997372526480006,0.5294117647058824,4.602060317993164,8684.574786 +62799,Binary classification,Voting,SMTP,0.9997133666677283,0.5,4.518564224243164,9122.220249 +64702,Binary classification,Voting,SMTP,0.9997217971901516,0.5,4.562410354614258,9566.245368 +66605,Binary classification,Voting,SMTP,0.9997297459612036,0.5,4.587350845336914,10016.770375 +68508,Binary classification,Voting,SMTP,0.9997226560789408,0.5128205128205129,4.58268928527832,10473.685786 +70411,Binary classification,Voting,SMTP,0.9997301519670502,0.5128205128205129,4.552003860473633,10937.213874000001 +72314,Binary classification,Voting,SMTP,0.9997372533292769,0.5128205128205129,4.568490982055664,11407.187031000001 +74217,Binary classification,Voting,SMTP,0.9997439905141748,0.5128205128205129,4.501398086547852,11883.434676 +76120,Binary classification,Voting,SMTP,0.9997503908354025,0.5128205128205129,4.530572891235352,12366.124718000001 +78023,Binary classification,Voting,SMTP,0.999756478941837,0.5128205128205129,4.565195083618164,12855.350972 +79926,Binary classification,Voting,SMTP,0.9997622771348139,0.5128205128205129,4.555276870727539,13350.750206 +81829,Binary classification,Voting,SMTP,0.9997678056411008,0.5128205128205129,4.523683547973633,13852.638924 +83732,Binary classification,Voting,SMTP,0.9997730828486463,0.5128205128205129,4.51640510559082,14360.452684 +85635,Binary classification,Voting,SMTP,0.9997781255108952,0.5128205128205129,4.554742813110352,14874.156807 +87538,Binary classification,Voting,SMTP,0.9997829489244549,0.5128205128205129,4.55351448059082,15393.589182 +89441,Binary classification,Voting,SMTP,0.9997763864042933,0.5,4.576028823852539,15919.905447 +91344,Binary classification,Voting,SMTP,0.9997700973254655,0.4878048780487804,4.509759902954102,16451.368555 +93247,Binary classification,Voting,SMTP,0.9997747892671,0.4878048780487804,4.633722305297852,16987.639193000003 +95150,Binary classification,Voting,SMTP,0.9997792935290964,0.4878048780487804,4.606340408325195,17528.67073 +95156,Binary classification,Voting,SMTP,0.9997793074457464,0.4878048780487804,4.602045059204102,18069.786262 +106,Binary classification,[baseline] Last Class,Bananas,0.5333333333333333,0.5242718446601942,0.0005102157592773438,0.004468 +212,Binary classification,[baseline] Last Class,Bananas,0.5876777251184834,0.5538461538461539,0.0005102157592773438,0.067972 +318,Binary classification,[baseline] Last Class,Bananas,0.5457413249211357,0.5102040816326531,0.0005102157592773438,0.134988 +424,Binary classification,[baseline] Last Class,Bananas,0.5460992907801419,0.5025906735751295,0.0005102157592773438,0.20522 +530,Binary classification,[baseline] Last Class,Bananas,0.5671077504725898,0.5096359743040686,0.0005102157592773438,0.337716 +636,Binary classification,[baseline] Last Class,Bananas,0.5464566929133858,0.4875444839857651,0.0005102157592773438,0.474055 +742,Binary classification,[baseline] Last Class,Bananas,0.5573549257759784,0.4875,0.0005102157592773438,0.646583 +848,Binary classification,[baseline] Last Class,Bananas,0.5501770956316411,0.4816326530612245,0.0005102157592773438,0.822555 +954,Binary classification,[baseline] Last Class,Bananas,0.5487932843651626,0.4794188861985472,0.0005102157592773438,1.00209 +1060,Binary classification,[baseline] Last Class,Bananas,0.5448536355051936,0.46799116997792495,0.0005102157592773438,1.292978 +1166,Binary classification,[baseline] Last Class,Bananas,0.534763948497854,0.4590818363273453,0.0005102157592773438,1.5875979999999998 +1272,Binary classification,[baseline] Last Class,Bananas,0.5287175452399685,0.456935630099728,0.0005102157592773438,1.885535 +1378,Binary classification,[baseline] Last Class,Bananas,0.5286855482933914,0.45232067510548524,0.0005102157592773438,2.211477 +1484,Binary classification,[baseline] Last Class,Bananas,0.5252865812542145,0.44913928012519555,0.0005102157592773438,2.547239 +1590,Binary classification,[baseline] Last Class,Bananas,0.5204531151667715,0.4437956204379563,0.0005102157592773438,2.88734 +1696,Binary classification,[baseline] Last Class,Bananas,0.5227138643067847,0.4455106237148732,0.0005102157592773438,3.258534 +1802,Binary classification,[baseline] Last Class,Bananas,0.524153248195447,0.4523961661341854,0.0005102157592773438,3.633124 +1908,Binary classification,[baseline] Last Class,Bananas,0.5233350812794966,0.456664674237896,0.0005102157592773438,4.01125 +2014,Binary classification,[baseline] Last Class,Bananas,0.5171385991058122,0.4563758389261745,0.0005102157592773438,4.505139000000001 +2120,Binary classification,[baseline] Last Class,Bananas,0.5143935818782445,0.45813586097946285,0.0005102157592773438,5.002779 +2226,Binary classification,[baseline] Last Class,Bananas,0.5114606741573033,0.45459106874059213,0.0005102157592773438,5.503925000000001 +2332,Binary classification,[baseline] Last Class,Bananas,0.510939510939511,0.45506692160611856,0.0005102157592773438,6.074663000000001 +2438,Binary classification,[baseline] Last Class,Bananas,0.5104636848584325,0.4530032095369097,0.0005102157592773438,6.648598000000001 +2544,Binary classification,[baseline] Last Class,Bananas,0.5084545812033032,0.45462478184991273,0.0005102157592773438,7.226634000000001 +2650,Binary classification,[baseline] Last Class,Bananas,0.5096262740656852,0.458072590738423,0.0005102157592773438,7.8632990000000005 +2756,Binary classification,[baseline] Last Class,Bananas,0.5092558983666061,0.45746388443017655,0.0005102157592773438,8.503527 +2862,Binary classification,[baseline] Last Class,Bananas,0.5103110800419434,0.4563445867287544,0.0005102157592773438,9.147193 +2968,Binary classification,[baseline] Last Class,Bananas,0.5133131108864173,0.457957957957958,0.0005102157592773438,9.82546 +3074,Binary classification,[baseline] Last Class,Bananas,0.5099251545720794,0.4563176895306859,0.0005102157592773438,10.507099 +3180,Binary classification,[baseline] Last Class,Bananas,0.5102233406731677,0.45387583304103823,0.0005102157592773438,11.191893 +3286,Binary classification,[baseline] Last Class,Bananas,0.5095890410958904,0.45222713362801764,0.0005102157592773438,11.975438 +3392,Binary classification,[baseline] Last Class,Bananas,0.5107637864936597,0.4558871761233191,0.0005102157592773438,12.764918 +3498,Binary classification,[baseline] Last Class,Bananas,0.5124392336288247,0.45579316948611553,0.0005102157592773438,13.557573 +3604,Binary classification,[baseline] Last Class,Bananas,0.5134610047182903,0.45440398381574854,0.0005102157592773438,14.3795 +3710,Binary classification,[baseline] Last Class,Bananas,0.5122674575357239,0.4546276756104914,0.0005102157592773438,15.204998 +3816,Binary classification,[baseline] Last Class,Bananas,0.510615989515072,0.4536142815335089,0.0005102157592773438,16.116361 +3922,Binary classification,[baseline] Last Class,Bananas,0.5090538128028564,0.45078459343794575,0.0005102157592773438,17.035489000000002 +4028,Binary classification,[baseline] Last Class,Bananas,0.5108020859200397,0.45247359644246804,0.0005102157592773438,17.958008000000003 +4134,Binary classification,[baseline] Last Class,Bananas,0.5102830873457537,0.4517876489707476,0.0005102157592773438,18.927027000000002 +4240,Binary classification,[baseline] Last Class,Bananas,0.5102618542108988,0.4525316455696203,0.0005102157592773438,19.900154000000004 +4346,Binary classification,[baseline] Last Class,Bananas,0.5074798619102416,0.4490216271884655,0.0005102157592773438,20.876623000000006 +4452,Binary classification,[baseline] Last Class,Bananas,0.5099977533138621,0.45132075471698113,0.0005102157592773438,21.913356000000007 +4558,Binary classification,[baseline] Last Class,Bananas,0.5099846390168971,0.45390070921985815,0.0005102157592773438,22.953869000000008 +4664,Binary classification,[baseline] Last Class,Bananas,0.5099721209521767,0.4553039332538737,0.0005102157592773438,23.99911400000001 +4770,Binary classification,[baseline] Last Class,Bananas,0.5110085971901867,0.4556489262371615,0.0005102157592773438,25.08372100000001 +4876,Binary classification,[baseline] Last Class,Bananas,0.5109743589743589,0.4539624370132845,0.0005102157592773438,26.17171900000001 +4982,Binary classification,[baseline] Last Class,Bananas,0.5099377635013049,0.45379279480868207,0.0005102157592773438,27.26320500000001 +5088,Binary classification,[baseline] Last Class,Bananas,0.5099272655789266,0.45364891518737677,0.0005102157592773438,28.44143600000001 +5194,Binary classification,[baseline] Last Class,Bananas,0.5097246293086848,0.4531786941580756,0.0005102157592773438,29.62357400000001 +5300,Binary classification,[baseline] Last Class,Bananas,0.5095301000188714,0.4529572721532309,0.0005102157592773438,30.80903600000001 +906,Binary classification,[baseline] Last Class,Elec2,0.8530386740331491,0.8500563697857948,0.0005102157592773438,0.224121 +1812,Binary classification,[baseline] Last Class,Elec2,0.8619547211485368,0.8287671232876712,0.0005102157592773438,0.785464 +2718,Binary classification,[baseline] Last Class,Elec2,0.8450496871549503,0.80958842152872,0.0005102157592773438,1.64751 +3624,Binary classification,[baseline] Last Class,Elec2,0.8418437758763456,0.8056968463886063,0.0005102157592773438,2.8059529999999997 +4530,Binary classification,[baseline] Last Class,Elec2,0.8388165157871494,0.7960893854748604,0.0005102157592773438,4.158177 +5436,Binary classification,[baseline] Last Class,Elec2,0.8413983440662374,0.7995348837209302,0.0005102157592773438,5.857693 +6342,Binary classification,[baseline] Last Class,Elec2,0.8370919413341744,0.7958094485076103,0.0005102157592773438,7.811494 +7248,Binary classification,[baseline] Last Class,Elec2,0.8359321098385539,0.7948231233822259,0.0005102157592773438,10.005109 +8154,Binary classification,[baseline] Last Class,Elec2,0.8352753587636453,0.8021799970540581,0.0005102157592773438,12.510532 +9060,Binary classification,[baseline] Last Class,Elec2,0.8358538470029805,0.8069081937410726,0.0005102157592773438,15.278307999999999 +9966,Binary classification,[baseline] Last Class,Elec2,0.8372303060712494,0.8118765947575969,0.0005102157592773438,18.289258999999998 +10872,Binary classification,[baseline] Last Class,Elec2,0.8368135406126391,0.8140461215932915,0.0005102157592773438,21.565545999999998 +11778,Binary classification,[baseline] Last Class,Elec2,0.8374798335739153,0.8150724637681159,0.0005102157592773438,25.041396 +12684,Binary classification,[baseline] Last Class,Elec2,0.8384451628163684,0.8161177420802298,0.0005102157592773438,28.814916 +13590,Binary classification,[baseline] Last Class,Elec2,0.842004562513798,0.8223417459660736,0.0005102157592773438,32.85712 +14496,Binary classification,[baseline] Last Class,Elec2,0.8448430493273542,0.8264794383149447,0.0005102157592773438,37.134508000000004 +15402,Binary classification,[baseline] Last Class,Elec2,0.8460489578598792,0.8270983738058776,0.0005102157592773438,41.682175 +16308,Binary classification,[baseline] Last Class,Elec2,0.844851904090268,0.8251313243019076,0.0005102157592773438,46.494991 +17214,Binary classification,[baseline] Last Class,Elec2,0.8443618195549875,0.8222177981286084,0.0005102157592773438,51.515798 +18120,Binary classification,[baseline] Last Class,Elec2,0.8450797505381091,0.8227792158595871,0.0005102157592773438,56.800748 +19026,Binary classification,[baseline] Last Class,Elec2,0.8462023653088042,0.8224083515416363,0.0005102157592773438,62.372633 +19932,Binary classification,[baseline] Last Class,Elec2,0.847523957653906,0.8255753888538139,0.0005102157592773438,68.180409 +20838,Binary classification,[baseline] Last Class,Elec2,0.84661899505687,0.8249917862227577,0.0005102157592773438,74.270057 +21744,Binary classification,[baseline] Last Class,Elec2,0.8452835395299637,0.8209495422610177,0.0005102157592773438,80.612623 +22650,Binary classification,[baseline] Last Class,Elec2,0.8444081416398075,0.8188733552631579,0.0005102157592773438,87.17507 +23556,Binary classification,[baseline] Last Class,Elec2,0.8451284228401613,0.8194595664654062,0.0005102157592773438,93.968638 +24462,Binary classification,[baseline] Last Class,Elec2,0.8464903315481788,0.8198781599270878,0.0005102157592773438,100.983267 +25368,Binary classification,[baseline] Last Class,Elec2,0.8462963692986951,0.8199492034172247,0.0005102157592773438,108.278888 +26274,Binary classification,[baseline] Last Class,Elec2,0.8477524454763445,0.8213168944876262,0.0005102157592773438,115.769594 +27180,Binary classification,[baseline] Last Class,Elec2,0.8495529636851982,0.8240457851026293,0.0005102157592773438,123.465792 +28086,Binary classification,[baseline] Last Class,Elec2,0.8509880719245149,0.825107610012955,0.0005102157592773438,131.36678899999998 +28992,Binary classification,[baseline] Last Class,Elec2,0.8521265220240765,0.8258237516759436,0.0005102157592773438,139.55273799999998 +29898,Binary classification,[baseline] Last Class,Elec2,0.8531959728400843,0.8268160833366216,0.0005102157592773438,147.964309 +30804,Binary classification,[baseline] Last Class,Elec2,0.8537480115573158,0.8267107743201139,0.0005102157592773438,156.664426 +31710,Binary classification,[baseline] Last Class,Elec2,0.8530385694913116,0.8259895444361464,0.0005102157592773438,165.606267 +32616,Binary classification,[baseline] Last Class,Elec2,0.8536869538555879,0.8269760696156635,0.0005102157592773438,174.782391 +33522,Binary classification,[baseline] Last Class,Elec2,0.8541511291429253,0.8276032300151628,0.0005102157592773438,184.217189 +34428,Binary classification,[baseline] Last Class,Elec2,0.8549684840386905,0.8286724084685859,0.0005102157592773438,193.875362 +35334,Binary classification,[baseline] Last Class,Elec2,0.8555175048821215,0.8284321962695346,0.0005102157592773438,203.79136499999998 +36240,Binary classification,[baseline] Last Class,Elec2,0.8545213720025387,0.8259146744155329,0.0005102157592773438,213.957306 +37146,Binary classification,[baseline] Last Class,Elec2,0.854354556467896,0.8252696854208386,0.0005102157592773438,224.37720199999998 +38052,Binary classification,[baseline] Last Class,Elec2,0.8545636119944285,0.8247736052181622,0.0005102157592773438,234.998191 +38958,Binary classification,[baseline] Last Class,Elec2,0.8548142824139435,0.8254213223038459,0.0005102157592773438,245.89740899999998 +39864,Binary classification,[baseline] Last Class,Elec2,0.8546521837292728,0.8262981172802495,0.0005102157592773438,257.034489 +40770,Binary classification,[baseline] Last Class,Elec2,0.8540067207927592,0.8267652366261132,0.0005102157592773438,268.379106 +41676,Binary classification,[baseline] Last Class,Elec2,0.8537012597480504,0.8274320002264302,0.0005102157592773438,279.987419 +42582,Binary classification,[baseline] Last Class,Elec2,0.8536201592259458,0.8277177368086459,0.0005102157592773438,291.808183 +43488,Binary classification,[baseline] Last Class,Elec2,0.853473451836181,0.8276626818845675,0.0005102157592773438,303.899029 +44394,Binary classification,[baseline] Last Class,Elec2,0.8533777847858897,0.8271686890948196,0.0005102157592773438,316.239245 +45300,Binary classification,[baseline] Last Class,Elec2,0.8533521711296055,0.8273155007928462,0.0005102157592773438,328.81397599999997 +45312,Binary classification,[baseline] Last Class,Elec2,0.8533027300214076,0.8272294856132872,0.0005102157592773438,341.389555 +25,Binary classification,[baseline] Last Class,Phishing,0.625,0.64,0.0005102157592773438,0.001863 +50,Binary classification,[baseline] Last Class,Phishing,0.6530612244897959,0.6222222222222223,0.0005102157592773438,0.005016 +75,Binary classification,[baseline] Last Class,Phishing,0.5675675675675675,0.5555555555555556,0.0005102157592773438,0.009415 +100,Binary classification,[baseline] Last Class,Phishing,0.5555555555555556,0.5416666666666666,0.0005102157592773438,0.115037 +125,Binary classification,[baseline] Last Class,Phishing,0.5241935483870968,0.5123966942148761,0.0005102157592773438,0.22212700000000002 +150,Binary classification,[baseline] Last Class,Phishing,0.5234899328859061,0.5298013245033113,0.0005102157592773438,0.330326 +175,Binary classification,[baseline] Last Class,Phishing,0.5229885057471264,0.496969696969697,0.0005102157592773438,0.439628 +200,Binary classification,[baseline] Last Class,Phishing,0.507537688442211,0.47872340425531923,0.0005102157592773438,0.550035 +225,Binary classification,[baseline] Last Class,Phishing,0.5,0.45098039215686275,0.0005102157592773438,0.6616070000000001 +250,Binary classification,[baseline] Last Class,Phishing,0.5180722891566265,0.4782608695652174,0.0005102157592773438,0.774476 +275,Binary classification,[baseline] Last Class,Phishing,0.5218978102189781,0.4738955823293172,0.0005102157592773438,0.8884620000000001 +300,Binary classification,[baseline] Last Class,Phishing,0.5217391304347826,0.460377358490566,0.0005102157592773438,1.0035580000000002 +325,Binary classification,[baseline] Last Class,Phishing,0.5216049382716049,0.44839857651245546,0.0005102157592773438,1.151113 +350,Binary classification,[baseline] Last Class,Phishing,0.5329512893982808,0.4511784511784511,0.0005102157592773438,1.299965 +375,Binary classification,[baseline] Last Class,Phishing,0.5267379679144385,0.4380952380952381,0.0005102157592773438,1.4500600000000001 +400,Binary classification,[baseline] Last Class,Phishing,0.5263157894736842,0.43243243243243246,0.0005102157592773438,1.6013830000000002 +425,Binary classification,[baseline] Last Class,Phishing,0.5424528301886793,0.436046511627907,0.0005102157592773438,1.7539290000000003 +450,Binary classification,[baseline] Last Class,Phishing,0.5367483296213809,0.4222222222222222,0.0005102157592773438,1.9077010000000003 +475,Binary classification,[baseline] Last Class,Phishing,0.5358649789029536,0.43298969072164945,0.0005102157592773438,2.0627030000000004 +500,Binary classification,[baseline] Last Class,Phishing,0.5370741482965932,0.44604316546762596,0.0005102157592773438,2.2669650000000003 +525,Binary classification,[baseline] Last Class,Phishing,0.5400763358778626,0.43822843822843827,0.0005102157592773438,2.491531 +550,Binary classification,[baseline] Last Class,Phishing,0.5391621129326047,0.44150110375275936,0.0005102157592773438,2.717762 +575,Binary classification,[baseline] Last Class,Phishing,0.5418118466898955,0.4416135881104034,0.0005102157592773438,2.945135 +600,Binary classification,[baseline] Last Class,Phishing,0.5509181969949917,0.443064182194617,0.0005102157592773438,3.175983 +625,Binary classification,[baseline] Last Class,Phishing,0.5560897435897436,0.43584521384928715,0.0005102157592773438,3.407996 +650,Binary classification,[baseline] Last Class,Phishing,0.551617873651772,0.4393063583815029,0.0005102157592773438,3.641123 +675,Binary classification,[baseline] Last Class,Phishing,0.5459940652818991,0.44363636363636366,0.0005102157592773438,3.8753569999999997 +700,Binary classification,[baseline] Last Class,Phishing,0.5464949928469242,0.4389380530973452,0.0005102157592773438,4.110698999999999 +725,Binary classification,[baseline] Last Class,Phishing,0.5441988950276243,0.44630872483221484,0.0005102157592773438,4.380075 +750,Binary classification,[baseline] Last Class,Phishing,0.5367156208277704,0.44122383252818037,0.0005102157592773438,4.6504829999999995 +775,Binary classification,[baseline] Last Class,Phishing,0.5310077519379846,0.43369734789391573,0.0005102157592773438,4.940408 +800,Binary classification,[baseline] Last Class,Phishing,0.5294117647058824,0.4388059701492537,0.0005102157592773438,5.231503 +825,Binary classification,[baseline] Last Class,Phishing,0.5266990291262136,0.43965517241379315,0.0005102157592773438,5.523716 +850,Binary classification,[baseline] Last Class,Phishing,0.5241460541813898,0.4341736694677871,0.0005102157592773438,5.817038 +875,Binary classification,[baseline] Last Class,Phishing,0.522883295194508,0.4311050477489768,0.0005102157592773438,6.111637 +900,Binary classification,[baseline] Last Class,Phishing,0.5272525027808677,0.4340878828229028,0.0005102157592773438,6.407366 +925,Binary classification,[baseline] Last Class,Phishing,0.5227272727272727,0.43388960205391536,0.0005102157592773438,6.766113 +950,Binary classification,[baseline] Last Class,Phishing,0.5205479452054794,0.43896424167694204,0.0005102157592773438,7.1265789999999996 +975,Binary classification,[baseline] Last Class,Phishing,0.5174537987679672,0.43373493975903615,0.0005102157592773438,7.4884189999999995 +1000,Binary classification,[baseline] Last Class,Phishing,0.5185185185185185,0.4361078546307151,0.0005102157592773438,7.851253 +1025,Binary classification,[baseline] Last Class,Phishing,0.517578125,0.43863636363636366,0.0005102157592773438,8.215067 +1050,Binary classification,[baseline] Last Class,Phishing,0.5138226882745471,0.4370860927152318,0.0005102157592773438,8.579858 +1075,Binary classification,[baseline] Last Class,Phishing,0.5111731843575419,0.43729903536977494,0.0005102157592773438,8.945611 +1100,Binary classification,[baseline] Last Class,Phishing,0.5122838944494995,0.4393305439330544,0.0005102157592773438,9.312327999999999 +1125,Binary classification,[baseline] Last Class,Phishing,0.5124555160142349,0.44534412955465585,0.0005102157592773438,9.680001999999998 +1150,Binary classification,[baseline] Last Class,Phishing,0.5143603133159269,0.44642857142857145,0.0005102157592773438,10.125450999999998 +1175,Binary classification,[baseline] Last Class,Phishing,0.5187393526405452,0.4509232264334305,0.0005102157592773438,10.572147999999999 +1200,Binary classification,[baseline] Last Class,Phishing,0.5187656380316931,0.448901623686724,0.0005102157592773438,11.020091999999998 +1225,Binary classification,[baseline] Last Class,Phishing,0.5171568627450981,0.4471468662301216,0.0005102157592773438,11.469230999999999 +1250,Binary classification,[baseline] Last Class,Phishing,0.5156124899919936,0.4474885844748858,0.0005102157592773438,11.919638999999998 +1903,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,0.335236 +3806,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,1.143886 +5709,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,2.36402 +7612,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,4.028138 +9515,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,6.117771 +11418,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,8.657701 +13321,Binary classification,[baseline] Last Class,SMTP,1.0,0.0,0.00048351287841796875,11.631 +15224,Binary classification,[baseline] Last Class,SMTP,0.9985548183669447,0.0,0.0005102157592773438,15.050370000000001 +17127,Binary classification,[baseline] Last Class,SMTP,0.9984818404764685,0.0,0.0005102157592773438,18.876176 +19030,Binary classification,[baseline] Last Class,SMTP,0.9986336644069578,0.0,0.0005102157592773438,23.061029 +20933,Binary classification,[baseline] Last Class,SMTP,0.9987578826676858,0.0,0.0005102157592773438,27.619004 +22836,Binary classification,[baseline] Last Class,SMTP,0.9988613969783228,0.0,0.0005102157592773438,32.587888 +24739,Binary classification,[baseline] Last Class,SMTP,0.9989489853666425,0.0,0.0005102157592773438,37.966612 +26642,Binary classification,[baseline] Last Class,SMTP,0.9989489884013363,0.0,0.0005102157592773438,43.747015999999995 +28545,Binary classification,[baseline] Last Class,SMTP,0.9990190582959642,0.0,0.0005102157592773438,49.923314999999995 +30448,Binary classification,[baseline] Last Class,SMTP,0.9990803691660919,0.0,0.0005102157592773438,56.476617 +32351,Binary classification,[baseline] Last Class,SMTP,0.9991344667697063,0.0,0.0005102157592773438,63.442318 +34254,Binary classification,[baseline] Last Class,SMTP,0.999182553352991,0.0,0.0005102157592773438,70.796392 +36157,Binary classification,[baseline] Last Class,SMTP,0.9992255780506694,0.0,0.0005102157592773438,78.554987 +38060,Binary classification,[baseline] Last Class,SMTP,0.9992643001655325,0.0,0.0005102157592773438,86.688101 +39963,Binary classification,[baseline] Last Class,SMTP,0.9992993343676493,0.0,0.0005102157592773438,95.30254000000001 +41866,Binary classification,[baseline] Last Class,SMTP,0.9993311835662247,0.0,0.0005102157592773438,104.31052400000002 +43769,Binary classification,[baseline] Last Class,SMTP,0.9993602632059952,0.0,0.0005102157592773438,113.72316700000002 +45672,Binary classification,[baseline] Last Class,SMTP,0.9993869194893915,0.0,0.0005102157592773438,123.54962900000001 +47575,Binary classification,[baseline] Last Class,SMTP,0.9994114432252911,0.0,0.0005102157592773438,133.72455100000002 +49478,Binary classification,[baseline] Last Class,SMTP,0.99943408048184,0.0,0.0005102157592773438,144.361296 +51381,Binary classification,[baseline] Last Class,SMTP,0.9994161152199299,0.0625,0.0005102157592773438,155.342577 +53284,Binary classification,[baseline] Last Class,SMTP,0.9994369686391532,0.0625,0.0005102157592773438,166.71976 +55187,Binary classification,[baseline] Last Class,SMTP,0.9994563838654731,0.0625,0.0005102157592773438,178.568204 +57090,Binary classification,[baseline] Last Class,SMTP,0.9994394717020793,0.36,0.0005102157592773438,190.760423 +58993,Binary classification,[baseline] Last Class,SMTP,0.9994575535665853,0.36,0.0005102157592773438,203.318295 +60896,Binary classification,[baseline] Last Class,SMTP,0.9994745052960013,0.36,0.0005102157592773438,216.324675 +62799,Binary classification,[baseline] Last Class,SMTP,0.9994585814834868,0.37037037037037035,0.0005102157592773438,229.75194900000002 +64702,Binary classification,[baseline] Last Class,SMTP,0.9994745058036197,0.37037037037037035,0.0005102157592773438,243.519605 +66605,Binary classification,[baseline] Last Class,SMTP,0.99948952014894,0.37037037037037035,0.0005102157592773438,257.632512 +68508,Binary classification,[baseline] Last Class,SMTP,0.9994745062548352,0.3793103448275862,0.0005102157592773438,272.164854 +70411,Binary classification,[baseline] Last Class,SMTP,0.9994887089902003,0.3793103448275862,0.0005102157592773438,287.040678 +72314,Binary classification,[baseline] Last Class,SMTP,0.9995021642028404,0.3793103448275862,0.0005102157592773438,302.255295 +74217,Binary classification,[baseline] Last Class,SMTP,0.9995149293952786,0.3793103448275862,0.0005102157592773438,317.85437 +76120,Binary classification,[baseline] Last Class,SMTP,0.99952705631971,0.3793103448275862,0.0005102157592773438,333.81575100000003 +78023,Binary classification,[baseline] Last Class,SMTP,0.99953859167927,0.3793103448275862,0.0005102157592773438,350.111597 +79926,Binary classification,[baseline] Last Class,SMTP,0.999549577729121,0.3793103448275862,0.0005102157592773438,366.757049 +81829,Binary classification,[baseline] Last Class,SMTP,0.9995600527936648,0.3793103448275862,0.0005102157592773438,383.769929 +83732,Binary classification,[baseline] Last Class,SMTP,0.9995700517132244,0.3793103448275862,0.0005102157592773438,401.12604899999997 +85635,Binary classification,[baseline] Last Class,SMTP,0.9995796062311698,0.3793103448275862,0.0005102157592773438,418.85540799999995 +87538,Binary classification,[baseline] Last Class,SMTP,0.999588745330546,0.3793103448275862,0.0005102157592773438,436.927356 +89441,Binary classification,[baseline] Last Class,SMTP,0.9995751341681575,0.36666666666666664,0.0005102157592773438,455.364423 +91344,Binary classification,[baseline] Last Class,SMTP,0.9995839856365567,0.36666666666666664,0.0005102157592773438,474.187698 +93247,Binary classification,[baseline] Last Class,SMTP,0.999592475816657,0.36666666666666664,0.0005102157592773438,493.377496 +95150,Binary classification,[baseline] Last Class,SMTP,0.9996006263859841,0.36666666666666664,0.0005102157592773438,512.867881 +95156,Binary classification,[baseline] Last Class,SMTP,0.9996006515684935,0.36666666666666664,0.0005102157592773438,532.358985 diff --git a/docs/benchmarks/Binary classification/index.md b/docs/benchmarks/Binary classification/index.md new file mode 100644 index 0000000000..3d8c8e14c0 --- /dev/null +++ b/docs/benchmarks/Binary classification/index.md @@ -0,0 +1,37950 @@ +# Binary classification + + + +=== "Table" + + | Model | Dataset | Accuracy | F1 | Memory in Mb | Time in s | + |:----------------------------------|:----------|-----------:|-----------:|---------------:|------------:| + | ADWIN Bagging | Bananas | 0.625967 | 0.448218 | 0.400658 | 942.73 | + | ADWIN Bagging | Elec2 | 0.823773 | 0.776587 | 0.598438 | 8970.15 | + | ADWIN Bagging | Phishing | 0.893515 | 0.879201 | 1.31008 | 568.218 | + | ADWIN Bagging | SMTP | 0.999685 | 0 | 0.164217 | 8006.78 | + | ALMA | Bananas | 0.506415 | 0.482595 | 0.0029211 | 68.9731 | + | ALMA | Elec2 | 0.906427 | 0.889767 | 0.00435829 | 836.498 | + | ALMA | Phishing | 0.8256 | 0.810764 | 0.0045805 | 29.7613 | + | ALMA | SMTP | 0.764986 | 0.00178548 | 0.00309372 | 1361.61 | + | AdaBoost | Bananas | 0.677864 | 0.645041 | 0.453154 | 876.714 | + | AdaBoost | Elec2 | 0.880581 | 0.858687 | 13.5424 | 10153.7 | + | AdaBoost | Phishing | 0.878303 | 0.863555 | 0.873312 | 552.609 | + | AdaBoost | SMTP | 0.999443 | 0.404494 | 1.33633 | 6617.5 | + | Adaptive Random Forest | Bananas | 0.88696 | 0.871707 | 15.3551 | 2603.02 | + | Adaptive Random Forest | Elec2 | 0.876608 | 0.852391 | 22.3949 | 12397.6 | + | Adaptive Random Forest | Phishing | 0.907926 | 0.896116 | 4.10291 | 743.377 | + | Adaptive Random Forest | SMTP | 0.999685 | 0 | 0.327095 | 11543.4 | + | Aggregated Mondrian Forest | Bananas | 0.889413 | 0.874249 | 17.2377 | 2954.75 | + | Aggregated Mondrian Forest | Elec2 | 0.849904 | 0.819731 | 287.315 | 18206.6 | + | Aggregated Mondrian Forest | Phishing | 0.904724 | 0.892112 | 3.39106 | 807.573 | + | Aggregated Mondrian Forest | SMTP | 0.999863 | 0.734694 | 0.211749 | 5848.87 | + | Bagging | Bananas | 0.634082 | 0.459437 | 0.703124 | 1170.85 | + | Bagging | Elec2 | 0.840436 | 0.80208 | 2.28896 | 13164.5 | + | Bagging | Phishing | 0.893515 | 0.879201 | 1.38826 | 633.136 | + | Bagging | SMTP | 0.999685 | 0 | 0.207971 | 8814.84 | + | Hoeffding Adaptive Tree | Bananas | 0.616531 | 0.42825 | 0.0618467 | 163.516 | + | Hoeffding Adaptive Tree | Elec2 | 0.821258 | 0.787344 | 0.435328 | 2980.69 | + | Hoeffding Adaptive Tree | Phishing | 0.874299 | 0.856095 | 0.142962 | 77.865 | + | Hoeffding Adaptive Tree | SMTP | 0.999685 | 0 | 0.0241137 | 2094.95 | + | Hoeffding Tree | Bananas | 0.642197 | 0.503405 | 0.0594654 | 93.5302 | + | Hoeffding Tree | Elec2 | 0.795635 | 0.750834 | 0.938466 | 1485.98 | + | Hoeffding Tree | Phishing | 0.879904 | 0.860595 | 0.132719 | 54.2758 | + | Hoeffding Tree | SMTP | 0.999685 | 0 | 0.0170441 | 1543.56 | + | Leveraging Bagging | Bananas | 0.828269 | 0.802689 | 3.23571 | 2747.95 | + | Leveraging Bagging | Elec2 | 0.892653 | 0.871966 | 7.56535 | 18763.3 | + | Leveraging Bagging | Phishing | 0.894315 | 0.877323 | 4.0114 | 1619.65 | + | Leveraging Bagging | SMTP | 0.999674 | 0 | 0.164603 | 17549.6 | + | Logistic regression | Bananas | 0.543208 | 0.197015 | 0.00424099 | 82.0689 | + | Logistic regression | Elec2 | 0.822144 | 0.777086 | 0.005373 | 953.54 | + | Logistic regression | Phishing | 0.8872 | 0.871233 | 0.00556469 | 29.2066 | + | Logistic regression | SMTP | 0.999769 | 0.421053 | 0.00438309 | 1531.37 | + | Naive Bayes | Bananas | 0.61521 | 0.413912 | 0.0140247 | 97.154 | + | Naive Bayes | Elec2 | 0.728741 | 0.603785 | 0.0510378 | 1230.66 | + | Naive Bayes | Phishing | 0.884708 | 0.871429 | 0.05723 | 38.528 | + | Naive Bayes | SMTP | 0.993484 | 0.0490798 | 0.0201406 | 1826.47 | + | Stacking | Bananas | 0.876203 | 0.859649 | 19.1946 | 5236.84 | + | Stacking | Elec2 | 0.885458 | 0.864157 | 40.7547 | 22944.4 | + | Stacking | Phishing | 0.895116 | 0.882722 | 8.72124 | 2411.41 | + | Stacking | SMTP | 0.999685 | 0 | 4.88868 | 24733.2 | + | Streaming Random Patches | Bananas | 0.871674 | 0.854265 | 10.5381 | 3551.41 | + | Streaming Random Patches | Elec2 | 0.868884 | 0.843009 | 107.322 | 22969 | + | Streaming Random Patches | Phishing | 0.913531 | 0.901996 | 6.59559 | 1436.69 | + | Streaming Random Patches | SMTP | 0.999685 | 0 | 0.17817 | 18142.3 | + | Voting | Bananas | 0.872617 | 0.849162 | 4.58403 | 2790.97 | + | Voting | Elec2 | 0.84368 | 0.797958 | 5.7575 | 13925.5 | + | Voting | Phishing | 0.896717 | 0.884512 | 4.8203 | 1436.72 | + | Voting | SMTP | 0.999779 | 0.487805 | 4.60205 | 18069.8 | + | Vowpal Wabbit logistic regression | Bananas | 0.551321 | 0 | 0.000646591 | 88.7248 | + | Vowpal Wabbit logistic regression | Elec2 | 0.697475 | 0.459592 | 0.000646591 | 937.011 | + | Vowpal Wabbit logistic regression | Phishing | 0.7736 | 0.669778 | 0.000646591 | 27.8334 | + | Vowpal Wabbit logistic regression | SMTP | 0.999695 | 0.121212 | 0.000646591 | 1631.37 | + | [baseline] Last Class | Bananas | 0.50953 | 0.452957 | 0.000510216 | 30.809 | + | [baseline] Last Class | Elec2 | 0.853303 | 0.827229 | 0.000510216 | 341.39 | + | [baseline] Last Class | Phishing | 0.515612 | 0.447489 | 0.000510216 | 11.9196 | + | [baseline] Last Class | SMTP | 0.999601 | 0.366667 | 0.000510216 | 532.359 | + | k-Nearest Neighbors | Bananas | 0.885073 | 0.870838 | 4.50996 | 2974.33 | + | k-Nearest Neighbors | Elec2 | 0.853148 | 0.823642 | 4.76604 | 13503.4 | + | k-Nearest Neighbors | Phishing | 0.881505 | 0.867145 | 4.59643 | 1552.65 | + | k-Nearest Neighbors | SMTP | 0.999821 | 0.666667 | 4.51822 | 17961.1 | + | sklearn SGDClassifier | Bananas | 0.546415 | 0.205026 | 0.00557804 | 621.426 | + | sklearn SGDClassifier | Elec2 | 0.819099 | 0.772892 | 0.00680161 | 4291.77 | + | sklearn SGDClassifier | Phishing | 0.8896 | 0.876122 | 0.00701618 | 167.984 | + | sklearn SGDClassifier | SMTP | 0.999706 | 0.363636 | 0.00574303 | 7118.18 | + +=== "Chart" + + *Try reloading the page if something is buggy* + + 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"Time in s": 272.164854 + }, + { + "step": 70411, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9994887089902004, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 287.040678 + }, + { + "step": 72314, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995021642028404, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 302.255295 + }, + { + "step": 74217, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995149293952786, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 317.85437 + }, + { + "step": 76120, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.99952705631971, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 333.81575100000003 + }, + { + "step": 78023, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.99953859167927, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 350.111597 + }, + { + "step": 79926, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999549577729121, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 366.757049 + }, + { + "step": 81829, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995600527936648, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 383.769929 + }, + { + "step": 83732, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995700517132244, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 401.126049 + }, + { + "step": 85635, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995796062311698, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 418.855408 + }, + { + "step": 87538, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999588745330546, + "F1": 0.3793103448275862, + "Memory in Mb": 0.0005102157592773, + "Time in s": 436.927356 + }, + { + "step": 89441, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995751341681576, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 455.364423 + }, + { + "step": 91344, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9995839856365568, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 474.187698 + }, + { + "step": 93247, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999592475816657, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 493.377496 + }, + { + "step": 95150, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.999600626385984, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 512.867881 + }, + { + "step": 95156, + "track": "Binary classification", + "model": "[baseline] Last Class", + "dataset": "SMTP", + "Accuracy": 0.9996006515684936, + "F1": 0.3666666666666666, + "Memory in Mb": 0.0005102157592773, + "Time in s": 532.358985 + } + ] + }, + "params": [ + { + "name": "models", + "select": { + "type": "point", + "fields": [ + "model" + ] + }, + "bind": "legend" + }, + { + "name": "Dataset", + "value": "Bananas", + "bind": { + "input": "select", + "options": [ + "Bananas", + "Elec2", + "Phishing", + "SMTP" + ] + } + }, + { + "name": "grid", + "select": "interval", + "bind": "scales" + } + ], + "transform": [ + { + "filter": { + "field": "dataset", + "equal": { + "expr": "Dataset" + } + } + } + ], + "repeat": { + "row": [ + "Accuracy", + "F1", + "Memory in Mb", + "Time in s" + ] + }, + "spec": { + "width": "container", + "mark": "line", + "encoding": { + "x": { + "field": "step", + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18, + "title": "Instance" + } + }, + "y": { + "field": { + "repeat": "row" + }, + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18 + } + }, + "color": { + "field": "model", + "type": "ordinal", + "scale": { + "scheme": "category20b" + }, + "title": "Models", + "legend": { + "titleFontSize": 18, + "labelFontSize": 18, + "labelLimit": 500 + } + }, + "opacity": { + "condition": { + "param": "models", + "value": 1 + }, + "value": 0.2 + } + } + } + } + ``` + + + +## Datasets + +???- abstract "Bananas" + + Bananas dataset. + + An artificial dataset where instances belongs to several clusters with a banana shape. + There are two attributes that correspond to the x and y axis, respectively. + + Name Bananas + Task Binary classification + Samples 5,300 + Features 2 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/banana.zip + + + +???- abstract "Elec2" + + Electricity prices in New South Wales. + + This is a binary classification task, where the goal is to predict if the price of electricity + will go up or down. + + This data was collected from the Australian New South Wales Electricity Market. In this market, + prices are not fixed and are affected by demand and supply of the market. They are set every + five minutes. Electricity transfers to/from the neighboring state of Victoria were done to + alleviate fluctuations. + + Name Elec2 + Task Binary classification + Samples 45,312 + Features 8 + Sparse False + Path /Users/mastelini/river_data/Elec2/electricity.csv + URL https://maxhalford.github.io/files/datasets/electricity.zip + Size 2.95 MB + Downloaded True + + + +???- abstract "Phishing" + + Phishing websites. + + This dataset contains features from web pages that are classified as phishing or not. + + Name Phishing + Task Binary classification + Samples 1,250 + Features 9 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/phishing.csv.gz + + + +???- abstract "SMTP" + + SMTP dataset from the KDD 1999 cup. + + The goal is to predict whether or not an SMTP connection is anomalous or not. The dataset only + contains 2,211 (0.4%) positive labels. + + Name SMTP + Task Binary classification + Samples 95,156 + Features 3 + Sparse False + Path /Users/mastelini/river_data/SMTP/smtp.csv + URL https://maxhalford.github.io/files/datasets/smtp.zip + Size 5.23 MB + Downloaded True + + + +## Models + +???- example "Logistic regression" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      LogisticRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.005
+          )
+        )
+        loss=Log (
+          weight_pos=1.
+          weight_neg=1.
+        )
+        l2=0.
+        l1=0.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      )
+    )
+ + + +???- example "Aggregated Mondrian Forest" + + 
[]
+ + + +???- example "ALMA" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      ALMAClassifier (
+        p=2
+        alpha=0.9
+        B=1.111111
+        C=1.414214
+      )
+    )
+ + + +???- example "sklearn SGDClassifier" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SKL2RiverClassifier (
+        estimator=SGDClassifier(eta0=0.005, learning_rate='constant', loss='log_loss',
+                    penalty=None)
+        classes=[False, True]
+      )
+    )
+ + + +???- example "Vowpal Wabbit logistic regression" + + 
VW2RiverClassifier ()
+ + + +???- example "Naive Bayes" + + 
GaussianNB ()
+ + + +???- example "Hoeffding Tree" + + 
HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )
+ + + +???- example "Hoeffding Adaptive Tree" + + 
HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=True
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=42
+    )
+ + + +???- example "Adaptive Random Forest" + + 
[]
+ + + +???- example "Streaming Random Patches" + + 
SRPClassifier (
+      model=HoeffdingTreeClassifier (
+        grace_period=50
+        max_depth=inf
+        split_criterion="info_gain"
+        delta=0.01
+        tau=0.05
+        leaf_prediction="nba"
+        nb_threshold=0
+        nominal_attributes=None
+        splitter=GaussianSplitter (
+          n_splits=10
+        )
+        binary_split=False
+        min_branch_fraction=0.01
+        max_share_to_split=0.99
+        max_size=100.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+      )
+      n_models=10
+      subspace_size=0.6
+      training_method="patches"
+      lam=6
+      drift_detector=ADWIN (
+        delta=1e-05
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      warning_detector=ADWIN (
+        delta=0.0001
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      disable_detector="off"
+      disable_weighted_vote=False
+      seed=None
+      metric=Accuracy (
+        cm=ConfusionMatrix (
+          classes=[]
+        )
+      )
+    )
+ + + +???- example "k-Nearest Neighbors" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )
+ + + +???- example "ADWIN Bagging" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "AdaBoost" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "Bagging" + + 
[HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    )]
+ + + +???- example "Leveraging Bagging" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "Stacking" + + 
[Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SoftmaxRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=CrossEntropy (
+          class_weight={}
+        )
+        l2=0
+      )
+    ), GaussianNB (), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )]
+ + + +???- example "Voting" + + 
VotingClassifier (
+      models=[Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SoftmaxRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=CrossEntropy (
+          class_weight={}
+        )
+        l2=0
+      )
+    ), GaussianNB (), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )]
+      use_probabilities=True
+    )
+ + + +???- example "[baseline] Last Class" + + 
NoChangeClassifier ()
+ + + +## Environment + +
Python implementation: CPython
+Python version       : 3.10.13
+IPython version      : 8.16.1
+
+river       : 0.19.0
+numpy       : 1.25.2
+scikit-learn: 1.3.1
+pandas      : 2.1.1
+scipy       : 1.11.3
+
+Compiler    : Clang 14.0.6 
+OS          : Darwin
+Release     : 22.6.0
+Machine     : arm64
+Processor   : arm
+CPU cores   : 8
+Architecture: 64bit
+
+ diff --git a/docs/benchmarks/Multiclass classification/index.md b/docs/benchmarks/Multiclass classification/index.md new file mode 100644 index 0000000000..de3b47ecae --- /dev/null +++ b/docs/benchmarks/Multiclass classification/index.md @@ -0,0 +1,24908 @@ +# Multiclass classification + + + +=== "Table" + + | Model | Dataset | Accuracy | MicroF1 | MacroF1 | Memory in Mb | Time in s | + |:---------------------------|:--------------|-----------:|----------:|----------:|---------------:|------------:| + | ADWIN Bagging | ImageSegments | 0.777826 | 0.777826 | 0.765011 | 4.11628 | 3543.55 | + | ADWIN Bagging | Insects | 0.579465 | 0.579465 | 0.570198 | 15.3074 | 60279.4 | + | ADWIN Bagging | Keystroke | 0.81656 | 0.81656 | 0.815908 | 37.8558 | 41308 | + | AdaBoost | ImageSegments | 0.804677 | 0.804677 | 0.79777 | 4.09839 | 3350.88 | + | AdaBoost | Insects | 0.563532 | 0.563532 | 0.554622 | 27.943 | 60335.7 | + | AdaBoost | Keystroke | 0.834796 | 0.834796 | 0.836062 | 194.794 | 51861.3 | + | Adaptive Random Forest | ImageSegments | 0.818536 | 0.818536 | 0.814535 | 3.06348 | 1574.18 | + | Adaptive Random Forest | Insects | 0.745378 | 0.745378 | 0.743302 | 0.361794 | 25383.5 | + | Adaptive Random Forest | Keystroke | 0.969116 | 0.969116 | 0.969111 | 1.63546 | 7363.05 | + | Aggregated Mondrian Forest | ImageSegments | 0.901689 | 0.901689 | 0.900381 | 17.0502 | 2997.7 | + | Aggregated Mondrian Forest | Insects | 0.646981 | 0.646981 | 0.644352 | 1365.41 | 76295.7 | + | Aggregated Mondrian Forest | Keystroke | 0.881073 | 0.881073 | 0.879928 | 338.139 | 35528.4 | + | Bagging | ImageSegments | 0.77696 | 0.77696 | 0.764564 | 4.15507 | 3634.88 | + | Bagging | Insects | 0.606392 | 0.606392 | 0.598542 | 3.69162 | 65237 | + | Bagging | Keystroke | 0.669739 | 0.669739 | 0.669981 | 50.3449 | 55411.4 | + | Hoeffding Adaptive Tree | ImageSegments | 0.774361 | 0.774361 | 0.763362 | 0.423797 | 457.311 | + | Hoeffding Adaptive Tree | Insects | 0.613337 | 0.613337 | 0.604219 | 0.143826 | 11292.9 | + | Hoeffding Adaptive Tree | Keystroke | 0.723124 | 0.723124 | 0.721825 | 0.724475 | 8998.46 | + | Hoeffding Tree | ImageSegments | 0.776094 | 0.776094 | 0.763137 | 0.417154 | 328.067 | + | Hoeffding Tree | Insects | 0.537306 | 0.537306 | 0.527364 | 2.51923 | 7445.36 | + | Hoeffding Tree | Keystroke | 0.648218 | 0.648218 | 0.647249 | 5.09445 | 7138.73 | + | Leveraging Bagging | ImageSegments | 0.778259 | 0.778259 | 0.766016 | 4.1005 | 8561.3 | + | Leveraging Bagging | Insects | 0.695858 | 0.695858 | 0.690508 | 13.831 | 99120.2 | + | Leveraging Bagging | Keystroke | 0.956616 | 0.956616 | 0.95665 | 7.40999 | 37049.1 | + | Naive Bayes | ImageSegments | 0.731919 | 0.731919 | 0.730419 | 0.390004 | 248.959 | + | Naive Bayes | Insects | 0.506897 | 0.506897 | 0.493019 | 0.611693 | 4263.77 | + | Naive Bayes | Keystroke | 0.652532 | 0.652532 | 0.651577 | 4.86901 | 3544.69 | + | Stacking | ImageSegments | 0.867908 | 0.867908 | 0.865603 | 9.18162 | 5416.88 | + | Stacking | Insects | 0.754745 | 0.754745 | 0.752818 | 10.5864 | 72115 | + | Stacking | Keystroke | 0.975489 | 0.975489 | 0.975486 | 18.7111 | 42471.8 | + | Streaming Random Patches | ImageSegments | 0.766999 | 0.766999 | 0.764707 | 8.92653 | 6441.81 | + | Streaming Random Patches | Insects | 0.736163 | 0.736163 | 0.734622 | 9.632 | 90031.6 | + | Streaming Random Patches | Keystroke | 0.955929 | 0.955929 | 0.95592 | 39.636 | 31009.8 | + | Voting | ImageSegments | 0.80641 | 0.80641 | 0.798999 | 6.07392 | 3157.94 | + | Voting | Insects | 0.648533 | 0.648533 | 0.638 | 9.40652 | 48163.7 | + | Voting | Keystroke | 0.779107 | 0.779107 | 0.784136 | 16.3925 | 29779.2 | + | [baseline] Last Class | ImageSegments | 0.148116 | 0.148116 | 0.148116 | 0.00136948 | 31.4159 | + | [baseline] Last Class | Insects | 0.289761 | 0.289761 | 0.289763 | 0.00138664 | 679.004 | + | [baseline] Last Class | Keystroke | 0.997549 | 0.997549 | 0.997549 | 0.00504208 | 274.675 | + | k-Nearest Neighbors | ImageSegments | 0.873538 | 0.873538 | 0.872136 | 5.26871 | 2666.29 | + | k-Nearest Neighbors | Insects | 0.713115 | 0.713115 | 0.711381 | 6.27269 | 40639.9 | + | k-Nearest Neighbors | Keystroke | 0.910486 | 0.910486 | 0.910328 | 6.32511 | 21326.5 | + +=== "Chart" + + *Try reloading the page if something is buggy* + + ```vegalite + { + "$schema": "https://vega.github.io/schema/vega-lite/v5.json", + "data": { + "values": [ + { + "step": 46, + "track": "Multiclass classification", + "model": "Naive Bayes", + "dataset": "ImageSegments", + "Accuracy": 0.4666666666666667, + "MicroF1": 0.4666666666666667, + "MacroF1": 0.4009102009102009, + "Memory in Mb": 0.3899507522583008, + "Time in s": 0.450679 + }, + { + "step": 92, + "track": "Multiclass classification", + "model": "Naive Bayes", + "dataset": "ImageSegments", + "Accuracy": 0.5604395604395604, + "MicroF1": 0.5604395604395604, + "MacroF1": 0.5279334700387331, + "Memory in Mb": 0.3899507522583008, + "Time in s": 1.152847 + }, + { + "step": 138, + "track": "Multiclass 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"type": "ordinal", + "scale": { + "scheme": "category20b" + }, + "title": "Models", + "legend": { + "titleFontSize": 18, + "labelFontSize": 18, + "labelLimit": 500 + } + }, + "opacity": { + "condition": { + "param": "models", + "value": 1 + }, + "value": 0.2 + } + } + } + } + ``` + + + +## Datasets + +???- abstract "ImageSegments" + + Image segments classification. + + This dataset contains features that describe image segments into 7 classes: brickface, sky, + foliage, cement, window, path, and grass. + + Name ImageSegments + Task Multi-class classification + Samples 2,310 + Features 18 + Classes 7 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/segment.csv.zip + + + +???- abstract "Insects" + + Insects dataset. + + This dataset has different variants, which are: + + - abrupt_balanced + - abrupt_imbalanced + - gradual_balanced + - gradual_imbalanced + - incremental-abrupt_balanced + - incremental-abrupt_imbalanced + - incremental-reoccurring_balanced + - incremental-reoccurring_imbalanced + - incremental_balanced + - incremental_imbalanced + - out-of-control + + The number of samples and the difficulty change from one variant to another. The number of + classes is always the same (6), except for the last variant (24). + + Name Insects + Task Multi-class classification + Samples 52,848 + Features 33 + Classes 6 + Sparse False + Path /Users/mastelini/river_data/Insects/INSECTS-abrupt_balanced_norm.arff + URL http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff + Size 15.66 MB + Downloaded True + Variant abrupt_balanced + + Parameters + ---------- + variant + Indicates which variant of the dataset to load. + + + +???- abstract "Keystroke" + + CMU keystroke dataset. + + Users are tasked to type in a password. The task is to determine which user is typing in the + password. + + The only difference with the original dataset is that the "sessionIndex" and "rep" attributes + have been dropped. + + Name Keystroke + Task Multi-class classification + Samples 20,400 + Features 31 + Classes 51 + Sparse False + Path /Users/mastelini/river_data/Keystroke/DSL-StrongPasswordData.csv + URL http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv + Size 4.45 MB + Downloaded True + + + +## Models + +???- example "Naive Bayes" + + 
GaussianNB ()
+ + + +???- example "Hoeffding Tree" + + 
HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )
+ + + +???- example "Hoeffding Adaptive Tree" + + 
HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=True
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=42
+    )
+ + + +???- example "Adaptive Random Forest" + + 
[]
+ + + +???- example "Aggregated Mondrian Forest" + + 
[]
+ + + +???- example "Streaming Random Patches" + + 
SRPClassifier (
+      model=HoeffdingTreeClassifier (
+        grace_period=50
+        max_depth=inf
+        split_criterion="info_gain"
+        delta=0.01
+        tau=0.05
+        leaf_prediction="nba"
+        nb_threshold=0
+        nominal_attributes=None
+        splitter=GaussianSplitter (
+          n_splits=10
+        )
+        binary_split=False
+        min_branch_fraction=0.01
+        max_share_to_split=0.99
+        max_size=100.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+      )
+      n_models=10
+      subspace_size=0.6
+      training_method="patches"
+      lam=6
+      drift_detector=ADWIN (
+        delta=1e-05
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      warning_detector=ADWIN (
+        delta=0.0001
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      disable_detector="off"
+      disable_weighted_vote=False
+      seed=None
+      metric=Accuracy (
+        cm=ConfusionMatrix (
+          classes=[]
+        )
+      )
+    )
+ + + +???- example "k-Nearest Neighbors" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )
+ + + +???- example "ADWIN Bagging" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "AdaBoost" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "Bagging" + + 
[HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    ), HoeffdingAdaptiveTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      bootstrap_sampling=False
+      drift_window_threshold=300
+      drift_detector=ADWIN (
+        delta=0.002
+        clock=32
+        max_buckets=5
+        min_window_length=5
+        grace_period=10
+      )
+      switch_significance=0.05
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+      seed=None
+    )]
+ + + +???- example "Leveraging Bagging" + + 
[HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    )]
+ + + +???- example "Stacking" + + 
[Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SoftmaxRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=CrossEntropy (
+          class_weight={}
+        )
+        l2=0
+      )
+    ), GaussianNB (), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )]
+ + + +???- example "Voting" + + 
VotingClassifier (
+      models=[Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SoftmaxRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=CrossEntropy (
+          class_weight={}
+        )
+        l2=0
+      )
+    ), GaussianNB (), HoeffdingTreeClassifier (
+      grace_period=200
+      max_depth=inf
+      split_criterion="info_gain"
+      delta=1e-07
+      tau=0.05
+      leaf_prediction="nba"
+      nb_threshold=0
+      nominal_attributes=None
+      splitter=GaussianSplitter (
+        n_splits=10
+      )
+      binary_split=False
+      min_branch_fraction=0.01
+      max_share_to_split=0.99
+      max_size=100.
+      memory_estimate_period=1000000
+      stop_mem_management=False
+      remove_poor_attrs=False
+      merit_preprune=True
+    ), Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNClassifier (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        weighted=True
+        cleanup_every=0
+        softmax=False
+      )
+    )]
+      use_probabilities=True
+    )
+ + + +???- example "[baseline] Last Class" + + 
NoChangeClassifier ()
+ + + +## Environment + +
Python implementation: CPython
+Python version       : 3.10.13
+IPython version      : 8.16.1
+
+river       : 0.19.0
+numpy       : 1.25.2
+scikit-learn: 1.3.1
+pandas      : 2.1.1
+scipy       : 1.11.3
+
+Compiler    : Clang 14.0.6 
+OS          : Darwin
+Release     : 22.6.0
+Machine     : arm64
+Processor   : arm
+CPU cores   : 8
+Architecture: 64bit
+
+ diff --git a/docs/benchmarks/Multiclass classification/multiclass_classification.csv b/docs/benchmarks/Multiclass classification/multiclass_classification.csv new file mode 100644 index 0000000000..9394fc8dfd --- /dev/null +++ b/docs/benchmarks/Multiclass classification/multiclass_classification.csv @@ -0,0 +1,2129 @@ +step,track,model,dataset,Accuracy,MicroF1,MacroF1,Memory in Mb,Time in s +46,Multiclass classification,Naive Bayes,ImageSegments,0.4666666666666667,0.4666666666666667,0.4009102009102009,0.3899507522583008,0.450679 +92,Multiclass classification,Naive Bayes,ImageSegments,0.5604395604395604,0.5604395604395604,0.5279334700387331,0.3899507522583008,1.152847 +138,Multiclass classification,Naive Bayes,ImageSegments,0.5474452554744526,0.5474452554744526,0.5191892873237387,0.38997745513916016,2.278305 +184,Multiclass classification,Naive Bayes,ImageSegments,0.5573770491803278,0.5573770491803278,0.5225713529323662,0.3899507522583008,3.449742 +230,Multiclass classification,Naive Bayes,ImageSegments,0.5545851528384279,0.5545851528384279,0.5217226223148511,0.38997745513916016,4.939578 +276,Multiclass classification,Naive Bayes,ImageSegments,0.56,0.56,0.5450388711329709,0.38997745513916016,6.667081 +322,Multiclass classification,Naive Bayes,ImageSegments,0.5825545171339563,0.5825545171339563,0.5566705826058684,0.39000415802001953,8.548779999999999 +368,Multiclass classification,Naive Bayes,ImageSegments,0.5940054495912807,0.5940054495912807,0.5613773296963412,0.39000415802001953,10.607026 +414,Multiclass classification,Naive Bayes,ImageSegments,0.5980629539951574,0.5980629539951574,0.5624927052752284,0.39000415802001953,12.811145 +460,Multiclass classification,Naive Bayes,ImageSegments,0.599128540305011,0.599128540305011,0.5669821167583783,0.38997745513916016,15.144022 +506,Multiclass classification,Naive Bayes,ImageSegments,0.6099009900990099,0.6099009900990099,0.592228619098681,0.39000415802001953,17.683543 +552,Multiclass classification,Naive Bayes,ImageSegments,0.6116152450090744,0.6116152450090744,0.5983340184133136,0.3899507522583008,20.357047 +598,Multiclass classification,Naive Bayes,ImageSegments,0.6180904522613065,0.6180904522613065,0.611527101723203,0.38997745513916016,23.213992 +644,Multiclass classification,Naive Bayes,ImageSegments,0.6158631415241057,0.6158631415241057,0.6113311881078581,0.38997745513916016,26.205369 +690,Multiclass classification,Naive Bayes,ImageSegments,0.6182873730043541,0.6182873730043541,0.6150189987146761,0.38997745513916016,29.350024 +736,Multiclass classification,Naive Bayes,ImageSegments,0.617687074829932,0.617687074829932,0.6157912419016742,0.38997745513916016,32.567265 +782,Multiclass classification,Naive Bayes,ImageSegments,0.6274007682458387,0.6274007682458387,0.6216325704223051,0.38997745513916016,36.027093 +828,Multiclass classification,Naive Bayes,ImageSegments,0.6324062877871826,0.6324062877871826,0.6280704917469789,0.38997745513916016,39.646129 +874,Multiclass classification,Naive Bayes,ImageSegments,0.6426116838487973,0.6426116838487973,0.6349558095046656,0.38997745513916016,43.417442 +920,Multiclass classification,Naive Bayes,ImageSegments,0.6485310119695321,0.6485310119695321,0.6384515982514894,0.38997745513916016,47.360213 +966,Multiclass classification,Naive Bayes,ImageSegments,0.6507772020725389,0.6507772020725389,0.6399118827528387,0.38997745513916016,51.459671 +1012,Multiclass classification,Naive Bayes,ImageSegments,0.6508407517309595,0.6508407517309595,0.6387857120889422,0.38997745513916016,55.677121 +1058,Multiclass classification,Naive Bayes,ImageSegments,0.6537369914853358,0.6537369914853358,0.6398811322847953,0.38997745513916016,60.129657 +1104,Multiclass classification,Naive Bayes,ImageSegments,0.658204895738894,0.658204895738894,0.6463297068165035,0.38997745513916016,64.716333 +1150,Multiclass classification,Naive Bayes,ImageSegments,0.6640557006092254,0.6640557006092254,0.6508930463144657,0.39000415802001953,69.425449 +1196,Multiclass classification,Naive Bayes,ImageSegments,0.6702928870292887,0.6702928870292887,0.6599370641329333,0.39000415802001953,74.368592 +1242,Multiclass classification,Naive Bayes,ImageSegments,0.6736502820306205,0.6736502820306205,0.669511465798708,0.39000415802001953,79.42749 +1288,Multiclass classification,Naive Bayes,ImageSegments,0.6822066822066822,0.6822066822066822,0.6790074545382362,0.39000415802001953,84.676077 +1334,Multiclass classification,Naive Bayes,ImageSegments,0.6841710427606902,0.6841710427606902,0.6834974476087327,0.39000415802001953,90.04929600000001 +1380,Multiclass classification,Naive Bayes,ImageSegments,0.6874546773023931,0.6874546773023931,0.687676692272135,0.39000415802001953,95.54439700000002 +1426,Multiclass classification,Naive Bayes,ImageSegments,0.6919298245614035,0.6919298245614035,0.6930786661709784,0.39000415802001953,101.25523300000002 +1472,Multiclass classification,Naive Bayes,ImageSegments,0.698844323589395,0.698844323589395,0.6985606658027719,0.38997745513916016,107.09626300000002 +1518,Multiclass classification,Naive Bayes,ImageSegments,0.7027027027027027,0.7027027027027027,0.7017787722939461,0.39000415802001953,113.17857300000003 +1564,Multiclass classification,Naive Bayes,ImageSegments,0.7056941778630839,0.7056941778630839,0.7062915374924865,0.38997745513916016,119.38367200000003 +1610,Multiclass classification,Naive Bayes,ImageSegments,0.7078931013051585,0.7078931013051585,0.7081385387673028,0.38997745513916016,125.72760100000004 +1656,Multiclass classification,Naive Bayes,ImageSegments,0.7093655589123867,0.7093655589123867,0.7109488618373111,0.3899507522583008,132.27559300000004 +1702,Multiclass classification,Naive Bayes,ImageSegments,0.7101704879482658,0.7101704879482658,0.7132092257742534,0.38997745513916016,138.94755600000005 +1748,Multiclass classification,Naive Bayes,ImageSegments,0.7143674871207785,0.7143674871207784,0.7178399485500211,0.3899507522583008,145.68584300000003 +1794,Multiclass classification,Naive Bayes,ImageSegments,0.7172336865588399,0.7172336865588399,0.7191260584555579,0.38997745513916016,152.67811600000005 +1840,Multiclass classification,Naive Bayes,ImageSegments,0.7199564980967917,0.7199564980967917,0.7217017555070446,0.39000415802001953,159.82058900000004 +1886,Multiclass classification,Naive Bayes,ImageSegments,0.7204244031830239,0.7204244031830238,0.7234495525792994,0.39000415802001953,167.13449700000004 +1932,Multiclass classification,Naive Bayes,ImageSegments,0.7219057483169342,0.7219057483169342,0.7238483512148008,0.39000415802001953,174.57489300000003 +1978,Multiclass classification,Naive Bayes,ImageSegments,0.723823975720789,0.723823975720789,0.7251399238639739,0.39000415802001953,182.21825900000005 +2024,Multiclass classification,Naive Bayes,ImageSegments,0.726643598615917,0.726643598615917,0.7268553573885639,0.39000415802001953,189.97396200000006 +2070,Multiclass classification,Naive Bayes,ImageSegments,0.7269212179797003,0.7269212179797003,0.7276782991451582,0.39000415802001953,197.92708900000005 +2116,Multiclass classification,Naive Bayes,ImageSegments,0.7286052009456265,0.7286052009456266,0.7283656039279267,0.39000415802001953,206.04766600000005 +2162,Multiclass classification,Naive Bayes,ImageSegments,0.7306802406293382,0.7306802406293383,0.7303992643507475,0.39000415802001953,214.36632800000004 +2208,Multiclass classification,Naive Bayes,ImageSegments,0.733574988672406,0.733574988672406,0.7322842940126589,0.39000415802001953,222.77231300000003 +2254,Multiclass classification,Naive Bayes,ImageSegments,0.7314691522414558,0.7314691522414558,0.7300322879925133,0.39000415802001953,231.40748800000003 +2300,Multiclass classification,Naive Bayes,ImageSegments,0.7316224445411048,0.7316224445411048,0.7300416811383057,0.39000415802001953,240.14309800000004 +2310,Multiclass classification,Naive Bayes,ImageSegments,0.7319185794716327,0.7319185794716329,0.7304188192194185,0.39000415802001953,248.95897400000004 +1056,Multiclass classification,Naive Bayes,Insects,0.623696682464455,0.623696682464455,0.5870724729616662,0.6116933822631836,4.116407 +2112,Multiclass classification,Naive Bayes,Insects,0.6148744670772146,0.6148744670772146,0.5800776869595596,0.6116933822631836,12.008893 +3168,Multiclass classification,Naive Bayes,Insects,0.6065677297126618,0.6065677297126618,0.5714781230184183,0.6116933822631836,23.636521000000002 +4224,Multiclass classification,Naive Bayes,Insects,0.6043097324177126,0.6043097324177126,0.5697541737710122,0.6116933822631836,38.735534 +5280,Multiclass classification,Naive Bayes,Insects,0.6088274294373934,0.6088274294373934,0.5727560614138387,0.6116933822631836,57.253764000000004 +6336,Multiclass classification,Naive Bayes,Insects,0.6023677979479084,0.6023677979479084,0.5679597008529512,0.6116933822631836,79.038555 +7392,Multiclass classification,Naive Bayes,Insects,0.5995129211202814,0.5995129211202814,0.5652603100832261,0.6116933822631836,104.109779 +8448,Multiclass classification,Naive Bayes,Insects,0.6019888717888008,0.6019888717888008,0.5673514925692325,0.6116933822631836,132.296427 +9504,Multiclass classification,Naive Bayes,Insects,0.5993896664211301,0.5993896664211301,0.5644951651039589,0.6116933822631836,163.68164199999998 +10560,Multiclass classification,Naive Bayes,Insects,0.5994885879344635,0.5994885879344635,0.564565538599863,0.6116933822631836,198.25211399999998 +11616,Multiclass classification,Naive Bayes,Insects,0.5972449418854929,0.5972449418854929,0.5631227877868952,0.6116933822631836,235.999104 +12672,Multiclass classification,Naive Bayes,Insects,0.6001894088864336,0.6001894088864336,0.5684733590606373,0.6116933822631836,276.973484 +13728,Multiclass classification,Naive Bayes,Insects,0.6120783856632913,0.6120783856632913,0.5935173038317552,0.6116933822631836,321.087465 +14784,Multiclass classification,Naive Bayes,Insects,0.6024487587093282,0.6024487587093282,0.5841270876002982,0.6116933822631836,368.414891 +15840,Multiclass classification,Naive Bayes,Insects,0.5676494728202538,0.5676494728202538,0.5507155080701159,0.6116933822631836,418.92674800000003 +16896,Multiclass classification,Naive Bayes,Insects,0.5418762947617638,0.5418762947617638,0.5256197352354142,0.6116933822631836,472.67283100000003 +17952,Multiclass classification,Naive Bayes,Insects,0.5232020500250683,0.5232020500250683,0.5066898143269706,0.6116933822631836,529.5973250000001 +19008,Multiclass classification,Naive Bayes,Insects,0.5118640500868101,0.5118640500868101,0.4926543583964285,0.6116933822631836,589.87103 +20064,Multiclass classification,Naive Bayes,Insects,0.5103922643672432,0.5103922643672432,0.4900586962359796,0.6116933822631836,653.257514 +21120,Multiclass classification,Naive Bayes,Insects,0.5115772527108291,0.5115772527108291,0.4910837640903744,0.6116933822631836,719.720849 +22176,Multiclass classification,Naive Bayes,Insects,0.5140022547914318,0.5140022547914318,0.49325418882319577,0.6116933822631836,789.2473650000001 +23232,Multiclass classification,Naive Bayes,Insects,0.5154319659076234,0.5154319659076234,0.49430134175999263,0.6116933822631836,861.9200270000001 +24288,Multiclass classification,Naive Bayes,Insects,0.5184254951208466,0.5184254951208466,0.4965832238311332,0.6116933822631836,937.6283820000001 +25344,Multiclass classification,Naive Bayes,Insects,0.5225111470623052,0.5225111470623052,0.499893079239698,0.6116933822631836,1016.4339050000001 +26400,Multiclass classification,Naive Bayes,Insects,0.5257396113489148,0.5257396113489148,0.5022487669255871,0.6116933822631836,1098.325454 +27456,Multiclass classification,Naive Bayes,Insects,0.5301402294663996,0.5301402294663996,0.5051550433324518,0.6116933822631836,1183.302333 +28512,Multiclass classification,Naive Bayes,Insects,0.5277261407877661,0.5277261407877661,0.5036945145235058,0.6116933822631836,1271.323869 +29568,Multiclass classification,Naive Bayes,Insects,0.5204450908107011,0.5204450908107011,0.4989008712312768,0.6116933822631836,1362.446785 +30624,Multiclass classification,Naive Bayes,Insects,0.5147111648107632,0.5147111648107632,0.49582684007363204,0.6116933822631836,1456.7074690000002 +31680,Multiclass classification,Naive Bayes,Insects,0.5105590454244137,0.5105590454244137,0.4941101813344875,0.6116933822631836,1553.9918670000002 +32736,Multiclass classification,Naive Bayes,Insects,0.5075607148312204,0.5075607148312204,0.4931947798921405,0.6116933822631836,1654.3550870000001 +33792,Multiclass classification,Naive Bayes,Insects,0.5044538486579266,0.5044538486579266,0.4905626123916189,0.6116933822631836,1757.6376 +34848,Multiclass classification,Naive Bayes,Insects,0.5020231296811777,0.5020231296811777,0.48787984248812405,0.6116933822631836,1863.925375 +35904,Multiclass classification,Naive Bayes,Insects,0.49987466228448874,0.49987466228448874,0.48534350611524757,0.6116933822631836,1973.177917 +36960,Multiclass classification,Naive Bayes,Insects,0.49679374441949187,0.49679374441949187,0.4819418474093529,0.6116933822631836,2085.445724 +38016,Multiclass classification,Naive Bayes,Insects,0.49559384453505195,0.49559384453505195,0.4801892436835747,0.6116933822631836,2200.821931 +39072,Multiclass classification,Naive Bayes,Insects,0.49402370044278365,0.49402370044278365,0.47838078382052607,0.6116933822631836,2319.178703 +40128,Multiclass classification,Naive Bayes,Insects,0.493508111745209,0.493508111745209,0.47852138016706713,0.6116933822631836,2440.443075 +41184,Multiclass classification,Naive Bayes,Insects,0.49369885632421145,0.49369885632421145,0.47942014994272747,0.6116933822631836,2564.583087 +42240,Multiclass classification,Naive Bayes,Insects,0.4938800634484718,0.4938800634484718,0.48023774975329353,0.6116933822631836,2691.651665 +43296,Multiclass classification,Naive Bayes,Insects,0.49437579397159026,0.49437579397159026,0.4812132921167227,0.6116933822631836,2821.6013359999997 +44352,Multiclass classification,Naive Bayes,Insects,0.49403621113390905,0.49403621113390905,0.48123889196184183,0.6116933822631836,2954.3777659999996 +45408,Multiclass classification,Naive Bayes,Insects,0.4944832294580131,0.4944832294580131,0.4818441874360225,0.6116933822631836,3089.8310679999995 +46464,Multiclass classification,Naive Bayes,Insects,0.4945225232981082,0.4945225232981082,0.4820791268335544,0.6116933822631836,3227.9665449999993 +47520,Multiclass classification,Naive Bayes,Insects,0.4956333256171216,0.4956333256171216,0.48331686360214987,0.6116933822631836,3368.688097999999 +48576,Multiclass classification,Naive Bayes,Insects,0.4970869788986104,0.4970869788986104,0.48467037716343636,0.6116933822631836,3511.887438999999 +49632,Multiclass classification,Naive Bayes,Insects,0.4987608551107171,0.4987608551107171,0.4862426724473749,0.6116933822631836,3657.6494079999993 +50688,Multiclass classification,Naive Bayes,Insects,0.5009568528419516,0.5009568528419516,0.4881725476999718,0.6116933822631836,3806.0112589999994 +51744,Multiclass classification,Naive Bayes,Insects,0.5034497419940862,0.5034497419940862,0.4903712806540024,0.6116933822631836,3956.8935159999996 +52800,Multiclass classification,Naive Bayes,Insects,0.5068467205818292,0.5068467205818292,0.4930025316136313,0.6116933822631836,4110.278735 +52848,Multiclass classification,Naive Bayes,Insects,0.5068972694760346,0.5068972694760346,0.49301906278314944,0.6116933822631836,4263.766907 +408,Multiclass classification,Naive Bayes,Keystroke,0.9852579852579852,0.9852579852579852,0.6962686567164179,0.19356441497802734,0.780775 +816,Multiclass classification,Naive Bayes,Keystroke,0.947239263803681,0.947239263803681,0.7418606503288051,0.28890228271484375,2.463269 +1224,Multiclass classification,Naive Bayes,Keystroke,0.884709730171709,0.884709730171709,0.8705899666065842,0.38424015045166016,5.15507 +1632,Multiclass classification,Naive Bayes,Keystroke,0.8933169834457388,0.8933169834457388,0.8791291775937072,0.47957801818847656,8.960951 +2040,Multiclass classification,Naive Bayes,Keystroke,0.8921039725355566,0.8921039725355566,0.8831785360852743,0.575160026550293,14.051639 +2448,Multiclass classification,Naive Bayes,Keystroke,0.851655087862689,0.851655087862689,0.8581984289516641,0.6704978942871094,20.582881999999998 +2856,Multiclass classification,Naive Bayes,Keystroke,0.8598949211908932,0.8598949211908932,0.8469962214365346,0.7658357620239258,28.649143 +3264,Multiclass classification,Naive Bayes,Keystroke,0.8513637756665645,0.8513637756665645,0.8281280134770846,0.8611736297607422,38.532046 +3672,Multiclass classification,Naive Bayes,Keystroke,0.8422773086352493,0.8422773086352493,0.8409307955747314,0.9565114974975586,50.273206 +4080,Multiclass classification,Naive Bayes,Keystroke,0.8367246874233881,0.8367246874233881,0.8249418657104467,1.0523834228515625,63.882498 +4488,Multiclass classification,Naive Bayes,Keystroke,0.8203699576554491,0.8203699576554491,0.8300896799820437,1.147721290588379,79.531469 +4896,Multiclass classification,Naive Bayes,Keystroke,0.8192032686414709,0.8192032686414709,0.8269731591910484,1.2430591583251953,97.310117 +5304,Multiclass classification,Naive Bayes,Keystroke,0.8172732415613804,0.8172732415613804,0.8027823390848743,1.3383970260620117,117.35519000000001 +5712,Multiclass classification,Naive Bayes,Keystroke,0.7961828051129399,0.7961828051129399,0.8002006091139847,1.4337348937988281,139.817583 +6120,Multiclass classification,Naive Bayes,Keystroke,0.793920575257395,0.793920575257395,0.7746960355921345,1.5290727615356445,164.727582 +6528,Multiclass classification,Naive Bayes,Keystroke,0.7688064960931515,0.7688064960931515,0.7622487598340326,1.624410629272461,192.15170700000002 +6936,Multiclass classification,Naive Bayes,Keystroke,0.7568853640951694,0.7568853640951694,0.757813781660983,1.7197484970092773,222.24358600000002 +7344,Multiclass classification,Naive Bayes,Keystroke,0.7669889690862045,0.7669889690862046,0.7643943615019536,1.8150863647460938,255.230678 +7752,Multiclass classification,Naive Bayes,Keystroke,0.7676428847890595,0.7676428847890595,0.7655695901071293,1.9104242324829102,291.218411 +8160,Multiclass classification,Naive Bayes,Keystroke,0.7714180659394534,0.7714180659394533,0.7672011803374248,2.0057621002197266,330.398823 +8568,Multiclass classification,Naive Bayes,Keystroke,0.7702813120112058,0.7702813120112058,0.7699263138193525,2.1021223068237305,372.82766399999997 +8976,Multiclass classification,Naive Bayes,Keystroke,0.7680222841225627,0.7680222841225627,0.7682287234686136,2.197460174560547,418.63014999999996 +9384,Multiclass classification,Naive Bayes,Keystroke,0.7659597143770649,0.7659597143770649,0.7643546547243014,2.2927980422973633,468.01111099999997 +9792,Multiclass classification,Naive Bayes,Keystroke,0.7586559084873864,0.7586559084873864,0.7552148692020618,2.3881359100341797,521.0847249999999 +10200,Multiclass classification,Naive Bayes,Keystroke,0.7505637807628199,0.7505637807628199,0.7430512224080145,2.483473777770996,577.917349 +10608,Multiclass classification,Naive Bayes,Keystroke,0.7290468558499105,0.7290468558499106,0.715756093271779,2.5788116455078125,638.790947 +11016,Multiclass classification,Naive Bayes,Keystroke,0.7217430776214253,0.7217430776214253,0.7173640789896896,2.674149513244629,703.666983 +11424,Multiclass classification,Naive Bayes,Keystroke,0.7151361288628206,0.7151361288628206,0.7011862635194489,2.7694873809814453,772.6431349999999 +11832,Multiclass classification,Naive Bayes,Keystroke,0.705603921900093,0.705603921900093,0.6976881379682607,2.8648252487182617,845.8350979999999 +12240,Multiclass classification,Naive Bayes,Keystroke,0.7094533867146009,0.7094533867146009,0.7058405389403433,2.960163116455078,923.50335 +12648,Multiclass classification,Naive Bayes,Keystroke,0.7053846762077963,0.7053846762077963,0.6965736948063982,3.0555009841918945,1005.7536769999999 +13056,Multiclass classification,Naive Bayes,Keystroke,0.6927613941018766,0.6927613941018766,0.6842255816736498,3.150838851928711,1092.707972 +13464,Multiclass classification,Naive Bayes,Keystroke,0.6890737577063062,0.6890737577063062,0.6845669389392289,3.2461767196655273,1184.483965 +13872,Multiclass classification,Naive Bayes,Keystroke,0.6873332852714296,0.6873332852714296,0.68390545518227,3.3415145874023438,1281.216395 +14280,Multiclass classification,Naive Bayes,Keystroke,0.682960991666083,0.682960991666083,0.6781566371919944,3.43685245513916,1383.0399089999999 +14688,Multiclass classification,Naive Bayes,Keystroke,0.686185061619119,0.686185061619119,0.6843713776162116,3.5321903228759766,1489.909884 +15096,Multiclass classification,Naive Bayes,Keystroke,0.6928784365684001,0.6928784365684001,0.6911392400672977,3.627528190612793,1601.996709 +15504,Multiclass classification,Naive Bayes,Keystroke,0.6913500612784622,0.6913500612784622,0.687359772989117,3.7228660583496094,1719.445985 +15912,Multiclass classification,Naive Bayes,Keystroke,0.6819810194205267,0.6819810194205267,0.674915944935936,3.818203926086426,1842.197498 +16320,Multiclass classification,Naive Bayes,Keystroke,0.6726515105092223,0.6726515105092223,0.6670192172011686,3.913541793823242,1970.358299 +16728,Multiclass classification,Naive Bayes,Keystroke,0.6695163508100676,0.6695163508100676,0.6664051037977977,4.008879661560059,2103.939399 +17136,Multiclass classification,Naive Bayes,Keystroke,0.6650131310183834,0.6650131310183834,0.6608988619616458,4.1063079833984375,2242.845385 +17544,Multiclass classification,Naive Bayes,Keystroke,0.6568431853160804,0.6568431853160804,0.6531382897719189,4.201645851135254,2386.822189 +17952,Multiclass classification,Naive Bayes,Keystroke,0.6556180714166342,0.6556180714166342,0.6538448358590968,4.29698371887207,2536.044428 +18360,Multiclass classification,Naive Bayes,Keystroke,0.6614194672912468,0.6614194672912468,0.6603186829199909,4.392321586608887,2690.5476860000003 +18768,Multiclass classification,Naive Bayes,Keystroke,0.6669686151222891,0.6669686151222891,0.6662293616554571,4.487659454345703,2850.3140810000004 +19176,Multiclass classification,Naive Bayes,Keystroke,0.6579921773142112,0.6579921773142112,0.6554177118629491,4.5829973220825195,3015.4823350000006 +19584,Multiclass classification,Naive Bayes,Keystroke,0.6622580809886126,0.6622580809886126,0.6609360990360078,4.678335189819336,3186.2814100000005 +19992,Multiclass classification,Naive Bayes,Keystroke,0.6562453103896754,0.6562453103896754,0.6545704957554572,4.773673057556152,3362.6238980000007 +20400,Multiclass classification,Naive Bayes,Keystroke,0.6525319868621011,0.6525319868621011,0.6515767870317885,4.869010925292969,3544.6906370000006 +46,Multiclass classification,Hoeffding Tree,ImageSegments,0.35555555555555557,0.35555555555555557,0.25379424497071557,0.4170856475830078,0.290301 +92,Multiclass classification,Hoeffding Tree,ImageSegments,0.4945054945054945,0.4945054945054945,0.5043329927491418,0.4170818328857422,0.82046 +138,Multiclass classification,Hoeffding Tree,ImageSegments,0.5328467153284672,0.5328467153284672,0.5564033878668025,0.4171772003173828,1.6754229999999999 +184,Multiclass classification,Hoeffding Tree,ImageSegments,0.6010928961748634,0.6010928961748634,0.622766496539645,0.4171772003173828,2.801183 +230,Multiclass classification,Hoeffding Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6539827168809461,0.41720008850097656,4.271522 +276,Multiclass classification,Hoeffding Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.6671561759164943,0.4172496795654297,5.954744 +322,Multiclass classification,Hoeffding Tree,ImageSegments,0.67601246105919,0.67601246105919,0.6756614325426025,0.4172496795654297,7.864603 +368,Multiclass classification,Hoeffding Tree,ImageSegments,0.7029972752043597,0.7029972752043597,0.6993447851636564,0.4172229766845703,10.008665 +414,Multiclass classification,Hoeffding Tree,ImageSegments,0.7142857142857143,0.7142857142857143,0.7108606838045498,0.4171428680419922,12.399438 +460,Multiclass classification,Hoeffding Tree,ImageSegments,0.7145969498910676,0.7145969498910676,0.7090365931960759,0.4172191619873047,15.01004 +506,Multiclass classification,Hoeffding Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7126631585949761,0.4172191619873047,17.873655 +552,Multiclass classification,Hoeffding Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7157730164623107,0.4171123504638672,20.946970999999998 +598,Multiclass classification,Hoeffding Tree,ImageSegments,0.7286432160804021,0.7286432160804021,0.7216745323124732,0.41713523864746094,24.255884 +644,Multiclass classification,Hoeffding Tree,ImageSegments,0.7278382581648523,0.7278382581648523,0.7229105183087501,0.41710853576660156,27.838411999999998 +690,Multiclass classification,Hoeffding Tree,ImageSegments,0.7314949201741655,0.7314949201741654,0.7263583447448078,0.41710853576660156,31.647636 +736,Multiclass classification,Hoeffding Tree,ImageSegments,0.7333333333333333,0.7333333333333333,0.729431071218305,0.41713523864746094,35.743157 +782,Multiclass classification,Hoeffding Tree,ImageSegments,0.7387964148527529,0.7387964148527529,0.7349287389986899,0.41713523864746094,40.063089999999995 +828,Multiclass classification,Hoeffding Tree,ImageSegments,0.7376058041112454,0.7376058041112454,0.7356226390109741,0.41713523864746094,44.599844 +874,Multiclass classification,Hoeffding Tree,ImageSegments,0.7445589919816724,0.7445589919816724,0.7409366047432264,0.41713523864746094,49.398728999999996 +920,Multiclass classification,Hoeffding Tree,ImageSegments,0.7453754080522307,0.7453754080522307,0.7408438328939173,0.41710853576660156,54.404894 +966,Multiclass classification,Hoeffding Tree,ImageSegments,0.7471502590673575,0.7471502590673575,0.7416651838589269,0.41710853576660156,59.665949 +1012,Multiclass classification,Hoeffding Tree,ImageSegments,0.7467853610286844,0.7467853610286844,0.7416356251822,0.41710853576660156,65.211169 +1058,Multiclass classification,Hoeffding Tree,ImageSegments,0.7492904446546831,0.7492904446546831,0.7430778844390783,0.41710853576660156,70.961377 +1104,Multiclass classification,Hoeffding Tree,ImageSegments,0.7515865820489573,0.7515865820489573,0.7451256886686588,0.4171581268310547,76.969446 +1150,Multiclass classification,Hoeffding Tree,ImageSegments,0.7536988685813751,0.7536988685813751,0.7468312166689606,0.4171581268310547,83.201851 +1196,Multiclass classification,Hoeffding Tree,ImageSegments,0.7564853556485356,0.7564853556485356,0.7503479321738041,0.4171581268310547,89.604352 +1242,Multiclass classification,Hoeffding Tree,ImageSegments,0.7566478646253022,0.7566478646253022,0.7509717522131719,0.4171581268310547,96.30702600000001 +1288,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614607614607615,0.7614607614607615,0.7547643483779538,0.4171581268310547,103.26246200000001 +1334,Multiclass classification,Hoeffding Tree,ImageSegments,0.7614403600900225,0.7614403600900225,0.7551060921605869,0.4171581268310547,110.41488900000002 +1380,Multiclass classification,Hoeffding Tree,ImageSegments,0.7621464829586657,0.7621464829586658,0.7562209880685912,0.4171581268310547,117.79988600000001 +1426,Multiclass classification,Hoeffding Tree,ImageSegments,0.7642105263157895,0.7642105263157895,0.7575332274919562,0.4171581268310547,125.46176800000002 +1472,Multiclass classification,Hoeffding Tree,ImageSegments,0.7688647178789939,0.768864717878994,0.760438686053582,0.4171581268310547,133.360363 +1518,Multiclass classification,Hoeffding Tree,ImageSegments,0.7705998681608438,0.7705998681608438,0.7612069012840872,0.4171581268310547,141.48549400000002 +1564,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709532949456174,0.7709532949456174,0.7622701654854867,0.4171581268310547,149.83563600000002 +1610,Multiclass classification,Hoeffding Tree,ImageSegments,0.7712865133623369,0.771286513362337,0.7617247271717752,0.41718101501464844,158.439217 +1656,Multiclass classification,Hoeffding Tree,ImageSegments,0.7709969788519637,0.7709969788519637,0.7615629120572474,0.41718101501464844,167.22864700000002 +1702,Multiclass classification,Hoeffding Tree,ImageSegments,0.770135214579659,0.770135214579659,0.7627316365695143,0.41718101501464844,176.30742800000002 +1748,Multiclass classification,Hoeffding Tree,ImageSegments,0.7727532913566113,0.7727532913566113,0.7649467707214076,0.41718101501464844,185.609237 +1794,Multiclass classification,Hoeffding Tree,ImageSegments,0.7741215839375348,0.7741215839375348,0.7649332326562149,0.41715431213378906,195.10730800000002 +1840,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754214246873301,0.7754214246873301,0.7664700790631908,0.41715431213378906,204.88888000000003 +1886,Multiclass classification,Hoeffding Tree,ImageSegments,0.7740053050397878,0.7740053050397878,0.7655121135276625,0.41715431213378906,214.87796100000003 +1932,Multiclass classification,Hoeffding Tree,ImageSegments,0.7742102537545313,0.7742102537545313,0.7648034036287765,0.41715431213378906,225.10774000000004 +1978,Multiclass classification,Hoeffding Tree,ImageSegments,0.7754172989377845,0.7754172989377845,0.7656013068970459,0.41715431213378906,235.56491900000003 +2024,Multiclass classification,Hoeffding Tree,ImageSegments,0.7770637666831438,0.7770637666831438,0.7660878232247856,0.41715431213378906,246.31694000000005 +2070,Multiclass classification,Hoeffding Tree,ImageSegments,0.7762203963267279,0.7762203963267279,0.7654829214385931,0.41715431213378906,257.28426500000006 +2116,Multiclass classification,Hoeffding Tree,ImageSegments,0.7768321513002364,0.7768321513002364,0.7653071619305024,0.41715431213378906,268.5154150000001 +2162,Multiclass classification,Hoeffding Tree,ImageSegments,0.7778806108283203,0.7778806108283203,0.7659351904174982,0.41715431213378906,279.94414300000005 +2208,Multiclass classification,Hoeffding Tree,ImageSegments,0.7797915722700498,0.7797915722700498,0.7668192864082087,0.41715431213378906,291.65328600000004 +2254,Multiclass classification,Hoeffding Tree,ImageSegments,0.7767421216156236,0.7767421216156236,0.7637794374955548,0.41715431213378906,303.618395 +2300,Multiclass classification,Hoeffding Tree,ImageSegments,0.7759895606785558,0.7759895606785558,0.763026662835187,0.41715431213378906,315.80512400000003 +2310,Multiclass classification,Hoeffding Tree,ImageSegments,0.776093546990039,0.776093546990039,0.7631372452021826,0.41715431213378906,328.06738900000005 +1056,Multiclass classification,Hoeffding Tree,Insects,0.6218009478672986,0.6218009478672986,0.5852663107194211,0.6579360961914062,7.68277 +2112,Multiclass classification,Hoeffding Tree,Insects,0.6153481762198011,0.6153481762198011,0.5806436317780949,0.6579360961914062,22.565114 +3168,Multiclass classification,Hoeffding Tree,Insects,0.6071992421850332,0.6071992421850332,0.572248584718361,0.6579360961914062,43.997682 +4224,Multiclass classification,Hoeffding Tree,Insects,0.6043097324177126,0.6043097324177126,0.5697573109597247,0.6579360961914062,71.858443 +5280,Multiclass classification,Hoeffding Tree,Insects,0.6088274294373934,0.6088274294373934,0.5727379077413696,0.6579360961914062,105.92483999999999 +6336,Multiclass classification,Hoeffding Tree,Insects,0.6026835043409629,0.6026835043409629,0.568251333238805,0.6579360961914062,146.287253 +7392,Multiclass classification,Hoeffding Tree,Insects,0.600189419564335,0.600189419564335,0.5659762112716077,0.6579360961914062,192.863981 +8448,Multiclass classification,Hoeffding Tree,Insects,0.60258079791642,0.60258079791642,0.5679781484640408,0.6579360961914062,245.806734 +9504,Multiclass classification,Hoeffding Tree,Insects,0.5998105861306956,0.5998105861306956,0.5649597336877693,0.6579360961914062,305.14044 +10560,Multiclass classification,Hoeffding Tree,Insects,0.5998674116867128,0.5998674116867128,0.5650173260529011,0.6579360961914062,370.68089100000003 +11616,Multiclass classification,Hoeffding Tree,Insects,0.5974171330176495,0.5974171330176495,0.5633067089377387,0.6579360961914062,442.33844300000004 +12672,Multiclass classification,Hoeffding Tree,Insects,0.6001894088864336,0.6001894088864336,0.5684760329567131,0.6579360961914062,520.121563 +13728,Multiclass classification,Hoeffding Tree,Insects,0.6120783856632913,0.6120783856632913,0.5935956771555828,0.6579360961914062,604.039429 +14784,Multiclass classification,Hoeffding Tree,Insects,0.6024487587093282,0.6024487587093282,0.5842148300149193,0.6579360961914062,694.113241 +15840,Multiclass classification,Hoeffding Tree,Insects,0.5677757434181451,0.5677757434181451,0.5509250187877572,0.6579360961914062,790.19156 +16896,Multiclass classification,Hoeffding Tree,Insects,0.5419354838709678,0.5419354838709678,0.5257359157219258,0.6579360961914062,892.361186 +17952,Multiclass classification,Hoeffding Tree,Insects,0.5233691716338923,0.5233691716338923,0.5068581838352059,0.6579360961914062,1000.4717479999999 +19008,Multiclass classification,Hoeffding Tree,Insects,0.5121271110643447,0.5121271110643447,0.49292899065094153,0.6579360961914062,1114.494528 +20064,Multiclass classification,Hoeffding Tree,Insects,0.5120370831879579,0.5120370831879579,0.4920970323041603,1.3099584579467773,1234.2056499999999 +21120,Multiclass classification,Hoeffding Tree,Insects,0.5173066906577016,0.5173066906577016,0.4973447169836249,1.310713768005371,1358.925583 +22176,Multiclass classification,Hoeffding Tree,Insects,0.5229312288613304,0.5229312288613304,0.5026343687424488,1.310713768005371,1488.370808 +23232,Multiclass classification,Hoeffding Tree,Insects,0.5301536739701261,0.5301536739701261,0.5095132087733324,1.310713768005371,1622.41448 +24288,Multiclass classification,Hoeffding Tree,Insects,0.5351422571746202,0.5351422571746202,0.5135975374357353,1.310713768005371,1760.8970379999998 +25344,Multiclass classification,Hoeffding Tree,Insects,0.5403069881229531,0.5403069881229531,0.5180803411538233,1.310713768005371,1903.5911449999999 +26400,Multiclass classification,Hoeffding Tree,Insects,0.5441493995984696,0.5441493995984696,0.5209012984387186,1.310713768005371,2050.469487 +27456,Multiclass classification,Hoeffding Tree,Insects,0.5475869604807867,0.5475869604807867,0.5230407124785976,1.310713768005371,2201.55681 +28512,Multiclass classification,Hoeffding Tree,Insects,0.5442460804601733,0.5442460804601733,0.5199893698637053,1.310713768005371,2356.711105 +29568,Multiclass classification,Hoeffding Tree,Insects,0.5439848479724017,0.5439848479724017,0.5225387960194383,1.310713768005371,2516.62263 +30624,Multiclass classification,Hoeffding Tree,Insects,0.5449825294713124,0.5449825294713124,0.5260472440529832,1.310713768005371,2681.5460789999997 +31680,Multiclass classification,Hoeffding Tree,Insects,0.5469238296663405,0.5469238296663405,0.5300194392617626,1.310713768005371,2851.622305 +32736,Multiclass classification,Hoeffding Tree,Insects,0.5492286543455017,0.5492286543455017,0.5337692045397758,1.310713768005371,3026.797274 +33792,Multiclass classification,Hoeffding Tree,Insects,0.5448196265277737,0.5448196265277737,0.5298516474077153,1.310713768005371,3207.119826 +34848,Multiclass classification,Hoeffding Tree,Insects,0.539357763939507,0.539357763939507,0.5246413689313029,1.310713768005371,3392.4010240000002 +35904,Multiclass classification,Hoeffding Tree,Insects,0.5352756037099964,0.5352756037099964,0.5204658240271913,1.310713768005371,3582.6817720000004 +36960,Multiclass classification,Hoeffding Tree,Insects,0.5307232338537298,0.5307232338537298,0.5158458403074864,1.310713768005371,3778.3092850000003 +38016,Multiclass classification,Hoeffding Tree,Insects,0.5287912666052874,0.5287912666052874,0.5138605376143625,1.8479537963867188,3978.8224330000003 +39072,Multiclass classification,Hoeffding Tree,Insects,0.5245322617798367,0.5245322617798367,0.5100329616180462,1.9625730514526367,4184.1075280000005 +40128,Multiclass classification,Hoeffding Tree,Insects,0.5244847608841927,0.5244847608841927,0.5114466799524962,1.9625730514526367,4393.646320000001 +41184,Multiclass classification,Hoeffding Tree,Insects,0.5269650098341548,0.5269650098341548,0.5145630920489553,1.9625730514526367,4606.675677000001 +42240,Multiclass classification,Hoeffding Tree,Insects,0.5290608205686688,0.5290608205686688,0.5171452370879218,1.9625730514526367,4823.052294000001 +43296,Multiclass classification,Hoeffding Tree,Insects,0.5316318281556762,0.5316318281556762,0.5200714653059241,1.9625730514526367,5042.794587000001 +44352,Multiclass classification,Hoeffding Tree,Insects,0.5332912448422809,0.5332912448422809,0.521951703681177,1.9633283615112305,5266.308108000001 +45408,Multiclass classification,Hoeffding Tree,Insects,0.5350937080185875,0.5350937080185875,0.5236272112757866,1.9633283615112305,5493.659660000001 +46464,Multiclass classification,Hoeffding Tree,Insects,0.5374168693368917,0.5374168693368917,0.5257977177437826,1.9633283615112305,5724.562244000002 +47520,Multiclass classification,Hoeffding Tree,Insects,0.5359540394368568,0.5359540394368568,0.5247049329892776,1.9633283615112305,5959.275286000002 +48576,Multiclass classification,Hoeffding Tree,Insects,0.5333196088522902,0.5333196088522902,0.5224640186909637,1.9633283615112305,6197.987866000002 +49632,Multiclass classification,Hoeffding Tree,Insects,0.5314017448771937,0.5314017448771937,0.5209076603734537,1.9633283615112305,6440.583835000002 +50688,Multiclass classification,Hoeffding Tree,Insects,0.5322271982954209,0.5322271982954209,0.5219695808096345,2.081958770751953,6687.224874000002 +51744,Multiclass classification,Hoeffding Tree,Insects,0.5377345727924551,0.5377345727924551,0.5274876060436412,2.3156700134277344,6937.746409000002 +52800,Multiclass classification,Hoeffding Tree,Insects,0.5370366863008769,0.5370366863008769,0.5270872650003847,2.519227981567383,7191.466386000002 +52848,Multiclass classification,Hoeffding Tree,Insects,0.5373058073305959,0.5373058073305959,0.5273644947479657,2.519227981567383,7445.3631460000015 +408,Multiclass classification,Hoeffding Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.2240447998046875,0.863228 +816,Multiclass classification,Hoeffding Tree,Keystroke,0.9423312883435583,0.9423312883435583,0.7661667470992702,0.3196687698364258,3.107641 +1224,Multiclass classification,Hoeffding Tree,Keystroke,0.8830744071954211,0.883074407195421,0.8761191747044462,0.41529273986816406,7.048775 +1632,Multiclass classification,Hoeffding Tree,Keystroke,0.8902513795217658,0.8902513795217658,0.8767853151263398,0.5114049911499023,13.087731999999999 +2040,Multiclass classification,Hoeffding Tree,Keystroke,0.8891613536047082,0.8891613536047082,0.8807858055314012,0.6185035705566406,21.551524999999998 +2448,Multiclass classification,Hoeffding Tree,Keystroke,0.848385778504291,0.848385778504291,0.8522513926518692,0.7141275405883789,32.816222999999994 +2856,Multiclass classification,Hoeffding Tree,Keystroke,0.8563922942206655,0.8563922942206655,0.8440193478447516,0.8097515106201172,47.080318999999996 +3264,Multiclass classification,Hoeffding Tree,Keystroke,0.8482991112473184,0.8482991112473184,0.8269786301577753,0.9053754806518555,64.636989 +3672,Multiclass classification,Hoeffding Tree,Keystroke,0.8392808499046581,0.8392808499046581,0.8374924160046072,1.0009994506835938,85.706576 +4080,Multiclass classification,Hoeffding Tree,Keystroke,0.8323118411375338,0.8323118411375338,0.8182261307945194,1.1217241287231445,110.70978199999999 +4488,Multiclass classification,Hoeffding Tree,Keystroke,0.8159126365054602,0.8159126365054602,0.8260965842218733,1.2173480987548828,139.812165 +4896,Multiclass classification,Hoeffding Tree,Keystroke,0.8149131767109296,0.8149131767109296,0.8221314665977922,1.312972068786621,173.369773 +5304,Multiclass classification,Hoeffding Tree,Keystroke,0.8125589289081652,0.8125589289081652,0.797613058026624,1.4085960388183594,211.780209 +5712,Multiclass classification,Hoeffding Tree,Keystroke,0.7907546839432674,0.7907546839432674,0.7936708037520236,1.5042200088500977,255.27399100000002 +6120,Multiclass classification,Hoeffding Tree,Keystroke,0.7886909625755842,0.7886909625755842,0.7694478218498494,1.599843978881836,304.294734 +6528,Multiclass classification,Hoeffding Tree,Keystroke,0.7635973647924008,0.7635973647924008,0.75687960152136,1.6954679489135742,359.144129 +6936,Multiclass classification,Hoeffding Tree,Keystroke,0.75155010814708,0.7515501081470799,0.7521509466338959,1.7910919189453125,420.22114200000004 +7344,Multiclass classification,Hoeffding Tree,Keystroke,0.7611330518861501,0.7611330518861501,0.7576671162861806,1.8881807327270508,487.76956500000006 +7752,Multiclass classification,Hoeffding Tree,Keystroke,0.7617081666881693,0.7617081666881692,0.7593340838982119,1.983804702758789,562.1432000000001 +8160,Multiclass classification,Hoeffding Tree,Keystroke,0.7655349920333374,0.7655349920333374,0.7610505848438686,2.0794286727905273,643.5514560000001 +8568,Multiclass classification,Hoeffding Tree,Keystroke,0.7644449632310026,0.7644449632310025,0.7639417799779614,2.223102569580078,732.3349550000001 +8976,Multiclass classification,Hoeffding Tree,Keystroke,0.7624512534818941,0.7624512534818941,0.7625605608371232,2.3187265396118164,828.9274100000001 +9384,Multiclass classification,Hoeffding Tree,Keystroke,0.7605243525524885,0.7605243525524885,0.7588384348689571,2.4143505096435547,933.4845880000001 +9792,Multiclass classification,Hoeffding Tree,Keystroke,0.753344908589521,0.753344908589521,0.7499438215834663,2.509974479675293,1046.19484 +10200,Multiclass classification,Hoeffding Tree,Keystroke,0.7450730463770958,0.7450730463770959,0.7369660419615974,2.6055984497070312,1167.344916 +10608,Multiclass classification,Hoeffding Tree,Keystroke,0.7240501555576506,0.7240501555576506,0.7111305646829175,2.7012224197387695,1296.919782 +11016,Multiclass classification,Hoeffding Tree,Keystroke,0.7166591012256015,0.7166591012256015,0.7122511515574346,2.796846389770508,1434.7760759999999 +11424,Multiclass classification,Hoeffding Tree,Keystroke,0.710146196270682,0.710146196270682,0.6963016796632095,2.892470359802246,1580.7280859999998 +11832,Multiclass classification,Hoeffding Tree,Keystroke,0.7005324993660722,0.7005324993660722,0.6925666211338901,2.9880943298339844,1735.0271709999997 +12240,Multiclass classification,Hoeffding Tree,Keystroke,0.7043876133671052,0.7043876133671052,0.7007845610449206,3.0837182998657227,1897.6526119999996 +12648,Multiclass classification,Hoeffding Tree,Keystroke,0.7004032576895707,0.7004032576895707,0.6915775762792657,3.179342269897461,2069.0860809999995 +13056,Multiclass classification,Hoeffding Tree,Keystroke,0.6877058598238223,0.6877058598238223,0.6789768292873962,3.274966239929199,2249.3891769999996 +13464,Multiclass classification,Hoeffding Tree,Keystroke,0.6838743222164451,0.6838743222164451,0.6791243465680947,3.3705902099609375,2438.6931489999997 +13872,Multiclass classification,Hoeffding Tree,Keystroke,0.6822146925239708,0.6822146925239708,0.6786558938530485,3.466214179992676,2637.6841019999997 +14280,Multiclass classification,Hoeffding Tree,Keystroke,0.6777085230058127,0.6777085230058127,0.6725285130045525,3.561838150024414,2845.8288079999998 +14688,Multiclass classification,Hoeffding Tree,Keystroke,0.6807380676788997,0.6807380676788997,0.6786761142186741,3.6574621200561523,3062.9942149999997 +15096,Multiclass classification,Hoeffding Tree,Keystroke,0.6873799271281882,0.6873799271281882,0.6854839306484398,3.7530860900878906,3290.055422 +15504,Multiclass classification,Hoeffding Tree,Keystroke,0.6858027478552539,0.6858027478552539,0.6816808496509055,3.848710060119629,3526.69202 +15912,Multiclass classification,Hoeffding Tree,Keystroke,0.6765759537426937,0.6765759537426937,0.6694713281964946,3.944334030151367,3772.997519 +16320,Multiclass classification,Hoeffding Tree,Keystroke,0.6673815797536614,0.6673815797536614,0.6617321933140904,4.0399580001831055,4029.133223 +16728,Multiclass classification,Hoeffding Tree,Keystroke,0.6643151790518323,0.6643151790518323,0.661178029358405,4.135581970214844,4295.086238 +17136,Multiclass classification,Hoeffding Tree,Keystroke,0.6598774438284214,0.6598774438284214,0.655734247886306,4.3294572830200195,4570.827071 +17544,Multiclass classification,Hoeffding Tree,Keystroke,0.6518269395200365,0.6518269395200365,0.6481085155228206,4.425081253051758,4856.254143 +17952,Multiclass classification,Hoeffding Tree,Keystroke,0.6507158375577963,0.6507158375577963,0.6489368995854258,4.520705223083496,5151.869359 +18360,Multiclass classification,Hoeffding Tree,Keystroke,0.6566806470940683,0.6566806470940683,0.6555764711123695,4.616329193115234,5457.498716 +18768,Multiclass classification,Hoeffding Tree,Keystroke,0.662279533223211,0.662279533223211,0.6615432060687808,4.711953163146973,5772.982264 +19176,Multiclass classification,Hoeffding Tree,Keystroke,0.6534028683181226,0.6534028683181226,0.6508089832432514,4.807577133178711,6098.679956 +19584,Multiclass classification,Hoeffding Tree,Keystroke,0.6577643874789358,0.6577643874789358,0.6564201177589184,4.903201103210449,6434.678037 +19992,Multiclass classification,Hoeffding Tree,Keystroke,0.6518433294982742,0.6518433294982742,0.6501496360982542,4.9988250732421875,6781.324361 +20400,Multiclass classification,Hoeffding Tree,Keystroke,0.6482180499044071,0.6482180499044071,0.6472493759146578,5.094449043273926,7138.730487 +46,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,0.42345714569091797,0.325579 +92,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5164835164835165,0.5164835164835165,0.5335477748411618,0.42351436614990234,1.056326 +138,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.5474452554744526,0.5474452554744526,0.5743273066802479,0.42363643646240234,2.202996 +184,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6120218579234973,0.6120218579234973,0.6355989308336889,0.4237203598022461,3.699294 +230,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6375545851528385,0.6375545851528385,0.6557923943920432,0.4237203598022461,5.564336 +276,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.6509090909090909,0.6509090909090909,0.66910740948952,0.4237699508666992,7.749814 +322,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.67601246105919,0.67601246105919,0.678427291711157,0.4238309860229492,10.278631 +368,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7002724795640327,0.7002724795640327,0.6988359939675117,0.42380428314208984,13.125556000000001 +414,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.711864406779661,0.711864406779661,0.7104564330601258,0.4237241744995117,16.369918000000002 +460,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7124183006535948,0.7124183006535948,0.7087721216219991,0.4238004684448242,19.921878000000003 +506,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7207920792079208,0.7207920792079208,0.7145025942185106,0.4238004684448242,23.844357000000002 +552,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7223230490018149,0.7223230490018149,0.7174926871575792,0.4236936569213867,28.111685 +598,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7269681742043551,0.7269681742043551,0.7216367248754637,0.42371654510498047,32.752989 +644,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7262830482115086,0.7262830482115085,0.7230014848259525,0.4237508773803711,37.712808 +690,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7300435413642961,0.7300435413642961,0.7265684058467008,0.4237508773803711,43.006145000000004 +736,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7319727891156462,0.7319727891156461,0.7296570819427115,0.42377758026123047,48.68780100000001 +782,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.737516005121639,0.737516005121639,0.7350906419548328,0.42377758026123047,54.691720000000004 +828,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7363966142684402,0.7363966142684402,0.7359651798179677,0.42377758026123047,60.98272 +874,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7422680412371134,0.7422680412371134,0.7398886847335938,0.42377758026123047,67.641769 +920,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7421109902067464,0.7421109902067464,0.738912026501458,0.4237508773803711,74.649906 +966,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7419689119170985,0.7419689119170985,0.7379593683174607,0.4237508773803711,81.98079 +1012,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7418397626112759,0.741839762611276,0.7380802548116379,0.4237508773803711,89.699811 +1058,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7436140018921475,0.7436140018921475,0.7390703652035102,0.4237508773803711,97.73816099999999 +1104,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7461468721668177,0.7461468721668177,0.7413714574148674,0.4238004684448242,106.141078 +1150,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7476066144473456,0.7476066144473456,0.742441565911322,0.4238004684448242,114.87573499999999 +1196,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7506276150627615,0.7506276150627615,0.7460917536510117,0.4234342575073242,123.97312099999999 +1242,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7510072522159549,0.7510072522159549,0.7470578866974922,0.4235563278198242,133.391788 +1288,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.756021756021756,0.7560217560217559,0.7510482446555896,0.4236173629760742,143.113173 +1334,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7569392348087022,0.7569392348087022,0.7522366633133313,0.4236173629760742,153.228885 +1380,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7585206671501088,0.7585206671501088,0.7544196711061472,0.4236783981323242,163.64661999999998 +1426,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7614035087719299,0.7614035087719299,0.7567964121564391,0.4236783981323242,174.36664399999998 +1472,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7654656696125085,0.7654656696125085,0.7591802078998249,0.4236783981323242,185.463757 +1518,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7673038892551087,0.7673038892551087,0.7600352016074767,0.4237394332885742,196.90308 +1564,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7677543186180422,0.7677543186180422,0.7612494392404334,0.4237394332885742,208.647576 +1610,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7675574891236793,0.7675574891236793,0.7602773300593106,0.42376232147216797,220.786107 +1656,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.76797583081571,0.76797583081571,0.7607906010792568,0.42376232147216797,233.21939999999998 +1702,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7677836566725456,0.7677836566725456,0.7627036277641847,0.42376232147216797,245.952092 +1748,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7710360618202633,0.7710360618202633,0.7657334796773966,0.42376232147216797,259.02702999999997 +1794,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7724484104852203,0.7724484104852203,0.7657758298578787,0.4237356185913086,272.39410699999996 +1840,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7737901033170201,0.77379010331702,0.767302943564198,0.4237966537475586,286.06776199999996 +1886,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7724137931034483,0.7724137931034483,0.7666353585191567,0.4237966537475586,300.095471 +1932,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7731745209735889,0.7731745209735889,0.7666634536176192,0.4237966537475586,314.417396 +1978,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7738998482549317,0.7738998482549316,0.7665909326930368,0.4237966537475586,329.067854 +2024,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7750865051903114,0.7750865051903113,0.7662611838286661,0.4237966537475586,344.01511700000003 +2070,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7747704204929918,0.7747704204929918,0.7660645062500586,0.4237966537475586,359.290159 +2116,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7754137115839244,0.7754137115839244,0.7658988206988366,0.4237966537475586,374.882405 +2162,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7760296159185562,0.7760296159185563,0.7660708746783081,0.4237966537475586,390.75768 +2208,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.777979157227005,0.7779791572270048,0.7670029065892423,0.4237966537475586,407.002801 +2254,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7749667110519307,0.7749667110519308,0.7639707440456852,0.4237966537475586,423.546299 +2300,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7742496737712049,0.7742496737712049,0.7632394528829524,0.4237966537475586,440.39336399999996 +2310,Multiclass classification,Hoeffding Adaptive Tree,ImageSegments,0.7743611953226505,0.7743611953226506,0.7633622232911937,0.4237966537475586,457.310729 +1056,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6161137440758294,0.6161137440758294,0.581384151333148,0.6645784378051758,11.249192 +2112,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6120322122216959,0.6120322122216959,0.5792161554760864,0.6646394729614258,32.358705 +3168,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6049889485317335,0.6049889485317335,0.5721633809277145,0.6647005081176758,62.851539 +4224,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.603125739995264,0.603125739995264,0.5703574432462961,0.6647005081176758,102.700179 +5280,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6061754120098504,0.6061754120098504,0.5722430970062696,0.6647615432739258,151.914202 +6336,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5995264404104184,0.5995264404104184,0.5671511237518188,0.6647615432739258,210.432187 +7392,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5972128264104992,0.5972128264104992,0.5650210504998666,0.6647615432739258,278.26775499999997 +8448,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5989108559251806,0.5989108559251806,0.566418690076869,0.6647615432739258,355.20493799999997 +9504,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5962327685993897,0.5962327685993897,0.5633780031885509,0.6647615432739258,441.186739 +10560,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5964579979164694,0.5964579979164694,0.5634236596216465,0.6648225784301758,536.283653 +11616,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.594317692638829,0.594317692638829,0.5620068495149612,0.6648225784301758,640.2689049999999 +12672,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5975061163286244,0.5975061163286244,0.567518061449456,0.6648225784301758,753.0441599999999 +13728,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6097472135207984,0.6097472135207984,0.5927729676671933,0.6648225784301758,874.528885 +14784,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001488195900697,0.6001488195900697,0.5832911478837771,0.6645174026489258,1004.5501099999999 +15840,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5673969316244712,0.5673969316244712,0.5522471754341495,0.8876123428344727,1142.6522839999998 +16896,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5712340929269014,0.5712340929269014,0.5590383236849579,1.4319400787353516,1288.8770269999998 +17952,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5741184335134533,0.5741184335134533,0.5632919959429028,1.8629226684570312,1445.4718369999998 +19008,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5867312042931552,0.5867312042931552,0.5723846445183198,0.4819307327270508,1609.073978 +20064,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5966704879629168,0.5966704879629168,0.5796820575913003,0.6649179458618164,1780.2710459999998 +21120,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5984658364505895,0.5984658364505895,0.5810209140208816,0.6650400161743164,1958.8195809999997 +22176,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6001803833145434,0.6001803833145434,0.5822125955100945,1.2073478698730469,2144.7260309999997 +23232,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6020403770823468,0.6020403770823468,0.5837921358595156,1.3215751647949219,2339.5310459999996 +24288,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6047268085807221,0.6047268085807221,0.5859785990228289,1.3216361999511719,2543.839083 +25344,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6069131515605887,0.6069131515605887,0.587737290445056,1.3217582702636719,2757.2066809999997 +26400,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6094927838175689,0.6094927838175689,0.5895162861993263,1.3217582702636719,2979.334505 +27456,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6105991622655254,0.6105991622655254,0.5896134687358237,1.3219413757324219,3211.0823579999997 +28512,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6106064326049595,0.6106064326049595,0.5910741826972655,1.3219413757324219,3451.5448549999996 +29568,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6099029323231981,0.6099029323231981,0.5935355609859342,1.3219413757324219,3700.7129539999996 +30624,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6088887437546942,0.6088887437546942,0.5952474102625339,1.3214530944824219,3958.5322249999995 +31680,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6088891694813598,0.6088891694813598,0.5975058139751561,1.3216972351074219,4224.837575 +32736,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6095921796242554,0.6095921796242554,0.5998546240309938,1.3217582702636719,4499.473663 +33792,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6043917019324673,0.6043917019324673,0.595080118632132,0.6649713516235352,4783.331389999999 +34848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6034378856142566,0.6034378856142566,0.5941773754098104,0.6650934219360352,5073.360361999999 +35904,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6029022644347269,0.6029022644347269,0.5935512429191343,0.6651544570922852,5369.406481999999 +36960,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6013690846613815,0.6013690846613815,0.5919623858291095,0.6651544570922852,5671.388488999999 +38016,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6010259108246745,0.6010259108246745,0.5912597483191937,0.6651544570922852,5979.127636999999 +39072,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6003429653707353,0.6003429653707353,0.5902279082897147,0.6648492813110352,6292.481400999999 +40128,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5961322800109652,0.5961322800109652,0.5867765456240649,0.6648492813110352,6611.499413 +41184,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5939829541315591,0.5939829541315591,0.585290407267574,0.6650323867797852,6936.132393 +42240,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5925803167688629,0.5925803167688629,0.5844470095695741,0.6650934219360352,7266.407125 +43296,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5911306155445202,0.5911306155445202,0.5835517912214992,0.6651544570922852,7602.391688 +44352,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.58959211742689,0.58959211742689,0.58246410272577,1.1046571731567383,7943.862096 +45408,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5875746030347744,0.5875746030347744,0.5808874407233396,1.3207244873046875,8291.951918 +46464,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5862083808621914,0.5862083808621914,0.5791892600330408,1.3209075927734375,8644.890712 +47520,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5879332477535302,0.5879332477535302,0.5810233099134106,1.3210525512695312,9004.012781000001 +48576,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5928152341739578,0.5928152341739578,0.5858160887305829,1.3216018676757812,9370.107000000002 +49632,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.5979327436481231,0.5979327436481231,0.5906079347867982,1.3215408325195312,9743.028377000002 +50688,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6027383747311934,0.6027383747311934,0.594893758427483,1.3217239379882812,10122.858893000002 +51744,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6077923583866417,0.6077923583866417,0.5993180348311721,1.3217239379882812,10509.572003000003 +52800,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.612985094414667,0.612985094414667,0.6039181082054342,0.14382553100585938,10901.200853000002 +52848,Multiclass classification,Hoeffding Adaptive Tree,Insects,0.6133366132420005,0.6133366132420005,0.604218855594392,0.14382553100585938,11292.868844000002 +408,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.23062610626220703,0.871514 +816,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,0.3262500762939453,3.583779 +1224,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8863450531479967,0.8863450531479967,0.8786592421362933,0.4218740463256836,8.686347999999999 +1632,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.891477621091355,0.891477621091355,0.8818548670971931,0.5179252624511719,16.685395 +2040,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.889651790093183,0.889651790093183,0.8812768038030504,0.6251459121704102,28.245741 +2448,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8414384961176952,0.8414384961176952,0.8420581397672002,0.7206478118896484,43.571154 +2856,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8500875656742557,0.8500875656742557,0.8345582037188519,0.8163328170776367,63.099422000000004 +3264,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8406374501992032,0.8406374501992032,0.8151418555553325,0.911895751953125,87.33095300000001 +3672,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8321983110868973,0.8321983110868973,0.8307198315203921,1.0075807571411133,116.498805 +4080,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.826182887962736,0.826182887962736,0.8123767856033619,1.128366470336914,151.118073 +4488,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.809226654780477,0.809226654780477,0.8196273526663149,1.2239294052124023,191.82030500000002 +4896,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8081716036772216,0.8081716036772216,0.815232111826365,1.3194313049316406,239.06161600000001 +5304,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.8057703186875353,0.8057703186875353,0.7903391475861199,1.415055274963379,293.29488000000003 +5712,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7860269655051655,0.7860269655051656,0.7895763142947654,1.5108013153076172,355.22640600000005 +6120,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.784441902271613,0.784441902271613,0.7657785418705475,1.6062421798706055,425.24061900000004 +6528,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7585414432357898,0.7585414432357898,0.751418836389106,1.7020492553710938,503.46722600000004 +6936,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7473684210526316,0.7473684210526316,0.7484284412750404,1.797490119934082,590.6999010000001 +7344,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7565027917744791,0.7565027917744791,0.7526701844923946,1.8947620391845703,687.248946 +7752,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7577086827506129,0.7577086827506129,0.755735065870518,1.9903860092163086,793.498598 +8160,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7617355068023042,0.7617355068023042,0.7576049653668414,2.085948944091797,909.902095 +8568,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7604762460604646,0.7604762460604646,0.7596175662696861,2.2296838760375977,1036.556796 +8976,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.756991643454039,0.7569916434540391,0.7575313939177277,2.325368881225586,1173.4366320000001 +9384,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7558350207822658,0.7558350207822658,0.7548436696787698,2.420870780944824,1320.145727 +9792,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.748340312531917,0.7483403125319169,0.744390859626019,2.5164337158203125,1476.9925130000001 +10200,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7393862143347387,0.7393862143347387,0.7315892779928432,2.612057685852051,1644.1937280000002 +10608,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7196191194494201,0.7196191194494201,0.7089541376321258,2.707803726196289,1822.1938220000002 +11016,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7123921924648207,0.7123921924648208,0.7092068316988943,2.8033666610717773,2011.0989090000003 +11424,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7062943184802591,0.7062943184802591,0.6946713230955313,2.8989906311035156,2211.8042590000005 +11832,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6967289324655566,0.6967289324655566,0.690232830798306,2.994553565979004,2423.5715250000003 +12240,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7007108423890841,0.7007108423890841,0.6983689907908355,3.090177536010742,2646.6754960000003 +12648,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6969241717403337,0.6969241717403337,0.6892508246262707,3.1858625411987305,2881.7592360000003 +13056,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6836461126005362,0.6836461126005362,0.6755391962059192,3.2815475463867188,3128.5577150000004 +13464,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6793433855752804,0.6793433855752804,0.6754035266161623,3.377110481262207,3387.3558160000002 +13872,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6769519140653161,0.6769519140653161,0.6742482232309566,3.4728565216064453,3658.0697 +14280,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6728762518383641,0.6728762518383641,0.6689356443053495,3.5684194564819336,3940.688111 +14688,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6762442976782188,0.6762442976782188,0.6753292472514647,3.663982391357422,4235.610853 +15096,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6830076184166942,0.6830076184166942,0.6822311287838643,3.75966739654541,4542.8267670000005 +15504,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6818035218989873,0.6818035218989873,0.6788656596145114,3.8552303314208984,4862.152597 +15912,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6816039218150964,0.6816039218150964,0.6801525397911032,0.2705574035644531,5190.397888 +16320,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6858263373981249,0.6858263373981249,0.685191280018575,0.46213340759277344,5522.880902000001 +16728,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6896634184253004,0.6896634184253004,0.6890226069872224,0.6535873413085938,5860.018685000001 +17136,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6925007295010213,0.6925007295010213,0.6918635442211969,0.9691534042358398,6202.345681000001 +17544,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.6990252522373597,0.6990252522373597,0.6986638608261282,0.2649049758911133,6547.073149000001 +17952,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7038605091638349,0.7038605091638349,0.7032543903990934,0.579315185546875,6893.121988000001 +18360,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.710114930007081,0.7101149300070809,0.70950849929648,0.2349414825439453,7240.035665000001 +18768,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.715351414717323,0.715351414717323,0.7146010079934133,0.3305654525756836,7588.155090000001 +19176,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7179139504563233,0.7179139504563233,0.7169858006379833,0.4260063171386719,7937.751954000001 +19584,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7223612316805392,0.7223612316805392,0.7214649429496548,0.5217523574829102,8289.115139000001 +19992,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7219248661897854,0.7219248661897855,0.7206428236711905,0.6287288665771484,8642.591702000002 +20400,Multiclass classification,Hoeffding Adaptive Tree,Keystroke,0.7231236825334575,0.7231236825334575,0.7218249685926471,0.7244749069213867,8998.461289 +46,Multiclass classification,Adaptive Random Forest,ImageSegments,0.4222222222222222,0.4222222222222222,0.3590236094437775,0.9685115814208984,1.326052 +92,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5604395604395604,0.5604395604395604,0.5746538615446178,1.0556058883666992,4.053487 +138,Multiclass classification,Adaptive Random Forest,ImageSegments,0.5766423357664233,0.5766423357664233,0.598257695340355,1.344954490661621,8.154789999999998 +184,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6229508196721312,0.6229508196721312,0.6451744040758778,1.4133405685424805,13.553012999999998 +230,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6506550218340611,0.6506550218340611,0.6680655280025949,1.5576086044311523,20.188933 +276,Multiclass classification,Adaptive Random Forest,ImageSegments,0.6727272727272727,0.6727272727272727,0.6900672130049011,1.7550430297851562,28.051384 +322,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7040498442367601,0.7040498442367601,0.7087861936875776,1.832967758178711,37.153949999999995 +368,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7302452316076294,0.7302452316076294,0.7285991575377422,1.971024513244629,47.432601999999996 +414,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7457627118644068,0.7457627118644068,0.7430362907281778,1.991847038269043,58.97377399999999 +460,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7342047930283224,0.7342047930283224,0.7271744800226857,1.8101978302001953,71.823928 +506,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7405940594059406,0.7405940594059406,0.7304322149686578,1.7132930755615234,85.827474 +552,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7368421052631579,0.7368421052631579,0.7267508109083203,1.5079193115234375,101.049314 +598,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7403685092127303,0.7403685092127302,0.7318978254380314,1.6471452713012695,117.42015599999999 +644,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7325038880248833,0.7325038880248833,0.7248107612258207,1.7740907669067383,135.017443 +690,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7242380261248186,0.7242380261248187,0.7153272190465999,1.913142204284668,153.656893 +736,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7251700680272108,0.725170068027211,0.7148466398758337,2.0619029998779297,173.429455 +782,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7259923175416133,0.7259923175416134,0.7134712280209221,2.0208959579467773,194.315292 +828,Multiclass classification,Adaptive Random Forest,ImageSegments,0.727932285368803,0.727932285368803,0.7177600265828429,2.224555015563965,216.352158 +874,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7353951890034365,0.7353951890034366,0.7262567978322628,2.300021171569824,239.599524 +920,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7431991294885746,0.7431991294885745,0.7345004589126253,2.4412155151367188,263.99359400000003 +966,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7471502590673575,0.7471502590673575,0.7368855656689403,2.474191665649414,289.66420500000004 +1012,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7546983184965381,0.754698318496538,0.7446216664767904,2.5655078887939453,316.44421900000003 +1058,Multiclass classification,Adaptive Random Forest,ImageSegments,0.760643330179754,0.760643330179754,0.7502594177262459,2.798956871032715,344.45448600000003 +1104,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7624660018132366,0.7624660018132366,0.7523020427630668,2.48898983001709,373.71735 +1150,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7650130548302873,0.7650130548302874,0.7555087521342715,2.3284912109375,404.061966 +1196,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7690376569037657,0.7690376569037657,0.7603504370239861,2.0560731887817383,435.51003499999996 +1242,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7719580983078163,0.7719580983078163,0.7638249032322542,2.1193370819091797,467.96964099999997 +1288,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7746697746697747,0.7746697746697747,0.7668828628349821,2.277647018432617,501.44875399999995 +1334,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7771942985746436,0.7771942985746436,0.7696789046658701,2.3871631622314453,535.887669 +1380,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7817258883248731,0.7817258883248731,0.7754511149783997,2.3104944229125977,571.357393 +1426,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7866666666666666,0.7866666666666666,0.7797171864703156,2.4089183807373047,607.784244 +1472,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7912984364377974,0.7912984364377974,0.7836430453045393,2.5425024032592773,645.2286509999999 +1518,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7963085036255768,0.7963085036255768,0.7883976288226553,2.6389265060424805,683.7451019999999 +1564,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7978246960972489,0.7978246960972489,0.790949738475821,2.283763885498047,723.3862519999999 +1610,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798011187072716,0.7980111870727161,0.7914720525222512,2.519012451171875,764.1312649999999 +1656,Multiclass classification,Adaptive Random Forest,ImageSegments,0.7981873111782477,0.7981873111782477,0.7919320984228655,2.307619094848633,806.0160599999999 +1702,Multiclass classification,Adaptive Random Forest,ImageSegments,0.798941798941799,0.7989417989417988,0.7945012991620244,2.40640926361084,848.960292 +1748,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8019461934745278,0.8019461934745278,0.797056036319667,2.447686195373535,893.037184 +1794,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8047964305633017,0.8047964305633019,0.7993493873930555,2.5208606719970703,938.202728 +1840,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8069603045133225,0.8069603045133223,0.8019867749609348,2.8025121688842773,984.592034 +1886,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8084880636604774,0.8084880636604774,0.8043300839686539,2.9287471771240234,1032.221691 +1932,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8114966338684619,0.8114966338684619,0.8071482324590065,2.977842330932617,1081.048247 +1978,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8148710166919575,0.8148710166919576,0.8107088256390683,3.110445022583008,1130.9949649999999 +2024,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8161146811665843,0.8161146811665844,0.8110472160986095,3.3117494583129883,1182.226115 +2070,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8173030449492509,0.8173030449492509,0.8127793203399477,2.7790603637695312,1234.703432 +2116,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8193853427895981,0.8193853427895981,0.8144282151100146,2.8652515411376953,1288.356269 +2162,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8199907450254512,0.8199907450254512,0.8150157846003385,2.925917625427246,1343.2838700000002 +2208,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8205709107385591,0.8205709107385591,0.8153449009635614,2.785597801208496,1399.3252850000001 +2254,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8175765645805593,0.8175765645805593,0.813116129924445,2.868098258972168,1456.6402850000002 +2300,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8186167899086559,0.8186167899086559,0.8144518819207099,3.062863349914551,1515.2003170000003 +2310,Multiclass classification,Adaptive Random Forest,ImageSegments,0.8185361628410567,0.8185361628410566,0.8145347387119569,3.063481330871582,1574.1800910000002 +1056,Multiclass classification,Adaptive Random Forest,Insects,0.6682464454976303,0.6682464454976303,0.6049011732627783,7.181946754455566,32.418226 +2112,Multiclass classification,Adaptive Random Forest,Insects,0.6944576030317385,0.6944576030317385,0.6288311688548281,9.897843360900879,94.87356399999999 +3168,Multiclass classification,Adaptive Random Forest,Insects,0.6984527944426903,0.6984527944426903,0.625371849015863,13.448436737060547,186.837042 +4224,Multiclass classification,Adaptive Random Forest,Insects,0.706369879232773,0.706369879232773,0.6266042661686886,17.43436622619629,307.272577 +5280,Multiclass classification,Adaptive Random Forest,Insects,0.7107406705815495,0.7107406705815495,0.6273487761971507,20.93905258178711,452.99825 +6336,Multiclass classification,Adaptive Random Forest,Insects,0.7108129439621153,0.7108129439621153,0.6274052515282983,25.022296905517578,622.602665 +7392,Multiclass classification,Adaptive Random Forest,Insects,0.7127587606548504,0.7127587606548504,0.6273117178459473,28.819257736206055,816.020547 +8448,Multiclass classification,Adaptive Random Forest,Insects,0.7164673848703682,0.7164673848703682,0.6293431255193823,32.802799224853516,1032.257355 +9504,Multiclass classification,Adaptive Random Forest,Insects,0.721666842049879,0.721666842049879,0.63170101976307,32.88048076629639,1271.699652 +10560,Multiclass classification,Adaptive Random Forest,Insects,0.724405720238659,0.724405720238659,0.6339052025360064,29.71586036682129,1533.827375 +11616,Multiclass classification,Adaptive Random Forest,Insects,0.7244080929832114,0.7244080929832114,0.6334336343217646,33.71169948577881,1818.162347 +12672,Multiclass classification,Adaptive Random Forest,Insects,0.7225949017441402,0.7225949017441402,0.6332595599893077,29.649346351623535,2125.062078 +13728,Multiclass classification,Adaptive Random Forest,Insects,0.7416769869600058,0.7416769869600057,0.7385871869253197,11.750191688537598,2443.2361889999997 +14784,Multiclass classification,Adaptive Random Forest,Insects,0.7472096326861936,0.7472096326861937,0.7473000008879964,7.712667465209961,2772.2292589999997 +15840,Multiclass classification,Adaptive Random Forest,Insects,0.7404507860344719,0.7404507860344719,0.7427443120881612,5.854048728942871,3118.2806729999998 +16896,Multiclass classification,Adaptive Random Forest,Insects,0.73666765315182,0.73666765315182,0.7407696345938622,9.543391227722168,3480.1200929999995 +17952,Multiclass classification,Adaptive Random Forest,Insects,0.7295972369227341,0.7295972369227341,0.7347001031972082,14.625198364257812,3856.6322749999995 +19008,Multiclass classification,Adaptive Random Forest,Insects,0.739780081022781,0.7397800810227809,0.7407912307996387,5.110816955566406,4245.133706999999 +20064,Multiclass classification,Adaptive Random Forest,Insects,0.7434581069630664,0.7434581069630664,0.7402037922066672,3.8148155212402344,4646.574114999999 +21120,Multiclass classification,Adaptive Random Forest,Insects,0.745111037454425,0.7451110374544251,0.7386209934273732,7.313493728637695,5063.578879 +22176,Multiclass classification,Adaptive Random Forest,Insects,0.7462006764374295,0.7462006764374295,0.7365944363606786,12.210733413696289,5495.973683 +23232,Multiclass classification,Adaptive Random Forest,Insects,0.7483965391072274,0.7483965391072274,0.7360584061499352,11.241872787475586,5944.2105440000005 +24288,Multiclass classification,Adaptive Random Forest,Insects,0.7495779635195784,0.7495779635195785,0.7345443205753824,12.262273788452148,6407.867088000001 +25344,Multiclass classification,Adaptive Random Forest,Insects,0.7508582251509293,0.7508582251509293,0.7336140903014292,15.815716743469238,6885.995097000001 +26400,Multiclass classification,Adaptive Random Forest,Insects,0.7510890564036516,0.7510890564036516,0.7317409587301968,20.072275161743164,7378.034229000001 +27456,Multiclass classification,Adaptive Random Forest,Insects,0.7520670187579676,0.7520670187579677,0.7304776676466566,23.249674797058105,7884.702304 +28512,Multiclass classification,Adaptive Random Forest,Insects,0.7487285609063169,0.7487285609063169,0.7285292321096271,2.7024307250976562,8406.670172 +29568,Multiclass classification,Adaptive Random Forest,Insects,0.7464741096492712,0.7464741096492712,0.7309964825863351,6.2935638427734375,8940.901067 +30624,Multiclass classification,Adaptive Random Forest,Insects,0.7457793162002416,0.7457793162002416,0.7347045068936117,9.350909233093262,9487.044090000001 +31680,Multiclass classification,Adaptive Random Forest,Insects,0.745036143817671,0.745036143817671,0.7375864352537521,14.599569320678711,10044.836672000001 +32736,Multiclass classification,Adaptive Random Forest,Insects,0.7451962731021842,0.7451962731021842,0.7406480970104784,19.125198364257812,10615.117300000002 +33792,Multiclass classification,Adaptive Random Forest,Insects,0.7402858749371134,0.7402858749371134,0.7370798749337869,6.808139801025391,11202.653786000003 +34848,Multiclass classification,Adaptive Random Forest,Insects,0.7366200820730623,0.7366200820730623,0.7333315604235389,5.8602495193481445,11807.700361000003 +35904,Multiclass classification,Adaptive Random Forest,Insects,0.733921956382475,0.7339219563824751,0.7303171015411175,9.36469554901123,12429.747970000002 +36960,Multiclass classification,Adaptive Random Forest,Insects,0.7304039611461349,0.7304039611461349,0.7265687877692525,14.848862648010254,13069.446785000002 +38016,Multiclass classification,Adaptive Random Forest,Insects,0.7276864395633302,0.7276864395633302,0.7236022807953257,19.807891845703125,13727.939023000003 +39072,Multiclass classification,Adaptive Random Forest,Insects,0.7250134370760922,0.7250134370760921,0.7209989950382084,16.71243381500244,14405.601845000003 +40128,Multiclass classification,Adaptive Random Forest,Insects,0.7235028783612032,0.7235028783612032,0.7198278735760195,8.331427574157715,15101.835691000002 +41184,Multiclass classification,Adaptive Random Forest,Insects,0.723623825364835,0.723623825364835,0.7203262236880287,6.9819841384887695,15814.868539000003 +42240,Multiclass classification,Adaptive Random Forest,Insects,0.7240464973129098,0.7240464973129098,0.7211005399097123,10.71219539642334,16543.112989 +43296,Multiclass classification,Adaptive Random Forest,Insects,0.7245409400623629,0.7245409400623629,0.721844297210525,10.330558776855469,17285.760894000003 +44352,Multiclass classification,Adaptive Random Forest,Insects,0.7248765529525828,0.7248765529525828,0.7223628081683402,13.299851417541504,18041.694028 +45408,Multiclass classification,Adaptive Random Forest,Insects,0.7254167859581122,0.7254167859581122,0.7228420559832612,15.662115097045898,18810.181113000002 +46464,Multiclass classification,Adaptive Random Forest,Insects,0.7263844349267159,0.7263844349267159,0.7236482152790997,19.25161361694336,19591.516438000002 +47520,Multiclass classification,Adaptive Random Forest,Insects,0.7265304404553968,0.7265304404553967,0.7240124567772878,14.065608024597168,20387.990038000004 +48576,Multiclass classification,Adaptive Random Forest,Insects,0.7304374678332476,0.7304374678332476,0.7281756207358935,7.354809761047363,21197.413376000004 +49632,Multiclass classification,Adaptive Random Forest,Insects,0.7344603171404969,0.7344603171404969,0.7322565876518081,7.006095886230469,22016.972025000003 +50688,Multiclass classification,Adaptive Random Forest,Insects,0.7380590684001815,0.7380590684001815,0.7356981427827818,10.14159107208252,22847.182754 +51744,Multiclass classification,Adaptive Random Forest,Insects,0.7420134124422627,0.7420134124422627,0.7394134340953542,13.563420295715332,23688.037606 +52800,Multiclass classification,Adaptive Random Forest,Insects,0.7451466883842497,0.7451466883842497,0.7430487162081567,0.3614501953125,24535.706056000003 +52848,Multiclass classification,Adaptive Random Forest,Insects,0.7453781671618067,0.7453781671618067,0.7433023109254195,0.36179351806640625,25383.518073000003 +408,Multiclass classification,Adaptive Random Forest,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,0.3354053497314453,3.23067 +816,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730061349693252,0.9730061349693252,0.8116978142719798,0.988037109375,11.21298 +1224,Multiclass classification,Adaptive Random Forest,Keystroke,0.9730171708912511,0.9730171708912511,0.9579161898493525,2.195523262023926,25.427007 +1632,Multiclass classification,Adaptive Random Forest,Keystroke,0.9693439607602697,0.9693439607602697,0.9069773132409142,3.526730537414551,46.453053999999995 +2040,Multiclass classification,Adaptive Random Forest,Keystroke,0.9666503187837175,0.9666503187837175,0.9303026980117671,5.496582984924316,74.431187 +2448,Multiclass classification,Adaptive Random Forest,Keystroke,0.9660809154066203,0.9660809154066203,0.9555178664837441,2.29970645904541,107.969459 +2856,Multiclass classification,Adaptive Random Forest,Keystroke,0.9691768826619965,0.9691768826619965,0.9674134048328416,3.376467704772949,146.96126800000002 +3264,Multiclass classification,Adaptive Random Forest,Keystroke,0.9672080907140668,0.9672080907140668,0.9546197483047236,4.62060546875,192.073824 +3672,Multiclass classification,Adaptive Random Forest,Keystroke,0.9684009806592209,0.968400980659221,0.9654409635782653,3.119338035583496,243.354323 +4080,Multiclass classification,Adaptive Random Forest,Keystroke,0.9644520715861731,0.9644520715861731,0.95030552665756,4.705347061157227,301.433133 +4488,Multiclass classification,Adaptive Random Forest,Keystroke,0.9661243592600847,0.9661243592600847,0.9659906155964958,1.508072853088379,365.412759 +4896,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677221654749745,0.9677221654749745,0.96768641848376,2.487558364868164,434.672843 +5304,Multiclass classification,Adaptive Random Forest,Keystroke,0.9685083914765227,0.9685083914765227,0.9677400809149086,2.8771514892578125,509.85413800000003 +5712,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690071791279986,0.9690071791279986,0.9686984277929261,4.140267372131348,591.7133210000001 +6120,Multiclass classification,Adaptive Random Forest,Keystroke,0.9671514953423762,0.9671514953423762,0.9635575047511442,5.121949195861816,681.1937680000001 +6528,Multiclass classification,Adaptive Random Forest,Keystroke,0.9675195342423778,0.9675195342423778,0.9673223823066149,2.1385393142700195,777.2102420000001 +6936,Multiclass classification,Adaptive Random Forest,Keystroke,0.9685652487382841,0.9685652487382841,0.9688652926813892,2.7864933013916016,879.0640510000001 +7344,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686776521857552,0.9686776521857552,0.9682274153773371,3.314570426940918,987.0629210000001 +7752,Multiclass classification,Adaptive Random Forest,Keystroke,0.9682621597213262,0.9682621597213262,0.9674704101631952,4.690197944641113,1101.141854 +8160,Multiclass classification,Adaptive Random Forest,Keystroke,0.96727540139723,0.96727540139723,0.9662379529396136,5.223731994628906,1221.487909 +8568,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677833547332788,0.9677833547332788,0.9678822443058487,4.885932922363281,1347.980617 +8976,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686908077994429,0.9686908077994429,0.9690861219789195,6.402636528015137,1480.694289 +9384,Multiclass classification,Adaptive Random Forest,Keystroke,0.9683470105509965,0.9683470105509965,0.9680699356268633,6.928671836853027,1620.259773 +9792,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686446736799101,0.9686446736799101,0.9687197530276813,5.552419662475586,1766.078849 +10200,Multiclass classification,Adaptive Random Forest,Keystroke,0.9684282772820865,0.9684282772820866,0.9682582636163196,2.695918083190918,1917.758924 +10608,Multiclass classification,Adaptive Random Forest,Keystroke,0.9673800320543038,0.9673800320543038,0.9668238422002586,3.239151954650879,2074.190769 +11016,Multiclass classification,Adaptive Random Forest,Keystroke,0.9676804357694053,0.9676804357694053,0.9678040910458205,4.023995399475098,2235.420867 +11424,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677842948437363,0.9677842948437363,0.9678364439490078,4.695375442504883,2402.1641919999997 +11832,Multiclass classification,Adaptive Random Forest,Keystroke,0.9677119432000676,0.9677119432000676,0.9677086079179034,5.258674621582031,2574.8706989999996 +12240,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687065936759539,0.9687065936759539,0.9690716756618885,6.001680374145508,2753.3671299999996 +12648,Multiclass classification,Adaptive Random Forest,Keystroke,0.9688463667272871,0.9688463667272871,0.9689334511448673,5.217698097229004,2937.7699639999996 +13056,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687476062811183,0.9687476062811183,0.968764477893114,5.266051292419434,3127.8024029999997 +13464,Multiclass classification,Adaptive Random Forest,Keystroke,0.9687291094109782,0.9687291094109782,0.9687736841624996,6.279603958129883,3323.370395 +13872,Multiclass classification,Adaptive Random Forest,Keystroke,0.9695047220820416,0.9695047220820416,0.9697384724636318,4.041820526123047,3524.041026 +14280,Multiclass classification,Adaptive Random Forest,Keystroke,0.9682750892919673,0.9682750892919673,0.9680357071263168,2.1731691360473633,3729.110149 +14688,Multiclass classification,Adaptive Random Forest,Keystroke,0.9686797848437394,0.9686797848437394,0.9688099431838716,2.4900379180908203,3938.3384300000002 +15096,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692613448161643,0.9692613448161643,0.9694122553904638,2.7789316177368164,4151.996270000001 +15504,Multiclass classification,Adaptive Random Forest,Keystroke,0.9694897761723538,0.9694897761723538,0.969571649124791,3.946505546569824,4370.227344000001 +15912,Multiclass classification,Adaptive Random Forest,Keystroke,0.9694550939601534,0.9694550939601534,0.9694916672888816,4.345325469970703,4594.050341000001 +16320,Multiclass classification,Adaptive Random Forest,Keystroke,0.9695447024940254,0.9695447024940254,0.9695954968773723,3.909954071044922,4823.361799000001 +16728,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692114545345848,0.9692114545345848,0.9692084456743588,1.764338493347168,5057.2303470000015 +17136,Multiclass classification,Adaptive Random Forest,Keystroke,0.9696527575138605,0.9696527575138605,0.9697329621491685,1.7167367935180664,5295.013901000001 +17544,Multiclass classification,Adaptive Random Forest,Keystroke,0.9696745140511885,0.9696745140511885,0.9697082565052514,2.8143720626831055,5537.319837000001 +17952,Multiclass classification,Adaptive Random Forest,Keystroke,0.968748259149908,0.968748259149908,0.968705960089485,2.951136589050293,5784.484584000001 +18360,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690070265264993,0.9690070265264993,0.9690448168177233,3.5441465377807617,6036.117893000001 +18768,Multiclass classification,Adaptive Random Forest,Keystroke,0.9690946874833485,0.9690946874833485,0.9691164520527107,4.379698753356934,6292.729193000001 +19176,Multiclass classification,Adaptive Random Forest,Keystroke,0.968761408083442,0.968761408083442,0.9687617227117352,3.8120603561401367,6554.348831000001 +19584,Multiclass classification,Adaptive Random Forest,Keystroke,0.9689526630240515,0.9689526630240515,0.9689629146490384,2.019772529602051,6819.891372000001 +19992,Multiclass classification,Adaptive Random Forest,Keystroke,0.9692861787804512,0.9692861787804512,0.9692901573177237,1.2564506530761719,7089.584863000001 +20400,Multiclass classification,Adaptive Random Forest,Keystroke,0.9691161331437815,0.9691161331437815,0.9691108096285476,1.6354646682739258,7363.046142000001 +46,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.5333333333333333,0.5333333333333333,0.5005728607232367,0.8510866165161133,0.941842 +92,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6153846153846154,0.6153846153846154,0.596131344383025,1.5052366256713867,2.918201 +138,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6496350364963503,0.6496350364963503,0.6567305057749026,2.146304130554199,6.147886 +184,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.6994535519125683,0.6994535519125683,0.7070190759413217,2.7665939331054688,10.824064 +230,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7379912663755459,0.7379912663755459,0.7433871451842025,3.2484235763549805,16.931166 +276,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7490909090909091,0.7490909090909091,0.7566070103930901,3.776392936706543,24.729994 +322,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7694704049844237,0.7694704049844237,0.7681721604320974,4.142314910888672,34.173162000000005 +368,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.784741144414169,0.7847411444141691,0.7789718513534348,4.497910499572754,45.384105000000005 +414,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7990314769975787,0.7990314769975787,0.7943771701942021,4.869846343994141,58.265676000000006 +460,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.7973856209150327,0.7973856209150327,0.7916511033189314,5.3911848068237305,73.08883800000001 +506,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.805940594059406,0.805940594059406,0.8010859843658406,5.806554794311523,89.87625100000001 +552,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8076225045372051,0.8076225045372051,0.8036838079612314,6.295863151550293,108.59930000000001 +598,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8174204355108877,0.8174204355108878,0.8156009215135775,6.727802276611328,129.48595400000002 +644,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8211508553654744,0.8211508553654744,0.8207645722848749,7.18087100982666,152.525841 +690,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8229317851959361,0.8229317851959362,0.8226135245892084,7.561182022094727,177.86541200000002 +736,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8231292517006803,0.8231292517006803,0.8228959515200417,7.975464820861816,205.49957600000002 +782,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8309859154929577,0.8309859154929577,0.8306123687436626,8.301925659179688,235.39408200000003 +828,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8343409915356711,0.834340991535671,0.835521648488366,8.722038269042969,267.718494 +874,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8407789232531501,0.8407789232531501,0.8414965916969209,9.057206153869629,302.46008700000004 +920,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8443960826985855,0.8443960826985855,0.8446110045111287,9.382828712463379,339.623661 +966,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8466321243523316,0.8466321243523316,0.8462590694093756,9.696897506713867,379.342347 +1012,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8516320474777448,0.8516320474777448,0.8504483916737715,9.949009895324707,421.625642 +1058,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8571428571428571,0.8571428571428571,0.8557487568785946,10.2299222946167,466.542637 +1104,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8603807796917498,0.8603807796917498,0.8594481550185353,10.524299621582031,514.218423 +1150,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8624891209747607,0.8624891209747607,0.8612253786789881,10.737759590148926,564.599929 +1196,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8652719665271966,0.8652719665271966,0.8642881992026393,11.010127067565918,617.836337 +1242,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8670427074939565,0.8670427074939565,0.8663181473795101,11.261144638061523,674.05967 +1288,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8694638694638694,0.8694638694638694,0.8687259920464652,11.505732536315918,733.385389 +1334,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8709677419354839,0.8709677419354839,0.870193396369452,11.826444625854492,796.067675 +1380,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8745467730239304,0.8745467730239304,0.874089581073643,12.086430549621582,861.584672 +1426,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8771929824561403,0.8771929824561403,0.8759011931352845,12.29430866241455,930.2045189999999 +1472,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8796736913664174,0.8796736913664174,0.877566397675441,12.500163078308105,1001.8141389999998 +1518,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8826631509558339,0.8826631509558339,0.8803270226288138,12.740474700927734,1076.4882149999999 +1564,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8841970569417786,0.8841970569417786,0.8822041640143002,12.987508773803711,1154.350794 +1610,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.886886264760721,0.886886264760721,0.8850836875294148,13.252826690673828,1235.463166 +1656,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.888821752265861,0.888821752265861,0.8870702351165313,13.500110626220703,1319.86391 +1702,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8912404467960023,0.8912404467960025,0.8905987472429445,13.767583847045898,1407.541035 +1748,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8929593589009731,0.892959358900973,0.8920318510221457,14.030475616455078,1498.676265 +1794,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.894032348020078,0.894032348020078,0.8925886559949978,14.271255493164062,1593.100327 +1840,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8945078847199565,0.8945078847199565,0.8931986390525462,14.574835777282715,1691.005218 +1886,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.896551724137931,0.896551724137931,0.8956464025201587,14.834091186523438,1792.408733 +1932,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8964267219057483,0.8964267219057483,0.8951782213786073,15.134613037109375,1897.156299 +1978,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8973191704602934,0.8973191704602934,0.8961901832930852,15.326050758361816,2005.31409 +2024,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8986653484923381,0.8986653484923381,0.8970310627029995,15.549851417541504,2116.877653 +2070,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.8994683421942967,0.8994683421942967,0.8980105869909577,15.816215515136719,2232.114727 +2116,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.900709219858156,0.900709219858156,0.8989778942952686,15.957537651062012,2350.8625340000003 +2162,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9000462748727441,0.9000462748727441,0.8982611856050026,16.206623077392578,2473.2186990000005 +2208,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9012233801540552,0.9012233801540552,0.8993036839855942,16.400617599487305,2599.1257530000003 +2254,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9014647137150466,0.9014647137150466,0.8999821457114682,16.693093299865723,2728.6827460000004 +2300,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9016963897346673,0.9016963897346673,0.9003174232892135,16.988688468933105,2861.834306 +2310,Multiclass classification,Aggregated Mondrian Forest,ImageSegments,0.9016890428757037,0.9016890428757037,0.9003808534937335,17.050235748291016,2997.696193 +1056,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6511848341232227,0.6511848341232227,0.5805974192561721,27.882014274597168,41.422615 +2112,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6830885836096636,0.6830885836096636,0.6159001145696381,53.900901794433594,137.16292 +3168,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6889801073571203,0.6889801073571203,0.6135176771695448,79.45620250701904,291.10856 +4224,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6954771489462468,0.6954771489462468,0.6159765684907534,104.90542316436768,501.70617000000004 +5280,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7003220306876302,0.7003220306876302,0.6217575035584229,130.7021541595459,768.289754 +6336,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7021310181531176,0.7021310181531176,0.622391174421368,156.0168752670288,1090.319759 +7392,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7027465836828576,0.7027465836828576,0.6232948240709647,180.83974838256836,1466.44078 +8448,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7040369361903634,0.7040369361903634,0.6235946437988805,205.63252925872803,1896.370643 +9504,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7105124697463959,0.7105124697463959,0.6284709935917355,229.19151210784912,2381.431795 +10560,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7140827729898664,0.7140827729898664,0.6302854833117341,253.17632389068604,2925.98814 +11616,Multiclass classification,Aggregated Mondrian Forest,Insects,0.71562634524322,0.7156263452432199,0.6305326785921538,277.4567346572876,3530.8520359999998 +12672,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7145450240707126,0.7145450240707125,0.6284185449457835,301.7114896774292,4201.052974 +13728,Multiclass classification,Aggregated Mondrian Forest,Insects,0.7057623661397247,0.7057623661397247,0.6885364031919957,327.2237205505371,4936.820951 +14784,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6967462625989312,0.6967462625989312,0.69194472505998,352.6018476486206,5738.465999 +15840,Multiclass classification,Aggregated Mondrian Forest,Insects,0.676684134099375,0.676684134099375,0.673854549025314,384.9730758666992,6613.285946 +16896,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6698431488606097,0.6698431488606097,0.668750254945471,415.2214603424072,7559.921071 +17952,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6646983454960727,0.6646983454960727,0.6646134205077884,444.32067584991455,8589.520858 +19008,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6620192560635555,0.6620192560635555,0.6605985532750915,472.75781440734863,9705.665905 +20064,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6597717190848826,0.6597717190848826,0.6570293922418718,499.48760890960693,10901.076562 +21120,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6539608882996354,0.6539608882996354,0.6496192149174075,528.8777961730957,12166.701144 +22176,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6547463359639233,0.6547463359639233,0.6484047117859243,557.1920728683472,13501.384366 +23232,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6583444535319185,0.6583444535319185,0.6499882024630633,584.0554361343384,14901.095396 +24288,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6611767612302878,0.6611767612302878,0.6506059068013808,610.3706150054932,16366.700533000001 +25344,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6659827171210986,0.6659827171210986,0.6532433614752314,635.6853046417236,17901.739193 +26400,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6702526610856472,0.6702526610856472,0.6554263220708306,660.4025926589966,19504.786084 +27456,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6745947914769623,0.6745947914769623,0.6575507550972549,684.36501121521,21172.086014 +28512,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6705482094630143,0.6705482094630143,0.6539581966383304,712.6770572662354,22902.746936 +29568,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6644231744850678,0.6644231744850678,0.6512239029866641,743.5559530258179,24691.477434 +30624,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6622799856317148,0.6622799856317148,0.6527566844616065,772.5478630065918,26538.585641 +31680,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6621736797247388,0.6621736797247388,0.6557760097374935,800.5439138412476,28440.416189000003 +32736,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6623797159004124,0.6623797159004124,0.6584479912704261,827.4998264312744,30418.714512000002 +33792,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6575123553608949,0.6575123553608949,0.6541419435809196,857.7161102294922,32439.386127 +34848,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6519069073377909,0.6519069073377909,0.6481893367707658,888.8327789306641,34499.720344 +35904,Multiclass classification,Aggregated Mondrian Forest,Insects,0.647550343982397,0.647550343982397,0.643407015045196,919.6311988830566,36599.09766 +36960,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6444438431775752,0.6444438431775752,0.6400224052225335,949.7819452285767,38735.650911 +38016,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6425358411153492,0.6425358411153492,0.6377821595167165,979.4456567764282,40896.763764999996 +39072,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6414476209976709,0.6414476209976709,0.6370415360917451,1009.0255756378174,43085.847267 +40128,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6409898572033793,0.6409898572033793,0.636858231937463,1037.841980934143,45303.144751 +41184,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6414782798727631,0.6414782798727631,0.637272014233453,1065.1163549423218,47540.369251 +42240,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6428419233409882,0.6428419233409882,0.6385110475108609,1091.8334274291992,49803.522006 +43296,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6441159487238711,0.6441159487238711,0.6396283228479406,1118.1560363769531,52086.93226 +44352,Multiclass classification,Aggregated Mondrian Forest,Insects,0.645058735992424,0.645058735992424,0.6403851797193834,1144.4119939804077,54391.61903 +45408,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6469266853128373,0.6469266853128373,0.6418265850265934,1169.9601306915283,56719.958571 +46464,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6487742935238792,0.6487742935238792,0.643191402092947,1194.6403436660767,59073.094705 +47520,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6459521454576065,0.6459521454576065,0.6406800374556137,1224.6073780059814,61451.967815 +48576,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6443643849716932,0.6443643849716932,0.6398250343320808,1254.4350862503052,63857.884093 +49632,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6446172754931394,0.6446172754931394,0.6406945505071863,1282.3891849517822,66293.298766 +50688,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6461222798745241,0.6461222798745241,0.6426238276925219,1309.4736614227295,68755.018108 +51744,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6489186943161394,0.6489186943161394,0.6457243405011626,1334.444143295288,71244.151045 +52800,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6470577094263149,0.6470577094263149,0.6443966707674731,1363.999231338501,73759.934152 +52848,Multiclass classification,Aggregated Mondrian Forest,Insects,0.6469809071470471,0.6469809071470471,0.6443518314696601,1365.409776687622,76295.692169 +408,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9901719901719902,0.9901719901719902,0.8308395677472984,0.12276840209960938,1.485322 +816,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9914110429447853,0.9914110429447853,0.960934413925625,0.41584110260009766,6.729082 +1224,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9893704006541292,0.9893704006541292,0.9580466011674303,1.2467107772827148,20.148490000000002 +1632,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9889638258736971,0.9889638258736971,0.9786672150923964,2.28104305267334,50.264957 +2040,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.988719960765081,0.988719960765081,0.9803510904896324,3.352717399597168,91.64343099999999 +2448,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9885574172456069,0.9885574172456069,0.9830468792370581,4.983606338500977,148.278076 +2856,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9852889667250437,0.9852889667250437,0.9737767108051043,6.963967323303223,227.073424 +3264,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9825314128102973,0.9825314128102973,0.9734338986941852,9.8344087600708,324.702985 +3672,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9822936529555979,0.9822936529555979,0.9788760747631073,12.7888765335083,446.35643500000003 +4080,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9806325079676391,0.9806325079676391,0.9749453255203757,16.445659637451172,594.71846 +4488,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9801649208825496,0.9801649208825496,0.9779116862524243,20.943636894226074,768.8230350000001 +4896,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9801838610827375,0.9801838610827375,0.978782474664832,24.856953620910645,967.6114420000001 +5304,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9768055817461814,0.9768055817461814,0.9702080932270808,28.10527801513672,1191.0213970000002 +5712,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9746104009805638,0.9746104009805638,0.9718234131704067,32.14579772949219,1440.171835 +6120,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9697663016832816,0.9697663016832816,0.9621279568251032,36.40912055969238,1717.813161 +6528,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9656810173127011,0.9656810173127011,0.9634765255010708,42.20043754577637,2023.330442 +6936,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9653929343907715,0.9653929343907715,0.9646253117338193,46.972042083740234,2355.811343 +7344,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9635026555903582,0.9635026555903582,0.9611034281810401,50.8284969329834,2716.693574 +7752,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9610372855115469,0.9610372855115469,0.9585597537512924,55.062747955322266,3108.019641 +8160,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9593087388160314,0.9593087388160314,0.9577319445930262,59.75967216491699,3529.9614579999998 +8568,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9598459203922026,0.9598459203922026,0.9601713780248281,65.88526916503906,3981.5521329999997 +8976,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.959108635097493,0.959108635097493,0.9586518345557712,71.85272026062012,4465.222941 +9384,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9573697111797932,0.9573697111797932,0.9561353164275519,78.18439388275146,4984.801354 +9792,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9555714431620876,0.9555714431620876,0.9546392488298882,85.86389446258545,5537.4752260000005 +10200,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9486224139621532,0.9486224139621532,0.9433099305923253,94.35744285583496,6127.477763000001 +10608,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9431507495050439,0.9431507495050439,0.9403442056943527,104.1574821472168,6756.480909000001 +11016,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9408987743985474,0.9408987743985474,0.9399975161043574,113.0038013458252,7421.284759000001 +11424,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9380197846450145,0.9380197846450145,0.936341059397272,121.46645069122314,8125.715779000001 +11832,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9322965091708224,0.9322965091708224,0.9294143034054053,131.1031150817871,8872.899296000001 +12240,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9326742380913473,0.9326742380913473,0.9327603226303838,137.88959789276123,9652.703167000001 +12648,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.927571756147703,0.927571756147703,0.9249549620362734,145.5888376235962,10475.658214000001 +13056,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9247797778628878,0.9247797778628878,0.9237072084771099,154.53871536254883,11346.213643000001 +13464,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9238654088984625,0.9238654088984625,0.9233692422863465,161.3583574295044,12266.045404 +13872,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9202653017086007,0.9202653017086007,0.9191663953636944,170.12918186187744,13231.481182 +14280,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9163106660130261,0.9163106660130261,0.9150341930556871,179.0350112915039,14245.599713 +14688,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9161162933206237,0.9161162933206237,0.9160540991607554,184.834698677063,15311.95464 +15096,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9145412388208016,0.9145412388208016,0.91429667624259,191.58009719848633,16433.239395 +15504,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9105979487841063,0.9105979487841064,0.9097163708309961,200.08039951324463,17613.909715 +15912,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9068568914587393,0.9068568914587393,0.9060681758481206,210.5000762939453,18848.20155 +16320,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9031190636681169,0.9031190636681169,0.9023660107991418,221.55222129821777,20131.794746000003 +16728,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.9005799007592515,0.9005799007592515,0.9001704241319546,231.28063201904297,21466.799965000002 +17136,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8989203384884739,0.8989203384884739,0.8987537815839572,248.11264038085938,22840.808564000003 +17544,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.893746793592886,0.8937467935928861,0.892807745348426,267.53482723236084,24265.711089000004 +17952,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8894212021614395,0.8894212021614395,0.8884694521151855,281.7739496231079,25739.620728000005 +18360,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8911705430579008,0.8911705430579007,0.8908032768807751,288.0978307723999,27256.627666000004 +18768,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8911387009111739,0.8911387009111739,0.8906428613252552,296.05272102355957,28820.747713000004 +19176,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8886049543676662,0.8886049543676662,0.8879368647002966,307.68266773223877,30435.231458000006 +19584,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8895470561201042,0.8895470561201042,0.889061241536932,313.4344787597656,32089.799369000008 +19992,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8862488119653844,0.8862488119653844,0.8855123768505595,324.9442596435547,33786.733744000005 +20400,Multiclass classification,Aggregated Mondrian Forest,Keystroke,0.8810726015981175,0.8810726015981175,0.8799282628097613,338.1390075683594,35528.434162000005 +46,Multiclass classification,Streaming Random Patches,ImageSegments,0.35555555555555557,0.35555555555555557,0.24684873949579833,2.5926971435546875,5.672061 +92,Multiclass classification,Streaming Random Patches,ImageSegments,0.5274725274725275,0.5274725274725275,0.5392220990960486,2.5963096618652344,17.740863 +138,Multiclass classification,Streaming Random Patches,ImageSegments,0.5401459854014599,0.5401459854014599,0.5661177456005042,2.5979232788085938,35.937815 +184,Multiclass classification,Streaming Random Patches,ImageSegments,0.5956284153005464,0.5956284153005464,0.6144104879239446,2.6004638671875,59.975895 +230,Multiclass classification,Streaming Random Patches,ImageSegments,0.6200873362445415,0.6200873362445415,0.6319742698014011,2.6008224487304688,89.681265 +276,Multiclass classification,Streaming Random Patches,ImageSegments,0.6327272727272727,0.6327272727272727,0.6440706793955739,2.601276397705078,125.043898 +322,Multiclass classification,Streaming Random Patches,ImageSegments,0.6573208722741433,0.6573208722741433,0.6535377647060517,2.6028709411621094,166.036362 +368,Multiclass classification,Streaming Random Patches,ImageSegments,0.6784741144414169,0.6784741144414169,0.6717418242612484,2.6031723022460938,212.735146 +414,Multiclass classification,Streaming Random Patches,ImageSegments,0.6900726392251816,0.6900726392251816,0.6823551618652942,2.603717803955078,265.17548899999997 +460,Multiclass classification,Streaming Random Patches,ImageSegments,0.6971677559912854,0.6971677559912854,0.686858403065277,2.6037940979003906,323.486791 +506,Multiclass classification,Streaming Random Patches,ImageSegments,0.699009900990099,0.699009900990099,0.6869845800125663,2.604084014892578,387.600808 +552,Multiclass classification,Streaming Random Patches,ImageSegments,0.6987295825771325,0.6987295825771325,0.6895132041566728,2.6040496826171875,457.32381699999996 +598,Multiclass classification,Streaming Random Patches,ImageSegments,0.7035175879396985,0.7035175879396985,0.6939747146282641,2.6041183471679688,532.682096 +644,Multiclass classification,Streaming Random Patches,ImageSegments,0.6998444790046656,0.6998444790046656,0.6913714585468268,2.6053123474121094,613.490084 +690,Multiclass classification,Streaming Random Patches,ImageSegments,0.7024673439767779,0.7024673439767779,0.6944906634267102,2.6058921813964844,699.880084 +736,Multiclass classification,Streaming Random Patches,ImageSegments,0.7020408163265306,0.7020408163265306,0.69548275919944,2.605987548828125,791.65329 +782,Multiclass classification,Streaming Random Patches,ImageSegments,0.706786171574904,0.706786171574904,0.6991539785967766,2.6064224243164062,888.981823 +828,Multiclass classification,Streaming Random Patches,ImageSegments,0.7085852478839177,0.7085852478839177,0.70309750989463,2.6064682006835938,991.6474969999999 +874,Multiclass classification,Streaming Random Patches,ImageSegments,0.715922107674685,0.7159221076746849,0.7073525059690206,2.6064682006835938,1099.573439 +920,Multiclass classification,Streaming Random Patches,ImageSegments,0.7170837867247007,0.7170837867247007,0.707165908654469,2.6064682006835938,1212.915424 +966,Multiclass classification,Streaming Random Patches,ImageSegments,0.7160621761658031,0.716062176165803,0.7063689525089133,2.6064682006835938,1331.5593390000001 +1012,Multiclass classification,Streaming Random Patches,ImageSegments,0.7151335311572701,0.7151335311572701,0.7047830593764105,2.6064910888671875,1455.3800170000002 +1058,Multiclass classification,Streaming Random Patches,ImageSegments,0.7152317880794702,0.7152317880794702,0.7037726227430311,2.606658935546875,1584.3270810000001 +1104,Multiclass classification,Streaming Random Patches,ImageSegments,0.71441523118767,0.71441523118767,0.7026447500373862,2.6067771911621094,1718.2459470000001 +1150,Multiclass classification,Streaming Random Patches,ImageSegments,0.7162750217580505,0.7162750217580505,0.7030218527348165,2.6067771911621094,1857.0229310000002 +1196,Multiclass classification,Streaming Random Patches,ImageSegments,0.7179916317991631,0.7179916317991631,0.705575475090573,2.379610061645508,2000.273372 +1242,Multiclass classification,Streaming Random Patches,ImageSegments,0.7155519742143432,0.7155519742143431,0.7053749246401603,3.185004234313965,2147.034729 +1288,Multiclass classification,Streaming Random Patches,ImageSegments,0.7156177156177156,0.7156177156177156,0.7041730806550314,3.633350372314453,2296.192092 +1334,Multiclass classification,Streaming Random Patches,ImageSegments,0.7149287321830458,0.7149287321830458,0.7045092702498074,4.368736267089844,2447.850078 +1380,Multiclass classification,Streaming Random Patches,ImageSegments,0.7186366932559826,0.7186366932559827,0.7102131417787841,4.724300384521484,2601.871177 +1426,Multiclass classification,Streaming Random Patches,ImageSegments,0.7256140350877193,0.7256140350877193,0.7174099613082184,4.89253044128418,2758.036552 +1472,Multiclass classification,Streaming Random Patches,ImageSegments,0.7273963290278722,0.7273963290278722,0.7183919320082559,5.412370681762695,2916.512936 +1518,Multiclass classification,Streaming Random Patches,ImageSegments,0.7211601845748187,0.7211601845748187,0.7136134581802791,6.487729072570801,3077.492565 +1564,Multiclass classification,Streaming Random Patches,ImageSegments,0.7172104926423545,0.7172104926423546,0.7129536273040751,6.405126571655273,3241.109 +1610,Multiclass classification,Streaming Random Patches,ImageSegments,0.7209446861404599,0.7209446861404599,0.7163536024764182,6.857941627502441,3407.317179 +1656,Multiclass classification,Streaming Random Patches,ImageSegments,0.7238670694864048,0.7238670694864048,0.7196892738307762,7.034061431884766,3576.2587320000002 +1702,Multiclass classification,Streaming Random Patches,ImageSegments,0.7260435038212816,0.7260435038212816,0.7238533950478148,7.623349189758301,3747.8646120000003 +1748,Multiclass classification,Streaming Random Patches,ImageSegments,0.7309673726388094,0.7309673726388093,0.7286270619416129,8.106144905090332,3922.0639630000005 +1794,Multiclass classification,Streaming Random Patches,ImageSegments,0.7361963190184049,0.7361963190184049,0.7329274067865035,8.185744285583496,4098.8506050000005 +1840,Multiclass classification,Streaming Random Patches,ImageSegments,0.7389885807504079,0.7389885807504077,0.7360694376974826,8.929247856140137,4278.2789060000005 +1886,Multiclass classification,Streaming Random Patches,ImageSegments,0.7411140583554376,0.7411140583554376,0.7396669191579938,9.100563049316406,4460.600751000001 +1932,Multiclass classification,Streaming Random Patches,ImageSegments,0.7431382703262558,0.7431382703262558,0.7411378754700444,9.223885536193848,4645.683986000001 +1978,Multiclass classification,Streaming Random Patches,ImageSegments,0.7465857359635811,0.746585735963581,0.744200926808846,9.401692390441895,4833.458731000001 +2024,Multiclass classification,Streaming Random Patches,ImageSegments,0.7508650519031141,0.7508650519031143,0.7476945996538615,9.481804847717285,5024.230846 +2070,Multiclass classification,Streaming Random Patches,ImageSegments,0.7549540840985983,0.7549540840985983,0.7524477298078486,9.431160926818848,5217.969956000001 +2116,Multiclass classification,Streaming Random Patches,ImageSegments,0.7583924349881797,0.7583924349881797,0.7554386161495508,9.549637794494629,5414.604524000001 +2162,Multiclass classification,Streaming Random Patches,ImageSegments,0.7607589079130033,0.7607589079130033,0.7577216433051415,10.151451110839844,5614.378132000002 +2208,Multiclass classification,Streaming Random Patches,ImageSegments,0.7648391481649298,0.7648391481649298,0.7614528787516565,9.14443588256836,5817.274926000002 +2254,Multiclass classification,Streaming Random Patches,ImageSegments,0.7652019529516201,0.7652019529516201,0.762166830901651,8.801234245300293,6023.198429000002 +2300,Multiclass classification,Streaming Random Patches,ImageSegments,0.7672901261418008,0.7672901261418008,0.7647372124971393,8.857858657836914,6232.074878000002 +2310,Multiclass classification,Streaming Random Patches,ImageSegments,0.7669987007362494,0.7669987007362494,0.7647069285577738,8.926526069641113,6441.814766000002 +1056,Multiclass classification,Streaming Random Patches,Insects,0.6388625592417062,0.6388625592417062,0.6031100134310133,8.730474472045898,177.190345 +2112,Multiclass classification,Streaming Random Patches,Insects,0.659403126480341,0.659403126480341,0.6244477305834598,21.138185501098633,477.66892900000005 +3168,Multiclass classification,Streaming Random Patches,Insects,0.6722450268392801,0.6722450268392801,0.6321534006670183,28.433568000793457,884.814326 +4224,Multiclass classification,Streaming Random Patches,Insects,0.680322045938906,0.680322045938906,0.6340126191391743,39.2259521484375,1406.7405990000002 +5280,Multiclass classification,Streaming Random Patches,Insects,0.6878196628149271,0.6878196628149271,0.6395508722492685,32.51231288909912,2046.4837620000003 +6336,Multiclass classification,Streaming Random Patches,Insects,0.6876085240726125,0.6876085240726125,0.641396967542699,34.576416969299316,2792.1074670000003 +7392,Multiclass classification,Streaming Random Patches,Insects,0.6924638073332431,0.6924638073332431,0.6467777725107727,40.06835174560547,3634.422347 +8448,Multiclass classification,Streaming Random Patches,Insects,0.6949212738250267,0.6949212738250267,0.6476372139610082,43.01965808868408,4571.1184410000005 +9504,Multiclass classification,Streaming Random Patches,Insects,0.6992528675155214,0.6992528675155214,0.6494082466298291,45.71251583099365,5608.992399000001 +10560,Multiclass classification,Streaming Random Patches,Insects,0.7013921772895161,0.7013921772895161,0.6506452100316108,50.310431480407715,6744.395149000001 +11616,Multiclass classification,Streaming Random Patches,Insects,0.7043478260869566,0.7043478260869566,0.6524912605091605,59.279197692871094,7964.619750000001 +12672,Multiclass classification,Streaming Random Patches,Insects,0.7079946334148843,0.7079946334148843,0.6596828376773001,72.61201000213623,9269.589572 +13728,Multiclass classification,Streaming Random Patches,Insects,0.7208421359364756,0.7208421359364756,0.7145906055666686,38.78250694274902,10625.218377000001 +14784,Multiclass classification,Streaming Random Patches,Insects,0.7285395386592708,0.7285395386592708,0.7256542392368915,15.546477317810059,12027.231464 +15840,Multiclass classification,Streaming Random Patches,Insects,0.7206263021655408,0.7206263021655408,0.7196216319492748,14.88278579711914,13491.676191 +16896,Multiclass classification,Streaming Random Patches,Insects,0.7171352471145309,0.7171352471145309,0.7175260611854538,21.28149700164795,15025.812845 +17952,Multiclass classification,Streaming Random Patches,Insects,0.7121051751991533,0.7121051751991533,0.7136617513297842,29.336480140686035,16621.157643 +19008,Multiclass classification,Streaming Random Patches,Insects,0.7205240174672489,0.720524017467249,0.7180961996594418,20.976608276367188,18267.655244999998 +20064,Multiclass classification,Streaming Random Patches,Insects,0.7261625878482779,0.7261625878482779,0.7198561207408494,15.994047164916992,19960.279520999997 +21120,Multiclass classification,Streaming Random Patches,Insects,0.7272598134381363,0.7272598134381363,0.7183389579277755,17.52824878692627,21708.029386999995 +22176,Multiclass classification,Streaming Random Patches,Insects,0.7281623449830891,0.7281623449830891,0.7167723651435352,22.240838050842285,23503.558300999994 +23232,Multiclass classification,Streaming Random Patches,Insects,0.7307477078042272,0.7307477078042272,0.7170791531651185,26.114503860473633,25348.434525999994 +24288,Multiclass classification,Streaming Random Patches,Insects,0.7325318071396221,0.7325318071396222,0.7165563330554671,27.974491119384766,27240.683159999993 +25344,Multiclass classification,Streaming Random Patches,Insects,0.7353904431203883,0.7353904431203884,0.7174524973348954,37.12833023071289,29182.513668999993 +26400,Multiclass classification,Streaming Random Patches,Insects,0.7367703322095533,0.7367703322095533,0.7168965346030137,31.575971603393555,31184.058177999992 +27456,Multiclass classification,Streaming Random Patches,Insects,0.738371881260244,0.738371881260244,0.7164257197178175,35.22733116149902,33213.34443999999 +28512,Multiclass classification,Streaming Random Patches,Insects,0.7366279681526429,0.7366279681526429,0.7161250847684691,17.50509262084961,35271.15690999999 +29568,Multiclass classification,Streaming Random Patches,Insects,0.7354483038522678,0.7354483038522677,0.719616514898752,22.40646266937256,37354.25785299999 +30624,Multiclass classification,Streaming Random Patches,Insects,0.7348724814681775,0.7348724814681775,0.7237598149406717,31.226743698120117,39459.628930999985 +31680,Multiclass classification,Streaming Random Patches,Insects,0.7347769815966413,0.7347769815966413,0.7275990709197302,35.24118995666504,41587.294746999985 +32736,Multiclass classification,Streaming Random Patches,Insects,0.7351458683366427,0.7351458683366427,0.7308983066693725,48.40772724151611,43729.930447999985 +33792,Multiclass classification,Streaming Random Patches,Insects,0.7303423988636027,0.7303423988636027,0.7274356410957497,77.28174114227295,45887.55412199999 +34848,Multiclass classification,Streaming Random Patches,Insects,0.726805750853732,0.726805750853732,0.723911701718825,53.16175174713135,48068.300588999984 +35904,Multiclass classification,Streaming Random Patches,Insects,0.7248976408656658,0.7248976408656659,0.7218080521646734,41.53026580810547,50265.619736999986 +36960,Multiclass classification,Streaming Random Patches,Insects,0.7215833761735978,0.7215833761735979,0.7182506744185386,35.33352756500244,52485.185911999986 +38016,Multiclass classification,Streaming Random Patches,Insects,0.7196369854004998,0.7196369854004999,0.7160236415660819,44.00273513793945,54721.05808499999 +39072,Multiclass classification,Streaming Random Patches,Insects,0.7175142688950884,0.7175142688950884,0.713988650041017,46.12203025817871,56978.30571199999 +40128,Multiclass classification,Streaming Random Patches,Insects,0.7158023276098388,0.7158023276098388,0.7126852582249207,27.841010093688965,59249.54765999999 +41184,Multiclass classification,Streaming Random Patches,Insects,0.7157322196051769,0.715732219605177,0.7129296468122535,21.849401473999023,61534.70422899999 +42240,Multiclass classification,Streaming Random Patches,Insects,0.7156656170837378,0.7156656170837377,0.7131576552849198,28.021278381347656,63833.596648999985 +43296,Multiclass classification,Streaming Random Patches,Insects,0.715925626515764,0.715925626515764,0.7137513847694824,36.50454139709473,66146.66403399999 +44352,Multiclass classification,Streaming Random Patches,Insects,0.7161958016730176,0.7161958016730177,0.7143198962298327,46.888444900512695,68474.34057599999 +45408,Multiclass classification,Streaming Random Patches,Insects,0.7170260092056291,0.7170260092056291,0.7151715877390813,47.08374786376953,70816.527322 +46464,Multiclass classification,Streaming Random Patches,Insects,0.7181843617502098,0.7181843617502098,0.7162864260409335,43.18325901031494,73172.526917 +47520,Multiclass classification,Streaming Random Patches,Insects,0.7179023127591069,0.7179023127591069,0.716246618663062,54.80090522766113,75543.857611 +48576,Multiclass classification,Streaming Random Patches,Insects,0.7211528564076171,0.7211528564076171,0.719707905487922,60.4919376373291,77931.086228 +49632,Multiclass classification,Streaming Random Patches,Insects,0.7250710241582882,0.7250710241582882,0.7236001513027165,35.55128765106201,80332.853368 +50688,Multiclass classification,Streaming Random Patches,Insects,0.7288259316984631,0.7288259316984631,0.7271241427068512,21.017152786254883,82746.274567 +51744,Multiclass classification,Streaming Random Patches,Insects,0.7329107318864387,0.7329107318864386,0.7308784460773333,26.768343925476074,85169.877802 +52800,Multiclass classification,Streaming Random Patches,Insects,0.7359230288452433,0.7359230288452432,0.7343606492383059,9.57052230834961,87600.592492 +52848,Multiclass classification,Streaming Random Patches,Insects,0.7361628853104244,0.7361628853104244,0.7346220154259927,9.63199520111084,90031.55993999999 +408,Multiclass classification,Streaming Random Patches,Keystroke,0.9901719901719902,0.9901719901719902,0.8308395677472984,1.027322769165039,17.348128 +816,Multiclass classification,Streaming Random Patches,Keystroke,0.9877300613496932,0.9877300613496932,0.9320293882508496,2.6651391983032227,54.406226000000004 +1224,Multiclass classification,Streaming Random Patches,Keystroke,0.9828291087489779,0.9828291087489779,0.9464059415055075,5.679329872131348,114.007104 +1632,Multiclass classification,Streaming Random Patches,Keystroke,0.9828326180257511,0.9828326180257511,0.9632097550305241,8.335807800292969,201.582874 +2040,Multiclass classification,Streaming Random Patches,Keystroke,0.9749877390877881,0.9749877390877881,0.9373958892668122,12.631415367126465,318.920922 +2448,Multiclass classification,Streaming Random Patches,Keystroke,0.9701675521046179,0.9701675521046179,0.957381800109682,16.891732215881348,465.74626 +2856,Multiclass classification,Streaming Random Patches,Keystroke,0.9660245183887916,0.9660245183887916,0.9394754450400101,22.937668800354004,641.328845 +3264,Multiclass classification,Streaming Random Patches,Keystroke,0.9616916947594238,0.9616916947594238,0.9454054748805115,29.12161636352539,847.207258 +3672,Multiclass classification,Streaming Random Patches,Keystroke,0.9607736311631708,0.9607736311631708,0.953605417859829,28.669262886047363,1081.626023 +4080,Multiclass classification,Streaming Random Patches,Keystroke,0.9578328021573915,0.9578328021573915,0.9463612240153171,34.20732402801514,1345.493591 +4488,Multiclass classification,Streaming Random Patches,Keystroke,0.9589926454201025,0.9589926454201025,0.9613092683363473,18.652557373046875,1636.499529 +4896,Multiclass classification,Streaming Random Patches,Keystroke,0.9607763023493361,0.9607763023493361,0.9605208703626084,20.81053066253662,1952.783692 +5304,Multiclass classification,Streaming Random Patches,Keystroke,0.9615312087497643,0.9615312087497643,0.9603033149830379,27.91543483734131,2294.145458 +5712,Multiclass classification,Streaming Random Patches,Keystroke,0.9616529504465068,0.9616529504465068,0.9605387671994151,32.60424041748047,2660.691575 +6120,Multiclass classification,Streaming Random Patches,Keystroke,0.9597973525085798,0.9597973525085798,0.9561203427932812,39.11091995239258,3053.1932039999997 +6528,Multiclass classification,Streaming Random Patches,Keystroke,0.9589397885705531,0.9589397885705531,0.9571591040678328,29.255366325378418,3470.643733 +6936,Multiclass classification,Streaming Random Patches,Keystroke,0.959913482335977,0.959913482335977,0.9605956598361813,31.930577278137207,3910.602191 +7344,Multiclass classification,Streaming Random Patches,Keystroke,0.9602342366880022,0.9602342366880022,0.9598619882355601,26.562703132629395,4374.151898 +7752,Multiclass classification,Streaming Random Patches,Keystroke,0.9601341762353245,0.9601341762353245,0.9596510454605859,31.588034629821777,4858.190889 +8160,Multiclass classification,Streaming Random Patches,Keystroke,0.9584507905380562,0.9584507905380562,0.9567204261955369,39.12565612792969,5363.543193 +8568,Multiclass classification,Streaming Random Patches,Keystroke,0.9579782887825377,0.9579782887825377,0.957794146577291,44.816758155822754,5892.06228 +8976,Multiclass classification,Streaming Random Patches,Keystroke,0.9579944289693594,0.9579944289693594,0.9581242571113369,49.5586576461792,6445.036349 +9384,Multiclass classification,Streaming Random Patches,Keystroke,0.9570499840136417,0.9570499840136417,0.9565283447410108,50.185367584228516,7025.065903 +9792,Multiclass classification,Streaming Random Patches,Keystroke,0.9563885200694515,0.9563885200694515,0.9560487952418978,54.40623474121094,7630.795018999999 +10200,Multiclass classification,Streaming Random Patches,Keystroke,0.9532307088930287,0.9532307088930287,0.9512518567217172,66.82855319976807,8267.200675 +10608,Multiclass classification,Streaming Random Patches,Keystroke,0.9519185443574998,0.9519185443574998,0.9512557409849248,41.17615795135498,8934.408603 +11016,Multiclass classification,Streaming Random Patches,Keystroke,0.9528824330458465,0.9528824330458465,0.953398407731189,32.87209510803223,9625.903537 +11424,Multiclass classification,Streaming Random Patches,Keystroke,0.953689923837871,0.953689923837871,0.9540175301991308,28.078542709350586,10339.782646 +11832,Multiclass classification,Streaming Random Patches,Keystroke,0.9542726734849125,0.9542726734849125,0.9545119777330118,20.280012130737305,11070.86282 +12240,Multiclass classification,Streaming Random Patches,Keystroke,0.955470218155078,0.955470218155078,0.9559406438939211,21.54300308227539,11820.445116 +12648,Multiclass classification,Streaming Random Patches,Keystroke,0.9559579346880683,0.9559579346880683,0.9561632451269845,26.89114284515381,12588.394717000001 +13056,Multiclass classification,Streaming Random Patches,Keystroke,0.9558789735733435,0.9558789735733435,0.9559075747932771,21.382742881774902,13375.587705000002 +13464,Multiclass classification,Streaming Random Patches,Keystroke,0.9563247418851668,0.9563247418851668,0.9565051554876024,21.864919662475586,14180.689980000001 +13872,Multiclass classification,Streaming Random Patches,Keystroke,0.9569605652079879,0.9569605652079879,0.9571856017401091,25.72835636138916,15004.794290000002 +14280,Multiclass classification,Streaming Random Patches,Keystroke,0.9566496253239022,0.9566496253239022,0.9566382966080723,21.764866828918457,15848.339318000002 +14688,Multiclass classification,Streaming Random Patches,Keystroke,0.957241097569279,0.9572410975692791,0.957426459656079,25.14582061767578,16708.048820000004 +15096,Multiclass classification,Streaming Random Patches,Keystroke,0.9580655846306724,0.9580655846306724,0.9582773620158959,26.658535957336426,17588.164126000003 +15504,Multiclass classification,Streaming Random Patches,Keystroke,0.9584596529703928,0.9584596529703928,0.9585840009788793,30.767892837524414,18489.703328000003 +15912,Multiclass classification,Streaming Random Patches,Keystroke,0.9580793161963421,0.9580793161963421,0.9580713134265897,27.786094665527344,19412.324967000004 +16320,Multiclass classification,Streaming Random Patches,Keystroke,0.958514614866107,0.958514614866107,0.9586173296332885,25.79348850250244,20355.979039000005 +16728,Multiclass classification,Streaming Random Patches,Keystroke,0.9577330065164106,0.9577330065164106,0.9576699214368118,33.630208015441895,21321.132478000007 +17136,Multiclass classification,Streaming Random Patches,Keystroke,0.9576305806828129,0.9576305806828129,0.9576693803774444,33.920249938964844,22315.653729000005 +17544,Multiclass classification,Streaming Random Patches,Keystroke,0.956506868836573,0.956506868836573,0.9564470129227677,31.505155563354492,23335.132930000003 +17952,Multiclass classification,Streaming Random Patches,Keystroke,0.9563812600969306,0.9563812600969306,0.9564135249623555,19.79563045501709,24372.247222 +18360,Multiclass classification,Streaming Random Patches,Keystroke,0.9569148646440437,0.9569148646440437,0.9569804233582649,23.70892333984375,25428.663478000002 +18768,Multiclass classification,Streaming Random Patches,Keystroke,0.9574252677572335,0.9574252677572335,0.957475477736454,21.893744468688965,26504.246634000003 +19176,Multiclass classification,Streaming Random Patches,Keystroke,0.9568187744458931,0.9568187744458931,0.956806677474395,28.04871368408203,27598.635240000003 +19584,Multiclass classification,Streaming Random Patches,Keystroke,0.9567992646683348,0.9567992646683348,0.9568012672257533,32.11082458496094,28712.463090000005 +19992,Multiclass classification,Streaming Random Patches,Keystroke,0.9565304386974138,0.9565304386974138,0.9565268274864178,40.13526153564453,29849.822929000005 +20400,Multiclass classification,Streaming Random Patches,Keystroke,0.9559292122162851,0.9559292122162851,0.9559196349550496,39.63601016998291,31009.846621000004 +46,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.5111111111111111,0.5111111111111111,0.40938578329882686,0.09116363525390625,0.155273 +92,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6043956043956044,0.6043956043956044,0.5940974230447915,0.16827392578125,0.72683 +138,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.6715328467153284,0.6715328467153284,0.6806196928151186,0.24543190002441406,1.742293 +184,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7049180327868853,0.7049180327868853,0.7184732466987995,0.32204627990722656,3.3407109999999998 +230,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.74235807860262,0.74235807860262,0.7523809662907407,0.39917659759521484,5.610709 +276,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7490909090909091,0.7490909090909091,0.7611097615339608,0.47675609588623047,8.7745 +322,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7663551401869159,0.766355140186916,0.7725898650917747,0.5538606643676758,12.918764 +368,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.784741144414169,0.7847411444141691,0.7844949397573193,0.6304874420166016,18.189003 +414,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7990314769975787,0.7990314769975787,0.7976353129150817,0.7076187133789062,24.731741 +460,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7952069716775599,0.7952069716775599,0.7930763833747545,0.7847471237182617,32.681045 +506,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.7960396039603961,0.7960396039603961,0.7941234022368324,3.003793716430664,61.211909999999996 +552,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8021778584392014,0.8021778584392014,0.8007250644998717,3.2264842987060547,91.16202899999999 +598,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8090452261306532,0.8090452261306531,0.8095532779239047,3.4499826431274414,122.67972799999998 +644,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8164852255054432,0.8164852255054433,0.8176018556357175,3.6760778427124023,155.77468699999997 +690,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8214804063860668,0.8214804063860668,0.8221151176242331,3.8941650390625,190.53727699999996 +736,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8272108843537415,0.8272108843537415,0.8281233770721121,4.128121376037598,227.08550299999996 +782,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8361075544174136,0.8361075544174136,0.8364659566156888,4.367749214172363,265.419762 +828,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8403869407496977,0.8403869407496977,0.8412749002251585,4.601743698120117,305.543518 +874,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.845360824742268,0.845360824742268,0.8465057584066101,4.840575218200684,347.501906 +920,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8487486398258978,0.8487486398258978,0.8489576083149123,5.074535369873047,391.43023400000004 +966,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8538860103626943,0.8538860103626943,0.8530581393966605,5.3079938888549805,437.316456 +1012,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8585558852621167,0.8585558852621167,0.8570252804249208,5.479596138000488,485.23685 +1058,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8628192999053926,0.8628192999053927,0.8611045332429007,5.435150146484375,535.278485 +1104,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8631006346328196,0.8631006346328196,0.8616288881212748,5.355225563049316,587.436372 +1150,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8668407310704961,0.8668407310704961,0.8650902600877293,5.281754493713379,641.538536 +1196,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8719665271966527,0.8719665271966527,0.8702683106604537,5.235520362854004,697.554251 +1242,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8759065269943593,0.8759065269943593,0.8740479640614998,5.142333984375,755.471933 +1288,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8787878787878788,0.8787878787878788,0.8772603222806128,5.092559814453125,815.138635 +1334,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8777194298574643,0.8777194298574643,0.8760741143565023,5.055940628051758,876.491339 +1380,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8796229151559101,0.8796229151559101,0.8783130803325612,4.964084625244141,939.483975 +1426,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8785964912280702,0.8785964912280702,0.8768931648451159,4.951287269592285,1004.010152 +1472,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8769544527532291,0.8769544527532291,0.8748964905672628,4.969002723693848,1070.168576 +1518,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8727752142386289,0.8727752142386289,0.8705110235515202,5.101251602172852,1138.304608 +1564,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8688419705694178,0.8688419705694178,0.8667015278861958,5.262187957763672,1208.4659419999998 +1610,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8651336233685519,0.8651336233685519,0.8631350462642483,5.320252418518066,1280.5305069999997 +1656,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8640483383685801,0.8640483383685801,0.8620479268968886,5.35189151763916,1354.3819709999998 +1702,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8647854203409759,0.8647854203409759,0.8635043959538364,5.359102249145508,1430.0286029999997 +1748,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.866056096164854,0.866056096164854,0.864439618601765,5.402237892150879,1507.5136589999997 +1794,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8683770217512549,0.8683770217512549,0.8664209902402824,5.3993330001831055,1586.7199259999998 +1840,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8694942903752039,0.8694942903752039,0.867597342266498,5.4049272537231445,1667.6587319999999 +1886,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8710875331564987,0.8710875331564986,0.8694766742923737,5.4121294021606445,1750.3169159999998 +1932,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8705334023821854,0.8705334023821854,0.8686918451193435,5.405803680419922,1834.6099799999997 +1978,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8715225088517956,0.8715225088517956,0.8698703895904014,5.395906448364258,1920.5171279999997 +2024,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8729609490855166,0.8729609490855166,0.870902914954928,5.386837959289551,2008.1064549999996 +2070,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8733687771870469,0.8733687771870469,0.8714525187304558,5.375288963317871,2097.403794 +2116,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.875177304964539,0.875177304964539,0.8730645404016979,5.353263854980469,2188.326937 +2162,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8745950948634891,0.8745950948634891,0.872417325547954,5.322790145874023,2280.8823749999997 +2208,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8753964657906661,0.8753964657906661,0.8732500176589647,5.30323600769043,2374.9757969999996 +2254,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8748335552596538,0.8748335552596538,0.8732733602208504,5.278659820556641,2470.7327649999997 +2300,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8742931709438887,0.8742931709438887,0.8727466012343671,5.262259483337402,2568.119206 +2310,Multiclass classification,k-Nearest Neighbors,ImageSegments,0.8735383282806409,0.8735383282806409,0.8721361121313428,5.268708229064941,2666.293295 +1056,Multiclass classification,k-Nearest Neighbors,Insects,0.6597156398104266,0.6597156398104266,0.5853273709738578,6.371035575866699,65.756564 +2112,Multiclass classification,k-Nearest Neighbors,Insects,0.6807200378967314,0.6807200378967314,0.5992086579995298,6.2783002853393555,182.184591 +3168,Multiclass classification,k-Nearest Neighbors,Insects,0.6842437638143354,0.6842437638143354,0.6001715208792017,6.298460006713867,341.998975 +4224,Multiclass classification,k-Nearest Neighbors,Insects,0.6848212171442103,0.6848212171442103,0.6051604277089342,6.265153884887695,541.5068689999999 +5280,Multiclass classification,k-Nearest Neighbors,Insects,0.6872513733661678,0.6872513733661678,0.611100448555976,6.2555742263793945,777.52113 +6336,Multiclass classification,k-Nearest Neighbors,Insects,0.6842936069455406,0.6842936069455406,0.6118525331169307,6.3140106201171875,1048.588472 +7392,Multiclass classification,k-Nearest Neighbors,Insects,0.6852929238262752,0.6852929238262752,0.6157762907660722,6.288516998291016,1352.458798 +8448,Multiclass classification,k-Nearest Neighbors,Insects,0.6828459808215934,0.6828459808215934,0.6148503710479976,6.31680965423584,1687.096094 +9504,Multiclass classification,k-Nearest Neighbors,Insects,0.6851520572450805,0.6851520572450805,0.6155258331015067,6.223039627075195,2051.649807 +10560,Multiclass classification,k-Nearest Neighbors,Insects,0.6861445212614831,0.6861445212614831,0.6169474950376627,6.253497123718262,2444.130945 +11616,Multiclass classification,k-Nearest Neighbors,Insects,0.6873009040034438,0.6873009040034438,0.6200568175672779,6.251482009887695,2863.1289039999997 +12672,Multiclass classification,k-Nearest Neighbors,Insects,0.6866072133217583,0.6866072133217583,0.623883491026523,6.276742935180664,3309.0829679999997 +13728,Multiclass classification,k-Nearest Neighbors,Insects,0.7020470605376266,0.7020470605376266,0.6991473808978487,6.26933479309082,3781.2775089999996 +14784,Multiclass classification,k-Nearest Neighbors,Insects,0.7077724413177299,0.7077724413177299,0.7078402863830927,6.244691848754883,4278.402760999999 +15840,Multiclass classification,k-Nearest Neighbors,Insects,0.7016857124818486,0.7016857124818486,0.704840832390747,6.350223541259766,4805.403565999999 +16896,Multiclass classification,k-Nearest Neighbors,Insects,0.6992009470257473,0.6992009470257473,0.7048178275842342,6.243149757385254,5357.683726999999 +17952,Multiclass classification,k-Nearest Neighbors,Insects,0.6922734109520361,0.6922734109520361,0.6995766929659905,6.218992233276367,5935.240105999998 +19008,Multiclass classification,k-Nearest Neighbors,Insects,0.6974272636397116,0.6974272636397116,0.7006862112488368,6.24652099609375,6538.276384999998 +20064,Multiclass classification,k-Nearest Neighbors,Insects,0.699845486716842,0.699845486716842,0.6985118222305657,6.205791473388672,7167.459726999999 +21120,Multiclass classification,k-Nearest Neighbors,Insects,0.7016904209479615,0.7016904209479615,0.6971610909052677,6.218420028686523,7825.840650999999 +22176,Multiclass classification,k-Nearest Neighbors,Insects,0.7039909808342728,0.7039909808342728,0.6964197759629052,6.236072540283203,8511.079801999998 +23232,Multiclass classification,k-Nearest Neighbors,Insects,0.7076320433902974,0.7076320433902974,0.697368621848442,6.279313087463379,9222.986801999998 +24288,Multiclass classification,k-Nearest Neighbors,Insects,0.7098447729237863,0.7098447729237863,0.6967477548491564,6.2948198318481445,9960.927248999997 +25344,Multiclass classification,k-Nearest Neighbors,Insects,0.7127017322337529,0.712701732233753,0.6972185032799825,6.224791526794434,10724.419586999997 +26400,Multiclass classification,k-Nearest Neighbors,Insects,0.7145346414636918,0.7145346414636918,0.6967850611237018,6.263523101806641,11512.986172999998 +27456,Multiclass classification,k-Nearest Neighbors,Insects,0.7156437807321071,0.7156437807321071,0.6955595874776194,6.272575378417969,12326.628602999997 +28512,Multiclass classification,k-Nearest Neighbors,Insects,0.7130931921012942,0.7130931921012942,0.6943090782068162,6.22489070892334,13165.433758999998 +29568,Multiclass classification,k-Nearest Neighbors,Insects,0.7117732607298678,0.7117732607298677,0.6978751959025926,6.217726707458496,14028.837757999998 +30624,Multiclass classification,k-Nearest Neighbors,Insects,0.7122097769650263,0.7122097769650264,0.7026862643890369,6.243690490722656,14916.319238999999 +31680,Multiclass classification,k-Nearest Neighbors,Insects,0.7113545250797058,0.7113545250797058,0.7052714328980031,6.277059555053711,15827.904481999998 +32736,Multiclass classification,k-Nearest Neighbors,Insects,0.7111959676187567,0.7111959676187566,0.7078689284492299,6.295280456542969,16762.400180999997 +33792,Multiclass classification,k-Nearest Neighbors,Insects,0.7067562368678051,0.7067562368678051,0.704703743720216,6.183221817016602,17721.950532 +34848,Multiclass classification,k-Nearest Neighbors,Insects,0.7030734353028956,0.7030734353028956,0.7010614031639846,6.343389511108398,18710.094933 +35904,Multiclass classification,k-Nearest Neighbors,Insects,0.6998022449377489,0.6998022449377489,0.6976694331042329,6.273009300231934,19725.980479 +36960,Multiclass classification,k-Nearest Neighbors,Insects,0.6967179847939609,0.6967179847939609,0.6945045780432343,6.264690399169922,20767.047301000002 +38016,Multiclass classification,k-Nearest Neighbors,Insects,0.6941470472182033,0.6941470472182033,0.6917813776610243,6.265054702758789,21835.556239 +39072,Multiclass classification,k-Nearest Neighbors,Insects,0.691996621535154,0.691996621535154,0.6898060776768534,6.200959205627441,22932.108791000002 +40128,Multiclass classification,k-Nearest Neighbors,Insects,0.6904328756199068,0.6904328756199068,0.6882031611963276,6.413609504699707,24054.967875000002 +41184,Multiclass classification,k-Nearest Neighbors,Insects,0.6916446106403128,0.6916446106403128,0.6892941373261507,6.3101043701171875,25204.026441 +42240,Multiclass classification,k-Nearest Neighbors,Insects,0.692535334643339,0.692535334643339,0.6900712004452627,6.22797966003418,26378.286294 +43296,Multiclass classification,k-Nearest Neighbors,Insects,0.6935904838895947,0.6935904838895947,0.6909354899104013,6.220904350280762,27574.213319000002 +44352,Multiclass classification,k-Nearest Neighbors,Insects,0.6941895334941715,0.6941895334941715,0.691322114366645,6.22946834564209,28791.926615000004 +45408,Multiclass classification,k-Nearest Neighbors,Insects,0.6950690422181602,0.6950690422181602,0.6917362410920441,6.2850341796875,30030.670852000003 +46464,Multiclass classification,k-Nearest Neighbors,Insects,0.6964466349568473,0.6964466349568473,0.6926338572817136,6.2335004806518555,31290.345387 +47520,Multiclass classification,k-Nearest Neighbors,Insects,0.6963530377322755,0.6963530377322755,0.6929015597977773,6.2439117431640625,32571.46119 +48576,Multiclass classification,k-Nearest Neighbors,Insects,0.7006073082861555,0.7006073082861555,0.697843135408715,6.247167587280273,33874.398319 +49632,Multiclass classification,k-Nearest Neighbors,Insects,0.7046805424029337,0.7046805424029337,0.7023003034160373,6.246943473815918,35198.416197 +50688,Multiclass classification,k-Nearest Neighbors,Insects,0.7083867658373942,0.7083867658373942,0.7061355873839065,6.210485458374023,36536.506394 +51744,Multiclass classification,k-Nearest Neighbors,Insects,0.7126567844925884,0.7126567844925883,0.7104085577951368,6.270394325256348,37890.628371 +52800,Multiclass classification,k-Nearest Neighbors,Insects,0.7128544101214038,0.7128544101214038,0.7110869129037599,6.247260093688965,39264.666928 +52848,Multiclass classification,k-Nearest Neighbors,Insects,0.7131152194069673,0.7131152194069672,0.7113808258412672,6.272693634033203,40639.937472 +408,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.0294876098632812,8.610537 +816,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9251533742331288,0.9251533742331288,0.8588670451436246,5.3865966796875,60.879099000000004 +1224,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9247751430907605,0.9247751430907604,0.888226412135106,6.246823310852051,137.71097600000002 +1632,Multiclass classification,k-Nearest Neighbors,Keystroke,0.927038626609442,0.927038626609442,0.893336805209695,6.212030410766602,236.990146 +2040,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9298675821481118,0.9298675821481118,0.911424130088645,6.329436302185059,359.011082 +2448,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9239885574172456,0.9239885574172456,0.9121555472954921,6.208271026611328,503.2068 +2856,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9169877408056042,0.9169877408056042,0.8816257260944811,6.284844398498535,667.387655 +3264,Multiclass classification,k-Nearest Neighbors,Keystroke,0.908979466748391,0.908979466748391,0.9011431783951355,6.232160568237305,850.70223 +3672,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133751021520021,0.9133751021520021,0.9125908871445695,6.234919548034668,1054.346916 +4080,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9141946555528315,0.9141946555528315,0.9054789816810689,6.308258056640625,1277.35851 +4488,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9057276576777357,0.9057276576777357,0.9087691557812896,6.2749834060668945,1518.241674 +4896,Multiclass classification,k-Nearest Neighbors,Keystroke,0.908682328907048,0.908682328907048,0.9101970481905531,6.210176467895508,1774.8436390000002 +5304,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9092966245521403,0.9092966245521403,0.9045962329696907,6.311163902282715,2048.2991580000003 +5712,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9110488530905271,0.9110488530905271,0.9114244990736602,6.304790496826172,2337.0378760000003 +6120,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9089720542572316,0.9089720542572316,0.9032533666541098,6.217576026916504,2640.3521840000003 +6528,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9077677340278841,0.9077677340278841,0.9071900335968285,6.258184432983398,2958.617579 +6936,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9105984138428262,0.9105984138428262,0.9126270814361048,6.1720380783081055,3289.9253120000003 +7344,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9120250578782514,0.9120250578782514,0.9125633522308233,6.2164154052734375,3633.8320570000005 +7752,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133015094826474,0.9133015094826474,0.9136330015220733,6.260525703430176,3992.0508240000004 +8160,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9148179924010296,0.9148179924010296,0.9154195917586071,6.291139602661133,4363.477247000001 +8568,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9166569394186996,0.9166569394186995,0.9177086422960681,6.216147422790527,4747.269196000001 +8976,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9179944289693593,0.9179944289693593,0.9193727618453186,6.204837799072266,5142.613476000001 +9384,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9180432697431525,0.9180432697431525,0.9181590265081533,6.197464942932129,5549.1165660000015 +9792,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9166581554488816,0.9166581554488816,0.9168210748678531,6.232473373413086,5968.023607000002 +10200,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9154819099911756,0.9154819099911756,0.9145218669909496,6.197787284851074,6399.011787000002 +10608,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9126048835674555,0.9126048835674555,0.9111025938131312,6.2185258865356445,6842.990199000003 +11016,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9106672719019518,0.9106672719019518,0.911227786024665,6.261377334594727,7300.041981000002 +11424,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9103562986956141,0.9103562986956141,0.9101104800687125,6.227293968200684,7769.979179000002 +11832,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9092215366410278,0.9092215366410278,0.9094186121236619,6.287784576416016,8252.975566000001 +12240,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9110221423318898,0.9110221423318898,0.9118339797691071,6.31197452545166,8747.696417000001 +12648,Multiclass classification,k-Nearest Neighbors,Keystroke,0.912627500593026,0.912627500593026,0.9131272841889786,6.279851913452148,9255.008595000001 +13056,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9129069322098813,0.9129069322098813,0.913006147591119,6.346117973327637,9774.692327 +13464,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9136150932184506,0.9136150932184506,0.9138210444048112,6.238006591796875,10306.725428000002 +13872,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9137769447047798,0.9137769447047797,0.913931693448659,6.288792610168457,10851.686584000001 +14280,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9121787240002801,0.9121787240002801,0.9118234284090696,6.252389907836914,11409.551357 +14688,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9133247089262613,0.9133247089262613,0.9136581918124823,6.268362998962402,11980.223294 +15096,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9134150380920835,0.9134150380920835,0.9134700562544149,6.304409027099609,12563.090191 +15504,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9127910726956073,0.9127910726956073,0.9127632118282708,6.222077369689941,13158.500651999999 +15912,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9127019043429074,0.9127019043429074,0.9127365496247325,6.263586044311523,13766.752283999998 +16320,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113303511244562,0.9113303511244562,0.9110956080213111,6.256609916687012,14388.121761999999 +16728,Multiclass classification,k-Nearest Neighbors,Keystroke,0.910384408441442,0.9103844084414419,0.9103324360258119,6.2080230712890625,15023.088924 +17136,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113510358914503,0.9113510358914503,0.9114963483082135,6.223179817199707,15670.063673 +17544,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9113606566721769,0.9113606566721769,0.9113826667045093,6.23558235168457,16331.265293 +17952,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9114255473232689,0.911425547323269,0.9114384409485988,6.255133628845215,17006.553944 +18360,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9126858761370445,0.9126858761370445,0.9127656000580755,6.270404815673828,17693.792261 +18768,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9125059945649278,0.9125059945649276,0.9124701420883569,6.180520057678223,18393.849908 +19176,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9125945241199479,0.9125945241199479,0.9125632790621417,6.265439987182617,19106.227212 +19584,Multiclass classification,k-Nearest Neighbors,Keystroke,0.913547464637696,0.913547464637696,0.9135225066457016,6.303133964538574,19831.701162 +19992,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9111099994997749,0.9111099994997749,0.910917465793804,6.225028991699219,20571.760391 +20400,Multiclass classification,k-Nearest Neighbors,Keystroke,0.9104858081278494,0.9104858081278494,0.9103279821226861,6.325108528137207,21326.45228 +46,Multiclass classification,ADWIN Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.245764972655729,4.105147361755371,2.153154 +92,Multiclass classification,ADWIN Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.4934752395581889,4.108363151550293,6.907408 +138,Multiclass classification,ADWIN Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646677,4.108027458190918,14.639156 +184,Multiclass classification,ADWIN Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.614143164890895,4.108977317810059,25.443956 +230,Multiclass classification,ADWIN Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,3.881842613220215,39.254234 +276,Multiclass classification,ADWIN Bagging,ImageSegments,0.64,0.64,0.6559607038460422,3.996514320373535,55.768073 +322,Multiclass classification,ADWIN Bagging,ImageSegments,0.6697819314641744,0.6697819314641744,0.6706320385346652,4.112936019897461,74.877199 +368,Multiclass classification,ADWIN Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6897433526546475,4.112924575805664,96.687005 +414,Multiclass classification,ADWIN Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.706570530482581,4.117301940917969,121.290167 +460,Multiclass classification,ADWIN Bagging,ImageSegments,0.7145969498910676,0.7145969498910676,0.7071122267088654,4.116390228271484,148.551711 +506,Multiclass classification,ADWIN Bagging,ImageSegments,0.7247524752475247,0.7247524752475247,0.7147973207987898,4.115703582763672,178.365171 +552,Multiclass classification,ADWIN Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7210771168277493,4.115436553955078,210.796119 +598,Multiclass classification,ADWIN Bagging,ImageSegments,0.7336683417085427,0.7336683417085426,0.7250288715672424,4.115207672119141,245.953277 +644,Multiclass classification,ADWIN Bagging,ImageSegments,0.7325038880248833,0.7325038880248833,0.7258924883659029,4.118658065795898,283.80303000000004 +690,Multiclass classification,ADWIN Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7302536378735861,4.118425369262695,324.296282 +736,Multiclass classification,ADWIN Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7329631379486719,4.118097305297852,367.43284 +782,Multiclass classification,ADWIN Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7387105187530085,4.117616653442383,413.28289 +828,Multiclass classification,ADWIN Bagging,ImageSegments,0.7460701330108828,0.7460701330108827,0.7425025596154723,4.117326736450195,461.953497 +874,Multiclass classification,ADWIN Bagging,ImageSegments,0.7514318442153494,0.7514318442153494,0.7467163857842193,4.117303848266602,513.2937440000001 +920,Multiclass classification,ADWIN Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7453933609147309,4.117105484008789,567.431634 +966,Multiclass classification,ADWIN Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.7451117895470661,4.116701126098633,624.2209760000001 +1012,Multiclass classification,ADWIN Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.744963080481548,4.116399765014648,683.8404210000001 +1058,Multiclass classification,ADWIN Bagging,ImageSegments,0.7511825922421949,0.7511825922421949,0.7446315489945475,4.117582321166992,746.2097300000001 +1104,Multiclass classification,ADWIN Bagging,ImageSegments,0.7533998186763372,0.7533998186763373,0.7466082689908061,4.117956161499023,811.1743250000002 +1150,Multiclass classification,ADWIN Bagging,ImageSegments,0.7563098346388164,0.7563098346388164,0.7491651771194966,4.117490768432617,878.6809830000002 +1196,Multiclass classification,ADWIN Bagging,ImageSegments,0.7589958158995815,0.7589958158995815,0.7526420027035883,4.117303848266602,948.8627130000002 +1242,Multiclass classification,ADWIN Bagging,ImageSegments,0.75825946817083,0.7582594681708301,0.7524016178277559,4.11713981628418,1021.5806660000002 +1288,Multiclass classification,ADWIN Bagging,ImageSegments,0.7637917637917638,0.7637917637917638,0.75666252908711,4.117353439331055,1096.9757510000002 +1334,Multiclass classification,ADWIN Bagging,ImageSegments,0.7636909227306826,0.7636909227306825,0.7569484848610158,4.11726188659668,1175.015685 +1380,Multiclass classification,ADWIN Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7590436403579585,4.11729621887207,1255.693831 +1426,Multiclass classification,ADWIN Bagging,ImageSegments,0.767719298245614,0.767719298245614,0.761211289695921,4.117136001586914,1338.965745 +1472,Multiclass classification,ADWIN Bagging,ImageSegments,0.7722637661454793,0.7722637661454793,0.764056696643358,4.117197036743164,1424.822234 +1518,Multiclass classification,ADWIN Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.764234133414765,4.117246627807617,1513.291691 +1564,Multiclass classification,ADWIN Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7653316001442944,4.117277145385742,1604.2857379999998 +1610,Multiclass classification,ADWIN Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7647353044337893,4.11713981628418,1697.9838939999997 +1656,Multiclass classification,ADWIN Bagging,ImageSegments,0.7734138972809668,0.7734138972809667,0.7645730180903108,4.116628646850586,1794.3974679999997 +1702,Multiclass classification,ADWIN Bagging,ImageSegments,0.7724867724867724,0.7724867724867724,0.7656182355666586,4.116819381713867,1893.3012909999998 +1748,Multiclass classification,ADWIN Bagging,ImageSegments,0.7750429307384087,0.7750429307384087,0.7677424040514297,4.116933822631836,1994.6627269999997 +1794,Multiclass classification,ADWIN Bagging,ImageSegments,0.7763524818739542,0.7763524818739542,0.7677176136548693,4.116861343383789,2098.663831 +1840,Multiclass classification,ADWIN Bagging,ImageSegments,0.7775965198477434,0.7775965198477434,0.7691578918725354,4.11646842956543,2205.2268019999997 +1886,Multiclass classification,ADWIN Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7681560201617949,4.116430282592773,2314.3219019999997 +1932,Multiclass classification,ADWIN Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7674170460709655,4.116365432739258,2425.9767019999995 +1978,Multiclass classification,ADWIN Bagging,ImageSegments,0.7769347496206374,0.7769347496206374,0.7672843880004774,4.116201400756836,2540.2532379999993 +2024,Multiclass classification,ADWIN Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7681802739952505,4.116155624389648,2657.1841359999994 +2070,Multiclass classification,ADWIN Bagging,ImageSegments,0.778153697438376,0.7781536974383759,0.767530439166732,4.116151809692383,2776.6077329999994 +2116,Multiclass classification,ADWIN Bagging,ImageSegments,0.7787234042553192,0.778723404255319,0.7673415220519754,4.116128921508789,2898.5867789999993 +2162,Multiclass classification,ADWIN Bagging,ImageSegments,0.7797316057380842,0.7797316057380842,0.7679341969633587,4.116201400756836,3023.0016299999993 +2208,Multiclass classification,ADWIN Bagging,ImageSegments,0.7816039873130947,0.7816039873130947,0.7687944234581563,4.11619758605957,3150.0382249999993 +2254,Multiclass classification,ADWIN Bagging,ImageSegments,0.7785175321793165,0.7785175321793165,0.7657018899401807,4.116170883178711,3279.4760369999995 +2300,Multiclass classification,ADWIN Bagging,ImageSegments,0.7777294475859069,0.7777294475859068,0.7649119672933203,4.116254806518555,3411.2017669999996 +2310,Multiclass classification,ADWIN Bagging,ImageSegments,0.7778258986574275,0.7778258986574276,0.765010539660814,4.116277694702148,3543.5486899999996 +1056,Multiclass classification,ADWIN Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762562,6.4894914627075195,90.340432 +2112,Multiclass classification,ADWIN Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.490170478820801,257.284509 +3168,Multiclass classification,ADWIN Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244894,6.491124153137207,490.807794 +4224,Multiclass classification,ADWIN Bagging,Insects,0.6107032914989344,0.6107032914989344,0.574850135712032,6.4912004470825195,783.3577379999999 +5280,Multiclass classification,ADWIN Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225517,6.491948127746582,1129.937274 +6336,Multiclass classification,ADWIN Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789626,6.490735054016113,1525.7763839999998 +7392,Multiclass classification,ADWIN Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867537,6.490704536437988,1967.9282259999998 +8448,Multiclass classification,ADWIN Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.490643501281738,2456.1055429999997 +9504,Multiclass classification,ADWIN Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297488,6.491009712219238,2990.761064 +10560,Multiclass classification,ADWIN Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.491185188293457,3571.5807019999997 +11616,Multiclass classification,ADWIN Bagging,Insects,0.6005165733964701,0.6005165733964701,0.56512832395729,6.491345405578613,4198.711386999999 +12672,Multiclass classification,ADWIN Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306638,6.491543769836426,4874.277407999999 +13728,Multiclass classification,ADWIN Bagging,Insects,0.6152108982297662,0.6152108982297662,0.5959760515786451,5.97607421875,5593.125681999999 +14784,Multiclass classification,ADWIN Bagging,Insects,0.6060339579246432,0.6060339579246432,0.5869142505177357,6.496403694152832,6355.050274999999 +15840,Multiclass classification,ADWIN Bagging,Insects,0.5713744554580465,0.5713744554580465,0.5537658591956377,6.4967546463012695,7160.569073999999 +16896,Multiclass classification,ADWIN Bagging,Insects,0.545546019532406,0.545546019532406,0.5286479939306438,6.381303787231445,8010.073855999999 +17952,Multiclass classification,ADWIN Bagging,Insects,0.526767311013314,0.526767311013314,0.509587529402725,6.497265815734863,8901.233847 +19008,Multiclass classification,ADWIN Bagging,Insects,0.517756615983585,0.517756615983585,0.4976462434137419,4.6858930587768555,9829.81162 +20064,Multiclass classification,ADWIN Bagging,Insects,0.5296815032647162,0.5296815032647162,0.5080882715573688,10.369908332824707,10791.950057 +21120,Multiclass classification,ADWIN Bagging,Insects,0.539750935176855,0.539750935176855,0.5184934777423561,10.92272663116455,11801.930673 +22176,Multiclass classification,ADWIN Bagging,Insects,0.5468771138669674,0.5468771138669674,0.525970977438283,10.920933723449707,12856.592968 +23232,Multiclass classification,ADWIN Bagging,Insects,0.5551633593043778,0.5551633593043778,0.5340735310276195,12.231526374816895,13957.460176 +24288,Multiclass classification,ADWIN Bagging,Insects,0.5615761518507844,0.5615761518507844,0.5396852076547556,12.87682819366455,15100.290122 +25344,Multiclass classification,ADWIN Bagging,Insects,0.5679280274632048,0.5679280274632048,0.5455634192548013,13.528642654418945,16285.362621 +26400,Multiclass classification,ADWIN Bagging,Insects,0.5727868479866661,0.5727868479866661,0.5496374434570932,13.632143020629883,17508.053461 +27456,Multiclass classification,ADWIN Bagging,Insects,0.5754143143325442,0.5754143143325442,0.5513680135969626,13.630533218383789,18766.96928 +28512,Multiclass classification,ADWIN Bagging,Insects,0.5772859598049875,0.5772859598049875,0.5551350356863173,13.627862930297852,20061.957755000003 +29568,Multiclass classification,ADWIN Bagging,Insects,0.577772516657084,0.577772516657084,0.5590861332292512,13.626611709594727,21394.440888000005 +30624,Multiclass classification,ADWIN Bagging,Insects,0.578225516768442,0.578225516768442,0.5625516131192055,12.769641876220703,22755.311286000004 +31680,Multiclass classification,ADWIN Bagging,Insects,0.5795637488557088,0.5795637488557088,0.5663363640160616,12.768932342529297,24150.336726000005 +32736,Multiclass classification,ADWIN Bagging,Insects,0.5811211241790133,0.5811211241790133,0.5696723582178381,12.768062591552734,25577.802283000005 +33792,Multiclass classification,ADWIN Bagging,Insects,0.575804208221124,0.575804208221124,0.5647934119551398,12.981294631958008,27039.455958000006 +34848,Multiclass classification,ADWIN Bagging,Insects,0.5701495107182828,0.5701495107182828,0.559068023359177,12.981256484985352,28537.493337000007 +35904,Multiclass classification,ADWIN Bagging,Insects,0.5657744478177311,0.5657744478177311,0.5542573482740075,12.983362197875977,30069.702466000006 +36960,Multiclass classification,ADWIN Bagging,Insects,0.5611894261208366,0.5611894261208366,0.5493152777162592,13.52482795715332,31635.746830000007 +38016,Multiclass classification,ADWIN Bagging,Insects,0.558779429172695,0.558779429172695,0.5463982360776033,13.526559829711914,33235.41425300001 +39072,Multiclass classification,ADWIN Bagging,Insects,0.5546825010877633,0.5546825010877633,0.5426283860139581,14.304903030395508,34865.73115700001 +40128,Multiclass classification,ADWIN Bagging,Insects,0.5542153662122761,0.5542153662122761,0.5429626632180721,15.152182579040527,36525.24862800001 +41184,Multiclass classification,ADWIN Bagging,Insects,0.5541364155112547,0.5541364155112547,0.5435420562964655,15.252725601196289,38211.297236000006 +42240,Multiclass classification,ADWIN Bagging,Insects,0.5542981604678141,0.5542981604678141,0.544391400018036,15.251314163208008,39923.86574200001 +43296,Multiclass classification,ADWIN Bagging,Insects,0.554151749624668,0.554151749624668,0.5448486588729107,13.424749374389648,41664.47056700001 +44352,Multiclass classification,ADWIN Bagging,Insects,0.5536290049829767,0.5536290049829767,0.5448029815059025,13.648665428161621,43429.160590000014 +45408,Multiclass classification,ADWIN Bagging,Insects,0.5541436342414165,0.5541436342414165,0.5454957405719211,14.18911075592041,45215.90786900002 +46464,Multiclass classification,ADWIN Bagging,Insects,0.5553020683124207,0.5553020683124207,0.546961663735647,15.156656265258789,47024.86244100002 +47520,Multiclass classification,ADWIN Bagging,Insects,0.5579662871693428,0.5579662871693428,0.5498636684303295,14.218542098999023,48856.78116300002 +48576,Multiclass classification,ADWIN Bagging,Insects,0.5627586206896552,0.5627586206896552,0.5545030394801858,14.845645904541016,50711.770017000024 +49632,Multiclass classification,ADWIN Bagging,Insects,0.5677701436602124,0.5677701436602124,0.5591808574875289,15.233248710632324,52589.43568900003 +50688,Multiclass classification,ADWIN Bagging,Insects,0.5730463432438297,0.5730463432438297,0.5639878919164368,15.890131950378418,54487.48381000003 +51744,Multiclass classification,ADWIN Bagging,Insects,0.5791894555785324,0.5791894555785324,0.5695807960578061,16.1916446685791,56406.77001200003 +52800,Multiclass classification,ADWIN Bagging,Insects,0.5794427924771303,0.5794427924771303,0.5701512686040561,15.307219505310059,58342.70756100003 +52848,Multiclass classification,ADWIN Bagging,Insects,0.5794652487369197,0.5794652487369197,0.5701984940722999,15.307356834411621,60279.413314000034 +408,Multiclass classification,ADWIN Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.100947380065918,5.705318 +816,Multiclass classification,ADWIN Bagging,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,3.048105239868164,25.352994000000002 +1224,Multiclass classification,ADWIN Bagging,Keystroke,0.8912510220768601,0.8912510220768601,0.8617021305177772,3.9921913146972656,63.032035 +1632,Multiclass classification,ADWIN Bagging,Keystroke,0.9031269160024524,0.9031269160024524,0.8868998230762756,4.944231986999512,123.60027500000001 +2040,Multiclass classification,ADWIN Bagging,Keystroke,0.898970083374203,0.898970083374203,0.888705938214812,5.993730545043945,211.611387 +2448,Multiclass classification,ADWIN Bagging,Keystroke,0.8598283612586841,0.8598283612586841,0.8569666636755086,6.37155818939209,330.620683 +2856,Multiclass classification,ADWIN Bagging,Keystroke,0.8669001751313485,0.8669001751313484,0.8547854134985733,7.318934440612793,479.663409 +3264,Multiclass classification,ADWIN Bagging,Keystroke,0.8581060373889059,0.8581060373889059,0.8327540420876277,8.264909744262695,660.474615 +3672,Multiclass classification,ADWIN Bagging,Keystroke,0.8490874421138654,0.8490874421138654,0.8463961237855363,8.926459312438965,875.371562 +4080,Multiclass classification,ADWIN Bagging,Keystroke,0.8421181662172101,0.84211816621721,0.8299816031455575,10.074895858764648,1125.854174 +4488,Multiclass classification,ADWIN Bagging,Keystroke,0.8301760641854246,0.8301760641854244,0.8400819204125556,11.044804573059082,1412.144879 +4896,Multiclass classification,ADWIN Bagging,Keystroke,0.8314606741573034,0.8314606741573034,0.8387821748480373,8.862092971801758,1728.261118 +5304,Multiclass classification,ADWIN Bagging,Keystroke,0.8333019045823119,0.8333019045823119,0.8299513887279447,9.715648651123047,2074.079546 +5712,Multiclass classification,ADWIN Bagging,Keystroke,0.8255997198389073,0.8255997198389075,0.831498100235552,10.513428688049316,2449.732459 +6120,Multiclass classification,ADWIN Bagging,Keystroke,0.8241542735741134,0.8241542735741134,0.813971923025991,11.555256843566895,2856.066254 +6528,Multiclass classification,ADWIN Bagging,Keystroke,0.8043511567335683,0.8043511567335683,0.8048077550156274,12.298343658447266,3295.046229 +6936,Multiclass classification,ADWIN Bagging,Keystroke,0.8002883922134102,0.8002883922134101,0.8062362865697692,11.726844787597656,3768.4733180000003 +7344,Multiclass classification,ADWIN Bagging,Keystroke,0.8093422306959008,0.8093422306959008,0.813125473572493,9.433514595031738,4267.304275 +7752,Multiclass classification,ADWIN Bagging,Keystroke,0.8157657076506257,0.8157657076506257,0.8184378776785012,10.539642333984375,4791.9677950000005 +8160,Multiclass classification,ADWIN Bagging,Keystroke,0.8199534256649099,0.81995342566491,0.8213128379144453,11.364830017089844,5345.1630700000005 +8568,Multiclass classification,ADWIN Bagging,Keystroke,0.8247928096183028,0.8247928096183028,0.8275146627418534,12.672901153564453,5929.400246 +8976,Multiclass classification,ADWIN Bagging,Keystroke,0.8295264623955432,0.8295264623955433,0.8318915513040454,13.833264350891113,6547.972923 +9384,Multiclass classification,ADWIN Bagging,Keystroke,0.8319300863263348,0.8319300863263348,0.8336463894938194,14.741169929504395,7204.877164 +9792,Multiclass classification,ADWIN Bagging,Keystroke,0.8342355224185476,0.8342355224185476,0.8362542817352725,16.02083969116211,7900.9214680000005 +10200,Multiclass classification,ADWIN Bagging,Keystroke,0.8343955289734287,0.8343955289734286,0.833886744496364,17.251243591308594,8638.28045 +10608,Multiclass classification,ADWIN Bagging,Keystroke,0.8258697086829452,0.8258697086829452,0.823298887298616,18.484009742736816,9418.818077 +11016,Multiclass classification,ADWIN Bagging,Keystroke,0.825692237857467,0.825692237857467,0.827229896548608,17.053231239318848,10241.512178 +11424,Multiclass classification,ADWIN Bagging,Keystroke,0.8263153287227524,0.8263153287227524,0.8251000136898328,18.097841262817383,11105.510711 +11832,Multiclass classification,ADWIN Bagging,Keystroke,0.8257966359563857,0.8257966359563859,0.8251092059206939,19.1904354095459,12012.672379000001 +12240,Multiclass classification,ADWIN Bagging,Keystroke,0.8289892965111528,0.8289892965111528,0.8300645161883343,17.2155818939209,12963.554855000002 +12648,Multiclass classification,ADWIN Bagging,Keystroke,0.8324503834901558,0.8324503834901558,0.8328446288662702,17.090572357177734,13955.688030000003 +13056,Multiclass classification,ADWIN Bagging,Keystroke,0.8295672156261968,0.8295672156261968,0.8279815503081916,18.284998893737793,14990.870602000003 +13464,Multiclass classification,ADWIN Bagging,Keystroke,0.828121518235163,0.828121518235163,0.8279872572314477,18.759904861450195,16071.989638000003 +13872,Multiclass classification,ADWIN Bagging,Keystroke,0.8300771393554899,0.8300771393554899,0.8300312724960401,19.937789916992188,17198.739284000003 +14280,Multiclass classification,ADWIN Bagging,Keystroke,0.8329714966034036,0.8329714966034036,0.8330653900337638,21.172300338745117,18373.548754000003 +14688,Multiclass classification,ADWIN Bagging,Keystroke,0.8360454823994008,0.8360454823994008,0.8362319050195895,22.12139320373535,19591.296889000005 +15096,Multiclass classification,ADWIN Bagging,Keystroke,0.8391520370983769,0.8391520370983769,0.8393677597260801,22.688467979431152,20852.364231000003 +15504,Multiclass classification,ADWIN Bagging,Keystroke,0.8400309617493389,0.8400309617493388,0.8398031059873,23.806550979614258,22157.639176000004 +15912,Multiclass classification,ADWIN Bagging,Keystroke,0.8335114072025642,0.8335114072025642,0.8310693286634668,25.06292724609375,23499.792247000005 +16320,Multiclass classification,ADWIN Bagging,Keystroke,0.8283595808566702,0.8283595808566702,0.826721014765785,26.060873985290527,24886.958001000006 +16728,Multiclass classification,ADWIN Bagging,Keystroke,0.826747175225683,0.8267471752256829,0.8259678903415486,27.245673179626465,26317.085564000008 +17136,Multiclass classification,ADWIN Bagging,Keystroke,0.821943390720747,0.821943390720747,0.8202405231953956,28.675668716430664,27793.820666000007 +17544,Multiclass classification,ADWIN Bagging,Keystroke,0.8182180926865417,0.8182180926865417,0.8170173651382093,29.74225902557373,29319.213378000008 +17952,Multiclass classification,ADWIN Bagging,Keystroke,0.81878446883182,0.81878446883182,0.8179349229322325,30.879840850830078,30892.431607000006 +18360,Multiclass classification,ADWIN Bagging,Keystroke,0.821123154855929,0.821123154855929,0.8204502524156659,32.019548416137695,32513.434007000007 +18768,Multiclass classification,ADWIN Bagging,Keystroke,0.8235200085256035,0.8235200085256035,0.8229965581236837,33.157379150390625,34181.929457000006 +19176,Multiclass classification,ADWIN Bagging,Keystroke,0.819973924380704,0.819973924380704,0.8189812465563673,34.295823097229004,35895.74071300001 +19584,Multiclass classification,ADWIN Bagging,Keystroke,0.821733135883164,0.821733135883164,0.8211010404575377,35.31475067138672,37655.039070000006 +19992,Multiclass classification,ADWIN Bagging,Keystroke,0.8188684908208694,0.8188684908208694,0.8180458262517715,36.57470226287842,39458.702899 +20400,Multiclass classification,ADWIN Bagging,Keystroke,0.816559635276239,0.816559635276239,0.8159075588016685,37.855767250061035,41308.014952000005 +46,Multiclass classification,AdaBoost,ImageSegments,0.1111111111111111,0.1111111111111111,0.0815018315018315,3.4160032272338867,1.208286 +92,Multiclass classification,AdaBoost,ImageSegments,0.23076923076923078,0.23076923076923078,0.2226391771283412,4.099128723144531,4.553382 +138,Multiclass classification,AdaBoost,ImageSegments,0.4233576642335766,0.4233576642335766,0.44635377186191566,4.099002838134766,10.351245 +184,Multiclass classification,AdaBoost,ImageSegments,0.5355191256830601,0.5355191256830601,0.5617062146473912,4.099178314208984,18.765494 +230,Multiclass classification,AdaBoost,ImageSegments,0.5938864628820961,0.5938864628820961,0.6236530662596055,4.0991668701171875,30.180838 +276,Multiclass classification,AdaBoost,ImageSegments,0.6290909090909091,0.6290909090909091,0.6558170665459355,4.099109649658203,44.412895 +322,Multiclass classification,AdaBoost,ImageSegments,0.660436137071651,0.660436137071651,0.678574720261515,4.098438262939453,61.214822 +368,Multiclass classification,AdaBoost,ImageSegments,0.6920980926430518,0.6920980926430518,0.7041680355881775,4.0984954833984375,80.427396 +414,Multiclass classification,AdaBoost,ImageSegments,0.7167070217917676,0.7167070217917676,0.7259075149442813,4.097980499267578,102.254145 +460,Multiclass classification,AdaBoost,ImageSegments,0.7254901960784313,0.7254901960784313,0.7325011710849479,4.098300933837891,127.09125599999999 +506,Multiclass classification,AdaBoost,ImageSegments,0.7386138613861386,0.7386138613861386,0.7428621938273078,4.098552703857422,154.35838199999998 +552,Multiclass classification,AdaBoost,ImageSegments,0.7422867513611615,0.7422867513611615,0.7453719085253248,4.098358154296875,184.30369299999998 +598,Multiclass classification,AdaBoost,ImageSegments,0.7487437185929648,0.7487437185929648,0.7504522188790486,4.098468780517578,216.734559 +644,Multiclass classification,AdaBoost,ImageSegments,0.7465007776049767,0.7465007776049767,0.7482323503576439,4.098541259765625,252.13907799999998 +690,Multiclass classification,AdaBoost,ImageSegments,0.7489114658925979,0.748911465892598,0.7488472102580618,4.098594665527344,290.044846 +736,Multiclass classification,AdaBoost,ImageSegments,0.7523809523809524,0.7523809523809524,0.7518283723099097,4.098430633544922,330.661237 +782,Multiclass classification,AdaBoost,ImageSegments,0.7541613316261203,0.7541613316261204,0.7531089046321314,4.098361968994141,373.819675 +828,Multiclass classification,AdaBoost,ImageSegments,0.7557436517533253,0.7557436517533253,0.7552013614952863,4.098308563232422,419.68676000000005 +874,Multiclass classification,AdaBoost,ImageSegments,0.7617411225658648,0.7617411225658649,0.7601066395856337,4.098381042480469,467.91378800000007 +920,Multiclass classification,AdaBoost,ImageSegments,0.763873775843308,0.763873775843308,0.7623480483274478,4.098400115966797,518.622959 +966,Multiclass classification,AdaBoost,ImageSegments,0.7678756476683938,0.7678756476683938,0.7646598072570266,4.098423004150391,571.868767 +1012,Multiclass classification,AdaBoost,ImageSegments,0.7705242334322453,0.7705242334322453,0.7668271197983111,4.098529815673828,627.754669 +1058,Multiclass classification,AdaBoost,ImageSegments,0.7757805108798487,0.7757805108798487,0.7714920336037777,4.098388671875,686.3790640000001 +1104,Multiclass classification,AdaBoost,ImageSegments,0.7760652765185857,0.7760652765185856,0.7719206139767609,4.098537445068359,747.748718 +1150,Multiclass classification,AdaBoost,ImageSegments,0.7789382071366405,0.7789382071366405,0.7750313949659527,4.098442077636719,811.629001 +1196,Multiclass classification,AdaBoost,ImageSegments,0.7849372384937239,0.7849372384937239,0.7820003890472508,4.098487854003906,878.060098 +1242,Multiclass classification,AdaBoost,ImageSegments,0.7856567284448026,0.7856567284448026,0.7827470902102026,4.098438262939453,947.241797 +1288,Multiclass classification,AdaBoost,ImageSegments,0.7894327894327894,0.7894327894327894,0.785982924599392,4.0983428955078125,1018.793017 +1334,Multiclass classification,AdaBoost,ImageSegments,0.7906976744186046,0.7906976744186046,0.7876424482584368,4.098438262939453,1093.0782689999999 +1380,Multiclass classification,AdaBoost,ImageSegments,0.7933284989122552,0.7933284989122552,0.7906471924204205,4.098392486572266,1169.7117919999998 +1426,Multiclass classification,AdaBoost,ImageSegments,0.7978947368421052,0.7978947368421052,0.7945020166797493,4.098480224609375,1248.577134 +1472,Multiclass classification,AdaBoost,ImageSegments,0.8028552005438477,0.8028552005438477,0.7982243751921434,4.098472595214844,1329.680821 +1518,Multiclass classification,AdaBoost,ImageSegments,0.8035596572181938,0.8035596572181938,0.7981876534181912,4.098491668701172,1413.4983189999998 +1564,Multiclass classification,AdaBoost,ImageSegments,0.8035828534868842,0.8035828534868842,0.798634974540431,4.098518371582031,1499.9052179999999 +1610,Multiclass classification,AdaBoost,ImageSegments,0.8048477315102548,0.8048477315102549,0.7997380784882049,4.098381042480469,1588.8368169999999 +1656,Multiclass classification,AdaBoost,ImageSegments,0.8066465256797583,0.8066465256797583,0.80161945439383,4.098377227783203,1680.2495139999999 +1702,Multiclass classification,AdaBoost,ImageSegments,0.8059964726631393,0.8059964726631393,0.8024858564723997,4.098514556884766,1774.345382 +1748,Multiclass classification,AdaBoost,ImageSegments,0.8070978820835718,0.8070978820835718,0.8029124203507955,4.098423004150391,1871.065136 +1794,Multiclass classification,AdaBoost,ImageSegments,0.8081427774679308,0.8081427774679307,0.8029834045630979,4.098461151123047,1970.0377899999999 +1840,Multiclass classification,AdaBoost,ImageSegments,0.8069603045133225,0.8069603045133223,0.801927622716254,4.098594665527344,2071.588776 +1886,Multiclass classification,AdaBoost,ImageSegments,0.8053050397877984,0.8053050397877984,0.8006727596367825,4.098400115966797,2175.772942 +1932,Multiclass classification,AdaBoost,ImageSegments,0.8047643707923355,0.8047643707923355,0.7995493059800365,4.098396301269531,2282.41088 +1978,Multiclass classification,AdaBoost,ImageSegments,0.8057663125948407,0.8057663125948407,0.8003960406612564,4.0984344482421875,2391.59611 +2024,Multiclass classification,AdaBoost,ImageSegments,0.8072170044488384,0.8072170044488384,0.8005625942078284,4.098430633544922,2503.16971 +2070,Multiclass classification,AdaBoost,ImageSegments,0.8066698888351861,0.8066698888351861,0.8002110568368,4.098316192626953,2617.3694960000003 +2116,Multiclass classification,AdaBoost,ImageSegments,0.807565011820331,0.807565011820331,0.8005131307885663,4.0983428955078125,2733.922308 +2162,Multiclass classification,AdaBoost,ImageSegments,0.8079592781119852,0.8079592781119852,0.8006755955605837,4.098320007324219,2852.747139 +2208,Multiclass classification,AdaBoost,ImageSegments,0.8087902129587675,0.8087902129587675,0.8009921695193862,4.098320007324219,2973.740681 +2254,Multiclass classification,AdaBoost,ImageSegments,0.8060363959165557,0.8060363959165557,0.7987732120640717,4.0983428955078125,3097.6149410000003 +2300,Multiclass classification,AdaBoost,ImageSegments,0.8051326663766856,0.8051326663766856,0.798077892809675,4.0983428955078125,3223.9293610000004 +2310,Multiclass classification,AdaBoost,ImageSegments,0.8046773495019489,0.8046773495019489,0.7977695866822911,4.098388671875,3350.8763620000004 +1056,Multiclass classification,AdaBoost,Insects,0.6360189573459716,0.6360189573459716,0.5992691812827112,6.474042892456055,88.969298 +2112,Multiclass classification,AdaBoost,Insects,0.6110847939365229,0.6110847939365229,0.5773210074897359,6.473905563354492,256.045675 +3168,Multiclass classification,AdaBoost,Insects,0.6043574360593622,0.6043574360593622,0.5704368753709179,6.473470687866211,489.83548 +4224,Multiclass classification,AdaBoost,Insects,0.6014681506038362,0.6014681506038362,0.5676969561642586,6.473196029663086,783.3519140000001 +5280,Multiclass classification,AdaBoost,Insects,0.6057965523773442,0.6057965523773442,0.5710016183775801,6.473196029663086,1130.801617 +6336,Multiclass classification,AdaBoost,Insects,0.5966850828729282,0.5966850828729282,0.5635903588556204,6.473356246948242,1527.884634 +7392,Multiclass classification,AdaBoost,Insects,0.5957245298335814,0.5957245298335814,0.5625002603439991,6.473814010620117,1971.450931 +8448,Multiclass classification,AdaBoost,Insects,0.5982005445720374,0.5982005445720374,0.5646892369665863,6.474157333374023,2461.351589 +9504,Multiclass classification,AdaBoost,Insects,0.596337998526781,0.596337998526781,0.5627085514562804,6.47450065612793,2997.75158 +10560,Multiclass classification,AdaBoost,Insects,0.5965527038545316,0.5965527038545316,0.5631320282838163,6.47468376159668,3580.189969 +11616,Multiclass classification,AdaBoost,Insects,0.5953508394317693,0.5953508394317693,0.562671447170627,6.47468376159668,4209.202801 +12672,Multiclass classification,AdaBoost,Insects,0.5979796385447084,0.5979796385447084,0.5680559575776837,6.474340438842773,4886.872526 +13728,Multiclass classification,AdaBoost,Insects,0.610767101333139,0.610767101333139,0.5941277335666079,6.473836898803711,5609.570035 +14784,Multiclass classification,AdaBoost,Insects,0.6019752418318338,0.6019752418318338,0.5851264744797859,6.473745346069336,6378.998584999999 +15840,Multiclass classification,AdaBoost,Insects,0.5705536965717533,0.5705536965717533,0.5545059657048704,6.473974227905273,7193.860588999999 +16896,Multiclass classification,AdaBoost,Insects,0.548091151228174,0.548091151228174,0.5320735507355622,6.474386215209961,8051.816493999999 +17952,Multiclass classification,AdaBoost,Insects,0.5307225224221492,0.5307225224221492,0.5138536287616571,6.474637985229492,8952.059017 +19008,Multiclass classification,AdaBoost,Insects,0.5182827379386542,0.5182827379386542,0.4990809738484312,6.47486686706543,9893.706361 +20064,Multiclass classification,AdaBoost,Insects,0.5182176145142801,0.5182176145142801,0.497867701567998,8.642622947692871,10881.966771000001 +21120,Multiclass classification,AdaBoost,Insects,0.5272503432927695,0.5272503432927695,0.5067114684709674,15.437758445739746,11917.076256 +22176,Multiclass classification,AdaBoost,Insects,0.533032694475761,0.533032694475761,0.5127471323280748,16.81709384918213,12999.258268 +23232,Multiclass classification,AdaBoost,Insects,0.5410442942619775,0.5410442942619775,0.5207771198745245,17.041016578674316,14124.681759 +24288,Multiclass classification,AdaBoost,Insects,0.5459710956478775,0.5459710956478775,0.5251711652768184,17.038064002990723,15290.808705 +25344,Multiclass classification,AdaBoost,Insects,0.5532099593576135,0.5532099593576135,0.5314216535856217,17.036622047424316,16491.42669 +26400,Multiclass classification,AdaBoost,Insects,0.5607788173794462,0.5607788173794462,0.5375130024626694,17.14900016784668,17723.115047 +27456,Multiclass classification,AdaBoost,Insects,0.5667091604443635,0.5667091604443635,0.5418496825562071,17.261820793151855,18986.675500999998 +28512,Multiclass classification,AdaBoost,Insects,0.5692890463329943,0.5692890463329943,0.5455529487931667,17.26294231414795,20283.404340999998 +29568,Multiclass classification,AdaBoost,Insects,0.5688436432509216,0.5688436432509216,0.5481992899375988,17.26337718963623,21617.011828 +30624,Multiclass classification,AdaBoost,Insects,0.5687228553701467,0.5687228553701467,0.5505043481720591,17.26330852508545,22980.237824 +31680,Multiclass classification,AdaBoost,Insects,0.5691467533697402,0.5691467533697402,0.5529220328647554,17.262804985046387,24378.053405 +32736,Multiclass classification,AdaBoost,Insects,0.5703986558729189,0.5703986558729189,0.5556828084411201,17.262507438659668,25809.039082 +33792,Multiclass classification,AdaBoost,Insects,0.5650025154626972,0.5650025154626972,0.5507695387439543,17.487704277038574,27274.413313999998 +34848,Multiclass classification,AdaBoost,Insects,0.5587281545039745,0.5587281545039745,0.5445349362041821,17.929275512695312,28775.770082 +35904,Multiclass classification,AdaBoost,Insects,0.5541876723393588,0.5541876723393588,0.5396635045593164,18.030207633972168,30311.158156999998 +36960,Multiclass classification,AdaBoost,Insects,0.549122000054114,0.549122000054114,0.5343517375956978,19.681435585021973,31880.342243 +38016,Multiclass classification,AdaBoost,Insects,0.5473628830724714,0.5473628830724714,0.5321033552605493,21.718143463134766,33482.866823 +39072,Multiclass classification,AdaBoost,Insects,0.5426787131120269,0.5426787131120269,0.52803892360078,22.487850189208984,35116.535538 +40128,Multiclass classification,AdaBoost,Insects,0.5419293742367982,0.5419293742367982,0.5284857300708793,23.76238250732422,36778.160188999995 +41184,Multiclass classification,AdaBoost,Insects,0.5417769467984362,0.5417769467984362,0.5296361895775551,23.860816955566406,38464.674338 +42240,Multiclass classification,AdaBoost,Insects,0.5422240109851085,0.5422240109851085,0.5313111510734391,24.196860313415527,40175.576412999995 +43296,Multiclass classification,AdaBoost,Insects,0.5444970550871925,0.5444970550871925,0.5344195798463859,24.296037673950195,41906.99288399999 +44352,Multiclass classification,AdaBoost,Insects,0.5463461928705102,0.5463461928705102,0.5369578677381479,24.84640598297119,43659.477484999996 +45408,Multiclass classification,AdaBoost,Insects,0.5482855066399454,0.5482855066399454,0.5392181145139481,25.28636360168457,45430.571796 +46464,Multiclass classification,AdaBoost,Insects,0.5506532079288896,0.5506532079288896,0.5419048601727473,25.498303413391113,47220.970484 +47520,Multiclass classification,AdaBoost,Insects,0.5514846692901787,0.5514846692901787,0.5429796926051395,25.823355674743652,49033.125825999996 +48576,Multiclass classification,AdaBoost,Insects,0.5515388574369532,0.5515388574369532,0.543031483592694,25.821112632751465,50867.580247 +49632,Multiclass classification,AdaBoost,Insects,0.551953416211642,0.551953416211642,0.5433574148660688,26.098894119262695,52723.913942 +50688,Multiclass classification,AdaBoost,Insects,0.5563359441276856,0.5563359441276856,0.5472854805195191,26.366034507751465,54600.511786999996 +51744,Multiclass classification,AdaBoost,Insects,0.5623562607502464,0.5623562607502464,0.552981536157949,27.10032081604004,56496.789585 +52800,Multiclass classification,AdaBoost,Insects,0.5634576412432054,0.5634576412432054,0.5545218292020726,27.943178176879883,58415.671716 +52848,Multiclass classification,AdaBoost,Insects,0.5635324616345299,0.5635324616345299,0.5546220283668154,27.942995071411133,60335.727695999994 +408,Multiclass classification,AdaBoost,Keystroke,0.9877149877149877,0.9877149877149877,0.7696139476961394,2.1207275390625,4.211814 +816,Multiclass classification,AdaBoost,Keystroke,0.9889570552147239,0.9889570552147239,0.9592655637573824,2.9369373321533203,23.739993000000002 +1224,Multiclass classification,AdaBoost,Keystroke,0.9836467702371219,0.9836467702371219,0.9326470331192014,4.590028762817383,86.527265 +1632,Multiclass classification,AdaBoost,Keystroke,0.9828326180257511,0.9828326180257511,0.9594506659780556,5.819695472717285,184.232691 +2040,Multiclass classification,AdaBoost,Keystroke,0.9705738106915155,0.9705738106915155,0.9304838721924584,8.549582481384277,323.308574 +2448,Multiclass classification,AdaBoost,Keystroke,0.9607682876992235,0.9607682876992235,0.9455756842664337,10.061903953552246,491.40663400000005 +2856,Multiclass classification,AdaBoost,Keystroke,0.9541155866900175,0.9541155866900175,0.9254688528922778,12.678574562072754,687.150288 +3264,Multiclass classification,AdaBoost,Keystroke,0.9436101746858719,0.9436101746858719,0.9191430707434157,16.086813926696777,906.458431 +3672,Multiclass classification,AdaBoost,Keystroke,0.9403432307273223,0.9403432307273223,0.9284235615798526,18.255277633666992,1151.002639 +4080,Multiclass classification,AdaBoost,Keystroke,0.9338073057121844,0.9338073057121844,0.9182429705382059,21.65336036682129,1427.8669570000002 +4488,Multiclass classification,AdaBoost,Keystroke,0.9318029864051705,0.9318029864051705,0.9319119487505448,22.768765449523926,1733.8095280000002 +4896,Multiclass classification,AdaBoost,Keystroke,0.9317671092951992,0.9317671092951992,0.9296889978700974,24.966033935546875,2062.8329900000003 +5304,Multiclass classification,AdaBoost,Keystroke,0.9281538751650009,0.9281538751650009,0.9197653564039141,29.062508583068848,2423.7318920000002 +5712,Multiclass classification,AdaBoost,Keystroke,0.9227805988443355,0.9227805988443355,0.9201475418022375,32.12655830383301,2815.063536 +6120,Multiclass classification,AdaBoost,Keystroke,0.9177970256577872,0.9177970256577872,0.9072843264203106,37.27707767486572,3238.118927 +6528,Multiclass classification,AdaBoost,Keystroke,0.9115979776313774,0.9115979776313774,0.909931232789514,41.43412208557129,3706.915394 +6936,Multiclass classification,AdaBoost,Keystroke,0.9129055515501081,0.9129055515501081,0.9153430596364791,44.48411560058594,4207.758914 +7344,Multiclass classification,AdaBoost,Keystroke,0.9135230832084978,0.9135230832084978,0.9124682676754273,47.44067192077637,4740.722969 +7752,Multiclass classification,AdaBoost,Keystroke,0.9121403689846471,0.9121403689846471,0.9121831707972875,51.03960132598877,5307.755902000001 +8160,Multiclass classification,AdaBoost,Keystroke,0.9086897904154921,0.9086897904154921,0.9062633734460517,56.064818382263184,5918.019803000001 +8568,Multiclass classification,AdaBoost,Keystroke,0.9059180576631259,0.9059180576631259,0.9058259471519292,61.820496559143066,6575.962782000001 +8976,Multiclass classification,AdaBoost,Keystroke,0.9042896935933148,0.9042896935933148,0.9043251050138335,64.74030494689941,7280.842530000002 +9384,Multiclass classification,AdaBoost,Keystroke,0.9018437599914739,0.9018437599914739,0.9009662752730246,68.81300067901611,8035.1031440000015 +9792,Multiclass classification,AdaBoost,Keystroke,0.8971504442855683,0.8971504442855683,0.8956423708961025,74.25286674499512,8855.073479000002 +10200,Multiclass classification,AdaBoost,Keystroke,0.8926365329934307,0.8926365329934307,0.8903074227158838,79.6785535812378,9744.976722000003 +10608,Multiclass classification,AdaBoost,Keystroke,0.8846987838220043,0.8846987838220043,0.8819820059100918,85.28873825073242,10726.537155000004 +11016,Multiclass classification,AdaBoost,Keystroke,0.8791647753064004,0.8791647753064004,0.8795835231396919,89.59383392333984,11788.438997000003 +11424,Multiclass classification,AdaBoost,Keystroke,0.8759520266129738,0.8759520266129738,0.8744149508862001,94.86630344390869,12917.703791000004 +11832,Multiclass classification,AdaBoost,Keystroke,0.872200152142676,0.872200152142676,0.8717012117300328,100.15169906616211,14117.771334000003 +12240,Multiclass classification,AdaBoost,Keystroke,0.8736824903995425,0.8736824903995425,0.8749440738646468,101.93578433990479,15365.994884000003 +12648,Multiclass classification,AdaBoost,Keystroke,0.8717482406894915,0.8717482406894915,0.8710221211412438,107.46908473968506,16670.526324000002 +13056,Multiclass classification,AdaBoost,Keystroke,0.8661815396399847,0.8661815396399847,0.8651994621744733,112.71690273284912,18043.485216 +13464,Multiclass classification,AdaBoost,Keystroke,0.8642204560647702,0.8642204560647702,0.8645487273027374,116.56386756896973,19480.298866 +13872,Multiclass classification,AdaBoost,Keystroke,0.8619421815298104,0.8619421815298104,0.862314869215492,121.52821636199951,20980.471725 +14280,Multiclass classification,AdaBoost,Keystroke,0.859163806989285,0.859163806989285,0.8592780138529494,125.80194187164307,22534.622977 +14688,Multiclass classification,AdaBoost,Keystroke,0.8591952066453326,0.8591952066453326,0.8604793833246808,129.6016607284546,24135.768583999998 +15096,Multiclass classification,AdaBoost,Keystroke,0.8607485922490891,0.8607485922490891,0.8621609789956539,132.68816757202148,25779.863983 +15504,Multiclass classification,AdaBoost,Keystroke,0.8604141133974069,0.8604141133974069,0.8613237595899307,136.05841445922852,27480.057181 +15912,Multiclass classification,AdaBoost,Keystroke,0.8536861290930803,0.8536861290930803,0.853192144751886,141.70578575134277,29254.423593 +16320,Multiclass classification,AdaBoost,Keystroke,0.8493167473497151,0.849316747349715,0.8496464102754333,147.49746799468994,31089.50572 +16728,Multiclass classification,AdaBoost,Keystroke,0.846296407006636,0.846296407006636,0.8470383589757107,153.1935510635376,32973.577156 +17136,Multiclass classification,AdaBoost,Keystroke,0.8411438576014006,0.8411438576014006,0.8410396667771575,156.28132915496826,34948.88082 +17544,Multiclass classification,AdaBoost,Keystroke,0.8365729920766117,0.8365729920766117,0.8367907010021001,161.03080940246582,36967.519165 +17952,Multiclass classification,AdaBoost,Keystroke,0.8355523369171634,0.8355523369171634,0.8362918425397341,166.63249397277832,39016.229588999995 +18360,Multiclass classification,AdaBoost,Keystroke,0.837572852551882,0.8375728525518821,0.8385662484273668,171.47760772705078,41092.028592999995 +18768,Multiclass classification,AdaBoost,Keystroke,0.8390259498055097,0.8390259498055097,0.8401126675526959,175.70373821258545,43194.947863999994 +19176,Multiclass classification,AdaBoost,Keystroke,0.8376531942633637,0.8376531942633637,0.838676297522501,180.87701034545898,45330.650987999994 +19584,Multiclass classification,AdaBoost,Keystroke,0.8390951335341879,0.8390951335341879,0.8403338496937821,185.05438709259033,47482.84587799999 +19992,Multiclass classification,AdaBoost,Keystroke,0.8372767745485469,0.8372767745485468,0.8385640183306876,190.1383810043335,49661.235199999996 +20400,Multiclass classification,AdaBoost,Keystroke,0.8347958233246727,0.8347958233246727,0.8360623278174891,194.794171333313,51861.27850099999 +46,Multiclass classification,Bagging,ImageSegments,0.3111111111111111,0.3111111111111111,0.24576497265572897,4.149084091186523,2.196675 +92,Multiclass classification,Bagging,ImageSegments,0.4835164835164835,0.4835164835164835,0.4934752395581889,4.152299880981445,7.023639 +138,Multiclass classification,Bagging,ImageSegments,0.5328467153284672,0.5328467153284672,0.5528821792646678,4.15202522277832,15.046926 +184,Multiclass classification,Bagging,ImageSegments,0.5956284153005464,0.5956284153005464,0.6141431648908949,4.152608871459961,26.297795 +230,Multiclass classification,Bagging,ImageSegments,0.62882096069869,0.62882096069869,0.6441389332893815,4.151983261108398,40.50873 +276,Multiclass classification,Bagging,ImageSegments,0.64,0.64,0.6559607038460421,4.152521133422852,57.698206 +322,Multiclass classification,Bagging,ImageSegments,0.6666666666666666,0.6666666666666666,0.6673617488913626,4.152231216430664,77.585785 +368,Multiclass classification,Bagging,ImageSegments,0.6948228882833788,0.6948228882833788,0.6911959597548878,4.152448654174805,100.185488 +414,Multiclass classification,Bagging,ImageSegments,0.711864406779661,0.711864406779661,0.7079630503641953,4.152788162231445,125.71728800000001 +460,Multiclass classification,Bagging,ImageSegments,0.7124183006535948,0.7124183006535948,0.7065500352371009,4.152704238891602,154.000542 +506,Multiclass classification,Bagging,ImageSegments,0.7207920792079208,0.7207920792079208,0.7127593158348896,4.152563095092773,184.883226 +552,Multiclass classification,Bagging,ImageSegments,0.7259528130671506,0.7259528130671506,0.7192025503807162,4.152528762817383,218.482328 +598,Multiclass classification,Bagging,ImageSegments,0.7319932998324958,0.7319932998324957,0.7251188986558661,4.152769088745117,254.840787 +644,Multiclass classification,Bagging,ImageSegments,0.7309486780715396,0.7309486780715396,0.7259740406437201,4.152563095092773,294.12903800000004 +690,Multiclass classification,Bagging,ImageSegments,0.7358490566037735,0.7358490566037735,0.7304359912942561,4.152692794799805,336.073433 +736,Multiclass classification,Bagging,ImageSegments,0.7374149659863946,0.7374149659863947,0.733149934717071,4.152753829956055,380.701162 +782,Multiclass classification,Bagging,ImageSegments,0.7426376440460948,0.7426376440460948,0.7385597120510639,4.152643203735352,428.175969 +828,Multiclass classification,Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7412375783772316,4.152631759643555,478.460063 +874,Multiclass classification,Bagging,ImageSegments,0.7491408934707904,0.7491408934707904,0.7454343548790067,4.153181076049805,531.417765 +920,Multiclass classification,Bagging,ImageSegments,0.7486398258977149,0.7486398258977149,0.7441307384051415,4.153326034545898,587.1362770000001 +966,Multiclass classification,Bagging,ImageSegments,0.7492227979274612,0.749222797927461,0.7439306216964366,4.153120040893555,645.6842 +1012,Multiclass classification,Bagging,ImageSegments,0.7487636003956478,0.7487636003956478,0.7437900284473965,4.153234481811523,707.105172 +1058,Multiclass classification,Bagging,ImageSegments,0.750236518448439,0.7502365184484389,0.7448138061687654,4.153268814086914,771.2868930000001 +1104,Multiclass classification,Bagging,ImageSegments,0.7524932003626473,0.7524932003626473,0.7468314646869904,4.153234481811523,838.222518 +1150,Multiclass classification,Bagging,ImageSegments,0.7554395126196692,0.7554395126196692,0.7493227137357602,4.153413772583008,907.556087 +1196,Multiclass classification,Bagging,ImageSegments,0.7581589958158996,0.7581589958158996,0.7527652773681007,4.153318405151367,979.5797180000001 +1242,Multiclass classification,Bagging,ImageSegments,0.7574536663980661,0.7574536663980661,0.7525915384194216,4.153432846069336,1054.216781 +1288,Multiclass classification,Bagging,ImageSegments,0.7622377622377622,0.7622377622377621,0.7563448085202398,4.153615951538086,1131.5718310000002 +1334,Multiclass classification,Bagging,ImageSegments,0.7621905476369092,0.7621905476369092,0.7566636999776912,4.153776168823242,1211.5912470000003 +1380,Multiclass classification,Bagging,ImageSegments,0.7635968092820885,0.7635968092820886,0.7587252257765656,4.153825759887695,1294.4019940000003 +1426,Multiclass classification,Bagging,ImageSegments,0.7663157894736842,0.7663157894736842,0.7609139797315134,4.153848648071289,1379.8910190000004 +1472,Multiclass classification,Bagging,ImageSegments,0.7709041468388851,0.7709041468388851,0.7637689949207689,4.153989791870117,1467.9946540000003 +1518,Multiclass classification,Bagging,ImageSegments,0.7719182597231378,0.7719182597231378,0.7639714255563932,4.154367446899414,1558.8129900000004 +1564,Multiclass classification,Bagging,ImageSegments,0.7722328854766475,0.7722328854766475,0.7650721335080709,4.154550552368164,1652.2028900000003 +1610,Multiclass classification,Bagging,ImageSegments,0.7725295214418894,0.7725295214418892,0.764505787280341,4.154642105102539,1748.3782850000002 +1656,Multiclass classification,Bagging,ImageSegments,0.7716012084592145,0.7716012084592145,0.7634170612719108,4.15452766418457,1847.3163560000003 +1702,Multiclass classification,Bagging,ImageSegments,0.7713109935332157,0.7713109935332157,0.7652815676598499,4.154825210571289,1948.7028940000002 +1748,Multiclass classification,Bagging,ImageSegments,0.77389811104751,0.77389811104751,0.7674409436090757,4.155008316040039,2052.533374 +1794,Multiclass classification,Bagging,ImageSegments,0.7752370329057445,0.7752370329057446,0.7674318582149376,4.155046463012695,2159.053176 +1840,Multiclass classification,Bagging,ImageSegments,0.7765089722675367,0.7765089722675368,0.7688731808749575,4.154977798461914,2268.233507 +1886,Multiclass classification,Bagging,ImageSegments,0.7750663129973475,0.7750663129973475,0.7678921362145585,4.154905319213867,2379.8377889999997 +1932,Multiclass classification,Bagging,ImageSegments,0.7752459865354738,0.7752459865354739,0.7671636716269125,4.155000686645508,2494.085284 +1978,Multiclass classification,Bagging,ImageSegments,0.7759231158320687,0.7759231158320687,0.7670573130332384,4.154901504516602,2611.0522539999997 +2024,Multiclass classification,Bagging,ImageSegments,0.7775580820563519,0.7775580820563519,0.7671264358471986,4.154878616333008,2730.5624909999997 +2070,Multiclass classification,Bagging,ImageSegments,0.77670372160464,0.7767037216046399,0.7665050383810529,4.15495491027832,2852.4395529999997 +2116,Multiclass classification,Bagging,ImageSegments,0.7773049645390071,0.7773049645390071,0.766340416614934,4.15495491027832,2976.9921299999996 +2162,Multiclass classification,Bagging,ImageSegments,0.7783433595557612,0.7783433595557612,0.766965714748886,4.155027389526367,3104.012504 +2208,Multiclass classification,Bagging,ImageSegments,0.780244676030811,0.780244676030811,0.7678552364681828,4.155023574829102,3233.6609839999996 +2254,Multiclass classification,Bagging,ImageSegments,0.7776298268974701,0.7776298268974701,0.7652407320979201,4.154973983764648,3365.6406429999997 +2300,Multiclass classification,Bagging,ImageSegments,0.7768595041322314,0.7768595041322314,0.764461061100325,4.15504264831543,3499.962334 +2310,Multiclass classification,Bagging,ImageSegments,0.7769597228237333,0.7769597228237333,0.7645642360301897,4.155065536499023,3634.8810719999997 +1056,Multiclass classification,Bagging,Insects,0.6360189573459716,0.6360189573459716,0.5970323052762561,6.533428192138672,93.097088 +2112,Multiclass classification,Bagging,Insects,0.62482235907153,0.62482235907153,0.5890580890213498,6.533924102783203,264.682132 +3168,Multiclass classification,Bagging,Insects,0.6157246605620461,0.6157246605620461,0.5802533923244892,6.534633636474609,504.28420900000003 +4224,Multiclass classification,Bagging,Insects,0.6107032914989344,0.6107032914989344,0.5748501357120321,6.535015106201172,804.5259470000001 +5280,Multiclass classification,Bagging,Insects,0.614889183557492,0.614889183557492,0.5777842549225517,6.535823822021484,1159.582019 +6336,Multiclass classification,Bagging,Insects,0.608997632202052,0.608997632202052,0.5733157350789625,6.535648345947266,1564.000203 +7392,Multiclass classification,Bagging,Insects,0.6057367068055743,0.6057367068055743,0.5703382690867537,6.535068511962891,2016.3102330000002 +8448,Multiclass classification,Bagging,Insects,0.6069610512608027,0.6069610512608027,0.5711427916016896,6.534946441650391,2516.339397 +9504,Multiclass classification,Bagging,Insects,0.6039145532989583,0.6039145532989583,0.5678102867297489,6.535068511962891,3064.243813 +10560,Multiclass classification,Bagging,Insects,0.6034662373330808,0.6034662373330808,0.567425153452482,6.535427093505859,3659.768381 +11616,Multiclass classification,Bagging,Insects,0.6005165733964701,0.6005165733964701,0.56512832395729,6.535404205322266,4303.8464189999995 +12672,Multiclass classification,Bagging,Insects,0.6031883829216321,0.6031883829216321,0.5703828979306639,6.535358428955078,4997.310473 +13728,Multiclass classification,Bagging,Insects,0.6147009543235958,0.6147009543235958,0.5955104002005771,6.534030914306641,5738.022631999999 +14784,Multiclass classification,Bagging,Insects,0.6051545694378678,0.6051545694378678,0.586271708420286,6.533008575439453,6524.316427 +15840,Multiclass classification,Bagging,Insects,0.5703642906749163,0.5703642906749163,0.5530031721301686,6.534244537353516,7355.370967999999 +16896,Multiclass classification,Bagging,Insects,0.5440662918023084,0.5440662918023084,0.5274181049148582,6.532741546630859,8230.882624 +17952,Multiclass classification,Bagging,Insects,0.524650437301543,0.524650437301543,0.5077439094080566,6.533657073974609,9149.482306 +19008,Multiclass classification,Bagging,Insects,0.5142842110801283,0.5142842110801283,0.4945495171544722,5.423342704772949,10110.1367 +20064,Multiclass classification,Bagging,Insects,0.5202611772915317,0.5202611772915317,0.499632175624185,13.463048934936523,11121.939621 +21120,Multiclass classification,Bagging,Insects,0.5284814621904447,0.5284814621904447,0.5082299437323158,14.233846664428711,12202.117709999999 +22176,Multiclass classification,Bagging,Insects,0.5344757609921083,0.5344757609921083,0.5148729059414189,14.772774696350098,13344.394484999999 +23232,Multiclass classification,Bagging,Insects,0.5430674529723215,0.5430674529723215,0.5233933209280776,14.684733390808105,14542.607494 +24288,Multiclass classification,Bagging,Insects,0.5502120475974801,0.5502120475974801,0.5298443248135049,16.20911407470703,15791.070918 +25344,Multiclass classification,Bagging,Insects,0.5564061081955569,0.5564061081955569,0.5355525016331893,16.199478149414062,17093.843057 +26400,Multiclass classification,Bagging,Insects,0.561460661388689,0.561460661388689,0.5398397773012414,16.192718505859375,18441.382026 +27456,Multiclass classification,Bagging,Insects,0.564742305590967,0.564742305590967,0.5421523628031605,15.229331016540527,19838.570208 +28512,Multiclass classification,Bagging,Insects,0.5680614499666795,0.5680614499666795,0.5472893783055924,13.71937370300293,21280.868604 +29568,Multiclass classification,Bagging,Insects,0.5701288598775662,0.5701288598775662,0.55295508639855,11.343052864074707,22768.358846 +30624,Multiclass classification,Bagging,Insects,0.5724128922705156,0.5724128922705156,0.5585792537754973,9.387857437133789,24294.822849 +31680,Multiclass classification,Bagging,Insects,0.5749865841724802,0.5749865841724802,0.5636037623129485,9.38664436340332,25857.719223 +32736,Multiclass classification,Bagging,Insects,0.5781884832747823,0.5781884832747823,0.5684564968293649,9.385660171508789,27456.12944 +33792,Multiclass classification,Bagging,Insects,0.575656239827173,0.575656239827173,0.5663415557568727,7.860757827758789,29092.739018 +34848,Multiclass classification,Bagging,Insects,0.5754584325766924,0.5754584325766924,0.565994999425249,7.205549240112305,30762.023764 +35904,Multiclass classification,Bagging,Insects,0.5763863743976827,0.5763863743976827,0.5665127709334143,6.54947566986084,32461.363070000003 +36960,Multiclass classification,Bagging,Insects,0.5758813820720258,0.5758813820720258,0.56571927622701,6.547377586364746,34189.07998 +38016,Multiclass classification,Bagging,Insects,0.5767460213073786,0.5767460213073786,0.5661110063916132,6.546515464782715,35945.284935 +39072,Multiclass classification,Bagging,Insects,0.5764633615725219,0.5764633615725219,0.5659285794545608,6.543356895446777,37730.818682000005 +40128,Multiclass classification,Bagging,Insects,0.573454282652578,0.573454282652578,0.5636611811263741,8.510072708129883,39542.33547700001 +41184,Multiclass classification,Bagging,Insects,0.5726391957846685,0.5726391957846685,0.5633960246210544,8.712862014770508,41378.34519600001 +42240,Multiclass classification,Bagging,Insects,0.5723146854802433,0.5723146854802433,0.5635786987292998,10.13754653930664,43237.00688100001 +43296,Multiclass classification,Bagging,Insects,0.5717981291142165,0.5717981291142165,0.5635967907133216,10.13637924194336,45117.76335600001 +44352,Multiclass classification,Bagging,Insects,0.571103244571712,0.571103244571712,0.5633625241299441,10.135028839111328,47020.66134100001 +45408,Multiclass classification,Bagging,Insects,0.5712335102517233,0.5712335102517233,0.563808836162261,11.334146499633789,48947.97157600001 +46464,Multiclass classification,Bagging,Insects,0.5728213847577642,0.5728213847577642,0.5658781423773395,12.350201606750488,50897.74209700001 +47520,Multiclass classification,Bagging,Insects,0.576863991245607,0.576863991245607,0.5703778478941884,16.125893592834473,52890.49897200001 +48576,Multiclass classification,Bagging,Insects,0.5828512609366958,0.5828512609366958,0.5764029561430954,15.266244888305664,54904.91240000001 +49632,Multiclass classification,Bagging,Insects,0.5890270194031956,0.5890270194031956,0.5823661991476956,14.839654922485352,56940.07330000001 +50688,Multiclass classification,Bagging,Insects,0.5947087024286306,0.5947087024286306,0.5876086024291545,12.465810775756836,58994.29364000001 +51744,Multiclass classification,Bagging,Insects,0.600718937827339,0.600718937827339,0.5930357853224563,11.884730339050293,61065.77177200001 +52800,Multiclass classification,Bagging,Insects,0.6060342051932802,0.6060342051932802,0.5982060206393416,3.691446304321289,63151.215841000005 +52848,Multiclass classification,Bagging,Insects,0.6063920373909588,0.6063920373909588,0.5985419438128344,3.691621780395508,65236.99615100001 +408,Multiclass classification,Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.1448841094970703,5.867596 +816,Multiclass classification,Bagging,Keystroke,0.943558282208589,0.943558282208589,0.7669956277713079,3.0916757583618164,25.808269 +1224,Multiclass classification,Bagging,Keystroke,0.8912510220768601,0.8912510220768601,0.8617021305177773,4.035944938659668,63.939426 +1632,Multiclass classification,Bagging,Keystroke,0.9031269160024524,0.9031269160024524,0.8868998230762758,4.988290786743164,125.34339 +2040,Multiclass classification,Bagging,Keystroke,0.898970083374203,0.898970083374203,0.888705938214812,6.037667274475098,214.307845 +2448,Multiclass classification,Bagging,Keystroke,0.8594196975888844,0.8594196975888844,0.8547805855679916,6.993380546569824,335.016386 +2856,Multiclass classification,Bagging,Keystroke,0.8651488616462347,0.8651488616462347,0.8483773016417727,7.939821243286133,488.12155800000005 +3264,Multiclass classification,Bagging,Keystroke,0.8553478394115844,0.8553478394115844,0.8302147847543373,8.885003089904785,675.394352 +3672,Multiclass classification,Bagging,Keystroke,0.8452737673658404,0.8452737673658404,0.8411086163638233,9.830622673034668,899.024814 +4080,Multiclass classification,Bagging,Keystroke,0.8374601618043638,0.8374601618043638,0.8238000521910981,11.003908157348633,1161.3046 +4488,Multiclass classification,Bagging,Keystroke,0.8250501448629374,0.8250501448629373,0.8343531144302688,11.974610328674316,1461.738376 +4896,Multiclass classification,Bagging,Keystroke,0.8232890704800817,0.8232890704800817,0.8292209535545839,12.919659614562988,1801.820426 +5304,Multiclass classification,Bagging,Keystroke,0.8199132566471808,0.819913256647181,0.8044565992905442,13.86521053314209,2181.861898 +5712,Multiclass classification,Bagging,Keystroke,0.7998599194536858,0.7998599194536857,0.8029484507582976,14.811628341674805,2601.779179 +6120,Multiclass classification,Bagging,Keystroke,0.7970256577872201,0.7970256577872201,0.7783451709211457,15.75713062286377,3063.5971010000003 +6528,Multiclass classification,Bagging,Keystroke,0.7720239007200858,0.7720239007200858,0.767005590841987,16.704151153564453,3570.6766780000003 +6936,Multiclass classification,Bagging,Keystroke,0.7645277577505407,0.7645277577505407,0.766187831914561,17.649503707885742,4126.519897 +7344,Multiclass classification,Bagging,Keystroke,0.773389622769985,0.7733896227699851,0.770832075885354,18.61162567138672,4733.5217410000005 +7752,Multiclass classification,Bagging,Keystroke,0.7737066185008385,0.7737066185008385,0.7718493223486268,19.557814598083496,5395.069721000001 +8160,Multiclass classification,Bagging,Keystroke,0.7765657556073048,0.7765657556073047,0.7724710929560354,20.503721237182617,6113.943535 +8568,Multiclass classification,Bagging,Keystroke,0.7730827594257033,0.7730827594257033,0.7727491763630034,21.882675170898438,6890.839823 +8976,Multiclass classification,Bagging,Keystroke,0.7714763231197772,0.7714763231197772,0.7717207236627096,22.87528133392334,7728.212391 +9384,Multiclass classification,Bagging,Keystroke,0.7702227432590856,0.7702227432590856,0.7694267539223918,23.822596549987793,8626.275614 +9792,Multiclass classification,Bagging,Keystroke,0.7656010621999796,0.7656010621999795,0.7644081311179032,24.768078804016113,9586.24664 +10200,Multiclass classification,Bagging,Keystroke,0.757623296401608,0.757623296401608,0.749720417225094,25.71299648284912,10618.940127 +10608,Multiclass classification,Bagging,Keystroke,0.737154709154332,0.737154709154332,0.7245707699101513,26.660439491271973,11726.561153 +11016,Multiclass classification,Bagging,Keystroke,0.729822968679074,0.7298229686790739,0.7256689004292383,27.605186462402344,12907.41343 +11424,Multiclass classification,Bagging,Keystroke,0.7229274271207213,0.7229274271207213,0.7092514304350318,28.551199913024902,14153.769988 +11832,Multiclass classification,Bagging,Keystroke,0.7133801031189249,0.7133801031189249,0.7054771135814562,29.4963436126709,15465.906612 +12240,Multiclass classification,Bagging,Keystroke,0.7177874009314487,0.7177874009314487,0.7138351093258007,30.441871643066406,16835.329364 +12648,Multiclass classification,Bagging,Keystroke,0.7147149521625682,0.7147149521625682,0.7065885995198201,31.388431549072266,18265.757575 +13056,Multiclass classification,Bagging,Keystroke,0.7031788586748372,0.7031788586748372,0.6954173783902821,32.33424186706543,19760.458564 +13464,Multiclass classification,Bagging,Keystroke,0.7011067369828419,0.7011067369828419,0.6966368809795416,33.27959156036377,21319.158047 +13872,Multiclass classification,Bagging,Keystroke,0.7007425564126595,0.7007425564126595,0.6971102154727419,34.22630214691162,22941.129728 +14280,Multiclass classification,Bagging,Keystroke,0.6961972126899643,0.6961972126899643,0.691133802747568,35.17108726501465,24623.129677 +14688,Multiclass classification,Bagging,Keystroke,0.698781235105876,0.698781235105876,0.696592906911097,36.11711597442627,26362.470984 +15096,Multiclass classification,Bagging,Keystroke,0.7048029148724744,0.7048029148724744,0.702773358939844,37.0643196105957,28156.052692 +15504,Multiclass classification,Bagging,Keystroke,0.7047668193252918,0.7047668193252918,0.7013012225519919,38.00920104980469,30004.434818 +15912,Multiclass classification,Bagging,Keystroke,0.6956822324178241,0.6956822324178241,0.6887843659114408,38.955204010009766,31904.325566000003 +16320,Multiclass classification,Bagging,Keystroke,0.6869906244255163,0.6869906244255163,0.6817298949676788,39.901418685913086,33880.147234000004 +16728,Multiclass classification,Bagging,Keystroke,0.6840437615830693,0.6840437615830693,0.6809878840610977,40.84670162200928,35894.19480500001 +17136,Multiclass classification,Bagging,Keystroke,0.6798949518529326,0.6798949518529326,0.6760668667678135,42.678324699401855,37945.35177200001 +17544,Multiclass classification,Bagging,Keystroke,0.6725759562218548,0.6725759562218548,0.6693298574086026,43.72208595275879,40033.19473500001 +17952,Multiclass classification,Bagging,Keystroke,0.6715503314578575,0.6715503314578575,0.6700615486077944,44.668694496154785,42156.41544100001 +18360,Multiclass classification,Bagging,Keystroke,0.6768887194291628,0.6768887194291628,0.6760264883444682,45.61451721191406,44306.462280000014 +18768,Multiclass classification,Bagging,Keystroke,0.6818884211648105,0.6818884211648105,0.6814185274246665,46.561092376708984,46484.07667300002 +19176,Multiclass classification,Bagging,Keystroke,0.6739504563233377,0.6739504563233377,0.6724064481498903,47.50611400604248,48682.185033000016 +19584,Multiclass classification,Bagging,Keystroke,0.677883878874534,0.677883878874534,0.6774885006147249,48.451809883117676,50904.928535000014 +19992,Multiclass classification,Bagging,Keystroke,0.6733530088539843,0.6733530088539843,0.6729949515014169,49.39821243286133,53145.742009000016 +20400,Multiclass classification,Bagging,Keystroke,0.6697387126819943,0.6697387126819943,0.6699810213452306,50.34487438201904,55411.38251600001 +46,Multiclass classification,Leveraging Bagging,ImageSegments,0.37777777777777777,0.37777777777777777,0.2811210847975554,4.0974016189575195,6.997987 +92,Multiclass classification,Leveraging Bagging,ImageSegments,0.5164835164835165,0.5164835164835165,0.5316649744849407,4.0979814529418945,22.017115 +138,Multiclass classification,Leveraging Bagging,ImageSegments,0.5547445255474452,0.5547445255474452,0.5804654781117263,4.0981035232543945,44.610383999999996 +184,Multiclass classification,Leveraging Bagging,ImageSegments,0.6174863387978142,0.6174863387978142,0.6394923756219437,4.0987138748168945,74.61421299999999 +230,Multiclass classification,Leveraging Bagging,ImageSegments,0.6506550218340611,0.6506550218340611,0.66859135700569,4.0987138748168945,111.65332099999999 +276,Multiclass classification,Leveraging Bagging,ImageSegments,0.6618181818181819,0.6618181818181819,0.6795855359270878,4.098832130432129,156.076644 +322,Multiclass classification,Leveraging Bagging,ImageSegments,0.6853582554517134,0.6853582554517134,0.6872635633687633,4.099373817443848,207.57491499999998 +368,Multiclass classification,Leveraging Bagging,ImageSegments,0.7111716621253406,0.7111716621253404,0.7098417316927395,4.099347114562988,266.34173899999996 +414,Multiclass classification,Leveraging Bagging,ImageSegments,0.7215496368038741,0.7215496368038742,0.7201557312728714,4.09926700592041,332.356571 +460,Multiclass classification,Leveraging Bagging,ImageSegments,0.7211328976034859,0.721132897603486,0.7175330036146421,4.099320411682129,405.380301 +506,Multiclass classification,Leveraging Bagging,ImageSegments,0.7287128712871287,0.7287128712871287,0.7233455022590812,4.099320411682129,485.520305 +552,Multiclass classification,Leveraging Bagging,ImageSegments,0.7295825771324864,0.7295825771324864,0.7255599965917697,4.099240303039551,572.983507 +598,Multiclass classification,Leveraging Bagging,ImageSegments,0.7353433835845896,0.7353433835845896,0.7308494254186014,4.0992631912231445,667.526521 +644,Multiclass classification,Leveraging Bagging,ImageSegments,0.7340590979782271,0.7340590979782271,0.7314183982762247,4.099823951721191,768.914228 +690,Multiclass classification,Leveraging Bagging,ImageSegments,0.737300435413643,0.737300435413643,0.7343909641298695,4.099823951721191,877.069835 +736,Multiclass classification,Leveraging Bagging,ImageSegments,0.7387755102040816,0.7387755102040816,0.7369557659594496,4.099850654602051,992.1901310000001 +782,Multiclass classification,Leveraging Bagging,ImageSegments,0.7439180537772087,0.7439180537772088,0.7419020281650245,4.099850654602051,1114.103609 +828,Multiclass classification,Leveraging Bagging,ImageSegments,0.7436517533252721,0.7436517533252721,0.7432199627682998,4.099850654602051,1242.576589 +874,Multiclass classification,Leveraging Bagging,ImageSegments,0.7502863688430699,0.7502863688430699,0.7482089866208982,4.099850654602051,1377.530874 +920,Multiclass classification,Leveraging Bagging,ImageSegments,0.750816104461371,0.750816104461371,0.7477650187313974,4.099823951721191,1518.374517 +966,Multiclass classification,Leveraging Bagging,ImageSegments,0.7512953367875648,0.7512953367875648,0.747322646811651,4.099823951721191,1664.8953589999999 +1012,Multiclass classification,Leveraging Bagging,ImageSegments,0.7507418397626113,0.7507418397626113,0.7469783619055548,4.099823951721191,1817.1987969999998 +1058,Multiclass classification,Leveraging Bagging,ImageSegments,0.7530747398297067,0.7530747398297066,0.7482363934596314,4.099823951721191,1975.1124209999998 +1104,Multiclass classification,Leveraging Bagging,ImageSegments,0.7552130553037172,0.7552130553037172,0.750118495060715,4.0998735427856445,2138.658016 +1150,Multiclass classification,Leveraging Bagging,ImageSegments,0.7571801566579635,0.7571801566579635,0.7516199800653577,4.0998735427856445,2307.825702 +1196,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598326359832636,0.7598326359832636,0.7548841797367702,4.0998735427856445,2482.820129 +1242,Multiclass classification,Leveraging Bagging,ImageSegments,0.7598710717163578,0.7598710717163577,0.7553301531902636,4.0998735427856445,2663.4478950000002 +1288,Multiclass classification,Leveraging Bagging,ImageSegments,0.7645687645687645,0.7645687645687647,0.7590078532621816,4.1004838943481445,2849.419913 +1334,Multiclass classification,Leveraging Bagging,ImageSegments,0.7644411102775694,0.7644411102775694,0.7591993978414527,4.100506782531738,3040.9965970000003 +1380,Multiclass classification,Leveraging Bagging,ImageSegments,0.7650471356055112,0.7650471356055112,0.7601575050520947,4.100506782531738,3238.5768190000003 +1426,Multiclass classification,Leveraging Bagging,ImageSegments,0.7670175438596492,0.7670175438596492,0.7613339877221927,4.100506782531738,3441.4807240000005 +1472,Multiclass classification,Leveraging Bagging,ImageSegments,0.7715839564921821,0.7715839564921821,0.7641396475218201,4.100552558898926,3649.5015090000006 +1518,Multiclass classification,Leveraging Bagging,ImageSegments,0.7732366512854317,0.7732366512854317,0.7648275341801108,4.100552558898926,3862.6942700000004 +1564,Multiclass classification,Leveraging Bagging,ImageSegments,0.7735124760076776,0.7735124760076776,0.7657569341108763,4.100552558898926,4080.8910560000004 +1610,Multiclass classification,Leveraging Bagging,ImageSegments,0.7737725295214419,0.7737725295214419,0.7651494083475014,4.1005754470825195,4304.577590000001 +1656,Multiclass classification,Leveraging Bagging,ImageSegments,0.7740181268882175,0.7740181268882175,0.7654813489818475,4.100529670715332,4533.710142000001 +1702,Multiclass classification,Leveraging Bagging,ImageSegments,0.7730746619635509,0.7730746619635509,0.766493027961906,4.100529670715332,4767.793233000001 +1748,Multiclass classification,Leveraging Bagging,ImageSegments,0.7756153405838581,0.7756153405838581,0.7686072256536652,4.100529670715332,5007.029776000001 +1794,Multiclass classification,Leveraging Bagging,ImageSegments,0.7769102063580591,0.7769102063580591,0.7685414235990152,4.100502967834473,5251.440116000002 +1840,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781402936378466,0.7781402936378466,0.7699957723931323,4.100502967834473,5500.964415000001 +1886,Multiclass classification,Leveraging Bagging,ImageSegments,0.7761273209549071,0.7761273209549071,0.7684985598909853,4.100502967834473,5755.503987000001 +1932,Multiclass classification,Leveraging Bagging,ImageSegments,0.7762817193164163,0.7762817193164163,0.7677434418046419,4.100502967834473,6014.862306000001 +1978,Multiclass classification,Leveraging Bagging,ImageSegments,0.7774405665149215,0.7774405665149215,0.7684788817649146,4.100502967834473,6279.121569000001 +2024,Multiclass classification,Leveraging Bagging,ImageSegments,0.7790410281759763,0.7790410281759763,0.7689103339153599,4.100502967834473,6548.278113000001 +2070,Multiclass classification,Leveraging Bagging,ImageSegments,0.7786370227162881,0.7786370227162881,0.7686288077529282,4.100502967834473,6822.363214000001 +2116,Multiclass classification,Leveraging Bagging,ImageSegments,0.7791962174940898,0.7791962174940898,0.768391950800897,4.100502967834473,7101.096348000001 +2162,Multiclass classification,Leveraging Bagging,ImageSegments,0.7801943544655252,0.7801943544655253,0.768962628827985,4.100525856018066,7384.333285000001 +2208,Multiclass classification,Leveraging Bagging,ImageSegments,0.7820570910738559,0.7820570910738559,0.7698068761587117,4.100499153137207,7672.298476000001 +2254,Multiclass classification,Leveraging Bagging,ImageSegments,0.7789613848202397,0.7789613848202397,0.7667173742344939,4.100499153137207,7965.117559000001 +2300,Multiclass classification,Leveraging Bagging,ImageSegments,0.7781644193127447,0.7781644193127447,0.7659138381656089,4.100499153137207,8262.647904000001 +2310,Multiclass classification,Leveraging Bagging,ImageSegments,0.7782589865742746,0.7782589865742745,0.7660163657276376,4.100499153137207,8561.303246000001 +1056,Multiclass classification,Leveraging Bagging,Insects,0.6218009478672986,0.6218009478672986,0.5857016652718549,6.471495628356934,220.837673 +2112,Multiclass classification,Leveraging Bagging,Insects,0.6196115585030791,0.6196115585030791,0.5856756432415233,10.302834510803223,598.297395 +3168,Multiclass classification,Leveraging Bagging,Insects,0.628986422481844,0.628986422481844,0.5949930595607559,19.024110794067383,1103.516793 +4224,Multiclass classification,Leveraging Bagging,Insects,0.6294103717736207,0.6294103717736207,0.5952675443708706,19.52926254272461,1735.893967 +5280,Multiclass classification,Leveraging Bagging,Insects,0.6364841826103429,0.6364841826103429,0.5994911272790603,18.82306957244873,2497.807238 +6336,Multiclass classification,Leveraging Bagging,Insects,0.6352012628255722,0.6352012628255722,0.5993891820807258,20.00343894958496,3379.788115 +7392,Multiclass classification,Leveraging Bagging,Insects,0.638749830875389,0.638749830875389,0.6030343276880051,20.9547061920166,4385.582643 +8448,Multiclass classification,Leveraging Bagging,Insects,0.6405824553095774,0.6405824553095774,0.6028521616895871,23.98197650909424,5520.259032 +9504,Multiclass classification,Leveraging Bagging,Insects,0.6449542249815847,0.6449542249815847,0.6055705492028415,24.687146186828613,6764.141036999999 +10560,Multiclass classification,Leveraging Bagging,Insects,0.6485462638507434,0.6485462638507434,0.6081614166360887,28.76917839050293,8102.806145 +11616,Multiclass classification,Leveraging Bagging,Insects,0.6490744726646578,0.6490744726646578,0.6078786452761632,30.803756713867188,9530.02909 +12672,Multiclass classification,Leveraging Bagging,Insects,0.6514876489621971,0.6514876489621971,0.6111938480023122,35.14385414123535,11044.697830000001 +13728,Multiclass classification,Leveraging Bagging,Insects,0.6707947840023312,0.6707947840023312,0.6607574394823457,17.51351547241211,12617.098737 +14784,Multiclass classification,Leveraging Bagging,Insects,0.6821348846648176,0.6821348846648176,0.6733632096765088,9.275564193725586,14250.678949000001 +15840,Multiclass classification,Leveraging Bagging,Insects,0.6778205694803965,0.6778205694803965,0.670556396248407,11.964457511901855,15956.730999000001 +16896,Multiclass classification,Leveraging Bagging,Insects,0.6754661142349808,0.6754661142349808,0.6690281338426608,12.60369873046875,17732.973803 +17952,Multiclass classification,Leveraging Bagging,Insects,0.6721631106902123,0.6721631106902123,0.6660357480506892,12.93508529663086,19577.321708 +19008,Multiclass classification,Leveraging Bagging,Insects,0.6856947440416689,0.6856947440416689,0.6751812770122833,14.563780784606934,21465.395048 +20064,Multiclass classification,Leveraging Bagging,Insects,0.6926680954991776,0.6926680954991776,0.6785701715539604,23.61655616760254,23398.659989 +21120,Multiclass classification,Leveraging Bagging,Insects,0.6942090061082438,0.6942090061082438,0.6784920731228882,30.020954132080078,25401.280766 +22176,Multiclass classification,Leveraging Bagging,Insects,0.6958737316798196,0.6958737316798196,0.6784853924286285,31.293453216552734,27443.688764 +23232,Multiclass classification,Leveraging Bagging,Insects,0.6989798114588266,0.6989798114588266,0.6799590657327791,29.59604263305664,29526.276676999998 +24288,Multiclass classification,Leveraging Bagging,Insects,0.7011981718614897,0.7011981718614897,0.680282364066019,32.615909576416016,31645.669427999997 +25344,Multiclass classification,Leveraging Bagging,Insects,0.7031527443475516,0.7031527443475516,0.6805566439417602,33.91432285308838,33792.819539 +26400,Multiclass classification,Leveraging Bagging,Insects,0.7051782264479716,0.7051782264479716,0.6809495737401271,35.12977695465088,35966.301701 +27456,Multiclass classification,Leveraging Bagging,Insects,0.7065743944636678,0.7065743944636678,0.6805936316849747,38.84447956085205,38159.78466 +28512,Multiclass classification,Leveraging Bagging,Insects,0.7054820946301428,0.7054820946301428,0.681225779493031,34.570815086364746,40377.715598999996 +29568,Multiclass classification,Leveraging Bagging,Insects,0.7045692833226231,0.7045692833226231,0.6849598194839713,20.382534980773926,42611.23284999999 +30624,Multiclass classification,Leveraging Bagging,Insects,0.7031316330862424,0.7031316330862424,0.6877640955933652,22.55568027496338,44864.71640799999 +31680,Multiclass classification,Leveraging Bagging,Insects,0.7032418952618454,0.7032418952618454,0.6917227552448634,26.177990913391113,47133.482559 +32736,Multiclass classification,Leveraging Bagging,Insects,0.7037421719871697,0.7037421719871697,0.6952024388211077,25.761178016662598,49415.922784999995 +33792,Multiclass classification,Leveraging Bagging,Insects,0.7002160338551685,0.7002160338551685,0.6931280234945141,25.958494186401367,51714.47139399999 +34848,Multiclass classification,Leveraging Bagging,Insects,0.6973627571957414,0.6973627571957414,0.6902163957562899,18.894118309020996,54032.337051999995 +35904,Multiclass classification,Leveraging Bagging,Insects,0.6951786758766677,0.6951786758766677,0.6877287571005829,18.049145698547363,56371.370632 +36960,Multiclass classification,Leveraging Bagging,Insects,0.6919830081982737,0.6919830081982737,0.6843647347906762,22.045016288757324,58731.919307 +38016,Multiclass classification,Leveraging Bagging,Insects,0.6900697093252663,0.6900697093252663,0.68217396069655,25.079078674316406,61114.705623999995 +39072,Multiclass classification,Leveraging Bagging,Insects,0.688720534411712,0.688720534411712,0.6808510434728485,19.794261932373047,63520.517782999996 +40128,Multiclass classification,Leveraging Bagging,Insects,0.6867695068158597,0.6867695068158597,0.6796002866264578,10.854747772216797,65948.78476699999 +41184,Multiclass classification,Leveraging Bagging,Insects,0.6843843333414273,0.6843843333414273,0.6779529807793833,10.474969863891602,68395.63477799999 +42240,Multiclass classification,Leveraging Bagging,Insects,0.6822131205757712,0.6822131205757712,0.6764872431583758,14.707494735717773,70864.05938699999 +43296,Multiclass classification,Leveraging Bagging,Insects,0.6795472918350849,0.6795472918350849,0.674587653669649,12.672552108764648,73351.02096199998 +44352,Multiclass classification,Leveraging Bagging,Insects,0.6769633153705666,0.6769633153705666,0.6725984110786069,13.144417762756348,75857.66838799998 +45408,Multiclass classification,Leveraging Bagging,Insects,0.6748959411544475,0.6748959411544475,0.6710316194917795,14.719610214233398,78383.45415699997 +46464,Multiclass classification,Leveraging Bagging,Insects,0.6743215031315241,0.6743215031315241,0.670959098678123,15.027325630187988,80927.72302099997 +47520,Multiclass classification,Leveraging Bagging,Insects,0.6765293882447021,0.6765293882447021,0.6733002712216741,17.283148765563965,83488.02074099997 +48576,Multiclass classification,Leveraging Bagging,Insects,0.6805970149253732,0.6805970149253732,0.6770692638556323,17.906007766723633,86063.52222099998 +49632,Multiclass classification,Leveraging Bagging,Insects,0.6848340754770205,0.6848340754770205,0.6808344811077705,18.8202543258667,88653.18323199998 +50688,Multiclass classification,Leveraging Bagging,Insects,0.6890524197525992,0.6890524197525992,0.6843657264244208,21.507144927978516,91255.74433499998 +51744,Multiclass classification,Leveraging Bagging,Insects,0.6932531936687089,0.6932531936687089,0.6877873898777546,23.154582023620605,93870.63731899999 +52800,Multiclass classification,Leveraging Bagging,Insects,0.6956002954601412,0.6956002954601412,0.6902433463100389,14.128369331359863,96495.21656399999 +52848,Multiclass classification,Leveraging Bagging,Insects,0.6958578538043787,0.6958578538043787,0.6905081705907102,13.831001281738281,99120.19143899999 +408,Multiclass classification,Leveraging Bagging,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,2.0028390884399414,23.27864 +816,Multiclass classification,Leveraging Bagging,Keystroke,0.9521472392638037,0.9521472392638037,0.8408896590786493,4.076430320739746,76.761208 +1224,Multiclass classification,Leveraging Bagging,Keystroke,0.9533932951757972,0.9533932951757972,0.9542235338779169,5.6716413497924805,164.877928 +1632,Multiclass classification,Leveraging Bagging,Keystroke,0.9589209074187615,0.9589209074187615,0.9361222534860761,8.122180938720703,291.606081 +2040,Multiclass classification,Leveraging Bagging,Keystroke,0.9573320255026974,0.9573320255026974,0.9445755787125868,10.5212984085083,455.912148 +2448,Multiclass classification,Leveraging Bagging,Keystroke,0.9607682876992235,0.9607682876992235,0.9588299190873342,9.065413475036621,649.921126 +2856,Multiclass classification,Leveraging Bagging,Keystroke,0.9618213660245184,0.9618213660245184,0.9516555143941907,13.188368797302246,870.236672 +3264,Multiclass classification,Leveraging Bagging,Keystroke,0.9589334967821024,0.9589334967821024,0.9492703335352553,13.21088695526123,1122.958204 +3672,Multiclass classification,Leveraging Bagging,Keystroke,0.9585943884500137,0.9585943884500137,0.9531276848185062,16.65507411956787,1406.732623 +4080,Multiclass classification,Leveraging Bagging,Keystroke,0.9541554302525129,0.9541554302525129,0.9416377826660955,17.091320037841797,1722.764314 +4488,Multiclass classification,Leveraging Bagging,Keystroke,0.9529752618676176,0.9529752618676176,0.9549694463549354,10.336687088012695,2070.686487 +4896,Multiclass classification,Leveraging Bagging,Keystroke,0.9550561797752809,0.9550561797752809,0.95517907029875,11.520882606506348,2449.358224 +5304,Multiclass classification,Leveraging Bagging,Keystroke,0.9568168960965491,0.9568168960965491,0.9575833276239932,13.737529754638672,2856.6015070000003 +5712,Multiclass classification,Leveraging Bagging,Keystroke,0.9574505340570828,0.9574505340570828,0.9570632809827344,15.842782020568848,3290.691514 +6120,Multiclass classification,Leveraging Bagging,Keystroke,0.9557117176009152,0.9557117176009152,0.9522483041543378,20.042810440063477,3760.43818 +6528,Multiclass classification,Leveraging Bagging,Keystroke,0.9566416424084572,0.9566416424084572,0.9568246790885271,11.687369346618652,4258.095146 +6936,Multiclass classification,Leveraging Bagging,Keystroke,0.9574621485219899,0.9574621485219899,0.9579855320572277,12.487288475036621,4780.363237 +7344,Multiclass classification,Leveraging Bagging,Keystroke,0.9568296336647147,0.9568296336647147,0.9563404233689646,15.327423095703125,5334.336646 +7752,Multiclass classification,Leveraging Bagging,Keystroke,0.9565217391304348,0.9565217391304348,0.9563017119581124,18.70553493499756,5920.99825 +8160,Multiclass classification,Leveraging Bagging,Keystroke,0.9545287412673121,0.9545287412673121,0.9527980459948603,24.086796760559082,6539.621381999999 +8568,Multiclass classification,Leveraging Bagging,Keystroke,0.9545932064900199,0.9545932064900199,0.9549210113442089,21.21516990661621,7187.28418 +8976,Multiclass classification,Leveraging Bagging,Keystroke,0.9550974930362117,0.9550974930362117,0.9553627160759579,17.566545486450195,7867.8027329999995 +9384,Multiclass classification,Leveraging Bagging,Keystroke,0.9555579239049344,0.9555579239049344,0.9558253322166266,15.710055351257324,8578.363562999999 +9792,Multiclass classification,Leveraging Bagging,Keystroke,0.9552650393218262,0.9552650393218262,0.9553715117788079,18.717252731323242,9321.146735999999 +10200,Multiclass classification,Leveraging Bagging,Keystroke,0.9533287577213452,0.9533287577213452,0.9523119157834915,15.605277061462402,10099.276789999998 +10608,Multiclass classification,Leveraging Bagging,Keystroke,0.9521070990855096,0.9521070990855096,0.9515822083565743,11.186952590942383,10903.979313999998 +11016,Multiclass classification,Leveraging Bagging,Keystroke,0.953427144802542,0.953427144802542,0.9541201209142027,7.581887245178223,11728.435273 +11424,Multiclass classification,Leveraging Bagging,Keystroke,0.953689923837871,0.953689923837871,0.9538275342826803,10.15964126586914,12573.844722999998 +11832,Multiclass classification,Leveraging Bagging,Keystroke,0.9535964838137098,0.9535964838137098,0.9538502960885477,11.061944961547852,13441.174568999999 +12240,Multiclass classification,Leveraging Bagging,Keystroke,0.9541629218073372,0.9541629218073372,0.9544632162431566,11.249642372131348,14331.138910999998 +12648,Multiclass classification,Leveraging Bagging,Keystroke,0.9548509527951293,0.9548509527951293,0.9551609875055331,13.203255653381348,15243.410521999998 +13056,Multiclass classification,Leveraging Bagging,Keystroke,0.9551895825354271,0.955189582535427,0.9553883557595891,9.36058521270752,16176.429406999998 +13464,Multiclass classification,Leveraging Bagging,Keystroke,0.955953353635891,0.955953353635891,0.9562606797905644,11.575583457946777,17130.275872 +13872,Multiclass classification,Leveraging Bagging,Keystroke,0.9561675437964098,0.9561675437964098,0.9563487774281333,11.42638874053955,18106.072389999998 +14280,Multiclass classification,Leveraging Bagging,Keystroke,0.9549688353526157,0.9549688353526157,0.9548529395574759,10.249165534973145,19109.380900999997 +14688,Multiclass classification,Leveraging Bagging,Keystroke,0.9552665622659495,0.9552665622659495,0.955472434271787,8.168793678283691,20137.264238999996 +15096,Multiclass classification,Leveraging Bagging,Keystroke,0.9560781715799934,0.9560781715799934,0.9563263247313607,9.020037651062012,21191.151532999997 +15504,Multiclass classification,Leveraging Bagging,Keystroke,0.9563955363478036,0.9563955363478036,0.9565429512012837,8.031278610229492,22273.408126999995 +15912,Multiclass classification,Leveraging Bagging,Keystroke,0.9566337753755264,0.9566337753755264,0.9567672375037608,10.967172622680664,23379.117397999995 +16320,Multiclass classification,Leveraging Bagging,Keystroke,0.9563085973405233,0.9563085973405233,0.9563585840602682,11.29026985168457,24508.785403999995 +16728,Multiclass classification,Leveraging Bagging,Keystroke,0.955580797513003,0.955580797513003,0.9555776398983683,9.525394439697266,25660.924918999994 +17136,Multiclass classification,Leveraging Bagging,Keystroke,0.9564050189670266,0.9564050189670267,0.9565585833577668,10.421767234802246,26839.493165999993 +17544,Multiclass classification,Leveraging Bagging,Keystroke,0.9566778772159836,0.9566778772159836,0.9567660151847867,11.633780479431152,28038.177978999993 +17952,Multiclass classification,Leveraging Bagging,Keystroke,0.9564369672998718,0.9564369672998718,0.9564736297242662,8.448995590209961,29257.620389999993 +18360,Multiclass classification,Leveraging Bagging,Keystroke,0.9567514570510376,0.9567514570510375,0.9568227044222711,7.821832656860352,30499.29393699999 +18768,Multiclass classification,Leveraging Bagging,Keystroke,0.9568924175414291,0.9568924175414291,0.9569505378685396,9.859258651733398,31763.565401999993 +19176,Multiclass classification,Leveraging Bagging,Keystroke,0.9567144719687093,0.9567144719687093,0.956766336746882,11.256629943847656,33053.804573999994 +19584,Multiclass classification,Leveraging Bagging,Keystroke,0.9568503293673084,0.9568503293673084,0.9569026376832065,11.690522193908691,34367.36625199999 +19992,Multiclass classification,Leveraging Bagging,Keystroke,0.9564303936771548,0.9564303936771548,0.9564653381379459,12.451186180114746,35701.899461999994 +20400,Multiclass classification,Leveraging Bagging,Keystroke,0.9566155203686455,0.9566155203686455,0.9566498206969933,7.4099931716918945,37049.10208799999 +46,Multiclass classification,Stacking,ImageSegments,0.4,0.4000000000000001,0.3289160825620571,1.89190673828125,1.901401 +92,Multiclass classification,Stacking,ImageSegments,0.5494505494505495,0.5494505494505495,0.5607526488856412,2.084074020385742,6.467373 +138,Multiclass classification,Stacking,ImageSegments,0.5693430656934306,0.5693430656934306,0.5872103411959265,2.357966423034668,13.822826 +184,Multiclass classification,Stacking,ImageSegments,0.6174863387978142,0.6174863387978142,0.6372989403156369,2.7369613647460938,24.259991 +230,Multiclass classification,Stacking,ImageSegments,0.6375545851528385,0.6375545851528385,0.6548159763148107,2.862431526184082,37.817904999999996 +276,Multiclass classification,Stacking,ImageSegments,0.6618181818181819,0.6618181818181819,0.6802187985971371,2.982741355895996,54.565380999999995 +322,Multiclass classification,Stacking,ImageSegments,0.6915887850467289,0.6915887850467289,0.6955507555363084,3.080752372741699,74.633343 +368,Multiclass classification,Stacking,ImageSegments,0.7111716621253406,0.7111716621253404,0.7105739026832886,3.232259750366211,98.20470399999999 +414,Multiclass classification,Stacking,ImageSegments,0.7263922518159807,0.7263922518159807,0.7261041400072307,3.505929946899414,125.52754499999999 +460,Multiclass classification,Stacking,ImageSegments,0.7276688453159041,0.7276688453159043,0.72519869331257,3.7872886657714844,156.78717 +506,Multiclass classification,Stacking,ImageSegments,0.7425742574257426,0.7425742574257425,0.7379486431795568,6.240692138671875,210.52347600000002 +552,Multiclass classification,Stacking,ImageSegments,0.7422867513611615,0.7422867513611615,0.7388440561615693,6.313092231750488,268.009607 +598,Multiclass classification,Stacking,ImageSegments,0.7520938023450586,0.7520938023450586,0.749839509127547,6.682056427001953,329.26112900000004 +644,Multiclass classification,Stacking,ImageSegments,0.7573872472783826,0.7573872472783826,0.7582793237949303,7.269444465637207,394.37227100000007 +690,Multiclass classification,Stacking,ImageSegments,0.7634252539912917,0.7634252539912917,0.7648953830992049,7.531791687011719,463.2777280000001 +736,Multiclass classification,Stacking,ImageSegments,0.7673469387755102,0.7673469387755102,0.7694390547687558,7.987269401550293,536.1609010000001 +782,Multiclass classification,Stacking,ImageSegments,0.7772087067861716,0.7772087067861717,0.7788980835102386,8.317158699035645,613.0067590000001 +828,Multiclass classification,Stacking,ImageSegments,0.7823458282950423,0.7823458282950423,0.7854763667551727,8.613452911376953,693.8523060000001 +874,Multiclass classification,Stacking,ImageSegments,0.7915234822451317,0.7915234822451317,0.7933203073280156,8.694649696350098,778.7710210000001 +920,Multiclass classification,Stacking,ImageSegments,0.7986942328618063,0.7986942328618062,0.7996826842527437,8.824880599975586,867.8856220000001 +966,Multiclass classification,Stacking,ImageSegments,0.8041450777202073,0.8041450777202073,0.8044659150084363,9.089361190795898,961.2082950000001 +1012,Multiclass classification,Stacking,ImageSegments,0.8100890207715133,0.8100890207715133,0.8093994872208631,9.280214309692383,1058.9817440000002 +1058,Multiclass classification,Stacking,ImageSegments,0.8145695364238411,0.814569536423841,0.8133421993203876,9.165953636169434,1161.0697040000002 +1104,Multiclass classification,Stacking,ImageSegments,0.8213961922030825,0.8213961922030824,0.8206569542548617,8.760258674621582,1267.2341280000003 +1150,Multiclass classification,Stacking,ImageSegments,0.824194952132289,0.824194952132289,0.8228781271733864,8.742037773132324,1377.3471480000003 +1196,Multiclass classification,Stacking,ImageSegments,0.8292887029288702,0.8292887029288704,0.8281638601893785,8.87535572052002,1491.4919770000004 +1242,Multiclass classification,Stacking,ImageSegments,0.8340048348106366,0.8340048348106366,0.833490204478907,8.332135200500488,1609.3898390000004 +1288,Multiclass classification,Stacking,ImageSegments,0.8360528360528361,0.8360528360528361,0.8353480055004047,8.416248321533203,1730.8650650000004 +1334,Multiclass classification,Stacking,ImageSegments,0.8394598649662416,0.8394598649662416,0.8389194005130135,8.469959259033203,1855.8596220000004 +1380,Multiclass classification,Stacking,ImageSegments,0.8419144307469181,0.8419144307469181,0.8414934007209077,8.578604698181152,1984.2269550000003 +1426,Multiclass classification,Stacking,ImageSegments,0.8449122807017544,0.8449122807017544,0.8435602800871403,8.689190864562988,2115.814455 +1472,Multiclass classification,Stacking,ImageSegments,0.8484024473147519,0.8484024473147518,0.8459519552383536,8.800261497497559,2250.6136890000002 +1518,Multiclass classification,Stacking,ImageSegments,0.8503625576796309,0.8503625576796308,0.8475723684173131,9.025433540344238,2388.852428 +1564,Multiclass classification,Stacking,ImageSegments,0.8522072936660269,0.8522072936660269,0.8497128793769615,8.811847686767578,2530.498155 +1610,Multiclass classification,Stacking,ImageSegments,0.8527035425730267,0.8527035425730267,0.8503048238231962,8.729784965515137,2675.5358680000004 +1656,Multiclass classification,Stacking,ImageSegments,0.8531722054380665,0.8531722054380665,0.8508343416398155,8.761359214782715,2823.7565440000003 +1702,Multiclass classification,Stacking,ImageSegments,0.8571428571428571,0.8571428571428571,0.8561317791292776,8.798370361328125,2975.2442650000003 +1748,Multiclass classification,Stacking,ImageSegments,0.8580423583285632,0.8580423583285632,0.8567712479140972,8.86152172088623,3129.874549 +1794,Multiclass classification,Stacking,ImageSegments,0.8611266034578918,0.8611266034578918,0.8591986188286931,8.932531356811523,3287.541657 +1840,Multiclass classification,Stacking,ImageSegments,0.8618814573137574,0.8618814573137574,0.8601172531559075,8.819746017456055,3448.3205970000004 +1886,Multiclass classification,Stacking,ImageSegments,0.8636604774535809,0.8636604774535809,0.8623243992773615,9.007128715515137,3612.1367020000002 +1932,Multiclass classification,Stacking,ImageSegments,0.8648368720870016,0.8648368720870016,0.8630569076841595,9.368453979492188,3779.147863 +1978,Multiclass classification,Stacking,ImageSegments,0.8649468892261002,0.8649468892261002,0.8631362872103546,8.952109336853027,3949.363777 +2024,Multiclass classification,Stacking,ImageSegments,0.8665348492338112,0.8665348492338112,0.8639071890295129,9.146061897277832,4122.536804 +2070,Multiclass classification,Stacking,ImageSegments,0.8680521991300145,0.8680521991300145,0.8658036637930728,8.80567455291748,4298.84894 +2116,Multiclass classification,Stacking,ImageSegments,0.8695035460992908,0.8695035460992909,0.8667661913422944,8.892473220825195,4478.129032 +2162,Multiclass classification,Stacking,ImageSegments,0.8690421101341971,0.869042110134197,0.8663186552920692,8.910783767700195,4660.4030729999995 +2208,Multiclass classification,Stacking,ImageSegments,0.8699592206615315,0.8699592206615315,0.8669965232275297,8.99278450012207,4845.573407999999 +2254,Multiclass classification,Stacking,ImageSegments,0.869063470927652,0.8690634709276521,0.8666022158227548,9.09610366821289,5033.674223999999 +2300,Multiclass classification,Stacking,ImageSegments,0.8686385384949978,0.8686385384949978,0.8662053097556822,9.110825538635254,5224.6921839999995 +2310,Multiclass classification,Stacking,ImageSegments,0.8679081853616284,0.8679081853616284,0.8656034675726049,9.181622505187988,5416.881651 +1056,Multiclass classification,Stacking,Insects,0.6511848341232227,0.6511848341232227,0.5864257754346489,12.51792049407959,137.265242 +2112,Multiclass classification,Stacking,Insects,0.6873519658929418,0.6873519658929418,0.6004104483953082,15.371862411499023,366.367491 +3168,Multiclass classification,Stacking,Insects,0.6978212819703189,0.6978212819703189,0.602242348585179,17.772335052490234,671.574116 +4224,Multiclass classification,Stacking,Insects,0.7054226852948141,0.7054226852948141,0.6059831617919115,20.14197826385498,1043.757912 +5280,Multiclass classification,Stacking,Insects,0.7080886531540065,0.7080886531540066,0.6082411118035554,23.246225357055664,1476.569185 +6336,Multiclass classification,Stacking,Insects,0.708602999210734,0.708602999210734,0.6091818949546898,28.13547992706299,1970.1501170000001 +7392,Multiclass classification,Stacking,Insects,0.7104586659450683,0.7104586659450683,0.6104104212994758,30.164710998535156,2526.789716 +8448,Multiclass classification,Stacking,Insects,0.7130342133301764,0.7130342133301764,0.6119778058667307,27.698996543884277,3146.1270590000004 +9504,Multiclass classification,Stacking,Insects,0.717773334736399,0.717773334736399,0.6149023583636667,27.04288387298584,3829.1831980000006 +10560,Multiclass classification,Stacking,Insects,0.7215645420967894,0.7215645420967894,0.617635708330779,23.96706485748291,4572.729772000001 +11616,Multiclass classification,Stacking,Insects,0.7213086526043909,0.721308652604391,0.6182075626749539,26.15617847442627,5374.612830000001 +12672,Multiclass classification,Stacking,Insects,0.7240943887617394,0.7240943887617394,0.6351065980046956,25.051542282104492,6233.453892000001 +13728,Multiclass classification,Stacking,Insects,0.7432796678079697,0.7432796678079697,0.7402334392509421,15.30208683013916,7142.743199000001 +14784,Multiclass classification,Stacking,Insects,0.7491713454643848,0.7491713454643848,0.7487081677599373,11.128735542297363,8102.097506000001 +15840,Multiclass classification,Stacking,Insects,0.7424079803017867,0.7424079803017867,0.7445532404968841,16.950417518615723,9128.379042 +16896,Multiclass classification,Stacking,Insects,0.7382657591003255,0.7382657591003255,0.7427378731329454,18.26229953765869,10214.572621000001 +17952,Multiclass classification,Stacking,Insects,0.7309342097933262,0.7309342097933262,0.7368436311738037,23.776363372802734,11358.099531000002 +19008,Multiclass classification,Stacking,Insects,0.7429368127531962,0.7429368127531962,0.7441354243297112,12.958039283752441,12553.803014000001 +20064,Multiclass classification,Stacking,Insects,0.7475950755121368,0.7475950755121367,0.7439196968116685,12.612845420837402,13796.415893000001 +21120,Multiclass classification,Stacking,Insects,0.7492305506889531,0.7492305506889531,0.7418613509588597,16.95127773284912,15088.238885 +22176,Multiclass classification,Stacking,Insects,0.7509808342728298,0.7509808342728299,0.7400929587109365,17.926865577697754,16424.025269 +23232,Multiclass classification,Stacking,Insects,0.7532176832680471,0.7532176832680472,0.7391930166872092,20.939698219299316,17798.240955 +24288,Multiclass classification,Stacking,Insects,0.7550129699015935,0.7550129699015935,0.7379653286035112,25.43882942199707,19212.969178 +25344,Multiclass classification,Stacking,Insects,0.7569743124334136,0.7569743124334136,0.7375346698329149,29.94521999359131,20668.368585 +26400,Multiclass classification,Stacking,Insects,0.7580590173870222,0.7580590173870221,0.7363169253318035,34.1699275970459,22166.950006 +27456,Multiclass classification,Stacking,Insects,0.7593880896011656,0.7593880896011656,0.7352131419868576,32.93678665161133,23706.536377 +28512,Multiclass classification,Stacking,Insects,0.7573217354705202,0.7573217354705202,0.7350502568377754,21.273219108581543,25286.984696 +29568,Multiclass classification,Stacking,Insects,0.7555382690161329,0.7555382690161329,0.7386915112539557,20.747055053710938,26906.631088 +30624,Multiclass classification,Stacking,Insects,0.7544982529471312,0.7544982529471312,0.7426503125712552,24.910794258117676,28562.795387 +31680,Multiclass classification,Stacking,Insects,0.7531487736355315,0.7531487736355315,0.7453200395899969,32.13512706756592,30253.076649 +32736,Multiclass classification,Stacking,Insects,0.7530471971895525,0.7530471971895525,0.7484606399297139,36.17057991027832,31977.334616 +33792,Multiclass classification,Stacking,Insects,0.7480986061377289,0.748098606137729,0.7448942365218528,13.298456192016602,33736.870240000004 +34848,Multiclass classification,Stacking,Insects,0.7436795133010016,0.7436795133010016,0.7403442775964885,15.221885681152344,35530.857132000005 +35904,Multiclass classification,Stacking,Insects,0.7404952232403977,0.7404952232403977,0.7368033013057004,16.932289123535156,37356.72126300001 +36960,Multiclass classification,Stacking,Insects,0.7371411564165697,0.7371411564165696,0.7332530467261859,22.237309455871582,39213.91358200001 +38016,Multiclass classification,Stacking,Insects,0.7341049585689859,0.7341049585689859,0.7299460315219516,22.86026954650879,41100.10013700001 +39072,Multiclass classification,Stacking,Insects,0.7343042154027284,0.7343042154027284,0.7301016033872143,21.91624164581299,43017.482867000006 +40128,Multiclass classification,Stacking,Insects,0.7327734443143021,0.7327734443143021,0.728948208474553,20.388718605041504,44961.93393100001 +41184,Multiclass classification,Stacking,Insects,0.7327538061821626,0.7327538061821626,0.7292630064673854,15.630711555480957,46951.12268600001 +42240,Multiclass classification,Stacking,Insects,0.7331849712351145,0.7331849712351144,0.7301128191332076,20.110919952392578,48959.16072000001 +43296,Multiclass classification,Stacking,Insects,0.7337567848481349,0.7337567848481349,0.7309969621648841,24.057676315307617,50985.068017000005 +44352,Multiclass classification,Stacking,Insects,0.7342111790038556,0.7342111790038556,0.731637560144403,28.529647827148438,53028.942305000004 +45408,Multiclass classification,Stacking,Insects,0.7351289448763406,0.7351289448763407,0.7324911060941295,28.861422538757324,55091.119898000004 +46464,Multiclass classification,Stacking,Insects,0.7357682457008803,0.7357682457008803,0.7329742877599967,33.076725006103516,57170.52325500001 +47520,Multiclass classification,Stacking,Insects,0.7366947957659041,0.736694795765904,0.7341498113226347,21.352835655212402,59267.07909500001 +48576,Multiclass classification,Stacking,Insects,0.7403602676273804,0.7403602676273804,0.7381372580344014,19.381468772888184,61379.50627500001 +49632,Multiclass classification,Stacking,Insects,0.7442122866756664,0.7442122866756663,0.742109373234967,21.8067569732666,63507.849119000006 +50688,Multiclass classification,Stacking,Insects,0.7475289521968157,0.7475289521968157,0.7453466445950636,21.65154266357422,65647.81315 +51744,Multiclass classification,Stacking,Insects,0.7510581141410433,0.7510581141410433,0.7487124138061083,22.870601654052734,67797.610737 +52800,Multiclass classification,Stacking,Insects,0.7545218659444307,0.7545218659444307,0.752582163258218,10.554459571838379,69956.07865499999 +52848,Multiclass classification,Stacking,Insects,0.7547448294132117,0.7547448294132117,0.7528178949021433,10.58643913269043,72115.038215 +408,Multiclass classification,Stacking,Keystroke,0.9803439803439803,0.9803439803439803,0.49503722084367247,1.786503791809082,20.578742 +816,Multiclass classification,Stacking,Keystroke,0.9815950920245399,0.98159509202454,0.9278568842209168,6.9002227783203125,101.28008299999999 +1224,Multiclass classification,Stacking,Keystroke,0.9803761242845462,0.9803761242845462,0.9574942570636209,9.112634658813477,223.840162 +1632,Multiclass classification,Stacking,Keystroke,0.9779276517473943,0.9779276517473943,0.9432755457272627,10.40715503692627,381.844655 +2040,Multiclass classification,Stacking,Keystroke,0.973516429622364,0.973516429622364,0.9361356188587967,12.656171798706055,575.269036 +2448,Multiclass classification,Stacking,Keystroke,0.9726195341234164,0.9726195341234164,0.9612590316809274,8.745987892150879,802.257021 +2856,Multiclass classification,Stacking,Keystroke,0.9754816112084063,0.9754816112084063,0.9751469891413959,9.931495666503906,1061.688609 +3264,Multiclass classification,Stacking,Keystroke,0.9754826846460313,0.9754826846460313,0.9697604489278108,10.511832237243652,1352.298412 +3672,Multiclass classification,Stacking,Keystroke,0.9733042767638246,0.9733042767638246,0.9642745555297418,11.800049781799316,1675.3338330000001 +4080,Multiclass classification,Stacking,Keystroke,0.9722971316499142,0.9722971316499142,0.9666413905932107,12.42660903930664,2030.1772580000002 +4488,Multiclass classification,Stacking,Keystroke,0.9734789391575663,0.9734789391575663,0.9728883985144964,9.746350288391113,2413.735444 +4896,Multiclass classification,Stacking,Keystroke,0.9740551583248213,0.9740551583248213,0.9730015599884005,10.666529655456543,2823.5055469999998 +5304,Multiclass classification,Stacking,Keystroke,0.9741655666603809,0.9741655666603809,0.9728266773902405,11.775634765625,3261.739577 +5712,Multiclass classification,Stacking,Keystroke,0.9747855016634565,0.9747855016634565,0.9744326987999562,12.58005428314209,3727.994683 +6120,Multiclass classification,Stacking,Keystroke,0.9751593397613989,0.9751593397613989,0.9747223863351728,13.55466365814209,4223.159482999999 +6528,Multiclass classification,Stacking,Keystroke,0.9751800214493642,0.9751800214493642,0.9745255481694279,11.360074043273926,4747.988555 +6936,Multiclass classification,Stacking,Keystroke,0.9763518385003604,0.9763518385003604,0.9769458779347455,11.155635833740234,5300.577354999999 +7344,Multiclass classification,Stacking,Keystroke,0.9765763311997822,0.9765763311997822,0.9763923596721359,12.33658504486084,5881.2072339999995 +7752,Multiclass classification,Stacking,Keystroke,0.9771642368726616,0.9771642368726616,0.9773496343719735,13.116165161132812,6492.775159 +8160,Multiclass classification,Stacking,Keystroke,0.976590268415247,0.976590268415247,0.9759275084076021,13.303799629211426,7137.299991 +8568,Multiclass classification,Stacking,Keystroke,0.9768880588303958,0.9768880588303958,0.9769304999907085,13.133574485778809,7814.540539 +8976,Multiclass classification,Stacking,Keystroke,0.9774930362116991,0.9774930362116991,0.9777587646121523,13.50635814666748,8523.072866 +9384,Multiclass classification,Stacking,Keystroke,0.9767664925929873,0.9767664925929873,0.9763135719034829,15.166536331176758,9263.702674 +9792,Multiclass classification,Stacking,Keystroke,0.9765090389132877,0.9765090389132877,0.9763153416047449,16.169885635375977,10037.443626 +10200,Multiclass classification,Stacking,Keystroke,0.9758799882341406,0.9758799882341406,0.9755246287395946,14.205968856811523,10844.068015 +10608,Multiclass classification,Stacking,Keystroke,0.9755821627227302,0.9755821627227302,0.9754319444516873,12.997503280639648,11685.064117 +11016,Multiclass classification,Stacking,Keystroke,0.9759418974126192,0.9759418974126192,0.9761027289556774,12.962043762207031,12559.39796 +11424,Multiclass classification,Stacking,Keystroke,0.9760133064869124,0.9760133064869124,0.9760613734021468,14.090433120727539,13467.395857 +11832,Multiclass classification,Stacking,Keystroke,0.9754881244188995,0.9754881244188995,0.9753195915858492,14.295487403869629,14408.853786 +12240,Multiclass classification,Stacking,Keystroke,0.9759784296102623,0.9759784296102623,0.9761779987511395,15.044499397277832,15385.688043 +12648,Multiclass classification,Stacking,Keystroke,0.9762789594370206,0.9762789594370206,0.9764127823145236,15.120206832885742,16404.149055 +13056,Multiclass classification,Stacking,Keystroke,0.9758713136729222,0.9758713136729221,0.975797420384815,15.049361228942871,17460.942559000003 +13464,Multiclass classification,Stacking,Keystroke,0.9757112084973631,0.9757112084973631,0.9757165619520196,15.162266731262207,18558.798501 +13872,Multiclass classification,Stacking,Keystroke,0.9759930790858626,0.9759930790858626,0.9761084708221816,15.711796760559082,19695.838422 +14280,Multiclass classification,Stacking,Keystroke,0.9754884795854052,0.9754884795854052,0.975424480421301,16.988737106323242,20872.643227 +14688,Multiclass classification,Stacking,Keystroke,0.975624702117519,0.975624702117519,0.9757017096421697,17.869779586791992,22084.930025 +15096,Multiclass classification,Stacking,Keystroke,0.9757535607817158,0.9757535607817158,0.9758249143111629,17.579912185668945,23330.568569000003 +15504,Multiclass classification,Stacking,Keystroke,0.9755531187512094,0.9755531187512094,0.9755669148190674,16.59157657623291,24610.336028 +15912,Multiclass classification,Stacking,Keystroke,0.9756772044497517,0.9756772044497517,0.9757389077528199,16.193113327026367,25925.188427 +16320,Multiclass classification,Stacking,Keystroke,0.9759176420123782,0.9759176420123782,0.9759886766110538,16.353660583496094,27266.573062 +16728,Multiclass classification,Stacking,Keystroke,0.9756680815448078,0.9756680815448078,0.9756766431570708,17.00908374786377,28641.859399 +17136,Multiclass classification,Stacking,Keystroke,0.9758389261744966,0.9758389261744966,0.975891563489883,18.364989280700684,30047.550521 +17544,Multiclass classification,Stacking,Keystroke,0.9753747933648749,0.9753747933648749,0.975363882573194,17.298136711120605,31485.782589000002 +17952,Multiclass classification,Stacking,Keystroke,0.9753217090969862,0.9753217090969862,0.9753429667022142,16.72727108001709,32956.927282000004 +18360,Multiclass classification,Stacking,Keystroke,0.9754888610490767,0.9754888610490767,0.9755190387029732,17.51059913635254,34461.639008000006 +18768,Multiclass classification,Stacking,Keystroke,0.9757553151809026,0.9757553151809026,0.9757835195290103,18.871691703796387,35998.873267 +19176,Multiclass classification,Stacking,Keystroke,0.9754367666232073,0.9754367666232073,0.9754369138844643,17.42948341369629,37568.082084 +19584,Multiclass classification,Stacking,Keystroke,0.9754889444926722,0.9754889444926722,0.9754964783302286,17.978480339050293,39170.395427 +19992,Multiclass classification,Stacking,Keystroke,0.9756390375669051,0.9756390375669051,0.975642520227376,19.26256561279297,40805.125646 +20400,Multiclass classification,Stacking,Keystroke,0.9754889945585568,0.9754889945585568,0.9754863274548964,18.711057662963867,42471.761869 +46,Multiclass classification,Voting,ImageSegments,0.4666666666666667,0.4666666666666667,0.3890768588137009,0.9137420654296875,0.663852 +92,Multiclass classification,Voting,ImageSegments,0.6153846153846154,0.6153846153846154,0.617040786788686,0.9906883239746094,2.032737 +138,Multiclass classification,Voting,ImageSegments,0.6715328467153284,0.6715328467153284,0.6884491245817251,1.0679149627685547,4.226265 +184,Multiclass classification,Voting,ImageSegments,0.7049180327868853,0.7049180327868853,0.7194266051408907,1.1443958282470703,7.386208 +230,Multiclass classification,Voting,ImageSegments,0.7292576419213974,0.7292576419213974,0.7448338459304749,1.2214689254760742,11.723904000000001 +276,Multiclass classification,Voting,ImageSegments,0.7381818181818182,0.7381818181818182,0.7559766728000937,1.2995519638061523,17.331033 +322,Multiclass classification,Voting,ImageSegments,0.7538940809968847,0.7538940809968847,0.7616248500949714,1.3766565322875977,24.26159 +368,Multiclass classification,Voting,ImageSegments,0.773841961852861,0.7738419618528611,0.7772939373537765,1.4532833099365234,32.770568000000004 +414,Multiclass classification,Voting,ImageSegments,0.7820823244552058,0.7820823244552059,0.7854200812154107,1.5304145812988281,42.983195 +460,Multiclass classification,Voting,ImageSegments,0.7777777777777778,0.7777777777777778,0.7796254955467015,1.6075658798217773,54.886431 +506,Multiclass classification,Voting,ImageSegments,0.7861386138613862,0.7861386138613862,0.7886239053396241,3.8640270233154297,87.00222099999999 +552,Multiclass classification,Voting,ImageSegments,0.7858439201451906,0.7858439201451906,0.7889431335032357,4.088808059692383,121.00394599999998 +598,Multiclass classification,Voting,ImageSegments,0.7906197654941374,0.7906197654941374,0.7944387660679091,4.304059028625488,157.00397999999998 +644,Multiclass classification,Voting,ImageSegments,0.7853810264385692,0.7853810264385692,0.7901251252871709,4.532710075378418,195.073691 +690,Multiclass classification,Voting,ImageSegments,0.7895500725689405,0.7895500725689405,0.7935315861788143,4.759090423583984,235.272046 +736,Multiclass classification,Voting,ImageSegments,0.7863945578231293,0.7863945578231294,0.7911065855691086,4.991429328918457,277.59961999999996 +782,Multiclass classification,Voting,ImageSegments,0.7887323943661971,0.7887323943661971,0.792926322670609,5.219735145568848,322.07131499999997 +828,Multiclass classification,Voting,ImageSegments,0.7896009673518742,0.7896009673518742,0.7950712422059908,5.452417373657227,368.82718 +874,Multiclass classification,Voting,ImageSegments,0.7938144329896907,0.7938144329896907,0.7979586706142276,5.699496269226074,417.885664 +920,Multiclass classification,Voting,ImageSegments,0.794341675734494,0.794341675734494,0.7973145688626199,5.9376373291015625,469.16999000000004 +966,Multiclass classification,Voting,ImageSegments,0.7937823834196891,0.7937823834196891,0.7958827691316667,6.182188987731934,522.9385980000001 +1012,Multiclass classification,Voting,ImageSegments,0.7912957467853611,0.7912957467853611,0.7931630938612351,6.34267520904541,579.2700850000001 +1058,Multiclass classification,Voting,ImageSegments,0.793755912961211,0.7937559129612108,0.7947921362588558,6.295009613037109,638.2805060000001 +1104,Multiclass classification,Voting,ImageSegments,0.7941976427923844,0.7941976427923844,0.7951664828862093,6.2213640213012695,699.725726 +1150,Multiclass classification,Voting,ImageSegments,0.7954743255004352,0.7954743255004351,0.7958304956922065,6.151959419250488,763.5071 +1196,Multiclass classification,Voting,ImageSegments,0.796652719665272,0.796652719665272,0.7972397572733622,6.087224006652832,829.5043310000001 +1242,Multiclass classification,Voting,ImageSegments,0.7953263497179693,0.7953263497179693,0.795947547023496,6.001987457275391,897.6078460000001 +1288,Multiclass classification,Voting,ImageSegments,0.7995337995337995,0.7995337995337995,0.799082939294124,5.924266815185547,967.7224320000001 +1334,Multiclass classification,Voting,ImageSegments,0.7981995498874719,0.7981995498874719,0.7978549794399667,5.872907638549805,1039.926656 +1380,Multiclass classification,Voting,ImageSegments,0.7991298042059464,0.7991298042059464,0.799072028035076,5.784454345703125,1114.0085680000002 +1426,Multiclass classification,Voting,ImageSegments,0.8007017543859649,0.8007017543859649,0.799801266098334,5.781437873840332,1190.125915 +1472,Multiclass classification,Voting,ImageSegments,0.8042148198504419,0.8042148198504419,0.8016037490391381,5.805401802062988,1268.322504 +1518,Multiclass classification,Voting,ImageSegments,0.8048780487804879,0.8048780487804877,0.8013581039030082,5.915700912475586,1348.933518 +1564,Multiclass classification,Voting,ImageSegments,0.8048624440179143,0.8048624440179143,0.8017038254481382,6.0695037841796875,1431.999326 +1610,Multiclass classification,Voting,ImageSegments,0.8048477315102548,0.8048477315102549,0.8009666848419111,6.138180732727051,1517.316045 +1656,Multiclass classification,Voting,ImageSegments,0.804833836858006,0.804833836858006,0.8009346118743482,6.1542863845825195,1604.689641 +1702,Multiclass classification,Voting,ImageSegments,0.8048206937095826,0.8048206937095828,0.802987300619633,6.14796257019043,1694.105126 +1748,Multiclass classification,Voting,ImageSegments,0.8065254722381225,0.8065254722381225,0.8041280306488863,6.185528755187988,1785.6451299999999 +1794,Multiclass classification,Voting,ImageSegments,0.8070273284997211,0.8070273284997211,0.8033862119520573,6.18717098236084,1879.2213629999999 +1840,Multiclass classification,Voting,ImageSegments,0.8085916258836324,0.8085916258836324,0.8051706679397826,6.228180885314941,1974.8859899999998 +1886,Multiclass classification,Voting,ImageSegments,0.8074270557029177,0.8074270557029178,0.8044133208197751,6.244633674621582,2072.712055 +1932,Multiclass classification,Voting,ImageSegments,0.8073537027446919,0.8073537027446919,0.8036280810428232,6.232837677001953,2172.610836 +1978,Multiclass classification,Voting,ImageSegments,0.808295397066262,0.808295397066262,0.8041943782356388,6.225313186645508,2274.5024089999997 +2024,Multiclass classification,Voting,ImageSegments,0.8096885813148789,0.809688581314879,0.8043903689108628,6.209332466125488,2378.336668 +2070,Multiclass classification,Voting,ImageSegments,0.8086031899468342,0.8086031899468342,0.8034099584264852,6.192641258239746,2484.108554 +2116,Multiclass classification,Voting,ImageSegments,0.808983451536643,0.808983451536643,0.8029929757635029,6.163993835449219,2591.83622 +2162,Multiclass classification,Voting,ImageSegments,0.8093475242943082,0.8093475242943081,0.8028985652670257,6.160528182983398,2701.493184 +2208,Multiclass classification,Voting,ImageSegments,0.8110557317625736,0.8110557317625736,0.8037088502350873,6.127141952514648,2812.975729 +2254,Multiclass classification,Voting,ImageSegments,0.8078118064802485,0.8078118064802485,0.8004652010359966,6.094814300537109,2926.3842619999996 +2300,Multiclass classification,Voting,ImageSegments,0.8064375815571988,0.8064375815571988,0.7990276111502428,6.073050498962402,3041.734776 +2310,Multiclass classification,Voting,ImageSegments,0.8064097011693374,0.8064097011693374,0.7989986920740723,6.073922157287598,3157.9431529999997 +1056,Multiclass classification,Voting,Insects,0.6293838862559241,0.6293838862559241,0.5938169901557457,7.681754112243652,78.197886 +2112,Multiclass classification,Voting,Insects,0.6290857413548081,0.6290857413548081,0.5936238360694311,7.563845634460449,217.436369 +3168,Multiclass classification,Voting,Insects,0.625197347647616,0.625197347647616,0.5890732389154221,7.54627799987793,406.781755 +4224,Multiclass classification,Voting,Insects,0.624437603599337,0.624437603599337,0.5890978975177876,7.509035110473633,643.136123 +5280,Multiclass classification,Voting,Insects,0.6309907179390036,0.6309907179390036,0.5943307513870396,7.529419898986816,922.055301 +6336,Multiclass classification,Voting,Insects,0.6249408050513023,0.6249408050513023,0.5899587518293812,7.541637420654297,1240.879558 +7392,Multiclass classification,Voting,Insects,0.6242727641726424,0.6242727641726424,0.589208790087756,7.5199432373046875,1598.2590730000002 +8448,Multiclass classification,Voting,Insects,0.6266129986977625,0.6266129986977625,0.5910042020201396,7.600367546081543,1990.9287910000003 +9504,Multiclass classification,Voting,Insects,0.6255919183415763,0.6255919183415763,0.5892477749449755,7.551809310913086,2416.671036 +10560,Multiclass classification,Voting,Insects,0.6269533099725353,0.6269533099725353,0.5906555376897765,7.57810115814209,2875.240995 +11616,Multiclass classification,Voting,Insects,0.6254842875591907,0.6254842875591907,0.5899069142128334,7.574300765991211,3366.8452850000003 +12672,Multiclass classification,Voting,Insects,0.6276536974193039,0.6276536974193039,0.5948280902959312,7.593076705932617,3891.533291 +13728,Multiclass classification,Voting,Insects,0.6419465287389816,0.6419465287389816,0.6240594787506325,7.568525314331055,4449.097087 +14784,Multiclass classification,Voting,Insects,0.6349861327200162,0.6349861327200162,0.6168664949740267,7.497129440307617,5038.3500540000005 +15840,Multiclass classification,Voting,Insects,0.6042048109097796,0.6042048109097796,0.5876183517420878,7.622871398925781,5663.9066330000005 +16896,Multiclass classification,Voting,Insects,0.5831311038768866,0.5831311038768866,0.5677288238088704,7.5406084060668945,6323.428796 +17952,Multiclass classification,Voting,Insects,0.5683805916104953,0.5683805916104953,0.5530005563922373,7.511743545532227,7015.247243 +19008,Multiclass classification,Voting,Insects,0.5655811016993739,0.5655811016993739,0.5465928919365096,7.569133758544922,7739.601247 +20064,Multiclass classification,Voting,Insects,0.5718985196630614,0.5718985196630614,0.5506497035356593,8.179316520690918,8496.204598999999 +21120,Multiclass classification,Voting,Insects,0.5817510298783086,0.5817510298783086,0.55937505855693,8.13927173614502,9285.092110999998 +22176,Multiclass classification,Voting,Insects,0.5905298759864712,0.5905298759864712,0.5668099949242361,8.13715648651123,10104.551325999999 +23232,Multiclass classification,Voting,Insects,0.6004907236020834,0.6004907236020834,0.5756153967719769,8.254791259765625,10955.282647999999 +24288,Multiclass classification,Voting,Insects,0.6088854119487792,0.6088854119487792,0.5822871692574689,8.217899322509766,11836.441737999998 +25344,Multiclass classification,Voting,Insects,0.617014560233595,0.617014560233595,0.5890646667396601,8.13050651550293,12747.590801999997 +26400,Multiclass classification,Voting,Insects,0.6237357475661957,0.6237357475661957,0.5942060376379845,8.178851127624512,13688.250944999996 +27456,Multiclass classification,Voting,Insects,0.6299763248952832,0.6299763248952832,0.5983574644866619,8.215079307556152,14661.447404999995 +28512,Multiclass classification,Voting,Insects,0.6312651257409421,0.6312651257409421,0.6016879522351425,8.160200119018555,15669.084531999995 +29568,Multiclass classification,Voting,Insects,0.6310751851726587,0.6310751851726587,0.6062390002054064,8.153844833374023,16709.899933999994 +30624,Multiclass classification,Voting,Insects,0.6313228619011854,0.6313228619011854,0.610710416812842,8.221953392028809,17785.196262999994 +31680,Multiclass classification,Voting,Insects,0.6320590927743931,0.6320590927743931,0.614817700164209,8.237210273742676,18894.010558999995 +32736,Multiclass classification,Voting,Insects,0.6331144035436077,0.6331144035436077,0.6184679282473909,8.208189964294434,20033.816622999995 +33792,Multiclass classification,Voting,Insects,0.6291616110798733,0.6291616110798733,0.6151628967287334,8.149331092834473,21206.789184999994 +34848,Multiclass classification,Voting,Insects,0.6245587855482538,0.6245587855482538,0.6103108800280445,8.270771980285645,22409.569843999994 +35904,Multiclass classification,Voting,Insects,0.6211737180736986,0.6211737180736986,0.6063163580543118,8.246885299682617,23639.112908999996 +36960,Multiclass classification,Voting,Insects,0.6171433209772992,0.6171433209772992,0.6018416894357856,8.222872734069824,24895.212450999996 +38016,Multiclass classification,Voting,Insects,0.6153360515585953,0.6153360515585953,0.5996210858832133,8.711487770080566,26177.407049999994 +39072,Multiclass classification,Voting,Insects,0.613472908295155,0.613472908295155,0.5980758777202522,8.84398365020752,27486.887242999994 +40128,Multiclass classification,Voting,Insects,0.6139008647544048,0.6139008647544048,0.5993833357378361,9.00393295288086,28821.579146999993 +41184,Multiclass classification,Voting,Insects,0.6157395041643396,0.6157395041643396,0.6018873090815099,8.895415306091309,30174.675792999995 +42240,Multiclass classification,Voting,Insects,0.6179833802883591,0.6179833802883591,0.6047393094362844,8.820836067199707,31551.592344999994 +43296,Multiclass classification,Voting,Insects,0.6202101859337106,0.6202101859337106,0.60743097275183,8.80302619934082,32950.21258099999 +44352,Multiclass classification,Voting,Insects,0.6221054767648982,0.6221054767648982,0.6097047537791253,8.807188034057617,34370.71930599999 +45408,Multiclass classification,Voting,Insects,0.623736428304006,0.623736428304006,0.6112415003179203,8.906554222106934,35814.22252799999 +46464,Multiclass classification,Voting,Insects,0.6259389191399608,0.6259389191399608,0.6133867892257391,8.822076797485352,37279.498286999995 +47520,Multiclass classification,Voting,Insects,0.6274542814453166,0.6274542814453166,0.6153714367024555,8.875716209411621,38770.246245999995 +48576,Multiclass classification,Voting,Insects,0.6317858980957283,0.6317858980957283,0.6202967225132047,8.86828327178955,40284.403256 +49632,Multiclass classification,Voting,Insects,0.6360137817090125,0.6360137817090125,0.6247992459885968,8.835649490356445,41820.805007999996 +50688,Multiclass classification,Voting,Insects,0.6403811628228145,0.6403811628228145,0.6293790828873279,8.924153327941895,43378.957976 +51744,Multiclass classification,Voting,Insects,0.6455559206076185,0.6455559206076185,0.6346828420183047,9.218049049377441,44959.107880999996 +52800,Multiclass classification,Voting,Insects,0.648269853595712,0.648269853595712,0.6377385869395499,9.400546073913574,46560.782 +52848,Multiclass classification,Voting,Insects,0.6485325562472799,0.6485325562472799,0.637999701607352,9.406517028808594,48163.738895 +408,Multiclass classification,Voting,Keystroke,0.9828009828009828,0.9828009828009828,0.6067632850241546,1.4587059020996094,10.139614 +816,Multiclass classification,Voting,Keystroke,0.9496932515337423,0.9496932515337423,0.7435135353411919,6.019382476806641,66.737739 +1224,Multiclass classification,Voting,Keystroke,0.9149632052330335,0.9149632052330335,0.9012024099743488,7.076447486877441,151.07716299999998 +1632,Multiclass classification,Voting,Keystroke,0.9258123850398529,0.9258123850398529,0.913338738884437,7.232892990112305,261.540164 +2040,Multiclass classification,Voting,Keystroke,0.9230014713094654,0.9230014713094654,0.9086113906821328,7.553393363952637,397.83621500000004 +2448,Multiclass classification,Voting,Keystroke,0.8961994278708623,0.8961994278708623,0.8992132713257572,7.640434265136719,558.733108 +2856,Multiclass classification,Voting,Keystroke,0.9001751313485113,0.9001751313485113,0.8860451027148403,7.9326982498168945,743.600486 +3264,Multiclass classification,Voting,Keystroke,0.8924302788844621,0.8924302788844621,0.8761196773917237,8.074724197387695,952.077233 +3672,Multiclass classification,Voting,Keystroke,0.8874965949332607,0.8874965949332607,0.8846937712308092,8.20841121673584,1184.393658 +4080,Multiclass classification,Voting,Keystroke,0.8815886246629075,0.8815886246629075,0.868452721773406,8.525882720947266,1441.2089369999999 +4488,Multiclass classification,Voting,Keystroke,0.8760864720303098,0.8760864720303098,0.8834419600614621,8.681946754455566,1719.7568239999998 +4896,Multiclass classification,Voting,Keystroke,0.8737487231869254,0.8737487231869254,0.8797220914000274,8.834684371948242,2018.9742069999998 +5304,Multiclass classification,Voting,Keystroke,0.8693192532528757,0.8693192532528757,0.8538682361373632,9.067034721374512,2339.6996679999997 +5712,Multiclass classification,Voting,Keystroke,0.8607949571003327,0.8607949571003327,0.8654889627515672,9.271133422851562,2680.904224 +6120,Multiclass classification,Voting,Keystroke,0.8561856512502043,0.8561856512502043,0.84095068957581,9.378315925598145,3042.663698 +6528,Multiclass classification,Voting,Keystroke,0.8434196414891987,0.8434196414891987,0.8427350578509161,9.608606338500977,3424.478417 +6936,Multiclass classification,Voting,Keystroke,0.8392213410237923,0.8392213410237923,0.8447429510460126,9.751982688903809,3824.86879 +7344,Multiclass classification,Voting,Keystroke,0.8454310227427482,0.8454310227427482,0.847842289102327,9.957889556884766,4243.00141 +7752,Multiclass classification,Voting,Keystroke,0.8456973293768546,0.8456973293768547,0.8480563212460421,10.19985294342041,4680.993142 +8160,Multiclass classification,Voting,Keystroke,0.8469175144012747,0.8469175144012746,0.8472851046009279,10.418806076049805,5138.9878340000005 +8568,Multiclass classification,Voting,Keystroke,0.8469709349830746,0.8469709349830746,0.8501227536717817,10.607142448425293,5616.664707000001 +8976,Multiclass classification,Voting,Keystroke,0.8475766016713092,0.8475766016713092,0.8507851780426926,10.772598266601562,6113.940894000001 +9384,Multiclass classification,Voting,Keystroke,0.8459980816370031,0.8459980816370031,0.8471668648040658,10.97368335723877,6631.342845000001 +9792,Multiclass classification,Voting,Keystroke,0.8418956184250843,0.8418956184250843,0.8426049398612477,11.192140579223633,7169.901201000001 +10200,Multiclass classification,Voting,Keystroke,0.8344935778017453,0.8344935778017454,0.8308153568434791,11.354521751403809,7729.92345 +10608,Multiclass classification,Voting,Keystroke,0.817384745922504,0.817384745922504,0.8105787344487394,11.59365177154541,8312.440227000001 +11016,Multiclass classification,Voting,Keystroke,0.8127099409895597,0.8127099409895597,0.8142119266109252,11.793928146362305,8918.030696000002 +11424,Multiclass classification,Voting,Keystroke,0.8079313665411888,0.8079313665411888,0.8037472320719128,11.945178031921387,9547.170938000001 +11832,Multiclass classification,Voting,Keystroke,0.8040740427689967,0.8040740427689967,0.8039730126613296,12.203582763671875,10200.281645000001 +12240,Multiclass classification,Voting,Keystroke,0.8072554947299616,0.8072554947299616,0.8097160881214022,12.414502143859863,10877.318664 +12648,Multiclass classification,Voting,Keystroke,0.8043014153554202,0.8043014153554202,0.8038043720799647,12.561456680297852,11578.515438 +13056,Multiclass classification,Voting,Keystroke,0.7996936039831483,0.7996936039831483,0.8010057260657798,12.889472007751465,12304.325005 +13464,Multiclass classification,Voting,Keystroke,0.7974448488449826,0.7974448488449826,0.7996515087686575,12.99599838256836,13054.609905000001 +13872,Multiclass classification,Voting,Keystroke,0.7978516329031793,0.7978516329031793,0.8006715750629478,13.20394229888916,13829.291085 +14280,Multiclass classification,Voting,Keystroke,0.797674907206387,0.7976749072063871,0.8002875748518964,13.364522933959961,14628.347686000001 +14688,Multiclass classification,Voting,Keystroke,0.8007761966364813,0.8007761966364813,0.8043248634763072,13.53370189666748,15451.756014 +15096,Multiclass classification,Voting,Keystroke,0.8051010268300762,0.8051010268300763,0.8085780284871096,13.774932861328125,16299.960754 +15504,Multiclass classification,Voting,Keystroke,0.8052634973876024,0.8052634973876024,0.8077470357827514,13.933537483215332,17172.988913 +15912,Multiclass classification,Voting,Keystroke,0.7978756834894098,0.7978756834894098,0.7983136026998061,14.138628005981445,18070.675966000003 +16320,Multiclass classification,Voting,Keystroke,0.793369691770329,0.7933696917703291,0.7956625263629296,14.30509090423584,18993.333450000002 +16728,Multiclass classification,Voting,Keystroke,0.7901596221677527,0.7901596221677527,0.7932579365729884,14.447582244873047,19941.842904 +17136,Multiclass classification,Voting,Keystroke,0.7861686606361249,0.7861686606361248,0.7888822346867281,14.767212867736816,20916.572711 +17544,Multiclass classification,Voting,Keystroke,0.780425240836801,0.780425240836801,0.7838193866310822,14.989240646362305,21922.215184 +17952,Multiclass classification,Voting,Keystroke,0.7802907915993538,0.7802907915993537,0.7845235361146662,15.200251579284668,22957.213951 +18360,Multiclass classification,Voting,Keystroke,0.783975162045863,0.783975162045863,0.7883700169311393,15.375930786132812,24020.765336 +18768,Multiclass classification,Voting,Keystroke,0.7869664837214259,0.7869664837214259,0.7913854757843782,15.5132417678833,25114.453204 +19176,Multiclass classification,Voting,Keystroke,0.7816427640156454,0.7816427640156454,0.7858184292134073,15.77665901184082,26236.293864000003 +19584,Multiclass classification,Voting,Keystroke,0.7846090997293571,0.7846090997293571,0.7893723685613512,15.996115684509277,27388.205854000003 +19992,Multiclass classification,Voting,Keystroke,0.7807013155920164,0.7807013155920164,0.785620728786203,16.12063980102539,28569.915626 +20400,Multiclass classification,Voting,Keystroke,0.7791068189617139,0.7791068189617139,0.7841355172773921,16.39253330230713,29779.243894000003 +46,Multiclass classification,[baseline] Last Class,ImageSegments,0.17777777777777778,0.17777777777777778,0.15260266049739735,0.0013666152954101562,0.110776 +92,Multiclass classification,[baseline] Last Class,ImageSegments,0.13186813186813187,0.13186813186813187,0.1213108980966124,0.0013637542724609375,0.225611 +138,Multiclass classification,[baseline] Last Class,ImageSegments,0.12408759124087591,0.12408759124087591,0.11874455065544491,0.001369476318359375,0.34363900000000003 +184,Multiclass classification,[baseline] Last Class,ImageSegments,0.12568306010928962,0.12568306010928962,0.12262983423071581,0.0013647079467773438,0.48452400000000007 +230,Multiclass classification,[baseline] Last Class,ImageSegments,0.12663755458515283,0.12663755458515283,0.12503852041208066,0.0013637542724609375,0.6292090000000001 +276,Multiclass classification,[baseline] Last Class,ImageSegments,0.12727272727272726,0.12727272727272726,0.12427907918144998,0.0013666152954101562,0.7861950000000001 +322,Multiclass classification,[baseline] Last Class,ImageSegments,0.13395638629283488,0.13395638629283488,0.13210036596246022,0.0013666152954101562,1.0166240000000002 +368,Multiclass classification,[baseline] Last Class,ImageSegments,0.13896457765667575,0.13896457765667575,0.13745011462972964,0.0013675689697265625,1.2507780000000002 +414,Multiclass classification,[baseline] Last Class,ImageSegments,0.14043583535108958,0.14043583535108958,0.14035813096947544,0.0013666152954101562,1.5223060000000002 +460,Multiclass classification,[baseline] Last Class,ImageSegments,0.14596949891067537,0.14596949891067537,0.14563148710727947,0.00136566162109375,1.7974560000000002 +506,Multiclass classification,[baseline] Last Class,ImageSegments,0.13861386138613863,0.13861386138613863,0.13833816102314941,0.0013666152954101562,2.0756200000000002 +552,Multiclass classification,[baseline] Last Class,ImageSegments,0.1397459165154265,0.1397459165154265,0.13938652491777898,0.0013666152954101562,2.402759 +598,Multiclass classification,[baseline] Last Class,ImageSegments,0.1373534338358459,0.1373534338358459,0.13727981043458612,0.0013675689697265625,2.771723 +644,Multiclass classification,[baseline] Last Class,ImageSegments,0.13996889580093314,0.13996889580093314,0.14017571709017965,0.0013666152954101562,3.149556 +690,Multiclass classification,[baseline] Last Class,ImageSegments,0.1378809869375907,0.1378809869375907,0.13801517784553327,0.001369476318359375,3.580436 +736,Multiclass classification,[baseline] Last Class,ImageSegments,0.1401360544217687,0.1401360544217687,0.14031088927958282,0.0013675689697265625,4.0152470000000005 +782,Multiclass classification,[baseline] Last Class,ImageSegments,0.14212548015364918,0.14212548015364918,0.1420930265541123,0.0013647079467773438,4.453992 +828,Multiclass classification,[baseline] Last Class,ImageSegments,0.14268440145102781,0.14268440145102781,0.14229874553046912,0.0013666152954101562,4.959761 +874,Multiclass classification,[baseline] Last Class,ImageSegments,0.13860252004581902,0.13860252004581902,0.13845352694595275,0.0013647079467773438,5.469480000000001 +920,Multiclass classification,[baseline] Last Class,ImageSegments,0.13492927094668117,0.13492927094668117,0.1348083913046733,0.0013666152954101562,6.0005820000000005 +966,Multiclass classification,[baseline] Last Class,ImageSegments,0.13367875647668392,0.13367875647668392,0.13349177774445276,0.0013637542724609375,6.5350530000000004 +1012,Multiclass classification,[baseline] Last Class,ImageSegments,0.13254203758654798,0.13254203758654798,0.1324936677659038,0.0013675689697265625,7.07275 +1058,Multiclass classification,[baseline] Last Class,ImageSegments,0.13339640491958374,0.13339640491958374,0.1331834965440007,0.00136566162109375,7.6454260000000005 +1104,Multiclass classification,[baseline] Last Class,ImageSegments,0.13417951042611062,0.13417951042611062,0.13402826529501538,0.0013666152954101562,8.221471000000001 +1150,Multiclass classification,[baseline] Last Class,ImageSegments,0.134029590948651,0.134029590948651,0.13406391150519123,0.0013637542724609375,8.800858000000002 +1196,Multiclass classification,[baseline] Last Class,ImageSegments,0.13640167364016736,0.13640167364016736,0.1363948420172951,0.001369476318359375,9.430169000000001 +1242,Multiclass classification,[baseline] Last Class,ImageSegments,0.13940370668815472,0.13940370668815472,0.13919772383892226,0.0013637542724609375,10.062783000000001 +1288,Multiclass classification,[baseline] Last Class,ImageSegments,0.1414141414141414,0.1414141414141414,0.14118715023210152,0.0013666152954101562,10.698372 +1334,Multiclass classification,[baseline] Last Class,ImageSegments,0.14328582145536384,0.14328582145536384,0.14302553278156666,0.0013637542724609375,11.387531000000001 +1380,Multiclass classification,[baseline] Last Class,ImageSegments,0.14358230601885424,0.14358230601885424,0.1433209000486506,0.001369476318359375,12.080639000000001 +1426,Multiclass classification,[baseline] Last Class,ImageSegments,0.14175438596491227,0.14175438596491227,0.14145466559291123,0.001369476318359375,12.777602000000002 +1472,Multiclass classification,[baseline] Last Class,ImageSegments,0.13936097892590074,0.13936097892590074,0.13907629713942624,0.0013647079467773438,13.546128000000001 +1518,Multiclass classification,[baseline] Last Class,ImageSegments,0.13974950560316415,0.13974950560316415,0.13951366685898453,0.0013666152954101562,14.318195000000001 +1564,Multiclass classification,[baseline] Last Class,ImageSegments,0.13691618682021753,0.13691618682021753,0.13664170474395118,0.0013666152954101562,15.093811 +1610,Multiclass classification,[baseline] Last Class,ImageSegments,0.13610938471100062,0.13610938471100062,0.13597683881903072,0.0013637542724609375,15.942934000000001 +1656,Multiclass classification,[baseline] Last Class,ImageSegments,0.1365558912386707,0.1365558912386707,0.13633224623774592,0.001369476318359375,16.795246000000002 +1702,Multiclass classification,[baseline] Last Class,ImageSegments,0.13932980599647266,0.13932980599647266,0.13901296274399097,0.0013675689697265625,17.650687 +1748,Multiclass classification,[baseline] Last Class,ImageSegments,0.14195764167143674,0.14195764167143674,0.14147197312723642,0.00136566162109375,18.510738 +1794,Multiclass classification,[baseline] Last Class,ImageSegments,0.14221974344673732,0.14221974344673732,0.14194103966110075,0.0013647079467773438,19.374685 +1840,Multiclass classification,[baseline] Last Class,ImageSegments,0.14138118542686243,0.14138118542686243,0.14114329766598663,0.0013675689697265625,20.242449999999998 +1886,Multiclass classification,[baseline] Last Class,ImageSegments,0.140053050397878,0.140053050397878,0.13973258713820755,0.0013666152954101562,21.182872999999997 +1932,Multiclass classification,[baseline] Last Class,ImageSegments,0.14293112377006734,0.14293112377006734,0.14275229229825853,0.0013666152954101562,22.126859999999997 +1978,Multiclass classification,[baseline] Last Class,ImageSegments,0.14618108244815378,0.14618108244815378,0.14597158151605963,0.001369476318359375,23.074112999999997 +2024,Multiclass classification,[baseline] Last Class,ImageSegments,0.14434008897676717,0.14434008897676717,0.14416625237761066,0.001369476318359375,24.067370999999998 +2070,Multiclass classification,[baseline] Last Class,ImageSegments,0.14403093281778637,0.14403093281778637,0.14385543497127623,0.0013666152954101562,25.063920999999997 +2116,Multiclass classification,[baseline] Last Class,ImageSegments,0.14468085106382977,0.14468085106382977,0.14460362317776573,0.0013637542724609375,26.063629999999996 +2162,Multiclass classification,[baseline] Last Class,ImageSegments,0.14530310041647385,0.14530310041647385,0.14520465913821792,0.001369476318359375,27.083891999999995 +2208,Multiclass classification,[baseline] Last Class,ImageSegments,0.14499320344358857,0.14499320344358857,0.14491109851991696,0.001369476318359375,28.107943999999996 +2254,Multiclass classification,[baseline] Last Class,ImageSegments,0.14647137150466044,0.14647137150466044,0.14640425534129609,0.0013666152954101562,29.207950999999998 +2300,Multiclass classification,[baseline] Last Class,ImageSegments,0.14789038712483688,0.14789038712483688,0.14788688524810298,0.0013675689697265625,30.311507 +2310,Multiclass classification,[baseline] Last Class,ImageSegments,0.14811606756171503,0.14811606756171503,0.14811566784252675,0.001369476318359375,31.415920999999997 +1056,Multiclass classification,[baseline] Last Class,Insects,0.15829383886255924,0.15829383886255924,0.1376212379233521,0.0013856887817382812,0.57267 +2112,Multiclass classification,[baseline] Last Class,Insects,0.16579819990525818,0.16579819990525818,0.15110451064118433,0.0013856887817382812,1.690872 +3168,Multiclass classification,[baseline] Last Class,Insects,0.17019261130407326,0.17019261130407326,0.15681512355039637,0.0013885498046875,3.2981429999999996 +4224,Multiclass classification,[baseline] Last Class,Insects,0.16599573762727918,0.16599573762727918,0.15254433156050665,0.0013856887817382812,5.4736839999999995 +5280,Multiclass classification,[baseline] Last Class,Insects,0.17086569426027656,0.17086569426027656,0.15676679113993588,0.0013837814331054688,8.202311 +6336,Multiclass classification,[baseline] Last Class,Insects,0.17379636937647988,0.17379636937647988,0.16137568195972998,0.0013837814331054688,11.448991 +7392,Multiclass classification,[baseline] Last Class,Insects,0.1752130970098769,0.1752130970098769,0.16189407904134778,0.0013837814331054688,15.242684 +8448,Multiclass classification,[baseline] Last Class,Insects,0.17722268260921037,0.17722268260921037,0.163740045170864,0.0013818740844726562,19.537217000000002 +9504,Multiclass classification,[baseline] Last Class,Insects,0.17731242765442493,0.17731242765442493,0.1637492974453096,0.0013885498046875,24.318802 +10560,Multiclass classification,[baseline] Last Class,Insects,0.17908892887584052,0.17908892887584052,0.16564210767474952,0.0013837814331054688,29.683683000000002 +11616,Multiclass classification,[baseline] Last Class,Insects,0.17899268187688333,0.17899268187688333,0.16559253835337612,0.0013856887817382812,35.598037000000005 +12672,Multiclass classification,[baseline] Last Class,Insects,0.18530502722752742,0.1853050272275274,0.18269809988409802,0.0013866424560546875,41.981502000000006 +13728,Multiclass classification,[baseline] Last Class,Insects,0.24797843665768193,0.24797843665768193,0.26603936845528803,0.0013866424560546875,48.94863000000001 +14784,Multiclass classification,[baseline] Last Class,Insects,0.2795778935263478,0.2795778935263478,0.28229742751715126,0.0013818740844726562,56.43945000000001 +15840,Multiclass classification,[baseline] Last Class,Insects,0.27615379758823155,0.27615379758823155,0.2847375853365436,0.0013818740844726562,64.48233200000001 +16896,Multiclass classification,[baseline] Last Class,Insects,0.2723290914471737,0.2723290914471737,0.2859139704285301,0.0013856887817382812,73.03679300000002 +17952,Multiclass classification,[baseline] Last Class,Insects,0.2720739791655061,0.2720739791655061,0.2880143206503878,0.0013866424560546875,82.10379000000002 +19008,Multiclass classification,[baseline] Last Class,Insects,0.28252748987215237,0.28252748987215237,0.2877504429321087,0.0013866424560546875,91.70347300000002 +20064,Multiclass classification,[baseline] Last Class,Insects,0.28724517769027563,0.28724517769027563,0.28667392366619265,0.0013818740844726562,101.81113500000002 +21120,Multiclass classification,[baseline] Last Class,Insects,0.28306264501160094,0.28306264501160094,0.28164766024255256,0.0013837814331054688,112.42818900000002 +22176,Multiclass classification,[baseline] Last Class,Insects,0.2805411499436302,0.2805411499436302,0.27862960725280095,0.0013866424560546875,123.55266000000002 +23232,Multiclass classification,[baseline] Last Class,Insects,0.2797124531875511,0.2797124531875511,0.27719419757933417,0.0013856887817382812,135.22034100000002 +24288,Multiclass classification,[baseline] Last Class,Insects,0.2777205912628155,0.2777205912628155,0.2745878480946635,0.0013866424560546875,147.32084400000002 +25344,Multiclass classification,[baseline] Last Class,Insects,0.2756579726157124,0.2756579726157124,0.27233803052028965,0.0013818740844726562,159.88729300000003 +26400,Multiclass classification,[baseline] Last Class,Insects,0.27394977082465244,0.27394977082465244,0.26996904425699914,0.0013837814331054688,172.95537600000003 +27456,Multiclass classification,[baseline] Last Class,Insects,0.27189947186304864,0.27189947186304864,0.26719485323886244,0.0013866424560546875,186.52082400000003 +28512,Multiclass classification,[baseline] Last Class,Insects,0.2723860965942969,0.2723860965942969,0.2686965366571337,0.0013885498046875,200.59564800000004 +29568,Multiclass classification,[baseline] Last Class,Insects,0.2738187844556431,0.2738187844556431,0.2720266804437783,0.0013885498046875,215.16150500000003 +30624,Multiclass classification,[baseline] Last Class,Insects,0.27538124938771513,0.27538124938771513,0.27486986638103517,0.0013885498046875,230.19075300000003 +31680,Multiclass classification,[baseline] Last Class,Insects,0.2780390795163989,0.2780390795163989,0.2784141751235631,0.0013856887817382812,245.71900300000004 +32736,Multiclass classification,[baseline] Last Class,Insects,0.279670077898274,0.279670077898274,0.28021922512452757,0.0013837814331054688,261.76959600000004 +33792,Multiclass classification,[baseline] Last Class,Insects,0.2808440117190968,0.2808440117190968,0.28119627453717067,0.0013856887817382812,278.22772000000003 +34848,Multiclass classification,[baseline] Last Class,Insects,0.2772405085086234,0.2772405085086234,0.27819051828647573,0.0013837814331054688,295.19763900000004 +35904,Multiclass classification,[baseline] Last Class,Insects,0.2739325404562293,0.2739325404562293,0.2754200456137155,0.0013856887817382812,312.64260700000005 +36960,Multiclass classification,[baseline] Last Class,Insects,0.271246516410076,0.271246516410076,0.27333283767820205,0.0013818740844726562,330.5037730000001 +38016,Multiclass classification,[baseline] Last Class,Insects,0.26855188741286334,0.26855188741286334,0.2710722002891223,0.0013856887817382812,348.8496650000001 +39072,Multiclass classification,[baseline] Last Class,Insects,0.277034117376059,0.277034117376059,0.2770619820799866,0.0013866424560546875,367.6207990000001 +40128,Multiclass classification,[baseline] Last Class,Insects,0.27617315024796274,0.27617315024796274,0.2760769006623072,0.0013837814331054688,386.8573710000001 +41184,Multiclass classification,[baseline] Last Class,Insects,0.27567200058276475,0.27567200058276475,0.27543526329721163,0.0013837814331054688,406.52795400000014 +42240,Multiclass classification,[baseline] Last Class,Insects,0.27401216884869434,0.27401216884869434,0.27359461935885426,0.0013885498046875,426.69962200000015 +43296,Multiclass classification,[baseline] Last Class,Insects,0.2738422450629403,0.2738422450629403,0.27319488690835786,0.0013856887817382812,447.37129900000014 +44352,Multiclass classification,[baseline] Last Class,Insects,0.2729588960790061,0.2729588960790061,0.27209116538690487,0.0013866424560546875,468.49129600000015 +45408,Multiclass classification,[baseline] Last Class,Insects,0.27205056489087587,0.27205056489087587,0.2708084959373003,0.0013866424560546875,490.06234300000017 +46464,Multiclass classification,[baseline] Last Class,Insects,0.27137722488862104,0.27137722488862104,0.2698631410415437,0.0013837814331054688,512.0778290000002 +47520,Multiclass classification,[baseline] Last Class,Insects,0.27235421620825356,0.27235421620825356,0.27170627983222856,0.0013837814331054688,534.5781510000002 +48576,Multiclass classification,[baseline] Last Class,Insects,0.2741327843540916,0.2741327843540916,0.27449463409742436,0.0013818740844726562,557.5265480000002 +49632,Multiclass classification,[baseline] Last Class,Insects,0.27535209848683284,0.27535209848683284,0.27650368764304034,0.0013818740844726562,580.9705880000001 +50688,Multiclass classification,[baseline] Last Class,Insects,0.2768362696549411,0.2768362696549411,0.27863440912734966,0.0013837814331054688,604.9012140000001 +51744,Multiclass classification,[baseline] Last Class,Insects,0.27827918752294994,0.27827918752294994,0.2805971515128955,0.0013885498046875,629.3033230000001 +52800,Multiclass classification,[baseline] Last Class,Insects,0.28911532415386654,0.28911532415386654,0.28929532027297566,0.0013866424560546875,654.1512880000001 +52848,Multiclass classification,[baseline] Last Class,Insects,0.2897610081934642,0.2897610081934642,0.28976272570313216,0.0013866424560546875,679.0036960000001 +408,Multiclass classification,[baseline] Last Class,Keystroke,0.9975429975429976,0.9975429975429976,0.9660408844388819,0.0006122589111328125,0.255536 +816,Multiclass classification,[baseline] Last Class,Keystroke,0.9975460122699387,0.9975460122699387,0.9879967903427672,0.0006628036499023438,0.794196 +1224,Multiclass classification,[baseline] Last Class,Keystroke,0.9975470155355682,0.9975470155355682,0.9931179599499375,0.000713348388671875,1.53447 +1632,Multiclass classification,[baseline] Last Class,Keystroke,0.9975475168608215,0.9975475168608215,0.9950750839342831,0.0012521743774414062,2.469131 +2040,Multiclass classification,[baseline] Last Class,Keystroke,0.9975478175576263,0.9975478175576263,0.9960150346160551,0.0013027191162109375,3.675833 +2448,Multiclass classification,[baseline] Last Class,Keystroke,0.9975480179812015,0.9975480179812015,0.9965317313935653,0.0013532638549804688,5.030286 +2856,Multiclass classification,[baseline] Last Class,Keystroke,0.9975481611208407,0.9975481611208407,0.9968424283169279,0.00140380859375,6.586031 +3264,Multiclass classification,[baseline] Last Class,Keystroke,0.9975482684646031,0.9975482684646031,0.9970416021996,0.0014543533325195312,8.377109 +3672,Multiclass classification,[baseline] Last Class,Keystroke,0.9975483519476982,0.9975483519476982,0.9971755428551425,0.0015048980712890625,10.331252000000001 +4080,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484187300809,0.9975484187300809,0.9972690115789393,0.0015554428100585938,12.525489 +4488,Multiclass classification,[baseline] Last Class,Keystroke,0.9975484733675062,0.9975484733675062,0.9973361791525123,0.001605987548828125,14.940819000000001 +4896,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485188968335,0.9975485188968335,0.9973856025730918,0.0016565322875976562,17.495259 +5304,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485574203281,0.9975485574203281,0.9974226798335742,0.0017070770263671875,20.336762 +5712,Multiclass classification,[baseline] Last Class,Keystroke,0.9975485904395027,0.9975485904395027,0.99745094204078,0.0017576217651367188,23.402208 +6120,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486190554013,0.9975486190554013,0.9974727709453766,0.00180816650390625,26.661861000000002 +6528,Multiclass classification,[baseline] Last Class,Keystroke,0.9975486440937643,0.9975486440937643,0.997489815700999,0.0018587112426757812,30.164710000000003 +6936,Multiclass classification,[baseline] Last Class,Keystroke,0.997548666186013,0.997548666186013,0.9975032443691146,0.0019092559814453125,33.838397 +7344,Multiclass classification,[baseline] Last Class,Keystroke,0.997548685823233,0.997548685823233,0.9975139007887865,0.0034246444702148438,37.738436 +7752,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487033931105,0.9975487033931105,0.9975224052755716,0.003475189208984375,41.800015 +8160,Multiclass classification,[baseline] Last Class,Keystroke,0.997548719205785,0.997548719205785,0.9975292209193424,0.0035257339477539062,46.105028000000004 +8568,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487335123147,0.9975487335123147,0.9975346982235258,0.0035762786865234375,50.63279300000001 +8976,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487465181059,0.9975487465181059,0.9975391057693664,0.0036268234252929688,55.447067000000004 +9384,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487583928381,0.9975487583928381,0.997542651662671,0.0036773681640625,60.387128000000004 +9792,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487692779083,0.9975487692779083,0.9975454987794795,0.0037279129028320312,65.547582 +10200,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487792920874,0.9975487792920874,0.9975477757646256,0.0037784576416015625,70.981052 +10608,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487885358726,0.9975487885358726,0.9975495850737114,0.0038290023803710938,76.594226 +11016,Multiclass classification,[baseline] Last Class,Keystroke,0.9975487970948707,0.9975487970948707,0.9975510089260562,0.003879547119140625,82.44596800000001 +11424,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488050424582,0.9975488050424582,0.9975521137613483,0.003930091857910156,88.533094 +11832,Multiclass classification,[baseline] Last Class,Keystroke,0.99754881244189,0.99754881244189,0.9975529536110199,0.0039806365966796875,94.81874400000001 +12240,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488193479859,0.9975488193479859,0.9975535726732964,0.004031181335449219,101.331754 +12648,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488258084921,0.9975488258084921,0.9975540072976319,0.00408172607421875,108.05167800000001 +13056,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488318651857,0.9975488318651857,0.997554287526727,0.004132270812988281,114.99668100000001 +13464,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488375547797,0.9975488375547797,0.9975544383040469,0.0041828155517578125,122.1119 +13872,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488429096676,0.9975488429096676,0.9975544804262362,0.004233360290527344,129.47010500000002 +14280,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488479585405,0.9975488479585405,0.9975544312994101,0.004283905029296875,136.988051 +14688,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488527269013,0.9975488527269013,0.9975543055435039,0.004334449768066406,144.742896 +15096,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488572374959,0.9975488572374959,0.9975541154780816,0.0043849945068359375,152.648866 +15504,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488615106753,0.9975488615106753,0.9975538715150367,0.004435539245605469,160.767465 +15912,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488655647037,0.9975488655647037,0.9975535824776959,0.004486083984375,169.09858 +16320,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488694160182,0.9975488694160182,0.9975532558614028,0.004536628723144531,177.653336 +16728,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488730794524,0.9975488730794524,0.997552898047314,0.0045871734619140625,186.438203 +17136,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488765684272,0.9975488765684272,0.9975525144785747,0.004637718200683594,195.44716799999998 +17544,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488798951149,0.9975488798951149,0.9975521098061079,0.004688262939453125,204.56363499999998 +17952,Multiclass classification,[baseline] Last Class,Keystroke,0.997548883070581,0.997548883070581,0.9975516880097278,0.004738807678222656,213.933058 +18360,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488861049077,0.9975488861049077,0.997551252499137,0.0047893524169921875,223.513668 +18768,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488890073001,0.9975488890073001,0.9975508061984416,0.004839897155761719,233.322943 +19176,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488917861799,0.9975488917861799,0.9975503516171184,0.00489044189453125,243.357771 +19584,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488944492672,0.9975488944492672,0.9975498909097889,0.004940986633300781,253.567103 +19992,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488970036517,0.9975488970036517,0.9975494259267257,0.0049915313720703125,264.004285 +20400,Multiclass classification,[baseline] Last Class,Keystroke,0.9975488994558557,0.9975488994558557,0.9975489582566448,0.005042076110839844,274.675054 diff --git a/docs/benchmarks/Regression/index.md b/docs/benchmarks/Regression/index.md new file mode 100644 index 0000000000..ac378dd8ac --- /dev/null +++ b/docs/benchmarks/Regression/index.md @@ -0,0 +1,20664 @@ +# Regression + + + +=== "Table" + + | Model | Dataset | MAE | RMSE | R2 | Memory in Mb | Time in s | + |:-----------------------------------------|:--------------|-----------:|-----------:|--------------:|---------------:|------------:| + | Adaptive Model Rules | ChickWeights | 24.1943 | 37.2166 | 0.725319 | 0.046977 | 5.25855 | + | Adaptive Model Rules | TrumpApproval | 1.39847 | 2.43336 | -1.02372 | 0.114429 | 9.38293 | + | Adaptive Random Forest | ChickWeights | 26.1016 | 40.8094 | 0.669725 | 1.19043 | 56.006 | + | Adaptive Random Forest | TrumpApproval | 0.800378 | 2.11495 | -0.528761 | 1.28462 | 87.4457 | + | Aggregated Mondrian Forest | ChickWeights | 25.6742 | 41.7123 | 0.65479 | 8.21412 | 127.415 | + | Aggregated Mondrian Forest | TrumpApproval | 0.268533 | 0.349421 | 0.958184 | 16.9323 | 186.034 | + | Bagging | ChickWeights | 23.1143 | 36.6311 | 0.733893 | 0.628034 | 38.0203 | + | Bagging | TrumpApproval | 0.908203 | 2.23718 | -0.710572 | 1.31579 | 82.0689 | + | Exponentially Weighted Average | ChickWeights | 121.818 | 141.004 | -2.94294 | 3.09241 | 55.8851 | + | Exponentially Weighted Average | TrumpApproval | 40.7546 | 40.7905 | -567.663 | 5.27613 | 141.452 | + | Hoeffding Adaptive Tree | ChickWeights | 23.3739 | 37.6579 | 0.718766 | 0.0947332 | 7.99029 | + | Hoeffding Adaptive Tree | TrumpApproval | 0.921313 | 2.23942 | -0.713986 | 0.138225 | 16.7576 | + | Hoeffding Tree | ChickWeights | 23.1619 | 36.7336 | 0.732402 | 0.0440512 | 6.29305 | + | Hoeffding Tree | TrumpApproval | 0.956103 | 2.24987 | -0.730022 | 0.148639 | 11.7656 | + | Linear Regression | ChickWeights | 23.7587 | 37.0377 | 0.727954 | 0.00421047 | 3.21471 | + | Linear Regression | TrumpApproval | 1.31455 | 3.91198 | -4.23035 | 0.00497341 | 11.5379 | + | Linear Regression with l1 regularization | ChickWeights | 23.7577 | 37.078 | 0.727361 | 0.00444126 | 9.7485 | + | Linear Regression with l1 regularization | TrumpApproval | 1.15377 | 3.82872 | -4.01007 | 0.0052042 | 13.3595 | + | Linear Regression with l2 regularization | ChickWeights | 25.2738 | 38.5885 | 0.704694 | 0.00423336 | 1.22128 | + | Linear Regression with l2 regularization | TrumpApproval | 1.87151 | 4.13052 | -4.83107 | 0.0049963 | 4.15677 | + | Passive-Aggressive Regressor, mode 1 | ChickWeights | 24.3423 | 37.596 | 0.71969 | 0.00345898 | 1.10187 | + | Passive-Aggressive Regressor, mode 1 | TrumpApproval | 4.98403 | 6.97667 | -15.6354 | 0.00443554 | 2.99338 | + | Passive-Aggressive Regressor, mode 2 | ChickWeights | 100.624 | 143.066 | -3.05911 | 0.00345898 | 1.16798 | + | Passive-Aggressive Regressor, mode 2 | TrumpApproval | 31.0933 | 34.6257 | -408.765 | 0.00443554 | 4.72475 | + | River MLP | ChickWeights | 51.4078 | 80.9203 | -0.298584 | 0.0123129 | 28.2295 | + | River MLP | TrumpApproval | 1.58058 | 5.03392 | -7.66066 | 0.0133505 | 32.2432 | + | Stochastic Gradient Tree | ChickWeights | 68.7588 | 80.358 | -0.280601 | 1.12059 | 22.3803 | + | Stochastic Gradient Tree | TrumpApproval | 9.42975 | 17.9379 | -108.972 | 3.08244 | 52.4507 | + | Streaming Random Patches | ChickWeights | 23.7097 | 38.4416 | 0.706938 | 0.355182 | 93.4014 | + | Streaming Random Patches | TrumpApproval | 0.656697 | 1.98434 | -0.345761 | 1.06461 | 134.903 | + | [baseline] Mean predictor | ChickWeights | 50.2509 | 71.1144 | -0.00292947 | 0.000490189 | 0.302835 | + | [baseline] Mean predictor | TrumpApproval | 1.56755 | 2.20286 | -0.658483 | 0.000490189 | 1.08177 | + | k-Nearest Neighbors | ChickWeights | 24.8406 | 39.2016 | 0.695236 | 2.88522 | 40.0878 | + | k-Nearest Neighbors | TrumpApproval | 0.641679 | 1.59417 | 0.131425 | 5.03263 | 123.301 | + +=== "Chart" + + *Try reloading the page if something is buggy* + + ```vegalite + { + "$schema": "https://vega.github.io/schema/vega-lite/v5.json", + "data": { + "values": [ + { + "step": 11, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 30.432219699626994, + "RMSE": 31.267456151778337, + "R2": -1257.4692714745631, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.000963 + }, + { + "step": 22, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 20.75760844034268, + "RMSE": 23.632210645041404, + "R2": -590.4769976066937, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.002374 + }, + { + "step": 33, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 14.555240079240876, + "RMSE": 19.34929493332969, + "R2": -259.0232069515881, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.004113 + }, + { + "step": 44, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 11.14363365913676, + "RMSE": 16.767243978820222, + "R2": -220.3452424437857, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.006175 + }, + { + "step": 55, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 10.841164000616114, + "RMSE": 17.714902804136145, + "R2": -60.2608923989398, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.008581 + }, + { + "step": 66, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 10.32598508406065, + "RMSE": 16.527353468164844, + "R2": -21.985729074745297, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.01133 + }, + { + "step": 77, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 9.718401993814265, + "RMSE": 15.52109639018614, + "R2": -12.587024696233003, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.014424 + }, + { + "step": 88, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 8.767755200283737, + "RMSE": 14.552446235427842, + "R2": -9.829280875288257, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.017858 + }, + { + "step": 99, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 7.977130626229444, + "RMSE": 13.740429605807138, + "R2": -7.074807888709797, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0216349999999999 + }, + { + "step": 110, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 7.506893871110683, + "RMSE": 13.098273311725844, + "R2": -4.124041411671393, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0257519999999999 + }, + { + "step": 121, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 7.252833276832352, + "RMSE": 12.607637144454216, + "R2": -2.6562249812820733, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0302089999999999 + }, + { + "step": 132, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 6.896359231575217, + "RMSE": 12.121970224209305, + "R2": -1.7624336939368233, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0350039999999999 + }, + { + "step": 143, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 6.581914741629191, + "RMSE": 11.688367143429067, + "R2": -1.080274127204615, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0401389999999999 + }, + { + "step": 154, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 6.347682986169337, + "RMSE": 11.314945909537578, + "R2": -0.6567859420078188, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0456129999999999 + }, + { + "step": 165, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 6.47676439389405, + "RMSE": 11.21748999353191, + "R2": -0.3089959076061037, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0514269999999999 + }, + { + "step": 176, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 6.552290709218319, + "RMSE": 11.100632967129414, + "R2": -0.0335718949744832, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0575809999999999 + }, + { + "step": 187, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 6.503097179992549, + "RMSE": 10.915357728148932, + "R2": 0.1817591258850298, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0640729999999999 + }, + { + "step": 198, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 6.420443618722296, + "RMSE": 10.727647067877951, + "R2": 0.3713230272376924, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.070904 + }, + { + "step": 209, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 6.54715053669462, + "RMSE": 10.814712106795348, + "R2": 0.4732913339801876, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0780719999999999 + }, + { + "step": 220, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 7.075852889975692, + "RMSE": 11.488147441481184, + "R2": 0.479648982578291, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0855759999999999 + }, + { + "step": 231, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 7.197265349840174, + "RMSE": 11.527376107146, + "R2": 0.5518657524511614, + "Memory in Mb": 0.0041303634643554, + "Time in s": 0.0934159999999999 + }, + { + "step": 242, + "track": "Regression", + "model": "Linear Regression", + "dataset": "ChickWeights", + "MAE": 7.359957454348683, + "RMSE": 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-0.6584830635688459, + "Memory in Mb": 0.0004901885986328, + "Time in s": 1.081765 + } + ] + }, + "params": [ + { + "name": "models", + "select": { + "type": "point", + "fields": [ + "model" + ] + }, + "bind": "legend" + }, + { + "name": "Dataset", + "value": "ChickWeights", + "bind": { + "input": "select", + "options": [ + "ChickWeights", + "TrumpApproval" + ] + } + }, + { + "name": "grid", + "select": "interval", + "bind": "scales" + } + ], + "transform": [ + { + "filter": { + "field": "dataset", + "equal": { + "expr": "Dataset" + } + } + } + ], + "repeat": { + "row": [ + "MAE", + "RMSE", + "R2", + "Memory in Mb", + "Time in s" + ] + }, + "spec": { + "width": "container", + "mark": "line", + "encoding": { + "x": { + "field": "step", + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18, + "title": "Instance" + } + }, + "y": { + "field": { + "repeat": "row" + }, + "type": "quantitative", + "axis": { + "titleFontSize": 18, + "labelFontSize": 18 + } + }, + "color": { + "field": "model", + "type": "ordinal", + "scale": { + "scheme": "category20b" + }, + "title": "Models", + "legend": { + "titleFontSize": 18, + "labelFontSize": 18, + "labelLimit": 500 + } + }, + "opacity": { + "condition": { + "param": "models", + "value": 1 + }, + "value": 0.2 + } + } + } + } + ``` + + + +## Datasets + +???- abstract "ChickWeights" + + Chick weights along time. + + The stream contains 578 items and 3 features. The goal is to predict the weight of each chick + along time, according to the diet the chick is on. The data is ordered by time and then by + chick. + + Name ChickWeights + Task Regression + Samples 578 + Features 3 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/chick-weights.csv + + + +???- abstract "TrumpApproval" + + Donald Trump approval ratings. + + This dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald + Trump's approval ratings. It contains 5 features, which are approval ratings collected by + 5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of + this task is to see if we can reproduce FiveThirtyEight's model. + + Name TrumpApproval + Task Regression + Samples 1,001 + Features 6 + Sparse False + Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/trump_approval.csv.gz + + + +## Models + +???- example "Linear Regression" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      LinearRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=Squared ()
+        l2=0.
+        l1=0.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      )
+    )
+ + + +???- example "Linear Regression with l1 regularization" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      LinearRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=Squared ()
+        l2=0.
+        l1=1.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      )
+    )
+ + + +???- example "Linear Regression with l2 regularization" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      LinearRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=Squared ()
+        l2=1.
+        l1=0.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      )
+    )
+ + + +???- example "Passive-Aggressive Regressor, mode 1" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      PARegressor (
+        C=1.
+        mode=1
+        eps=0.1
+        learn_intercept=True
+      )
+    )
+ + + +???- example "Passive-Aggressive Regressor, mode 2" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      PARegressor (
+        C=1.
+        mode=2
+        eps=0.1
+        learn_intercept=True
+      )
+    )
+ + + +???- example "k-Nearest Neighbors" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      KNNRegressor (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        aggregation_method="mean"
+      )
+    )
+ + + +???- example "Hoeffding Tree" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      HoeffdingTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+      )
+    )
+ + + +???- example "Hoeffding Adaptive Tree" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=True
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=42
+      )
+    )
+ + + +???- example "Stochastic Gradient Tree" + + 
SGTRegressor (
+      delta=1e-07
+      grace_period=200
+      init_pred=0.
+      max_depth=inf
+      lambda_value=0.1
+      gamma=1.
+      nominal_attributes=[]
+      feature_quantizer=StaticQuantizer (
+        n_bins=64
+        warm_start=100
+        buckets=None
+      )
+    )
+ + + +???- example "Adaptive Random Forest" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      []
+    )
+ + + +???- example "Aggregated Mondrian Forest" + + 
[]
+ + + +???- example "Adaptive Model Rules" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      AMRules (
+        n_min=200
+        delta=1e-07
+        tau=0.05
+        pred_type="adaptive"
+        pred_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        fading_factor=0.99
+        anomaly_threshold=-0.75
+        m_min=30
+        ordered_rule_set=True
+        min_samples_split=5
+      )
+    )
+ + + +???- example "Streaming Random Patches" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      SRPRegressor (
+        model=HoeffdingTreeRegressor (
+          grace_period=50
+          max_depth=inf
+          delta=0.01
+          tau=0.05
+          leaf_prediction="adaptive"
+          leaf_model=LinearRegression (
+            optimizer=SGD (
+              lr=Constant (
+                learning_rate=0.01
+              )
+            )
+            loss=Squared ()
+            l2=0.
+            l1=0.
+            intercept_init=0.
+            intercept_lr=Constant (
+              learning_rate=0.01
+            )
+            clip_gradient=1e+12
+            initializer=Zeros ()
+          )
+          model_selector_decay=0.95
+          nominal_attributes=None
+          splitter=TEBSTSplitter (
+            digits=1
+          )
+          min_samples_split=5
+          binary_split=False
+          max_size=500.
+          memory_estimate_period=1000000
+          stop_mem_management=False
+          remove_poor_attrs=False
+          merit_preprune=True
+        )
+        n_models=10
+        subspace_size=0.6
+        training_method="patches"
+        lam=6
+        drift_detector=ADWIN (
+          delta=1e-05
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        warning_detector=ADWIN (
+          delta=0.0001
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        disable_detector="off"
+        disable_weighted_vote=True
+        drift_detection_criteria="error"
+        aggregation_method="mean"
+        seed=42
+        metric=MAE ()
+      )
+    )
+ + + +???- example "Bagging" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      [HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=False
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      )]
+    )
+ + + +???- example "Exponentially Weighted Average" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      [LinearRegression (
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.01
+          )
+        )
+        loss=Squared ()
+        l2=0.
+        l1=0.
+        intercept_init=0.
+        intercept_lr=Constant (
+          learning_rate=0.01
+        )
+        clip_gradient=1e+12
+        initializer=Zeros ()
+      ), HoeffdingAdaptiveTreeRegressor (
+        grace_period=200
+        max_depth=inf
+        delta=1e-07
+        tau=0.05
+        leaf_prediction="adaptive"
+        leaf_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        model_selector_decay=0.95
+        nominal_attributes=None
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        min_samples_split=5
+        bootstrap_sampling=True
+        drift_window_threshold=300
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        switch_significance=0.05
+        binary_split=False
+        max_size=500.
+        memory_estimate_period=1000000
+        stop_mem_management=False
+        remove_poor_attrs=False
+        merit_preprune=True
+        seed=None
+      ), KNNRegressor (
+        n_neighbors=5
+        engine=SWINN (
+          graph_k=20
+          dist_func=FunctionWrapper (
+            distance_function=functools.partial(, p=2)
+          )
+          maxlen=1000
+          warm_up=500
+          max_candidates=50
+          delta=0.0001
+          prune_prob=0.
+          n_iters=10
+          seed=None
+        )
+        aggregation_method="mean"
+      ), AMRules (
+        n_min=200
+        delta=1e-07
+        tau=0.05
+        pred_type="adaptive"
+        pred_model=LinearRegression (
+          optimizer=SGD (
+            lr=Constant (
+              learning_rate=0.01
+            )
+          )
+          loss=Squared ()
+          l2=0.
+          l1=0.
+          intercept_init=0.
+          intercept_lr=Constant (
+            learning_rate=0.01
+          )
+          clip_gradient=1e+12
+          initializer=Zeros ()
+        )
+        splitter=TEBSTSplitter (
+          digits=1
+        )
+        drift_detector=ADWIN (
+          delta=0.002
+          clock=32
+          max_buckets=5
+          min_window_length=5
+          grace_period=10
+        )
+        fading_factor=0.99
+        anomaly_threshold=-0.75
+        m_min=30
+        ordered_rule_set=True
+        min_samples_split=5
+      )]
+    )
+ + + +???- example "River MLP" + + 
Pipeline (
+      StandardScaler (
+        with_std=True
+      ),
+      MLPRegressor (
+        hidden_dims=(5,)
+        activations=(, , )
+        loss=Squared ()
+        optimizer=SGD (
+          lr=Constant (
+            learning_rate=0.001
+          )
+        )
+        seed=42
+      )
+    )
+ + + +???- example "[baseline] Mean predictor" + + 
StatisticRegressor (
+      statistic=Mean ()
+    )
+ + + +## Environment + +
Python implementation: CPython
+Python version       : 3.10.13
+IPython version      : 8.16.1
+
+river       : 0.19.0
+numpy       : 1.25.2
+scikit-learn: 1.3.1
+pandas      : 2.1.1
+scipy       : 1.11.3
+
+Compiler    : Clang 14.0.6 
+OS          : Darwin
+Release     : 22.6.0
+Machine     : arm64
+Processor   : arm
+CPU cores   : 8
+Architecture: 64bit
+
+ diff --git a/docs/benchmarks/Regression/regression.csv b/docs/benchmarks/Regression/regression.csv new file mode 100644 index 0000000000..6d0b88b0a6 --- /dev/null +++ b/docs/benchmarks/Regression/regression.csv @@ -0,0 +1,1769 @@ +step,track,model,dataset,MAE,RMSE,R2,Memory in Mb,Time in s +11,Regression,Linear Regression,ChickWeights,30.432219699626994,31.267456151778337,-1257.4692714745631,0.004130363464355469,0.000963 +22,Regression,Linear Regression,ChickWeights,20.75760844034268,23.632210645041404,-590.4769976066937,0.004130363464355469,0.002374 +33,Regression,Linear Regression,ChickWeights,14.555240079240876,19.349294933329695,-259.0232069515881,0.004130363464355469,0.004113 +44,Regression,Linear Regression,ChickWeights,11.143633659136759,16.767243978820222,-220.34524244378574,0.004130363464355469,0.006175 +55,Regression,Linear Regression,ChickWeights,10.841164000616114,17.714902804136145,-60.2608923989398,0.004130363464355469,0.008581 +66,Regression,Linear Regression,ChickWeights,10.32598508406065,16.527353468164844,-21.985729074745297,0.004130363464355469,0.01133 +77,Regression,Linear Regression,ChickWeights,9.718401993814265,15.521096390186141,-12.587024696233003,0.004130363464355469,0.014424 +88,Regression,Linear Regression,ChickWeights,8.767755200283737,14.552446235427842,-9.829280875288257,0.004130363464355469,0.017858 +99,Regression,Linear Regression,ChickWeights,7.977130626229444,13.740429605807138,-7.074807888709797,0.004130363464355469,0.021634999999999998 +110,Regression,Linear Regression,ChickWeights,7.506893871110683,13.098273311725844,-4.124041411671393,0.004130363464355469,0.025751999999999997 +121,Regression,Linear Regression,ChickWeights,7.252833276832352,12.607637144454216,-2.6562249812820733,0.004130363464355469,0.030208999999999996 +132,Regression,Linear Regression,ChickWeights,6.896359231575217,12.121970224209305,-1.7624336939368233,0.004130363464355469,0.03500399999999999 +143,Regression,Linear Regression,ChickWeights,6.581914741629191,11.688367143429069,-1.080274127204615,0.004130363464355469,0.040138999999999994 +154,Regression,Linear Regression,ChickWeights,6.347682986169337,11.314945909537578,-0.6567859420078188,0.004130363464355469,0.045612999999999994 +165,Regression,Linear Regression,ChickWeights,6.47676439389405,11.21748999353191,-0.30899590760610374,0.004130363464355469,0.051426999999999994 +176,Regression,Linear Regression,ChickWeights,6.552290709218319,11.100632967129414,-0.03357189497448321,0.004130363464355469,0.05758099999999999 +187,Regression,Linear Regression,ChickWeights,6.503097179992549,10.915357728148932,0.18175912588502985,0.004130363464355469,0.06407299999999999 +198,Regression,Linear Regression,ChickWeights,6.420443618722296,10.727647067877953,0.3713230272376924,0.004130363464355469,0.070904 +209,Regression,Linear Regression,ChickWeights,6.54715053669462,10.814712106795348,0.47329133398018763,0.004130363464355469,0.07807199999999999 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Regression,ChickWeights,12.40252640363099,20.24468752454989,0.7068188127948265,0.004210472106933594,0.8529599999999999 +385,Regression,Linear Regression,ChickWeights,12.78041264925886,20.84297745742841,0.7250508110390363,0.004210472106933594,0.972583 +396,Regression,Linear Regression,ChickWeights,12.908163646252072,20.82655299121286,0.7440434321899679,0.004210472106933594,1.093238 +407,Regression,Linear Regression,ChickWeights,13.78624220521945,22.297725224665914,0.7272822586077066,0.004210472106933594,1.2149269999999999 +418,Regression,Linear Regression,ChickWeights,14.56231380927385,23.732773749874315,0.7099846963904786,0.004210472106933594,1.3375199999999998 +429,Regression,Linear Regression,ChickWeights,15.109717404902197,24.642068489898374,0.7221580232945248,0.004210472106933594,1.4604629999999998 +440,Regression,Linear Regression,ChickWeights,15.287005413554732,24.721522560240437,0.7401560140604169,0.004210472106933594,1.583729 +451,Regression,Linear Regression,ChickWeights,15.806865735774078,25.331119330890413,0.7387809061287051,0.004210472106933594,1.707315 +462,Regression,Linear Regression,ChickWeights,16.912347710111163,27.450327347193873,0.7118740092210123,0.004210472106933594,1.831218 +473,Regression,Linear Regression,ChickWeights,17.68786801080465,28.748046923071918,0.7209603573249957,0.004210472106933594,1.955435 +484,Regression,Linear Regression,ChickWeights,18.02230431978895,29.040370094251127,0.7308604085348502,0.004210472106933594,2.079964 +495,Regression,Linear Regression,ChickWeights,18.476434617297652,29.565622398548214,0.7375811559076941,0.004210472106933594,2.204806 +506,Regression,Linear Regression,ChickWeights,19.368862660258834,31.016595939650866,0.7195863076124669,0.004210472106933594,2.32996 +517,Regression,Linear Regression,ChickWeights,20.093492725340727,32.00802507821089,0.7181912437784894,0.004210472106933594,2.455434 +528,Regression,Linear Regression,ChickWeights,20.883641447975457,33.20140091570763,0.727385103943677,0.004210472106933594,2.581219 +539,Regression,Linear Regression,ChickWeights,21.055940734584826,33.19901872731025,0.7386798629639011,0.004210472106933594,2.707313 +550,Regression,Linear Regression,ChickWeights,22.046658398851132,34.818142407426606,0.7214274205964286,0.004210472106933594,2.833717 +561,Regression,Linear Regression,ChickWeights,22.750150790689958,35.737018888500465,0.7193638350430389,0.004210472106933594,2.960429 +572,Regression,Linear Regression,ChickWeights,23.60149518688988,36.92142939550449,0.722919218201958,0.004210472106933594,3.087448 +578,Regression,Linear Regression,ChickWeights,23.758656678867762,37.03767126301035,0.7279537206511313,0.004210472106933594,3.2147080000000003 +20,Regression,Linear Regression,TrumpApproval,20.715375599336316,24.276120972986362,-1381.3340079163324,0.004813194274902344,0.003774 +40,Regression,Linear Regression,TrumpApproval,12.956746822999646,17.85530816845139,-127.17403450091604,0.004813194274902344,0.008234 +60,Regression,Linear Regression,TrumpApproval,10.540337295823328,15.264267507077205,-125.28803290438402,0.004813194274902344,0.013346 +80,Regression,Linear Regression,TrumpApproval,8.92648259034571,13.436420463778147,-97.15695382305036,0.004813194274902344,0.019104 +100,Regression,Linear Regression,TrumpApproval,7.5495393499287236,12.076339439187347,-48.75014684916543,0.004813194274902344,0.025552 +120,Regression,Linear Regression,TrumpApproval,6.5712666531069654,11.058195411086313,-34.388513465790076,0.004813194274902344,0.032651 +140,Regression,Linear Regression,TrumpApproval,5.868178209177549,10.265658199354172,-30.515672886293014,0.004813194274902344,0.040397 +160,Regression,Linear Regression,TrumpApproval,5.226493262391851,9.609365926739029,-23.352843972650145,0.004813194274902344,0.048786 +180,Regression,Linear Regression,TrumpApproval,4.806672346419344,9.079121174210673,-18.092824435696784,0.004813194274902344,0.057857000000000006 +200,Regression,Linear Regression,TrumpApproval,4.400421129740624,8.617551092451054,-16.252012396913173,0.004813194274902344,0.06766 +220,Regression,Linear Regression,TrumpApproval,4.083414123099576,8.223437931584808,-15.946617088642817,0.004813194274902344,0.07815899999999999 +240,Regression,Linear Regression,TrumpApproval,3.8235343884157706,7.87966547036827,-14.67643164713968,0.004813194274902344,0.08935399999999999 +260,Regression,Linear Regression,TrumpApproval,3.5733429968046226,7.572887494545769,-13.674649599158814,0.004973411560058594,0.10124299999999999 +280,Regression,Linear Regression,TrumpApproval,3.399764262602937,7.307305033384193,-13.305426773388605,0.004973411560058594,0.11382999999999999 +300,Regression,Linear Regression,TrumpApproval,3.2435269592384794,7.069212717011484,-12.166742621467943,0.004973411560058594,0.21774699999999997 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+600,Regression,Linear Regression,TrumpApproval,1.950641059884997,5.038426259116397,-6.58092298894084,0.004973411560058594,3.3455269999999993 +620,Regression,Linear Regression,TrumpApproval,1.9139787950639096,4.959092402037442,-6.2321238970256,0.004973411560058594,3.560656999999999 +640,Regression,Linear Regression,TrumpApproval,1.8644177203659011,4.8815607080230725,-5.87676544995844,0.004973411560058594,3.776474999999999 +660,Regression,Linear Regression,TrumpApproval,1.8242147858959745,4.808190620674182,-5.62363098938706,0.004973411560058594,3.9929739999999994 +680,Regression,Linear Regression,TrumpApproval,1.7745110240786572,4.737042423784333,-5.530654039159668,0.004973411560058594,4.2677439999999995 +700,Regression,Linear Regression,TrumpApproval,1.73663030353679,4.669916427921507,-5.51357997146441,0.004973411560058594,4.544746 +720,Regression,Linear Regression,TrumpApproval,1.692679144669073,4.604703216269991,-5.472066122280332,0.004973411560058594,4.823995 +740,Regression,Linear Regression,TrumpApproval,1.6517738073879173,4.542197076044804,-5.293761488897717,0.004973411560058594,5.105493 +760,Regression,Linear Regression,TrumpApproval,1.6176019850850996,4.482676429973872,-5.196305855003581,0.004973411560058594,5.389141 +780,Regression,Linear Regression,TrumpApproval,1.5865007641193463,4.425455260516019,-5.066178353973196,0.004973411560058594,5.673523 +800,Regression,Linear Regression,TrumpApproval,1.5595678531598225,4.370690133148669,-4.970481738375755,0.004973411560058594,5.9679660000000005 +820,Regression,Linear Regression,TrumpApproval,1.5359483450738913,4.318357063182573,-4.892367885242343,0.004973411560058594,6.2645230000000005 +840,Regression,Linear Regression,TrumpApproval,1.5094802852221159,4.267276732370252,-4.807214337073276,0.004973411560058594,6.563154000000001 +860,Regression,Linear Regression,TrumpApproval,1.4815681878661566,4.217864876321593,-4.663732871777943,0.004973411560058594,6.863868000000001 +880,Regression,Linear Regression,TrumpApproval,1.4526831778170481,4.169823323470254,-4.507942651608292,0.004973411560058594,7.276079000000001 +900,Regression,Linear Regression,TrumpApproval,1.425504815240136,4.123417367951589,-4.4087270270070205,0.004973411560058594,7.880966000000001 +920,Regression,Linear Regression,TrumpApproval,1.401135420694234,4.078757160785335,-4.379153600942964,0.004973411560058594,8.488067000000001 +940,Regression,Linear Regression,TrumpApproval,1.3798894262867005,4.035722473386745,-4.310917809364017,0.004973411560058594,9.096757 +960,Regression,Linear Regression,TrumpApproval,1.3578157698337674,3.993911445090692,-4.255827563021541,0.004973411560058594,9.706153 +980,Regression,Linear Regression,TrumpApproval,1.3349554985290681,3.953168904153961,-4.2491478554421755,0.004973411560058594,10.316233 +1000,Regression,Linear Regression,TrumpApproval,1.3157385915327033,3.9139344489617316,-4.232086679588724,0.004973411560058594,10.926998000000001 +1001,Regression,Linear Regression,TrumpApproval,1.3145482000473083,3.9119809164882438,-4.230354806784151,0.004973411560058594,11.537908000000002 +11,Regression,Linear Regression with l1 regularization,ChickWeights,30.519429760441792,31.341724959881887,-1263.4547929656035,0.004361152648925781,0.001889 +22,Regression,Linear Regression with l1 regularization,ChickWeights,20.93274945698016,23.730069634788823,-595.3856524245364,0.004361152648925781,0.005264 +33,Regression,Linear Regression with l1 regularization,ChickWeights,14.671976905269485,19.432784890847977,-261.2719879213097,0.004361152648925781,0.045495999999999995 +44,Regression,Linear Regression with l1 regularization,ChickWeights,11.206218788565426,16.83704009498573,-222.1918420065333,0.004361152648925781,0.086209 +55,Regression,Linear Regression with l1 regularization,ChickWeights,10.7873677371092,17.69725945175844,-60.138926246201024,0.004361152648925781,0.127302 +66,Regression,Linear Regression with l1 regularization,ChickWeights,10.358479420064798,16.54420972880916,-22.032639310332936,0.004361152648925781,0.168766 +77,Regression,Linear Regression with l1 regularization,ChickWeights,9.753598876381378,15.536347024393615,-12.613738343052718,0.004361152648925781,0.21060099999999998 +88,Regression,Linear Regression with l1 regularization,ChickWeights,8.774706713989955,14.560860647391403,-9.841807755380493,0.004361152648925781,0.252804 +99,Regression,Linear Regression with l1 regularization,ChickWeights,7.976543403311107,13.74760854733656,-7.083247758311314,0.004361152648925781,0.295373 +110,Regression,Linear Regression with l1 regularization,ChickWeights,7.5284067705618165,13.110785837893241,-4.133835882207287,0.004361152648925781,0.338308 +121,Regression,Linear Regression with l1 regularization,ChickWeights,7.271666718491515,12.6229442838289,-2.665108536473531,0.004361152648925781,0.38161 +132,Regression,Linear Regression with l1 regularization,ChickWeights,6.91845605456336,12.134014714075713,-1.7679259750984961,0.004361152648925781,0.425278 +143,Regression,Linear Regression with l1 regularization,ChickWeights,6.610383809165891,11.700505099139125,-1.084596952740374,0.004361152648925781,0.469311 +154,Regression,Linear Regression with l1 regularization,ChickWeights,6.3485668448406924,11.31852948419668,-0.6578355548574832,0.004361152648925781,0.51371 +165,Regression,Linear Regression with l1 regularization,ChickWeights,6.473998962981321,11.222073845492618,-0.3100659276219817,0.004361152648925781,0.558476 +176,Regression,Linear Regression with l1 regularization,ChickWeights,6.543521830550948,11.096254270292283,-0.032756661210885385,0.004361152648925781,0.603607 +187,Regression,Linear Regression with l1 regularization,ChickWeights,6.493894355635018,10.908553918682982,0.18277886707380187,0.004361152648925781,0.649102 +198,Regression,Linear Regression with l1 regularization,ChickWeights,6.432058292739276,10.739983052449066,0.36987633376979445,0.004361152648925781,0.6949599999999999 +209,Regression,Linear Regression with l1 regularization,ChickWeights,6.530905166315106,10.805387069826965,0.47419925648761396,0.004361152648925781,0.74118 +220,Regression,Linear Regression with l1 regularization,ChickWeights,7.049069109840064,11.46222613381468,0.4819945238144716,0.004361152648925781,0.7877609999999999 +231,Regression,Linear Regression with l1 regularization,ChickWeights,7.185364391622807,11.520615160379734,0.5523912707049028,0.004361152648925781,0.834703 +242,Regression,Linear Regression with l1 regularization,ChickWeights,7.384443509591489,11.759466507882767,0.6247424700583044,0.004361152648925781,0.882006 +253,Regression,Linear Regression with l1 regularization,ChickWeights,7.370825288025247,11.706644644448966,0.6770052015955412,0.004361152648925781,0.929669 +264,Regression,Linear Regression with l1 regularization,ChickWeights,7.997212264968545,12.688148058774217,0.6533323093865229,0.004441261291503906,0.977694 +275,Regression,Linear Regression with l1 regularization,ChickWeights,8.45564901988644,13.583827871673952,0.6503637760490552,0.004441261291503906,1.026082 +286,Regression,Linear Regression with l1 regularization,ChickWeights,8.687395226209604,13.953064893865328,0.6804867014487179,0.004441261291503906,1.074833 +297,Regression,Linear Regression with l1 regularization,ChickWeights,8.660171229881424,13.910099225377925,0.7245931722706233,0.004441261291503906,1.1239519999999998 +308,Regression,Linear Regression with l1 regularization,ChickWeights,9.16625719191718,14.612234985526298,0.7293304097140514,0.004441261291503906,1.1734349999999998 +319,Regression,Linear Regression with l1 regularization,ChickWeights,10.250950211093048,17.0718306278326,0.664728869016383,0.004441261291503906,1.2232849999999997 +330,Regression,Linear Regression with l1 regularization,ChickWeights,10.679670450254022,17.65395670255975,0.6931807697512926,0.004441261291503906,1.3407639999999996 +341,Regression,Linear Regression with l1 regularization,ChickWeights,10.873729384474112,17.73873175202587,0.7226130148559202,0.004441261291503906,1.6983979999999996 +352,Regression,Linear Regression with l1 regularization,ChickWeights,11.018541118771262,17.831871437600412,0.745188204067577,0.004441261291503906,2.0571989999999998 +363,Regression,Linear Regression with l1 regularization,ChickWeights,11.899574150448762,19.190338217602402,0.7134289333715201,0.004441261291503906,2.417118 +374,Regression,Linear Regression with l1 regularization,ChickWeights,12.408282768986876,20.289550367060546,0.7055179762102581,0.004441261291503906,2.778154 +385,Regression,Linear Regression with l1 regularization,ChickWeights,12.788104615245373,20.897902847676004,0.7235998101431352,0.004441261291503906,3.1404569999999996 +396,Regression,Linear Regression with l1 regularization,ChickWeights,12.908222014164421,20.86950621812891,0.7429865604297317,0.004441261291503906,3.5031369999999997 +407,Regression,Linear Regression with l1 regularization,ChickWeights,13.785647364051679,22.333927174809716,0.726395986248676,0.004441261291503906,3.8661689999999997 +418,Regression,Linear Regression with l1 regularization,ChickWeights,14.562464823979756,23.771461386342615,0.709038397249883,0.004441261291503906,4.229544 +429,Regression,Linear Regression with l1 regularization,ChickWeights,15.115712915071189,24.692790084324347,0.7210130632693055,0.004441261291503906,4.59326 +440,Regression,Linear Regression with l1 regularization,ChickWeights,15.290646451171162,24.766775019882367,0.7392038606135755,0.004441261291503906,4.9573149999999995 +451,Regression,Linear Regression with l1 regularization,ChickWeights,15.806610158983217,25.370563596297366,0.7379667599208486,0.004441261291503906,5.321708999999999 +462,Regression,Linear Regression with l1 regularization,ChickWeights,16.91167446753811,27.489289014578034,0.711055524573946,0.004441261291503906,5.686440999999999 +473,Regression,Linear Regression with l1 regularization,ChickWeights,17.69453441784174,28.803034656505247,0.7198918720890418,0.004441261291503906,6.051508999999999 +484,Regression,Linear Regression with l1 regularization,ChickWeights,18.025914293879836,29.08166628667707,0.7300944167213836,0.004441261291503906,6.416912999999999 +495,Regression,Linear Regression with l1 regularization,ChickWeights,18.47687089345869,29.604201733284565,0.7368958634072684,0.004441261291503906,6.782651999999999 +506,Regression,Linear Regression with l1 regularization,ChickWeights,19.37032815671457,31.058772984483273,0.7188231637639817,0.004441261291503906,7.148725999999999 +517,Regression,Linear Regression with l1 regularization,ChickWeights,20.096649322747314,32.051830787895724,0.717419357352562,0.004441261291503906,7.515149999999999 +528,Regression,Linear Regression with l1 regularization,ChickWeights,20.88685610593147,33.24610520798377,0.7266504806846955,0.004441261291503906,7.882236999999999 +539,Regression,Linear Regression with l1 regularization,ChickWeights,21.052957054073875,33.24035912136826,0.7380286507287471,0.004441261291503906,8.25334 +550,Regression,Linear Regression with l1 regularization,ChickWeights,22.046178761536364,34.86098206113683,0.7207414968982613,0.004441261291503906,8.62558 +561,Regression,Linear Regression with l1 regularization,ChickWeights,22.751953045975853,35.78242297978339,0.7186502822700677,0.004441261291503906,8.998921 +572,Regression,Linear Regression with l1 regularization,ChickWeights,23.603432973098663,36.96472548228527,0.7222689970347711,0.004441261291503906,9.373355 +578,Regression,Linear Regression with l1 regularization,ChickWeights,23.757667537133976,37.078025255419426,0.7273605875689941,0.004441261291503906,9.748496 +20,Regression,Linear Regression with l1 regularization,TrumpApproval,20.96628233331211,24.387937149248955,-1394.0974368768457,0.005043983459472656,0.003367 +40,Regression,Linear Regression with l1 regularization,TrumpApproval,12.95809265443779,17.886947111698607,-127.62867621055315,0.005043983459472656,0.060679000000000004 +60,Regression,Linear Regression with l1 regularization,TrumpApproval,10.43403375286247,15.198987179765494,-124.2101566950438,0.005043983459472656,0.118829 +80,Regression,Linear Regression with l1 regularization,TrumpApproval,8.76952679896777,13.348146279436204,-95.87145335263979,0.005043983459472656,0.177742 +100,Regression,Linear Regression with l1 regularization,TrumpApproval,7.318348711169017,11.969856517585775,-47.87667264392048,0.005043983459472656,0.237441 +120,Regression,Linear Regression with l1 regularization,TrumpApproval,6.2853039116310185,10.94189036106609,-33.648027646243705,0.005043983459472656,0.297866 +140,Regression,Linear Regression with l1 regularization,TrumpApproval,5.5208355911538485,10.138862242229527,-29.74195117722151,0.005043983459472656,0.35901700000000003 +160,Regression,Linear Regression with l1 regularization,TrumpApproval,4.9080595636493145,9.487746704217276,-22.740310036230184,0.005043983459472656,0.420891 +180,Regression,Linear Regression with l1 regularization,TrumpApproval,4.437342628193194,8.948953859899,-17.549281500204398,0.005043983459472656,0.483487 +200,Regression,Linear Regression with l1 regularization,TrumpApproval,4.020740144728086,8.490404067975657,-15.746680942149272,0.005043983459472656,0.546844 +220,Regression,Linear Regression with l1 regularization,TrumpApproval,3.702540763677515,8.09713522450445,-15.430052960036054,0.005043983459472656,0.610978 +240,Regression,Linear Regression with l1 regularization,TrumpApproval,3.449057445346116,7.7551931287900455,-14.185073150160106,0.005043983459472656,0.675884 +260,Regression,Linear Regression with l1 regularization,TrumpApproval,3.201640426877581,7.451485247160068,-13.20791735379428,0.005204200744628906,0.741568 +280,Regression,Linear Regression with l1 regularization,TrumpApproval,2.9861522146348123,7.180696949733205,-12.814002869999907,0.005204200744628906,0.808037 +300,Regression,Linear Regression with l1 regularization,TrumpApproval,2.8260389726991693,6.939608203297966,-11.688379207589731,0.005204200744628906,0.926618 +320,Regression,Linear Regression with l1 regularization,TrumpApproval,2.694730270614988,6.722171113188908,-11.495217468089896,0.005204200744628906,1.2155880000000001 +340,Regression,Linear Regression with l1 regularization,TrumpApproval,2.572442774284147,6.524300196624447,-11.438471282384336,0.005204200744628906,1.5070640000000002 +360,Regression,Linear Regression with l1 regularization,TrumpApproval,2.4832798669216825,6.3452949037256134,-10.86190793294698,0.005204200744628906,1.8008990000000002 +380,Regression,Linear Regression with l1 regularization,TrumpApproval,2.371542642654472,6.177015076243767,-10.629949316856383,0.005204200744628906,2.0970690000000003 +400,Regression,Linear Regression with l1 regularization,TrumpApproval,2.263251524870982,6.020874949010495,-10.361708857360679,0.005204200744628906,2.4016 +420,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1669018257777095,5.8760767656227735,-10.179937857653263,0.005204200744628906,2.706944 +440,Regression,Linear Regression with l1 regularization,TrumpApproval,2.1025509089011916,5.743886676480224,-9.489284487381322,0.005204200744628906,3.013076 +460,Regression,Linear Regression with l1 regularization,TrumpApproval,2.0360304025506277,5.61908468135586,-8.519416508014515,0.005204200744628906,3.319991 +480,Regression,Linear Regression with l1 regularization,TrumpApproval,1.9657178079674962,5.50138701729293,-7.914879120336785,0.005204200744628906,3.627685 +500,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8948913466896102,5.390446783732167,-7.379388774419297,0.005204200744628906,3.9361800000000002 +520,Regression,Linear Regression with l1 regularization,TrumpApproval,1.8304411336225566,5.286008256480869,-7.071904701569496,0.005204200744628906,4.245555 +540,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7733791235095338,5.187623645241403,-6.74573862520947,0.005204200744628906,4.555757000000001 +560,Regression,Linear Regression with l1 regularization,TrumpApproval,1.7328732375480083,5.096231477200102,-6.653340289034931,0.005204200744628906,4.866786000000001 +580,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6922671720641331,5.009032279128942,-6.5765398617523605,0.005204200744628906,5.1996410000000015 +600,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6600221636451293,4.9270067527590165,-6.249341959517198,0.005204200744628906,5.545038000000002 +620,Regression,Linear Regression with l1 regularization,TrumpApproval,1.6169171465584515,4.847662648980224,-5.910766757861972,0.005204200744628906,5.892753000000002 +640,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5787668849144931,4.771995268006674,-5.5715350899413965,0.005204200744628906,6.242777000000002 +660,Regression,Linear Regression with l1 regularization,TrumpApproval,1.535700232104731,4.69925054984221,-5.326885534626132,0.005204200744628906,6.599796000000002 +680,Regression,Linear Regression with l1 regularization,TrumpApproval,1.5003699975160405,4.630081239411466,-5.239062722957792,0.005204200744628906,6.957619000000002 +700,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4782734303433982,4.565354365023557,-5.225160013321354,0.005204200744628906,7.316272000000002 +720,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4563696019956498,4.503833132228122,-5.19161922746511,0.005204200744628906,7.675697000000002 +740,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4392280778003554,4.445645440595998,-5.02903742417401,0.005204200744628906,8.035893000000002 +760,Regression,Linear Regression with l1 regularization,TrumpApproval,1.4073407178561264,4.387021097703184,-4.9346827726614455,0.005204200744628906,8.396854000000001 +780,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3782504190107006,4.330701361336262,-4.809192109617374,0.005204200744628906,8.758623000000002 +800,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3571814777264213,4.277370073659861,-4.718248073230613,0.005204200744628906,9.121240000000002 +820,Regression,Linear Regression with l1 regularization,TrumpApproval,1.3328025450945626,4.2253925636381995,-4.641399853721709,0.005204200744628906,9.484674000000002 +840,Regression,Linear Regression with l1 regularization,TrumpApproval,1.311715211433691,4.175582527272098,-4.560327645533724,0.005204200744628906,9.848922000000002 +860,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2897432923325247,4.127236925138345,-4.422957957045758,0.005204200744628906,10.213983000000002 +880,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2672991203860131,4.080383024210964,-4.274192362897992,0.005204200744628906,10.579853000000002 +900,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2421842209255052,4.03488734209176,-4.178968845613636,0.005204200744628906,10.965988000000001 +920,Regression,Linear Regression with l1 regularization,TrumpApproval,1.220808929344255,3.991045752926761,-4.15028972045945,0.005204200744628906,11.354575 +940,Regression,Linear Regression with l1 regularization,TrumpApproval,1.2057181063421543,3.9494511154557617,-4.0862825167589865,0.005204200744628906,11.745471 +960,Regression,Linear Regression with l1 regularization,TrumpApproval,1.188437369603739,3.9086583836793856,-4.0338431039290805,0.005204200744628906,12.138778 +980,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1710173649101312,3.8690392813429555,-4.028105053503917,0.005204200744628906,12.545048 +1000,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1544521877618488,3.8306020851942315,-4.0116636492940465,0.005204200744628906,12.952187 +1001,Regression,Linear Regression with l1 regularization,TrumpApproval,1.1537672749321948,3.8287168981917103,-4.010074752320696,0.005204200744628906,13.359495 +11,Regression,Linear Regression with l2 regularization,ChickWeights,30.6062254572366,31.39938120772091,-1268.1112549740517,0.004153251647949219,0.000711 +22,Regression,Linear Regression with l2 regularization,ChickWeights,21.412737763681047,23.97862157826266,-607.9443275975191,0.004153251647949219,0.001889 +33,Regression,Linear Regression with l2 regularization,ChickWeights,15.119104680903606,19.655410372524667,-267.315679768846,0.004153251647949219,0.003406 +44,Regression,Linear Regression with l2 regularization,ChickWeights,11.691588950452092,17.042779535378298,-227.6797328948204,0.004153251647949219,0.00525 +55,Regression,Linear Regression with l2 regularization,ChickWeights,11.128477598777668,17.570968714531574,-59.26944361385635,0.004153251647949219,0.0074210000000000005 +66,Regression,Linear Regression with l2 regularization,ChickWeights,10.755656716101159,16.483156797846284,-21.862958739409084,0.004153251647949219,0.009919 +77,Regression,Linear Regression with l2 regularization,ChickWeights,10.454334080303978,15.644372833730271,-12.803711937026078,0.004153251647949219,0.012745000000000001 +88,Regression,Linear Regression with l2 regularization,ChickWeights,9.893519322025275,14.807378680481822,-10.212022929829027,0.004153251647949219,0.015896 +99,Regression,Linear Regression with l2 regularization,ChickWeights,9.219705201317108,14.044546137802199,-7.436202462041329,0.004153251647949219,0.019372 +110,Regression,Linear Regression with l2 regularization,ChickWeights,8.828389618716818,13.455080798744472,-4.4070097733575375,0.004153251647949219,0.023173 +121,Regression,Linear Regression with l2 regularization,ChickWeights,8.61456960864212,13.037583740326507,-2.9098467157738415,0.004153251647949219,0.027299 +132,Regression,Linear Regression with l2 regularization,ChickWeights,8.52880743945525,12.690080989153241,-2.0274307958032884,0.004153251647949219,0.031782 +143,Regression,Linear Regression with l2 regularization,ChickWeights,8.39143583855712,12.359614263508039,-1.3260696348061907,0.004153251647949219,0.036638 +154,Regression,Linear Regression with l2 regularization,ChickWeights,8.12180315101294,12.009375103170282,-0.866389387173786,0.004153251647949219,0.041874999999999996 +165,Regression,Linear Regression with l2 regularization,ChickWeights,8.136940986261356,11.920551719153746,-0.47822185831879493,0.004153251647949219,0.04749199999999999 +176,Regression,Linear Regression with l2 regularization,ChickWeights,8.284290032332207,11.93362687305613,-0.19451089204457617,0.004153251647949219,0.05348299999999999 +187,Regression,Linear Regression with l2 regularization,ChickWeights,8.390309464431912,11.903488345267945,0.026908395403585694,0.004153251647949219,0.05984699999999999 +198,Regression,Linear Regression with l2 regularization,ChickWeights,8.350219958465262,11.791481226840993,0.2404518209934976,0.004153251647949219,0.06659 +209,Regression,Linear Regression with l2 regularization,ChickWeights,8.499019855105985,11.958125495095471,0.3560283448388185,0.004153251647949219,0.073713 +220,Regression,Linear Regression with l2 regularization,ChickWeights,8.90272187978296,12.527163169679886,0.3812690011074207,0.004153251647949219,0.081218 +231,Regression,Linear Regression with l2 regularization,ChickWeights,9.171291167504231,12.73748746029564,0.45283948771259386,0.004153251647949219,0.08971 +242,Regression,Linear Regression with l2 regularization,ChickWeights,9.37629466014084,13.047657656056804,0.538024139715424,0.004153251647949219,0.099295 +253,Regression,Linear Regression with l2 regularization,ChickWeights,9.440817816219349,13.0964165059942,0.5957634168273553,0.004153251647949219,0.110134 +264,Regression,Linear Regression with l2 regularization,ChickWeights,9.906487060964153,13.855497684527965,0.5866088718530376,0.004233360290527344,0.12224199999999999 +275,Regression,Linear Regression with l2 regularization,ChickWeights,10.387009537918406,14.786939232799543,0.5856869069436603,0.004233360290527344,0.13552899999999998 +286,Regression,Linear Regression with l2 regularization,ChickWeights,10.701469010841246,15.270898285463774,0.6172820078624095,0.004233360290527344,0.14989199999999997 +297,Regression,Linear Regression with l2 regularization,ChickWeights,10.689852199892528,15.284847538688991,0.6674656839655615,0.004233360290527344,0.17720199999999997 +308,Regression,Linear Regression with l2 regularization,ChickWeights,11.168487287417783,16.008183102465477,0.6751444757196481,0.004233360290527344,0.20585199999999998 +319,Regression,Linear Regression with l2 regularization,ChickWeights,12.085867087734242,18.170753499240714,0.6201764868699093,0.004233360290527344,0.23488799999999999 +330,Regression,Linear Regression with l2 regularization,ChickWeights,12.672501856506585,19.05837058612535,0.6424226539377311,0.004233360290527344,0.26428199999999996 +341,Regression,Linear Regression with l2 regularization,ChickWeights,12.822446828447035,19.13937756684808,0.6770787925994421,0.004233360290527344,0.29402799999999996 +352,Regression,Linear Regression with l2 regularization,ChickWeights,13.055746883990931,19.312136445778254,0.7011272480618885,0.004233360290527344,0.32412699999999994 +363,Regression,Linear Regression with l2 regularization,ChickWeights,13.79008745873622,20.396105048894267,0.6762859401979866,0.004233360290527344,0.35457799999999995 +374,Regression,Linear Regression with l2 regularization,ChickWeights,14.293199062265238,21.539399675842862,0.6681199603719434,0.004233360290527344,0.38537999999999994 +385,Regression,Linear Regression with l2 regularization,ChickWeights,14.740320816630273,22.311026164960477,0.6849554171717112,0.004233360290527344,0.42616299999999996 +396,Regression,Linear Regression with l2 regularization,ChickWeights,14.862968645899144,22.294096988116678,0.7067005430463744,0.004233360290527344,0.467315 +407,Regression,Linear Regression with l2 regularization,ChickWeights,15.699705023283963,23.67314903355933,0.6925996644733732,0.004233360290527344,0.508826 +418,Regression,Linear Regression with l2 regularization,ChickWeights,16.38213993729544,25.048095107979137,0.6769473375050636,0.004233360290527344,0.55069 +429,Regression,Linear Regression with l2 regularization,ChickWeights,16.967894830794286,26.153201890569886,0.6870368010887093,0.004233360290527344,0.592905 +440,Regression,Linear Regression with l2 regularization,ChickWeights,17.10728249235129,26.204092785638924,0.7080553660644732,0.004233360290527344,0.6354690000000001 +451,Regression,Linear Regression with l2 regularization,ChickWeights,17.603016925007317,26.772391386711114,0.7082099437723521,0.004233360290527344,0.6783830000000001 +462,Regression,Linear Regression with l2 regularization,ChickWeights,18.614531201761594,28.786744962703725,0.6831362914484524,0.004233360290527344,0.7216460000000001 +473,Regression,Linear Regression with l2 regularization,ChickWeights,19.488293352005442,30.38515335394973,0.6882746780375071,0.004233360290527344,0.7652570000000001 +484,Regression,Linear Regression with l2 regularization,ChickWeights,19.755002868307955,30.52390276571354,0.7026599444855313,0.004233360290527344,0.8092140000000001 +495,Regression,Linear Regression with l2 regularization,ChickWeights,20.22217092676305,31.08727194033441,0.7098743070293987,0.004233360290527344,0.8535240000000001 +506,Regression,Linear Regression with l2 regularization,ChickWeights,21.03670858216615,32.44431034253017,0.6931769059461363,0.004233360290527344,0.898204 +517,Regression,Linear Regression with l2 regularization,ChickWeights,21.78200415465676,33.496021791915204,0.6913806254796178,0.004233360290527344,0.943264 +528,Regression,Linear Regression with l2 regularization,ChickWeights,22.56258004106143,34.768391171729405,0.7010449079513538,0.004233360290527344,0.9886969999999999 +539,Regression,Linear Regression with l2 regularization,ChickWeights,22.68725373887437,34.77075336357408,0.7133508993505916,0.004233360290527344,1.0345 +550,Regression,Linear Regression with l2 regularization,ChickWeights,23.627725892037507,36.324416048782524,0.6968033114915981,0.004233360290527344,1.080674 +561,Regression,Linear Regression with l2 regularization,ChickWeights,24.347376192466918,37.30920796407717,0.6941284720923248,0.004233360290527344,1.127216 +572,Regression,Linear Regression with l2 regularization,ChickWeights,25.18573737545828,38.51358935872805,0.698506895988072,0.004233360290527344,1.174127 +578,Regression,Linear Regression with l2 regularization,ChickWeights,25.27380465992389,38.58852748240754,0.7046942807227952,0.004233360290527344,1.2212839999999998 +20,Regression,Linear Regression with l2 regularization,TrumpApproval,20.994354275814885,24.339467027537435,-1388.5575385664913,0.004836082458496094,0.002841 +40,Regression,Linear Regression with l2 regularization,TrumpApproval,12.808927193108108,17.83271591943186,-126.84988353201342,0.004836082458496094,0.0066630000000000005 +60,Regression,Linear Regression with l2 regularization,TrumpApproval,10.864002308096953,15.320672400398038,-126.22308256175273,0.004836082458496094,0.011298 +80,Regression,Linear Regression with l2 regularization,TrumpApproval,8.882777304938948,13.38981065066765,-96.4771385394691,0.004836082458496094,0.01675 +100,Regression,Linear Regression with l2 regularization,TrumpApproval,7.231639558854497,11.98203471414171,-47.97617801736401,0.004836082458496094,0.023066000000000003 +120,Regression,Linear Regression with l2 regularization,TrumpApproval,6.334108393931037,10.98237795329033,-33.904913895880355,0.004836082458496094,0.030179000000000004 +140,Regression,Linear Regression with l2 regularization,TrumpApproval,5.563493982833803,10.178707085968126,-29.98405233271513,0.004836082458496094,0.038033000000000004 +160,Regression,Linear Regression with l2 regularization,TrumpApproval,5.002122045077101,9.533278572445496,-22.968717144675633,0.004836082458496094,0.04831800000000001 +180,Regression,Linear Regression with l2 regularization,TrumpApproval,4.587842803317817,9.003737317880292,-17.777085610739057,0.004836082458496094,0.07294600000000001 +200,Regression,Linear Regression with l2 regularization,TrumpApproval,4.458683971614509,8.652080760634158,-16.390543570087573,0.004836082458496094,0.09946700000000001 +220,Regression,Linear Regression with l2 regularization,TrumpApproval,4.239995800771734,8.280452519944822,-16.182419642449048,0.004836082458496094,0.129303 +240,Regression,Linear Regression with l2 regularization,TrumpApproval,3.943592784264584,7.932077353220182,-14.885669934585447,0.004836082458496094,0.159907 +260,Regression,Linear Regression with l2 regularization,TrumpApproval,3.7846302486799286,7.646201644169009,-13.960159512708739,0.004996299743652344,0.191262 +280,Regression,Linear Regression with l2 regularization,TrumpApproval,3.6468171672887713,7.389977926170562,-13.630953412847402,0.004996299743652344,0.22336299999999998 +300,Regression,Linear Regression with l2 regularization,TrumpApproval,3.5261123680869226,7.1621871014808685,-12.51535851099468,0.004996299743652344,0.25877399999999995 +320,Regression,Linear Regression with l2 regularization,TrumpApproval,3.5074300839639245,6.985469271455791,-12.493228210862723,0.004996299743652344,0.29652399999999995 +340,Regression,Linear Regression with l2 regularization,TrumpApproval,3.434140699763514,6.814822943627961,-12.570888751300782,0.004996299743652344,0.33652199999999993 +360,Regression,Linear Regression with l2 regularization,TrumpApproval,3.4272200155971797,6.678288393486038,-12.13957341395007,0.004996299743652344,0.3787709999999999 +380,Regression,Linear Regression with l2 regularization,TrumpApproval,3.332029752839207,6.516115548498917,-11.941900403072644,0.004996299743652344,0.4232519999999999 +400,Regression,Linear Regression with l2 regularization,TrumpApproval,3.217390968362987,6.356555790563252,-11.66392028459017,0.004996299743652344,0.4696459999999999 +420,Regression,Linear Regression with l2 regularization,TrumpApproval,3.100825681509746,6.206562691759863,-11.472880484909139,0.004996299743652344,0.516851 +440,Regression,Linear Regression with l2 regularization,TrumpApproval,3.0187726323631243,6.072312098448126,-10.723095644711893,0.004996299743652344,0.5652379999999999 +460,Regression,Linear Regression with l2 regularization,TrumpApproval,2.947022825868371,5.94849802587685,-9.668265577306911,0.004996299743652344,0.6161209999999999 +480,Regression,Linear Regression with l2 regularization,TrumpApproval,2.867282537241402,5.828292237410032,-9.005843404687633,0.004996299743652344,0.6743809999999999 +500,Regression,Linear Regression with l2 regularization,TrumpApproval,2.8281006485905213,5.729646774374514,-8.467133754251039,0.004996299743652344,0.7334769999999999 +520,Regression,Linear Regression with l2 regularization,TrumpApproval,2.759113137285707,5.623931694381955,-8.136932704030892,0.004996299743652344,0.79343 +540,Regression,Linear Regression with l2 regularization,TrumpApproval,2.7113951403332286,5.52770300084093,-7.7945843187225226,0.004996299743652344,0.8541829999999999 +560,Regression,Linear Regression with l2 regularization,TrumpApproval,2.646739535451309,5.4320905955210534,-7.695343452205655,0.004996299743652344,0.9420199999999999 +580,Regression,Linear Regression with l2 regularization,TrumpApproval,2.5972398336076634,5.343168086286508,-7.621065103502998,0.004996299743652344,1.0306859999999998 +600,Regression,Linear Regression with l2 regularization,TrumpApproval,2.533455116608919,5.255265792942869,-7.247487071141652,0.004996299743652344,1.1201999999999999 +620,Regression,Linear Regression with l2 regularization,TrumpApproval,2.497138699914293,5.178243230235351,-6.88544757519055,0.004996299743652344,1.2104949999999999 +640,Regression,Linear Regression with l2 regularization,TrumpApproval,2.4712145738198297,5.107804033669319,-6.528964961790648,0.004996299743652344,1.3015299999999999 +660,Regression,Linear Regression with l2 regularization,TrumpApproval,2.429247896498525,5.0347117637840935,-6.262430681498119,0.004996299743652344,1.3932969999999998 +680,Regression,Linear Regression with l2 regularization,TrumpApproval,2.3980901245026116,4.967521902410674,-6.18160824182094,0.004996299743652344,1.4857959999999997 +700,Regression,Linear Regression with l2 regularization,TrumpApproval,2.360396901712673,4.90286744871834,-6.1796262474179136,0.004996299743652344,1.5790619999999997 +720,Regression,Linear Regression with l2 regularization,TrumpApproval,2.3150393936015323,4.836721469702358,-6.140716883970916,0.004996299743652344,1.6730619999999998 +740,Regression,Linear Regression with l2 regularization,TrumpApproval,2.267679208737699,4.77254376860168,-5.948293801048677,0.004996299743652344,1.7677929999999997 +760,Regression,Linear Regression with l2 regularization,TrumpApproval,2.2434173929652075,4.715867112708654,-5.857742612566196,0.004996299743652344,1.8632789999999997 +780,Regression,Linear Regression with l2 regularization,TrumpApproval,2.199009654391343,4.656030786420359,-5.714767077599112,0.004996299743652344,1.9595409999999998 +800,Regression,Linear Regression with l2 regularization,TrumpApproval,2.1596596811720175,4.59898830049537,-5.610494506343661,0.004996299743652344,2.0566079999999998 +820,Regression,Linear Regression with l2 regularization,TrumpApproval,2.1249482408574707,4.545176313025559,-5.527610390446187,0.004996299743652344,2.175652 +840,Regression,Linear Regression with l2 regularization,TrumpApproval,2.094058354623314,4.493551443258636,-5.439404045425388,0.004996299743652344,2.37025 +860,Regression,Linear Regression with l2 regularization,TrumpApproval,2.062104039794744,4.442864622918497,-5.284107374622643,0.004996299743652344,2.565666 +880,Regression,Linear Regression with l2 regularization,TrumpApproval,2.0307065941401414,4.393695684791879,-5.115247638705276,0.004996299743652344,2.7618669999999996 +900,Regression,Linear Regression with l2 regularization,TrumpApproval,2.003263565299311,4.347049681772325,-5.011317673951863,0.004996299743652344,2.9588469999999996 +920,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9706878923964866,4.300488305941944,-4.979898179552831,0.004996299743652344,3.1566579999999997 +940,Regression,Linear Regression with l2 regularization,TrumpApproval,1.949819924248383,4.257394252920471,-4.91037086709413,0.004996299743652344,3.355253 +960,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9258186229947107,4.214725843085415,-4.85305905428677,0.004996299743652344,3.5546249999999997 +980,Regression,Linear Regression with l2 regularization,TrumpApproval,1.9004260103609922,4.173138113177231,-4.849565137575967,0.004996299743652344,3.754774 +1000,Regression,Linear Regression with l2 regularization,TrumpApproval,1.872733130377695,4.13253797119814,-4.832859832721421,0.004996299743652344,3.955698 +1001,Regression,Linear Regression with l2 regularization,TrumpApproval,1.871510887330926,4.130524228438989,-4.8310671605777085,0.004996299743652344,4.156775 +11,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,26.624124996337724,28.77138517975663,-1064.5628215382144,0.0034055709838867188,0.000572 +22,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.0510878175865,20.931739283093208,-463.02330712701985,0.0034055709838867188,0.001645 +33,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.49930786476168,17.564629142555763,-213.26922094451623,0.0034055709838867188,0.003059 +44,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.378514545021682,15.405121473747096,-185.84310618709696,0.0034055709838867188,0.004809 +55,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.844108697295251,17.128215293517524,-56.27037115396167,0.0034055709838867188,0.0068920000000000006 +66,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.889488781892217,15.88743125142584,-20.240220516271876,0.0034055709838867188,0.009306 +77,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.103343480706034,14.91594241381016,-11.548186613409534,0.0034055709838867188,0.012052 +88,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.288900850158633,14.011374344891147,-9.038968322803399,0.0034055709838867188,0.015129 +99,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.736865157066078,13.281093172283262,-6.5439573170464564,0.0034055709838867188,0.018712 +110,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.618125386224052,12.858171267844924,-3.9379074608874927,0.0034055709838867188,0.022717 +121,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.580936033253089,12.515247629942861,-2.6028352541815996,0.0034055709838867188,0.027127000000000002 +132,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.191573127926202,12.024287681643044,-1.7180920054032294,0.0034055709838867188,0.031928 +143,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.001452140019149,11.63905100750295,-1.062756769701151,0.0034055709838867188,0.037113999999999994 +154,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.959260067984971,11.397763679955697,-0.6811278108981134,0.0034055709838867188,0.04268899999999999 +165,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.036161429677985,11.359538570018055,-0.3423577921849861,0.0034055709838867188,0.04865399999999999 +176,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.141200516910354,11.407680550849575,-0.09154063283497149,0.0034055709838867188,0.05501099999999999 +187,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.061965626679777,11.211626858308708,0.1367382583661435,0.0034055709838867188,0.06175399999999999 +198,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,6.988600359846859,11.023879576943443,0.33612315722527375,0.0034055709838867188,0.06888499999999999 +209,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.115468527113427,11.18859440458875,0.43624345152336885,0.0034055709838867188,0.07639599999999999 +220,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.571784360381598,11.998798941058181,0.4323613497222297,0.0034055709838867188,0.08428799999999999 +231,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.610536559977233,11.962244483436113,0.5174164020157423,0.0034055709838867188,0.09375399999999999 +242,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.753677752144043,12.109970858596688,0.6020391290764042,0.0034055709838867188,0.10451099999999999 +253,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,7.763402728464486,12.046916639776326,0.6579556132088519,0.0034055709838867188,0.123112 +264,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.37232599699494,12.938281421109101,0.6395292100633578,0.0034589767456054688,0.147917 +275,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,8.870502401884236,14.03783628218945,0.6266016212673495,0.0034589767456054688,0.173118 +286,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.125553299295866,14.312481045438886,0.6638140497188074,0.0034589767456054688,0.198688 +297,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.11642729851449,14.234872044017683,0.7115826499817814,0.0034589767456054688,0.225283 +308,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,9.63053955101658,15.01159987060024,0.7143329631149452,0.0034589767456054688,0.252265 +319,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,10.671899739762464,17.42953249336733,0.650531972004655,0.0034589767456054688,0.27961600000000003 +330,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.113559839827301,17.980470366868552,0.6817264420663893,0.0034589767456054688,0.307329 +341,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.368994570730054,18.183536514460908,0.7085274545262112,0.0034589767456054688,0.335405 +352,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,11.47998520043724,18.216810890558104,0.7340681346165732,0.0034589767456054688,0.363842 +363,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.490995837872445,19.84181186896939,0.693641639088806,0.0034589767456054688,0.392636 +374,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,12.988870134156189,20.81926805033374,0.6899406322869175,0.0034589767456054688,0.42178699999999997 +385,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.420579982415202,21.48960215237335,0.7077263415053474,0.0034589767456054688,0.45129199999999997 +396,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,13.424816444492956,21.37796604773129,0.7303103668220552,0.0034589767456054688,0.481151 +407,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,14.284688005634004,22.701575115822557,0.7173140296578691,0.0034589767456054688,0.514769 +418,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.038658536726118,24.042516108283174,0.7023651722582169,0.0034589767456054688,0.548784 +429,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.590290098257741,24.916858152232297,0.7159269075042054,0.0034589767456054688,0.583167 +440,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,15.812702077031824,25.072500493300815,0.7327254930224041,0.0034589767456054688,0.617914 +451,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,16.346042839206106,25.68091484988461,0.7315167856134144,0.0034589767456054688,0.653021 +462,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,17.370765923434053,27.689388199834635,0.7068336630361484,0.0034589767456054688,0.688487 +473,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.264179516209435,29.30099868065636,0.7101227966068765,0.0034589767456054688,0.724309 +484,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,18.63502154656571,29.559619400414906,0.7211497930314269,0.0034589767456054688,0.760485 +495,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.145243718121584,30.130361606680754,0.7274603750306702,0.0034589767456054688,0.7970149999999999 +506,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,19.98075812634153,31.43770898148617,0.7119202509985216,0.0034589767456054688,0.8338999999999999 +517,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,20.7046141289421,32.42665929478992,0.7107714616434232,0.0034589767456054688,0.8711439999999999 +528,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.54126059149082,33.75343345950398,0.7182443212146572,0.0034589767456054688,0.908739 +539,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,21.736037457517718,33.829762552174344,0.7286559632490104,0.0034589767456054688,0.946687 +550,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,22.674609740448528,35.33904665998618,0.7130297805712756,0.0034589767456054688,0.984985 +561,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,23.350956760305525,36.24046007710213,0.7114012814424304,0.0034589767456054688,1.023633 +572,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.20743030595361,37.47019278346573,0.7146215025224946,0.0034589767456054688,1.06263 +578,Regression,"Passive-Aggressive Regressor, mode 1",ChickWeights,24.342328163686027,37.59599019491026,0.7196900586014492,0.0034589767456054688,1.1018649999999999 +20,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,20.806898309502586,26.56763494383828,-1654.6182189603317,0.004302024841308594,0.003003 +40,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,14.866074912822512,20.957300378156614,-175.5777711351631,0.004302024841308594,0.009504 +60,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,11.772648582583251,17.555009093750932,-166.03688377592212,0.004302024841308594,0.016813 +80,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,10.372925375947808,15.758572852966298,-134.01675577859288,0.004302024841308594,0.024890000000000002 +100,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.950999863257042,14.807263848606526,-73.79513907078027,0.004302024841308594,0.033777 +120,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,9.131163180965077,13.743973626529105,-53.66614209724606,0.004302024841308594,0.043434 +140,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.532294463666167,12.935885124148239,-49.04322437944699,0.004302024841308594,0.05944 +160,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.33219708929472,12.40854626547306,-39.60710527883104,0.004302024841308594,0.077842 +180,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,8.281092452540433,12.043698542516514,-32.597139849683785,0.004302024841308594,0.09862299999999999 +200,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.889313429527772,11.548268653424005,-29.981738551789036,0.004302024841308594,0.121857 +220,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.555718436766954,11.115454500430198,-29.962115725262894,0.004302024841308594,0.149384 +240,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.300584612865839,10.768588372618428,-28.278525817685868,0.004302024841308594,0.177703 +260,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,7.073956995660685,10.455941089275187,-26.975057358486694,0.004435539245605469,0.206834 +280,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.879100927439736,10.179149173565092,-26.75935065850941,0.004435539245605469,0.236782 +300,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.698392466938299,9.935855831167725,-25.01038668400382,0.004435539245605469,0.26759 +320,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.496977203333427,9.674599820332077,-24.881575653507443,0.004435539245605469,0.299212 +340,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.319501534649956,9.433800456219284,-25.005937123592886,0.004435539245605469,0.33164299999999997 +360,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.189316591643737,9.224778235838508,-24.070488458586468,0.004435539245605469,0.36488499999999996 +380,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,6.05373584315195,9.026036673488779,-23.83217081250324,0.004435539245605469,0.39894499999999994 +400,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.893196935767096,8.831009888188627,-23.44247524737401,0.004435539245605469,0.43386499999999995 +420,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.787168115685009,8.669033337133486,-23.33356914323267,0.004435539245605469,0.46960899999999994 +440,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.789860410241021,8.633597516354541,-22.69832962851382,0.004435539245605469,0.5061589999999999 +460,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.751464501173282,8.532971696368575,-20.952287666855003,0.004435539245605469,0.5516559999999999 +480,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.758413491181221,8.476961067588123,-20.166616413985547,0.004435539245605469,0.599555 +500,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.682950272504451,8.3510488559106,-19.111518541810455,0.004435539245605469,0.6519079999999999 +520,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.627995468360723,8.245754355787446,-18.64179334000355,0.004435539245605469,0.705132 +540,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.546541731300828,8.130789587119862,-18.02792190581397,0.004435539245605469,0.7591289999999999 +560,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.474658569482086,8.019262742277965,-17.95054121046633,0.004435539245605469,0.813877 +580,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.409420416004319,7.920158789530457,-17.94222667017848,0.004435539245605469,0.869386 +600,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.394854582323811,7.870548110777217,-17.498743363524373,0.004435539245605469,0.941553 +620,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.360408122735632,7.801849933723111,-16.900148820132806,0.004435539245605469,1.016153 +640,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.332182524169608,7.745335706289596,-16.312002846243146,0.004435539245605469,1.093294 +660,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.286484086266954,7.672164501241343,-15.864310422998045,0.004435539245605469,1.1728 +680,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.240017672508232,7.591734569529257,-15.773518040276521,0.004435539245605469,1.259908 +700,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.203631741702394,7.526058935068808,-15.917522479350382,0.004435539245605469,1.347915 +720,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.1985518333986755,7.481861117849272,-16.086729414362967,0.004435539245605469,1.436748 +740,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.200051628353664,7.4433149034441595,-15.900950117878587,0.004435539245605469,1.526395 +760,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.146415466772512,7.367313347205083,-15.736951087672441,0.004435539245605469,1.61685 +780,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.164438314106662,7.352756459959702,-15.745558187055657,0.004435539245605469,1.708114 +800,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.199091748701669,7.381485816300255,-16.029304780133675,0.004435539245605469,1.8001699999999998 +820,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.184244405270293,7.343677512392477,-16.040406471643664,0.004435539245605469,1.892982 +840,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.162940711797175,7.2951968772545595,-15.972283354719572,0.004435539245605469,1.986541 +860,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.146772746928229,7.251973114148485,-15.742866229030533,0.004435539245605469,2.080851 +880,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.141562534384022,7.225910341165371,-15.54014218136778,0.004435539245605469,2.175912 +900,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.113671043317916,7.181653170625269,-15.40700562565575,0.004435539245605469,2.271722 +920,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.0827257569327715,7.134180367835326,-15.456838882747107,0.004435539245605469,2.368328 +940,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.049460198345376,7.092641752853287,-15.403746251323405,0.004435539245605469,2.491683 +960,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,5.012955702794688,7.041188655025779,-15.335641983730405,0.004435539245605469,2.616037 +980,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.992587411597517,7.002009756347646,-15.468099715303381,0.004435539245605469,2.7412199999999998 +1000,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.986581819477306,6.97972894589718,-15.638912943338369,0.004435539245605469,2.867218 +1001,Regression,"Passive-Aggressive Regressor, mode 1",TrumpApproval,4.984033991902679,6.9766666383395455,-15.63541499061877,0.004435539245605469,2.993378 +11,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,39.19936706045659,55.118879370280126,-3909.733983269086,0.0034055709838867188,0.001533 +22,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.495026158423794,43.23165104261441,-1978.3965328342838,0.0034055709838867188,0.004589 +33,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,30.680053698816124,39.985066603327745,-1109.3949268723327,0.0034055709838867188,0.008788 +44,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,29.375885022911746,37.29886968855784,-1094.3128086885838,0.0034055709838867188,0.014141 +55,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.707444751978134,40.753235251415205,-323.21264874535376,0.0034055709838867188,0.020635 +66,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,31.96097441162184,40.14945868859866,-134.64726490280094,0.0034055709838867188,0.028271 +77,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.25989567011213,39.82501544894248,-88.45229320906665,0.0034055709838867188,0.041195 +88,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.76307262878121,39.802536485586,-80.01195436020778,0.0034055709838867188,0.054526000000000005 +99,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,32.66411513705659,39.325402336106926,-65.1420916497486,0.0034055709838867188,0.06822700000000001 +110,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.19940912800194,40.704130728492046,-48.48362457590105,0.0034055709838867188,0.082293 +121,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,34.629161705635866,40.92880988729008,-37.532194399087835,0.0034055709838867188,0.096719 +132,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,35.29035427006805,41.59178542812187,-31.520750879588867,0.0034055709838867188,0.111503 +143,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.23638449140802,42.62018794050648,-26.659453761649477,0.0034055709838867188,0.126644 +154,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.725010132899186,42.91835139661213,-22.836775105518452,0.0034055709838867188,0.14214100000000002 +165,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,36.731745662210095,42.91744223234227,-18.16084111691443,0.0034055709838867188,0.15799600000000003 +176,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,37.94402632003076,44.39720610255875,-15.533183531813599,0.0034055709838867188,0.17420800000000003 +187,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,38.698580833397834,45.06856008203835,-12.949305609255877,0.0034055709838867188,0.19077600000000003 +198,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.18624064352699,46.68267333461602,-10.905017439998023,0.0034055709838867188,0.20770000000000002 +209,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,40.854323276826534,47.463811090322665,-9.145320047375163,0.0034055709838867188,0.224978 +220,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,41.36451127701117,48.41262940233051,-8.240888884363313,0.0034055709838867188,0.24261000000000002 +231,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,42.17342712408468,49.46918668675267,-7.253093192412523,0.0034055709838867188,0.260594 +242,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,43.81461612103895,51.73551020684679,-6.2632609796369465,0.0034055709838867188,0.27893 +253,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,44.90819615603068,53.07334253773936,-5.6387075541588505,0.0034055709838867188,0.297618 +264,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,46.45334973048907,55.82244674340302,-5.710187070138379,0.0034589767456054688,0.31665899999999997 +275,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,48.05643802527038,58.804796990371536,-5.552357606253864,0.0034589767456054688,0.33605399999999996 +286,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,49.41721923566732,60.72765972830183,-5.052332799264802,0.0034589767456054688,0.35580199999999995 +297,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,51.23299901747073,63.29154255446438,-4.701716347098143,0.0034589767456054688,0.37590499999999993 +308,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,52.82583967659276,65.36972550348784,-4.417008197806662,0.0034589767456054688,0.39636799999999994 +319,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,54.851023886215806,70.45860717413167,-4.71089374971376,0.0034589767456054688,0.41718399999999994 +330,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,56.58220488738844,72.62689780553444,-4.192702597603254,0.0034589767456054688,0.43835299999999994 +341,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,58.456862484765374,75.26810540469758,-3.994165343488624,0.0034589767456054688,0.45987999999999996 +352,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,59.98229295122657,76.97767263775137,-3.748486775975887,0.0034589767456054688,0.48176199999999997 +363,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,61.989108820835376,80.62951920841103,-4.0588898392459045,0.0034589767456054688,0.503996 +374,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,63.93796840595574,84.48840832488506,-4.106322034628877,0.0034589767456054688,0.526584 +385,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,65.15236861414519,85.79755918852514,-3.6588935912158114,0.0034589767456054688,0.551564 +396,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,66.90365663892747,87.9249329113371,-3.562003118430529,0.0034589767456054688,0.5777180000000001 +407,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,68.17917622540308,89.58756462611774,-3.402382103640547,0.0034589767456054688,0.6050150000000001 +418,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,70.80702754948452,94.96753809429286,-3.643808228470034,0.0034589767456054688,0.6334650000000001 +429,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,72.44730173566225,97.09455233033468,-3.313534410167211,0.0034589767456054688,0.663057 +440,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,74.29167351363806,99.40774027870643,-3.2014833153265316,0.0034589767456054688,0.693783 +451,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,75.83174494284101,101.77506329990584,-3.216760347648722,0.0034589767456054688,0.725638 +462,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,78.5111288104629,106.99570126481906,-3.3774383617967887,0.0034589767456054688,0.758621 +473,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,81.63741116996734,112.58139375423264,-3.2793958269429844,0.0034589767456054688,0.79391 +484,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,82.66628549198501,113.50934761838603,-3.1118432523868984,0.0034589767456054688,0.829617 +495,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,84.40016304476833,116.34990208847,-3.0639935553557365,0.0034589767456054688,0.865723 +506,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,86.52132256561038,120.30772815943004,-3.2188880650982723,0.0034589767456054688,0.902224 +517,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,87.79244029751037,121.63869088166206,-3.0698668478095374,0.0034589767456054688,0.9391200000000001 +528,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,90.43735682351402,126.62066541565774,-2.9650251625135784,0.0034589767456054688,0.9763980000000001 +539,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,91.59763342322412,127.62959496409519,-2.862114672867245,0.0034589767456054688,1.014034 +550,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,93.80067010965053,131.39026699356185,-2.9669210878459156,0.0034589767456054688,1.0520230000000002 +561,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,96.52355815418714,136.33091173427522,-3.0840934586689466,0.0034589767456054688,1.0903630000000002 +572,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,99.60515399822415,141.80943605664237,-3.0875177525301325,0.0034589767456054688,1.1290550000000001 +578,Regression,"Passive-Aggressive Regressor, mode 2",ChickWeights,100.62422612381133,143.06646930774232,-3.0591132110693486,0.0034589767456054688,1.167982 +20,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,48.24517612267716,65.52170729560882,-10068.892101934754,0.004302024841308594,0.0014 +40,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,41.96170708962665,54.398737007050464,-1188.7151382109587,0.004302024841308594,0.0037089999999999996 +60,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,37.75687919715097,48.78450375470138,-1288.953469480389,0.004302024841308594,0.0068 +80,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,34.906129137913965,44.99379649673769,-1099.675197364534,0.004302024841308594,0.010662 +100,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,33.91700787894482,42.88559259598606,-626.4029768570122,0.004302024841308594,0.015342999999999999 +120,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,33.25318798467783,41.41783833748641,-495.44216046034904,0.004302024841308594,0.020797999999999997 +140,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,32.454169303664,40.065346416262614,-479.0547686921885,0.004302024841308594,0.027020999999999996 +160,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.456143135335843,38.757475924320815,-395.16052146995384,0.004302024841308594,0.034013999999999996 +180,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.609503890456164,37.605439707525925,-326.55474603918685,0.004302024841308594,0.04177499999999999 +200,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.18524212396377,36.915721425331306,-315.5882175367347,0.004302024841308594,0.05032999999999999 +220,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.065472528043387,36.44442035805252,-331.8421153382835,0.004302024841308594,0.05964399999999999 +240,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.78865598017145,35.91292614465666,-324.6366197799479,0.004302024841308594,0.06971499999999999 +260,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.781114432924582,35.79236523689611,-326.81251307944893,0.004435539245605469,0.08530999999999998 +280,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.533238425747374,35.35890478021234,-333.95306350592915,0.004435539245605469,0.10330299999999998 +300,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.457831453745214,35.09485674586195,-323.50634592736793,0.004435539245605469,0.12379499999999997 +320,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.587592426740265,34.99099947403571,-337.56135797788454,0.004435539245605469,0.14665799999999998 +340,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.592186767063264,34.82748458593961,-353.4405109424598,0.004435539245605469,0.178415 +360,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.811871336212132,34.87255971663822,-357.27671195740294,0.004435539245605469,0.21101599999999998 +380,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,29.96085998978186,34.8837386376585,-369.9082961036221,0.004435539245605469,0.24444299999999997 +400,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.10861760053803,34.89146879370085,-380.5600928496674,0.004435539245605469,0.27875099999999997 +420,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.237056214581205,34.87113676993804,-392.72834237954817,0.004435539245605469,0.31389799999999995 +440,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.396870134836657,34.919939008119975,-386.68686883790514,0.004435539245605469,0.34987699999999994 +460,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.514209015244397,34.936230377424984,-366.98596965456716,0.004435539245605469,0.38662699999999994 +480,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.60304766371323,34.90556469597589,-357.88920799289923,0.004435539245605469,0.42413599999999996 +500,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.723498435612367,34.929338036322235,-350.83863344189655,0.004435539245605469,0.462404 +520,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.811640107301315,34.91688659850233,-351.20164208687333,0.004435539245605469,0.5014689999999999 +540,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.90684609870959,34.93305250730057,-350.23597283973027,0.004435539245605469,0.541291 +560,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.83222834631613,34.80844611918478,-356.0442181025925,0.004435539245605469,0.58187 +580,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.81214247339674,34.7464796172207,-363.5733105101423,0.004435539245605469,0.629234 +600,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.96266506693229,34.82326914665847,-361.13570824853826,0.004435539245605469,0.6774979999999999 +620,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.066025409450507,34.86865150113252,-356.54586556020155,0.004435539245605469,0.743837 +640,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.17687176552783,34.929241693507976,-351.0830657253844,0.004435539245605469,0.810979 +660,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.17965741293356,34.892251240403844,-347.8114699445998,0.004435539245605469,0.87889 +680,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.2564554130016,34.924087575336145,-353.970503405387,0.004435539245605469,0.947564 +700,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.205643809070587,34.8991435368638,-362.77351000506667,0.004435539245605469,1.017054 +720,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.176512353694502,34.84497410939031,-369.61240318757564,0.004435539245605469,1.087302 +740,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.10578554229227,34.74995813099662,-367.37224871607793,0.004435539245605469,1.158309 +760,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.047274834607858,34.691812272848594,-370.11801689051305,0.004435539245605469,1.23401 +780,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.10380007346799,34.703384372728905,-372.0292841604823,0.004435539245605469,1.319073 +800,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.08555480002386,34.670374973731704,-374.68721566223496,0.004435539245605469,1.406663 +820,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.19885148971359,34.750222550469864,-380.56447731408525,0.004435539245605469,1.496625 +840,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.254637155655836,34.7657556157579,-384.4513633760299,0.004435539245605469,1.5889890000000002 +860,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.316317159155098,34.79186578621207,-384.3655387384781,0.004435539245605469,1.693075 +880,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.241979864666273,34.706915019978055,-380.5804591262153,0.004435539245605469,2.065505 +900,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.224226006229813,34.673719949901624,-381.45588348920205,0.004435539245605469,2.440347 +920,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.14134426690263,34.58836614994504,-385.8283921715467,0.004435539245605469,2.817664 +940,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.997921544748234,34.45657797481166,-386.1428842704396,0.004435539245605469,3.197344 +960,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.060400411885407,34.566740304864304,-392.69608794190344,0.004435539245605469,3.57811 +980,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,30.96911325529305,34.515642414657464,-399.1566023636026,0.004435539245605469,3.959721 +1000,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.09609109084082,34.63142513356851,-408.62698817771025,0.004435539245605469,4.342153 +1001,Regression,"Passive-Aggressive Regressor, mode 2",TrumpApproval,31.093334457100017,34.62570772928248,-408.7651443035993,0.004435539245605469,4.724752 +11,Regression,k-Nearest Neighbors,ChickWeights,4.6439393939393945,12.708027567111456,-206.8805289598106,0.0208587646484375,0.001745 +22,Regression,k-Nearest Neighbors,ChickWeights,2.7674242424242426,9.021574170013263,-85.19732920009746,0.030094146728515625,0.005817 +33,Regression,k-Nearest Neighbors,ChickWeights,2.3601010101010105,7.4346315168437105,-37.38846247874159,0.0395355224609375,0.012524 +44,Regression,k-Nearest Neighbors,ChickWeights,1.9882575757575767,6.459864921032004,-31.8544119108943,0.0488433837890625,0.023287000000000002 +55,Regression,k-Nearest Neighbors,ChickWeights,2.201515151515152,6.079045396219125,-6.214006750846093,0.05837249755859375,0.035467 +66,Regression,k-Nearest Neighbors,ChickWeights,2.2709595959595963,5.693634951086079,-1.7279153546475992,0.06856918334960938,0.049542 +77,Regression,k-Nearest Neighbors,ChickWeights,2.6114718614718617,5.706903555891601,-0.8368793810695487,0.0782623291015625,0.06880900000000001 +88,Regression,k-Nearest Neighbors,ChickWeights,2.5236742424242427,5.412016943708686,-0.4977726858852578,0.08795547485351562,0.09929800000000001 +99,Regression,k-Nearest Neighbors,ChickWeights,2.4695286195286204,5.169211114529652,-0.1428260058474422,0.09764862060546875,0.13234700000000002 +110,Regression,k-Nearest Neighbors,ChickWeights,2.7553030303030313,5.269495069058163,0.1706792355598563,0.10734176635742188,0.16795900000000002 +121,Regression,k-Nearest Neighbors,ChickWeights,3.1511019283746564,5.580125306741311,0.2837685080447375,0.117034912109375,0.20637100000000003 +132,Regression,k-Nearest Neighbors,ChickWeights,3.3157828282828294,5.649452649212155,0.3999904226030885,0.12723159790039062,0.24803200000000003 +143,Regression,k-Nearest Neighbors,ChickWeights,3.6019813519813537,5.868270501527574,0.47563568627460706,0.13692474365234375,0.31342200000000003 +154,Regression,k-Nearest Neighbors,ChickWeights,3.7459956709956725,5.964828521670115,0.5395766265984425,0.14661788940429688,0.409887 +165,Regression,k-Nearest Neighbors,ChickWeights,4.050202020202021,6.4542180762994805,0.5666546129487657,0.15631103515625,0.575073 +176,Regression,k-Nearest Neighbors,ChickWeights,4.420928030303032,6.954884488253524,0.5942812793055753,0.16600418090820312,0.746583 +187,Regression,k-Nearest Neighbors,ChickWeights,4.757664884135474,7.278917476631412,0.6361362300357987,0.17569732666015625,0.9226570000000001 +198,Regression,k-Nearest Neighbors,ChickWeights,5.192340067340069,7.767087259749381,0.6704396407154757,0.18589401245117188,1.103506 +209,Regression,k-Nearest Neighbors,ChickWeights,5.571690590111645,8.414476478500024,0.6811438926382001,0.195587158203125,1.2899880000000001 +220,Regression,k-Nearest Neighbors,ChickWeights,6.017651515151518,9.535434778453542,0.641509702161033,0.20528030395507812,1.4817580000000001 +231,Regression,k-Nearest Neighbors,ChickWeights,6.514646464646468,10.15268578355149,0.652376522878304,0.21497344970703125,1.688886 +242,Regression,k-Nearest Neighbors,ChickWeights,7.006955922865016,10.883499074839365,0.6785664047839641,0.22466659545898438,1.9058080000000002 +253,Regression,k-Nearest Neighbors,ChickWeights,7.401119894598158,11.259257694820905,0.7012209269570091,0.2343597412109375,2.129833 +264,Regression,k-Nearest Neighbors,ChickWeights,7.873800505050509,12.237701558545494,0.6775097363055258,0.24460983276367188,2.3598440000000003 +275,Regression,k-Nearest Neighbors,ChickWeights,8.501393939393942,13.456617650281162,0.6568816796501455,0.254302978515625,2.605392 +286,Regression,k-Nearest Neighbors,ChickWeights,8.999592074592076,14.081405883193678,0.6745818706784585,0.2639961242675781,2.8696780000000004 +297,Regression,k-Nearest Neighbors,ChickWeights,9.403647586980924,14.487230370517851,0.7012657763253116,0.27368927001953125,3.1592620000000005 +308,Regression,k-Nearest Neighbors,ChickWeights,9.825595238095241,15.247017337775036,0.7053028346163965,0.2833824157714844,3.4714450000000006 +319,Regression,k-Nearest Neighbors,ChickWeights,10.570794148380358,17.082267622288043,0.6643188025566307,0.2930755615234375,3.8957660000000005 +330,Regression,k-Nearest Neighbors,ChickWeights,11.342676767676771,18.20491056057454,0.6737311884314376,0.3032722473144531,4.333389 +341,Regression,k-Nearest Neighbors,ChickWeights,11.756256109481921,18.5968301788559,0.6951271166039881,0.31296539306640625,4.790776 +352,Regression,k-Nearest Neighbors,ChickWeights,12.16955492424243,18.94133239132977,0.7124941202708752,0.3226585388183594,5.260338 +363,Regression,k-Nearest Neighbors,ChickWeights,12.609595959595964,19.7022738973151,0.6979354313341102,0.3323516845703125,5.750168 +374,Regression,k-Nearest Neighbors,ChickWeights,13.251024955436726,20.7851367099449,0.6909564285254863,0.3420448303222656,6.251716 +385,Regression,k-Nearest Neighbors,ChickWeights,13.78255411255412,21.481025974379733,0.7079595790244884,0.35224151611328125,6.769761 +396,Regression,k-Nearest Neighbors,ChickWeights,14.010311447811455,21.53574862211497,0.7263147242326703,0.3619346618652344,7.297159 +407,Regression,k-Nearest Neighbors,ChickWeights,14.576126126126132,22.56379182999173,0.720735043690873,0.3716278076171875,7.841892 +418,Regression,k-Nearest Neighbors,ChickWeights,15.256658692185015,23.708044463333223,0.710588766956741,0.38134765625,8.4256 +429,Regression,k-Nearest Neighbors,ChickWeights,15.863597513597522,24.650993900023582,0.7219567169230845,0.3910675048828125,9.118775999999999 +440,Regression,k-Nearest Neighbors,ChickWeights,16.15655303030304,24.89490243600041,0.7364984966983625,0.4007606506347656,9.834439999999999 +451,Regression,k-Nearest Neighbors,ChickWeights,16.474242424242437,25.235361878916873,0.7407521096740679,0.41095733642578125,10.564262999999999 +462,Regression,k-Nearest Neighbors,ChickWeights,17.206240981241,26.51959634874256,0.731081178462164,0.42067718505859375,11.311639 +473,Regression,k-Nearest Neighbors,ChickWeights,18.061486962649766,27.919441407022266,0.7368140706560946,0.430450439453125,12.077289 +484,Regression,k-Nearest Neighbors,ChickWeights,18.444800275482105,28.396609389438456,0.742660608098584,0.4401702880859375,12.86575 +495,Regression,k-Nearest Neighbors,ChickWeights,18.85067340067341,28.917019336286593,0.7489686179689856,0.4499168395996094,13.665871000000001 +506,Regression,k-Nearest Neighbors,ChickWeights,19.397397891963116,29.705616030262235,0.7427898649120724,2.5872955322265625,16.820609 +517,Regression,k-Nearest Neighbors,ChickWeights,20.115441650548043,30.735303248634356,0.7401565757784102,2.6296463012695312,20.027458000000003 +528,Regression,k-Nearest Neighbors,ChickWeights,20.836142676767683,31.986233829047414,0.7469752640852343,2.6746597290039062,23.267231000000002 +539,Regression,k-Nearest Neighbors,ChickWeights,21.017594310451457,32.125858524254696,0.7553011842320496,2.717792510986328,26.555547000000004 +550,Regression,k-Nearest Neighbors,ChickWeights,21.677242424242426,32.83678407493398,0.7522301799631583,2.769092559814453,29.885669000000004 +561,Regression,k-Nearest Neighbors,ChickWeights,22.80977421271539,35.198755082788004,0.727753941720713,2.8112449645996094,33.249092000000005 +572,Regression,k-Nearest Neighbors,ChickWeights,24.195600233100233,38.25560047694445,0.7025325582791198,2.857513427734375,36.653026000000004 +578,Regression,k-Nearest Neighbors,ChickWeights,24.840628604382932,39.201635479156685,0.6952358931227007,2.8852157592773438,40.087818000000006 +20,Regression,k-Nearest Neighbors,TrumpApproval,2.554585433333335,9.794739803036965,-224.02989290855143,0.033588409423828125,0.001545 +40,Regression,k-Nearest Neighbors,TrumpApproval,1.7993247666666672,6.973235588114817,-18.54942689237887,0.055484771728515625,0.0054 +60,Regression,k-Nearest Neighbors,TrumpApproval,1.366773144444445,5.705236645726316,-16.642396889136542,0.07735443115234375,0.012332 +80,Regression,k-Nearest Neighbors,TrumpApproval,1.1277757833333335,4.947712433075743,-12.30953248968821,0.09975433349609375,0.028826 +100,Regression,k-Nearest Neighbors,TrumpApproval,1.046201766666667,4.4398629296748915,-5.724544452799038,0.12165069580078125,0.050318 +120,Regression,k-Nearest Neighbors,TrumpApproval,1.000865705555556,4.0744555355418335,-3.804331488196434,0.14354705810546875,0.086896 +140,Regression,k-Nearest Neighbors,TrumpApproval,0.9447764619047624,3.7809361134406263,-3.275153002458012,0.16594696044921875,0.149837 +160,Regression,k-Nearest Neighbors,TrumpApproval,0.9352969166666671,3.5531790499707645,-2.3296179824080356,0.18784332275390625,0.23044599999999998 +180,Regression,k-Nearest Neighbors,TrumpApproval,0.9445764925925928,3.380979243961517,-1.647692611170827,0.20973968505859375,0.432465 +200,Regression,k-Nearest Neighbors,TrumpApproval,0.9456943733333335,3.2327893391999836,-1.427877878808435,0.23213958740234375,0.648003 +220,Regression,k-Nearest Neighbors,TrumpApproval,0.9124697575757575,3.0919339165015143,-1.3957229068060464,0.25403594970703125,0.888162 +240,Regression,k-Nearest Neighbors,TrumpApproval,0.9329223611111109,2.985727855147271,-1.2507750530936188,0.27593231201171875,1.171554 +260,Regression,k-Nearest Neighbors,TrumpApproval,0.9025974717948716,2.873740673763463,-1.11319648675526,0.2984657287597656,1.484213 +280,Regression,k-Nearest Neighbors,TrumpApproval,0.8654126523809523,2.773524640439575,-1.0608690746642817,0.3203620910644531,1.81098 +300,Regression,k-Nearest Neighbors,TrumpApproval,0.8525042622222223,2.688069339615046,-0.9037818439458585,0.3422584533691406,2.170241 +320,Regression,k-Nearest Neighbors,TrumpApproval,0.8265282395833334,2.6077957497476296,-0.880493509713772,0.3646583557128906,2.5581009999999997 +340,Regression,k-Nearest Neighbors,TrumpApproval,0.8137511019607846,2.539210136300266,-0.8840673465916704,0.3865547180175781,3.1375499999999996 +360,Regression,k-Nearest Neighbors,TrumpApproval,0.7887328240740744,2.4696835584739105,-0.7969398815662787,0.4084510803222656,3.7440789999999997 +380,Regression,k-Nearest Neighbors,TrumpApproval,0.7710879228070179,2.4087271831437693,-0.7684619785143365,0.4303474426269531,4.380375 +400,Regression,k-Nearest Neighbors,TrumpApproval,0.756179386666667,2.351105641867075,-0.7324819925835522,0.4527473449707031,5.04744 +420,Regression,k-Nearest Neighbors,TrumpApproval,0.7300392539682541,2.295700426816902,-0.7064552265553199,0.4746437072753906,5.735437 +440,Regression,k-Nearest Neighbors,TrumpApproval,0.7180258560606063,2.24592493832078,-0.6037054809307543,0.4965400695800781,6.669957 +460,Regression,k-Nearest Neighbors,TrumpApproval,0.7103659666666668,2.200554873752302,-0.45996881871915263,0.5189399719238281,7.633919000000001 +480,Regression,k-Nearest Neighbors,TrumpApproval,0.6905233472222223,2.1551860359584523,-0.36817166319202155,0.5408363342285156,8.635162000000001 +500,Regression,k-Nearest Neighbors,TrumpApproval,0.6835753693333335,2.1161668272230596,-0.2914054626850582,2.7066993713378906,12.111573000000002 +520,Regression,k-Nearest Neighbors,TrumpApproval,0.6741869282051286,2.0775236231845557,-0.24684449790743135,2.7946739196777344,15.640183000000002 +540,Regression,k-Nearest Neighbors,TrumpApproval,0.6635047197530868,2.0412653603832833,-0.19929175315985925,2.8836631774902344,19.224183000000004 +560,Regression,k-Nearest Neighbors,TrumpApproval,0.6666769047619049,2.01181749557566,-0.19269635778937388,2.973125457763672,22.863484000000003 +580,Regression,k-Nearest Neighbors,TrumpApproval,0.662313208045977,1.9804661409620818,-0.18439917312598686,3.0619163513183594,26.560120000000005 +600,Regression,k-Nearest Neighbors,TrumpApproval,0.6595208444444446,1.9515625148224913,-0.13735805248393262,3.158283233642578,30.314082000000006 +620,Regression,k-Nearest Neighbors,TrumpApproval,0.6603871010752689,1.924909501402362,-0.08963762017358134,3.248737335205078,34.12595900000001 +640,Regression,k-Nearest Neighbors,TrumpApproval,0.6518434010416667,1.8967107462711992,-0.038171332083393406,3.341320037841797,37.99311800000001 +660,Regression,k-Nearest Neighbors,TrumpApproval,0.6481796161616163,1.873162681009878,-0.005272423030620033,3.435527801513672,41.91854100000001 +680,Regression,k-Nearest Neighbors,TrumpApproval,0.6594073715686274,1.8574009428793898,-0.004045635504021261,3.5269508361816406,45.90599500000001 +700,Regression,k-Nearest Neighbors,TrumpApproval,0.6619153695238096,1.8376987056605067,-0.008672432190871993,3.615283966064453,49.957909000000015 +720,Regression,k-Nearest Neighbors,TrumpApproval,0.6538050537037038,1.8142062090777376,-0.004645759004504146,3.7059364318847656,54.07172000000001 +740,Regression,k-Nearest Neighbors,TrumpApproval,0.6437102684684685,1.7904191974020043,0.02211499739805689,3.8005104064941406,58.24412800000001 +760,Regression,k-Nearest Neighbors,TrumpApproval,0.6465423666666668,1.7722456151874884,0.03148604083873929,3.8912620544433594,62.47501600000001 +780,Regression,k-Nearest Neighbors,TrumpApproval,0.6423591829059828,1.752432393946061,0.0487781645727875,3.987659454345703,66.76475200000002 +800,Regression,k-Nearest Neighbors,TrumpApproval,0.6415445258333332,1.7335108155585357,0.060790557140155244,4.087154388427734,71.11932700000001 +820,Regression,k-Nearest Neighbors,TrumpApproval,0.641812437398374,1.7198679523833968,0.06536301290969171,4.179523468017578,75.53533700000001 +840,Regression,k-Nearest Neighbors,TrumpApproval,0.6391550126984127,1.7023246638821516,0.07583179507597027,4.276576995849609,80.015814 +860,Regression,k-Nearest Neighbors,TrumpApproval,0.6397551612403103,1.6865214638981003,0.0944734629735503,4.372867584228516,84.56990800000001 +880,Regression,k-Nearest Neighbors,TrumpApproval,0.6401663234848486,1.6719359262678322,0.11449022182092672,4.465221405029297,89.18993400000001 +900,Regression,k-Nearest Neighbors,TrumpApproval,0.6373928251851855,1.6559913256631793,0.1276383063389357,4.558887481689453,93.87754600000001 +920,Regression,k-Nearest Neighbors,TrumpApproval,0.6333341724637681,1.6410816825275085,0.12919955333528133,4.652858734130859,98.62624600000001 +940,Regression,k-Nearest Neighbors,TrumpApproval,0.637460545390071,1.6307722122541641,0.13281132177791266,4.746517181396484,103.437785 +960,Regression,k-Nearest Neighbors,TrumpApproval,0.6446958777777775,1.6213030711335545,0.13389079092896516,4.844425201416016,108.312072 +980,Regression,k-Nearest Neighbors,TrumpApproval,0.643768610068027,1.6085965270907718,0.1308548353743899,4.935100555419922,113.251739 +1000,Regression,k-Nearest Neighbors,TrumpApproval,0.6420156240666665,1.59493855356346,0.13116812210504825,5.030651092529297,118.255967 +1001,Regression,k-Nearest Neighbors,TrumpApproval,0.6416785025641023,1.5941707450098015,0.1314249186277071,5.032634735107422,123.301096 +11,Regression,Hoeffding Tree,ChickWeights,8.042756132756132,17.336048579080593,-385.86349170941764,0.016208648681640625,0.002632 +22,Regression,Hoeffding Tree,ChickWeights,4.456785613727984,12.282422261556867,-158.770726389092,0.017787933349609375,0.007319 +33,Regression,Hoeffding Tree,ChickWeights,3.4353973358733074,10.070376517434479,-69.4325218162971,0.023052215576171875,0.013907 +44,Regression,Hoeffding Tree,ChickWeights,2.736909422894262,8.732393473100391,-59.03623058514604,0.024105072021484375,0.021700999999999998 +55,Regression,Hoeffding Tree,ChickWeights,2.788577579622257,8.074088551816661,-11.726025456653014,0.030948638916015625,0.030334999999999997 +66,Regression,Hoeffding Tree,ChickWeights,3.3958800855981375,7.878422021930021,-4.223121571879303,0.040424346923828125,0.040093 +77,Regression,Hoeffding Tree,ChickWeights,3.8895265016210883,7.800910386370324,-2.432180745921895,0.046741485595703125,0.05116999999999999 +88,Regression,Hoeffding Tree,ChickWeights,4.072650698433535,7.572197783925699,-1.9320509270116553,0.052532196044921875,0.06356099999999999 +99,Regression,Hoeffding Tree,ChickWeights,4.410984939713907,7.55185413515251,-1.439151418709002,0.053585052490234375,0.07724199999999999 +110,Regression,Hoeffding Tree,ChickWeights,4.370948473977548,7.327634340090197,-0.6036593212329582,0.055164337158203125,0.09210199999999999 +121,Regression,Hoeffding Tree,ChickWeights,4.401973824893138,7.197046558152955,-0.19144536988389782,0.055164337158203125,0.10816699999999999 +132,Regression,Hoeffding Tree,ChickWeights,4.283071400630936,6.979735895990854,0.08415196835499827,0.055164337158203125,0.13239599999999999 +143,Regression,Hoeffding Tree,ChickWeights,4.169649051526778,6.77851615807502,0.3003478880703081,0.055690765380859375,0.16024499999999997 +154,Regression,Hoeffding Tree,ChickWeights,4.107721988217097,6.620782354691122,0.4327427443050297,0.055690765380859375,0.19148199999999999 +165,Regression,Hoeffding Tree,ChickWeights,4.3861341291386235,6.8739888422895685,0.5084535624523276,0.055690765380859375,0.23199899999999998 +176,Regression,Hoeffding Tree,ChickWeights,4.592324836010107,7.0395287886899816,0.5843455987500039,0.056217193603515625,0.273788 +187,Regression,Hoeffding Tree,ChickWeights,4.658423416973056,7.057579140031887,0.6579286220132116,0.056217193603515625,0.316974 +198,Regression,Hoeffding Tree,ChickWeights,4.6782517314261085,7.042640058036562,0.7290497323677609,0.056217193603515625,0.361531 +209,Regression,Hoeffding Tree,ChickWeights,4.8966529592561265,7.410861778989444,0.7526693351807108,0.02174663543701172,0.409543 +220,Regression,Hoeffding Tree,ChickWeights,5.507880191409123,8.546476599974424,0.7120144996082314,0.02806377410888672,0.458317 +231,Regression,Hoeffding Tree,ChickWeights,5.703958017872014,8.760797449465004,0.7411581545051223,0.03332805633544922,0.507954 +242,Regression,Hoeffding Tree,ChickWeights,5.934527728379076,9.145062262320872,0.7730513990797492,0.03806591033935547,0.576578 +253,Regression,Hoeffding Tree,ChickWeights,6.025889093973978,9.259481324724224,0.7979290061199974,0.04175090789794922,0.647861 +264,Regression,Hoeffding Tree,ChickWeights,6.701040765258382,10.569442782845146,0.7594412957229723,0.041831016540527344,0.7217790000000001 +275,Regression,Hoeffding Tree,ChickWeights,7.201977905163474,11.695812678726385,0.740801257827299,0.041831016540527344,0.7983520000000001 +286,Regression,Hoeffding Tree,ChickWeights,7.4760897436283305,12.176082777300051,0.7566872347890514,0.042357444763183594,0.889757 +297,Regression,Hoeffding Tree,ChickWeights,7.495029117947843,12.186858586615225,0.7886035011133373,0.042357444763183594,0.982264 +308,Regression,Hoeffding Tree,ChickWeights,8.05089484284177,13.06419009031293,0.7836428997387894,0.042357444763183594,1.075782 +319,Regression,Hoeffding Tree,ChickWeights,9.171875092169309,15.802620207207104,0.7127274179827436,0.042357444763183594,1.1703270000000001 +330,Regression,Hoeffding Tree,ChickWeights,9.626867556328977,16.443718231711543,0.7338058453397931,0.042357444763183594,1.26591 +341,Regression,Hoeffding Tree,ChickWeights,9.854283538219805,16.574189924013226,0.7578382368534643,0.042357444763183594,1.362543 +352,Regression,Hoeffding Tree,ChickWeights,10.034558550660114,16.72149964752778,0.7759339138910493,0.042357444763183594,1.460131 +363,Regression,Hoeffding Tree,ChickWeights,10.942839439265006,18.18973374364872,0.7425340708967089,0.042357444763183594,1.65219 +374,Regression,Hoeffding Tree,ChickWeights,11.480189522121245,19.36955258798825,0.7316181626186655,0.042357444763183594,1.847066 +385,Regression,Hoeffding Tree,ChickWeights,11.884428250077962,20.018801475409063,0.7463650656532205,0.042357444763183594,2.044712 +396,Regression,Hoeffding Tree,ChickWeights,12.037067702603977,20.025071614924446,0.7633646392298079,0.042357444763183594,2.245044 +407,Regression,Hoeffding Tree,ChickWeights,12.938689395183468,21.571547182252875,0.7447563988620904,0.039313316345214844,2.459951 +418,Regression,Hoeffding Tree,ChickWeights,13.737065020554605,23.070023559587742,0.7259561921053947,0.039839744567871094,2.675857 +429,Regression,Hoeffding Tree,ChickWeights,14.305628841534727,24.020997573013894,0.7359868139097058,0.040892601013183594,2.892761 +440,Regression,Hoeffding Tree,ChickWeights,14.503019064271445,24.118168317988548,0.7526847575357923,0.041419029235839844,3.110678 +451,Regression,Hoeffding Tree,ChickWeights,15.042001004765993,24.757154413851225,0.7504844548860922,0.042998313903808594,3.329579 +462,Regression,Hoeffding Tree,ChickWeights,16.165694044127083,26.934291479182736,0.7226050873941003,0.043524742126464844,3.549505 +473,Regression,Hoeffding Tree,ChickWeights,16.958578383564387,28.26726815061745,0.7302155620528221,0.043524742126464844,3.77047 +484,Regression,Hoeffding Tree,ChickWeights,17.309589456804158,28.5754148947933,0.7394096166099926,0.043524742126464844,4.010874 +495,Regression,Hoeffding Tree,ChickWeights,17.77955786237919,29.119281838039548,0.7454446166142166,0.043524742126464844,4.254034 +506,Regression,Hoeffding Tree,ChickWeights,18.687135400012505,30.600738447390604,0.7270552375925041,0.043524742126464844,4.499866 +517,Regression,Hoeffding Tree,ChickWeights,19.426270300418786,31.613839238226678,0.7250895764829616,0.043524742126464844,4.748399 +528,Regression,Hoeffding Tree,ChickWeights,20.230319490239392,32.829508990096734,0.7334580691909136,0.043524742126464844,5.00325 +539,Regression,Hoeffding Tree,ChickWeights,20.415951878027045,32.83473210597698,0.7443832332812113,0.043524742126464844,5.259172 +550,Regression,Hoeffding Tree,ChickWeights,21.41946931942451,34.477948502753435,0.726844465494657,0.043524742126464844,5.516121 +561,Regression,Hoeffding Tree,ChickWeights,22.135259536350134,35.412182207518484,0.7244424125617825,0.043524742126464844,5.774111 +572,Regression,Hoeffding Tree,ChickWeights,22.998428764364284,36.61317436816486,0.7275265693889857,0.044051170349121094,6.033148000000001 +578,Regression,Hoeffding Tree,ChickWeights,23.16185046142029,36.73359474841229,0.7324023432169282,0.044051170349121094,6.293050000000001 +20,Regression,Hoeffding Tree,TrumpApproval,4.834704431652337,13.708514217962266,-439.7934984576362,0.05008697509765625,0.001817 +40,Regression,Hoeffding Tree,TrumpApproval,3.4692310697037447,9.813795721313518,-37.72035957928713,0.07324981689453125,0.005553000000000001 +60,Regression,Hoeffding Tree,TrumpApproval,2.530247618203559,8.024836796214231,-33.90460110966681,0.08588409423828125,0.011369 +80,Regression,Hoeffding Tree,TrumpApproval,2.1398752670733447,6.982837000856316,-25.510487239912003,0.09588623046875,0.023421 +100,Regression,Hoeffding Tree,TrumpApproval,2.2521629689485394,6.362737158647257,-12.810573390910955,0.1053619384765625,0.037893 +120,Regression,Hoeffding Tree,TrumpApproval,2.2753311831165886,5.895687482983747,-9.059182991303912,0.1095733642578125,0.054815 +140,Regression,Hoeffding Tree,TrumpApproval,2.181766409647037,5.493495699082884,-8.025069637302263,0.1116790771484375,0.07421900000000001 +160,Regression,Hoeffding Tree,TrumpApproval,2.0635226048812747,5.165876255053421,-6.037983110569301,0.1158905029296875,0.09851900000000001 +180,Regression,Hoeffding Tree,TrumpApproval,1.9951428730766114,4.906287161641783,-4.575559841528811,0.1179962158203125,0.13018800000000003 +200,Regression,Hoeffding Tree,TrumpApproval,1.8700446037321659,4.662539866408188,-4.050299616280768,0.015085220336914062,0.16948700000000003 +220,Regression,Hoeffding Tree,TrumpApproval,1.7830718267282506,4.458344141345012,-3.981078161152351,0.031249046325683594,0.21017900000000003 +240,Regression,Hoeffding Tree,TrumpApproval,1.714887283408722,4.280191261764102,-3.6254927572925757,0.037039756774902344,0.27354600000000007 +260,Regression,Hoeffding Tree,TrumpApproval,1.6268995152596541,4.116599014627653,-3.336325373761703,0.044569969177246094,0.33844300000000005 +280,Regression,Hoeffding Tree,TrumpApproval,1.6037708656255951,3.992199218884993,-3.269831686495559,0.05755901336669922,0.40512500000000007 +300,Regression,Hoeffding Tree,TrumpApproval,1.5808413297038584,3.882244388071726,-2.9710192082752114,0.06756114959716797,0.4768960000000001 +320,Regression,Hoeffding Tree,TrumpApproval,1.5112246352788372,3.7620340381312185,-2.9135432145577016,0.07545757293701172,0.5560110000000001 +340,Regression,Hoeffding Tree,TrumpApproval,1.464954049061847,3.6574443601858126,-2.9089002921657214,0.08072185516357422,0.6372390000000001 +360,Regression,Hoeffding Tree,TrumpApproval,1.4845626481571885,3.5832345434246853,-2.782695640732784,0.08861827850341797,0.7206760000000001 +380,Regression,Hoeffding Tree,TrumpApproval,1.4519403327978173,3.4965427251184518,-2.72647470962537,0.09388256072998047,0.8063740000000001 +400,Regression,Hoeffding Tree,TrumpApproval,1.4093274160891025,3.4133346926199284,-2.6515915354000197,0.10125255584716797,0.8943350000000001 +420,Regression,Hoeffding Tree,TrumpApproval,1.3677964737960675,3.3343173536823296,-2.5997996751089016,0.10546398162841797,1.079576 +440,Regression,Hoeffding Tree,TrumpApproval,1.3357172246731819,3.2621145597551164,-2.3832380441779537,0.11125469207763672,1.2716070000000002 +460,Regression,Hoeffding Tree,TrumpApproval,1.3223220949397412,3.20054856097613,-2.088360697350681,0.11967754364013672,1.466366 +480,Regression,Hoeffding Tree,TrumpApproval,1.2961820395725512,3.1370925842333546,-1.8988499404168713,0.12757396697998047,1.663894 +500,Regression,Hoeffding Tree,TrumpApproval,1.2652762767168435,3.076750388249757,-1.7299037995212605,0.13231182098388672,1.864298 +520,Regression,Hoeffding Tree,TrumpApproval,1.2471740635308572,3.0222901376128295,-1.6387160551274738,0.13757610321044922,2.0709020000000002 +540,Regression,Hoeffding Tree,TrumpApproval,1.222508472081129,2.9683885282447466,-1.5361060189709668,0.13968181610107422,2.286544 +560,Regression,Hoeffding Tree,TrumpApproval,1.2073384071706728,2.920065266046622,-1.5126838513129575,0.14441967010498047,2.5053300000000003 +580,Regression,Hoeffding Tree,TrumpApproval,1.1845779132924192,2.8723790540044147,-1.4914188956527816,0.14705181121826172,2.7271330000000003 +600,Regression,Hoeffding Tree,TrumpApproval,1.1745692976588702,2.8296294830278073,-1.3910651808346999,0.14148998260498047,2.96944 +620,Regression,Hoeffding Tree,TrumpApproval,1.1708259630571383,2.7920061348512903,-1.2924200227078337,0.14412212371826172,3.214893 +640,Regression,Hoeffding Tree,TrumpApproval,1.1599967464968943,2.7528504813508814,-1.186915838733254,0.14622783660888672,3.481443 +660,Regression,Hoeffding Tree,TrumpApproval,1.1455993461288598,2.715465758170179,-1.112620243595547,0.09333324432373047,3.768351 +680,Regression,Hoeffding Tree,TrumpApproval,1.1331386715536063,2.679518493749607,-1.0895638535289454,0.10280895233154297,4.057625 +700,Regression,Hoeffding Tree,TrumpApproval,1.1287919059851137,2.648832972736431,-1.0956110522943683,0.11070537567138672,4.349483 +720,Regression,Hoeffding Tree,TrumpApproval,1.1090542602054634,2.6130484736329,-1.0841769561048746,0.11702251434326172,4.655905000000001 +740,Regression,Hoeffding Tree,TrumpApproval,1.0919225542546631,2.579731998640208,-1.0301471378292058,0.12070751190185547,4.969391000000001 +760,Regression,Hoeffding Tree,TrumpApproval,1.0729346607841277,2.546521266569091,-0.9996439724530697,0.12386608123779297,5.409378000000001 +780,Regression,Hoeffding Tree,TrumpApproval,1.0548522699101792,2.514796200212546,-0.958866579835745,0.13018321990966797,5.856313000000001 +800,Regression,Hoeffding Tree,TrumpApproval,1.0458975693179249,2.4863814517835756,-0.9321678603320387,0.14051342010498047,6.306401000000001 +820,Regression,Hoeffding Tree,TrumpApproval,1.042667475968943,2.463395040447954,-0.9174360179218257,0.14683055877685547,6.759550000000001 +840,Regression,Hoeffding Tree,TrumpApproval,1.0338402028724885,2.4371652901742165,-0.8942452584110789,0.15051555633544922,7.215791000000001 +860,Regression,Hoeffding Tree,TrumpApproval,1.0182769822752689,2.409744604248102,-0.8486703239118398,0.15209484100341797,7.680462000000001 +880,Regression,Hoeffding Tree,TrumpApproval,1.0072949101764561,2.3841216724611445,-0.8005738256179296,0.15525341033935547,8.149683000000001 +900,Regression,Hoeffding Tree,TrumpApproval,0.9984699415812968,2.359722022526475,-0.7713409518355698,0.15735912322998047,8.622145000000002 +920,Regression,Hoeffding Tree,TrumpApproval,0.9848390746890626,2.3349754381173082,-0.7628805257854674,0.12545299530029297,9.108636 +940,Regression,Hoeffding Tree,TrumpApproval,0.9804934467335737,2.3136297350671566,-0.7454793227879806,0.13442516326904297,9.599086 +960,Regression,Hoeffding Tree,TrumpApproval,0.9715993160407668,2.291923159938466,-0.7307898991199615,0.14021587371826172,10.092417 +980,Regression,Hoeffding Tree,TrumpApproval,0.96479276321034,2.271398262551761,-0.7329444574748756,0.14442729949951172,10.588738 +1000,Regression,Hoeffding Tree,TrumpApproval,0.9567764270416781,2.250974677037298,-0.7305695321170174,0.14863872528076172,11.174487 +1001,Regression,Hoeffding Tree,TrumpApproval,0.9561028857812052,2.249867758958838,-0.7300222157865335,0.14863872528076172,11.765557999999999 +11,Regression,Hoeffding Adaptive Tree,ChickWeights,8.051220648038832,17.336198122120386,-385.87016600913427,0.022922515869140625,0.002862 +22,Regression,Hoeffding Adaptive Tree,ChickWeights,4.498502947359929,12.285286375364281,-158.84524831763767,0.024562835693359375,0.008031 +33,Regression,Hoeffding Adaptive Tree,ChickWeights,3.4668695042339137,10.074636808082968,-69.49212762837747,0.029827117919921875,0.0152 +44,Regression,Hoeffding Adaptive Tree,ChickWeights,2.7637805804889553,8.735764655686483,-59.08259408516962,0.030941009521484375,0.027573 +55,Regression,Hoeffding Adaptive Tree,ChickWeights,2.814517498310432,8.074396776941786,-11.726997097138026,0.037784576416015625,0.040817 +66,Regression,Hoeffding Adaptive Tree,ChickWeights,3.396900059747575,7.862006773633152,-4.201378762014764,0.047260284423828125,0.055065 +77,Regression,Hoeffding Adaptive Tree,ChickWeights,3.8844336568547537,7.782255505653143,-2.415785129732385,0.053638458251953125,0.07050300000000001 +88,Regression,Hoeffding Adaptive Tree,ChickWeights,4.068768385552718,7.555909217267645,-1.9194502155140074,0.059429168701171875,0.08723500000000001 +99,Regression,Hoeffding Adaptive Tree,ChickWeights,4.319029347030655,7.489629607912237,-1.3991215781815165,0.060482025146484375,0.105314 +110,Regression,Hoeffding Adaptive Tree,ChickWeights,4.231978704025333,7.230698639905546,-0.5615110336669555,0.062061309814453125,0.124657 +121,Regression,Hoeffding Adaptive Tree,ChickWeights,4.279767976439616,7.114292598648662,-0.1642036472993016,0.062061309814453125,0.145348 +132,Regression,Hoeffding Adaptive Tree,ChickWeights,4.161677712403324,6.8979209349412445,0.1054968774084013,0.062061309814453125,0.183683 +143,Regression,Hoeffding Adaptive Tree,ChickWeights,4.036201943040193,6.686446116179646,0.3192250351622916,0.024164199829101562,0.23334700000000003 +154,Regression,Hoeffding Adaptive Tree,ChickWeights,4.002163310161137,6.555243218534794,0.4439177197734564,0.034926414489746094,0.28395200000000004 +165,Regression,Hoeffding Adaptive Tree,ChickWeights,4.269310553181931,6.794169336453219,0.5198027804498322,0.041365623474121094,0.33552800000000005 +176,Regression,Hoeffding Adaptive Tree,ChickWeights,4.394431170074558,6.916563516446891,0.5987399306940604,0.047156333923339844,0.38817000000000007 +187,Regression,Hoeffding Adaptive Tree,ChickWeights,4.429782113532627,6.896434310822903,0.6733712331422652,0.052016258239746094,0.44195900000000005 +198,Regression,Hoeffding Adaptive Tree,ChickWeights,4.448580123995543,6.86078369215091,0.7428621234581485,0.054648399353027344,0.49692500000000006 +209,Regression,Hoeffding Adaptive Tree,ChickWeights,4.634718338792146,7.17917659207716,0.7678921596594357,0.054648399353027344,0.5531490000000001 +220,Regression,Hoeffding Adaptive Tree,ChickWeights,5.229854791420841,8.435313620968111,0.7194573631198581,0.055296897888183594,0.6106500000000001 +231,Regression,Hoeffding Adaptive Tree,ChickWeights,5.4006373247873825,8.615072190659467,0.7496975788166091,0.055296897888183594,0.6850710000000001 +242,Regression,Hoeffding Adaptive Tree,ChickWeights,5.6226073005416035,8.982158345389516,0.781064800957145,0.055296897888183594,0.7630730000000001 +253,Regression,Hoeffding Adaptive Tree,ChickWeights,5.728895576419993,9.10264619767678,0.8047163053551843,0.055296897888183594,0.8439190000000001 +264,Regression,Hoeffding Adaptive Tree,ChickWeights,6.468790531655633,10.532848432020362,0.7611041743489119,0.05537700653076172,0.926058 +275,Regression,Hoeffding Adaptive Tree,ChickWeights,6.961259791220884,11.725202267966395,0.7394969764024641,0.05537700653076172,1.0095260000000001 +286,Regression,Hoeffding Adaptive Tree,ChickWeights,7.243017687832032,12.175095097400797,0.7567267064951951,0.05391216278076172,1.109836 +297,Regression,Hoeffding Adaptive Tree,ChickWeights,7.333189926829036,12.221129948725446,0.7874128689691341,0.05443859100341797,1.3004930000000001 +308,Regression,Hoeffding Adaptive Tree,ChickWeights,7.907494608974745,13.13418786953933,0.7813182108747583,0.05456066131591797,1.4945650000000001 +319,Regression,Hoeffding Adaptive Tree,ChickWeights,9.086203691627809,16.084282058543664,0.7023956098414756,0.05613994598388672,1.6919570000000002 +330,Regression,Hoeffding Adaptive Tree,ChickWeights,9.398286710797228,16.38837159928856,0.7355947540985646,0.05613994598388672,1.900794 +341,Regression,Hoeffding Adaptive Tree,ChickWeights,9.688169379844998,16.65705092991554,0.7554108572015372,0.05613994598388672,2.110987 +352,Regression,Hoeffding Adaptive Tree,ChickWeights,9.856066264187849,16.815734957180027,0.7734013139584004,0.05613994598388672,2.322457 +363,Regression,Hoeffding Adaptive Tree,ChickWeights,10.788654210226415,18.368645129880047,0.7374443731514406,0.05613994598388672,2.535213 +374,Regression,Hoeffding Adaptive Tree,ChickWeights,11.535989444086796,20.177763325541775,0.7087539856658172,0.0658864974975586,2.749718 +385,Regression,Hoeffding Adaptive Tree,ChickWeights,11.949331836981814,20.800028245688587,0.7261827687212361,0.0713338851928711,2.965855 +396,Regression,Hoeffding Adaptive Tree,ChickWeights,11.958714190964645,20.660643879084812,0.748105206776327,0.07817745208740234,3.1836569999999997 +407,Regression,Hoeffding Adaptive Tree,ChickWeights,12.807531574997112,22.01468171576837,0.7341619793955468,0.08257198333740234,3.4189649999999996 +418,Regression,Hoeffding Adaptive Tree,ChickWeights,13.71794187476778,23.73901232910809,0.7098322050491193,0.08467769622802734,3.6591389999999997 +429,Regression,Hoeffding Adaptive Tree,ChickWeights,14.269314924317156,24.652748132937095,0.7219171428567855,0.06568050384521484,3.9122609999999995 +440,Regression,Hoeffding Adaptive Tree,ChickWeights,14.511771919641937,24.834167752766053,0.7377826277560943,0.0706624984741211,4.16702 +451,Regression,Hoeffding Adaptive Tree,ChickWeights,15.00667707818897,25.401748915029017,0.7373221851710817,0.07874202728271484,4.423509 +462,Regression,Hoeffding Adaptive Tree,ChickWeights,16.106263610815663,27.4394567629727,0.7121021651653525,0.0857076644897461,4.681795 +473,Regression,Hoeffding Adaptive Tree,ChickWeights,16.950411373417108,28.951900473786843,0.7169889638801871,0.0888662338256836,4.941903 +484,Regression,Hoeffding Adaptive Tree,ChickWeights,17.321905164714362,29.29627092175635,0.7260962478080234,0.0889272689819336,5.203768 +495,Regression,Hoeffding Adaptive Tree,ChickWeights,17.829552469069228,29.855361574147427,0.732412614196017,0.0889272689819336,5.467412 +506,Regression,Hoeffding Adaptive Tree,ChickWeights,18.715769054600834,31.21095148117224,0.7160610523989874,0.08951473236083984,5.743327000000001 +517,Regression,Hoeffding Adaptive Tree,ChickWeights,19.54236471467993,32.39367117342827,0.7113596352744775,0.0743856430053711,6.026441000000001 +528,Regression,Hoeffding Adaptive Tree,ChickWeights,20.379374275832948,33.670378810622296,0.7196292071862618,0.0787191390991211,6.311317000000001 +539,Regression,Hoeffding Adaptive Tree,ChickWeights,20.522458105265056,33.639909372937744,0.7316929916628531,0.0872030258178711,6.597982000000001 +550,Regression,Hoeffding Adaptive Tree,ChickWeights,21.5114661084191,35.24478084224406,0.714558707096332,0.0935201644897461,6.886526000000001 +561,Regression,Hoeffding Adaptive Tree,ChickWeights,22.293418976341684,36.29050935662323,0.7106036021726428,0.09343624114990234,7.177067000000001 +572,Regression,Hoeffding Adaptive Tree,ChickWeights,23.158877831353536,37.47206255417766,0.7145930209145848,0.09461116790771484,7.581349000000001 +578,Regression,Hoeffding Adaptive Tree,ChickWeights,23.373902189510932,37.6579284312523,0.7187656938003131,0.09473323822021484,7.990285000000001 +20,Regression,Hoeffding Adaptive Tree,TrumpApproval,4.828377634536296,13.70786256219322,-439.7515918302183,0.05686187744140625,0.005477 +40,Regression,Hoeffding Adaptive Tree,TrumpApproval,3.453811275213839,9.811073218407973,-37.69887927291551,0.08008575439453125,0.014203 +60,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.5116544078850294,8.021960641037959,-33.879585508404254,0.09272003173828125,0.025216000000000002 +80,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.1224425015381523,6.9797990571526345,-25.487425023640153,0.102783203125,0.038581000000000004 +100,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.246653919301699,6.363694444016854,-12.814729355257526,0.1122589111328125,0.054574000000000004 +120,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.270681160376927,5.896666779393501,-9.062525006956841,0.1164703369140625,0.096737 +140,Regression,Hoeffding Adaptive Tree,TrumpApproval,2.162967815650222,5.491011289549727,-8.016908386196121,0.1185760498046875,0.14519300000000002 +160,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.9648637778298337,5.1475477542568076,-5.988130255135697,0.048110008239746094,0.20161800000000002 +180,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.8665278782891501,4.8758843309507505,-4.506673701927233,0.06455135345458984,0.259848 +200,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.773434994745299,4.638841370319518,-3.9990913279754245,0.07511425018310547,0.334681 +220,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.6594682627798778,4.42936028038101,-3.916524330360767,0.0809926986694336,0.415547 +240,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.5811297097344512,4.24689633509078,-3.553810703437006,0.0831594467163086,0.499019 +260,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4918706813368772,4.083314206963185,-3.2664860479391056,0.08697795867919922,0.584777 +280,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4582505621214346,3.950619643811522,-3.181352514384196,0.09651470184326172,0.672987 +300,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.4293807431017047,3.836527362327468,-2.8780450161882043,0.10505962371826172,0.763791 +320,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3766835460490845,3.7183907131031066,-2.8232679475596494,0.11137676239013672,0.862886 +340,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3285707966483495,3.6114631285578054,-2.8112330604866624,0.09692668914794922,1.077289 +360,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3305028688272291,3.538102571280229,-2.6880072396238157,0.10482311248779297,1.294249 +380,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.3086678355415842,3.4529556765760527,-2.6341471363086995,0.11014842987060547,1.513868 +400,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.256053624567095,3.3666460142322228,-2.552379472359031,0.11751842498779297,1.736306 +420,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.2254239545780012,3.2887455105144454,-2.5020714662192383,0.12172985076904297,1.9788649999999999 +440,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.204020924712129,3.2198773978896,-2.2961943419959137,0.12752056121826172,2.244474 +460,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1975328241312166,3.1601130927415366,-2.010817456858815,0.13547801971435547,2.513147 +480,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.186148143661266,3.1001176815841753,-1.8309188655239268,0.14337444305419922,2.784897 +500,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1667856894749518,3.0429667282148514,-1.6702825792738007,0.13623332977294922,3.07197 +520,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.153194144927427,2.98944402729251,-1.5816728306403074,0.14155864715576172,3.362254 +540,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1356058423088553,2.9370365647466374,-1.4828164968540292,0.14366436004638672,3.6556480000000002 +560,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.125648357086568,2.890393580385493,-1.4618789770567937,0.14840221405029297,3.952243 +580,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.1072323197222282,2.84377722554966,-1.4420491211959612,0.15103435516357422,4.252035 +600,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0962221602561253,2.8010574809052513,-1.343021715112041,0.15471935272216797,4.557891000000001 +620,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0955492072151647,2.7650292224496735,-1.2483344123018605,0.15787792205810547,4.883158000000001 +640,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.085957414095071,2.726589883354214,-1.1453910301968575,0.15998363494873047,5.354946000000001 +660,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0751762466913892,2.6908702968299423,-1.074523242859362,0.16366863250732422,5.834119000000001 +680,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0667684392102676,2.656475453821568,-1.0537791659469917,0.16630077362060547,6.316591000000001 +700,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0667188907522647,2.6278494556992995,-1.0625405514881172,0.15471935272216797,6.8062700000000005 +720,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0487756272096453,2.5923957614440996,-1.0513617944147002,0.15945720672607422,7.299083 +740,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0336933816342644,2.5596915816453274,-0.9987276211091367,0.16320323944091797,7.795134000000001 +760,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0143808347523189,2.5263993770636084,-0.9681675843117681,0.16478252410888672,8.298925 +780,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0004245938094416,2.495691505058861,-0.9292169429583497,0.16958141326904297,8.809149000000001 +800,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9976736219043986,2.469777786083391,-0.9064485942635294,0.16168498992919922,9.32665 +820,Regression,Hoeffding Adaptive Tree,TrumpApproval,1.0020392091388555,2.450590646975973,-0.8975546778436754,0.16431713104248047,9.853038000000002 +840,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9936292081382507,2.424886643827349,-0.8752066007627983,0.16800212860107422,10.484318000000002 +860,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9794930742877991,2.3980423354299125,-0.8307587924463844,0.17010784149169922,11.125036000000001 +880,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9694853941742789,2.372794343098121,-0.7835048635250907,0.17273998260498047,11.769143000000001 +900,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9594920424525858,2.348266033222206,-0.7541836724323567,0.11153697967529297,12.432199 +920,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9482726802907966,2.324135545417226,-0.7465505219679065,0.11639690399169922,13.105410000000001 +940,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9455376055826031,2.30345366329758,-0.7301587545146957,0.12230968475341797,13.781537 +960,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9379298129457146,2.2821811442731286,-0.7161074287562055,0.12967967987060547,14.460614 +980,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.930996996530802,2.261860474984104,-0.7184214614837348,0.13494396209716797,15.148323 +1000,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9214575102921838,2.2404008018877137,-0.714349138962711,0.13822460174560547,15.950631 +1001,Regression,Hoeffding Adaptive Tree,TrumpApproval,0.9213134079227226,2.239416179559339,-0.7139861919037982,0.13822460174560547,16.757639 +11,Regression,Stochastic Gradient Tree,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.009614944458007812,0.001328 +22,Regression,Stochastic Gradient Tree,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012609481811523438,0.003944 +33,Regression,Stochastic Gradient Tree,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.015787124633789062,0.007623 +44,Regression,Stochastic Gradient Tree,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.018873214721679688,0.012489 +55,Regression,Stochastic Gradient Tree,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.021825790405273438,0.019505 +66,Regression,Stochastic Gradient Tree,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.024618148803710938,0.027128 +77,Regression,Stochastic Gradient Tree,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.027502059936523438,0.035372 +88,Regression,Stochastic Gradient Tree,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.030019760131835938,0.047911 +99,Regression,Stochastic Gradient Tree,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.03290367126464844,0.072727 +110,Regression,Stochastic Gradient Tree,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.26967811584472656,0.103163 +121,Regression,Stochastic Gradient Tree,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.26967811584472656,0.146595 +132,Regression,Stochastic Gradient Tree,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.26967811584472656,0.196283 +143,Regression,Stochastic Gradient Tree,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.26967811584472656,0.25852200000000003 +154,Regression,Stochastic Gradient Tree,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.26967811584472656,0.329566 +165,Regression,Stochastic Gradient Tree,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.26967811584472656,0.40393 +176,Regression,Stochastic Gradient Tree,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.26967811584472656,0.481694 +187,Regression,Stochastic Gradient Tree,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.26967811584472656,0.681251 +198,Regression,Stochastic Gradient Tree,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.26967811584472656,0.884966 +209,Regression,Stochastic Gradient Tree,ChickWeights,55.16354936929098,57.0482200725598,-13.656313160472004,0.6838865280151367,1.1316950000000001 +220,Regression,Stochastic Gradient Tree,ChickWeights,53.62203856749311,56.03531795068661,-11.37998411824978,0.6869077682495117,1.3969520000000002 +231,Regression,Stochastic Gradient Tree,ChickWeights,52.77279286370195,55.29408706815337,-9.311090357596036,0.6899290084838867,1.6754760000000002 +242,Regression,Stochastic Gradient Tree,ChickWeights,52.49661908339594,55.007104536867395,-7.210918602421254,0.6929502487182617,1.9600240000000002 +253,Regression,Stochastic Gradient Tree,ChickWeights,52.25631812193077,54.713446605156875,-6.055353919833875,0.6947126388549805,2.2702780000000002 +264,Regression,Stochastic Gradient Tree,ChickWeights,51.62511478420569,54.312843786153664,-5.352168023774992,0.6947126388549805,2.586688 +275,Regression,Stochastic Gradient Tree,ChickWeights,51.4425344352617,54.29364548356293,-4.585603291722447,0.6947126388549805,2.915419 +286,Regression,Stochastic Gradient Tree,ChickWeights,51.75651621106165,54.635705044608144,-3.8989478253777694,0.6947126388549805,3.266148 +297,Regression,Stochastic Gradient Tree,ChickWeights,52.373839404142416,55.25476711535166,-3.3456400671942,0.6947126388549805,3.622985 +308,Regression,Stochastic Gradient Tree,ChickWeights,52.87239275875638,55.86677247417265,-2.9565197175813713,0.6947126388549805,3.98691 +319,Regression,Stochastic Gradient Tree,ChickWeights,52.69554478958866,56.2770501442128,-2.6433309475704183,0.6947126388549805,4.356941 +330,Regression,Stochastic Gradient Tree,ChickWeights,53.85316804407712,57.75044402630399,-2.2832890424968193,0.6947126388549805,4.733992 +341,Regression,Stochastic Gradient Tree,ChickWeights,54.90678041411178,59.01114057562677,-2.0697921090482247,0.6947126388549805,5.128946 +352,Regression,Stochastic Gradient Tree,ChickWeights,56.00533746556472,60.302245208561004,-1.9140207825503284,0.6947126388549805,5.54848 +363,Regression,Stochastic Gradient Tree,ChickWeights,55.99599298772852,60.54917173074773,-1.852879941931207,0.6947126388549805,6.1724879999999995 +374,Regression,Stochastic Gradient Tree,ChickWeights,56.87222492302705,61.81275171085535,-1.7331917323651345,0.6947126388549805,6.808446 +385,Regression,Stochastic Gradient Tree,ChickWeights,58.41786698150333,63.95254893573906,-1.5885028214279253,0.6947126388549805,7.450193 +396,Regression,Stochastic Gradient Tree,ChickWeights,59.7033976124885,65.46926983257002,-1.5293357430909813,0.6947126388549805,8.100657 +407,Regression,Stochastic Gradient Tree,ChickWeights,60.057805647389294,66.17359973042984,-1.4019380007417155,1.1097631454467773,8.796904 +418,Regression,Stochastic Gradient Tree,ChickWeights,59.7070864579051,66.11592086962122,-1.2507954049688483,1.1127843856811523,9.50192 +429,Regression,Stochastic Gradient Tree,ChickWeights,60.122823673891816,66.73609937588846,-1.0378169857688957,1.1158056259155273,10.222461000000001 +440,Regression,Stochastic Gradient Tree,ChickWeights,60.39504675635191,66.96100690444877,-0.906365593827489,1.1188268661499023,10.951743 +451,Regression,Stochastic Gradient Tree,ChickWeights,60.27126048587789,66.93502892662679,-0.8239085862185902,1.120589256286621,11.696828 +462,Regression,Stochastic Gradient Tree,ChickWeights,60.340686610373176,67.43825007380137,-0.7390015352251049,1.120589256286621,12.465469 +473,Regression,Stochastic Gradient Tree,ChickWeights,61.40703262301831,69.11306667757516,-0.6127592621572406,1.120589256286621,13.248766 +484,Regression,Stochastic Gradient Tree,ChickWeights,61.95796621360106,69.71422620021941,-0.5510154280248158,1.120589256286621,14.047315 +495,Regression,Stochastic Gradient Tree,ChickWeights,62.59018166487368,70.55352405729404,-0.4943708535906215,1.120589256286621,14.854826 +506,Regression,Stochastic Gradient Tree,ChickWeights,62.49664579133251,70.88193125644693,-0.46447524520130457,1.120589256286621,15.674674999999999 +517,Regression,Stochastic Gradient Tree,ChickWeights,63.25224079915844,71.92080214464903,-0.4228062717918979,1.120589256286621,16.51129 +528,Regression,Stochastic Gradient Tree,ChickWeights,64.80783657170488,74.3681944005728,-0.36776422230083305,1.120589256286621,17.364023 +539,Regression,Stochastic Gradient Tree,ChickWeights,65.59959781369417,75.30113885843834,-0.3443906138479853,1.120589256286621,18.342071999999998 +550,Regression,Stochastic Gradient Tree,ChickWeights,65.79684627343133,76.01328745307667,-0.32771909731089166,1.120589256286621,19.334775999999998 +561,Regression,Stochastic Gradient Tree,ChickWeights,66.6512855136148,77.20436469287773,-0.30975691666695093,1.120589256286621,20.336346 +572,Regression,Stochastic Gradient Tree,ChickWeights,68.11975592628174,79.56492566870935,-0.2867456678376987,1.120589256286621,21.353617 +578,Regression,Stochastic Gradient Tree,ChickWeights,68.75877313437184,80.35800679505147,-0.2806007657015741,1.120589256286621,22.38029 +20,Regression,Stochastic Gradient Tree,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.019941329956054688,0.002168 +40,Regression,Stochastic Gradient Tree,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.03173637390136719,0.006794 +60,Regression,Stochastic Gradient Tree,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.04389762878417969,0.018434 +80,Regression,Stochastic Gradient Tree,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.05624198913574219,0.031286 +100,Regression,Stochastic Gradient Tree,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.5381031036376953,0.048039 +120,Regression,Stochastic Gradient Tree,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.5386066436767578,0.080711 +140,Regression,Stochastic Gradient Tree,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.5391101837158203,0.16679100000000002 +160,Regression,Stochastic Gradient Tree,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.5393619537353516,0.262676 +180,Regression,Stochastic Gradient Tree,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.5396137237548828,0.43318 +200,Regression,Stochastic Gradient Tree,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.5077581405639648,0.638958 +220,Regression,Stochastic Gradient Tree,TrumpApproval,36.497516001377406,38.019453444701035,-361.2329206514933,1.3602590560913086,0.9135530000000001 +240,Regression,Stochastic Gradient Tree,TrumpApproval,33.64243104419191,36.40668421494773,-333.65237138497804,1.360762596130371,1.221179 +260,Regression,Stochastic Gradient Tree,TrumpApproval,31.222114965034955,34.98371838354962,-312.16748668977897,1.3610143661499023,1.570709 +280,Regression,Stochastic Gradient Tree,TrumpApproval,29.182059468614717,33.71869814960704,-303.5986275675674,1.361769676208496,1.939253 +300,Regression,Stochastic Gradient Tree,TrumpApproval,27.34275770505051,32.57805191350732,-278.63174197976707,1.3620214462280273,2.3245549999999997 +320,Regression,Stochastic Gradient Tree,TrumpApproval,25.81388747443183,31.5521424826706,-274.2849072221064,1.3630285263061523,2.8771169999999997 +340,Regression,Stochastic Gradient Tree,TrumpApproval,24.51835124153299,30.62414457186519,-273.0482727941538,1.3640356063842773,3.4469999999999996 +360,Regression,Stochastic Gradient Tree,TrumpApproval,23.451930423400693,29.787924926455332,-260.4155562259403,1.3660497665405273,4.029196 +380,Regression,Stochastic Gradient Tree,TrumpApproval,22.468440533492842,29.014219480552867,-255.59151052979877,1.3665533065795898,4.629964999999999 +400,Regression,Stochastic Gradient Tree,TrumpApproval,21.594907007575774,28.301677882839346,-250.0434007116766,0.510127067565918,5.253793 +420,Regression,Stochastic Gradient Tree,TrumpApproval,20.62268781294523,27.62086591367872,-246.0239415518119,1.3623762130737305,5.968102 +440,Regression,Stochastic Gradient Tree,TrumpApproval,19.786863931462925,26.990398924900393,-230.60756767519214,1.3643903732299805,6.700306 +460,Regression,Stochastic Gradient Tree,TrumpApproval,19.05732899619648,26.404670160589287,-209.2038511633616,1.3666563034057617,7.451319000000001 +480,Regression,Stochastic Gradient Tree,TrumpApproval,18.376512097202227,25.854792215140314,-195.90337768575387,1.3701810836791992,8.221931000000001 +500,Regression,Stochastic Gradient Tree,TrumpApproval,17.755044410127518,25.338820973360427,-184.15507530651482,1.3716917037963867,9.124580000000002 +520,Regression,Stochastic Gradient Tree,TrumpApproval,17.16611419898163,24.851444862058347,-177.4118263333629,1.3737058639526367,10.044684000000002 +540,Regression,Stochastic Gradient Tree,TrumpApproval,16.628565596068775,24.392285078947275,-170.25012213753183,1.3747129440307617,10.981068000000002 +560,Regression,Stochastic Gradient Tree,TrumpApproval,16.091244232649693,23.955027361350904,-168.10096043791202,1.3752164840698242,11.990243000000003 +580,Regression,Stochastic Gradient Tree,TrumpApproval,15.590768135673304,23.54051091957351,-166.33817208986073,1.3764753341674805,13.016881000000003 +600,Regression,Stochastic Gradient Tree,TrumpApproval,15.168708628495342,23.15108754841241,-159.05714501634571,0.5124959945678711,14.090212000000003 +620,Regression,Stochastic Gradient Tree,TrumpApproval,14.742446374247313,22.779539618023726,-151.59887848495535,3.0642080307006836,15.285325000000004 +640,Regression,Stochastic Gradient Tree,TrumpApproval,14.319364852585176,22.42187566882095,-144.08105420081068,3.0679845809936523,16.529242000000004 +660,Regression,Stochastic Gradient Tree,TrumpApproval,13.916412195872256,22.080274918425697,-138.68241285181185,3.0712575912475586,17.842975000000003 +680,Regression,Stochastic Gradient Tree,TrumpApproval,13.515604789075645,21.753254558457893,-136.71797028279042,3.074782371520996,19.280557 +700,Regression,Stochastic Gradient Tree,TrumpApproval,13.16391092204058,21.44141764506316,-136.3120101768532,3.0773000717163086,20.753146 +720,Regression,Stochastic Gradient Tree,TrumpApproval,12.828283113852926,21.142484202016185,-135.44313416922282,3.078558921813965,22.284495 +740,Regression,Stochastic Gradient Tree,TrumpApproval,12.504466467012781,20.855361315179096,-131.6825380828392,3.0800695419311523,23.930702 +760,Regression,Stochastic Gradient Tree,TrumpApproval,12.187542748969033,20.57929219886472,-129.592708960364,3.0813283920288086,25.608717 +780,Regression,Stochastic Gradient Tree,TrumpApproval,11.899403743710545,20.31464229706916,-126.82553676745258,3.08359432220459,27.347365999999997 +800,Regression,Stochastic Gradient Tree,TrumpApproval,11.634366305883285,20.06137952581079,-124.7856004590591,3.084601402282715,29.130084999999998 +820,Regression,Stochastic Gradient Tree,TrumpApproval,11.363415331478278,19.815492221289514,-123.0687724200615,3.08560848236084,30.98707 +840,Regression,Stochastic Gradient Tree,TrumpApproval,11.106640469158773,19.57848368678801,-121.24309788996561,3.086615562438965,32.880055 +860,Regression,Stochastic Gradient Tree,TrumpApproval,10.873909665943762,19.350226189127362,-118.20364312373843,3.0871191024780273,34.808534 +880,Regression,Stochastic Gradient Tree,TrumpApproval,10.65545006969969,19.130035299019603,-114.92727947355435,3.0873708724975586,36.791638 +900,Regression,Stochastic Gradient Tree,TrumpApproval,10.439309697188909,18.916827199314994,-112.83532852765143,3.08762264251709,38.832751 +920,Regression,Stochastic Gradient Tree,TrumpApproval,10.21789524284777,18.710158789526105,-112.19133803320567,3.087874412536621,40.951802 +940,Regression,Stochastic Gradient Tree,TrumpApproval,10.012578535125467,18.510293787577226,-110.72583714230211,3.077906608581543,43.146806 +960,Regression,Stochastic Gradient Tree,TrumpApproval,9.811853150109151,18.316579311485903,-109.54344305213982,3.0804243087768555,45.38444 +980,Regression,Stochastic Gradient Tree,TrumpApproval,9.61909067795052,18.12881604876013,-109.39183420714343,3.080927848815918,47.662490999999996 +1000,Regression,Stochastic Gradient Tree,TrumpApproval,9.438738635632271,17.946847607318464,-109.00797869183796,3.0824384689331055,50.039238 +1001,Regression,Stochastic Gradient Tree,TrumpApproval,9.429746533156267,17.937886241411594,-108.97151968967047,3.0824384689331055,52.450717 +11,Regression,Adaptive Random Forest,ChickWeights,7.837563210503649,16.830121687224917,-363.61289911513376,0.1506052017211914,0.01357 +22,Regression,Adaptive Random Forest,ChickWeights,4.3557641651310055,11.925612892987612,-149.62275175212707,0.1761331558227539,0.033876 +33,Regression,Adaptive Random Forest,ChickWeights,3.3711466349197527,9.780434627556833,-65.4351822307151,0.21426868438720703,0.06570500000000001 +44,Regression,Adaptive Random Forest,ChickWeights,2.6922077728217833,8.482083592242564,-55.643739991610765,0.23125934600830078,0.11059100000000002 +55,Regression,Adaptive Random Forest,ChickWeights,2.74736475641488,7.825318026963682,-10.953904022002215,0.28695011138916016,0.16601600000000002 +66,Regression,Adaptive Random Forest,ChickWeights,2.8724679940162905,7.312536888278379,-3.4997438549991955,0.33327198028564453,0.24397800000000003 +77,Regression,Adaptive Random Forest,ChickWeights,3.0470429271529937,7.064245743713448,-1.8145642692685127,0.3119173049926758,0.346862 +88,Regression,Adaptive Random Forest,ChickWeights,2.9741223361578246,6.690154558226259,-1.288758200280824,0.3463144302368164,0.647937 +99,Regression,Adaptive Random Forest,ChickWeights,3.5306191185317584,6.892431773474538,-1.031779280787943,0.3914194107055664,0.960086 +110,Regression,Adaptive Random Forest,ChickWeights,3.8799314967396747,6.981555605673833,-0.45575716788658527,0.4218912124633789,1.292146 +121,Regression,Adaptive Random Forest,ChickWeights,4.113667008668635,7.033914104811044,-0.13804551272932075,0.4422159194946289,1.662194 +132,Regression,Adaptive Random Forest,ChickWeights,4.34164975929163,7.0584702899254435,0.06337311670854429,0.46953678131103516,2.044753 +143,Regression,Adaptive Random Forest,ChickWeights,4.57586761829926,7.15786747745719,0.2198462773680444,0.5039682388305664,2.459644 +154,Regression,Adaptive Random Forest,ChickWeights,4.72768375743327,7.245199860946492,0.3206991207112422,0.5465364456176758,2.897301 +165,Regression,Adaptive Random Forest,ChickWeights,5.104360720447454,7.731417459148682,0.3781793296599212,0.5543031692504883,3.478625 +176,Regression,Adaptive Random Forest,ChickWeights,5.614563299993537,8.384781892618234,0.41030323544665537,0.577855110168457,4.083596 +187,Regression,Adaptive Random Forest,ChickWeights,6.030281219875818,8.796345271037008,0.46861442712072365,0.5973634719848633,4.712472 +198,Regression,Adaptive Random Forest,ChickWeights,6.128233569544692,8.84845009665535,0.572286448100142,0.6156282424926758,5.362321 +209,Regression,Adaptive Random Forest,ChickWeights,6.65587905711115,9.652732357425101,0.5803946009681837,0.637272834777832,6.0687109999999995 +220,Regression,Adaptive Random Forest,ChickWeights,7.106977341119842,10.677274056234571,0.550512947323095,0.6516351699829102,6.792503 +231,Regression,Adaptive Random Forest,ChickWeights,7.51605472684967,11.121858780588035,0.582840670024279,0.6455926895141602,7.540065 +242,Regression,Adaptive Random Forest,ChickWeights,7.8763674823035235,11.54794620868086,0.6381207504866175,0.654301643371582,8.314509000000001 +253,Regression,Adaptive Random Forest,ChickWeights,8.048654689630025,11.785882981718466,0.6726179175042853,0.6542215347290039,9.228486 +264,Regression,Adaptive Random Forest,ChickWeights,8.558470564817128,12.694815113306078,0.6529678969258632,0.7006998062133789,10.165772 +275,Regression,Adaptive Random Forest,ChickWeights,9.011287699636803,13.865710758190522,0.6357023625954032,0.7181978225708008,11.12726 +286,Regression,Adaptive Random Forest,ChickWeights,9.454493871269733,14.39909947248495,0.6597325750664246,0.7397470474243164,12.112744 +297,Regression,Adaptive Random Forest,ChickWeights,9.455634964453314,14.370566123736594,0.7060577585099084,0.6961946487426758,13.208544 +308,Regression,Adaptive Random Forest,ChickWeights,9.98259297559382,15.278989711680778,0.7040656028742478,0.7122316360473633,14.327932 +319,Regression,Adaptive Random Forest,ChickWeights,10.896304985778038,17.680267148091307,0.6404050215106214,0.7230386734008789,15.472301 +330,Regression,Adaptive Random Forest,ChickWeights,11.34830207391465,18.238325787402868,0.6725323523293205,0.7481813430786133,16.699404 +341,Regression,Adaptive Random Forest,ChickWeights,11.700671911575691,18.698639858183288,0.6917798823884449,0.750828742980957,17.953966 +352,Regression,Adaptive Random Forest,ChickWeights,12.012928806619971,19.028065448277466,0.7098550919958697,0.779423713684082,19.24314 +363,Regression,Adaptive Random Forest,ChickWeights,12.590729727774809,20.061815233276363,0.6868102538385266,0.8219194412231445,20.584825000000002 +374,Regression,Adaptive Random Forest,ChickWeights,13.29572445199132,21.688967498502105,0.6634948622954009,0.8368387222290039,21.949532 +385,Regression,Adaptive Random Forest,ChickWeights,13.850252347511734,22.377982941031114,0.6830616430184708,0.8398981094360352,23.337733 +396,Regression,Adaptive Random Forest,ChickWeights,13.995508749414423,22.434927630401365,0.7029833246789492,0.8451242446899414,24.808931 +407,Regression,Adaptive Random Forest,ChickWeights,14.855647843034443,23.972462409994428,0.6847772413527866,0.8440675735473633,26.305221 +418,Regression,Adaptive Random Forest,ChickWeights,15.648428200057216,25.832735423225586,0.6563908585574095,0.8621377944946289,27.821711999999998 +429,Regression,Adaptive Random Forest,ChickWeights,16.477960681723363,27.016517310630082,0.6660339910533338,0.8826723098754883,29.421276999999996 +440,Regression,Adaptive Random Forest,ChickWeights,16.794784005292485,27.277386650758192,0.6836500576952018,0.8854074478149414,31.044845999999996 +451,Regression,Adaptive Random Forest,ChickWeights,17.2443539228967,27.815314781786782,0.6850336806962379,0.915654182434082,32.692344999999996 +462,Regression,Adaptive Random Forest,ChickWeights,18.21783864235053,29.965283642676138,0.6566601868655235,0.9476785659790039,34.453267999999994 +473,Regression,Adaptive Random Forest,ChickWeights,19.154558799374207,31.279498058996012,0.6696542166515442,0.9631280899047852,36.23863899999999 +484,Regression,Adaptive Random Forest,ChickWeights,19.653022199172934,31.710924929172922,0.6790841892096586,0.979741096496582,38.04845199999999 +495,Regression,Adaptive Random Forest,ChickWeights,20.17748759588543,32.35841629000369,0.6856630158751376,1.0035409927368164,39.89288999999999 +506,Regression,Adaptive Random Forest,ChickWeights,20.994447812000203,33.88452895368057,0.6653322556738073,1.0402307510375977,41.77183299999999 +517,Regression,Adaptive Random Forest,ChickWeights,21.74940325928189,34.92971521251369,0.6643962418834424,1.0591440200805664,43.682228999999985 +528,Regression,Adaptive Random Forest,ChickWeights,22.718068194641532,36.272080231437364,0.6746268651566016,1.0802621841430664,45.622796999999984 +539,Regression,Adaptive Random Forest,ChickWeights,22.976084812890594,36.32299861842887,0.6871862958215178,1.1105661392211914,47.62064799999998 +550,Regression,Adaptive Random Forest,ChickWeights,23.812560792713985,37.68037385984369,0.6737446986071818,1.1564149856567383,49.63871399999998 +561,Regression,Adaptive Random Forest,ChickWeights,24.744158926088524,38.956389615090316,0.6665241448790927,1.171940803527832,51.68634399999998 +572,Regression,Adaptive Random Forest,ChickWeights,25.965548256363952,40.779089345824126,0.6619939776632806,1.1861085891723633,53.83172099999997 +578,Regression,Adaptive Random Forest,ChickWeights,26.10164191353107,40.80941552099692,0.669724624616493,1.1904268264770508,56.00600499999997 +20,Regression,Adaptive Random Forest,TrumpApproval,4.656196028844478,13.301506400077992,-414.0076115498352,0.20158100128173828,0.057323 +40,Regression,Adaptive Random Forest,TrumpApproval,3.307191630717303,9.514843640593101,-35.39725790498291,0.28950977325439453,0.159522 +60,Regression,Adaptive Random Forest,TrumpApproval,2.3916587233350866,7.783560456255013,-31.83725667748105,0.32280826568603516,0.43784 +80,Regression,Adaptive Random Forest,TrumpApproval,2.0172424359013847,6.770328731809264,-23.921456088954436,0.36927127838134766,0.749169 +100,Regression,Adaptive Random Forest,TrumpApproval,2.069330341220504,6.141775226189047,-11.868015650386665,0.4076700210571289,1.082433 +120,Regression,Adaptive Random Forest,TrumpApproval,2.013474643057227,5.653544639730099,-8.249866206703038,0.4241609573364258,1.485703 +140,Regression,Adaptive Random Forest,TrumpApproval,1.8943659201342373,5.255534318342925,-7.260127227254786,0.44395923614501953,2.052392 +160,Regression,Adaptive Random Forest,TrumpApproval,1.9423634360618716,4.987168106592344,-5.559462264629689,0.45576953887939453,2.6505280000000004 +180,Regression,Adaptive Random Forest,TrumpApproval,1.9639788846395134,4.758402061618727,-4.244508869717663,0.47258472442626953,3.3297560000000006 +200,Regression,Adaptive Random Forest,TrumpApproval,1.9045329443413326,4.539431452034987,-3.787127034775958,0.5018167495727539,4.037089000000001 +220,Regression,Adaptive Random Forest,TrumpApproval,1.7801675790175082,4.332908187325825,-3.7047348470369075,0.539036750793457,4.871381000000001 +240,Regression,Adaptive Random Forest,TrumpApproval,1.7262455165213564,4.162317120423255,-3.3742337174670682,0.5583086013793945,5.726343000000002 +260,Regression,Adaptive Random Forest,TrumpApproval,1.6726006855046047,4.010034080286883,-3.1147254000977194,0.586766242980957,6.608691000000002 +280,Regression,Adaptive Random Forest,TrumpApproval,1.6001254820213158,3.8693841240389197,-3.01116046378164,0.6034517288208008,7.537396000000002 +300,Regression,Adaptive Random Forest,TrumpApproval,1.5903246290151523,3.758572865099384,-2.72204992570884,0.6344270706176758,8.563678000000001 +320,Regression,Adaptive Random Forest,TrumpApproval,1.5306703522535514,3.644833568467773,-2.673500446315358,0.6524057388305664,9.653495000000001 +340,Regression,Adaptive Random Forest,TrumpApproval,1.462120415173825,3.538151879462345,-2.658070572544154,0.6771516799926758,10.778313 +360,Regression,Adaptive Random Forest,TrumpApproval,1.4104873891633294,3.442715407420023,-2.491830651593505,0.712040901184082,12.006044000000001 +380,Regression,Adaptive Random Forest,TrumpApproval,1.3577274631021345,3.3535534396577877,-2.4279222429470106,0.7612085342407227,13.264975000000002 +400,Regression,Adaptive Random Forest,TrumpApproval,1.328889471148693,3.2750276755937473,-2.3616646758926834,0.7830896377563477,14.608014 +420,Regression,Adaptive Random Forest,TrumpApproval,1.2856838141339133,3.198005596242657,-2.3114858734875385,0.8153314590454102,15.987435000000001 +440,Regression,Adaptive Random Forest,TrumpApproval,1.2502461578606217,3.1277634460074983,-2.1102975482726696,0.8549776077270508,17.476653000000002 +460,Regression,Adaptive Random Forest,TrumpApproval,1.2118787702501406,3.0607885313580625,-1.8245276191275441,0.8641138076782227,19.005275 +480,Regression,Adaptive Random Forest,TrumpApproval,1.1755519926992437,2.997482691409013,-1.6465763687209671,0.904881477355957,20.582478000000002 +500,Regression,Adaptive Random Forest,TrumpApproval,1.1542746800420942,2.9412002898427465,-1.494663742459657,0.9429025650024414,22.213321 +520,Regression,Adaptive Random Forest,TrumpApproval,1.1232655769227813,2.8856256311301474,-1.4054721071787495,0.9187402725219727,23.93785 +540,Regression,Adaptive Random Forest,TrumpApproval,1.0927628011224122,2.8324064719208977,-1.3090698562992058,0.9784936904907227,25.72016 +560,Regression,Adaptive Random Forest,TrumpApproval,1.0798076211233283,2.7860066009246953,-1.2872677886963872,0.8415918350219727,27.559893 +580,Regression,Adaptive Random Forest,TrumpApproval,1.0533259806656756,2.7386650773118006,-1.2648586320750757,0.926945686340332,29.430927 +600,Regression,Adaptive Random Forest,TrumpApproval,1.0370277841126194,2.695306817676886,-1.1694452334238137,1.0152063369750977,31.394822 +620,Regression,Adaptive Random Forest,TrumpApproval,1.0220360797787769,2.6548714349996483,-1.0727572654712625,0.9687509536743164,33.420245 +640,Regression,Adaptive Random Forest,TrumpApproval,1.006223169156282,2.6150891537993277,-0.9735122270872276,0.8030519485473633,35.538976 +660,Regression,Adaptive Random Forest,TrumpApproval,0.9862189251721106,2.576116595691222,-0.901357605879683,0.7759256362915039,37.763356 +680,Regression,Adaptive Random Forest,TrumpApproval,0.9658028732124053,2.5385905860617046,-0.8755448750460146,0.8428354263305664,40.028227 +700,Regression,Adaptive Random Forest,TrumpApproval,0.9580702867531661,2.506070409170758,-0.8758066430098239,0.9465646743774414,42.353807 +720,Regression,Adaptive Random Forest,TrumpApproval,0.9436099236768006,2.472715624364642,-0.8663281360281503,1.0379304885864258,44.723793 +740,Regression,Adaptive Random Forest,TrumpApproval,0.9279645732871133,2.4402852992549255,-0.8166009677654511,1.1119890213012695,47.149898 +760,Regression,Adaptive Random Forest,TrumpApproval,0.9159099470417099,2.410261116608071,-0.7913739428902633,1.1737489700317383,49.62861 +780,Regression,Adaptive Random Forest,TrumpApproval,0.8968362370347621,2.379411736746614,-0.7536320120768303,1.261582374572754,52.179919 +800,Regression,Adaptive Random Forest,TrumpApproval,0.8878342141912964,2.3513921402434717,-0.7280625702741705,1.3552255630493164,54.784302 +820,Regression,Adaptive Random Forest,TrumpApproval,0.8775558321142263,2.3237456817971016,-0.7062000372474271,1.4321069717407227,57.452517 +840,Regression,Adaptive Random Forest,TrumpApproval,0.8672496542857573,2.297532908897418,-0.6834092983276716,1.4874773025512695,60.173386 +860,Regression,Adaptive Random Forest,TrumpApproval,0.8593706057522699,2.272389423762812,-0.6439286158863597,1.5595178604125977,62.976048 +880,Regression,Adaptive Random Forest,TrumpApproval,0.8551106332542915,2.2487703297155224,-0.6019328469057044,1.619084358215332,65.856537 +900,Regression,Adaptive Random Forest,TrumpApproval,0.8437512715146732,2.224375873084905,-0.5739713521606553,1.1261072158813477,68.798174 +920,Regression,Adaptive Random Forest,TrumpApproval,0.8344220404851989,2.2010168562801016,-0.5664083310843888,1.167832374572754,71.779904 +940,Regression,Adaptive Random Forest,TrumpApproval,0.825939320609599,2.179009982884269,-0.5482654902802699,1.1199464797973633,74.829165 +960,Regression,Adaptive Random Forest,TrumpApproval,0.8156984309758435,2.1571048007400404,-0.5331573747015668,1.1733713150024414,77.929016 +980,Regression,Adaptive Random Forest,TrumpApproval,0.806477335746804,2.1360065895495888,-0.5325097322303367,1.2283296585083008,81.05698100000001 +1000,Regression,Adaptive Random Forest,TrumpApproval,0.8008625237630099,2.1159877488140326,-0.5292346593649373,1.2836008071899414,84.241983 +1001,Regression,Adaptive Random Forest,TrumpApproval,0.800378499538596,2.1149541843634605,-0.5287610996295022,1.2846193313598633,87.445729 +11,Regression,Aggregated Mondrian Forest,ChickWeights,1.0878895070954884,1.3778002085324723,-1.2599207317049026,0.17917156219482422,0.003809 +22,Regression,Aggregated Mondrian Forest,ChickWeights,1.15171477394762,1.5218208011368886,-1.3974856828423898,0.33136653900146484,0.017797 +33,Regression,Aggregated Mondrian Forest,ChickWeights,1.2596040860169628,1.630698561429495,-0.8214033882315572,0.48356151580810547,0.056943 +44,Regression,Aggregated Mondrian Forest,ChickWeights,1.1470025325021567,1.5136945038262,-0.7860708992998826,0.6357030868530273,0.120895 +55,Regression,Aggregated Mondrian Forest,ChickWeights,1.7448650745312246,2.8901942810902064,-0.6023944619968462,0.795161247253418,0.34128800000000004 +66,Regression,Aggregated Mondrian Forest,ChickWeights,1.9741736434582027,3.1122799656868354,0.1967701507194095,0.949946403503418,0.602886 +77,Regression,Aggregated Mondrian Forest,ChickWeights,2.3465039451978784,3.868783481489585,0.16539043694478717,1.1044378280639648,0.885897 +88,Regression,Aggregated Mondrian Forest,ChickWeights,2.3152944739841907,3.751470845606434,0.286453015670338,1.2595434188842773,1.213122 +99,Regression,Aggregated Mondrian Forest,ChickWeights,2.4851266884813286,3.8753788781661265,0.3615628965518305,1.4176397323608398,1.708637 +110,Regression,Aggregated Mondrian Forest,ChickWeights,2.679180085056696,4.098463178184459,0.5005082908479199,1.580409049987793,2.233256 +121,Regression,Aggregated Mondrian Forest,ChickWeights,2.993112128155013,4.501608187312601,0.5353065115430311,1.7385053634643555,2.789253 +132,Regression,Aggregated Mondrian Forest,ChickWeights,3.049130101089184,4.474860576824222,0.624267329970883,1.8972959518432617,3.530617 +143,Regression,Aggregated Mondrian Forest,ChickWeights,3.129389359320645,4.535626207267123,0.6870855629914132,2.0540571212768555,4.307795 +154,Regression,Aggregated Mondrian Forest,ChickWeights,3.2350921629171503,4.614317779917637,0.7245583098520811,2.21335506439209,5.238077 +165,Regression,Aggregated Mondrian Forest,ChickWeights,3.615407192454655,5.434402308521257,0.6928112980835472,2.370730400085449,6.2153469999999995 +176,Regression,Aggregated Mondrian Forest,ChickWeights,3.842899644735678,5.8926781106586255,0.7087106044563829,2.5251951217651367,7.308114 +187,Regression,Aggregated Mondrian Forest,ChickWeights,3.939333513046091,5.936527515565436,0.7578865873655871,2.680434226989746,8.444739 +198,Regression,Aggregated Mondrian Forest,ChickWeights,4.1526220464224926,6.160116941975886,0.7926170753106898,2.8339643478393555,9.747256 +209,Regression,Aggregated Mondrian Forest,ChickWeights,4.486090256229248,6.857164593682279,0.7881489392686998,2.9901113510131836,11.107311 +220,Regression,Aggregated Mondrian Forest,ChickWeights,5.095083445923365,8.268326900050806,0.7303274183124314,3.147219657897949,12.601650999999999 +231,Regression,Aggregated Mondrian Forest,ChickWeights,5.345901760482457,8.651953805757511,0.7474084291359289,3.301150321960449,14.172197999999998 +242,Regression,Aggregated Mondrian Forest,ChickWeights,5.775936882313693,9.234098241635358,0.7685060952534608,3.4575910568237305,15.876551999999998 +253,Regression,Aggregated Mondrian Forest,ChickWeights,6.050411841877211,9.480574702158652,0.7880472652798773,3.6158742904663086,17.675130999999997 +264,Regression,Aggregated Mondrian Forest,ChickWeights,6.7396819662512994,10.861908099063555,0.7457953067175733,3.77274227142334,19.599012999999996 +275,Regression,Aggregated Mondrian Forest,ChickWeights,7.418933110619537,12.596893007879746,0.699156722346497,3.926619529724121,21.625010999999997 +286,Regression,Aggregated Mondrian Forest,ChickWeights,7.830180870941,13.02165358749325,0.7215679622698357,4.082179069519043,23.771257999999996 +297,Regression,Aggregated Mondrian Forest,ChickWeights,8.059624975297776,13.201631143135529,0.7517949935656911,4.237311363220215,26.095146999999997 +308,Regression,Aggregated Mondrian Forest,ChickWeights,8.517266870602596,14.029786197157003,0.750336177123377,4.390841484069824,28.501656999999998 +319,Regression,Aggregated Mondrian Forest,ChickWeights,9.872910663629112,17.67011178426297,0.6406578335650022,4.544772148132324,31.026265 +330,Regression,Aggregated Mondrian Forest,ChickWeights,10.355957081475973,18.251720539867826,0.671924837978655,4.698409080505371,33.677707999999996 +341,Regression,Aggregated Mondrian Forest,ChickWeights,10.779061369126929,18.644503325392748,0.6934349036095702,4.852313041687012,36.483968999999995 +352,Regression,Aggregated Mondrian Forest,ChickWeights,10.97013178962945,18.690294927177735,0.7199342471488321,5.009421348571777,39.412921 +363,Regression,Aggregated Mondrian Forest,ChickWeights,11.836385670325258,20.411474322578705,0.6756306209292051,5.165541648864746,42.477176 +374,Regression,Aggregated Mondrian Forest,ChickWeights,12.650208752532226,22.152599191433616,0.6487731216482631,5.323611259460449,45.685202 +385,Regression,Aggregated Mondrian Forest,ChickWeights,13.26433375884341,22.74111870549559,0.672536034672935,5.478930473327637,49.011466999999996 +396,Regression,Aggregated Mondrian Forest,ChickWeights,13.285084454056173,22.62858691877232,0.6976709876564118,5.63400936126709,52.447627 +407,Regression,Aggregated Mondrian Forest,ChickWeights,14.297859574888522,24.603705237609702,0.6677818184200794,5.789168357849121,55.986709 +418,Regression,Aggregated Mondrian Forest,ChickWeights,15.277775247208368,26.91758918665374,0.6267299649165277,5.945448875427246,59.629264 +429,Regression,Aggregated Mondrian Forest,ChickWeights,16.148002577856595,27.91235298687263,0.643353503146777,6.099112510681152,63.379205999999996 +440,Regression,Aggregated Mondrian Forest,ChickWeights,16.450833155107055,28.053185003016473,0.6652347923635655,6.252856254577637,67.23493599999999 +451,Regression,Aggregated Mondrian Forest,ChickWeights,16.938736394119786,28.680885446607185,0.6649461832952053,6.4079084396362305,71.205463 +462,Regression,Aggregated Mondrian Forest,ChickWeights,18.465286457846624,32.222162406640614,0.6027938253530374,6.562827110290527,75.286683 +473,Regression,Aggregated Mondrian Forest,ChickWeights,19.368786292726078,33.403615991184594,0.6231055060350865,6.717398643493652,79.474485 +484,Regression,Aggregated Mondrian Forest,ChickWeights,19.88015130963188,33.764210229402664,0.6360141173956066,6.872824668884277,83.769797 +495,Regression,Aggregated Mondrian Forest,ChickWeights,20.57744796303998,34.830627929035586,0.6356250399764956,7.029131889343262,88.176271 +506,Regression,Aggregated Mondrian Forest,ChickWeights,21.43571571603741,36.40788480688662,0.6134430891768862,7.184878349304199,92.694929 +517,Regression,Aggregated Mondrian Forest,ChickWeights,22.34914238062968,37.807266067412606,0.6066317565796657,7.340197563171387,97.332285 +528,Regression,Aggregated Mondrian Forest,ChickWeights,23.191315994328228,38.81894260965106,0.6271697641288401,7.495863914489746,102.073572 +539,Regression,Aggregated Mondrian Forest,ChickWeights,23.34075784343543,38.827434948624926,0.6423969913213963,7.652411460876465,106.92036399999999 +550,Regression,Aggregated Mondrian Forest,ChickWeights,24.125457325549842,39.99965605849559,0.6321733437483394,7.811335563659668,111.876093 +561,Regression,Aggregated Mondrian Forest,ChickWeights,24.859407485948264,40.9180834433101,0.6319179521646502,7.9698591232299805,116.942977 +572,Regression,Aggregated Mondrian Forest,ChickWeights,25.582967433016186,41.65667948828452,0.6471310448579161,8.12806224822998,122.12293 +578,Regression,Aggregated Mondrian Forest,ChickWeights,25.674172622955844,41.71227980537356,0.65479005999511,8.21412181854248,127.41509599999999 +20,Regression,Aggregated Mondrian Forest,TrumpApproval,0.38127899900663437,0.4856864156914124,0.4734504440676397,0.3661947250366211,0.012803 +40,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3903932807396207,0.4802129236445582,0.908098150441852,0.7077703475952148,0.066318 +60,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3553562094560649,0.44754485397583466,0.8908737885344612,1.0465993881225586,0.161186 +80,Regression,Aggregated Mondrian Forest,TrumpApproval,0.37850782280668976,0.48181042919820394,0.8725877843301283,1.386988639831543,0.310355 +100,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3456451525369771,0.45047631187257403,0.9301027529077078,1.7262754440307617,0.520575 +120,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3369671927041528,0.4421642502671299,0.9427860880043346,2.0684385299682617,0.828496 +140,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3170957614007029,0.42172730009164255,0.9461011323760549,2.404311180114746,1.349971 +160,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3307037984070857,0.4315519243898653,0.9502341257934384,2.7412595748901367,1.938101 +180,Regression,Aggregated Mondrian Forest,TrumpApproval,0.32391175568062514,0.4198186275021798,0.9586532343987925,3.0777502059936523,2.6707590000000003 +200,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3216990324082377,0.4152317221827294,0.959406710806771,3.4146718978881836,3.5252660000000002 +220,Regression,Aggregated Mondrian Forest,TrumpApproval,0.31954266191014236,0.40981538467826617,0.9573113010689299,3.7566747665405273,4.592894 +240,Regression,Aggregated Mondrian Forest,TrumpApproval,0.32058338930030683,0.40973424053171104,0.9569909127297425,4.096526145935059,5.7511 +260,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30963502752669864,0.3977121378847216,0.958918388812615,4.436213493347168,7.098098 +280,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3021514917324106,0.38814569807877647,0.9590118642619369,4.772730827331543,8.655558000000001 +300,Regression,Aggregated Mondrian Forest,TrumpApproval,0.302378432925199,0.38793022611752936,0.9597410141114792,5.1073408126831055,10.324296 +320,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30426355112773323,0.38808206022274244,0.9577019017103428,5.440821647644043,12.208908000000001 +340,Regression,Aggregated Mondrian Forest,TrumpApproval,0.30611168054734034,0.391104192749731,0.9546026616524738,5.773791313171387,14.314788 +360,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3092553456162419,0.39535170942611675,0.9532980843409116,6.1096906661987305,16.588556 +380,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3076082401740039,0.39602056260851237,0.9515479192781826,6.445483207702637,19.05639 +400,Regression,Aggregated Mondrian Forest,TrumpApproval,0.3014609513967445,0.3890658925747077,0.951945723428325,6.780200004577637,21.697867000000002 +420,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29711382936926317,0.3834166944817816,0.9518088379060109,7.1165571212768555,24.511965000000004 +440,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29823263394549426,0.3845174635941775,0.952475485177767,7.451247215270996,27.506667000000004 +460,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2946274143925286,0.38012238021570716,0.9560358463571957,7.7864484786987305,30.697063000000004 +480,Regression,Aggregated Mondrian Forest,TrumpApproval,0.29339680191338824,0.3767220961039539,0.9578595845961764,8.120524406433105,34.045785 +500,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2879691623817681,0.37098658104945786,0.9600287871527097,8.45413875579834,37.543006000000005 +520,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2858512762877924,0.36803361403114826,0.9606162857565064,8.791060447692871,41.194255000000005 +540,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28337361966731006,0.3643610952909248,0.9615619008486129,9.128493309020996,45.008334000000005 +560,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28829960115327347,0.3724151060029248,0.9588932716362207,9.463343620300293,48.989560000000004 +580,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28931551719826604,0.37295833877986023,0.957758997678865,9.801314353942871,53.13082800000001 +600,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28804453093670845,0.3714720231862385,0.9585805866298193,10.13992977142334,57.44083900000001 +620,Regression,Aggregated Mondrian Forest,TrumpApproval,0.285913070387579,0.3694806072400069,0.9596689430521099,10.477011680603027,61.917696000000014 +640,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2840899599351511,0.36696425721615267,0.9609793991220156,10.80936336517334,66.56801000000002 +660,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2804371592513609,0.3629082548929102,0.9621252180674722,11.1486234664917,71.37699000000002 +680,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2832721366095781,0.36464822523085283,0.9611623224331389,11.485033988952637,76.34970200000002 +700,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28537572628090535,0.3664704272728199,0.959741096022156,11.82703685760498,81.48889300000002 +720,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2846995678315971,0.3663721647933076,0.9588788359612517,12.1649808883667,86.79821800000002 +740,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28438969208914106,0.36562346066591106,0.9590812376103318,12.5026273727417,92.27027700000002 +760,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2812524955317089,0.36227858565351356,0.9593969665626949,12.84118938446045,97.89633500000002 +780,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2784736419799919,0.35904953945649953,0.9599459478528628,13.181014060974121,103.67983800000002 +800,Regression,Aggregated Mondrian Forest,TrumpApproval,0.28122680710979,0.3614117991183927,0.9590552347518728,13.522452354431152,109.62063100000002 +820,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2798414038154103,0.35991057058708614,0.9589530251045719,13.858756065368652,115.71824100000002 +840,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2792299366421054,0.358810295818463,0.9588293518961978,14.199065208435059,121.97670500000002 +860,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2757234931419036,0.35572944295397174,0.9596100235239657,14.53821849822998,128.39866500000002 +880,Regression,Aggregated Mondrian Forest,TrumpApproval,0.27250879183678145,0.3526976163964828,0.9604999212537026,14.877989768981934,134.987331 +900,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2707423985398595,0.35053221584455113,0.9608236269181287,15.214400291442871,141.746125 +920,Regression,Aggregated Mondrian Forest,TrumpApproval,0.27149682778681117,0.35071953709177356,0.960138783514385,15.551268577575684,148.677278 +940,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2704179121014184,0.350599255928843,0.9598313134304426,15.892088890075684,155.784091 +960,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2707082259086565,0.3516525290937312,0.9591696518431827,16.229090690612793,163.071856 +980,Regression,Aggregated Mondrian Forest,TrumpApproval,0.2709225398475326,0.3517696828828596,0.9583487676398438,16.574454307556152,170.541967 +1000,Regression,Aggregated Mondrian Forest,TrumpApproval,0.26869541998932756,0.349579763566054,0.9581740736545136,16.91306972503662,178.198313 +1001,Regression,Aggregated Mondrian Forest,TrumpApproval,0.268533139095725,0.34942113435685235,0.9581842100200093,16.932257652282715,186.03359 +11,Regression,Adaptive Model Rules,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.019614219665527344,0.000715 +22,Regression,Adaptive Model Rules,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.021178245544433594,0.002248 +33,Regression,Adaptive Model Rules,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.02634716033935547,0.0045000000000000005 +44,Regression,Adaptive Model Rules,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.027434349060058594,0.007522000000000001 +55,Regression,Adaptive Model Rules,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.034033775329589844,0.011341 +66,Regression,Adaptive Model Rules,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.043257713317871094,0.016081 +77,Regression,Adaptive Model Rules,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.04948711395263672,0.021988 +88,Regression,Adaptive Model Rules,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.05513286590576172,0.050945000000000004 +99,Regression,Adaptive Model Rules,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.05624675750732422,0.083616 +110,Regression,Adaptive Model Rules,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.057623863220214844,0.119642 +121,Regression,Adaptive Model Rules,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.05775737762451172,0.157993 +132,Regression,Adaptive Model Rules,ChickWeights,5.216037157212015,7.551012267266295,-0.0719037453282565,0.05781078338623047,0.197604 +143,Regression,Adaptive Model Rules,ChickWeights,5.030848211447775,7.321940337412501,0.18367095381254994,0.058394432067871094,0.238591 +154,Regression,Adaptive Model Rules,ChickWeights,4.907406922429448,7.137924382310331,0.3406662269828342,0.058447837829589844,0.28091 +165,Regression,Adaptive Model Rules,ChickWeights,5.132506734403487,7.341156657504303,0.439370571581684,0.058447837829589844,0.32452 +176,Regression,Adaptive Model Rules,ChickWeights,5.292049153445915,7.468652514259996,0.5321251372519638,0.059058189392089844,0.369448 +187,Regression,Adaptive Model Rules,ChickWeights,5.31698748044205,7.461166418014795,0.6176873420824156,0.059111595153808594,0.415771 +198,Regression,Adaptive Model Rules,ChickWeights,5.300228902480157,7.425148329077998,0.6988181014109027,0.059058189392089844,0.474994 +209,Regression,Adaptive Model Rules,ChickWeights,5.830499581707958,9.648698249017793,0.5807452540036802,0.02527141571044922,0.54427 +220,Regression,Adaptive Model Rules,ChickWeights,6.400718854692065,10.45569246424029,0.5689754490886993,0.03142070770263672,0.614353 +231,Regression,Adaptive Model Rules,ChickWeights,6.611665150046439,10.617456980307361,0.6198209084753062,0.036536216735839844,0.6853290000000001 +242,Regression,Adaptive Model Rules,ChickWeights,7.029624246247838,11.197269958950692,0.6597654020329642,0.041068077087402344,0.7572760000000001 +253,Regression,Adaptive Model Rules,ChickWeights,7.254490759785878,11.350610231674398,0.6963529412438163,0.044592857360839844,0.830219 +264,Regression,Adaptive Model Rules,ChickWeights,7.784750145903498,12.258358647532567,0.6764200982742594,0.044699668884277344,0.9041760000000001 +275,Regression,Adaptive Model Rules,ChickWeights,8.342804112650073,13.247943494163705,0.6674407623884211,0.044699668884277344,0.986871 +286,Regression,Adaptive Model Rules,ChickWeights,8.88061114203256,14.075280539927816,0.6748649197186086,0.045203208923339844,1.0705630000000002 +297,Regression,Adaptive Model Rules,ChickWeights,9.500784996803779,14.855892526018593,0.6858683144490312,0.04522991180419922,1.1552540000000002 +308,Regression,Adaptive Model Rules,ChickWeights,10.070788242104461,15.77018489177999,0.6847321098344714,0.045256614685058594,1.2409280000000003 +319,Regression,Adaptive Model Rules,ChickWeights,10.988840488902909,17.80174938329892,0.6354464447499208,0.045256614685058594,1.3276340000000002 +330,Regression,Adaptive Model Rules,ChickWeights,11.635092222175304,18.61329763011445,0.6589287557789436,0.04528331756591797,1.4153970000000002 +341,Regression,Adaptive Model Rules,ChickWeights,11.7817306308102,18.651657721342477,0.6933268021215234,0.04528331756591797,1.5042310000000003 +352,Regression,Adaptive Model Rules,ChickWeights,11.878812775671825,18.699040402285984,0.7198024587207095,0.04528331756591797,1.5941090000000002 +363,Regression,Adaptive Model Rules,ChickWeights,12.712200605470676,19.934033697107445,0.690787203614232,0.045310020446777344,1.6850070000000001 +374,Regression,Adaptive Model Rules,ChickWeights,13.202927457133043,20.9603625224819,0.6857237785454591,0.04533672332763672,1.7769460000000001 +385,Regression,Adaptive Model Rules,ChickWeights,13.5542070698499,21.51079994203591,0.707149447507495,0.04533672332763672,1.8699420000000002 +396,Regression,Adaptive Model Rules,ChickWeights,13.642433072457155,21.454130101613703,0.7283852775805406,0.04533672332763672,1.9639870000000001 +407,Regression,Adaptive Model Rules,ChickWeights,14.50232093628697,22.86556238504221,0.7132152539462153,0.045676231384277344,2.060696 +418,Regression,Adaptive Model Rules,ChickWeights,15.245933470432924,24.220098655355127,0.6979521608965717,0.045729637145996094,2.158386 +429,Regression,Adaptive Model Rules,ChickWeights,15.766409258920858,25.08619072251902,0.7120527209837302,0.045729637145996094,2.2570650000000003 +440,Regression,Adaptive Model Rules,ChickWeights,15.931210335947624,25.166941851240068,0.7307081986676882,0.04564952850341797,2.4439120000000005 +451,Regression,Adaptive Model Rules,ChickWeights,16.418312975299003,25.736739737917958,0.7303482652633313,0.046179771423339844,2.6336030000000004 +462,Regression,Adaptive Model Rules,ChickWeights,17.4982370763817,27.78944281741256,0.7047111429028308,0.04692363739013672,2.8262370000000003 +473,Regression,Adaptive Model Rules,ChickWeights,18.254684132762545,29.056725346353634,0.7149358826261665,0.04697704315185547,3.0200670000000005 +484,Regression,Adaptive Model Rules,ChickWeights,18.58513038702809,29.354635254956722,0.7250038129485413,0.046950340270996094,3.2149970000000003 +495,Regression,Adaptive Model Rules,ChickWeights,19.01404260598322,29.86038018890717,0.7323226450377984,0.046896934509277344,3.430483 +506,Regression,Adaptive Model Rules,ChickWeights,19.88342353555136,31.26600741511644,0.7150584356224581,0.04697704315185547,3.648791 +517,Regression,Adaptive Model Rules,ChickWeights,20.595063111922975,32.24616798680886,0.713982273554131,0.047003746032714844,3.869932 +528,Regression,Adaptive Model Rules,ChickWeights,21.380474467010046,33.43504054753495,0.7235347994633756,0.047003746032714844,4.098743 +539,Regression,Adaptive Model Rules,ChickWeights,21.53249764026729,33.42135235584957,0.735168024057878,0.047003746032714844,4.328606 +550,Regression,Adaptive Model Rules,ChickWeights,22.499187843294454,35.002118414433774,0.7184757368310433,0.04703044891357422,4.559521999999999 +561,Regression,Adaptive Model Rules,ChickWeights,23.191634121895575,35.912468657285935,0.7166015220750928,0.047003746032714844,4.791517 +572,Regression,Adaptive Model Rules,ChickWeights,24.040656821383894,37.100860859043735,0.7202195492645626,0.046950340270996094,5.02459 +578,Regression,Adaptive Model Rules,ChickWeights,24.194319129377014,37.21658551958108,0.7253190778127725,0.04697704315185547,5.258551 +20,Regression,Adaptive Model Rules,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.053809165954589844,0.004347 +40,Regression,Adaptive Model Rules,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.07615184783935547,0.011776 +60,Regression,Adaptive Model Rules,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.08839702606201172,0.021496 +80,Regression,Adaptive Model Rules,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.09804439544677734,0.033628000000000005 +100,Regression,Adaptive Model Rules,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.10713481903076172,0.04833900000000001 +120,Regression,Adaptive Model Rules,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.11132335662841797,0.06604700000000001 +140,Regression,Adaptive Model Rules,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.11333751678466797,0.086317 +160,Regression,Adaptive Model Rules,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.1174459457397461,0.109348 +180,Regression,Adaptive Model Rules,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.1194601058959961,0.13519 +200,Regression,Adaptive Model Rules,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.017618179321289062,0.169486 +220,Regression,Adaptive Model Rules,TrumpApproval,2.0738811328897206,4.417664564856108,-3.890594467356201,0.03579998016357422,0.205189 +240,Regression,Adaptive Model Rules,TrumpApproval,1.9726100065438286,4.2375242409752385,-3.5337340888030546,0.04149913787841797,0.24247600000000002 +260,Regression,Adaptive Model Rules,TrumpApproval,1.8594315384151245,4.074751007989252,-3.248610147038553,0.048842430114746094,0.28140600000000004 +280,Regression,Adaptive Model Rules,TrumpApproval,1.7773205119132678,3.936654153117972,-3.1518424972300867,0.06378841400146484,0.322149 +300,Regression,Adaptive Model Rules,TrumpApproval,1.8265705896173514,3.8591002097544127,-2.923813511442849,0.07349681854248047,0.364943 +320,Regression,Adaptive Model Rules,TrumpApproval,1.7442837931334845,3.739506488697679,-2.866813933026025,0.08107662200927734,0.40978200000000004 +340,Regression,Adaptive Model Rules,TrumpApproval,1.6994316865849048,3.6380049904847285,-2.8674589929341425,0.08619213104248047,0.45684600000000003 +360,Regression,Adaptive Model Rules,TrumpApproval,1.6868885299887,3.5545855692388106,-2.7224500036418355,0.0937795639038086,0.506202 +380,Regression,Adaptive Model Rules,TrumpApproval,1.637461983479605,3.464628975063406,-2.658760364179245,0.09889507293701172,0.560423 +400,Regression,Adaptive Model Rules,TrumpApproval,1.622197889515682,3.392154183911459,-2.6064142473473755,0.10611248016357422,0.624493 +420,Regression,Adaptive Model Rules,TrumpApproval,1.6252883623828789,3.3313119696358306,-2.5933132470830746,0.11011409759521484,0.691125 +440,Regression,Adaptive Model Rules,TrumpApproval,1.663593439145693,3.2993129689970107,-2.4608371725208844,0.11575984954833984,0.760369 +460,Regression,Adaptive Model Rules,TrumpApproval,1.6928806013876205,3.26900202016339,-2.221881423949668,0.12384319305419922,0.832438 +480,Regression,Adaptive Model Rules,TrumpApproval,1.6463369530072471,3.2036213976345094,-2.0231064080329655,0.13150310516357422,0.907505 +500,Regression,Adaptive Model Rules,TrumpApproval,1.6312675040418116,3.1569789450171624,-1.8741285299844175,0.07843685150146484,0.99002 +520,Regression,Adaptive Model Rules,TrumpApproval,1.6486177246548734,3.1232792518100463,-1.81800645719813,0.08357906341552734,1.082168 +540,Regression,Adaptive Model Rules,TrumpApproval,1.664948820150162,3.091452157271598,-1.7507490735781142,0.0873861312866211,1.179534 +560,Regression,Adaptive Model Rules,TrumpApproval,1.6361907885919602,3.043459997537018,-1.7295303491345493,0.08855342864990234,1.466782 +580,Regression,Adaptive Model Rules,TrumpApproval,1.6082012495575047,2.9965453347231947,-1.7114709556760634,0.08905696868896484,1.757406 +600,Regression,Adaptive Model Rules,TrumpApproval,1.622569336171024,2.97009213510141,-1.6343417506962359,0.09091472625732422,2.050417 +620,Regression,Adaptive Model Rules,TrumpApproval,1.6368903964872519,2.946158197159977,-1.5525460315178896,0.0923452377319336,2.345765 +640,Regression,Adaptive Model Rules,TrumpApproval,1.652159107256621,2.9245287804119107,-1.4681901897894076,0.09443950653076172,2.643466 +660,Regression,Adaptive Model Rules,TrumpApproval,1.6570267761004454,2.8972896524900835,-1.4050084478390592,0.09603023529052734,2.943569 +680,Regression,Adaptive Model Rules,TrumpApproval,1.6362052297782712,2.859601997032609,-1.379870428705038,0.09812450408935547,3.2460560000000003 +700,Regression,Adaptive Model Rules,TrumpApproval,1.608205636538717,2.8213269237454877,-1.377433396876134,0.10156917572021484,3.5543920000000004 +720,Regression,Adaptive Model Rules,TrumpApproval,1.5855230254631891,2.785659545407005,-1.3686218528413674,0.1027364730834961,3.8755710000000003 +740,Regression,Adaptive Model Rules,TrumpApproval,1.583695771004626,2.7597111871599203,-1.3233016566851918,0.1038503646850586,4.202987 +760,Regression,Adaptive Model Rules,TrumpApproval,1.5704020318609786,2.7290361106702816,-1.2965538228485634,0.1038503646850586,4.532954 +780,Regression,Adaptive Model Rules,TrumpApproval,1.5638796853366008,2.702190403614744,-1.2616800152467116,0.10642147064208984,4.876142 +800,Regression,Adaptive Model Rules,TrumpApproval,1.5494799828615766,2.674411214594314,-1.2354538504080876,0.10703182220458984,5.2219169999999995 +820,Regression,Adaptive Model Rules,TrumpApproval,1.533437809889996,2.6465115200139584,-1.2130964074464639,0.1085958480834961,5.570086999999999 +840,Regression,Adaptive Model Rules,TrumpApproval,1.5202839319169328,2.6201051582792827,-1.1892919715417847,0.11015987396240234,5.920738999999999 +860,Regression,Adaptive Model Rules,TrumpApproval,1.5178574341866524,2.5988091386120904,-1.1501373691585313,0.11077022552490234,6.280333 +880,Regression,Adaptive Model Rules,TrumpApproval,1.4962844530295305,2.571223801389781,-1.0942757338776041,0.11124706268310547,6.652339 +900,Regression,Adaptive Model Rules,TrumpApproval,1.4724252749133646,2.5436398469986066,-1.0582196084183888,0.11164379119873047,7.031402 +920,Regression,Adaptive Model Rules,TrumpApproval,1.4596881679466962,2.5220256913044325,-1.056635177134157,0.11167049407958984,7.413564 +940,Regression,Adaptive Model Rules,TrumpApproval,1.452139596196528,2.5028075284250018,-1.0425932823285438,0.11270427703857422,7.802334 +960,Regression,Adaptive Model Rules,TrumpApproval,1.4364147887122178,2.481230554777158,-1.0285162299402342,0.11320781707763672,8.193766 +980,Regression,Adaptive Model Rules,TrumpApproval,1.4186260884044517,2.45780687839372,-1.0290538610685447,0.11381816864013672,8.587828 +1000,Regression,Adaptive Model Rules,TrumpApproval,1.3997779646996387,2.434572696055838,-1.024386017127401,0.11442852020263672,8.984547 +1001,Regression,Adaptive Model Rules,TrumpApproval,1.3984653255896196,2.433357833975862,-1.0237164038272608,0.11442852020263672,9.38293 +11,Regression,Streaming Random Patches,ChickWeights,4.674710287324511,12.709622005759085,-206.93269654300337,0.14386653900146484,0.053261 +22,Regression,Streaming Random Patches,ChickWeights,2.741934273684416,9.017856101646904,-85.12629469646626,0.16807842254638672,0.14276 +33,Regression,Streaming Random Patches,ChickWeights,2.3543476002974106,7.430504888974863,-37.34585890537725,0.20960521697998047,0.266148 +44,Regression,Streaming Random Patches,ChickWeights,1.9327820011330463,6.452362261246447,-31.77814024428305,0.24174785614013672,0.646479 +55,Regression,Streaming Random Patches,ChickWeights,2.2606373648191784,6.1461368420669364,-6.374120366305681,0.30608272552490234,1.057843 +66,Regression,Streaming Random Patches,ChickWeights,2.3521495161457713,5.750947689984691,-1.7831107407377038,0.35672664642333984,1.521792 +77,Regression,Streaming Random Patches,ChickWeights,2.707478618787897,5.832856917221716,-0.9188552689556648,0.3732900619506836,2.259814 +88,Regression,Streaming Random Patches,ChickWeights,2.60389034076398,5.525482549715508,-0.5612341217350767,0.41286373138427734,3.03269 +99,Regression,Streaming Random Patches,ChickWeights,2.7646559934763433,5.466320467144536,-0.27797322073999386,0.4623746871948242,3.8541800000000004 +110,Regression,Streaming Random Patches,ChickWeights,2.880719733615897,5.407041915578862,0.12681954319141486,0.5318593978881836,4.717509000000001 +121,Regression,Streaming Random Patches,ChickWeights,3.0896780011355176,5.466874267225462,0.31254594053869156,0.5604543685913086,5.631386000000001 +132,Regression,Streaming Random Patches,ChickWeights,3.270943191870578,5.7618521847151385,0.37587775495273845,0.1946859359741211,6.649151000000001 +143,Regression,Streaming Random Patches,ChickWeights,3.2470159770350198,5.633009027852055,0.5168368848346436,0.22883892059326172,7.703019000000001 +154,Regression,Streaming Random Patches,ChickWeights,3.2192007860807728,5.520141144427338,0.6056681999848637,0.2577199935913086,8.869892000000002 +165,Regression,Streaming Random Patches,ChickWeights,3.5165819956804767,5.874797514643079,0.6409683688760216,0.26273250579833984,10.065309000000003 +176,Regression,Streaming Random Patches,ChickWeights,3.700430602978386,6.068185859760413,0.6911390854727877,0.29411983489990234,11.324773000000002 +187,Regression,Streaming Random Patches,ChickWeights,3.8037309027428843,6.1218084380222,0.7426259865968339,0.3320951461791992,12.629055000000003 +198,Regression,Streaming Random Patches,ChickWeights,4.006649900662983,6.578339511639692,0.7635979888713487,0.25274181365966797,13.981910000000003 +209,Regression,Streaming Random Patches,ChickWeights,4.229383118423565,6.982583803939909,0.780430075854037,0.32021617889404297,15.391493000000002 +220,Regression,Streaming Random Patches,ChickWeights,4.825249759558611,8.423350501384354,0.720252540483964,0.35108089447021484,16.880127 +231,Regression,Streaming Random Patches,ChickWeights,5.088028806665401,8.669832171958772,0.7465054715218886,0.32001399993896484,18.399834000000002 +242,Regression,Streaming Random Patches,ChickWeights,5.462442991686406,9.175585237136575,0.7715339230013022,0.3710927963256836,20.020863000000002 +253,Regression,Streaming Random Patches,ChickWeights,5.5636194675564115,9.260101660572655,0.797901929856006,0.41926097869873047,21.677784000000003 +264,Regression,Streaming Random Patches,ChickWeights,6.261116867150435,10.599777327157994,0.7580584961853923,0.3416013717651367,23.387217000000003 +275,Regression,Streaming Random Patches,ChickWeights,6.742618468073929,11.802240597789721,0.7360625543191792,0.3569021224975586,25.127202000000004 +286,Regression,Streaming Random Patches,ChickWeights,7.039594962415952,12.249488193444416,0.7537446936710837,0.39655208587646484,26.911059000000005 +297,Regression,Streaming Random Patches,ChickWeights,7.148800229885712,12.311677740983953,0.7842510327710687,0.4258260726928711,28.739538000000007 +308,Regression,Streaming Random Patches,ChickWeights,7.753683144422786,13.244190950829555,0.7776398094561477,0.45011425018310547,30.593856000000006 +319,Regression,Streaming Random Patches,ChickWeights,8.773143666519827,15.939753659782179,0.7077199430319765,0.4718656539916992,32.508295000000004 +330,Regression,Streaming Random Patches,ChickWeights,9.312574124937234,16.796832021919023,0.7222505421616592,0.3236379623413086,34.514388000000004 +341,Regression,Streaming Random Patches,ChickWeights,9.544591695703465,16.94083213248977,0.7470058810264122,0.3676939010620117,36.550995 +352,Regression,Streaming Random Patches,ChickWeights,9.680039805071173,17.006622056031052,0.7682275557667291,0.36295223236083984,38.648436000000004 +363,Regression,Streaming Random Patches,ChickWeights,10.417098847501563,18.381838377902795,0.7370670774420947,0.3571195602416992,40.771216 +374,Regression,Streaming Random Patches,ChickWeights,11.080869197293334,19.965812195666537,0.7148404591067161,0.39987850189208984,42.937715000000004 +385,Regression,Streaming Random Patches,ChickWeights,11.60940210623338,20.687378926969966,0.7291406299077132,0.3288450241088867,45.213373000000004 +396,Regression,Streaming Random Patches,ChickWeights,11.737814918904208,20.678726756260627,0.7476640805491588,0.4024953842163086,47.506899000000004 +407,Regression,Streaming Random Patches,ChickWeights,12.682108791666492,22.361726245252385,0.7257144517605874,0.4475545883178711,49.84079500000001 +418,Regression,Streaming Random Patches,ChickWeights,13.622708961705229,24.146094170569185,0.6997951546356598,0.4968290328979492,52.212878 +429,Regression,Streaming Random Patches,ChickWeights,14.165217959113354,24.88032134675199,0.7167593971826183,0.5376424789428711,54.630802 +440,Regression,Streaming Random Patches,ChickWeights,14.411006646174876,24.97835315387625,0.7347289581181171,0.5657072067260742,57.077112 +451,Regression,Streaming Random Patches,ChickWeights,14.766578325445964,25.376772271610328,0.7378384948653763,0.25832653045654297,59.570857 +462,Regression,Streaming Random Patches,ChickWeights,16.09445226127713,28.12961105819035,0.6974376845026461,0.3047628402709961,62.101037999999996 +473,Regression,Streaming Random Patches,ChickWeights,16.916275460891086,29.341089843915018,0.7093290035354769,0.3544912338256836,64.67061 +484,Regression,Streaming Random Patches,ChickWeights,17.222566694739786,29.549549967606488,0.7213397403469026,0.39832592010498047,67.26974899999999 +495,Regression,Streaming Random Patches,ChickWeights,17.854950072386483,30.34354672604944,0.7235900637963901,0.43242549896240234,69.89740799999998 +506,Regression,Streaming Random Patches,ChickWeights,18.84874733203415,31.79966974813451,0.7052484004379906,0.46784114837646484,72.66954099999998 +517,Regression,Streaming Random Patches,ChickWeights,19.785853660032195,33.20181112471305,0.6967783021275082,0.49755001068115234,75.49558399999998 +528,Regression,Streaming Random Patches,ChickWeights,20.52664258005787,34.100310164439925,0.7124234805234935,0.5351285934448242,78.36005299999998 +539,Regression,Streaming Random Patches,ChickWeights,20.766026265849113,34.21097619783695,0.7225061795517687,0.576685905456543,81.26779099999997 +550,Regression,Streaming Random Patches,ChickWeights,21.840503815170695,36.158966072689324,0.6995590139862528,0.4753904342651367,84.25568299999998 +561,Regression,Streaming Random Patches,ChickWeights,22.596906243133255,37.108967777985264,0.6974029137241997,0.5189352035522461,87.27382099999997 +572,Regression,Streaming Random Patches,ChickWeights,23.534320250737128,38.28067851879141,0.7021424273708647,0.5585355758666992,90.32555299999997 +578,Regression,Streaming Random Patches,ChickWeights,23.709683411591413,38.441629018276466,0.7069383356385298,0.3551816940307617,93.40141099999997 +20,Regression,Streaming Random Patches,TrumpApproval,2.677140920600926,9.804891856735377,-224.4966127051096,0.2373647689819336,0.078317 +40,Regression,Streaming Random Patches,TrumpApproval,2.42676306487335,7.1506632844470275,-19.556918338481843,0.3270711898803711,0.26006399999999996 +60,Regression,Streaming Random Patches,TrumpApproval,1.8116457742622056,5.852230884873156,-17.563213740222555,0.34931087493896484,0.628767 +80,Regression,Streaming Random Patches,TrumpApproval,1.5261658032230543,5.084894428469453,-13.057813649460385,0.3968191146850586,1.163603 +100,Regression,Streaming Random Patches,TrumpApproval,1.4048851039170587,4.580518627305071,-6.157363117938322,0.41078853607177734,1.7740749999999998 +120,Regression,Streaming Random Patches,TrumpApproval,1.2872329076385731,4.198963935277897,-4.1024421406573515,0.4562673568725586,2.4559889999999998 +140,Regression,Streaming Random Patches,TrumpApproval,1.4191481295394186,4.9019146331166,-6.185954638838571,0.20268917083740234,3.2282499999999996 +160,Regression,Streaming Random Patches,TrumpApproval,1.3292908695512107,4.594852312450113,-4.568052693162012,0.32157421112060547,4.121231 +180,Regression,Streaming Random Patches,TrumpApproval,1.2559271503392595,4.341680890984575,-3.3661470093978725,0.4017667770385742,5.147797 +200,Regression,Streaming Random Patches,TrumpApproval,1.169313410896163,4.12134361195162,-2.94593273053925,0.47729015350341797,6.3380719999999995 +220,Regression,Streaming Random Patches,TrumpApproval,1.1066399346497042,3.9344660517514094,-2.8792501333638807,0.5200605392456055,7.615136 +240,Regression,Streaming Random Patches,TrumpApproval,1.0535228972416337,3.7704097053754206,-2.5892915995637975,0.590418815612793,8.98983 +260,Regression,Streaming Random Patches,TrumpApproval,1.0002808672832586,3.6243313765079748,-2.3612477765650133,0.677699089050293,10.49221 +280,Regression,Streaming Random Patches,TrumpApproval,0.9484528900796008,3.4935839886686573,-2.269856713922535,0.7459287643432617,12.09809 +300,Regression,Streaming Random Patches,TrumpApproval,0.9319658343242508,3.3810597344709987,-2.011909605217061,0.8300580978393555,13.828035999999999 +320,Regression,Streaming Random Patches,TrumpApproval,0.9015525068993323,3.2761126415748776,-1.9678527090537403,0.8134641647338867,15.697273999999998 +340,Regression,Streaming Random Patches,TrumpApproval,0.9086073156973856,3.2065165500712434,-2.0044577988421626,0.7938528060913086,17.678026 +360,Regression,Streaming Random Patches,TrumpApproval,0.9209686764414103,3.130698586248577,-1.8875761151685357,0.8660383224487305,19.789752 +380,Regression,Streaming Random Patches,TrumpApproval,0.9054594388814018,3.0518145886207013,-1.8388130234405762,0.9304952621459961,22.025823 +400,Regression,Streaming Random Patches,TrumpApproval,0.9021459083449618,2.9892737243691805,-1.8006304820861794,0.9046812057495117,24.377516 +420,Regression,Streaming Random Patches,TrumpApproval,0.9000900027115483,2.937103674242639,-1.7932063526136273,0.2984609603881836,26.835964 +440,Regression,Streaming Random Patches,TrumpApproval,0.884833385913356,2.873027664171451,-1.6243016536608597,0.3453207015991211,29.357596 +460,Regression,Streaming Random Patches,TrumpApproval,0.8690754265879537,2.8131168800056585,-1.3859136859847618,0.3807516098022461,31.984181 +480,Regression,Streaming Random Patches,TrumpApproval,0.8473225380080763,2.7551095271995596,-1.235881587238783,0.44824886322021484,34.68911 +500,Regression,Streaming Random Patches,TrumpApproval,0.8286186581223807,2.701061117260836,-1.1039316995995572,0.49751949310302734,37.472375 +520,Regression,Streaming Random Patches,TrumpApproval,0.8308331247066605,2.6760879938297655,-1.0688124961584973,0.37538814544677734,40.349897 +540,Regression,Streaming Random Patches,TrumpApproval,0.8063786739892863,2.6263308571208617,-0.9852938090834253,0.40636730194091797,43.316687 +560,Regression,Streaming Random Patches,TrumpApproval,0.7997929461413226,2.582727501713279,-0.9656668153374994,0.43744945526123047,46.3679 +580,Regression,Streaming Random Patches,TrumpApproval,0.7908850979728871,2.5414571653673215,-0.950423138286411,0.49677181243896484,49.479346 +600,Regression,Streaming Random Patches,TrumpApproval,0.7789627943481009,2.500361162030882,-0.8669718089652279,0.569575309753418,52.654549 +620,Regression,Streaming Random Patches,TrumpApproval,0.7682254429218135,2.4616773328611172,-0.7820656947310429,0.6584272384643555,55.885269 +640,Regression,Streaming Random Patches,TrumpApproval,0.756836908225871,2.4246570785119217,-0.6965531574296129,0.7120962142944336,59.181412 +660,Regression,Streaming Random Patches,TrumpApproval,0.7406340846119412,2.388088765643962,-0.6339309775627988,0.8089780807495117,62.54643 +680,Regression,Streaming Random Patches,TrumpApproval,0.7257657440750075,2.3532857176647086,-0.6117268738432657,0.8398160934448242,65.979515 +700,Regression,Streaming Random Patches,TrumpApproval,0.7284794894639326,2.3250297797266612,-0.6145762958857721,0.9275884628295898,69.48963400000001 +720,Regression,Streaming Random Patches,TrumpApproval,0.7231955460113827,2.297270435922827,-0.6108826519065647,0.9087285995483398,73.071921 +740,Regression,Streaming Random Patches,TrumpApproval,0.7217944839950929,2.2699024953355416,-0.5717835473178023,0.8719320297241211,76.723943 +760,Regression,Streaming Random Patches,TrumpApproval,0.7121024512438853,2.241058668519108,-0.5486900768629306,0.9295186996459961,80.452493 +780,Regression,Streaming Random Patches,TrumpApproval,0.7019422114012909,2.213016497735788,-0.5169405784918113,1.0446271896362305,84.252351 +800,Regression,Streaming Random Patches,TrumpApproval,0.7005931807120314,2.1884283322336215,-0.49683523063919743,1.1031560897827148,88.13395700000001 +820,Regression,Streaming Random Patches,TrumpApproval,0.6997484436891046,2.169363820936814,-0.48702250637871436,1.0328702926635742,92.08914500000002 +840,Regression,Streaming Random Patches,TrumpApproval,0.6949195885567419,2.14957176369946,-0.4735678496288336,0.7475957870483398,96.10836100000002 +860,Regression,Streaming Random Patches,TrumpApproval,0.6920590805093112,2.1264870362465933,-0.439603572136809,0.8119535446166992,100.18224400000001 +880,Regression,Streaming Random Patches,TrumpApproval,0.6882027439938415,2.1038322601427426,-0.40209138862407245,0.8655576705932617,104.313167 +900,Regression,Streaming Random Patches,TrumpApproval,0.6818594219129391,2.085410616994055,-0.38345052454273,0.8467855453491211,108.50343900000001 +920,Regression,Streaming Random Patches,TrumpApproval,0.6756248333192205,2.0637081851469703,-0.37706620583821104,0.9400205612182617,112.75318600000001 +940,Regression,Streaming Random Patches,TrumpApproval,0.6689624136970388,2.0428592411141833,-0.3608300191067024,0.8168668746948242,117.06153900000001 +960,Regression,Streaming Random Patches,TrumpApproval,0.6627773066160889,2.022520414368002,-0.3478143420231987,0.9120321273803711,121.43268700000002 +980,Regression,Streaming Random Patches,TrumpApproval,0.6600305544016135,2.003593726688123,-0.34839580077130505,0.9782476425170898,125.86239700000002 +1000,Regression,Streaming Random Patches,TrumpApproval,0.657029407021691,1.9853014454830338,-0.3461726175729922,1.0593442916870117,130.37428000000003 +1001,Regression,Streaming Random Patches,TrumpApproval,0.6566965628025029,1.9843359394661053,-0.34576147632314735,1.0646085739135742,134.90266400000002 +11,Regression,Bagging,ChickWeights,10.955590565999659,17.7409835250609,-404.147256051216,0.1553668975830078,0.005094 +22,Regression,Bagging,ChickWeights,5.88626580700965,12.566688603347808,-166.25182631838038,0.1681652069091797,0.018278 +33,Regression,Bagging,ChickWeights,4.383857039198176,10.299865918219764,-72.67921052893462,0.20520591735839844,0.039075 +44,Regression,Bagging,ChickWeights,3.446496162870555,8.931116231999566,-61.79980671874969,0.2209186553955078,0.065167 +55,Regression,Bagging,ChickWeights,3.3513349782155033,8.247717183177938,-12.279242202465667,0.2687969207763672,0.09656600000000001 +66,Regression,Bagging,ChickWeights,3.889627188952696,8.0458642201752,-4.4474976461238604,0.3383464813232422,0.144605 +77,Regression,Bagging,ChickWeights,4.337751636727128,7.9681159743419645,-2.5808890563388096,0.3940753936767578,0.201306 +88,Regression,Bagging,ChickWeights,4.489908334389532,7.740787033322287,-2.0640641214272355,0.4405231475830078,0.274421 +99,Regression,Bagging,ChickWeights,4.7831270806190425,7.705843596650206,-1.5396388125269618,0.4615802764892578,0.37284300000000004 +110,Regression,Bagging,ChickWeights,4.73395080514245,7.47334250555501,-0.6680701376440403,0.4910602569580078,0.5845670000000001 +121,Regression,Bagging,ChickWeights,4.733710015085173,7.331306378435282,-0.23631246502535208,0.5042209625244141,0.806575 +132,Regression,Bagging,ChickWeights,4.565752852065114,7.0976416640915465,0.05294854472396571,0.5126438140869141,1.039517 +143,Regression,Bagging,ChickWeights,4.439022558662509,6.895745596080793,0.2759386934515202,0.5216274261474609,1.284562 +154,Regression,Bagging,ChickWeights,4.362170284876481,6.736533340066285,0.4127346685162743,0.5262050628662109,1.543379 +165,Regression,Bagging,ChickWeights,4.647894929983432,7.008861526290804,0.48897532974091285,0.5290126800537109,1.82575 +176,Regression,Bagging,ChickWeights,4.817744211127824,7.136288548419971,0.5728405597677722,0.5066156387329102,2.145522 +187,Regression,Bagging,ChickWeights,4.867036081233358,7.152118590173611,0.6487028411390494,0.4790925979614258,2.491984 +198,Regression,Bagging,ChickWeights,4.887061675652274,7.15502256260352,0.720333392468735,0.3514680862426758,2.944156 +209,Regression,Bagging,ChickWeights,5.070260383978678,7.484020932149266,0.7477619935177958,0.3277406692504883,3.412471 +220,Regression,Bagging,ChickWeights,5.671227628293087,8.602358503958763,0.7082361492582977,0.3353548049926758,3.897275 +231,Regression,Bagging,ChickWeights,5.872335352103937,8.83175934179542,0.7369479677711775,0.37228870391845703,4.389179 +242,Regression,Bagging,ChickWeights,6.107145463120707,9.222510821361377,0.769191114739663,0.3991250991821289,4.889463 +253,Regression,Bagging,ChickWeights,6.19844823305091,9.33633324769997,0.7945607849348244,0.42070865631103516,5.506109 +264,Regression,Bagging,ChickWeights,6.823605404288741,10.586090935492885,0.758682880699561,0.43335819244384766,6.131382 +275,Regression,Bagging,ChickWeights,7.289576170484155,11.670233638164651,0.7419337665758028,0.4418039321899414,6.777342 +286,Regression,Bagging,ChickWeights,7.579012857443305,12.145524073459754,0.75790700185782,0.45081424713134766,7.439278 +297,Regression,Bagging,ChickWeights,7.564986803201262,12.135208564512551,0.7903915740986345,0.45409488677978516,8.115354 +308,Regression,Bagging,ChickWeights,8.103353916061925,13.02855032884451,0.7848217554522041,0.4577798843383789,8.913327 +319,Regression,Bagging,ChickWeights,9.2182891996096,15.75502466975724,0.7144552709875674,0.4609994888305664,9.721339 +330,Regression,Bagging,ChickWeights,9.685083231372472,16.400765556025647,0.7351946818510116,0.4719209671020508,10.546217 +341,Regression,Bagging,ChickWeights,9.903299441393282,16.527032528363478,0.759214288686034,0.47708606719970703,11.387382 +352,Regression,Bagging,ChickWeights,10.047801743751695,16.62843798311862,0.778421004008311,0.48488330841064453,12.279877 +363,Regression,Bagging,ChickWeights,10.963674059851892,18.110346084278056,0.7447765461541069,0.48735523223876953,13.191450000000001 +374,Regression,Bagging,ChickWeights,11.492835005144466,19.28081121430548,0.7340717056357994,0.4739046096801758,14.120497000000002 +385,Regression,Bagging,ChickWeights,11.898657720927194,19.943213385356135,0.7482768269892894,0.4488801956176758,15.069160000000002 +396,Regression,Bagging,ChickWeights,12.0617729851989,19.965773188137263,0.7647640178574115,0.4161386489868164,16.114715 +407,Regression,Bagging,ChickWeights,12.97304553348899,21.57136186484404,0.7447607843499935,0.40607547760009766,17.182106 +418,Regression,Bagging,ChickWeights,13.747411939847145,23.06575414024212,0.7260576137332548,0.42958927154541016,18.259955 +429,Regression,Bagging,ChickWeights,14.305030376306712,23.986335540211613,0.7367482003695625,0.4628152847290039,19.347052 +440,Regression,Bagging,ChickWeights,14.526268288674354,24.074522605416067,0.7535790611891788,0.4899911880493164,20.478651000000003 +451,Regression,Bagging,ChickWeights,15.027594657922048,24.671918598232004,0.7521995995009789,0.5175485610961914,21.623166 +462,Regression,Bagging,ChickWeights,16.208274238827823,27.03842360672758,0.7204560380476568,0.5713090896606445,22.784126 +473,Regression,Bagging,ChickWeights,16.99589357541462,28.364120175265192,0.7283636722963454,0.5903940200805664,23.973479 +484,Regression,Bagging,ChickWeights,17.304815327407063,28.547476213614512,0.739918935022724,0.6084756851196289,25.225522 +495,Regression,Bagging,ChickWeights,17.747173803776352,29.064129392830434,0.7464079684248853,0.6325922012329102,26.517497000000002 +506,Regression,Bagging,ChickWeights,18.655435380807354,30.57773482627066,0.7274654471662664,0.6401453018188477,27.826703000000002 +517,Regression,Bagging,ChickWeights,19.38212758204995,31.566942752755278,0.7259045847606268,0.5900964736938477,29.161388000000002 +528,Regression,Bagging,ChickWeights,20.162555380752366,32.73116726334994,0.7350525453098611,0.6013956069946289,30.577640000000002 +539,Regression,Bagging,ChickWeights,20.34377517847412,32.75647044736101,0.7456003073902285,0.6148271560668945,32.006392000000005 +550,Regression,Bagging,ChickWeights,21.397093652240404,34.43808497807088,0.7274757471078548,0.6217546463012695,33.45442800000001 +561,Regression,Bagging,ChickWeights,22.130535392790673,35.39551310036421,0.7247017706467436,0.6181917190551758,34.916661000000005 +572,Regression,Bagging,ChickWeights,22.976096797270415,36.539264510866154,0.7286255260499503,0.6243486404418945,36.463466000000004 +578,Regression,Bagging,ChickWeights,23.114298050830314,36.631109645590286,0.7338934315030725,0.6280336380004883,38.020312000000004 +20,Regression,Bagging,TrumpApproval,6.57361785669815,13.877675781396096,-450.73930630825186,0.3875865936279297,0.030628 +40,Regression,Bagging,TrumpApproval,4.357601810962072,9.93598927447802,-38.690592530050864,0.5688495635986328,0.09352200000000001 +60,Regression,Bagging,TrumpApproval,3.120546196671925,8.124382016407804,-34.775930157070896,0.6946392059326172,0.180013 +80,Regression,Bagging,TrumpApproval,2.5823668216656817,7.068571931029129,-26.165472568815836,0.7988948822021484,0.365225 +100,Regression,Bagging,TrumpApproval,2.6103510398716643,6.439797187103485,-13.147122820254193,0.9021625518798828,0.589181 +120,Regression,Bagging,TrumpApproval,2.5653436103516496,5.96335184363353,-9.29140495411716,0.9421710968017578,0.842127 +140,Regression,Bagging,TrumpApproval,2.4314692166818666,5.556159680491977,-8.232140838080387,0.9627094268798828,1.43468 +160,Regression,Bagging,TrumpApproval,2.270493582871441,5.217534738727647,-6.179445803611509,1.0016803741455078,2.055929 +180,Regression,Bagging,TrumpApproval,2.1841879014169865,4.9594120506005,-4.6969569828406526,0.9622507095336914,2.757735 +200,Regression,Bagging,TrumpApproval,2.030794616399332,4.7110231793054895,-4.155876544063708,0.5397500991821289,3.551711 +220,Regression,Bagging,TrumpApproval,1.922882727301643,4.50300441964265,-4.081371242371108,0.37749576568603516,4.407055 +240,Regression,Bagging,TrumpApproval,1.8390508968191754,4.321014818317665,-3.7141474735663893,0.43126392364501953,5.281937999999999 +260,Regression,Bagging,TrumpApproval,1.7379678526387645,4.155226166492631,-3.4180849750109363,0.4941263198852539,6.197258999999999 +280,Regression,Bagging,TrumpApproval,1.7042826877160742,4.0269186303191065,-3.3444224917120184,0.5997896194458008,7.197467999999999 +300,Regression,Bagging,TrumpApproval,1.6796571065333832,3.9174008876388,-3.043265693703045,0.6906900405883789,8.219126999999999 +320,Regression,Bagging,TrumpApproval,1.5891460162001483,3.7936804881645676,-2.979662055693274,0.757817268371582,9.288978999999998 +340,Regression,Bagging,TrumpApproval,1.5335884019062007,3.685003453386448,-2.968029890458662,0.7969903945922852,10.383406999999998 +360,Regression,Bagging,TrumpApproval,1.54418408246079,3.606838798974545,-2.832696164635261,0.8751077651977539,11.515104999999998 +380,Regression,Bagging,TrumpApproval,1.5054402320411853,3.5177984273762375,-2.771919367898169,0.9264421463012695,12.700466999999998 +400,Regression,Bagging,TrumpApproval,1.4723491084260332,3.435525460733128,-2.699225318590951,0.9911317825317383,13.924277999999997 +420,Regression,Bagging,TrumpApproval,1.429579158986208,3.3562143901072865,-2.6472359354971333,0.9703760147094727,15.190464999999998 +440,Regression,Bagging,TrumpApproval,1.3992424504019558,3.2853835752695946,-2.4316761923151153,1.0316247940063477,16.527614999999997 +460,Regression,Bagging,TrumpApproval,1.365864594828797,3.217129802398014,-2.120443637805541,1.1097803115844727,17.906522 +480,Regression,Bagging,TrumpApproval,1.3301487592579586,3.151695763852486,-1.9259010739386908,1.1814966201782227,19.341224 +500,Regression,Bagging,TrumpApproval,1.2968821746115176,3.0904885141585767,-1.7543370405557224,1.2276010513305664,20.84359 +520,Regression,Bagging,TrumpApproval,1.2678501702074907,3.0331483120333496,-1.6577103317872361,1.2783823013305664,22.421222999999998 +540,Regression,Bagging,TrumpApproval,1.2343399552126226,2.9775564396478966,-1.551795811786203,1.2796869277954102,24.086657999999996 +560,Regression,Bagging,TrumpApproval,1.220715255826944,2.9300367331798807,-1.5298738258017646,1.2170305252075195,25.790580999999996 +580,Regression,Bagging,TrumpApproval,1.1924146311245054,2.8809984439523286,-1.5063937515255619,1.0756006240844727,27.649708999999994 +600,Regression,Bagging,TrumpApproval,1.1793821598427467,2.837096711415006,-1.4037015974174163,0.9519128799438477,29.574726999999996 +620,Regression,Bagging,TrumpApproval,1.1645342598465298,2.7961228756221597,-1.2991852345603885,0.9679117202758789,31.579397999999998 +640,Regression,Bagging,TrumpApproval,1.1398425529628198,2.753175690936408,-1.1874325743915652,0.936314582824707,33.628169 +660,Regression,Bagging,TrumpApproval,1.1198821044801988,2.7133180185933967,-1.1092797015680484,0.9601030349731445,35.724068 +680,Regression,Bagging,TrumpApproval,1.103543375947734,2.675681585437618,-1.0835838801311364,1.0132951736450195,37.850435000000004 +700,Regression,Bagging,TrumpApproval,1.0951788603095205,2.6436744635360436,-1.0874567422761272,1.0955934524536133,40.041881000000004 +720,Regression,Bagging,TrumpApproval,1.073603439060177,2.607328103868497,-1.0750617689055861,1.1506471633911133,42.267731000000005 +740,Regression,Bagging,TrumpApproval,1.052216298294681,2.5725220480150233,-1.0188151031858665,1.1900300979614258,44.534549000000005 +760,Regression,Bagging,TrumpApproval,1.0329729065757678,2.539265324444459,-0.9882648176410449,1.2228517532348633,46.85075200000001 +780,Regression,Bagging,TrumpApproval,1.0152010354431573,2.5074934380267417,-0.9475063222432678,1.2825212478637695,49.22632000000001 +800,Regression,Bagging,TrumpApproval,1.007374170791078,2.4793027589713232,-0.9211818120686948,1.3126497268676758,51.66223500000001 +820,Regression,Bagging,TrumpApproval,1.002386830132377,2.4547936379778226,-0.9040692252875042,1.3594255447387695,54.13772100000001 +840,Regression,Bagging,TrumpApproval,0.9929186057762197,2.428247138481691,-0.8804076589484491,1.397130012512207,56.66204300000001 +860,Regression,Bagging,TrumpApproval,0.9756374748391698,2.400488599154532,-0.8344958433267429,1.4288606643676758,59.23948900000001 +880,Regression,Bagging,TrumpApproval,0.964181960731443,2.37450147105602,-0.7860721035239808,1.4496355056762695,61.87688900000001 +900,Regression,Bagging,TrumpApproval,0.9549728240782616,2.3497625798451978,-0.7564202624419067,1.3498811721801758,64.59788000000002 +920,Regression,Bagging,TrumpApproval,0.9412862327577896,2.3247867434211136,-0.747529388757789,1.1945161819458008,67.39612200000002 +940,Regression,Bagging,TrumpApproval,0.934469347520636,2.302477656767798,-0.7286928787152502,1.2202577590942383,70.24630900000002 +960,Regression,Bagging,TrumpApproval,0.9259837543593707,2.280476047701142,-0.7135440601163805,1.2635469436645508,73.13750900000002 +980,Regression,Bagging,TrumpApproval,0.9196316545824527,2.2597103614150886,-0.715155968132227,1.2794008255004883,76.07904100000002 +1000,Regression,Bagging,TrumpApproval,0.9087747756519627,2.2382775608394114,-0.7111012811273396,1.3157854080200195,79.06112300000002 +1001,Regression,Bagging,TrumpApproval,0.9082029688272106,2.2371845268363217,-0.710571805505718,1.3157854080200195,82.06893700000002 +11,Regression,Exponentially Weighted Average,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.06525707244873047,0.004749 +22,Regression,Exponentially Weighted Average,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.07764339447021484,0.02229 +33,Regression,Exponentially Weighted Average,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.09733104705810547,0.042479 +44,Regression,Exponentially Weighted Average,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.10875988006591797,0.065964 +55,Regression,Exponentially Weighted Average,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.13167858123779297,0.11433299999999999 +66,Regression,Exponentially Weighted Average,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.1604146957397461,0.17057299999999997 +77,Regression,Exponentially Weighted Average,ChickWeights,43.506493506493506,43.70978671356627,-106.75487995129542,0.18252849578857422,0.23188899999999998 +88,Regression,Exponentially Weighted Average,ChickWeights,44.21590909090909,44.43649707984724,-99.97346126162999,0.2035512924194336,0.308411 +99,Regression,Exponentially Weighted Average,ChickWeights,45.05050505050505,45.309262771858165,-86.8022342468144,0.21530437469482422,0.393152 +110,Regression,Exponentially Weighted Average,ChickWeights,46.16363636363636,46.52487115902242,-63.64797006437341,0.2280874252319336,0.484559 +121,Regression,Exponentially Weighted Average,ChickWeights,47.21487603305785,47.67304278378361,-51.27707184490422,0.23778057098388672,0.583103 +132,Regression,Exponentially Weighted Average,ChickWeights,48.29545454545455,48.843054157105485,-43.84882422437649,0.24797725677490234,0.792791 +143,Regression,Exponentially Weighted Average,ChickWeights,49.44055944055945,50.100318941519305,-37.220279564063546,0.22072410583496094,1.018133 +154,Regression,Exponentially Weighted Average,ChickWeights,50.532467532467535,51.29137544271156,-33.04474826644667,0.2406787872314453,1.2542229999999999 +165,Regression,Exponentially Weighted Average,ChickWeights,51.690909090909095,52.61253451297311,-27.795548438273773,0.25681114196777344,1.5160939999999998 +176,Regression,Exponentially Weighted Average,ChickWeights,53.00568181818182,54.11860921749895,-23.566226925646234,0.2727985382080078,1.7876089999999998 +187,Regression,Exponentially Weighted Average,ChickWeights,54.41176470588235,55.733754017636336,-20.33250305682894,0.2873516082763672,2.0795459999999997 +198,Regression,Exponentially Weighted Average,ChickWeights,56.02525252525252,57.635786091488654,-17.146924852486976,0.30018043518066406,2.3857599999999994 +209,Regression,Exponentially Weighted Average,ChickWeights,57.5645933014354,59.46206220864915,-14.922837840066967,0.27625083923339844,2.7086299999999994 +220,Regression,Exponentially Weighted Average,ChickWeights,58.69090909090908,60.81327606250582,-13.581197962556498,0.2926349639892578,3.0480419999999993 +231,Regression,Exponentially Weighted Average,ChickWeights,60.25541125541125,62.66764529032318,-12.244451024360147,0.30736351013183594,3.437715999999999 +242,Regression,Exponentially Weighted Average,ChickWeights,62.17355371900826,65.06963847478845,-10.489760184397113,0.32158851623535156,3.8493299999999993 +253,Regression,Exponentially Weighted Average,ChickWeights,63.936758893280626,67.17295239601157,-9.634560128382748,0.3348064422607422,4.393024 +264,Regression,Exponentially Weighted Average,ChickWeights,65.10606060606062,68.57980310513724,-9.127665748505592,0.3451099395751953,4.977281 +275,Regression,Exponentially Weighted Average,ChickWeights,66.61454545454548,70.46451073219248,-8.408339126213217,0.35480308532714844,5.586244 +286,Regression,Exponentially Weighted Average,ChickWeights,68.48951048951052,72.8020594498525,-7.6983532427125105,0.3655261993408203,6.228505 +297,Regression,Exponentially Weighted Average,ChickWeights,70.55218855218858,75.3669362796119,-7.08492451355157,0.3711223602294922,6.899176000000001 +308,Regression,Exponentially Weighted Average,ChickWeights,72.39285714285718,77.65033596401675,-6.643510181414674,0.3809375762939453,7.597348 +319,Regression,Exponentially Weighted Average,ChickWeights,73.45454545454551,79.15086186624424,-6.206879640065647,0.39273643493652344,8.345726 +330,Regression,Exponentially Weighted Average,ChickWeights,75.77878787878792,82.20832738177494,-5.653192449779911,0.40398597717285156,9.225203 +341,Regression,Exponentially Weighted Average,ChickWeights,77.92375366568919,84.89106353805269,-5.352795814687307,0.4136791229248047,10.124705 +352,Regression,Exponentially Weighted Average,ChickWeights,80.04545454545458,87.49376601169416,-5.134510311668016,0.4233722686767578,11.048248000000001 +363,Regression,Exponentially Weighted Average,ChickWeights,80.99724517906337,88.57562798692558,-5.105139086016474,0.43306541442871094,11.989622 +374,Regression,Exponentially Weighted Average,ChickWeights,82.77807486631018,90.83029071422122,-4.901675845817959,0.45247840881347656,12.967698 +385,Regression,Exponentially Weighted Average,ChickWeights,85.1766233766234,93.99517810235533,-4.591702735915359,0.4681224822998047,14.032748 +396,Regression,Exponentially Weighted Average,ChickWeights,87.26767676767678,96.48964983485283,-4.494054297851511,0.48465919494628906,15.12168 +407,Regression,Exponentially Weighted Average,ChickWeights,89.00737100737103,98.71879502607636,-4.345544683073043,0.4991130828857422,16.236767999999998 +418,Regression,Exponentially Weighted Average,ChickWeights,90.57416267942587,100.72635724110245,-4.224084264201084,0.5109386444091797,17.416859999999996 +429,Regression,Exponentially Weighted Average,ChickWeights,93.12121212121215,104.19735398794236,-3.9677178403495805,0.5206584930419922,18.643557999999995 +440,Regression,Exponentially Weighted Average,ChickWeights,95.41818181818185,107.03565676064125,-3.8710119659250104,0.5303516387939453,19.910713999999995 +451,Regression,Exponentially Weighted Average,ChickWeights,97.16629711751665,109.07665280092142,-3.843505105397095,0.5280742645263672,21.225085999999994 +462,Regression,Exponentially Weighted Average,ChickWeights,98.71645021645023,111.17636431671961,-3.72620239405422,0.5443019866943359,22.607732999999993 +473,Regression,Exponentially Weighted Average,ChickWeights,101.54122621564484,115.20584573786859,-3.4812404756668593,0.5577869415283203,24.015635999999994 +484,Regression,Exponentially Weighted Average,ChickWeights,103.77066115702482,117.90601559037043,-3.4365483842712585,0.5712184906005859,25.474227999999993 +495,Regression,Exponentially Weighted Average,ChickWeights,106.02424242424244,120.71525892518193,-3.37467008920777,0.5825443267822266,26.96779499999999 +506,Regression,Exponentially Weighted Average,ChickWeights,107.31620553359684,122.26004165941237,-3.356924458603192,2.7805843353271484,30.43480299999999 +517,Regression,Exponentially Weighted Average,ChickWeights,109.39651837524178,124.91233289427785,-3.291877964737682,2.825040817260742,33.93932499999999 +528,Regression,Exponentially Weighted Average,ChickWeights,112.36553030303028,129.1106745698386,-3.1225038051323804,2.876035690307617,37.48648499999999 +539,Regression,Exponentially Weighted Average,ChickWeights,114.52504638218922,131.65752925403248,-3.109734667916423,2.927671432495117,41.07985699999999 +550,Regression,Exponentially Weighted Average,ChickWeights,115.89999999999996,133.35909826820617,-3.0866973064470367,2.9773387908935547,44.71417799999999 +561,Regression,Exponentially Weighted Average,ChickWeights,117.86452762923346,135.8046463151548,-3.0526234314410727,3.0208263397216797,48.39640499999999 +572,Regression,Exponentially Weighted Average,ChickWeights,120.54020979020974,139.4624607986965,-2.953338846956928,3.065652847290039,52.120071999999986 +578,Regression,Exponentially Weighted Average,ChickWeights,121.81833910034597,141.00422703423635,-2.942935834251463,3.092409133911133,55.885136999999986 +20,Regression,Exponentially Weighted Average,TrumpApproval,43.8732195,43.87807788634269,-4514.954899312423,0.1445150375366211,0.017697 +40,Regression,Exponentially Weighted Average,TrumpApproval,42.4932955,42.522552834216924,-725.9491167623446,0.21173763275146484,0.059669 +60,Regression,Exponentially Weighted Average,TrumpApproval,42.2167785,42.2386240157387,-966.0073736019044,0.25832653045654297,0.116093 +80,Regression,Exponentially Weighted Average,TrumpApproval,41.975705625,41.997608685598294,-957.9655948743646,0.3003568649291992,0.19454100000000002 +100,Regression,Exponentially Weighted Average,TrumpApproval,41.37550450000001,41.410913785433536,-583.9966399141301,0.3407926559448242,0.29552300000000004 +120,Regression,Exponentially Weighted Average,TrumpApproval,40.936110000000006,40.978293821977665,-484.9611418859003,0.3709287643432617,0.510712 +140,Regression,Exponentially Weighted Average,TrumpApproval,40.6885472857143,40.72961738075088,-495.1050461477588,0.3974485397338867,0.760252 +160,Regression,Exponentially Weighted Average,TrumpApproval,40.35105437500001,40.39801158334292,-429.4078677932073,0.3402233123779297,1.044132 +180,Regression,Exponentially Weighted Average,TrumpApproval,40.00981655555555,40.06373388340122,-370.7794659133543,0.3811016082763672,1.367502 +200,Regression,Exponentially Weighted Average,TrumpApproval,39.806330949999996,39.860362966711,-368.1089073295326,0.3127880096435547,1.794637 +220,Regression,Exponentially Weighted Average,TrumpApproval,39.727043136363626,39.77723500009918,-395.50198072931875,0.3578624725341797,2.266575 +240,Regression,Exponentially Weighted Average,TrumpApproval,39.56323079166665,39.61325406766278,-395.19837684116754,0.3875255584716797,2.775551 +260,Regression,Exponentially Weighted Average,TrumpApproval,39.42014538461535,39.46968290441584,-397.63185900832246,0.42084693908691406,3.358614 +280,Regression,Exponentially Weighted Average,TrumpApproval,39.33200189285712,39.37942345737111,-414.45601593500356,0.47014808654785156,4.097303 +300,Regression,Exponentially Weighted Average,TrumpApproval,39.18435719999999,39.23275803924839,-404.5402138221895,0.5117359161376953,4.882924 +320,Regression,Exponentially Weighted Average,TrumpApproval,39.13568690624999,39.1818628962716,-423.5167725219512,0.5480670928955078,5.740529 +340,Regression,Exponentially Weighted Average,TrumpApproval,39.14620944117645,39.18989510023786,-447.7943063391533,0.5615406036376953,6.72918 +360,Regression,Exponentially Weighted Average,TrumpApproval,39.24072974999997,39.28395553300239,-453.6543473793619,0.5990085601806641,7.76074 +380,Regression,Exponentially Weighted Average,TrumpApproval,39.29597665789471,39.337699215460226,-470.6701690846498,0.6312580108642578,8.924786000000001 +400,Regression,Exponentially Weighted Average,TrumpApproval,39.35730624999997,39.39781946688104,-485.4842825426507,0.6682605743408203,10.154856 +420,Regression,Exponentially Weighted Average,TrumpApproval,39.40549083333331,39.44465897881697,-502.77995042269276,0.6983966827392578,11.469727 +440,Regression,Exponentially Weighted Average,TrumpApproval,39.49730674999998,39.53710368662846,-495.9856416828035,0.7316226959228516,12.901862000000001 +460,Regression,Exponentially Weighted Average,TrumpApproval,39.61474728260867,39.65658853240579,-473.14358309219216,0.7705020904541016,14.448849000000001 +480,Regression,Exponentially Weighted Average,TrumpApproval,39.71032456249997,39.753047582709755,-464.4916761787406,0.8079357147216797,16.094285 +500,Regression,Exponentially Weighted Average,TrumpApproval,39.80313951999997,39.84667590965187,-456.87508245086684,2.9298267364501953,19.825027 +520,Regression,Exponentially Weighted Average,TrumpApproval,39.873547134615364,39.916931033645376,-459.2932847271911,3.0076160430908203,23.629967 +540,Regression,Exponentially Weighted Average,TrumpApproval,39.94649651851849,39.98996046818772,-459.28610565666287,3.1059207916259766,27.509295 +560,Regression,Exponentially Weighted Average,TrumpApproval,39.976066142857114,40.018487723609816,-470.92618770667195,3.203706741333008,31.453258 +580,Regression,Exponentially Weighted Average,TrumpApproval,40.00338510344825,40.044755101652726,-483.2331705341176,3.296670913696289,35.460995000000004 +600,Regression,Exponentially Weighted Average,TrumpApproval,40.07393431666663,40.11569326301364,-479.5746686678817,3.391347885131836,39.544454 +620,Regression,Exponentially Weighted Average,TrumpApproval,40.1459417741935,40.18827077358568,-473.96334667177865,3.4906063079833984,43.697410000000005 +640,Regression,Exponentially Weighted Average,TrumpApproval,40.219438156249964,40.26249426545423,-466.8085709746123,3.5870800018310547,47.924640000000004 +660,Regression,Exponentially Weighted Average,TrumpApproval,40.28296777272724,40.32626722721455,-464.9172853497744,3.6864986419677734,52.218165000000006 +680,Regression,Exponentially Weighted Average,TrumpApproval,40.31998279411761,40.36256991107017,-473.1325264408024,3.782560348510742,56.58359200000001 +700,Regression,Exponentially Weighted Average,TrumpApproval,40.31359012857138,40.35509446667054,-485.40526703956544,3.816682815551758,61.02307200000001 +720,Regression,Exponentially Weighted Average,TrumpApproval,40.31730695833329,40.357915759594896,-496.1610725544049,3.917215347290039,65.52718500000002 +740,Regression,Exponentially Weighted Average,TrumpApproval,40.36653568918915,40.407119416424955,-497.07428037101636,4.021100997924805,70.10250300000001 +760,Regression,Exponentially Weighted Average,TrumpApproval,40.403143671052604,40.443256311482514,-503.3712175162706,4.113973617553711,74.743136 +780,Regression,Exponentially Weighted Average,TrumpApproval,40.44545064102563,40.48534274444009,-506.6856716110208,4.221334457397461,79.45160200000001 +800,Regression,Exponentially Weighted Average,TrumpApproval,40.478548249999996,40.518050685964006,-512.1052117095793,4.33137321472168,84.22367000000001 +820,Regression,Exponentially Weighted Average,TrumpApproval,40.50894034146341,40.5479845946661,-518.5068774177179,4.393171310424805,89.06994900000001 +840,Regression,Exponentially Weighted Average,TrumpApproval,40.5406558690476,40.579310897365986,-524.140575335229,4.501546859741211,93.98653900000001 +860,Regression,Exponentially Weighted Average,TrumpApproval,40.58371181395347,40.62239247493601,-524.3496319016275,4.600507736206055,98.97766000000001 +880,Regression,Exponentially Weighted Average,TrumpApproval,40.62855514772725,40.66738601007716,-522.897851512946,4.697576522827148,104.03937600000002 +900,Regression,Exponentially Weighted Average,TrumpApproval,40.664104233333326,40.702738445808535,-526.020768835918,4.774164199829102,109.17863200000002 +920,Regression,Exponentially Weighted Average,TrumpApproval,40.68274704347825,40.72073961991632,-535.1540147256861,4.872934341430664,114.38942200000002 +940,Regression,Exponentially Weighted Average,TrumpApproval,40.70972619148935,40.74737437775791,-540.4099749760601,4.975519180297852,119.67171900000002 +960,Regression,Exponentially Weighted Average,TrumpApproval,40.734006364583315,40.771242977826994,-546.7118652484228,5.07771110534668,125.02115000000002 +980,Regression,Exponentially Weighted Average,TrumpApproval,40.74031829795916,40.776840159239676,-557.5026042066913,5.174932479858398,130.44017000000002 +1000,Regression,Exponentially Weighted Average,TrumpApproval,40.75359492299998,40.78950075300399,-567.2567645513548,5.274145126342773,135.92720000000003 +1001,Regression,Exponentially Weighted Average,TrumpApproval,40.75458054545452,40.7904615623717,-567.6629514867817,5.276128768920898,141.45243100000002 +11,Regression,River MLP,ChickWeights,41.63636363636363,41.64569169030137,-2231.5319148936137,0.012152671813964844,0.004659 +22,Regression,River MLP,ChickWeights,41.31818181818181,41.32960638133835,-1808.0547045951903,0.012152671813964844,0.035685 +33,Regression,River MLP,ChickWeights,41.12121212121212,41.13871582091424,-1174.393494897962,0.012152671813964844,0.08499899999999999 +44,Regression,River MLP,ChickWeights,41.159090909090914,41.174517715340755,-1333.7620984139928,0.012152671813964844,0.154054 +55,Regression,River MLP,ChickWeights,41.5090909090909,41.57075020645253,-336.3506066081568,0.012152671813964844,0.236038 +66,Regression,River MLP,ChickWeights,42.681818181818166,42.82080349691271,-153.29834830483878,0.012152671813964844,0.334153 +77,Regression,River MLP,ChickWeights,43.46421300395698,43.662828265715675,-106.52347713504811,0.012152671813964844,0.44537099999999996 +88,Regression,River MLP,ChickWeights,43.359772412267546,43.583709810639945,-96.13505707304522,0.012152671813964844,0.810611 +99,Regression,River MLP,ChickWeights,39.34760833403674,41.28110871337288,-71.88434940071843,0.012152671813964844,1.1795 +110,Regression,River MLP,ChickWeights,36.27694842893514,39.43109665219568,-45.43679588251114,0.012152671813964844,1.5519370000000001 +121,Regression,River MLP,ChickWeights,33.384495302116036,37.629851771248454,-31.570957412576163,0.012152671813964844,1.9276810000000002 +132,Regression,River MLP,ChickWeights,30.760956861305427,36.03410144813446,-23.410283906032372,0.012152671813964844,2.306782 +143,Regression,River MLP,ChickWeights,28.636527077105512,34.63559483719005,-17.266619380249022,0.012152671813964844,2.6893580000000004 +154,Regression,River MLP,ChickWeights,26.848366395937333,33.39569685051843,-13.432529594168455,0.012152671813964844,3.0876050000000004 +165,Regression,River MLP,ChickWeights,25.68994399106157,32.40192925941153,-9.921659843170621,0.012152671813964844,3.5625080000000002 +176,Regression,River MLP,ChickWeights,24.410830110997512,31.436328147747602,-7.289127728255313,0.012152671813964844,4.041374 +187,Regression,River MLP,ChickWeights,23.27797268834062,30.533192270092133,-5.402500905628177,0.012152671813964844,4.523515000000001 +198,Regression,River MLP,ChickWeights,22.21718064008457,29.702466677215767,-3.8195183008370286,0.012152671813964844,5.01074 +209,Regression,River MLP,ChickWeights,21.553196218218126,29.080353233595055,-2.8083766468986493,0.012152671813964844,5.506907 +220,Regression,River MLP,ChickWeights,21.477175147376318,28.875533005215566,-2.287429329651187,0.012152671813964844,6.006767 +231,Regression,River MLP,ChickWeights,20.952511346795177,28.337840419816686,-1.7081998467380504,0.012152671813964844,6.546588 +242,Regression,River MLP,ChickWeights,20.676711476832995,27.952207916118613,-1.120246781438643,0.012152671813964844,7.091828 +253,Regression,River MLP,ChickWeights,20.0995501155626,27.40917062551413,-0.77060809797316,0.012152671813964844,7.64039 +264,Regression,River MLP,ChickWeights,20.480542447806847,27.75611161124588,-0.6589532289622051,0.012312889099121094,8.194426 +275,Regression,River MLP,ChickWeights,20.614496901205563,28.041519628611827,-0.48996193124700227,0.012312889099121094,8.751968 +286,Regression,River MLP,ChickWeights,20.565278129073285,27.847512109279503,-0.27268965368266684,0.012312889099121094,9.312993 +297,Regression,River MLP,ChickWeights,20.274958424672484,27.625687239183637,-0.08627660996898667,0.012312889099121094,9.883614000000001 +308,Regression,River MLP,ChickWeights,20.45327995927562,27.912230969749725,0.012367732509979579,0.012312889099121094,10.459328000000001 +319,Regression,River MLP,ChickWeights,21.510251079645446,30.009813508826145,-0.03600670421086383,0.012312889099121094,11.038549000000001 +330,Regression,River MLP,ChickWeights,21.665590739767385,30.044848619277115,0.11133412773326878,0.012312889099121094,11.621241000000001 +341,Regression,River MLP,ChickWeights,21.89048226594194,30.334415208142868,0.18882940014056426,0.012312889099121094,12.207835000000001 +352,Regression,River MLP,ChickWeights,21.893952955210157,30.339570241246864,0.2623598804373043,0.012312889099121094,12.797939000000001 +363,Regression,River MLP,ChickWeights,22.90533819708786,32.054250800509514,0.20046341020600145,0.012312889099121094,13.393067000000002 +374,Regression,River MLP,ChickWeights,23.763742364378043,33.85198840163,0.1802483296948254,0.012312889099121094,13.997035000000002 +385,Regression,River MLP,ChickWeights,24.571542788077334,34.76894700117602,0.23490387687324887,0.012312889099121094,14.642537000000003 +396,Regression,River MLP,ChickWeights,24.36284577450351,34.48838301267373,0.2980969129506975,0.012312889099121094,15.292230000000002 +407,Regression,River MLP,ChickWeights,25.734101326034633,37.062399123466015,0.24654151138402014,0.012312889099121094,15.945417000000003 +418,Regression,River MLP,ChickWeights,27.11119186006235,39.928390080533305,0.17910517682254135,0.012312889099121094,16.606412000000002 +429,Regression,River MLP,ChickWeights,28.3590406143643,42.09339312084405,0.18927902325132573,0.012312889099121094,17.270793 +440,Regression,River MLP,ChickWeights,29.357663164641018,43.68705243766196,0.18853885804982917,0.012312889099121094,17.938627 +451,Regression,River MLP,ChickWeights,30.691095011752385,45.674890437608916,0.15071943546696187,0.012312889099121094,18.61722 +462,Regression,River MLP,ChickWeights,32.58469239048663,49.95891321104316,0.04563754616882043,0.012312889099121094,19.304175 +473,Regression,River MLP,ChickWeights,34.457204466549335,53.836031572602984,0.02142234380208996,0.012312889099121094,19.9947 +484,Regression,River MLP,ChickWeights,37.61656772325176,59.47905507802133,-0.1290194303344141,0.012312889099121094,20.688692000000003 +495,Regression,River MLP,ChickWeights,39.835053793820734,63.30264151550531,-0.20299724250356888,0.012312889099121094,21.389042000000003 +506,Regression,River MLP,ChickWeights,40.84375246428709,64.75828138125749,-0.2223668969879733,0.012312889099121094,22.096762000000002 +517,Regression,River MLP,ChickWeights,42.3259867290128,66.7403629812066,-0.22521937114525392,0.012312889099121094,22.959028000000004 +528,Regression,River MLP,ChickWeights,43.903763311459905,69.27444073484561,-0.18681443911135398,0.012312889099121094,23.825387000000003 +539,Regression,River MLP,ChickWeights,45.11725117254523,70.97054614693485,-0.19420442814828975,0.012312889099121094,24.695185000000002 +550,Regression,River MLP,ChickWeights,46.19146939493793,72.84904016514736,-0.2194804408254527,0.012312889099121094,25.570022 +561,Regression,River MLP,ChickWeights,48.0255382358928,75.55118081102547,-0.25426558573377567,0.012312889099121094,26.453531 +572,Regression,River MLP,ChickWeights,49.60861685612801,77.95895414232838,-0.23532548305844236,0.012312889099121094,27.340524000000002 +578,Regression,River MLP,ChickWeights,51.40782550111089,80.92025038917566,-0.298583502299673,0.012312889099121094,28.229463000000003 +20,Regression,River MLP,TrumpApproval,28.203089584036217,31.678254793976468,-2352.839799462937,0.013110160827636719,0.018592 +40,Regression,River MLP,TrumpApproval,17.631407237579232,23.536801219235826,-221.7205207554288,0.013110160827636719,0.053933999999999996 +60,Regression,River MLP,TrumpApproval,13.441671937224772,19.739075566761823,-210.18539534147195,0.013110160827636719,0.098078 +80,Regression,River MLP,TrumpApproval,11.196749290061339,17.292913087737123,-161.5886474703317,0.013110160827636719,0.159515 +100,Regression,River MLP,TrumpApproval,9.529407951935296,15.54264880746251,-81.40884208187767,0.013110160827636719,0.228076 +120,Regression,River MLP,TrumpApproval,8.478754286735066,14.272499783288554,-57.95136830581733,0.013110160827636719,0.332328 +140,Regression,River MLP,TrumpApproval,7.525552058981039,13.242333407520347,-51.44233495767236,0.013110160827636719,0.527456 +160,Regression,River MLP,TrumpApproval,6.729532853932534,12.401843141618142,-39.56324503441056,0.013110160827636719,0.7296090000000001 +180,Regression,River MLP,TrumpApproval,6.20494414148211,11.727398222866162,-30.855608065765253,0.013110160827636719,0.938725 +200,Regression,River MLP,TrumpApproval,5.707613016041334,11.135875265707485,-27.808506433676282,0.013110160827636719,1.173453 +220,Regression,River MLP,TrumpApproval,5.35235544657082,10.636236352263047,-27.34993958822678,0.013110160827636719,1.423387 +240,Regression,River MLP,TrumpApproval,4.997211310189409,10.191758203807838,-25.225868327846438,0.013110160827636719,1.696442 +260,Regression,River MLP,TrumpApproval,4.698339965696975,9.799142308635478,-23.570888426665658,0.013350486755371094,2.044166 +280,Regression,River MLP,TrumpApproval,4.429952698677103,9.448184269747657,-22.91569767610472,0.013350486755371094,2.398587 +300,Regression,River MLP,TrumpApproval,4.185436867704573,9.131292683908228,-20.968518634865898,0.013350486755371094,2.759636 +320,Regression,River MLP,TrumpApproval,3.989857840855361,8.848493522992882,-20.65027251080777,0.013350486755371094,3.127609 +340,Regression,River MLP,TrumpApproval,3.793510888989401,8.58729864958044,-20.548263158423392,0.013350486755371094,3.5072970000000003 +360,Regression,River MLP,TrumpApproval,3.624008920532304,8.350732680595982,-19.544713512132038,0.013350486755371094,3.994133 +380,Regression,River MLP,TrumpApproval,3.4723941591948395,8.130732570333006,-19.15022282865096,0.013350486755371094,4.488831 +400,Regression,River MLP,TrumpApproval,3.327129028584169,7.926491124989248,-18.691844486765735,0.013350486755371094,4.996594 +420,Regression,River MLP,TrumpApproval,3.1988914312464622,7.737168953530663,-18.383340677896445,0.013350486755371094,5.512642 +440,Regression,River MLP,TrumpApproval,3.0905418652204037,7.562941397323993,-17.185096454486196,0.013350486755371094,6.041213 +460,Regression,River MLP,TrumpApproval,2.9841087219087,7.398658950448711,-15.50384711789857,0.013350486755371094,6.671723 +480,Regression,River MLP,TrumpApproval,2.878027938067315,7.243616567021465,-14.455461195017083,0.013350486755371094,7.311147 +500,Regression,River MLP,TrumpApproval,2.790174040420949,7.099353406661882,-13.534510391092628,0.013350486755371094,7.962598 +520,Regression,River MLP,TrumpApproval,2.702735665583147,6.962851553400364,-13.005371203657658,0.013350486755371094,8.621042 +540,Regression,River MLP,TrumpApproval,2.619923274637493,6.8334980874356335,-12.440396167539378,0.013350486755371094,9.286045999999999 +560,Regression,River MLP,TrumpApproval,2.556848015725479,6.714676061099242,-12.286263553450382,0.013350486755371094,9.968405999999998 +580,Regression,River MLP,TrumpApproval,2.4920466556079988,6.599727625727584,-12.1527109643015,0.013350486755371094,10.662936999999998 +600,Regression,River MLP,TrumpApproval,2.4260558633236777,6.490118235625791,-11.578750092830703,0.013350486755371094,11.381531999999998 +620,Regression,River MLP,TrumpApproval,2.3708694907142864,6.387338726311831,-10.997792654674662,0.013350486755371094,12.131809999999998 +640,Regression,River MLP,TrumpApproval,2.309077397643504,6.287531812954472,-10.40846497658843,0.013350486755371094,12.902857999999998 +660,Regression,River MLP,TrumpApproval,2.253417256192923,6.192441467899733,-9.986430076809746,0.013350486755371094,13.765244 +680,Regression,River MLP,TrumpApproval,2.1933714736526424,6.100884478116631,-9.832475924452435,0.013350486755371094,14.638727 +700,Regression,River MLP,TrumpApproval,2.1444840100167193,6.014053532220149,-9.80279442231031,0.013350486755371094,15.530555 +720,Regression,River MLP,TrumpApproval,2.0889149793500317,5.930058413028094,-9.733901359629304,0.013350486755371094,16.434104 +740,Regression,River MLP,TrumpApproval,2.038014375475162,5.849577408393744,-9.43824097776182,0.013350486755371094,17.347853 +760,Regression,River MLP,TrumpApproval,1.990139846596363,5.772270602035659,-9.274280741061778,0.013350486755371094,18.296023 +780,Regression,River MLP,TrumpApproval,1.946515411702069,5.69815370877383,-9.05697999731458,0.013350486755371094,19.263123 +800,Regression,River MLP,TrumpApproval,1.9088971171085878,5.627293726045093,-8.897112526821198,0.013350486755371094,20.241821 +820,Regression,River MLP,TrumpApproval,1.8732261968689674,5.559226632327132,-8.765208356441784,0.013350486755371094,21.227083 +840,Regression,River MLP,TrumpApproval,1.8347271749400444,5.492864392938861,-8.621969919695442,0.013350486755371094,22.223141000000002 +860,Regression,River MLP,TrumpApproval,1.8001515928803729,5.4291765082898245,-8.383942958793503,0.013350486755371094,23.230427000000002 +880,Regression,River MLP,TrumpApproval,1.762610565031098,5.367211780988228,-8.125392758933815,0.013350486755371094,24.244373000000003 +900,Regression,River MLP,TrumpApproval,1.7278213800286457,5.307357454879676,-7.9606012045493255,0.013350486755371094,25.346781000000004 +920,Regression,River MLP,TrumpApproval,1.6959197142820022,5.249600105314111,-7.910676780558388,0.013350486755371094,26.455778000000002 +940,Regression,River MLP,TrumpApproval,1.6672680101890094,5.193857129216991,-7.796440957593809,0.013350486755371094,27.578615000000003 +960,Regression,River MLP,TrumpApproval,1.6372087427382507,5.139738169534271,-7.704147396017831,0.013350486755371094,28.708325000000002 +980,Regression,River MLP,TrumpApproval,1.6082702309133736,5.087224346398139,-7.692803163516414,0.013350486755371094,29.852185000000002 +1000,Regression,River MLP,TrumpApproval,1.582128560540168,5.036439630005545,-7.663534315499042,0.013350486755371094,31.047385000000002 +1001,Regression,River MLP,TrumpApproval,1.5805783932319006,5.033923389051291,-7.660658200179739,0.013350486755371094,32.243154000000004 +11,Regression,[baseline] Mean predictor,ChickWeights,4.664574314574316,12.707974531760701,-206.87879383707747,0.0004901885986328125,0.000258 +22,Regression,[baseline] Mean predictor,ChickWeights,2.767694704637076,9.018587183866769,-85.14025986830408,0.0004901885986328125,0.000737 +33,Regression,[baseline] Mean predictor,ChickWeights,2.3093367298127023,7.420500566500976,-37.24267181629702,0.0004901885986328125,0.00134 +44,Regression,[baseline] Mean predictor,ChickWeights,1.8923639683488078,6.441521936619904,-31.668094594906044,0.0004901885986328125,0.002066 +55,Regression,[baseline] Mean predictor,ChickWeights,2.1129412159858934,6.114058653243701,-6.297346571779499,0.0004901885986328125,0.0029100000000000003 +66,Regression,[baseline] Mean predictor,ChickWeights,2.832849782567835,6.236602142425367,-2.2730130120415795,0.0004901885986328125,0.0038720000000000004 +77,Regression,[baseline] Mean predictor,ChickWeights,3.4069290990236856,6.402381882180361,-1.3118663438824,0.0004901885986328125,0.004952000000000001 +88,Regression,[baseline] Mean predictor,ChickWeights,3.6503779711608075,6.321189272940957,-1.043267371916866,0.0004901885986328125,0.006149000000000001 +99,Regression,[baseline] Mean predictor,ChickWeights,4.035631404360372,6.4483291916176695,-0.7783857772357967,0.0004901885986328125,0.007464000000000001 +110,Regression,[baseline] Mean predictor,ChickWeights,4.693189868957898,7.0697740144659305,-0.49277927868413074,0.0004901885986328125,0.008896000000000001 +121,Regression,[baseline] Mean predictor,ChickWeights,5.274396860168236,7.6542276724395,-0.34762252544372596,0.0004901885986328125,0.010446 +132,Regression,[baseline] Mean predictor,ChickWeights,5.875758254207378,8.194624755054596,-0.2624191661321591,0.0004901885986328125,0.012113 +143,Regression,[baseline] Mean predictor,ChickWeights,6.530760796045927,8.870097879563003,-0.19803554240449484,0.0004901885986328125,0.013898 +154,Regression,[baseline] Mean predictor,ChickWeights,7.121466111912466,9.458403141043558,-0.15770278521517955,0.0004901885986328125,0.015801 +165,Regression,[baseline] Mean predictor,ChickWeights,7.772438504082036,10.375670403553157,-0.11989999304508925,0.0004901885986328125,0.017821999999999998 +176,Regression,[baseline] Mean predictor,ChickWeights,8.565827130563894,11.410434180005831,-0.09206765686265328,0.0004901885986328125,0.019960999999999996 +187,Regression,[baseline] Mean predictor,ChickWeights,9.429958588641576,12.495061319237752,-0.07221531716282037,0.0004901885986328125,0.022216999999999997 +198,Regression,[baseline] Mean predictor,ChickWeights,10.47731537859646,13.900491647656429,-0.05555027037575888,0.0004901885986328125,0.024589999999999997 +209,Regression,[baseline] Mean predictor,ChickWeights,11.43172675762076,15.229123619635446,-0.04445651287163721,0.0004901885986328125,0.027079 +220,Regression,[baseline] Mean predictor,ChickWeights,11.974320980081139,16.22368260926648,-0.03775608698471111,0.0004901885986328125,0.029685 +231,Regression,[baseline] Mean predictor,ChickWeights,12.9382196746461,17.489503190785292,-0.03157819728271183,0.0004901885986328125,0.032406 +242,Regression,[baseline] Mean predictor,ChickWeights,14.229204186206863,19.43725798629848,-0.02523677186741935,0.0004901885986328125,0.035243 +253,Regression,[baseline] Mean predictor,ChickWeights,15.339413196393396,20.820238312545918,-0.021649789303838762,0.0004901885986328125,0.041904 +264,Regression,[baseline] Mean predictor,ChickWeights,15.948617107030818,21.75817315507082,-0.019440185124094622,0.0004901885986328125,0.048726 +275,Regression,[baseline] Mean predictor,ChickWeights,16.794155127707494,23.16724301729152,-0.016999619323781356,0.0004901885986328125,0.055688 +286,Regression,[baseline] Mean predictor,ChickWeights,17.990009992534457,24.865985915258104,-0.014754713395529917,0.0004901885986328125,0.062787 +297,Regression,[baseline] Mean predictor,ChickWeights,19.34919450213405,26.676209297603677,-0.012890456560007202,0.0004901885986328125,0.070018 +308,Regression,[baseline] Mean predictor,ChickWeights,20.46881241431745,28.248013022827834,-0.011537481517321035,0.0004901885986328125,0.077383 +319,Regression,[baseline] Mean predictor,ChickWeights,20.993702124162965,29.638141143499492,-0.010503673119392376,0.0004901885986328125,0.08488399999999999 +330,Regression,[baseline] Mean predictor,ChickWeights,22.586872779548433,32.01796640002603,-0.009220237952050514,0.0004901885986328125,0.09251699999999999 +341,Regression,[baseline] Mean predictor,ChickWeights,23.973458872107372,33.821533603903084,-0.008387701903732392,0.0004901885986328125,0.10028199999999998 +352,Regression,[baseline] Mean predictor,ChickWeights,25.315991788770976,35.461698606860665,-0.007731302158646702,0.0004901885986328125,0.10817799999999998 +363,Regression,[baseline] Mean predictor,ChickWeights,25.615062978866305,35.981300981590465,-0.007443749031205149,0.0004901885986328125,0.11620599999999998 +374,Regression,[baseline] Mean predictor,ChickWeights,26.673321526932543,37.51836715700961,-0.006935846124255907,0.0004901885986328125,0.12436199999999997 +385,Regression,[baseline] Mean predictor,ChickWeights,28.27694482780972,39.8753298933956,-0.006332510983879436,0.0004901885986328125,0.13263999999999998 +396,Regression,[baseline] Mean predictor,ChickWeights,29.55612496209691,41.288487059450155,-0.005980181891907188,0.0004901885986328125,0.14104099999999997 +407,Regression,[baseline] Mean predictor,ChickWeights,30.561677112682855,42.81802042618151,-0.005646723150046551,0.0004901885986328125,0.14956599999999998 +418,Regression,[baseline] Mean predictor,ChickWeights,31.39346669137945,44.18765357092498,-0.0053697143301307815,0.0004901885986328125,0.15821399999999997 +429,Regression,[baseline] Mean predictor,ChickWeights,33.10612890637694,46.865579751152914,-0.004966366070605188,0.0004901885986328125,0.16698499999999997 +440,Regression,[baseline] Mean predictor,ChickWeights,34.54914638861108,48.61167278858254,-0.004716123854972665,0.0004901885986328125,0.17588299999999996 +451,Regression,[baseline] Mean predictor,ChickWeights,35.43263419295921,49.67507127970072,-0.004553693807187953,0.0004901885986328125,0.18490599999999996 +462,Regression,[baseline] Mean predictor,ChickWeights,36.308550382896186,51.2507761435036,-0.004357377489546899,0.0004901885986328125,0.19405499999999995 +473,Regression,[baseline] Mean predictor,ChickWeights,38.26330298063241,54.532250497281034,-0.004051661204895529,0.0004901885986328125,0.20332799999999995 +484,Regression,[baseline] Mean predictor,ChickWeights,39.598662348008276,56.08659790201894,-0.0039023944795495424,0.0004901885986328125,0.21272499999999994 +495,Regression,[baseline] Mean predictor,ChickWeights,40.94697327298068,57.823326559810994,-0.0037535911132069444,0.0004901885986328125,0.22224499999999994 +506,Regression,[baseline] Mean predictor,ChickWeights,41.42384714758024,58.679845942015916,-0.003665234721119459,0.0004901885986328125,0.23188899999999996 +517,Regression,[baseline] Mean predictor,ChickWeights,42.72663002099646,60.40151056768402,-0.0035345422299792872,0.0004901885986328125,0.24165999999999996 +528,Regression,[baseline] Mean predictor,ChickWeights,44.77321528369677,63.69509749878913,-0.0033415055563215112,0.0004901885986328125,0.25155399999999994 +539,Regression,[baseline] Mean predictor,ChickWeights,45.99579764939489,65.0494992510053,-0.0032526095626370655,0.0004901885986328125,0.26157099999999994 +550,Regression,[baseline] Mean predictor,ChickWeights,46.57020777663759,66.07332710234044,-0.0031815200825582313,0.0004901885986328125,0.2717109999999999 +561,Regression,[baseline] Mean predictor,ChickWeights,47.758257606406204,67.5643396193493,-0.0030950009187136196,0.0004901885986328125,0.28197199999999994 +572,Regression,[baseline] Mean predictor,ChickWeights,49.49138874897682,70.24569214117749,-0.002971942406188699,0.0004901885986328125,0.29235599999999995 +578,Regression,[baseline] Mean predictor,ChickWeights,50.250899455914585,71.11438743304103,-0.002929468639104371,0.0004901885986328125,0.30283499999999997 +20,Regression,[baseline] Mean predictor,TrumpApproval,2.695184981652336,9.807184976514188,-224.6021011118197,0.0004901885986328125,0.001338 +40,Regression,[baseline] Mean predictor,TrumpApproval,2.3994713447037435,7.102066178895935,-19.27845129783118,0.0004901885986328125,0.0038250000000000003 +60,Regression,[baseline] Mean predictor,TrumpApproval,1.8170744682035582,5.815253847056423,-17.329373299766118,0.0004901885986328125,0.00717 +80,Regression,[baseline] Mean predictor,TrumpApproval,1.604995404573344,5.081770494168446,-13.040545957103586,0.0004901885986328125,0.011356999999999999 +100,Regression,[baseline] Mean predictor,TrumpApproval,1.824259078948539,4.70488333223354,-6.5512954222403845,0.0004901885986328125,0.020929 +120,Regression,[baseline] Mean predictor,TrumpApproval,1.9187446081165878,4.412336880489357,-4.634185300646759,0.0004901885986328125,0.030834 +140,Regression,[baseline] Mean predictor,TrumpApproval,1.8761207739327506,4.13187920011476,-4.1056167996805835,0.0004901885986328125,0.041039 +160,Regression,[baseline] Mean predictor,TrumpApproval,1.961232939518506,3.9761734872745063,-3.1695661963674864,0.0004901885986328125,0.051538 +180,Regression,[baseline] Mean predictor,TrumpApproval,2.066134597500757,3.873731518767916,-2.4756944369169624,0.0004901885986328125,0.062312 +200,Regression,[baseline] Mean predictor,TrumpApproval,2.051125997923389,3.731810291394655,-2.23527456693896,0.0004901885986328125,0.073408 +220,Regression,[baseline] Mean predictor,TrumpApproval,1.9409519346841397,3.56902990398404,-2.19210047340805,0.0004901885986328125,0.084777 +240,Regression,[baseline] Mean predictor,TrumpApproval,1.9366756524315063,3.4612902974772624,-2.024876884626847,0.0004901885986328125,0.096419 +260,Regression,[baseline] Mean predictor,TrumpApproval,1.9250039777458068,3.363327951159923,-1.8945640461454523,0.0004901885986328125,0.108333 +280,Regression,[baseline] Mean predictor,TrumpApproval,1.8726934920539138,3.257010428159885,-1.8420037280027222,0.0004901885986328125,0.120517 +300,Regression,[baseline] Mean predictor,TrumpApproval,1.8907476896224937,3.1958821895815714,-1.6910252267675165,0.0004901885986328125,0.133002 +320,Regression,[baseline] Mean predictor,TrumpApproval,1.819623890420079,3.103812605138666,-1.663886258690169,0.0004901885986328125,0.145758 +340,Regression,[baseline] Mean predictor,TrumpApproval,1.7396293145937214,3.014220627768389,-1.654906383755708,0.0004901885986328125,0.158784 +360,Regression,[baseline] Mean predictor,TrumpApproval,1.7350691203787965,2.9569384317632506,-1.5759385016835008,0.0004901885986328125,0.172076 +380,Regression,[baseline] Mean predictor,TrumpApproval,1.6987131960417108,2.8893997308323693,-1.5446951110541192,0.0004901885986328125,0.185636 +400,Regression,[baseline] Mean predictor,TrumpApproval,1.673610627740774,2.82935583501861,-1.5089937655143242,0.0004901885986328125,0.199488 +420,Regression,[baseline] Mean predictor,TrumpApproval,1.6410137122925974,2.7701802079251965,-1.484737486096575,0.0004901885986328125,0.213608 +440,Regression,[baseline] Mean predictor,TrumpApproval,1.6565972573555454,2.7427790467379385,-1.391750010744973,0.0004901885986328125,0.227993 +460,Regression,[baseline] Mean predictor,TrumpApproval,1.699464840115161,2.7394674040138396,-1.2626191030939884,0.0004901885986328125,0.242643 +480,Regression,[baseline] Mean predictor,TrumpApproval,1.7224824441896143,2.7219018737730583,-1.182307732575659,0.0004901885986328125,0.25756 +500,Regression,[baseline] Mean predictor,TrumpApproval,1.7446092142173422,2.7058035442295596,-1.1113262021905803,0.0004901885986328125,0.272747 +520,Regression,[baseline] Mean predictor,TrumpApproval,1.7464998751860934,2.677192702589883,-1.0705208906620065,0.0004901885986328125,0.288233 +540,Regression,[baseline] Mean predictor,TrumpApproval,1.7535492786865425,2.653885630983747,-1.0271707062792519,0.0004901885986328125,0.303987 +560,Regression,[baseline] Mean predictor,TrumpApproval,1.7201019899937544,2.614359234374483,-1.0141103337708768,0.0004901885986328125,0.320009 +580,Regression,[baseline] Mean predictor,TrumpApproval,1.6887559504032665,2.5757257291728384,-1.0033760803823184,0.0004901885986328125,0.336298 +600,Regression,[baseline] Mean predictor,TrumpApproval,1.701917368353294,2.5614247637328695,-0.9592753712060649,0.0004901885986328125,0.35287999999999997 +620,Regression,[baseline] Mean predictor,TrumpApproval,1.7178157166185173,2.5513468959681562,-0.9142580419512063,0.0004901885986328125,0.369731 +640,Regression,[baseline] Mean predictor,TrumpApproval,1.7365901196485038,2.545046385321895,-0.8692105635365064,0.0004901885986328125,0.386852 +660,Regression,[baseline] Mean predictor,TrumpApproval,1.7465677425181807,2.532051562790666,-0.8368676529707118,0.0004901885986328125,0.40424 +680,Regression,[baseline] Mean predictor,TrumpApproval,1.731617734826669,2.5042261861708606,-0.8251107974736909,0.0004901885986328125,0.42189499999999996 +700,Regression,[baseline] Mean predictor,TrumpApproval,1.6973720107412233,2.4702678919797196,-0.8225927549994396,0.0004901885986328125,0.439849 +720,Regression,[baseline] Mean predictor,TrumpApproval,1.6698372433333928,2.4400355004771073,-0.81732226470892,0.0004901885986328125,0.458072 +740,Regression,[baseline] Mean predictor,TrumpApproval,1.6732482399922957,2.425592833263792,-0.7947920429290933,0.0004901885986328125,0.47656299999999996 +760,Regression,[baseline] Mean predictor,TrumpApproval,1.6653913599894004,2.404136439714782,-0.7822814452716051,0.0004901885986328125,0.49532099999999996 +780,Regression,[baseline] Mean predictor,TrumpApproval,1.6644612180457288,2.387561393188575,-0.7656652158374817,0.0004901885986328125,0.514347 +800,Regression,[baseline] Mean predictor,TrumpApproval,1.6556359332933146,2.368497267913513,-0.7532954885990883,0.0004901885986328125,0.533661 +820,Regression,[baseline] Mean predictor,TrumpApproval,1.6452077788467467,2.348678653798561,-0.7430103139622937,0.0004901885986328125,0.5532450000000001 +840,Regression,[baseline] Mean predictor,TrumpApproval,1.6374623223784903,2.3305035344735936,-0.7320713255917544,0.0004901885986328125,0.5730930000000001 +860,Regression,[baseline] Mean predictor,TrumpApproval,1.6419505315856449,2.3202080137162757,-0.7138439732116804,0.0004901885986328125,0.6284980000000001 +880,Regression,[baseline] Mean predictor,TrumpApproval,1.6490002164922652,2.3126155324510744,-0.6941855677649247,0.0004901885986328125,0.6842080000000001 +900,Regression,[baseline] Mean predictor,TrumpApproval,1.6474991175923384,2.299197536504521,-0.6816400531907807,0.0004901885986328125,0.7401880000000002 +920,Regression,[baseline] Mean predictor,TrumpApproval,1.6301006788336792,2.2779225390149764,-0.6777843948800273,0.0004901885986328125,0.7964830000000002 +940,Regression,[baseline] Mean predictor,TrumpApproval,1.6221876471839873,2.2623787372500574,-0.6690049120995847,0.0004901885986328125,0.8530460000000002 +960,Regression,[baseline] Mean predictor,TrumpApproval,1.6124120493571745,2.245866476718547,-0.6619276404267609,0.0004901885986328125,0.9098760000000002 +980,Regression,[baseline] Mean predictor,TrumpApproval,1.5867001120604314,2.223758235975506,-0.661013659831075,0.0004901885986328125,0.9669740000000002 +1000,Regression,[baseline] Mean predictor,TrumpApproval,1.5681359363812417,2.2037391763141216,-0.6587014308970958,0.0004901885986328125,1.0243380000000002 +1001,Regression,[baseline] Mean predictor,TrumpApproval,1.567554989468773,2.202858861923226,-0.6584830635688459,0.0004901885986328125,1.081765 diff --git a/docs/examples/batch-to-online.ipynb b/docs/examples/batch-to-online.ipynb index c47a138b13..60f5892f99 100644 --- a/docs/examples/batch-to-online.ipynb +++ b/docs/examples/batch-to-online.ipynb @@ -25,10 +25,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:35.636197Z", - "iopub.status.busy": "2023-01-29T19:26:35.635866Z", - "iopub.status.idle": "2023-01-29T19:26:38.347023Z", - "shell.execute_reply": "2023-01-29T19:26:38.343482Z" + "iopub.execute_input": "2023-12-04T17:49:59.272045Z", + "iopub.status.busy": "2023-12-04T17:49:59.271371Z", + "iopub.status.idle": "2023-12-04T17:49:59.753872Z", + "shell.execute_reply": "2023-12-04T17:49:59.715242Z" }, "tags": [] }, @@ -67,7 +67,7 @@ "scorer = metrics.make_scorer(metrics.roc_auc_score)\n", "scores = model_selection.cross_val_score(model, X, y, scoring=scorer, cv=cv)\n", "\n", - "# Display the average score and it's standard deviation\n", + "# Display the average score and its standard deviation\n", "print(f'ROC AUC: {scores.mean():.3f} (± {scores.std():.3f})')" ] }, @@ -94,7 +94,7 @@ "source": [ "## A hands-on introduction to incremental learning\n", "\n", - "Incremental learning is also often called *online learning* or *stream learning*, but if you [google online learning](https://www.google.com/search?q=online+learning) a lot of the results will point to educational websites. Hence, the terms \"incremental learning\" and \"stream learning\" (from which River derives it's name) are prefered. The point of incremental learning is to fit a model to a stream of data. In other words, the data isn't available in it's entirety, but rather the observations are provided one by one. As an example let's stream through the dataset used previously." + "Incremental learning is also often called *online learning* or *stream learning*, but if you [google online learning](https://www.google.com/search?q=online+learning) a lot of the results will point to educational websites. Hence, the terms \"incremental learning\" and \"stream learning\" (from which River derives its name) are preferred. The point of incremental learning is to fit a model to a stream of data. In other words, the data isn't available in its entirety, but rather the observations are provided one by one. As an example let's stream through the dataset used previously." ] }, { @@ -102,10 +102,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:38.377640Z", - "iopub.status.busy": "2023-01-29T19:26:38.377423Z", - "iopub.status.idle": "2023-01-29T19:26:38.392479Z", - "shell.execute_reply": "2023-01-29T19:26:38.392067Z" + "iopub.execute_input": "2023-12-04T17:49:59.761043Z", + "iopub.status.busy": "2023-12-04T17:49:59.759833Z", + "iopub.status.idle": "2023-12-04T17:49:59.779917Z", + "shell.execute_reply": "2023-12-04T17:49:59.778435Z" }, "tags": [] }, @@ -128,36 +128,27 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:38.395651Z", - "iopub.status.busy": "2023-01-29T19:26:38.394865Z", - "iopub.status.idle": "2023-01-29T19:26:38.419065Z", - "shell.execute_reply": "2023-01-29T19:26:38.418560Z" + "iopub.execute_input": "2023-12-04T17:49:59.790039Z", + "iopub.status.busy": "2023-12-04T17:49:59.788985Z", + "iopub.status.idle": "2023-12-04T17:49:59.840386Z", + "shell.execute_reply": "2023-12-04T17:49:59.837617Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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"output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -282,10 +235,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:38.454814Z", - "iopub.status.busy": "2023-01-29T19:26:38.454075Z", - "iopub.status.idle": "2023-01-29T19:26:38.634312Z", - "shell.execute_reply": "2023-01-29T19:26:38.633884Z" + "iopub.execute_input": "2023-12-04T17:49:59.876894Z", + "iopub.status.busy": "2023-12-04T17:49:59.875900Z", + "iopub.status.idle": "2023-12-04T17:50:00.034657Z", + "shell.execute_reply": "2023-12-04T17:50:00.034212Z" }, "tags": [] }, @@ -331,10 +284,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:38.636547Z", - "iopub.status.busy": "2023-01-29T19:26:38.636389Z", - "iopub.status.idle": "2023-01-29T19:26:38.657442Z", - "shell.execute_reply": "2023-01-29T19:26:38.656791Z" + "iopub.execute_input": "2023-12-04T17:50:00.036591Z", + "iopub.status.busy": "2023-12-04T17:50:00.036482Z", + "iopub.status.idle": "2023-12-04T17:50:00.054486Z", + "shell.execute_reply": "2023-12-04T17:50:00.054213Z" }, "tags": [] }, @@ -374,10 +327,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:38.659975Z", - "iopub.status.busy": "2023-01-29T19:26:38.659836Z", - "iopub.status.idle": "2023-01-29T19:26:38.673934Z", - "shell.execute_reply": "2023-01-29T19:26:38.673401Z" + "iopub.execute_input": "2023-12-04T17:50:00.056049Z", + "iopub.status.busy": "2023-12-04T17:50:00.055945Z", + "iopub.status.idle": "2023-12-04T17:50:00.066262Z", + "shell.execute_reply": "2023-12-04T17:50:00.066001Z" }, "tags": [] }, @@ -413,10 +366,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:38.676295Z", - "iopub.status.busy": "2023-01-29T19:26:38.676188Z", - "iopub.status.idle": "2023-01-29T19:26:38.732147Z", - "shell.execute_reply": "2023-01-29T19:26:38.731323Z" + "iopub.execute_input": "2023-12-04T17:50:00.067722Z", + "iopub.status.busy": "2023-12-04T17:50:00.067629Z", + "iopub.status.idle": "2023-12-04T17:50:00.100568Z", + "shell.execute_reply": "2023-12-04T17:50:00.100266Z" }, "tags": [] }, @@ -427,7 +380,7 @@ "scaler = preprocessing.StandardScaler()\n", "\n", "for xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer()):\n", - " scaler = scaler.learn_one(xi)" + " scaler.learn_one(xi)" ] }, { @@ -442,10 +395,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:38.735116Z", - "iopub.status.busy": "2023-01-29T19:26:38.734966Z", - "iopub.status.idle": "2023-01-29T19:26:38.795648Z", - "shell.execute_reply": "2023-01-29T19:26:38.795107Z" + "iopub.execute_input": "2023-12-04T17:50:00.102058Z", + "iopub.status.busy": "2023-12-04T17:50:00.101985Z", + "iopub.status.idle": "2023-12-04T17:50:00.150440Z", + "shell.execute_reply": "2023-12-04T17:50:00.150140Z" }, "tags": [] }, @@ -472,7 +425,8 @@ "for xi, yi in stream.iter_sklearn_dataset(datasets.load_breast_cancer(), shuffle=True, seed=42):\n", " \n", " # Scale the features\n", - " xi_scaled = scaler.learn_one(xi).transform_one(xi)\n", + " scaler.learn_one(xi)\n", + " xi_scaled = scaler.transform_one(xi)\n", " \n", " # Test the current model on the new \"unobserved\" sample\n", " yi_pred = log_reg.predict_proba_one(xi_scaled)\n", @@ -498,10 +452,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:38.798155Z", - "iopub.status.busy": "2023-01-29T19:26:38.797956Z", - "iopub.status.idle": "2023-01-29T19:26:38.923738Z", - "shell.execute_reply": "2023-01-29T19:26:38.923126Z" + "iopub.execute_input": "2023-12-04T17:50:00.151961Z", + "iopub.status.busy": "2023-12-04T17:50:00.151859Z", + "iopub.status.idle": "2023-12-04T17:50:00.270128Z", + "shell.execute_reply": "2023-12-04T17:50:00.269887Z" }, "tags": [] }, @@ -530,7 +484,7 @@ "# We compute the CV scores using the same CV scheme and the same scoring\n", "scores = model_selection.cross_val_score(model, X, y, scoring=scorer, cv=cv)\n", "\n", - "# Display the average score and it's standard deviation\n", + "# Display the average score and its standard deviation\n", "print(f'ROC AUC: {scores.mean():.3f} (± {scores.std():.3f})')" ] }, diff --git a/docs/examples/bike-sharing-forecasting.ipynb b/docs/examples/bike-sharing-forecasting.ipynb index 88a031a237..0c5f9d3fcb 100644 --- a/docs/examples/bike-sharing-forecasting.ipynb +++ b/docs/examples/bike-sharing-forecasting.ipynb @@ -19,10 +19,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:40.226039Z", - "iopub.status.busy": "2023-01-29T19:26:40.225911Z", - "iopub.status.idle": "2023-01-29T19:26:41.352854Z", - "shell.execute_reply": "2023-01-29T19:26:41.352324Z" + "iopub.execute_input": "2023-12-04T17:50:01.661427Z", + "iopub.status.busy": "2023-12-04T17:50:01.661176Z", + "iopub.status.idle": "2023-12-04T17:50:02.101176Z", + "shell.execute_reply": "2023-12-04T17:50:02.100862Z" }, "tags": [] }, @@ -31,8 +31,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading https://maxhalford.github.io/files/datasets/toulouse_bikes.zip (1.12 MB)\n", - "Uncompressing into /Users/max/river_data/Bikes\n", "{'clouds': 75,\n", " 'description': 'light rain',\n", " 'humidity': 81,\n", @@ -69,10 +67,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:41.355626Z", - "iopub.status.busy": "2023-01-29T19:26:41.355317Z", - "iopub.status.idle": "2023-01-29T19:26:47.015941Z", - "shell.execute_reply": "2023-01-29T19:26:47.015331Z" + "iopub.execute_input": "2023-12-04T17:50:02.102857Z", + "iopub.status.busy": "2023-12-04T17:50:02.102717Z", + "iopub.status.idle": "2023-12-04T17:50:07.739676Z", + "shell.execute_reply": "2023-12-04T17:50:07.739367Z" }, "tags": [] }, @@ -81,30 +79,27 @@ "name": "stdout", "output_type": "stream", "text": [ - 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get_hour
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y_mean_by_station_and_hour
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LinearRegression (\n", " optimizer=SGD (\n", " lr=Constant (\n", " learning_rate=0.001\n", @@ -259,19 +248,19 @@ " clip_gradient=1e+12\n", " initializer=Zeros ()\n", ")\n", - "\n", "
" + ], + "text/plain": [ + "Pipeline (\n", + " TransformerUnion (\n", + " Select (\n", + " clouds\n", + " humidity\n", + " pressure\n", + " temperature\n", + " wind\n", + " ),\n", + " Pipeline (\n", + " FuncTransformer (\n", + " func=\"get_hour\"\n", + " ),\n", + " TargetAgg (\n", + " by=['station', 'hour']\n", + " how=Mean ()\n", + " target_name=\"y\"\n", + " )\n", + " )\n", + " ),\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " LinearRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.001\n", + " )\n", + " )\n", + " loss=Squared ()\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + ")" ] }, "execution_count": 4, @@ -373,10 +412,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:55.768558Z", - "iopub.status.busy": "2023-01-29T19:26:55.768366Z", - "iopub.status.idle": "2023-01-29T19:26:56.138364Z", - "shell.execute_reply": "2023-01-29T19:26:56.138013Z" + "iopub.execute_input": "2023-12-04T17:50:16.823702Z", + "iopub.status.busy": "2023-12-04T17:50:16.823613Z", + "iopub.status.idle": "2023-12-04T17:50:17.222219Z", + "shell.execute_reply": "2023-12-04T17:50:17.221915Z" }, "tags": [] }, @@ -478,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:26:56.140872Z", - "iopub.status.busy": "2023-01-29T19:26:56.140738Z", - "iopub.status.idle": "2023-01-29T19:27:05.015470Z", - "shell.execute_reply": "2023-01-29T19:27:05.015051Z" + "iopub.execute_input": "2023-12-04T17:50:17.223831Z", + "iopub.status.busy": "2023-12-04T17:50:17.223728Z", + "iopub.status.idle": "2023-12-04T17:50:26.483009Z", + "shell.execute_reply": "2023-12-04T17:50:26.482763Z" }, "tags": [] }, @@ -490,30 +529,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "[20,000] MAE: 4.203433\n", - "[40,000] MAE: 4.195404\n", - "[60,000] MAE: 4.130316\n", - "[80,000] MAE: 4.122796\n", - "[100,000] MAE: 4.069826\n", - "[120,000] MAE: 4.066034\n", - "[140,000] MAE: 4.088604\n", - "[160,000] MAE: 4.059282\n", - "[180,000] MAE: 4.026821\n", - "[182,470] MAE: 4.024939\n" + "[20,000] MAE: 20.198137\n", + "[40,000] MAE: 12.199763\n", + "[60,000] MAE: 9.468279\n", + "[80,000] MAE: 8.126625\n", + "[100,000] MAE: 7.273133\n", + "[120,000] MAE: 6.735469\n", + "[140,000] MAE: 6.376704\n", + "[160,000] MAE: 6.06156\n", + "[180,000] MAE: 5.806744\n", + "[182,470] MAE: 5.780772\n" ] }, { "data": { - "text/html": [ - "
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"2023-12-04T17:50:28.349217Z" }, "tags": [] }, @@ -59,10 +59,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:20.988086Z", - "iopub.status.busy": "2023-05-05T10:38:20.987900Z", - "iopub.status.idle": "2023-05-05T10:38:21.016331Z", - "shell.execute_reply": "2023-05-05T10:38:21.016027Z" + "iopub.execute_input": "2023-12-04T17:50:28.351650Z", + "iopub.status.busy": "2023-12-04T17:50:28.351468Z", + "iopub.status.idle": "2023-12-04T17:50:28.371869Z", + "shell.execute_reply": "2023-12-04T17:50:28.371482Z" }, "tags": [] }, @@ -96,10 +96,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:21.018176Z", - "iopub.status.busy": "2023-05-05T10:38:21.018051Z", - "iopub.status.idle": "2023-05-05T10:38:21.281369Z", - "shell.execute_reply": "2023-05-05T10:38:21.280859Z" + "iopub.execute_input": "2023-12-04T17:50:28.373932Z", + "iopub.status.busy": "2023-12-04T17:50:28.373815Z", + "iopub.status.idle": "2023-12-04T17:50:28.574309Z", + "shell.execute_reply": "2023-12-04T17:50:28.574000Z" }, "tags": [] }, @@ -153,17 +153,17 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:21.283749Z", - "iopub.status.busy": "2023-05-05T10:38:21.283541Z", - "iopub.status.idle": "2023-05-05T10:38:21.452285Z", - "shell.execute_reply": "2023-05-05T10:38:21.451491Z" + "iopub.execute_input": "2023-12-04T17:50:28.576400Z", + "iopub.status.busy": "2023-12-04T17:50:28.576232Z", + "iopub.status.idle": "2023-12-04T17:50:28.872325Z", + "shell.execute_reply": "2023-12-04T17:50:28.871769Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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9JaudO3di9uzZyMzMxP3334+pU6fCYrFgw4YN+Pvf/46SkhKcffbZYV/r9/thMpmGeMbx5/P5YDabh3saRERERJSiUj7zIysy2gPuYftPVuSo5nn99dfDaDRi9erVuPDCCzFp0iSMGTMG55xzDj744AOcddZZoWMFQcBTTz2Fs88+Gw6HA/fddx8A4KmnnsLYsWNhNpsxYcIEvPDCC6HXVFVVQRAErFu3LjTW2toKQRCwZMkSAAcyKp9++ilmzpwJu92Oo446Clu2bFHN9cEHH0RBQQHS09NxzTXXwOPxRPxcVVVVOOGEEwAAWVlZEAQBV111FYDuZWo33ngjbr75ZuTm5uLUU0/tc569nQ8AZFnG7bffjuzsbBQWFuKuu+6K6vsnIiIiotSX8pmfTsmDn257etje/2/jfwansfe2201NTVi0aBHuv/9+OByOsMdou5LdddddePDBB/Hoo4/CaDTinXfewU033YRHH30Uc+bMwfvvv4+rr74apaWloWAhWr/97W/x5z//GXl5efjZz36GH//4x/j6668BAG+//TbuvvtuPPHEEzj66KPxwgsv4PHHH8eYMWPCnqusrAxvvfUW5s2bhy1btsDpdIb21wGA559/Htddd13o/H2J5ny33norVqxYgWXLluGqq67C7NmzcfLJJ8f0HRARERFR6kn54CcZbN++HYqiYMKECarx3NzcUFblhhtuwB/+8IfQc5deeimuvvrq0ONLLrkEV111Fa6//noAwK233orly5fjoYceijn4ue+++3DccccBAH7zm99g7ty58Hg8sFgseOKJJ/DjH/8Y11xzDQDg3nvvxeLFiyNmfwwGA7KzswEA+fn5yMzMVD0/fvx4/PGPfww9rqqq6nVufZ1v2rRp+L//+7/Quf/617/i008/ZfBDRERERKm/7C2ZrVy5EuvWrcOUKVPg9XpVz82cOVP1ePPmzZg9e7ZqbPbs2di8eXPM7ztt2rTQ/y4qKgIA1NfXAwC2bt2KWbNmqY6vrKyM+T2CZsyY0e/XhtNz7kD3/INzJyIiIkp1Tf4OPL3/Yzy2731UeXgPpMXMTwIYN24cBEHQ1dYEl5L1XNYVFGl5XCSi2B3nKooSGvP7/WGP7dk8IbjcTpajq12KlfZzxDLPcLSNHwRBGLS5ExERESWaf9R8gvWuKgDARtce/HnMVXAa9feSI1XKBz9pBiv+Nv5nw/r+fcnJycHJJ5+Mv/71r/j5z38ec2ADAJMmTcLXX3+N+fPnh8a+/vprTJ48GQCQl5cHAKipqcH06dMBQNVUIFoHHXQQVqxYoXqf5cuX9/qaYAc3SZL6PH8084zlfEREREQjhaTI+Na1O/S4U+rCopZ1uCCv/6t0Uk3KBz+iIPbZcCARPPnkk5g9ezZmzpyJu+66C9OmTYMoili1ahW+//77PpeH3Xbbbbjwwgsxffp0zJkzB++99x7efvttLF68GEB39ujII4/Egw8+iIqKCtTX1+POO++MeZ7XX389fvazn+Hwww/H7Nmz8dJLL+G7776L2PAAAEaNGgVBEPD+++/jjDPOgM1mQ1paWthjo5lnLOcjIiIiGik6pC4oUFRj/2n5BmfmzIBV5HYiAGt+EsbYsWPxzTffYM6cObjjjjtwyCGHYObMmfjLX/6CX/3qV/j973/f6+vPPfdcPPbYY3jooYcwZcoU/O1vf8OCBQtw/PHHh4559tlnEQgEMGPGDNx888249957Y57nvHnzcOedd+L222/HjBkzsHv3blx33XW9vqakpAR33303fvOb36CgoAA33nhjr8f3Nc9Yz0dEREQ0ErQF3Loxl+TBZ63fDcNsEpOg9CyuSBLt7e3IyMhAW1sbnE6n6jmPx4Ndu3ahoqICVmvfS84oeoqiwOVyweFw6FpvJyv+vHQLBAJYsWIFZs2aBaMx5RPCIwava2ridU1NvK6payiv7QbXbtxf/ZZuPMfkxKNjr4ZRMAzq+w+X3mIDLWZ+iIiIiIhSQIcUfuuRJn87lrVvCfvcSMPgh4iIiIgoBbSHWfYW9F7TaiThgq+4Y/BDRERERJQC2qWuiM/t8TZi3Q8tsEcyBj9ERERERCmgo5fMDwC817RqiGaSuBj8EBERERGlAG3mJ8+UoXq82b0X27pqhnJKCYfBDxERERFRCmiX1Jmfk7MOQZZRvRfiSM/+MPghIiIiIkoB7QF15ifHlI7Tsw9Tja3u2IG2gGsop5VQYg5+9u3bh8svvxw5OTmw2WyYOnUqVq9eHXpeURT87ne/Q1FREWw2G+bMmYNt27apztHc3IzLLrsMTqcTmZmZuOaaa9DZ2TnwT0NERERENEJpl72lG6w4KXMqTMKB/YUUKNjvaxnqqSWMmIKflpYWzJ49GyaTCR999BE2bdqEP//5z8jKygod88c//hGPP/44nn76aaxYsQIOhwOnnnoqPJ4Dfccvu+wyfPfdd/jkk0/w/vvv44svvsBPfvKT+H0qIiIiIqIRRFZkuDT7/DgNdtgNFmQaHarxzgj7AY0EMW0z+4c//AFlZWVYsGBBaKyioiL0vxVFwaOPPoo777wT55xzDgDgX//6FwoKCrBw4UJcfPHF2Lx5M/7zn/9g1apVmDlzJgDgL3/5C8444ww89NBDKC4ujsfnol5cddVVaG1txcKFCwEAxx9/PA499FA8+uij/T5nPM5BRERERP3TIXVBgXofH6fRBgBIN9jQ4G9THTtSxRT8vPvuuzj11FPxox/9CJ9//jlKSkpw/fXX49prrwUA7Nq1C7W1tZgzZ07oNRkZGZg1axaWLVuGiy++GMuWLUNmZmYo8AGAOXPmQBRFrFixAuedd57ufb1eL7xeb+hxe3s7ACAQCCAQCKiODQQCUBQl9F8yufrqq/H8888DAEwmE8rLy3HFFVfgf/7nf2A0xnSpohL8ft566y2YTKY+vy9FUfDFF19g7ty5aG5uRmZmZui5aM+RaII/J+F+lkYSSZKgKAokSRruqVAc8bqmJl7X1MTrmrqG6tq2eDt192E2xYRAIACHaFE91+ZzpdR9TyyfJaY76p07d+Kpp57Crbfeiv/5n//BqlWr8Itf/AJmsxnz589HbW0tAKCgoED1uoKCgtBztbW1yM/PV0/CaER2dnboGK0HHngAd999t2589erVcDjUaTxBEGC32+F2u5PuLxC/34+TTz4ZTz31FLxeLxYtWoRbb70ViqLgV7/6lepYn88Hs9nc7/cJBAJwubqL3SwWCwCEHkeiKApkWQ4dazKZQs9Fe45E4/V64fP58O233yZd4BZPsiyjo6MDK1euhCiyD0qq4HVNTbyuqYnXNXUN1bXdLbTAZTpwH2ZVTFizsrsuv93YBJd44LmN7d8jf1ty3Sf3Jpb7z5iCH1mWMXPmTNx///0AgOnTp2Pjxo14+umnMX/+/NhmGYM77rgDt956a+hxe3s7ysrKMHPmTDidTtWxHo8H1dXVsNvtsFqtUGQZinv4bsgFuwNClD/oJpMJdrsdY8aMAQBMmjQJH374IT7++GNUVVWhtbUVM2fOxJNPPgmLxYKdO3diz549+NWvfoVFixZBFEUcc8wxePTRRzF69GgA3b9tuO2227BgwQIYDAb8+Mc/htFohNFoDAWOJ5xwAg455JDQkjWv14vf/e53eOWVV1BfX4+ysjL85je/wYknnoizzjoLAFBWVgYAmD9/PhYsWKA7R0tLC26++Wa899578Hq9OO644/DYY49h/PjxAIDnnnsOt9xyC1599VXccsst2LNnD44++mg8++yzKCoqisdXHxWDwQCz2Yxx48bBarUO2fsmGkmSsGrVKhx++OEwGAzDPR2KE17X1MTrmpp4XVPXUF1bpWMrHDVbQo8LzVmYNXoWAGBrvQdVrR2h53KcBZhVOGvQ5jLUgqvCohFT8FNUVITJkyerxiZNmoS33noLAFBYWAgAqKurU93A1tXV4dBDDw0dU19frzpHIBBAc3Nz6PVaFosllFlQTf6Hm3jtmCAIof+ULjdafner7rVDJfv3j0BIS4/pNYIghP63zWZDU1MTAODTTz+F0+nEJ598AqD7ezvttNNQWVmJL7/8EkajEffeey9OP/10fPvttzCbzXj44Yfx/PPP49lnn8WkSZPw5z//Ge+88w5OPPFE1fsEvy+gO6BZtmwZHn/8cRxyyCHYtWsXGhsbUV5ejhdffBGXX345tmzZAqfTCZvNFnpdz3NcffXV2LZtG9599104nU78+te/xty5c7Fp0yaYTCYIggC3240///nPeOGFFyCKIi6//HLcdttteOmll/r/hccoOOdwP0sjjSAIMBgMI/57SDW8rqmJ1zU18bqmrqG4ti7Fp7q3yzDaQ++XYXaonnMpvpT6OYvls8T0qWfPno0tW7aoxrZu3YpRo0YB6G5+UFhYiE8//TQU7LS3t2PFihW47rrrAACVlZVobW3FmjVrMGPGDADAf//7X8iyjFmzUicCHShFUfDpp5/i448/xs9//nM0NDTA4XDgmWeeCS13e/HFFyHLMp555pnQD/SCBQuQmZmJJUuW4JRTTsGjjz6KO+64A+effz4A4Omnn8bHH38c8X23bt2K119/HZ988kmodiuYiVIUBdnZ2QCA/Px8Vc1PT8Gg5+uvv8ZRRx0FAHjppZdQVlaGhQsX4kc/+hGA7uV3Tz/9NMaOHQsAuPHGG3HPPfcM5GsjIiIiGpG0TQycRnvof6cbbL0eO5LEFPzccsstOOqoo3D//ffjwgsvxMqVK/H3v/8df//73wF0R7U333wz7r33XowfPx4VFRX43//9XxQXF+Pcc88F0J0pOu2003Dttdfi6aefht/vx4033oiLL76Ynd4AvP/++0hLS4Pf74csy7j00ktx11134YYbbsDUqVNVdT7r16/H9u3bkZ6uzix5PB7s2LEDbW1tqKmpUQWVRqMRM2fOjFjfsm7dOhgMBhx33HH9/gybN2+G0WhUvW9OTg4mTJiAzZs3h8bsdnso8AG6M4varCARERER9a1dcqseO3sEPGkG9dL+TgY/0Tn88MPxzjvv4I477sA999yDiooKPProo7jssstCx9x+++1wuVz4yU9+gtbWVhx99NH4z3/+o6qneOmll3DjjTfipJNOgiiKmDdvHh5//PH4faokdsIJJ+Cpp56C2WxGcXGxKo2nbe7Q2dmJGTNmhF0mlpeX16/3t9lsfR8UJz0bJgDdwfNIbjpARERE1F/tAU3mx9Bb5of7/ETtzDPPxJlnnhnxeUEQcM899/S6fCk7Oxsvv/xyrG/dL4LdgezfPzIk7xXp/WPhcDgwbty4qI497LDD8NprryE/P1/X+CGoqKgIK1aswLHHHgugu05ozZo1OOyww8IeP3XqVMiyjM8//1zVsjwoGLD01klv0qRJCAQCWLFiRWjZW1NTE7Zs2aKrGSMiIiKigdNlfoyRMz8uyQNZkSEKI6+zYMp/YkEUIaalD9t/0XZ664/LLrsMubm5OOecc/Dll19i165dWLJkCX7xi19g7969AICbbroJDz74IBYuXIjvv/8e119/PVpbWyOec/To0Zg/fz5+/OMfY+HChaFzvv766wCA8vJyCIKA999/Hw0NDejs7NSdY/z48TjnnHNw7bXX4quvvsL69etx+eWXo6SkJLT5LRERERHFj67mp0e2R5v5UaDAJXsxEqV88JPK7HY7vvjiC5SXl+P888/HpEmTcM0118Dj8YQyQb/85S9xxRVXYP78+aisrER6enrYjWR7euqpp3DBBRfg+uuvx8SJE3HttdeG+qcXFxfjrrvuwm9+8xsUFBTgxhtvDHuOBQsWYMaMGTjzzDNRWVkJRVHw4Ycf6pa6EREREdHAaZe9pfdoeOA06MsaOkfo0jdBScIii/b2dmRkZKCtrS3sPj+7du1CRUXFiN63ZTAoigKXywWHQ90uMZnx56VbcJnirFmzUqr15UjH65qaeF1TE69r6hqKaysrMi7//jEoOHBb/0DF5RhtzQ89nv/9X+BT/KHHd4+6GAfZU6PZWG+xgRYzP0RERERESaxT8qoCH0C/1C3dyHbXAIMfIiIiIqKkpm12AOiXuqVrmh6M1I5vDH6IiIiIiJKYNotjFy0wieoldtzotBuDHyIiIiKiJNYeUGd+tEvcAG50GsTgh4iIiIgoibXr2lzbdcdwo9NuKRv8yLI83FOgJMCfEyIiIkp2usxPmNbW2rGRmvlJuV6KZrMZoihi//79yMvLg9lsTpm2zMNNURR4vV4YDIak/04VRYHP50NDQwNEUYTZbB7uKRERERH1iy7zE9Wyt5GZ+Um54EcURVRUVKCmpgb79+8f7umklGDAkEoBpd1uR3l5OUQxZZOgRERElOK0zQvCbWrKhgfdUi74AbqzP+Xl5QgEApAkabinkzICgQC+/fZbjBs3LiU2YDMYDDAajSkTyBEREdHI1B5QBzIZxnA1P+rMj/Y1I0Xy38FGIAgCTCYTTCbTcE8lZQQCASiKAqvVmhLBDxEREVEqaJP6rvlJ09X8eKAoyoj7JTDX+hARERERJTFtw4Ow3d40dUAyZHTJvkGdVyJi8ENERERElKRkRdbX/ETR8AAYmXU/DH6IiIiIiJKUW/ZBgaIaC5f5sQommAR12cJI3OuHwQ8RERERUZLSLnkDwnd7EwQhTLtrZn6IiIiIiChJaPf4sYpmmMTwjanY7prBDxERERGNQIqioMbXgiZ/x3BPZUDaJW2zA33WJ0ib+RmJwQ/7FRMRERHRiCIrMp6uWYQv2zbBKBhwbdHJODZj8nBPq1+0+/U4w+zxE6QLfgKs+SEiIiIiSmn/afkGX7ZtAgAEFAlvNSwb5hn1XyyZH+1zrPkhIiIiIkph+73NeLX+a9VYvb8NkiIP04wGpkOT+Qm3wWmQdqNTbb3QSMDgh4iIiIhGBFmR8VTNx/ArAd1zyZoF0QYw2s1M1c9pu71x2RsRERERUUp6v3kNtnfVhH0uWfe80S97i1zzo80KMfghIiIiIkpBe7yNeKNhacTnk7XzmXbZW0avDQ/Y6prBDxERERGltIAi4cn9/0FAkSIeo+2alizaYmh4EC7zoyjKoMwrUTH4ISIiIqKU9u/Glajy1Pd6TDLW/CiKosve9NbwIF3T6tqvBOBR/IMyt0TF4IeIiIiIUpZL8uDfTatUYyXmHEyxl6vGkrHmxy17dV3qetvnJ1xgNNLqfhj8EBEREVHK2uGpU3V3EyDguuJTkW1KUx2XjPUv4VpV97bszSaaIWpu/7U1Q6mOwQ8RERERpazWgEv1uNSSg7G2QqRploAlY/CjDVzMggkW0RTxeEEQdJ87GZf7DQSDHyIiIiJKWW2a4CfT6ACgXwKWjMGPts11b53egpxG7efmsjciIiIiopTQEm3wk4TLv3QbnPay5C0oFTJeA8Hgh4iIiIhSVlsgfHYkFTb81H42bVYnnFT43APB4IeIiIiIUlZLoFP1OOuHzI9++VfyZUC0c+6t2UHQSN/olMEPEREREaUsXebHEH7Zm1v29roJaiLSbszqNPRd86Pd64fBDxERERFRitB2eztQ82PVHZtsS8C0DQ+iWfbGzA8RERERUQryywG4Za9qLBj8OMIEP8kWCGjnm96PzE+yBXwDxeCHiIiIiFKSttMbAGT+0PDAKBhgFy2q55ItEGj2q+uZMqNoda1v8Z1cn3mgGPwQERERUUpqk9TBj0kwqgKedM0yMW0NTSLzyQHdsrdck7PP16VCi++BYPBDRERERCmpNUyba0EQQo+TeaPTpkCHbizHlN7n67QBn0/xwycH4javRMfgh4iIiIhSUluEZgdBydz5rNHfrnrsMFhhE819vk67ySmQXJ97oBj8EBEREVFK0mZ+9MFPEmd+/OrMT66x76wPADhECwQIqrFk+twDxeCHiIiIiFKSrs21phtaMgc/jZrgJyeKeh8AEAVR1+kumT73QDH4ISIiIqKUpO32ltFX5ieJiv+1y95yo6j3CdIufUu2LncDweCHiIiIiFJSnzU/xuRt+6zN/MQS/GiDPgY/RERERERJrlXTCjorhWp+GgPqzE+0y96A5P7cA8Xgh4iIiIhSjqIousyPdtmbdvlXsgQBsiLrNjiNtuEBEK7LHTM/RERERERJyyV7EVAk1VimsfeGBx7ZB38S7HnTIXXBr6jnGc0Gp0HJXOs0UAx+iIiIiCjlaDu9AUCGptubUxMEAMmRBdHW+xgEURfY9Ubf8IDBDxERERFR0tIGPw6DFSbRqBvT7nmTDIGAttNbtjEdohD9bX0yN3oYKAY/RERERJRytPU+2mYHQHfGxG6wqMaSIRAYSKc3IHlrneKBwQ8RERERpRzdHj8GffADJGfns8aAdoPT2IIffavrxP/M8cLgh4iIiIhSTltA3eY6Uk1MMgY/TZplb3kxNDsA9J+5S/bBr2kOkaoY/BARERFRymmVem9zHZSMwY922VtODG2uAX2rawBwJcFyv3hg8ENEREREKUfb8CAzYvCTfHveaBsexNLmGgDSwna5S/ygLx4Y/BARERERAGCjqxoP730XC2r/i84kCAJ6ow9+Iix703Y+S/A9b7yyXxeoxFrzYxBEODRBX5Mmm5SqYgp+7rrrLgiCoPpv4sSJoec9Hg9uuOEG5OTkIC0tDfPmzUNdXZ3qHNXV1Zg7dy7sdjvy8/Nx2223IRBI/M2kiIiIiFJZtacBf9jzDlZ1bMeilnV4tf6r4Z7SgLTqan7CZ360e/0kegYkXJASa7c3ACgyZ6ke7/c193tOycTY9yFqU6ZMweLFiw+cwHjgFLfccgs++OADvPHGG8jIyMCNN96I888/H19//TUAQJIkzJ07F4WFhVi6dClqampw5ZVXwmQy4f7774/DxyEiIiKi/nit4WsEehS9r+vcNYyzGRi/Iuk6mEUKfrRLwBI9+NF2eksz2GAVzTGfp8iche1dNaHHNd6WAc8tGcQc/BiNRhQWFurG29ra8M9//hMvv/wyTjzxRADAggULMGnSJCxfvhxHHnkkFi1ahE2bNmHx4sUoKCjAoYceit///vf49a9/jbvuugtmc+wXjoiIiIgG5nv3Pqzt3KkaS4bal0jaNVkfoLdW18m1542+3if2rA8QLvMzMoKfmGt+tm3bhuLiYowZMwaXXXYZqqurAQBr1qyB3+/HnDlzQsdOnDgR5eXlWLZsGQBg2bJlmDp1KgoKCkLHnHrqqWhvb8d333030M9CRERERDFSFAWv1H+pG/cpfvjl5CxN0Nb7GAQRaZrNTIOSrdvbQDu9BRWbs1WPa0ZI8BNT5mfWrFl47rnnMGHCBNTU1ODuu+/GMcccg40bN6K2thZmsxmZmZmq1xQUFKC2thYAUFtbqwp8gs8Hn4vE6/XC6/WGHre3d0e8gUCA9UJDSJIkKIoCSRoZfeBHEl7b1MTrmpp4XVPTcF7Xbzp3YYt7X9jnWn2dyDKmDfGMBq7J2w5FUUKPnQYbZEmGDFl3rB1m1bEeyYcunwcmMeYFUmHF+9o2eFtV8802OPp1P5xvcKrO0+zvQIfXDZsh+VZixfL5Y7qqp59+euh/T5s2DbNmzcKoUaPw+uuvw2bTt8yLlwceeAB33323bnz16tVwOMKnMCn+ZFlGR0cHVq5cCVFko8BUwmubmnhdUxOva2oarusqQ8EC00q4BFfY579auwL5SvIFP+vEfXAZD3ymdMWAFStWhD22C364zOrP//mqr5EO/V44/RHva7vJuAMu8cB8W9vqsWJX+M/WmwBkuM1uKDgQAH285nMUKbG1zU4ELlf4n99wBhTSZmZm4qCDDsL27dtx8sknw+fzobW1VZX9qaurC9UIFRYWYuXKlapzBLvBhasjCrrjjjtw6623hh63t7ejrKwMM2fOhNOZfBcoWUmShFWrVuHwww+HwWAY7ulQHPHapiZe19TE65qahuu6ft3+Pdy1ChwI/8vkcaUTMMleOmTziZf9TSvgaNoTejzOUY5ZJbPCHisrMv65ba0qCBg/eTJGWfLiMpd4X9vXdn0Ph98XejyraDpmpY/v17ne2rUF9f620OO8wjLMck7s5RWJKbgqLBoDCn46OzuxY8cOXHHFFZgxYwZMJhM+/fRTzJs3DwCwZcsWVFdXo7KyEgBQWVmJ++67D/X19cjPzwcAfPLJJ3A6nZg8eXLE97FYLLBY9Os0jUajqtscDT5BEGAwGPi9pyBe29TE65qaeF1T01BfV78i4a3mFRAEIeIxbviT8uesQ/GoPleWOb3Xz5FmtKm6w7nhi+vnjte1lRUZLVKn6rPlWzP7fd5iSzYaAgcChzqpPSmvdyxzjin39qtf/Qqff/45qqqqsHTpUpx33nkwGAy45JJLkJGRgWuuuQa33norPvvsM6xZswZXX301KisrceSRRwIATjnlFEyePBlXXHEF1q9fj48//hh33nknbrjhhrDBDRERERENjv+2bEBDj9/6A4BJUN9EJnrxfyT6DU57L5PQd3xLzE53bZJb1Y4c6H+3N6A7+OmpZgTs9RNTaLd3715ccsklaGpqQl5eHo4++mgsX74ceXndacFHHnkEoihi3rx58Hq9OPXUU/Hkk0+GXm8wGPD+++/juuuuQ2VlJRwOB+bPn4977rknvp+KiIiIiCLyyn683bhcNTbeVgy7aMZ6V1VorDNBg4C+6Dc4tfd6fLrBhhoc6HbWEUjMoE/b6c0giMgw9P7ZelOsaXc9Evb6iSn4efXVV3t93mq14oknnsATTzwR8ZhRo0bhww8/jOVtiYiIiCiOvnPvQbukDhAuyT8a/23doBpzJW3wo878RNrjJ0jb7lq7QWqiaArT5loU+t9EIVy7a1mRB3TORJe6n4yIiIiIwtL+hn+MtRCT7KVIS7I9b8JRFAVtmuAnq69lb8bk+Nz6DU4H1viryKLO/PiUAJoCnQM6Z6Jj8ENEREQ0wtRran2Kf7gJTpbal950yT74FPW+L33V/KQlyefWBz/9r/cBgEyDA1ZRva9PjTe1634Y/BARERGNMNpGB/mmDAD6ICBRl3/1RrvkDQAy+qj5cSZJxktb85MzwMyPIAi6pW/7fald98Pgh4iIiGiEadBkEPJCwY+29iUxMyC9adPUMtlEMyyiqdfXaGt+EjX4aQqog5+BZn4AoEjb9IDBDxERERGlCkVRUO9TBz8F5kiZn+QLflq0zQ76WPIGhAt+EvNzazM/ucaBBz/Fmrqf/Sne7prBDxEREdEI0i654VP8qrG8H5ZP6Wt+uqAoypDNLR5ibXYAhAl+ErDVtUf26ZYhDrThAaDv+LafNT9ERERElCrqNUveRIjINqYB0C97U6CgS/YN2dziQZv56avZAaDv9uZT/PDK/ghHDw9t1gcAcgZh2VtzoBOeJLvmsWDwQ0RERDSCaOt9ck0H9orRLnsDErf+JRJt5qc/y96AxFvyp93jJ91g67OWKRpF5iwIEFRjtb7WAZ83UTH4ISIiIhpB6n2aTm8/1PsAgFUwwSgYVM8nWhDQl9aAuuFBpqH3Tm8AYBfNugAg0YI+bZvreGR9AMAsGnXnSuWObwx+iIiIiEYQbZvrYKc3oLv1sX7Pm8QKAvrSn8yPKIi67E97gn1ufae3gdf7BGmXvqVy3Q+DHyIiIqIRRLvBaZ7mJjrZO77pa376zvwA+qVvifa5dZ3e4hj8lGiaHqRyu2sGP0REREQjiLbmJ79H5gdI/CCgN5Ii6zJV0TQ8AIB0oybjlWAd37QZu3i0uQ4qGkHtrhn8EBEREY0QsiLrakfyzamT+WkNuKBA3Zo7mlbXgL7TXSIt9/vevQ/fu/epxgZz2VuNryXpWpxHi8EPERERUS8URYFL8iCgSMM9lQFrCbggKbJqTJv5SeQgoC91mi5lZsEIZxQND4BwG50mxuf2yQH8rWaRaswsGDHFURa399Du9eOV/WgOdMbt/InEONwTICIiIko0LYFObHRV41vXbmx0VaM14EKuyYlflZ6DUda84Z5ev2nrfcIFB8mc+dHWqhSYMyEIQoSj1cJt8JoI3mxchlrN5/pR3lFh25L3V7YxDRbRpNrbaL+vOW4d5RIJgx8iIiKiHyxr34J3Gldgj7dR91yjvx0Lm1bgppIzh2Fm8aFtc51nytAFB4kaBESj1t+qeqxdztWbRMz87OiqxftNq1VjY62FOCP7sLi+jyAIKDJnocpTHxqr8bVgqmNUXN8nEXDZGxERERGA3Z4GPL7vg7CBT1CVp2EIZxR/2mYHeWZ93Yh22VsyZX60y9567mHUF13wM8wND/yKhL/VLFLVMBkEET8rPjW0KW08aZe+pWq7awY/RERERADWdO7o85gmf0dSF4Jrl71p632A5F72VqsJfmLK/BgTK/Pz78aVukD8/NwjUWrJGZT30+31k6Ltrhn8EBEREQHYp/lNt1U0Y5pjtGrMrwTQLrmHcFbxpd/gNFzmRxv8DP/yr2jIiqzL/Axk2Vu71AVZ0xxiqOzxNmJh0wrV2ChLHs7OOXzQ3rPY0p35EWQFJl8ALa21kFqaITXUIVCzD3J7Wx9nSA6s+SEiIiICsNfbpHp8af4xOClzKi7//jHV0qNGfwcyomyfnGgafJplb2EyP9ogoEv2IaBIMAqGQZ3bQLUEXPApAdVYuMxWJNnGNNXjgCKhTXIjSzM+FD5oWqPqyidAwE+LTxnUa1AMByqX78IhG/bD7vYBAJqta3vUhAmwHn08HGdfCMFkGrR5DDZmfoiIiGjEkxRZt7FjqSUHoiDqOl41+juGcmpx41ckXfvigjA1MdqGBwDgSoKlb+HaXGsDmt5kGR264EIbLA6VbV01qsdzc2agwlowKO+lKAq8a1Yg7eFHULmiKhT4AEAAPTNfCjxffYa2v/4RUkuT/kRJgsEPERERjXh1vlbdPj4l5u7aCu3SsKbA8NwQD1STv123AWi4ZW+OMMFPRxIEPwNpcw0AoiDqNg7V1kgNBZ8c0H2WI9LHDcp7+XfvRPtjD6DjxX8AbW2670sKs7dVoHoX2h66B74t3w3KnAYbgx8iIiIa8fZolrw5DXY4fyiAT5XMT72m05tdtIQNdIyCATbRrBob7uL/aAykzXWQNhgcjuBnn69JF6SWWnLj/j6elV+j7dEH4N+9MzSmzXwFItQ8yW4X2p9+FO5F70ORh6cuqr9Y80NEREQj3j6fOvjp2VEr16jJ/CRr8KPb40ef9QlKM9jQJR9Y/pQMHd+0y94KzJkxn0NbI6T9zoZCtUfd4S3flKELRgdKammG680XAU2QZRBEbC/PwteVY9DutOL8wqNxRv4sSPU16HjuaUiN9T2OVuD+aCECu3ci7bJrINqTow6OmR8iIiIa8bTNDnoGP6mS+dF2euttDxxtx7dkqPnRtrku7E/wo/lOhuNaa9tblw1C1sf9wdtQ/H7VmCE3H5t+dCreOvcQ1BY64bab0WgMQDCZYCwpR8atd8J88HTduXybvkXX4g/jPsfBwuCHiIiIRjxt8FPWM/OjC36Ss+ZHt8FpL53QtMFPoi97G2ib66BEqPmp1gQ/5db4Bj/+3TvhXbNcNWaZcSQyf30PlMmTgR51P809gj/RZkf6j6+H46wLIPTYZNVYVAL7aWfHdY6DicEPERERjWiSIusKzEtUwY/6hrhdcsMnq1sqJwPtEq7e2kBr210nesODgba5jvSaRn+7quX0UNAFP5a8uJ1bURS433lVNSZYbXCcexEEo1HX1rsl4FIfKwiwnXganNfdCjHNCcFiRfpV10MwW+I2x8HGmh8iIiIa0WrDdHrrbdkbADQFOvqVWRhOusyPubeaH+1Gp4kd/OjbXJtianMdlK8JdBUoaPJ39LpEMJ7aA260aQKOeC57832zUtXgAADsp54FMa37ZzzLpK7badG0Rg8yjZ+IjF/+L+SGOhjyB6cF92Bh5oeIiIhGNO2SN6fBrsp82ESzritasi1988g+tEtu1VgsmZ/OBF/2ps3cFcbY5joo3WCDRVRv4KkNGgeTNutjEoz9ql0KR/F54X7vTdWYITcf1qNPDD3ONqoD/ZZAJ+QImS9DZhZM4yfGZW5DicEPERERjWjaTm/hftOeq7kpTLaOb+EK93vr9qYN9hI986Ntc93fgEEQBF0tlLZRxGDSNjsosWTDIMTndr1rySeQWtVBouPsH0EwHlgIlmVUZ34kRU74eq9YMfghIiKiEU3f6S1bd0yOJlAYymxAPGjrfZwGuy7D0VO6ruFBYgc/8WhzHaRrdz2EwY++3ic+S97kthZdRzbTuIkwHXyoaizT6IAAdcZMW/eT7Bj8EBER0Yim3eA03IaS2o5vyZb50d7A91XDoq/5Sezf/tfEoc11kLbup943dIHuHs/gtLl2f7gQit/XY0SA49wLdUsDDYKIDKNdNdYcoe4nWTH4ISIiohEroEio8TWrxkrCZH707a6TO/jpbckbEK7mxwNFUSIcPbxkRUZ9HNpcB2kbQQzVsjdZkXWBeDwyP1JTA7yrlqnGrEceA2NJedjjtXU/zX4GP0REREQpoc7XpmtlHO637dplb42B5Fr2pl2m11cbaG3mJ6BI8Cj+CEcPr3BtrgtMmf0+n77mZ2iudb2/DT7Nd1xuHXib665PP4LS42dcMFtgP+PciMdr634idXxLVgx+iIiIaMTS1vtkGB26G39A3/Cg2R+5C1YiavANLPMDJG7Tg3BtrrU38LHQfjctgU74h2BfJ229T7rBhgyDPcLR0ZFaW+BduVQ1Zj3qOIjpka9/tkndIpzL3oiIiIhSxF6vtsYiJ+xx2r1+/Eogqbpg6TI/fdT82ESzrvA9Uet+4tXmOihcVmwosj/VYep9BvI5AMCzZBEU6UDgJhiMsB1/Sq+v0W10ymVvRERERKlhr6bNdak5fPCTZXRA1Nw2NSRJ3U+n5IFb9qrG+lr2JghC2LqfRBSvNtdBdoNF1+p7KDq+adtcD3TJm9zZAc/Sz1VjllmzIWZk9vo6XfCTYt3ejH0fQkRERJSa9nq1zQ7CBz+iICLHlK4qfm/yt2OcrXBQ5xcP2g1ZBQjI1mSywkkzWFUboyZqu+t4trkOyjdlYFePzzskmZ84t7n2fPmpqsObIIiwnXh6n6+LZtnbn/b8GxlGO8osOSi15GC8rQhW0Tyg+Q4VBj9EREQ0IoXr9FYaIfgBuju+9Qx+kqXjm/bmNcNoh0kw9Pm6ZGl3rW1zPZBOb0F5Jid2eepCjwc78+OV/bogbiBtrmVPFzxf/lc1Zj7sCBhy+j5ntibz0yl1wS8HYBKNPzz2YG3nDtUxfxozv9c/O4mEy96IiIhoRKr1teo6vfV2A6et+2kMJEnwo6nZ0C5riiQtCZa9hWtzPdBlb4C+Jkq7SWy87fU2QcGBVuIChLCb7UbL+/USyF1u1ZhtzhlRvVYb/ADqAFrbJMQgiHH5zocKgx8iIiIakfZpbuIyI3R6C8o1qjtkNQ1RC+SBatXUbEQb/KRrvotEXPYW7zbXQdqaqMHO8mnrffLNGf1eRqb4feha8olqzDx1OoyFxVG93iaaYRZMqrGedT/aJiHF5mwYo8gkJgoGP0RERDQiaTeU7GvZjjbzM1T7vwyUdp+W7CjbQOsbHiTesrd4t7kO0ra7Huxlb/Gs9/Es/wpyp/pn0x5l1gfobnbRW91PrH9uEg2DHyIiIhqR9mk7vfVxw5mrCX6akrTmJ8sUXeZH2/EsEZe9xbvNdZB2o9NOqQtdsi/C0QOnzfz0t95HamlG18fvqsbME6bAWF4R03l0G536ewY/8ZnrcGHwQ0RERCOStnah1Nx7jUWuJhvQIXXBK/vjPq940+7TEq6mIxx9w4PEC37i3eY6KD/MJrCDWfej3eOn3BJ7m2vF70fHc09Bdqmvd7S1Pj1lazf1/SGAVhRFl/mJtDdWomLwQ0RERCNOd6c3ddagr99ga5e9AcmR/dHu0xJ9zY962Vsibuo6GG2uAcAkGpGpyX40DNLSt7aAS9VSHADKrbFnU1z/fh2B6l2qMfPB02Ece1DM58oyaTI/PwQ/7ZJbt/yRmR8iIiKiBBeu01tJH921bKJZtxQs0Tu++RVJd2MdbU2MtuFBImZ+BqPNdZB26dtg1f1o633MghEFfWxCq+VdvQyerz9TjRly8pB2ydX9WgaozQ4Gg59wc9XWRyU6Bj9EREQ04mgzBplGhy6wCSdXsxxIu4FootF2egOir/nRLntzS17ImoBxOA1Wm+sgbce3Bt/gXGttDU2pJReiEP0temD/HnS+/i/VmGAyIf3q6yDa7f2akzb4CbZL1y0VjXGuiSC5ZktEREQUB02ajI22nieSHM1xib7RqbbexyQYkSb2HeQB+n1+FCjolLxxm9tA1fhaB6XNdVC+WdvxLfbgp1Py4JvOXWhD5KyZtt4nlhoa2e1Cx4KnoPjVtWeOH10BY0l5bJPtQbs0siXggqIoYYKf/u9FNFyMwz0BIiIioqGmrdXJMerrecLJ03V8S+zMj7beJ9PoiHoZlHbZGwC4ZA+csIU5euit7dypepxpdMSlzXWQLvMT47K3Jn8H7t79Oup9rXCb3Shzj8VU52jdcdu7alWPo6338W1ch843X4Lcpq5dsx51HKyHHxXTXLW0ra79SgCdsidMm+vkqvcBGPwQERHRCKRt/6y92Ysk2TI/us8ZZbMDoLvo3yyY4FMOZBU6Al1xrasZiG80wc9haWPi0uY6SJsNbPC3Q1GUqN/j2dr/hgImBQreblqhC37aAi5dy/WDbCW9nlfuaIfr7ZfhXbda95yxvAKOcy+Oan69yQgTRDb5O8K0uU6uTm8Agx8iIiLqwzedu7CqYzsOshXh2IzJSbfGPxxt5ke7h08kybbXj3aDU20Xr76kG6xoChwIfhKl6UGn5MH37n2qscPSxsT1PYKZn7I9LRi/vQEWXwDN6xbAanNCsFggWKwwjqqAcfRYXUC0umM71nbuUI1t8eyHW/LCbrCExjZrPoNVNKPCmh92PoqiwLt6GdwLX4Ps1tdyiWlOpF/1MwgmU78+b08mwQCnwa5qlrG9qxYezV5HydbpDWDwQ0RERL3Y1lWDP+55BwDwWesG7PTU4eqCE+P6G/bh0Kzb+6afwU+gA7IiJ2xAqAt+Ysj8AN1ND3rWRyVKu+v1nbugQAk9NglGHOzof41LOJntHpzz3gaM3Xkg2+HZ9TkUUR1cmMYeBMc5F8FYNgoA0CX78FytuvMa0N2gYaO7Gkekjw+NbXbvVR0zwVYMQ5ifpUD1LrgWvgb/ru1h52qeMAWOC6+AISt+mZhsU5oq+Nng2q163i5aYsokJgoGP0RERBTRsvYtqseftKxHmsGKC/NmD9OMBk5RlH4vB8sxqpdCBRQJbZI75qBiqLT4+7fHT1C60Qb06HGQKJmftZ3q/WwOdpTBIg484wEAis+Lrk8/Qtd/P8aEzhb0bKkQUCRYoH4f/46taH34XlgOr4T9jPPwpvdbXUONoHWdVTjcMRaK1wPBYsUm9x7V85MdZarHclsLXO+/De/qZWHPJ9odsJ97ESwzK+P+C4lsYxqqUB96vNFVrXq+1JKTlL8EYfBDREREEVV56nVj7zSugEO0Ym7OjGGY0cB1SF3wa7qEhdvANJxMox0GQVTtEdTo70jY4KdZcxOeHWNDAG2760QIfgKKhHWa4OewtLFxObd/13Z0/uvvkFqbAQBGwYCAIqneOzwF3lVL0bF2GQyFEk43ipBEEZJBgCwIMLW5kO2TkeVehebAm1BkCbLFgsMzO1FcnIH9RZloyHVgcocRvtr1kBobINXXwLtqGRS/L+w7Wg6dCcf5l0JMH5x9drQ/025Z3ekvGZe8AQx+iIiIKAJFUXSbGga9WP85HAYLjs88eIhnNXDarI8AAZlRBgWiICLHmK7a8LLJ34HxtqK4zjFetN3esqMM8oK07a47E2DZ21b3ft2N+PS0igGfV2ptQfvfHoXiPRDgGQUDAMBtN2HzhAJMMBVgprkUitcLaW8VpFZ1p7VGdxPGb1e3nVYAyLIEUTRAAOC1WGEWjehyt6GitQ0VVd2BliAIyLRuRTt6z6YYcvLgOPcimA8+dMCfuTd9dc4rTcJmBwCDHyIiIoqgOdAJVy+/6f97zSewGyyqGoZkoG1SkGV0hK2ziCTHpA5+EnWj0y7ZpytQjzbIC9LuCdQxRJmfXZ46PFv7X3RJPlyUPxuHp48LPbdG0+VttDU/6sxdb9zvv6UKfADAaDBi7dRSLJs1Gl6rCS2O0Tih/HwAgOL3oevzxeha/CEUrwcdATe8sjrwyTQ60CF54MOBjFGX7IVZNMKjOdYqmoBeAh/BYoX9lDNhPeakuDQ16EtfgXKyZn4SszqPiIiIht1uT4PqsaC5MVOg4C/7PkS15rhEp63HiDUbom2BnKgd37QbnAJ9/zZfK904PMve/lGzGNu7arDP14RH9r6nagyg3d8nHl3e/Lt3wbtmuWrMNHosmm78KZYcNx5ea3ewUes7kOkRTGbY55yBrN/eD2FWJZokdZbNKBqQaXTALppV426pOyDt0gSmVs1xofcRRFgrj0XWb++H7cTThiTwAfr+WUnGNtfAAIOfBx98EIIg4Oabbw6NeTwe3HDDDcjJyUFaWhrmzZuHuro61euqq6sxd+5c2O125Ofn47bbbkMgEAAREREljt1edVAzwV6Ms3IOV40FFAkfNq8dymkNmL7TW2z1OtqObw0JmvnRLnmzi5aIN9iRDMeyN6/sxy7PgXtHBQr+uu9DtAe6UONrUQUgwMCDH0VR4F74qmpMsNqQfs2NyC+bqBpv8LfrsjtiuhOrTjkMz15+OJYcOw7LZo3GypnlSD/+NNiOORGYVYmlhxTjv8eNx7tzD8aCHx2CwK9uxzunTcDaQ0tRl58OWRRg++HaiGlOmEaPhWXGkbCfdjYybr8LaRdeOWi1PZH09ufCabDDabQP4Wzip9/L3latWoW//e1vmDZtmmr8lltuwQcffIA33ngDGRkZuPHGG3H++efj66+/BgBIkoS5c+eisLAQS5cuRU1NDa688kqYTCbcf//9A/s0REREFDfaZgejLHm4JO9otAVc+KJtU2hcGyQlOm3mJ9YlUzmattiJuuxN1+wgyo1ce0rXBD/agGowaGuygmNP7v8IUzTtrDOMjoj74kTL980q+KvUe/LYTzkTYlo6imWLalyBghpfC0Zr3rPKU4+WLDtasroDgpnp4zCu9GwAQInPjS/WPwyrwxbqjvaWsANbx+dj6/ju8zj8Ap4ovAhmZzYEqzrbNlx6y4gm65I3oJ+Zn87OTlx22WX4xz/+gaysA7v8trW14Z///CcefvhhnHjiiZgxYwYWLFiApUuXYvny7lTiokWLsGnTJrz44os49NBDcfrpp+P3v/89nnjiCfh84btZEBER0dDTNjsYZc2HIAg4JmOyarzG1wJFUZAstMvUtMFMX/LMGarHiZr5adUEKrHW+wBAgUn9Wdsl96AvfdPOO2i9qwpvNapbPh+WNmZAeywpfh/c772pGjPk5MF69IkAupei5Wm+g73eJt159vmaVY8n2kpC/9smmlEqZ6qe/7rte9XjMZnlsOQXJ0zgAwAO0QKTED5PUmrJHuLZxE+/Mj833HAD5s6dizlz5uDee+8Nja9ZswZ+vx9z5swJjU2cOBHl5eVYtmwZjjzySCxbtgxTp05FQUFB6JhTTz0V1113Hb777jtMnz5d935erxde74GuHu3t3X/JBAIBLpcbQpIkQVEUSFKkNo+UrHhtUxOva2oaquvqkf2o9baoNpIsMWYhEAggX3Sqgh2P5EOjty1h2z1rNfo6VPPPFO0x3U9kCXbV612SB23eTjgM/b9xHYzr2uBtV80zI8bPCQA5YhpEiJB6tHje424Y1O52DZ62iMG0R1L/ovxQ26gB3Qt6Pv0PpBZ1MGOZez4kQQB+OG+xKQv1vtbQ89VdDQg4DjT5UBQFezyNqjkXGjNC85IkCWPkbDQoNcAPf556/rkCgAmW4oS8p8002FXNPYKKf/i7IFHEMpeYg59XX30Va9euxapVq3TP1dbWwmw2IzMzUzVeUFCA2tra0DE9A5/g88HnwnnggQdw991368ZXr14NhyP232JQ/8iyjI6ODqxcuRKiyF4ZqYTXNjXxuqamobqu+4Q2dJoOLD8SIGD/+h1oQBVkKPCaPQj06GC1eO1XKFeywp0qoShQUG2ugYQD+/Ts3bwLUJp7eZVaADJcZjfQ4wb20zVfoUDpf8exwbium4zb4BIPZFHa2xuxYveKmM9jNkloFA6c57ONy9EsF8dljuGsEnfDZex7eZ0BItzf1WIF+rfsUnS7UPjvNyAEDtTweAtLsMXlBVYc+J4Chg64DAfms7rjO4zaYQg9boMHTWZ1HVL9xt1Yge66JVmWke+ywGVwIdKeoIHWZqzYHvu1GWwBo1v1MxTUsrUGKxT3MMwoPJcr+uWYMQU/e/bswU033YRPPvkE1iFMy91xxx249dZbQ4/b29tRVlaGmTNnwukc2uKvkUySJKxatQqHH344DAZD3y+gpMFrm5p4XVPTUF3XT1s3wFF/4BeMxeZsHD36qNDj8bt3qJbF5eSXYFbm1EGbT7x0SF2w7lDfZB5fURlz3c9rOzepalOKiyswcwAbbQ7Gdf1P9W44PAduCg/Nn4JZmYfEfJ7l+xuxqnN76HFaVi5m5c2KyxzD2VrvgaO1JvTYIpp0TQYA4BDHaBxdcpRuPFru156Hz2IGLD80gRAEFP2/GzG2pEx1nLctHRvrDvysy2YLZo0+8PnXu6rg2Lcu9NgmmjFn7LGh+h5JkoBVKzEqs0lXbwYAZsGIs8fNgUlIvL+nV9U0o7ljq2789LHHwW6whHnF8AiuCotGTMHPmjVrUF9fj8MOOyw0JkkSvvjiC/z1r3/Fxx9/DJ/Ph9bWVlX2p66uDoWFhQCAwsJCrFy5UnXeYDe44DFaFosFFov+CzYajTAauVXRUBIEAQaDgd97CuK1TU28rqlpKK7rXn9T6OYNACpsBar3K7JkY4/vwHKhBqkjKX7O2gJdqs8lQECuNSOmfX6A7rqflh6tjZvlzgF//nhf11bZrfqsuRZnv85das3FateBhgA1gdZBvdbaeZ+SfSh2dNWq2l0DwOHOcf2eR2BvNXyrl6NnKsZ6xGxYRuk3Sx1lz1fNp97fBkUETGL3e+8PtKqeL7XmwqRpRy0KIg5xjMZn7Rt155/gKIHNlDiBRE85ZqfqswHdNXJOS2KtvIrl5yCmP+knnXQSNmzYgHXr1oX+mzlzJi677LLQ/zaZTPj0009Dr9myZQuqq6tRWVkJAKisrMSGDRtQX3+gg8wnn3wCp9OJyZMn696TiIiIht5uXbODPNXjYrO64Hm/pv1wohroBqdB+bqmB4m114+iKGjRdE3rb01Wiaa4fZ83+iWC/aHtKJdrdOKG4tNVnedMghGH9TPTpigKXAtfRc9li4LFCvsZ54U9vljz+RUoqp/3fZoGCKUR9r851DE67Pgke2kUsx4e4dpdR/p8ySKmcDk9PR0HH3ywaszhcCAnJyc0fs011+DWW29FdnY2nE4nfv7zn6OyshJHHnkkAOCUU07B5MmTccUVV+CPf/wjamtrceedd+KGG24Im90hIiKioSUrMqo17avLNa1tC82ZqsfavVcS1UA3OA3K02x0Wu/TF4UPpw6pC5Iiq8Zi3c8oSBv8NPrb4ZMDMIuDk/3RtrrOMjmQY0rHHeXn45maT+GSPbgwb3a/WncDgG/DN/DvUC/lsp10OsSMzLDH20QzckxONPXo6rfX2xT6hYC2+1ukzT8n2UthFAwIKJJuPFFlhfmOk7nNNTCAfX4ieeSRRyCKIubNmwev14tTTz0VTz75ZOh5g8GA999/H9dddx0qKyvhcDgwf/583HPPPfGeChEREfVDvb9NV2OhzfwUmdXNDer8rZAUuV9ZlKE00A1Og7TtjxNtrx9tACFAQEY/N6XUXuvuzEezbq+beJAVGa0B7TXqDlArrAW4r+LSAZ1f8fvhfvcN1ZghMxu240/u9XWl5mxV8LPvhyWfiqLo2lyXmMMHPzbRjIn2Emx0VYfGTIIR46zhyz4SwYjP/ISzZMkS1WOr1YonnngCTzzxRMTXjBo1Ch9++OFA35qIiIgGwW6POuvjNNiRaVCv8S/U3BBLioxGfzsKNBmhRDPQDU6DtJmfBn93e2ZtfcRw0S4dSzfYYOxnQb1VNCPX5FQFePu8gxP8hMtYZfVjf6JIPF/9F1KT+ufbftYFEEzmXl9XasnBeldV6HFw6V9joAMe2ac7NpLpaRWq4GeCvThUO5SIwgU/yZ75SexfzxAREdGQ261Z8jbKmqe7qU83WHX72tQkwdK3gW5wGqQNfrpkH1yyN8LRQ09b79PfJWJBJZoaL222I160QZsAoV+bs4Yjd3bA/fF7qjHT6LEwTz+8z9dqA5o9P9TEaZe82URzr9nEEzOnhjJpBkHEhXmzo5r7cMkxpau+/3SDLeKyvmSRuKEmERERDYvdHk2zA0ue7hhBEFBkzsL2rgMtiWt8rTh0sCc3QNrlYP0NCnJM6RAgqDarbPC3IW0AG53GU0uclvcFFVuyNZmPpsgHD4B2WWJmPxtShOP+6N9QvB7VmP3ci6PK1mnrnmp9rfDLAezVNAYpteT2ej6raMYfxlyJ7V01KLXkqJo4JCKDIOInRafg2dpPoQC4quD4hM5URSO5Z09ERERxp838lFvDL3MpNGeqgp9Eb3qgKIru5rq/mR+jYEC2KV1VB1Lva0eFtaCXVw0dbQalv53egrSZn/2DlPnRNTsY4LyDAjX74F32hWrMMuNImMK0tg5HW8ejQEGtv1XX+S6aehiTYEjoJgda09Mq8Jdx/2+4pxE3XPZGREREIZ2SR3VDD4TP/AD6QvhEX/bWIXngVwKqsf7W/ABAfpi6n0Shb3M9sKVjJZqb+hpfi642Jx70wc/Al7wpsgzXO69C6TFfwWSCfe75UZ/DbrDosmd7vU3Yo2tzrQ4SKfEw+CEiIqKQak2zA6Ng0O1zEqRtepDowU+zptnBQOtJcnXBT+J0fNMv7+t/kAfol31Jioz6QQj2mv3aVuQDy/woigLX26/Av22zatx2wmkwZMUWqGgDwD3eplDXt6DSCJ3eKHEw+CEiIqIQ7ZK3UktOxC5hxZrgp8nfAb8cCHtsIojXBqdB+Qnc7lq7vG+gGZR0g01Xn7J/EDY7bY3zcj33B2/D8/VnqjHRmQnbiafFfC7tkrb1nVW6lvDaAIkSD2t+iIiIKETb5jrSkjcAurbWChTU+dsSdh+QeGdDtJmfwciE9EdAkdAhdanG4tExrcSSjZ2tuzF6dzPG7GyEtX0n2jJKYCguhbGkHIaSMhgLSyAMYNN63TUaQPDjXvwhuj79SDUmGAxIu+zH/Zqj9ud6p6dW9dgu6pfGUeJh8ENEREQh0TY7ALrb+mYZ01T1Jft9zQkb/GgzPwO9UdXX/LQnxF4/bQG3qgsdcGCj0P5QPB54Vy/DqStWADu3wyB1n1s0WOFv8cBftaPH0QJM4yfCcf4lMBYWx/xe8erG5/nqM7g/eFs1Jggi0q64FuaDJvfrnH0taSux5Az7tae+cdkbERERAejOGGj3LRll6X0jy0JN9qfW1xrnWcVPvDY4Dcozq5e9eWU/OiRPhKOHjjaAMAoGpPezBbfi8aD1kXvR+dZLyKuqDQU+AOBXpHCvgH/bZrQ9/Ht4ln8JRVHCHBOeTw7Apfn+Yl32pvh9cH/yATrfekn3nOPi+bAcMjOm8/WkrXvSSvb9b0YKZn6IiIgIQHfDgoDmhnaUNfKyN6C749tm997Q48GoA4mXeGd+so1pYff6cRqHd+8Wfb1PWr8zEq53X4dU3728yyyobxt9SuT6LsXvR+drz8O/dTMcF14B0dr3d6LtUAdEf40USYJ31VK4//Mu5DZ94w3HeRfDesTANhR1GKy6TGdPfQVHlBgY/BAREREAoFqzuWmOMb3PTTu1Hd8SOfOjzYgMNPNjEETkmpyqFtcN/naMtRUO6LwDpW0a0N96H9/338HTY28ck9jd+EIyCNhTmoVdo3Nwdc4xsNbWI7B/L+T6OlU7aQDwfrMSgepdSLvsGpgqxvX6ftrrYxXNsBt6r81RJAm+jevg/vCdUJCmZT/tHNiOndPreaJVYsmOGPyUWiIvEaXEweCHiIiIAAB1/lbV40gtrlXHJEm763hucNpTni74Gf6mB9qW3v3JcMldbrhee041ZrLYsejEadg6KgM+c/ct5Onlh2GqYxSA7iVyrvff0nVXk5oa0Pb4gzAWlsB82BGwHDYLhhx9oKC9PpGCNrmzA/7vN8K36Vv4t2yC7HaFPQ4A7CfPhe2UM/v8vNEqNedgo6s64nOU+Bj8EBEREQCg3qe+cS/QtHIOR5v5aZfccEvePn9jP9TivcFpUJ6u49vwt7tu0baL7kfTAPe7b0BqVQeyjrN/BFfxPvg8daGxfd7mUPAjWK1Iu+AymMZPROerz0HxqDvOBWr3IfDhO3B/+A5Mo8bAWD4agiMNoiMdgiMNXmkPLAY/vFYTAHXQpkgB+Navgeerz+DftQNA77VEpvET4ThzHozlFTF/9t5EamVtFy1x2ZCVBh+DHyIiIgKgb9Wcb+47+Mk3Z+jqXmp8LcO+9Esr3AanGUb7gM+rDX4afMOf+dEuy8qO8abct3kDPMu/VI2Zxk+C5ajjUFLzH+zqGfz49DVelkNmwFg6Cp0v/B3+3TvDvod/907dcyX+DvxM6cKuUTn4fkIBcg8dD9ntgnf5l+j64tOwtTxaxpJy2M+cB9OEyYPSeS1SJ8NSdnpLGgx+iIiICIA+a5FvyuzzNSbBgDyTUxU41SZg8KNtdpBpdETcvDUW2gCxUfM+w6HF3/+NQmW3G52v/Us1JlisSLt4PgRBQLFZvRRyn6Y7YJAhJxfOn9+Orv9+DM/XS6IKXCRFhkFSMG5nI8btbET65/vQYvoPFJ+3z9caS8phO/E0mA+dCUEcvGbGkYKfMtb7JA0GP0RERDFSFAVrOndgt6cBh6ePQ3kfHdGSgV+R0Ky5cS+IIvMDdHd86xn81CRg04N4NzsI0m502uBvG/a9frSZn1iCH/e/X9MFKo6zfwRDdvfNvTb42d9LjZdgMHbX3Jx0OgI7t8G7dgV869dErNHRdho0+yUoSvjARzCZYTpoEsyTp8E0eRoMmVlhj4u3NIMVGUYH2jRLC9npLXkw+CEiIorRkrbv8PeaRQCAD5vX4r6KS3W1L8mm0d+u2xgzP4qaH6A7+Fnvqgo9rk3ApgfxbnMdpP2OfEoA7ZIbGYNU/9Hk74BBECM2A/DIPrhldcAQ7Uahvu++hWfl16ox80GTYak8NvRYe5PfFnDBJXng6KUroCCKMI2bANO4CVDmXQr/lk3w79oOpaMdsqsTiqsTcmcnfPubgR7N4gyCPoNjGj0W1uNOhnnKIRBMpqg+V7yVmnN0wU+ibuxLegx+iIiIYvTf1g2h/+2WvXi3aTV+UnTyMM5o4LTNDhwGa9RNC7SBXyJmfuK9wWlQltEBgyBC6tHiud7fPijBz3tNq/FK/ZdQoOCivKNxbu4RumPqwtQcRdPqWna70Pn686oxwWKF4+KrVFmsQnMmRIiQe0Qp+7zNOMheHNVnEAxGmCdPg3nyNNW4oih46tuHUbGtFhO31qF8T0toWaIgiDAfOgPW406GadSYqN5nMJVYsvGdW93xjcFP8mDwQ0REFANFUXQbeX7VthkX582GMw4F9MNF2+ygIIp6n6Aiizb4aR72pV9a2jbK8cr8iIKIXKNT1Sa8wd+O8baiuJw/qEv24c2GZaHs3GsNX+HQtNEYbc1XHbe4Zb3qsdNgh00093l+1zuvQm7XBMDnXgRDljrTYxQMKDRnYn+PRgf7fdEHP4qi4M3GZVjdsQOTHWW4PP9YGAQRHVIX3GYB300pwndTiuDo9OIB96FwKAaYD5kBQ1biBBdlmkDHYbAi08BOb8li8CrCiIiIUlCH5NEtK/IrAXzaIxuUjLSZn3yzM8KRekWazE+X7EO75I7LvOJFm/nJjlPmBwDyzIPf8W2/txk+xa8ae7X+K9XjZn8nlrR9pxqbnTGxz3P7Nq6Dd/Uy1Zh54sGwzDo67PHapW/7vPqOb5F81b4ZbzcuR7W3Af9pXot/N63snrumTsmdZkXWsafAdvwpCRX4AMAURzkEHAjspzrKEyrQp94x+CEiIopBpHqWRS3r4NcUbCcTXZvrKOt9ACDHmAaToF5M0lshfLy1B9zolDwRnw+3wWluHDY4DZ1L1/Qg/nv9hNs8dr2rCptce0KPP2heo2oaYBQMODN7Zq/n7V7upunuZrXBcdH8iDf0JZrNPPf5wnd8C2dJqzo4+7i5+8+Ndm8ip9Eel258g6HInIX/VzQHpZYcTE+rwGX5x/b9IkoYXPZGREQUg3A3oQDQGnBhZftWzM6YNMQzio86TZ1OLMGPKIgoMGdgb4+2x3W+Vkyyl8ZrehG92bAMbzUug0kw4tL8Y3Ba9nTdMeE2OI22CUA0tN/VYAQ/kYLJVxq+wj32i9Epe7C45VvVc8dlTOnzc7refgVyh3q+jvMu7rV7WrFmmWO0gW57wI3N7r3qMcmNVR3b0SVpmjTEaVniYDkxcypOzJw63NOgfmDmh4iIKAa1vRTzf9j8DRSl953nE5GiKP3a4LQn7dK3ocj8tAVceLtxOYDupYfP132mCwAAYLe3XvVYgBBVE4BoaTc61X6X8VATZjNRANjeVYM1nTvwUfM3qmVxAgScldN71se74Rt41yxXjZknT4Pl8KN6fV2Rpt11va8tqqzn6s4duo6CQHedkjbzE0t7bqJYMPNDREQUg0iZHwDY6anFtq6aqIu/E0Wn7EGX7FONxdLwANB3fBuKdtd7vE26m+lnaz+F3WDBUc4JAIAdXbV4bN8HqmPitcFpkDb4afJ3QFZkiGFaNfdXjTfy9/lK/Vdo1QQPszMmosCcGfE1gdr9cGmWu4k2O9IuvKLP+pVizbVWoKDO19pnx7OV7dvCjm9270WgR7c8IL6ZOaKeGPwQERHFoK+b+g+b1yZd8KNtdiBAiLkVtPaGuLcgMV4awywvU6Dgyf0fwS6a4TBY8ED127rAbnpaRVznkffDsrecpk5UVDXDIMlo3vMuHCYHBKMRMJthGj0OhvyCfp1fVuRev8/9YbJC5+To22AHBfbvQftTD0PuVDeBcJx/CcSMvvershsscBrsqqYWtb6WXoMfl+TBRk176J62de1XPU70ZW+UvBj8EBERRUlRFN0eNtPTKvBN567Q45Ud29Dgb9dlAxJZnWaZVp7JGXaDyd5oswx1vra4Zz+0GjUblwZJioxH9r4HURDh0QQ+k+yluLzguLjOw+kDTv5sGw7esBfCD4kor+UdQFRvwmmZdhhsp54NY3FstVDNgU74NDVLeaYMNERYXndE+viIgUhgTxXan34EsludKTJPOQTmGUdGPacSSzba3QeCn76C3bWdO1V7IfUla5A2iSVizQ8REVGUum9C1e2GL80/FtYe+6goULCoZd0Qz2xg9G2uY6v3AfSZH78SQJOmfXG8hcv8BPmUgC7wmeoYhV+XnRfVvjfRUBQF3vWr0f7H/8P0jTWhwAdA2BoY77dr0fqnu9Dx3NMI1O7XPR+Jtn7KKppxdeGJEY+PlPXx79qOtice0gU+xvIKpF16TUztmrXLHPtqd72yY7vqcYW1QNUuWouZHxoszPwQERFFSdsRzSyYUGzOwrEZk1UBz39bNuD83CPjdpM92AaywWlQcDPNnkvManwtg5oB085buxSrp0PTKnBryVkwifG59ZFamuF66yX4vuveVNQkGBDAgYAn0EsDAO/61fCuXwNDXgHEjEyIzgwgLR1p9Y3wwg/JZodgtUKwWCHYHdhvbFC9vticjUMdozHJXqrrnjbNMRpjbAeW18kd7QhU70Kgehe6lnwCxafuqmYaMx7Oa2+CYLXG9Pm1DS56y/x0yT6s76xSjZ2SdShWdWzH2s4dYV8Tz32YiHpi8ENERBQl7W/gC82ZEAURp2VPVwU/btmL1R3bcUzG5CGeYf/EI/MjCAKKzNnY6akNjdV4WzDNMWrA84tEu+ztyoLjsLR9C9Z27lSNz0gbi5tK5sY18Gl75F5Vi+hgAwW/SUR1WRbKTDnIt5ZAkQKQ9u+F3KUNyhRIDbWQGn74vhQFGS4XujZ/A2gyMKUmP44fY8d3kwvRkJeOInMWBEHAxY7DsGDTehTUdcDR5YMhIOOENKADT0Lx+SDV7oPUGjkoMY2fBOc1N0KwWGL+DmKp8VrfWaVqNS5CxMz0scgyOiIGP1z2RoOFwQ8REVGUtJmfwh/qXIrMWTjEMRrrXVWh57Z21SRP8DOADU57KjJnqoKfwez4JisymjTBT4E5CzeVnImH9v4bG1y7AQBHOSfiuuJT49bdTZFldL70jG5vHJNgwK7R2fj0+IPQnmHDeFsxZo++uHuuXW54vljcnXnxdMX+nq5OHLauGYet24v6vDTkl7WjpfErZDfU4Sp/B9oD3YFVmsGKdPMmePs4HwCYJ01F+tXXQzCZ+j44jGKLut11h9SFTsmDNIM+g7SyQ93lbYqjDGkGK6Y6ylFgykSdv1U9N8EEuxh7QEYUDQY/REREUdJmfnou/ZniKFMFPzu6apEMAoqkq53pb/CjrQMZzI5vLQEXZKgL6PNM6TCLRvym7Dxscu+FWTDGvfOe57OP4d+xVTUmpjnhPf1svJO5LZS12eNtDDV8EG122E89G9Zj5sDz+Sfo+vwTKF5P1O/plw8soctv6ER+23ZIPwQZOaZ0pBmsUKCoas96YzlkBtIuv7a7E10/5ZmcECGqrkGNrxnjberv2y8HVA1BgO6GDED35rgnZU3Dy/VfqJ7PNqXFVH9EFAsGP0RERFHSZ34O3OyPtRaqntvtaYBPDsAcp6VWg6XJ36HbK6c/y96A7sxPT4MZ/DT4tZkXI5wGO4Dum+qDHeVxf8/Aniq4P1qoGjNkZiPjl/8LxQJg+4Gifo/sQ6O/Q/VdinY77KefA9uJpyGwpwpyexvk9lbIba1ob65FXdVW2NNzYQpIUDweKF4P5C43JE39kElQ/0xZxN6zN4LZAmNpOYxlo2GaeDBMEyYPOLgwCgYUmDNU13i/t0UX/Hzr2q1qPCFAwMz0saHHx2dOwRsNS1XL4rjkjQZTYv+NTERElCBkRdYtz+l5s19h6+5eFQwkZMio8tQn/J4/2jbXdtESdulSNLSZnwZ/O/yKBFMcNxQN0marck3pg5otULxedLzwDBSpZyAiIO2yayCmpSNbUeAwWOGSDmR09ngbwwaSgsUC07juTVgb/e14s2EZPm/di86D85GXno0/jL0y1Ciiau8mLF/0JKZsrkV6R/eCtp7fp2A0wVhaDkNhCQSzGTAaIZjMgNEA0ZkJY3kFDPmFEMT4N/gtMmergp9wwa52ydsEezEyewQ36QYbKp0H4Yu2TaGx3jZnJRooBj9ERERRaPR36Dp49Vz2ZhPNKLHkYK+3MTS23VOT8MFPPJodBGk7gClQUO9rQ4mmPiQetJmf3EHeV8n179cONCf4gX3OGaEgRhAElFtyVd3Xqr2NmNEjy9FTh9SFfzeuxKKW9fArgVDQ7Ja9eLdxJa4pmgMA2O80YGnlGCybVYGyvS04eI8Lk9Knw1hSBuOoChiKSyEYhud2rtichbU9HmuDn4AiYY2m+URwyVtP83Irsa6zCu2SG0bBgJMzDxmM6RIBYPBDREQUFe2NnV20IN1gU42NtRaogp8dXXVDMreB0Gaz+lvvAwB2g0XXbrrW1zIowY+22cFgttT2bVwHzzJ1XYqxbDRsp52lGivTBD97evwsBCmKgo9a1uKthuVwy+FbE6zs2I6rCk+EQRBR88P+OYoooLo8GxmTpiOtfN5AP1JcFFnUwe5+n3qvn02uPapMGBA++Mk3Z+D+isuwrasG421FyGGbaxpE3OSUiIgoCtrgp/CHdsM9jbMVqR4nQ9ODeGZ+AKBYd0M8OHU/2g51g5X5kTva0fnqc6oxwWRG+uXX6jIuZZZc1eNqjz74WdL2HV6o+zxi4AMA7ZIbm9x7AOh/7rTZteGknUudrw2ycqABgjbrM8ZaGDGwyTGl40jnQQx8aNAx+CEiIoqCtm1zuJvQcTZ104M6fyvaA7G3Nh5K2uVj/dngtLfXD1a7a23Nz2BkfhRFQecbL0B2darGHeddDEN+ge74ck3ws9/XDL8cUI0ta9+ie50IEQ5NndXy9q0/nEP9/RWb459F6y/tXj9+JaDae2mjq1r1/MwISwCJhhKDHyIioijURNjjp6cySy7Mmk5cPfe9SUS6PX4GnPlR35wPRsc3RVF0y94GI/Pj+2YVfBu+UY2ZD54Oy5HHhD2+zKoOfhQo2NdjKZikyNjWVaM6ZkbaWDw89iqclTVTNb6yYzsCiqTP/FgSJ/PjNNh1+/EEl741+Tt0y+AGc8Nbomgx+CEiIopCNJkfgyCiwqrOCGxP4KVvnZJHV5NRMICaH0AfFNZqgsZ4aJfc8CnqjEq8Mz9yRztcb72kGhMdaUi78IqIXeVsohl5mu+vZ93PHm+jqu0zAFxbNAcF5kzMSh+nGu+UuvBV2/e647XZluEkCELEYFeb9bGLFlRY84dsbkSRMPghIiLqg1+RdMvDwmV+AGCsZunbds1v+hNJgybrI0AYcM2FdllWS6ATXZob+IFq0GR9RIhx3xvG9dZLkN0u1Zhj3mUQ03sPsnqr+9ni3q96rsCciYwf5p1nykCRoj73wqYVqsdmwYhsY1p0H2CIaP8cBJfpbXSrg5+DHeUQBd520vDjTyEREVEf6n1tuo1AIxWea4OfHZ46KIoS9tjhpm12kGtywjjAPXkKTBkQoM6MaDeHHShtvU+OKT2uN9bedavhXb9GNWaZdhjMh86M8IoDtHU/1T0yP1u69qmem6DZEHSSpM4aar+3InNWwgUQ2mC3xtsCRVGwwaUPfogSQWL9CSIiIkpA2iVvToMddoMl7LHjrOrgp1Pq0tXVJApdvc8Al7wBgEk06rJH8e74pt/jJ34dwuTODv1yN7sDjgsuj2oT1XJN3U9w2ZuiKNji1gQ/9hLV44ly78vCEqneJ0i7DK/G14y9via0BdRZMwY/lCgY/BAREfUhlnbDeSYnnAa7aixR637q4tzmOkj7/cS749tgdnpzvf0y5E71srpolrsFaZe9tQQ60SF1oTHQgeaAumvcQZrMjxNWjLeq26X3VJRAnd6CtNe6OdCJ1R07VGO5JicKB9hFkCheGPwQERH1QVu0H6neB+guAh9rUy9f2pGgHd8GI/MD6G+I493xTRv8xKvTW9eX/4X3m1WqMfPU6TBPPzzqcxSZs2DSdPzb42nEVk3WJ81gQ0mYYGZWmE1Ag0oSqNlBUKE5S7fM8dOWb1WPD3aUR5U1IxoKDH6IiIj6oG3Z29dGk9rNThM18xPvDU6DCnWZn9a4nDdIu+wtHpkf38Z1cL/zqmpMtDuQFuVytyCDIKJE0wGt2tuI77vUzQ4OshWHPe8RaeN1wURQImZ+zKJRF3w2BdSZs6lscU0JhMEPERFRH7SF59qbe62xmrqfKk89AooU72kNiKTIYTY4jVfmJ1P1eL+3Oa5NH+Kd+QnsqULHv/4ORZFV444LLofojP070S592+NtxFZNp7cJdvWSt6BsU1rE5xKx5gfo+5cBU+ylQzQTor4x+CEiIuqFR/bpajW0N/da2o5vfiWg2u8lETT5OyBDfbM/WDU/btmLDs1+Qv3lkjy61tkDyfxILU1o/8fjUPzqc9pPPRuWGJa79aQNfra49+uuv7bTW09Hpk/QjWUZ02ATzf2az2Dr7c/DKEteqJ03USJg8ENERNSLcEu2+sr8pBmsumOGcunbus5duGf363h07/vY7N6re15WZCxp26gas4pmpInWuLx/rskJg6Ylc7yaHmizVQIEZPez25vc5UbH3x+H3KE+p2VmJWynntXvOWrbXe/zNalapRsFA8ZoNsPtaZZTv/Str+zKcOptOR6XvFGiMfZ9CBER0cilDX5yjOkwi33/8znWWqi64d/RVYuTsw6J9/R02gNdeGzfB/D8kB1Z0bEV09MqcHHe0Si35qHe14anaz7WBUUFpoy4FaUbBBEFpkxVrVSNrwUHRVjOFcm2rhpUexpwsKMcBT9kF7RL3rKMDphi3JtIkST41q9B1ycfIFCrbkRgGjcRaRfNH9B3oW13rTXGWgBTLz9DmUYHJtpLVNdI21I6kRT3shyPLa4p0TD4ISIi6oWuzXWUdRdjbYX4un1z6PFQdXz7zl0dCnyCvunchXWdVZiZPhYbXPrnAWBm+ri4zqPQnKUKfmJtevBR8zd4oW4JFChwGuy4a/RFKDJnocGvLqbPiWHJm+L3wbvia3QtWQSpqUH3vKGgCOlXXw/BOLDbo0yDA2kGGzqlrrDPRxMEnp1zuCr4OT7z4AHNaTBpNzoNMgqGiPVLRMOFwQ8REVEvtMFPtPuVjNPU/ezzNsMteSNujhove7xNYccVKFjVsV03LkDA3JwZOD93VlznUWzOwtoej7Ud83qzrnNXKPABgHbJjZfrv8QvS8/u1x4/UmsLvMu+gGfpEt0ePkFimhPOa2+CaLeHfT4WgiCgzJITdskhAEy0lYQd7+nQtArcXnYevnNVY0b6WF0dWSLJMjpgFkzwKX7V+HhbEawJWqdEIxeDHyIiol7s86pv2ostOVG9brQlDwZBhPRDBzEFCnZ66gZ9GVAsjRXyTBm4rvhUTBqEblzavZCizfzs9Tbh8X0fqGpkAGB1x3Zs69ofdfCjKAr82zbD+/US+Das03Vy68mQX4j0+T+FIaf35WqxGGXJixj8RLv8b3paBaanVcRtToNFFEQUmTOx26vOprHehxIRgx8iIqIIZEXGXk0mpdQS3V4rJtGIUZZ87Oyx3G04gp+pjlHY1lWjW+p2XMYUXFlw/KBlorQF+rW+VsiKDFGI3GupU/LgoT3/1nVzC3ql/it4ZHV2QdvmWpFl+NaugPuTDyDV977U0Fg2GraTTod56nQIYnx7QJVFqPspNmcj3WCL63slgiJLFoMfSgoMfoiIiCJo9HfolvKUmKPL/ADAGKs6+Kny1MdtbuF4ZJ9u49KL845GjikNCxtXYmn7FmQY7bggrxJHpI8f1LkUmrOQX9eOo1ZUoXRfK0RZQYN1A4xGMyAIEIwmmMaMg+Wo42AaPwmSoOCxfe+jzt8a8Zyb3Xt1XdBye3R682/dDNd7byCwt7rXuZknToHtxNNhHDchbk0etLTtroMm2Pte8paMtB3f7KIFY6z5wzQbosgY/BAREUWwT1OnYhctyIphz5JRmpu/wQ5+9nmbVcvFBAgosWTDIpowv/AEXFlw/KDd7PckNTXC9OHbuGLpWtXmpj6/BwYpuAywC95v18L77VoY8gqwYkoetpV7AaspdHyFOR9dXe3wtzYhvcOD9A4PnB1eWHwBuG0mdKRZkSvVwm/zo2vxh/B9v1E3lyDBbIFl5pGwzj4BxuLB33Sz1JIDAYJu+d5Bvezvk8wmaj7X9LQxvWb5iIYLgx8iIqIItEveSiw5MQUPozXBT62vFR7ZN2hF4NolbwXmTFjEA8HEYAc+stuFrk8+gOfL/0KRAjAJBviUQOj5gBwAwnz29trdyKn+Fj8zCAgYRBhkBWZZQKk5C27Zh0Zfu+41QZavnkFbL5/LUFAE6+wTYDm8EqJ16Jab2UQz8k0ZukzWxBTtfjbVMQqnZB2Kz9u+Q5klFxfnzx7uKRGFFVNI/tRTT2HatGlwOp1wOp2orKzERx99FHre4/HghhtuQE5ODtLS0jBv3jzU1dWpzlFdXY25c+fCbrcjPz8ft912GwKBgPatiIiIht0+Xb1P9EveAKDsh9/+BylQUO2JviFBrLSd3spinO9ASE2NaPvTXehasgiK1P3vuklQ/47Vp0hhX9se6G4JbZAUWHwSTJKCQlMGDIIB6QZbxD1xREGMGNAZCorgvOZGZP76HtiOOXFIA58gbd2P02BHQZTdApONIAi4uvBEPDfh5/j96Et0tVhEiSKmzE9paSkefPBBjB8/Hoqi4Pnnn8c555yDb775BlOmTMEtt9yCDz74AG+88QYyMjJw44034vzzz8fXX38NAJAkCXPnzkVhYSGWLl2KmpoaXHnllTCZTLj//vsH5QMSEdHQ6JJ9eK9pFRr87Tg585CYN7RMRP1tdhBkEU0osWSrzrPb2zBo34028xOp7iTeZLcb7f94DFKrui24WTSi2WDAysNHYXdZFkZZ8vDzotMARUFgbzW8Xy9BoG4/vJq6qmxjmipjlWV06GqZgO59ZLTENCfsp58Dy6yjIRhi2/w03kZb8rC6R3vxifaSIVl2SESRxRT8nHXWWarH9913H5566iksX74cpaWl+Oc//4mXX34ZJ554IgBgwYIFmDRpEpYvX44jjzwSixYtwqZNm7B48WIUFBTg0EMPxe9//3v8+te/xl133QWzmb3giYiS1av1X2FRyzoAwNdt3+O35RdgiqNseCc1AIqi6Gp+SmNodhA0ypKnDn48+s0142U4gh9FCqDjuacg1dWoxgWDAag8Fv8c1waPrfvf92YhAIwaDZNggKliHKxHn4B936/C5o/+hoO2N0CUu+tjHAar6lwOgxVKmoQ6h4CONAs8VhMcbh9Ge0wQfXbIbhfEdCesRx0P2/GnQLCqXz9cjs88GB+1fAOX5IFBEDE3e8ZwT4loxOt3zY8kSXjjjTfgcrlQWVmJNWvWwO/3Y86cOaFjJk6ciPLycixbtgxHHnkkli1bhqlTp6KgoCB0zKmnnorrrrsO3333HaZPnz6wT0NERMNCURQsb9964DEUPLLvPdw3+lIUaPZ7SRZNgQ5de+iSfiwjG2XNx9ft34ce7xqkpgcdUhdaAy7V2GAHP4qiwPXmy/Bv26waNxaWIP2aG2DNcsKz9cnQeECRUO1pCG3YKQgCdhan4cPTp+C/XX5kt7jgNDrw24oLAaMREA0QTGaIzgzU+mrwcvWbqveZmz0DswqOg+L3QzCZkGhyTOl4aMx8fO/eh9HWPBRq2n8T0dCLOfjZsGEDKisr4fF4kJaWhnfeeQeTJ0/GunXrYDabkZmZqTq+oKAAtbXdbT5ra2tVgU/w+eBzkXi9Xni93tDj9vbuwsdAIMB6oSEkSRIURYEkhV+zTcmL1zY1DeV1bQl0ok1z490Z6MIfqt/B3WUXwWZIvsz+bne9qlOZTTTDCWvM/+6UmbJV56n2NMDr98HQz05Yka5rlbtO9T4mwYhcMW1Q/530LFkEz7LPVWNCmhP2q6+HkpkNiwIUmjJR4zuwHG6Lax9GmQ4EZdtd+6EoCrqsRuwrykBh2lig+EDGUAEgAZhoKcYUWxk2ug+0sR5tzuv+fIIAJOj9QBosmGkfAwC9Xgv+PZy6eG0HXyx/z8Uc/EyYMAHr1q1DW1sb3nzzTcyfPx+ff/553y8cgAceeAB33323bnz16tVwOKJvOUoDI8syOjo6sHLlSohx3gyOhhevbWoayuu6Q2iEy+TSjW9zufC/Tc9hXmAaRCRXrcNKsRou44HPlCEbsXLlypjP44YPLrP6u/nPqs+Qq6T1a16Rrusaca9qvvlKOlavXNWv94iGdfcO5Hz2EdCzlbPBgPrj58K/bTuA7loXq1GCSzwwry93rEVGwBN6vML4nep5tLuwYs+KsO85E9nYZdqLeqEDE+R8KC3NWIHwxyYb/j2cunhtB5/Lpf/3J5KYgx+z2Yxx48YBAGbMmIFVq1bhsccew0UXXQSfz4fW1lZV9qeurg6Fhd3p7cLCQt0/HMFucMFjwrnjjjtw6623hh63t7ejrKwMM2fOhNPJbiJDRZIkrFq1CocffjgMw1xESvHFa5uahvK61jatgqMp/C+jatGFqqwALsk7elDnEG/f1nXA0XbgMx3iPAizCmf161zv7NyK5kBn6HFmYQlmOSf261yRrut3dS7VfA8dwHz7nEPNPnQsfAFw2FXj9it/gtJp6rqWtlYrdtUvCT32mo2YNbp7XgFFwtPbV8OhHJj3nJIjMdUxKuJ7z8Fx8MuBiB3gkhX/Hk5dvLaDL7gqLBoD/ptDlmV4vV7MmDEDJpMJn376KebNmwcA2LJlC6qrq1FZWQkAqKysxH333Yf6+nrk53fvffDJJ5/A6XRi8uTJEd/DYrHAYrHoJ280wmhMrb/8Ep0gCDAYDPzeUxCvbWoaquta7W/stYvVh61rMcqej2MzIv9dn2j2+1tUn6ncltfv77HCVoiWzh2hx3v8TQO6JuGu615/s2q+o235g3LdFa8XHS89073MrMf72eeeD/th+mBrgkPd4azG3wKvEIDDYMVeTzMCkFTPj08rgdHQ+7yNKbpNIf8eTl28toMrlu81ptzbHXfcgS+++AJVVVXYsGED7rjjDixZsgSXXXYZMjIycM011+DWW2/FZ599hjVr1uDqq69GZWUljjzySADAKaecgsmTJ+OKK67A+vXr8fHHH+POO+/EDTfcEDa4ISKi5KDtYHZcxhSImn9inqlZrCvIT1SKogx4j5+eRlnzVI93e+Pb8U1RFOzVdHorHaRmB66Fr+o6u1mPmA3bSaeHPb7cmqdrSb3T073qY0eXut4335SBNENidGojotQUU/BTX1+PK6+8EhMmTMBJJ52EVatW4eOPP8bJJ58MAHjkkUdw5plnYt68eTj22GNRWFiIt99+O/R6g8GA999/HwaDAZWVlbj88stx5ZVX4p577onvpyIioiHjlry6XexPy56OHxeeqBrzKwGs7tiBZNAc6ESXptPbgIIfizr42eVRN1MYqKZAh26+5YMQ/HjXroRn+ZeqMWNJORwXXB4x82cSDBhtzVeNbf8h6AkGQUHBLnBERIMlptzbP//5z16ft1qteOKJJ/DEE09EPGbUqFH48MMPY3lbIiJKYNoshggRJeZsjLbm45vOXVjTY7nX9q4azMmaNtRTjJk262MVzcgxpvf7fBWam3+X5EFzoBM5pv6fs6c9mvnaRQuyjf1rqBCJ1NSAztf/pRoTzBakX/nTPttMj7UWYHvXgWxRpOBnjFXdEZaIKN7YcoKIiAZEu+StzJITKkY/2FGuem5bl3q5VKLa61MHEyXm7F5rmvqSZ3LCLqqXd1fFcb8f7eampZacAc1XS5EC6PjX36F4ParxtB9dDkN+3wHLOFuR6vH2rhr45ACqPep5M/NDRIONwQ8REQ2I9ia+Z33LeM1N735fMzol9Q10ItrnbVY9HsiSN6C72Flb9zOYwU+8Nzd1f7AQgepdqjHLjCNhmVkZ1eu1QU275Mbazp2QIYfGBAi65XFERPHG4IeIiAZEu+yt5w1suTUPJkG9wlpb5J6I9mqWkZVYsgd8zlGaG/v+ND3wyn4sal2PpYYqtAXcofE9nsELfrxrVqDrs/+oxgx5BUi74PKoz1FoyoRD08hgUcs61eNiczZsYvJthktEyYXBDxER9ZtfkXRZh57BT7hi90Rf+tbdOU0b/Aws8wMAoy3azE9swU+drxX/W/UK/lW/BF8YduCO3S9ht6cBkiJjn0+dqSq3xif48X67Fp0vqet9BYMB6Vf8BII1+q5sgiBgrFWd/dns3qt6PMbGeh8iGnwMfoiIqN/2eZsgKbJqTNtlTLv0bXuCBz+tkgtu2asaKzUPPPjRLntr8LfBFeUSwA2u3fht1cuqQLNdcuOe3a/jy7bNCCiSer5xCNZ8mzeg819/g6K5vvazfgRjWeRNSCPpq55HGxwREQ0GBj9ERNRv2uxFnilDt7xJF/x4aiFrbqgTiTbrYxZMyI1DV7YSS45uvxttswgtRVHwQdMaPFD9dthAyS178beaj1VjmUYH0g22Ac3Vv30LOp59EoqkDqpsx82B9diT+nXOcX0EP2NsrPchosHH4IeIiPpNX++TpztGG/y4JA9qfa2DOa0B0TY7KLFkQxQG/s+lSTDoMjJV3shND3xyAE/W/Acv1n8OBdHvCTTQeh//zm1o/8fjUAJ+1bi18ljYz7mo313kxvbSxlqEiNEWBj9ENPhi2ueHiIiop92ajmXhunVlG9OQZUxDS6AzNLatqwbFcWgiMBi0mZ94LCELGmXJU3V5663u5x+1n+Crts268RlpY7G3Yx/qEH7JXH+DH/+u7ej673/g27hO95xlxpG9bmQajQyjA3mmDDT423TPlVtzQ+3RiYgGEzM/RETUL4qi6NtcW/SZH0EQkqruZ59v8IIfbXAYadmbXw5gadsW3fgFuZW4qegMzAtMwxFp48O+NpbgR5Fl+L5bj7a//AFtjz8YPvA5ZAbSLrkagjjwW4ZIS9+4uSkRDRX+moWIiPql3t+GLtmnGou0T8s4WyFWdmwLPd7uScx214qiYI+205s5fhkq7fez19sEvxzQZT32+ZpVe+AAwC9Lz8bM9HEIBAIwQMSNRafh2YbP8Hnbd6rjKqLYK0eRAvCuXQnPp/9BoG5/xOPMk6ch7fJrIRgMEY+JxVhrIZa164M6Bj9ENFQY/BARDTFFUbDeVYXWgBuznOOTdm8T7ZKtNIMN2ca0sMeO02R+dnsa4JF9sCbYZ2+X3LrGAvHM/Gg74cmQscfbpGvzrG0fnmtyYmb6ONWYKIj4SdHJSDfY8EHzGihQcET6eF1XuZ4Ujwee5V/C8/kiSK0tEY8TbXZYj50D20mnQzDG71YhUse3vjrBERHFC4MfIqIhJCsynq5ZhC/bNgEAPmxegwcrLo9LQf1QC9fsIFJNyBhrAQQIocJ9BQp2eeoxyV466POMxV5NswOzYESeyRm389sNFhSYM1HXo+HDLk+dLvip1gQ/2qApSBREXFZwLE7IPBgu2YtxEdpFK14vur5YDM+SRZDdrojzEzOyYDv+ZFiPPDamfXyiVWHNV/0cAIBJMMY1wCQi6g2DHyKiIaIoCp6vWxIKfIDu3/B/596DqY7Y900ZbrpmB71067KIJoyyqov9t3fVDGvwE1Ak7OiqxdauGtT5WlHnb9UteSuOU6e3niqs+ZrgR9/xbY9HHfz0VccTqXmEIknwrvgK7o/fhdyubzQQZCgogu3E02A5bFZcMz1aFtGEckuuKnAeZc3TtQAnIhosDH6IiIbIm43LsKhlnW58W1dNUgY/umYHvSy3ArpbXvd8zbYhbnqgKApqfC3Y4KrGt67d2OTeA4+mZkmrJA6bm2qNsRZgefvW0OOwwY83tuCnJ0WWobg64d+5De4P34FUH7m+ylQxDrYTT4Np8rS4NDSIxnh7sSr40S6JJCIaTAx+iIiGwEfNa/F24/Kwz+3oqhvi2Qxce6ALzT1aVwORmx0EjbcV4ZOW9aHHW7tqoCjKgNonR8svB/DIvvfwTeeumF43xVEW97lU9CjuNwRktNZXo0vaDLG9HVAA76gy3XdbbtUHP4rXA3/VPgR270SgehekxgbI7W1QXJ1Q+thE1jzlENhOOh2minG9HjcYTs+ajq/bNqNL9sEuWnBK1iFDPgciGrkY/BARDbIv2jbhX3VLIj6/fQiDgHjZrdmc0yQYUWTO6vU12t/wtwVcaAp0IDeONTWRfN62KabAR4CAo5wTcEzGpLjPpby2E+e++y2Katth6+reSLTFshlm0QQA8Mg+XJoZwPaxedg2Lg/tWQ4UuGT4dm6AVLsf/v17kb9+LdrefA6IYfNTADBPnAL7mfNgLCmP86eKXrElGw+PvRo7PXUYay1AhtExbHMhopGHwQ8R0SBa17kLT+//uNdj2iU3GgMdcS2sH2zaJW/lllwY+qiNKTRlwmGwqrqpbeuqGZLgZ7N7b8Tnyiy5GGMtQIE5E4XmTBSYuv+/3WCJ6xyk+jq4P3gLvm/X4iBvKwKyFHrOKwdCwY9PDqCwrgOFdR04eulOiGYL2g092lkrCkwuF+BwAFEGzMbSctjPugDmgybH9TP1V6bRgcPSxgz3NIhoBGLwQ0Q0SBRFwYLa/6o6WwHARXlH4/3m1aogYEdXbVIFP9o6lb6WvAEHNjtd1yMDs62rBpXOCXGfn9ZOj3pp4VTHKByTMQlTHaOQOciZB7mzA+6P34V36edQ5O7laBbBhAB6BD+KH+mwAQB8SkD1epskAP3qByDAWFwK20mnwXzo4UNW00NElMgY/BARDZJtXTWo96s7bJ2ZMxPn5ByO7917sd5VFRrf0VWLI50HDfEM+0dRFGxx71ONRRP8ANAFP9u7Bn+zU7fkRa1PvafNRXmzh2RvGf/2LehY8KSuvbRFNMJ1IPaBy6CgIK8QiqsDvhZNu20x8j/VgijCUFwK46ixMJaWQ8zIhJieATHdCSEtPW6bkxIRpQoGP0REg0S7k32ROQuX5h0DQRAw1laoCn62e4a289lA1PvbdAX5E+0lUb1WW/dT5amHXw7A1MsN/kBp9yMSIUbcNyee/Nu3oP3vj0Hx6zvKmR1OLJlWiN3l2ehIs0CymLBgws9hkBU89tkDKNu2D+N2NMLh8sEsdH83gsUKY2ExhLwCtLk8KDzhJFhGj4FgSqyNYomIEhmDHyKiQSArMpZ1bFWNHeWcGGpqoM067Oyqh6TIfdbNJIJNmvoZp8GOEnP4fWa0xlkLVZtc+pUAqrwNGD+I7Y53aZa8lVlyBjXYAiIHPoLRBOsxJ8F24klYu2dBj2cUVHsbkWG0Y2tpGraWTsB/jzsIeY2duDNvLrKKxkDMzIIgCAgEAuhcsQLGinGDuicPEVEq4t+aRESD4Hv3PrQF1Eudei5rG2dVBz8+xY893saol48Np03uParHk+ylUXeqsxssKLFkY2+PzUS3de0f5OAn9vqkgYgU+JgnTYXjR5fDkNW9d1ChOUu1HG+Xpx7ZprTQY0UU0FGYg9yDZiRVJ0AiokSW+L9iJCJKQks1S95GWfJQajmwYabTaEeeKUN1zI4hqH8ZKEVRsFlT7zPZURrTOQ6yFasef685X7xpO9ONsRVEOHLgIgU+lmmHIf2aG0KBD9C92WlPuzx12ONRb25aasll4ENEFEcMfoiI4iygSFjRsU01dmSYjmbjNEvfdngSf7PTBn87mvztqrHJ9tg2AtXWB33v3gdFiW2/mmh5ZB/2edUNBCoGKfPj37U9YuCTduVPIBjUiy2089jpqcMerzr4GYraJCKikYTBDxFRnH3n2oNOqUs1Vhmmk5s2+NnelfhND7T1PukGW9T1PkETbOrgp0Pqwj5fc4SjB2a3p0HValyAgFGW+Ac/clsrOhY8GXXgA+jrvqo9jboAuIzBDxFRXDH4ISKKM22Xt7HWQhSYM3XHjdXU/ez1NqFL1ncGSySbB1DvE5RnciLHmK4aG6ylb9p6nxJLTq+to/tDkQLo+NffIHeoM2K9BT5A91LInmTIupbc5VYGP0RE8cTgh4gojvxyAKs6tqvGjsoIv4lnhbUAAg4EDgoUXX1KotHV+8S45A3o3uw03NK3waDt9DZmEJa8ud97E/6d6mWOpvGTeg18gO7mD0XmrF7PzcwPEVF8MfghIoqjb1274Za9qrFZ6eE3LzWLRoyyqn/7n8hL3xr87WjQbNo6Kcr9fbQm2tVNEr7XLKeLF32nt/g2O/CuXYmuzxerxgyZ2UjvI/AJ0jY96CnbmIY0g3XAcyQiogMY/BARxZG2y9tEewlyTOkRjtYvfduewB3fNmsCFIfBqupgFwtt5qcp0IEGTSOFgfLJAVVLbSC+mZ9A7X50vva8akwwGJF+9XUQ0yJfc9V8euk8x6wPEVH8MfghIooTr+zHms4dqrHKMF3eetJ1fEvg4GeTS13vM9leCrGfm7KWmLORZrCpxuKd/dntrdc1OyjXZNr6S+5yo+PZJ6D41Fk+x7xLYSyviPo8Fb1kfspY70NEFHcMfogoYbgkD5r9ncM9jX77pnMXvLI/9FiAgFnp43t9zTjN5p5NgQ60BBLzO9BmfibZY9vfp6d41/10Sh7dprLaJW/F5mzYRHO/3yNIampE+2MPQmpQ1xNZZx0Na+WxMZ1rtDVfVffVE9tcExHFX3xb3hAR9dNnrRvxj5pPAADn5h6BC/NmD/OMYrekdaPq8RRHGTKMjl5fU2zOglU0w9Ojy9uOrlrMTB83KHPsryZ/B+p19T79D34AYKKtBKt7NIfob/CzqGUdnqv9DABwXu4sXJBbCUEQsLNLHZyMjsOSN/+OrehY8CRklzpANZaOgmPeZTGfzyaaUWTOwv4wrb657I2IKP6Y+SGiYbfH24hnahZD+eH/FjauRGOc6z8G2/fufVjvqlKN9bXkDQBEQdQVve/oSrzNTsPV+ww0M6HN/Oz3NeuyN33Z7WnAc7WfhX523m5cjkUt6wFA1zlvjG1gwY9nxVdof+rPusBHTHMi/errIJhM/TpvuKVvAoSY908iIqK+MfghomElKzKeqVkMGXJoTIEyaK2PB4OiKHil/kvVmNNgjyr4AfSbXW73JF7HN+3mphNtJf2u9wkabc2HRVQHDN+790f9ekVR8GL956q6HgB4vu4zrO3ciT2aZge91df0RmpqROebL6Hz1eegSJLqOWNBMTJuvgOG7P4HguGCsmJzNkxx3o+IiIi47I2IhtnnbZuwtUt/w7utqwZHZ0wahhkBkiKjNeBCmsGquzkP5xvXLt1nOC93VtT1JeGaHsiKPODgIp60mZ/JjoEteQMAgyDiIFsxNrh2h8a2dO3DLGfvdVJBazp3YKOrWjeuQMHDe99VBdSAflPR3iheL7zfroF3xdfw79gS9hjzpKlIu/InEK22sM9HK1xQ1t8uekRE1DsGP0Q0bNoDbrxU/0XY58IFREOh1teC+6vfDu1nk2F0oMCUgXxzBorMWTjaOQn55ozQ8bIi49X6r1TnyDNl4KTMqVG/p7bddZfsw35fy5DcAO/xNmKLex8m2ctQYgm/zKrZ34laX4tqrD+bm4Yz0V6iCn60QVYkfjmAF+vC/+wA3QFsT4XmLNgNlj7PG9hTBc+yL+BduxKK1xPxONvxp8B+1gUQxIEHqMGmBz0zWOXs9EZENCgY/BDRsHmx/gu4pPA3mLs9DeiSfXHpzhWL95pWqzbybAu40BZwhYKxtxuX4/8VzsHxmQcD6N7XZ4+3UXWOH+VVxrRkKceUjmxjGpp7dHn7zrVn0IOf7V21uGv3q5AUGQZBxM+L54bNumgDErtoiVsnMm3ThN2eBrglb5+Byn9a1qHO36oaKzZnh20cAAAVvTQ7UDweeNeugGfZ5wjs1WeSehIMBjh+dAWss47u9bhY2EQzyiy5qPY29JhvfDdjJSKibomzpoKIRpTvXHvwZdumiM8rUHTduoZCX5uMSoqMv9Uswot1n8MnB/B6w1LV86WWXMx2Toz5fSc71JmUb3tkQwbL560bQxkSSZHx1/0f6t7XJXnwYfNa1dhE+8DrfYLGWgth6HEuBUqfWb+2gAvvNC5XjY2zFeHeiksjLm0LF0wE9uxG5+v/QvNdv0LnGy/0GviIdgesR5+AzNvvjmvgE3R+7iyIP/yTPM5WhEMco+L+HkRExMwPEQ0DvxzAP2sXq8ZsohlOox11vtbQ2Nau/ZjiiM/yqmhIihwxc6D1QfMarOrYrsoSAcDFebP7FRgc4hiNr9o2hx5vcu+BX5FgEgwxnyta+zSfNaBIeHjvu/ht+TyMtxWjU/Lg/uq3sMujDkLjteQNAMyiEWOthahursKYXU1I7/Ci7buFcPdo9S3YbDCNnwRDYTEEQcAbDcvQ1aM1OADMLzgBNtGMX5adgzt3vYx2ya16Ppj5UTweeL9ZCc+yLxDYU9Xr3ARBhGnCZFhmHQ3zlEP63c0tGrOcB+Fhaz6aA504yFacUPVeRESphMEPEQ2595pXo0ZTQ3JR3tGo8TXjY9+60NhQ1/3U+VoRUNTdvG4sPgNtkgvbu2qxrF1d+K7d92a8rRiHpY3p13tPdZSrHntkH7Z31Qx4L53e9Aw0g7yyH3/YsxC3lpyFf9Utwe4eS7GA7hqoE35Y8jdQckc7fBu+wdnLViOwYytEubvmxSruhdvyne54Q3YuXBPHYVvGbhiKMiAoCgySjKPSxqPCa4G/fifS62rwm70mrNixEVktLpi9AYiiAYVp+9EiGiF3dkDxeXudlyEzG5Yjj4Fl1tEwZGbF5bNGo8CciQJz5pC9HxHRSMTgh4iGVL2vDQsbV6rGxlgLcXLWNCzv2IaPW9aFxrd11Qxp1zNta2SnwY7ZGQeWsE1zjMIztYt1xfRBl+QfDUEQ+vXeGUYHRlvzVXvTrO+sGrTgxyv7VTVGPbkkD35f/UbYOd5ZfkFUjQN6IzXWw/XOq/Bv3ghFkZEneVEnHyj29ygBKIqi+y6l5ka0LtmGC6QDWR9BEFBm+R4twoehsXQAR0l+NPq9ABTkmGxAoBXqsFZNEESYJk+F9ajjYJp4cFwaGRARUeJh8ENEQ+r5uiXwK4HQYwEC/l/RSRAFEQfZilTHuiQPanytEbuQxds+nzr4KdMU9R+feTAKzJl4eO976JS6VM8dmlYx4EBlmmOUOvhxVeFixL++BAif9elNtjENd476EYrMA8uEeNevQecrC1Sd1KzaphaKAo/ih01QjwcUCV2SerlbptEBQ5ilgXaDBeVRBGliRhasRx4Dy5HHDGmWh4iIhgeDHyIaMms7d2Jt5w7V2ClZh4SK0XOM6cgypqGlR0ZiW9f+IQt+9moyP+Hed5K9FPeOvgQP7f136HiTYMRFebMH/P6HOEbj3aZVocdVnnq0BVzIMDoGfG6tGk3w4zTYUWTOwpYu/eayuSYn/rf8R6oW37FSAgG4338TXZ8v1j0nCgLMogkug4S9JZkIGA2QrYWY7CiFIiuQ9u6G1NqsC3xEQUSGwR7zXARBhGnSwd1ZnklTmeUhIhpBGPwQ0ZDwywE8X/uZasxpsONHeUeFHguCgPG2Iqzs2BYa29pVE2orPdi0y97KIrSaLjBn4u5RF+PjlnXY72vGCRkHY3QvrZSjNd5eDKtohqdHMf8GV/WgbPZar2kTXWLJxi9Lz8bvd7+hqvMpMGXizlEXINfk7Pd7SS1N6HjuaQSqd+meE8wWmKdMQ90oG17IqkPA1J3FKbXk4E9j5gMAFEWBtH8Pln75L4ibN6GgrgNAd5OMnkvjBEGEmJsPQ0EhDPmFMBQUQXRmAIpy4D9BgLGkDGIGszxERCMRgx8iGhLvNa/WNQi4NP8YOAxW1dgEe7Eq+Nk2RE0PAoqEGk33s5Je9tmxGyw4L3dWXOdgEgyYbC9TZce+de0elOBHm/kpNGfBYbDiN+Xn46/7PsJ37mpMspfihuLTkWNK7/f7+LdvQceCJyG7XbrnLIfNQtqProBgtWK0ez8Cu18NPbfX24RGfztyTU4IggChuBT/np4F9yEzYe3yweYJ4NKi4zEhawoEoxEQDRAsZggG/rNGRESR8V8JIhp04ZocjLcV45gwN/XjNXU/e71N6JQ8SNMESfFW62vVNTLQ1vwMhWmOUbrgJ1zx/0DVarrtFf3QZSzT6MCdoy6ArMgQIAzofX0b16Hj+b9BCfhV44LRBMd5F8NSeWzo/ONshXAYrKpNb9d1VmFO1jQAwI6uWrjl/9/encdHVd3/43/dO/uSyb6SQNgJ+w4BZJdAUXGr1WpFS7VF6af9aDdaW6uftn5r+7PW1urn01qtC1atC4oIsikCEcIS9i1sYUlCyDZbZr339wdmyJ2ZkG0Wkryej4cPueecmTl3DoH75pzzPpeztLkMWrgMWhTkjoKqE4EZERH1PFzoTERR92qYJAffzpodNotbvi4DGkH57zJljRVR7+N5t3LWJ1FtinrAFc5Is/JwywafIyTddCQEJzzI0CQprkVB7FTg4yrZBts//xYS+KhS05H4g+XQT5mheH9REDHKlK9oW2q/skwu+PDVXF1ap2akiIioZ2LwQ0RRtcd+CruCkhxcnzyqxT0yGlEdOJCyybEYBD/n3JcU1y3t94m2LE0S0jXKxAL7gx78OytcmuvsCJ4v0/jZOthX/BNy0EyadtgoJD76S6hze4d93WhzvuL64FcHvQKXM981N8qkDBKJiIjagsEPEUXV+5e2K64tKiPuaJbkIJxBhhzFdSz2/ZwLSnOdq41P8CMIQsiDffCsR2eFS3MdicM1ZVmGc/X7cKx8K6ROP2kaEu5/CKKh5exsI4Pu2yV5cNR5Hna/CycaK6/aloiIqC0Y/BBR1Lglb8iStbsypoUkOQg20Kjc91PWWAmphYNFIyU0zXV8gh8AGBk0A3LEeV6RAa6zgpMdpKjN0ImaTr2nr/wUrH/5PZzrPg6pM8yaD9M3FkNQhZ7H01yi2oR++ixF2V77aRxwlEPGlUNQNYIaQ6J0+CsREXVvTHhARFFzynVR8dAqQMBky6BWXxc88+OSPCh3X4pIOulwLmd6UyYAiNeyNwAYZsyDACHw3flkPw47z2OMuW9E3j84zXVnZn389XVwrnoX7l1fhq033XAbDHMWtPn9RpnzcdJ1ZZan1HEKdsmlaFNgzIVW5F9fRETUfpz5IaKoCV6qlKdLg17Utvq6JLUpZN/L8Sju+wmX6S03jsGPUaULCQD32U8Hfi3LMmRZRkeFS3PdHrLXC+/xI3B88G/U/+7nYQMfQRBhvuPedgU+ADA6KOnBOXcNSmxlirJRZi55IyKijuE/nRFR1JS5lAHLAENWCy1DDTLkoLrZuUDHGi/g+uRREetbc8FL3pLV5laX5kWTLMuY1KBH6q6TyD9TiwS7Gwb5S9Ro3wV8Psh+H0SjCZqCEdCNHg/N4GEQNG1fttZSmusW++P3wV95Ad4Tx+A9egjesqOQPe4W26tS0mC6/W5oC0a0uU9NwqW8bv5rABgZFCARERG1FYMfIoqaE41Viuv+7Ql+jNnYaj0cuD7ujN7MT3DwE49ZH1mW4TtxDO49O+A5sBcj66uRFpR+2yc5oBIu75uRnA64d30J964vIej00I4YA+3goRBMZgh6AwSDEZJaA8Htgux2QxZw+SBQQWg1zbW/rgbe40fgO3sa/rNn4Dt/NiRldTiCTg/jvBugv25Ou4Kx5ppSXm+zHglbn6I2o5c2pUPvTURExOCHiKKiwedQzNwAQH99+2Z+mqvy1qPGa4vK2S5ng9Jcxzr4kVyNcLz9Ktx7SgJlOlEDURAViR4aJS/MYZIGyG4X3DuL4d5ZHFQhI8fhQMOHK4CvztSRVSrcItaiPC8ZZ/qk4ExeMrK1SfBXV8Gzbzfce3fBd/Z0u/ovCCJ0hdNhXLAIornz4zPa3HLwM9KcH/EDX4mIqOdg8ENEURE866MTNe1KItC0P6h5lrPNDYdwS9qkiPWxSfABp7EMfnxnz8D26v/Cf+liSJ1e1MDpv7K8zCV5On3wqtfrRqLbhREHKzDiYAUkUYApqwZ1dbWtv1hBgDq3NzRDhkE3vhDqzOzWX9JGV0tjzRTXRETUGQx+iCgqylzKZAf99JkQhbbnWFEJIiYlDMTnDQcDZZvqD2BR6oR2vU9rvGEyvcXijB9ZluHasgnOlW9D9vvCthHTM7CzF3C2VxK8GjXSDMl4tM+tgCjAe7IMnj074D1VFva1XtkPp88FNzwwwggRwlflys/SyALkNgY+qpQ0aAYMhmbwMGgGFURkliecppTXJ4N+DwkQMILBDxERdUK7gp+nnnoK7733Ho4cOQKDwYApU6bg97//PQYPHhxo43K58Oijj+Lf//433G43ioqK8Le//Q2ZmZmBNuXl5Vi6dCk2bdoEs9mMxYsX46mnnoJazViMqLsIzvTWnv0+TWYnjVAEP9XeBhx0no3oA3Clpw4SYpvpTXI64XjrFbj37Q6pEw1G6GfPh3bEGNRZZGw+/e9A3TnIaMzJQILKAHWv3jBcNxv++jp49u6C5+Be+K31qLdfQoO9Fj6XEzIASfBD9loD6ax9sl/xeRoh/J+7otEEzYDBUOX1gTovH+q8fIhGU8S+g9YEp7wGLv8e6uzMFxER9WztijY+//xzPPzww5gwYQJ8Ph9+/vOfY968eTh06BBMpst/Kf73f/83Pv74Y7zzzjtITEzEsmXLcOutt2Lr1q0AAL/fj4ULFyIrKwvbtm1DRUUF7r33Xmg0Gvzud7+L/B0SUczJsowTQQ+uA9qx36fJQEM2cnWpioQEG+v3RzT4ORuU7CBFbYZRpYvY+wfznjgG++v/gL8+dLZF06cfzPc+CFVKGgAgX/ZDK2jgka8kGzjqPI/xCQMC16qkZEjTpmF1gQ6f1R+E1X85bbUgydC4vfBY7UjQ6/GTXjchX52Kdec/w7ljO5F/pha55+uhwZU9RKLZAu2I0dCOGgfNgMEQVPH7B6kx5ny8f0mZQptL3oiIqLPa9TfbmjVrFNevvPIKMjIysGvXLkyfPh0NDQ146aWXsGLFCsyePRsA8PLLL6OgoABffvklJk+ejE8//RSHDh3C+vXrkZmZidGjR+N//ud/8NOf/hS//vWvodW2fgYIUXfV4HOg3udUlAkAsrXJ0HShQx0rvfUh6Yk7MvMjCAJmJ43Eq1WbAmUltjJYfU5Y1MZO9xMIl+ktLSLvG0z2+9H46UdoXLcactCZQgBgmFUE48JbFAGHRlBhoCEbB53lgbLDQcGPX5bwP2fewRl3tfLzRAFuvQYOvxayyYCVwmk8mj0Sxz0aHDblYfeYPKh8fnzbmY98ZEOVmg51v4EQxGvj+Lf++ixYVEZY/Vd+Hkab8+PXISIi6hY69TTV0HA5k1NKyuW0o7t27YLX68XcuXMDbYYMGYLevXujuLgYkydPRnFxMUaMGKFYBldUVISlS5fi4MGDGDNmTMjnuN1uuN1XNv1arVYAgM/ng88Xfq08RZ7f74csy/D7/a03pnZ75eImbKjfDxmhh1dqBQ1+mLMwav/yHemxPWo/pziEM1FlQiIMHfp5LTQNxApsDuxV8cl+fFZ3AF9LHhuRvp5trFb0NUeTFPE/V/y1l+B84yX4z5wMqROMJhjvvB+aoSPglwEEffYgfRYOOM4Erg87zir6t99RjtOu0GQJANB0W7IM7LSWodx5EZXuusD9+lQi9INGQP1VMOWXJEAKDczi5e606/Bi5aeQIGGqZQj6ajL4Zz74Z3F3xXHtvji20deevxs6HPxIkoQf/vCHmDp1KoYPHw4AqKyshFarRVJSkqJtZmYmKisrA22aBz5N9U114Tz11FN44oknQsp37twZWG5H0SdJEmw2G3bs2AHxGvnX4e6iXKjDSk3o/o8mDgC/P/oOlnqnKJYpRUqkx/Yz1VE4VI7AdbZkwI4dOzr8fr3UBhwUr/z58K59M1KOeSCg/SmPbXDDgyt/SO7VHIdDuDK74GyowfaT2zvcVwWfD+Yj+2DZWwLB6wmpdmf1Qt118+C3OYHt4T/TK9TBobnyXR5wnMIXFVuh/eqP79Wqw4rvGgDypCSMkLKxXnUcfskPh8MBQQD+uv99nBWVWfiqDp/Bdlk5+3WtUAG4D6PRKHiQVmfG9jMRGpcujn8Wd08c1+6LYxt9Doej9UZf6XDw8/DDD+PAgQPYsmVLR9+izZYvX45HHnkkcG21WpGXl4fx48fDYrFE/fPpMr/fj5KSEkyYMAGqMGeNUMcdqNoAU0Prgbw1XR+xGY/mIj22q8tPw+S6cj/TUsdgUuqEDr+fxdkLvz33buDaDSAxNxcFxtxWX2v3u3DQeRYHnOU44CxHtdca1EKACVf6OiuvEAM6sESvOdnvh2dnMVwbV0FuqAO0msv/NRFF6Ocvgm7mvFaXmY2WvPj4RJnivJ+kQXkYYeoNr+zHSyf2wCRd6f8daVNxU8p4AEDCxS146/xmGI0mCAJQDrviXgFg3oDp0IkdO5CU4oN/FndPHNfui2MbfU2rwtqiQ8HPsmXLsGrVKmzevBm5uVcePrKysuDxeFBfX6+Y/amqqkJWVlagTfC/AFdVVQXqwtHpdNDpQjcgq9VqZoiLMUEQoFKp+L1HkE/2o8RxQnFwY9OMRvASuI/rdqModUxUHlZbG1tJluCSvK0mA/BKPpR7LinuZ5A5p1O/Z4Yn9EG2LgWVzVJSb7YfxghLfouv2e84g7ert+FEY6Xie2ztgMw+pgyoO7i/SpYkePbvhnP1B/BfrGz6QEUbVWo6zN96EJo+fdv0nmqo0d+QhbLGikDZcU8lxiT2wz5bORplj+KeZiQPC3zXC1LG4j/nt0AQwt93itoMk9bQ3tukawD/LO6eOK7dF8c2utrzvbZr7k2WZSxbtgzvv/8+Nm7ciL59lX95jxs3DhqNBhs2bAiUHT16FOXl5SgsLAQAFBYWYv/+/bh48coa9XXr1sFisWDo0KHt6Q5Rt3DAUR6SHODPA5ZgRcF/48n8OxXlVr8TG+v3x7J7AC4nBfivspew5NjzeO78x/DKLa9bLndfCkmn3E+f2ULrtrmc+GC4omy79TjsQd9bkzqfHf/fuQ9R1lgRdg9VS/ro0mEQ2590RXLY0bhxDep/sxy2V168EvgE0U+ciqQfPd7mwKfJEGMvxfUR5zkAwDbrUUX5IEMOUjVXzt5JVBsxwt/y4aNN6a+JiIh6inaFnw8//DBWrFiBlStXIiEhIbBHJzExEQaDAYmJiViyZAkeeeQRpKSkwGKx4Pvf/z4KCwsxefJkAMC8efMwdOhQfOtb38LTTz+NyspKPPbYY3j44YfDzu4QdXfBD7ADDTlI11gCvx5h6oP9zTa8f1hTgrlJI2OW/U2SJfz1/GrU+GwAgGLrUfTWpePmtIlh25cFne+To02BKQJns0xPHIq3qrfC/9XyL6/sw5aGw5ifEpokpdh6FG7JG1J+NVnaZCzJntOu1/jOl8O1eQPcu3dA9rX8eZqBQ2C64Taoe7cv6GkyxNALq7AzcF3WWAmn341d9hOKdlMsQ0JeO9HfGydgDRsEZmmTO9QfIiKirqpdT08vvPACAGDmzJmK8pdffhn33XcfAOBPf/oTRFHEbbfdpjjktIlKpcKqVauwdOlSFBYWwmQyYfHixXjyySc7dydEXZBX8qHEVqYoK7QMUlzfnlaoCH7qfQ5sqN8f9qE/GoqtR0PSKL936UsUWgaFnTkoc1UorjuS4jqcRLUJ480DsN12LFC2sX4/ipJHhyzp2m49rrjWCmoMMeZihKk3hpt6I1eXGpIsQSW0fSLcV3EeztUfwHNgz1XbqXP7wHjjbdAO6tys9mBjLwgQAgGMV/bh3UtfwiVdSaIgQMAky8CQ16bAiPHm/ihxlIXUZXPmh4iIeph2BT/N08G2RK/X4/nnn8fzzz/fYps+ffpg9erV7floom5pj+NU6ANsgjL4GWTMwXBTbxxwXDnr5cOaEsxJGhH12R+v7Mfb1dvClPvwcuVG/DTvlpDA40TQzE//Dhxu2pLZySMUwc9Z9yWUuSox0HBlaVet145jjRcUr/tB7g0Ya+7X6c/3V1fBufYjuHdtB66ynE6dlw/D7PnQjhwbkXNzzCo9cnWpOOu+FChbU6sMvIYa85CkDp8048aUcWGDnwxNUqf7RkRE1JVw1xVRHH1pPaa4HmLshRSNOaTdrWmTFcFPnc+OjfUHUJQyOqr921i3Hxe9DWHr9jpOY7vtOCY3m6my+12oaJaUAECnM6c1N9yYh3RNIqqb9WlD3T5F8BM8k2YQtRhh7N2pz5VsVjg/+QDuL7eEPaAUAASVCtrRE6C/bjY0fTofaAUrMOYqgh8Jyn4Ezxg211efGRJAA5z5ISKinofJxonixCV5sMumPPRyimVw2LYFxlwMNeYpyj6s2QGvFL0DHxslD9679OVV2/yrahOc/isHEJ90Kc+Q0Qhq9NanR6xPoiBiVlDig2LrMUUfms8MAcC4hP4dniGTfT40fvYp6n77c7iKN4cNfESjCcb5NyH58T8g4Z7vRCXwAS4vfWuJCBETwyx5a+7GoFTjakHFhAdERNTjMPghipPd9lPwyFc2yQsQMDGh5QfY29MLFde1Pjs+azgYtf59UrsbVr9TUTY/RXnGUL3Pgbertwaug5e89dGnQyNE9kyDGYnDFPt1PLI3kDSiwefAEed5RfurfadX4zm8H/VP/xqOlW9DdodmlRN0ehiLbkLSL/8fjEU3QUyI7pljQwwtBz8jTL2RoLp6yuoRxt4obBZcz0sezfN9iIiox+GyN6I42dZwRHE93NQbFrWxxfYFxsuHeh7+Ks0xAHxcuwtzk0a2enZNe1l9TnxUs1NRNtrcF/dmzECFuxZ7HacD5Z/W7YVJpceJxkocatY3ABgQwf0+TVI0Zow191NkOttYvx9zk0eixHZCkdVMJ2owypTf4nvJfj/81VXwV5yHVFcDf10NpLpaSDWX4Ks8H/Y1gkYD/bQ5MMyZD9EUukQxWlI0ZmRqklDlrQ+pm5IYmuUtmCAIeChnPmYnjYAIoU0HxBIREXU3DH6I4sDpdysCCKDlJW/N3Zo2Gb8t/0/guspTjzPuauTrMyLavw9qdoQkYrgzfRoEQcC3s+bgRyf/Ba98ecmdDLnF5XGRyvQWbHbyCEXwc8pVhdOui9hhU2Z5G2vuB+1XS94kuw2+C2fhv3D+q/+fg7+q4qopqoPpxk2G8YbboEqKT4roIcZeqGqoV5SpBRXGm/u36fVqQYXhps7tfyIiIurKGPwQxUGJrUxxEKhKEDEhYUCrrxsWZsP/DtvxiAY/FZ46rKvbqyibmjgEfb7au5OhTcStaZPxVvWWq76PXtRipKlPxPrVRJZljHDoMfakDeqaGqTUOpFS70SDfRdmeByYYNTCadDAadBidIYEq+sYfBfOQrJZO/yZ6rx8mG65E5q+rY9RNA0x9sLnQUsdR5v7wqjiGWlERERtweCHqIMOOc7in5Ub4YMf92RMx/g2BC/A5Yf3rVblkrdRpr5tOghUEARMShiIVbVXlqR9aT2Gr6dNicjSN4ffhT+c+yAkMPt62hRFu4Wp47DVehjn3DWhfYSAAYZs3Jk+9arL+NrLX1MN967tcO/eDn9VBW722lHvcwTqPQC0ALSeRiTVN0IQBKRdOAxPJ74X0WyB8YZboZswJSIpqztrSJikB4VtmDEkIiKiyxj8EHWAJEt4oWItLnkvzyb8+fzH+FP/+5Gmufqmd6/kw0uVGxSHlgLAlKukKQ420TJAEfxUeOpw3lOLXF1qO+4glAQZz1euDUlVfX3yaGRoExVlGkGFH/S6AU+f/QDV3gYkq80YaeqDUeZ8jDD1gbkNgVxbyF4v3Du2wl2yDd4zysx4CWqDIvgJZhC1bQ4IxcRkqDIyoUpOhZiUAjEpGWJqGjR9B0LQXDtJATI1SRhgyEZZ4+WDZFPUZow1941zr4iIiLoOBj9EHVDuvhQIfADAJ/uxsqYES7LmtPiaGq8Nfzr3EU64lBnRdKIGYxPatmcDuHxoaIrajFqfPVC23XoMuUHZ4Nprk6oMhxw1ioChjy4dd6RPCds+V5eKZ/vfD7fsg17QRDzpgr/mEmyvvADfuTNh69WCCgaVFo1+T9j6cDNpgkYDVVYvqHNyocrJhTonD6rsXjFNXNAZTUkL3rv0JbySH4vSJkAvauPdLSIioi6DwQ9RBxx2hmYC+6z+AG5OnYhUTUJI3THnBTxz/iM0hJmpWJw5C4Z2PMCKgoiJlkFYU7s7ULbddhy3dSL4+bzhIEpU5TDBFCizqIx4NG/RVfsmCiIMQuQfvj0H98G+4iVIzpZndkSzBbrUbHypvYjaJCPqko3walQwNHqR0OjDgwmF0DS6IBiMlwOd7DyIaenXxPK1zsjWJuPhnAXx7gYREVGXxOCHqAOOBKV0Bi7P/nxYU4L7s2Yryr9oOIT/rfgU/qADMnWiBkuz52NSK4dThjMxYYAi+DnrvoQKTx2yte3PQnbUeR7/vLhJUaYSRDySeyPSW1nGF2myJKHxk5Vwrv84bL1oNEE7egJ04ydD3acfLIKMPx7/e8h5RGPN/ZGUtygWXSYiIqIuhMEPXdPOuWuwteEIbP5GRbmAy2mUpycOhSjE9l/yZVkOOUizyab6A1iUOhEpmsvLqA45zuKFC2sVZ88AQIYmET/KW4Q8XVqH+jDYkINEtUkxk7Tdehw3p01s1/vstp3ECxVr4W+W4AAAvpM1F4PDbK6PJslmhe21v8N7/HBInSo9C6ZFX4dmyDAIqit/bKkBzEwahg9rShTtOxJQEhERUffH4IeuWXU+O5448zbsQYFPk/X1+3DeXYu7M6fHtF+V3vqQmYYmXtmHj2pKsDhrFi55rfjz+Y9DAp8Rpj74r14LO5UUQBRETEwYoEhJvd12rM3Bj8PvwmtVn4ekTQaAr6WMw8yk4R3uW0d4DpTC/ta/INltIXW6UeNhvvM+CPrw39fspBH4qGZn4HvWCGqMNfeLan+JiIioa+rai9+pW9tYt7/FwKfJx7W7cKKx8qptIq2lWZ8mG+r346KnAc+c+ygkSJqXPBo/zbslItnQJiYoZzdOuy6iylPf6uv22k/jJydfDRv4jDTl4+6M6zrdt7aSXS7Y33oV1pf+GhL4CKII0813wrz4uy0GPgCQqU3CHelTIUCACBF3Z0yPWLY5IiIi6l4480PXJFmWUWw91no7yPhH5Xr8Jv+bUMVo+Vvwfp9BhhyccFUG9vR4ZR8eO70iZKneWHM/LM6cGbFlegXGXCSoDIrP2WErw42p48O2l2UZb1zcjI9rd4Wt7y+l4ftZC2K2jNB7+gTsb7wE/6WLIXWiJQkJ932vzYeK3pw2EbOTRgAALGpDRPtJRERE3QeDH7omnXVfwnmP8gDN6xKHwihqUe21Yrf9ypkvp10X8WldKRakjI1J34IzvU2xDEGeLg0b6vcFyoIDnyxtMh7KmR/RwEIliJiQMAAb6/cHynbYjrcY/BTbjoUNfPSiFvekXQddnQ0GVfTTJstuN5xrP4Lrs08hByWBAADtsFEw33kfRHNo1ryrYdBDRERErWHwQ9ek4FmfVI0F38ueB1EQ4ZX9+Pmp13HOfSU4ert6GyYmDAybZjqSarw2VHsbFGVDjL0wxtwXm+oPQELow7xe1OLR3JvCnjvTWRMTBiqCn7LGCtR4bWG/h/XN9gc1GWHqgwezr0eSYMR2bI94/4J5DpTC8e4K+OtrQ+oErQ6mW+6EbtK0iJ8ZRERERARwzw9dg2RZxjbrEUVZYcKgwKyJRlDh20GHibokD16t+izqfQve72MUdcjTpSJDm4gZScPCvmZpThFydalR6c8wU15IULXDdjykXbXXisNBy/XuSJ+K5Xm3Ii0G6az9dTWwvvRXWF/6a9jAR5PfH0k/fhz6ydcx8CEiIqKo4cwPXXNOuqpwMWh2pdAyWHFdYMzFjMRhik37O2zHsdt+MqqZvo40KoOfIcZegaBsUeoEfFZ/QJHd7Za0SSGJCSJJLagw3txf8T18Vn8Q85PHKIKIrQ3KYNIo6nBDyrioBRqyJMF39jS8Rw/Be+wQfKdPQPb7Q9oJKhUM198Aw9yvQVCpotIXIiIioiYMfuiaU2w9qrjO1Cahrz4jpN3dmdOxy35SkRHulcpNGNYvDzpRE5W+Bc/8DGl2Fk6mNgn3Zs7Ev6ouHxg6I3EYbk8rjEo/mptsGaQIfsrd1djrOI3R5r4ALs+kbWlQnp1TaBkMjRjZH3//pYuBYMd7/AikxvDpwJtoBgyB6fa7oc7Mjmg/iIiIiFrC4IeuKZIshQQ/hQmDw85QJKgMuDtjOv63Ym2grNrbgJU1JbgjfUrE+2bzN+Kc+5KibEjQQaDzU8ZgXEJ/uCVv1Ja6BRtp6oNsbTIqPHWBsg9rSgLBzxl3dUjyiKmJQzr9ubIkwXv4ADwHS+E9egj+2kutvwiAaLbAtOgOaMdN4hI3IiIiiikGP3RNOdZYgVqfXVE2JXFwC62BGYlDsbnhoGI/y2f1B/D1tMKIP1gfc15QXGsFNfL1mSHt0mOwh6Y5URBxY+oE/F/Fp4Gyw85zKGusxABDFr4ImvVJ01gw2JDT4c+TXI1wf7kFri0b4a+pbvPrBJUauknTYFx4C0SjqcOfT0RERNRRDH7omhI865OrS0WeLq3F9oIg4N7MmVh+6vVAWZ3PjnL3JfTRp0e0b8FL3gYacqARro19KtMsQ/BO9TbUNQscP6opwQ96LcTWoOQR0ywFHUq57auqgHvrZ3Dt2ArZ7WrTa9SZOdAMHgrNoKHQ9B901cNKiYiIiKKNwQ9dM/yyhC+DUlwHJzoIp48uHemaREUK6lL7qYgHP8HZ0oKXvMWTRlRjQcoYrLj4RaCsxFaGdXX70OBzKNq2dcmb7PXCe/IYvIf2w3NoX9jDSIOJZstXwU4BNIOGQpWU3L4bISIiIooiBj90zTjkPAurX7lJvi3BjyAIGGPui0/rSgNlpY5TWJQ2MWJ9a5Q8OOVSPvwPNnZ86Vg0zEkaiQ8u7YBTcgMAZMgh6b/76jNb3ovk98N3+gQ8p0/Ad+IYvCePQ/a4W/1cda/e0I2dCM2QYVBl53IfDxEREV2zGPzQNSN41idfn4FsbdtmDkYHBT9HnRfg8LsidrDofscZxQGmIkQMNFxbWcqMKh3mJo/EhzUlgbLgQ1enNZv1kWxW+M6chO/saXhOHkfOgb2w63RAG4IXQRChHTEa+ulzoe43kAEPERERdQkMfrqg7dbj2NxwCC7JoyhXCSIKjLm4KXUCVB3Y0xFPXtmP7UGHc05pw6xPk6HGXGgENbyyD8DlWY/9jnJMtgzqVL98sh8f1pTg3eovFeX9DJnQi9pOvXc0FCUMx46jnyPlYgPSL9mRUueEKMmQRBF+lYgJqRpY5WL4z5UrDxuVZQg+H6DTXfX9xQQLdOMLoZ86C6rUlvdiEREREV2LGPx0MXvtp/Hn86sUB2k2t99xBlZ/IxZnzoxtxzrpsOMsHH7lJvpJCW0PXHSiBkONudjrOB0oK7Wf6lTwc9Z9CX+7sAanXaF7XYYbe3f4fSNNslnh2vY5PPt2Q648j++662HzNYa0M6i0EC7shSfMe7RMgKZPX2gKRkA7dCRUvfIgiF0rsCYiIiJqwuCnC/HLEl6/+HmLgU+TtbV7MM0yBP0NWTHqWefttJ9QXPfXZyFDm9iu9xht7qsMfhynIclSuzOb+WUJH9XsxLuXiuGT/SH1fXTpWJAytl3vGQ2+s2fQuHk9PHtKIPt9gfJEtSls8GNu4xJAdVYvqAcMgqb/YGgGDIZoTohYn4mIiIjiicFPF7Kp/gDOuWtabSdDxj8q1+M3+d/sEsvfZFnGLpsy+BmfMKDd7zPanI9/VV25bvA5cMZdjb5hzuJpyTl3DV68sBYnXJUhdQIELEqdiFvTJkEjxu9Hx3v8CJyffADvqbKw9RpBBaNKB6f/SrICQRBgEkODH0GrgzqvD8TcPjjjdCNn4U3QJiZFq+tEREREccXgp4tolDx4p3qboixXl4o5SSMBAKddF/F5w8FA3WnXRXxatxcLUsbEtJ9NZFlu8yb4k66qkINNxyf0b/dnZmmTkaVNRqWnLlC2x36qTcGPJEv4uHY33qneFtg31FwvbSqW5hTFdTZNcjrgXPk2XDu2tto2KT0P+00NuJhmglunxih9HoaaBwF+HyDJEFPToO7TD6rMbAiiCJ/PB9f27RBN5hjcCREREVF8MPjpIlbV7AxJA704cxaGmy7vPfHKfpxwVSpmht6u3oqJCQOQqondsqWdtjK8WvU5VIKI29ImY1piQauv2RW05C1Lm4xe2pQOff5oUz7WNAt+Su2ncWva5Ku+psJThxcurMXxxgshdQIELEwdhzvSpsR1tse9dxcc774ByWYNWy+oNdCNnQjdhClQ5faGqDdgsP0UztfvRy9tMmamFUIbx/4TERERXQv4NNQF1HrtWFWzS1E22tw3EPgAl5c6fTtrDp4883agzCV58GrVZ/jv3Btj0s96nwPPnV8dmDl5/sInOOGqwj0Z06+6/G5n8JI3c/8Op04ebe6LNXV7AtdljRWw+RuRoDKEbX/WfQm/Ov3vkMx5wOUgbGl2EQbF8TwfydoAx7tvwL1vd9h6MTEZ+qkzoS+cHrI3Z4y5L8aY+8aim0RERERdAoOfLuCdS9vgkb2BawEC7s6YHtKuwJiLGYnDFMvfdtiOY4/9VEwegtfWloYsGVtTuxtnXZfwX70WwqIODUCqPPU4676kKBvXgSVvTYYac6EV1PA0S3m9z34GU5udb9Pc6trdIYGPAAELUsbiG+lT4zpb4t63G463/gXJ6QipE3R6GBfeCv2U6RBU/DEmIiIiaotrfzd8D3fGVY3P6w8qymYlDUeuLjVs+29mTIc5aJbj5cqNcEvesO0jxS15sa5+b9i6g85yPHZ6RdiU0cFL3iwqIwZ14vBQjajGMFOeoqzUcSpsW1mWsc9+WlGWqUnCL/t8Hd/KnBG3wEd2uWB/82XYXv5b2MBHO3Qkkn76JAzXzWbgQ0RERNQODH6ucSsufqFIba0TNbg9vbDF9ha1IWRWqNrbgI9qdkatjwCwueFQyDk9wX14/PS/Q7K6BS95G2Pu2+7U1MFGB81y7bVfTnkd7LynNiTRwo/yFqHAmNupz+8M76ky1P/h12GTGohGExLueQAJ3/k+VMkd2xNFRERE1JMx+LmG7Xecwb5m59YAwI0p45GsvnpGrhmJQ0Me4D+tK4VXCs1iFgmSLGF1rXJPSl99Zkg/PbIPz51fjQvuWgCAzd+II87zijYTOpDiOthokzL4sfkbcTLMrFPwd5uiNnc40UJnSdYGON57E9a/PA1/7aWQet3IsUha/hvoxk3q8H4oIiIiop6OwU8MFFuP4tETr+AXp1ZgVc1O2PyhB1AGk2UZ/76o/Nf/JLUJC1PHtfpaQRBwX+YsRZnN34ht1qPt63gb7bGfUqSXBoC7Mqbht32/iYEGZbIAj+zFXy6shlfyYbftpGJWSyuoFUkcOipDm4icoCCm1B669G2v/YzieqQ5P+aBhWS3wfHhO6j7zc/Q+MUGyEEzVIJOD/Nd98N831IeNkpERETUSdwwEGWVnjo8f+ET+L96qD3pqsTb1dswMWEg5iSPwBBDr7AP3DtsZTgZdNDmbWmF0IvaNn1ub306hpt644CjPFC2tq4U0xOHRvwB/+NaZSa63rp0DDf2hiAI+GXv2/FCxVoUNwu8Trsu4u3qbajy1iteN9KcD52oiUifRpv74kJtbeB6i/UwbkubHLh3j+TDYec5xWtGmfIj8tltITnsaNy0Fq4vNkL2uMO20fQdAPPd34EqNS1m/SIiIiLqzhj8RNnKmpJA4NPEK/uw1XoYW62H0VuXjqU5RcjXZwTq/bKEt6q3KF6TpU3GrKTh7frsouTRiuDnlKsKZa5KDOxEQoFgJxurQoKIr6WMDQQZGlGN72bPwxlXNS54rgQjq2p3Qi2oFK8bZ+54lrdgkxIGYnWzoKzKU4/DznMY+lUyhCPOc4rMdAKEiMw6tUZyOuDa9OnlWR53+D1SgkoFw/ybYJi9AILIyVkiIiKiSOGTVRTVeG34ouHQVduUu6vxP2feURxOurnhECqClpF9I33qVc/KCWesuR/SNBZF2dra0na9R2tWB836JKlNmBKUVlonavD9Xl8LCXZ8sj/wawECxpr7RaxfAw3ZIRnxNtYfCPx6r0O55K2/IQtmlT5inx9McjrhXPMh6p78KZzrPw4b+AiCCP2kaUha/lsY5y5k4ENEREQUYXy6iqJVNTsVsz4qQQwJAADAKbnxVPm7qPHa4JV8+E/1NkV9X30mJnYgEYAoiJiXPEpR9qXtKBp8oemTO6LGawvZRzQveTQ0Ye4xX5+Bb2Zc1+J7DTbmhD0HqKMEQQiZKdthOw77Vxnp9gUFPyNNfSL22c3562rg+Og/qP/Nz+Bc+2HLQc/EqUj6+W9gvvM+LnMjIiIiihIue4uSBp8DG+v3K8rmJI3EbWmF2NxwEOvr96HKUx+oq/XZ8f/OvocJCQNC0i/fmTGtw+mfZyYNxzvVxYElXn5Zwob6/bg1bXKH3q+5d6q3BSUs0OD6oGCrufnJY7DXfhp7g7KsAcB4c+ezvAWbZhmKFRe/CASgXtmHrQ1HMD6hP84FHawayf0+sizDd+IYXF9sgGd/aUgSgyaCIEI7diKM826EKiMzYp9PREREROEx+ImS1bW74Wm2p0SEiBtSx8OiNuCG1PG4PnkUflP+H5Q1VgTanHPXKJa/AcAwY2+MMHZ8L0qCyoAplsH4vOHKQanr6/bhptQJYWeh2mq3/aTiPQFgZtKwqy4dEwQBS3OK8JOTr8HqdyrqxidEbr9PE4vagAkJA/Cl9VigbGP9fuiCDi81ijr0N2RF5DM9xw7BufId+C6cvUorAboxE2AouhHqzMjtvyIiIiKiq+Oytyhw+F34tG6vouy6xAKkN9t/oxM1+EnezcjWJl/1ve7MmNbp7GxFKaMV13U+e8jhou1h97vw94p1ijK9qMWNqeNbfW2i2oSlOUWKsv76LGRqkzrcn6uZlTRCcV3urg7JTjfc1Lvd+6mCSQ477G++DOsLz1w18NGNGo+knz6BhHsfZOBDREREFGOc+YmCtXWlcEmewLUAATelTghpl6AyYHnv2/Cr02+iPsw+nIkJAzEgAjMSffWZGGjIwfHGC4GyT+tKMdkyqEPv9+rFz0P6+63MGSHJFVoy2twXS3Pm4/1L25GgMuDB7Os71I+2GG7MQ5rGgktea6AseHatM/t9ZFmGZ08JHO//G5LdGraNoNNDN6EQ+mmzGfAQERERxRGDnwhrlDz4pHaPomySZSBydClh26drLPhZ3q144sxbaAwKmL6ePiVi/SpKHq0Ifg47z+GMqxp99Olh2x9vrMDJxioMNGSjn+HKfpSj4kVss51UzEaNMuVjVmL70nBPTxyK6YlD23kX7ScKImYlDcc7QUkkmhtlzm/3+8qSBN+JY2j87FN4Du0L20aVmg79dXOgmzgFosHY7s8gIiIioshi8BNBXtmPf1Vugt3fqChflDrxqq/ro0/Hj3IX4amz7wXSPxcljw5J1dwZEy0DkXjRpMj0tqp2Jx7OWRDSdoftOP507qPAdb4+A3OSRmKYvhfWqo5CwJWDSI2iDg9kXx/xg1MjaUbiMPynuliRnKFJjjalzTNWsiTBd/oE3Ht2wLN3FyRbyzM9phtvh65wOtNVExEREV1DGPxESL3PgT+d+wjHms2uAMAYc1/FAaYtGWrKw//r+y1sqN+HTE0Srk8eGdH+aQQV5iaNxLuXigNlWxuO4Na0yYp9R17Zj9eqPle89rTrIl6qXA9ZluEUPDA1C34WZ81CqiYhon2NtFRNAkaZ81FqPxVS19qSN1mW4TtzEp7SErhLd0FqqLtqe+2wUTDdfg9USVffy0VEREREscfgJwJONFbimXMfhqSoFiHitrTCNr9PL10K7s2cGeHeXTEveTRW1e6EW/ICAGTI+ODSdizNmR9o81n9AcX+mKsZa+6P6ywFUelrpM1KGh4++Amz5E2WZfjPnoa7dBc8pSXw19WEtAkmmi0w3f5NaEeOu6ZnwYiIiIh6MgY/nbS54RD+UbE+cI5OE62gxvdyiiKWQjkSLGoD5iWPxkc1JYGyLxoO49a0ycjUJsEj+fD+pe1tei+TSo8Hsud2mQf9seZ+sKiMihTbakGFAmMvAF8taTtzEp69O+HZt6dNAQ8AqHPyoB0zAfopMyAaTVHpOxERERFFBoOfDvLLEt64uBmf1O4OqUvVWPCj3JvatNwt1m5IGYe1taXwyFdmf96/tB3fyynC+vq9qAuavfpmxnU44ryAPfaTij0zS7LmIEnddR721YIKc1X5OLJ/A7KrrNB6/MiCCZ7Pn4fb64W/ugqStaFt75WZA+2YCdCNmQBVxrUT3BIRERHR1TH46aBt1iNhA58CYy5+2OsGWNTXZnYvi9qIecmjsKp2Z6Bsc8MhfC1lLFZeKlG0HW7qjRtTJ+DGVKDGa8PG2n0osR3AzdnTUWgZHOuut5vs98N38jg8Rw7Ae+QgZpw/gwHu2kBSiRxdCjziwVbe5TJVeiZ0YyZAO2Yi1Fk50ew2EREREUUJg58OmmYpwE7bCeywHQ+UFSWPxj2ZM6AWVHHsWetuSB2HT+tK4flqqZ4MGb8tf1exJAwA7miWajtVk4BbUichp+zy+UPXMlmW4dm3G86P/gN/TXWgXBRE9NKlwCV5oRPVULUyTqrUdGhHj788w5OT12WW+BERERFReAx+OkgQBHwvpwgVp+tQ4anDkqw5mJnUvrNu4iVRbcLc5FFYXbsrUBYc+Iwx98VAQ9eb4fCVn4Ljg7fgPVUWtl4URBhVuhZfr87MgXbUWGhHjmXAQ0RERNTNMPjpBIOoxaO5N8Hqd3a5QOHG1PFYV7c3JFFDk0gesBoL/vo6OFe9C/euL6/aTtDpoRlYAFVGJgStFoJGC0GrA3Q6qHv3hTozO0Y9JiIiIqJYa/cJjJs3b8aNN96InJwcCIKADz74QFEvyzJ+9atfITs7GwaDAXPnzsXx48cVbWpra3H33XfDYrEgKSkJS5Ysgd2u3GjfVWRqk7pc4AMASWoT5rZwltDEhIHoq8+McY86zr1/DxqefrzFwEed1QvGuQuRuOwnSPnts7AseRimG2+HsegmGGbPh37aLOgnTGHgQ0RERNTNtTv4cTgcGDVqFJ5//vmw9U8//TSee+45vPjii9i+fTtMJhOKiorgcrkCbe6++24cPHgQ69atw6pVq7B582Y8+OCDHb8L6pAbU8dDIygn/wQIuD297WcTxZPs88Hx/r9h++fzkBqdIfWqlDQk3PtdJP7k1zAuvAWa/oMgqDjZSURERNRTtftJcMGCBViwYEHYOlmW8eyzz+Kxxx7DokWLAACvvvoqMjMz8cEHH+DOO+/E4cOHsWbNGpSUlGD8+PEAgL/85S/42te+hj/+8Y/Iyel6syhdVbLajDlJI7Cmbk+grNAyGHm6tDj2qm38NdWw/et/4Tt7OqRO0OlhnHcD9NfNgaDRxL5zRERERHRNavfMz9WcOnUKlZWVmDt3bqAsMTERkyZNQnFxMQCguLgYSUlJgcAHAObOnQtRFLF9e9sO2KTI+Xr6FAz6atleH1067s2cEecetc69dxfq//BE2MBHN2YCkh97CobZ8xn4EBEREZFCRNcAVVZWAgAyM5X7RTIzMwN1lZWVyMhQHv6pVquRkpISaBPM7XbD7XYHrq1WKwDA5/PB5wu/YZ/aRgsVfpl7O+x+F0yiDgKEFr9Tv98PWZbh9/tj3MvLZEmCa+2HcG/4JLRSrYFh0R3QTr4OkiBA4u+Ldon32FJ0cFy7J45r98Rx7b44ttHXnnigS2yAeOqpp/DEE0+ElO/cuRMmkykOPeqZJEmCzWbDjh07IIoRnTRsleBxI2Xzp9CfOx1S57MkoXbGfHhFHbBjR0z71V3Ec2wpejiu3RPHtXviuHZfHNvoczgcbW4b0eAnKysLAFBVVYXs7CuZs6qqqjB69OhAm4sXLype5/P5UFtbG3h9sOXLl+ORRx4JXFutVuTl5WH8+PGwWCyRvAW6Cr/fj5KSEkyYMAEqVewOcvVfrITj5b9BqqsGgoJdzdhJMN72TeTr9DHrT3cUr7Gl6OK4dk8c1+6J49p9cWyjr2lVWFtENPjp27cvsrKysGHDhkCwY7VasX37dixduhQAUFhYiPr6euzatQvjxo0DAGzcuBGSJGHSpElh31en00GnCz2YUq1WQ63uEpNX3YYgCFCpVDH73j3798D+xkuQ3S6g2YGjgijCePOd0E+bxYNIIyTWY0uxwXHtnjiu3RPHtfvi2EZXe77Xdo+A3W5HWVlZ4PrUqVMoLS1FSkoKevfujR/+8If4zW9+g4EDB6Jv37745S9/iZycHNx8880AgIKCAsyfPx8PPPAAXnzxRXi9Xixbtgx33nknM71RgGSzwvH+m3DvKQmpE01mJNy3FJoBg+PQMyIiIiLqqtod/OzcuROzZs0KXDctR1u8eDFeeeUV/OQnP4HD4cCDDz6I+vp6TJs2DWvWrIFef2VZ0htvvIFly5Zhzpw5EEURt912G5577rkI3A51dbIsw71jK5wfvgPJGbp+U92rNxK+/RBUKdd+Om4iIiIiura0O/iZOXMmZFlusV4QBDz55JN48sknW2yTkpKCFStWtPejqZvznS+HY+Xb8B4/ErZeN2YizHcuhqANXQJJRERERNQaLjykuJIlCd5D+9D4+Xp4y8IHPaLRBONNX4du4lTu7yEiIiKiDmPwQ3Ehu91w7dgC1+fr4a+pbrGdbswEmG65C2ICs/oRERERUecw+KGYkiUJ7p3FcH78HiRrQ4vtVEkpMN1+D7TDRsawd0RERETUnTH4oZjxnjgGxwdvwXfuTIttRLMF+mkzYZgxD4KeZ/cQERERUeQw+KGo89fVwLnybbj37mqxjTonD/oZc6EbMxGCRhPD3hERERFRT8Hgh6JGlmW4S7bB8d6blw8pDUM7aCgM1y+Euv8gJjMgIiIioqhi8ENRIdmssL/9KjwHSsPWq9KzYLr5DmgKRjDoISIiIqKYYPBDEefetxuOd16DZLeF1IlGEwzzb4J+ygwIKv72IyIiIqLY4dMnRYyv8gKcH78Pz4E9Yet14wthuuVOiEZTjHtGRERERMTghyLAX1+HxjUr4d6xDbIshdSL5gSYvv4t6EaOjUPviIiIiIguY/BDHSbZrGjctBauLzZC9nnDttEOHwPzHd/iIaVEREREFHcMfqjdpIZ6OL5YD9e2zyF7wwc9otEE46I7oJswhQkNiIiIiOiawOCH2kyqq0XSl5/B+v5rgN8Xto2g0cIwax70s4og6g0x7iERERERUcsY/FCrpEYnGtetRuPn62CyNgAmExA0myOIInSFM2CcdwNES2KcekpERERE1DIGP9Qi2e+Da9tmNK79EJLDDshySBtBFKEbXwjD9QuhSsuIQy+JiIiIiNqGwQ+FkCUJ3oN74Vj1LvwXK8O2EVQq6CZOhWHO16BKTYtxD4mIiIiI2o/BDwXIbjfcO7eh8fMN8FeHD3ogiNBOnQnT9TdAlZQc2w4SEREREXUCgx+C1FCPxi0b4d72OSSno8V2muFjUJXTF7nziqBS87cOEREREXUtfILtwWRJguuLjXCufh+yx91iO3VuH5gW3QEhvz9827fHsIdERERERJHD4KeH8lWch/3fr8BXfqrFNuqsXjDMWQDt2IkQRBE+X/j01kREREREXQGDnx5G9nrRuP5jNK5fDVmSwrbRDhkO/ax50Aws4AGlRERERNRtMPjpQfzVVbC99Dx8VRfC1usnToV+VhHUWTkx7hkRERERUfQx+OkhvGVHYXv5b2ETGqjSM2G+415oBgyOQ8+IiIiIiGKDwU8P4NqxFY63X4Xs9yvKBVGEYfYCGObdAEGjiVPviIiIiIhig8FPNyZLEho/WQnn+o9D6tQ5uTDfvQTqnLw49IyIiIiIKPYY/HRTss8H+4qX4N5TElKnHTYKCfc8AEGvj0PPiIiIiIjig8FPNyT7fbC/9n9w79sdUmeYMRfGm+6AIIpx6BkRERERUfww+OlmZL8f9tf+ERL4CIII0+13Qz9lRpx6RkREREQUXwx+uhHZ74f9jX/AvXenolzQ6pBw/0PQDhkWp54REREREcUfg59uQpYk2N98OWSPj6DRwPLAfzGNNRERERH1eNz40Q3IkgTHW/+Ce9eXinJBrYHlOwx8iIiIiIgABj9dnizLcLz/Jlw7tirKBbUGCd9ZBs2ggjj1jIiIiIjo2sLgp4tr/OQDuLZsUpQJKjUSvv0QtIO5x4eIiIiIqAmDny6scdNaONcpDzAVRBEJ9y+FtmBEnHpFRERERHRtYvDTRbmKN8Px4TtBpQLM9zwA7bBRcekTEREREdG1jMFPF+Qu3Qn726+FlJvv+BZ0YybEoUdERERERNc+Bj9djOdAKeyv/x2ArCg33Xg79IXT49MpIiIiIqIugMFPF+I5UArbKy9A9vsV5ca5C2GYPT9OvSIiIiIi6hoY/HQRLQU++mmzYPjazfHpFBERERFRF8LgpwvwHNwbPvCZNA2mW+6CIAhx6hkRERERUdfB4Oca5zm4D7aXwwQ+E6fCdMe9EEQOIRERERFRW6jj3QFqmXtnMexvvgxZkhTl+olTYfrGYgY+RERERETtwODnGiTLMlyb1sLx0X9C6hj4EBERERF1DIOfa4wsSXCufBuNm9eH1OkmTGHgQ0RERETUQQx+riGy1wv7ipfgLt0ZUmeYMRfGm+5g4ENERERE1EEMfq4RsssF6z//Cu/xIyF1ppu+DsOsojj0ioiIiIio+2Dwcw2QXS5Y/+9ZeE+VKcoFlQrmO++HbvzkOPWMiIiIiKj7YPATZ1KjE7b/fRbeMycV5YJOj4RvPwTtoKFx6hkRERERUffC4CeOJKcD1hf/BN/Z04py0WSG5XuPQJ3bOz4dIyIiIiLqhhj8xInksMP64jPwnStXlItmCywPPQp1dq849YyIiIiIqHti8BMHUkM9rP/3LHwXzinKxQQLLA/9COqsnDj1jIiIiIio+2LwE2P+mmpYX3gG/ppqRbloSULiwz+CKiMrTj0jIiIiIureGPzEkK/iPKwvPgPJ2qAoVyUlw/LQj6BKz4xTz4iIiIiIuj8GPzHiPVUG2z/+AsnpUJSrUtNhWfooVKlpceoZEREREVHPwOAnBjyH98P28guQvR5FuTonD5bv/hCiJTFOPSMiIiIi6jkY/ESRLElo/HQVnGs/AiAr6jR9ByDhO/8F0WiMT+eIiIiIiHoYBj9RItltsL/2d3iOHQqp0w4ZjoT7l0LQ6uLQMyIiIiKinkmM54c///zzyM/Ph16vx6RJk7Bjx454diciZFmGt+wo6v/4ZNjARzdmIhKWLGPgQ0REREQUY3Gb+XnrrbfwyCOP4MUXX8SkSZPw7LPPoqioCEePHkVGRka8utUhst8H34nj8BzaC8+BvSFprAFAEEQYb7gV+pnzIIhxjTmJiIiIiHqkuAU/zzzzDB544AHcf//9AIAXX3wRH3/8Mf75z3/iZz/7Wby61Way2w3PgVJ4DpbCc/gAZFdji21FSyISvvUgNAMGx7CHRERERETUXFyCH4/Hg127dmH58uWBMlEUMXfuXBQXF4e0d7vdcLvdgWur1QoA8Pl88Pl80e9wGJLDDttr/9dqO/XAITB+cwmEBEvc+hopfr8fsizD7/fHuysUYRzb7onj2j1xXLsnjmv3xbGNvvY8Y8cl+Ll06RL8fj8yM5WHemZmZuLIkSMh7Z966ik88cQTIeU7d+6EyWSKWj9bk6E3QVNzMWydZDTBVjAK9uFjgEOHY9yz6JAkCTabDTt27IDIpXvdCse2e+K4dk8c1+6J49p9cWyjz+FwtN7oK10i29vy5cvxyCOPBK6tVivy8vIwfvx4WCyWuPXLZb0E19oPA9eq3D7QDB0B9dBRUPXKgyAIcetbNPj9fpSUlGDChAlQqVTx7g5FEMe2e+K4dk8c1+6J49p9cWyjr2lVWFvEJfhJS0uDSqVCVVWVoryqqgpZWVkh7XU6HXS60OxoarUaanX84jf9qLGQzp2BdtgoaIaOhCopOW59iRVBEKBSqeL6vVN0cGy7J45r98Rx7Z44rt0Xxza62vO9xmXuTavVYty4cdiwYUOgTJIkbNiwAYWFhfHoUoeoc/JgeeC/oJ8yo0cEPkREREREXVncws9HHnkEixcvxvjx4zFx4kQ8++yzcDgcgexvREREREREkRS34Ocb3/gGqqur8atf/QqVlZUYPXo01qxZE5IEgYiIiIiIKBLiuvBw2bJlWLZsWTy7QEREREREPQTz7RERERERUY/A4IeIiIiIiHoEBj9ERERERNQjMPghIiIiIqIegcEPERERERH1CAx+iIiIiIioR2DwQ0REREREPQKDHyIiIiIi6hEY/BARERERUY/A4IeIiIiIiHoEBj9ERERERNQjMPghIiIiIqIegcEPERERERH1COp4d6AjZFkGAFit1jj3pGfx+XxwOBywWq1Qq7vkbx1qAce2e+K4dk8c1+6J49p9cWyjrykmaIoRrqZLjoDNZgMA5OXlxbknRERERER0LbDZbEhMTLxqG0FuS4h0jZEkCRcuXEBCQgIEQYh3d3oMq9WKvLw8nD17FhaLJd7doQji2HZPHNfuiePaPXFcuy+ObfTJsgybzYacnByI4tV39XTJmR9RFJGbmxvvbvRYFouFP7zdFMe2e+K4dk8c1+6J49p9cWyjq7UZnyZMeEBERERERD0Cgx8iIiIiIuoRGPxQm+l0Ojz++OPQ6XTx7gpFGMe2e+K4dk8c1+6J49p9cWyvLV0y4QEREREREVF7ceaHiIiIiIh6BAY/RERERETUIzD4ISIiIiKiHoHBDxERERER9QgMfnqYzZs348Ybb0ROTg4EQcAHH3ygqK+qqsJ9992HnJwcGI1GzJ8/H8ePH1e0mTlzJgRBUPz3ve99T9GmvLwcCxcuhNFoREZGBn784x/D5/NF+/Z6tFiM7d69e3HXXXchLy8PBoMBBQUF+POf/xyL2+uxYvUz26Smpga5ubkQBAH19fVRuiuK5bi+8sorGDlyJPR6PTIyMvDwww9H89Z6tFiNa0lJCebMmYOkpCQkJyejqKgIe/fujfbt9WiRGFsAKC4uxuzZs2EymWCxWDB9+nQ0NjYG6mtra3H33XfDYrEgKSkJS5Ysgd1uj/bt9SgMfnoYh8OBUaNG4fnnnw+pk2UZN998M06ePImVK1diz5496NOnD+bOnQuHw6Fo+8ADD6CioiLw39NPPx2o8/v9WLhwITweD7Zt24Z//etfeOWVV/CrX/0q6vfXk8VibHft2oWMjAy8/vrrOHjwIH7xi19g+fLl+Otf/xr1++upYjGuzS1ZsgQjR46Myr3QFbEa12eeeQa/+MUv8LOf/QwHDx7E+vXrUVRUFNV768liMa52ux3z589H7969sX37dmzZsgUJCQkoKiqC1+uN+j32VJEY2+LiYsyfPx/z5s3Djh07UFJSgmXLlkEUrzyO33333Th48CDWrVuHVatWYfPmzXjwwQdjco89hkw9FgD5/fffD1wfPXpUBiAfOHAgUOb3++X09HT573//e6BsxowZ8g9+8IMW33f16tWyKIpyZWVloOyFF16QLRaL7Ha7I3oPFF60xjachx56SJ41a1Znu0xtEO1x/dvf/ibPmDFD3rBhgwxArquri2DvqSXRGtfa2lrZYDDI69evj0a3qRXRGteSkhIZgFxeXh4o27dvnwxAPn78eETvgcLr6NhOmjRJfuyxx1p830OHDskA5JKSkkDZJ598IguCIJ8/fz6yN9GDceaHAtxuNwBAr9cHykRRhE6nw5YtWxRt33jjDaSlpWH48OFYvnw5nE5noK64uBgjRoxAZmZmoKyoqAhWqxUHDx6M8l1QOJEa23AaGhqQkpIS+U5TqyI5rocOHcKTTz6JV199VfGvkBR7kRrXdevWQZIknD9/HgUFBcjNzcUdd9yBs2fPxuZGSCFS4zp48GCkpqbipZdegsfjQWNjI1566SUUFBQgPz8/JvdCSm0Z24sXL2L79u3IyMjAlClTkJmZiRkzZijGvri4GElJSRg/fnygbO7cuRBFEdu3b4/R3XR//BuOAoYMGYLevXtj+fLlqKurg8fjwe9//3ucO3cOFRUVgXbf/OY38frrr2PTpk1Yvnw5XnvtNdxzzz2B+srKSkXgAyBwXVlZGZubIYVIjW2wbdu24a233uKUfJxEalzdbjfuuusu/OEPf0Dv3r3jcSvUTKTG9eTJk5AkCb/73e/w7LPP4j//+Q9qa2tx/fXXw+PxxOPWerRIjWtCQgI+++wzvP766zAYDDCbzVizZg0++eQTqNXqeNxaj9eWsT158iQA4Ne//jUeeOABrFmzBmPHjsWcOXMCe4MqKyuRkZGheG+1Wo2UlBQ+P0UQf0ooQKPR4L333sOSJUuQkpIClUqFuXPnYsGCBZBlOdCu+YPuiBEjkJ2djTlz5uDEiRPo379/PLpOrYjG2B44cACLFi3C448/jnnz5sXsXuiKSI3r8uXLUVBQcNVAl2InUuMqSRK8Xi+ee+65wM/om2++iaysLGzatIl7f2IsUuPa2NiIJUuWYOrUqXjzzTfh9/vxxz/+EQsXLkRJSQkMBkM8bq9Ha8vYSpIEAPjud7+L+++/HwAwZswYbNiwAf/85z/x1FNPxa3/PQ1nfkhh3LhxKC0tRX19PSoqKrBmzRrU1NSgX79+Lb5m0qRJAICysjIAQFZWFqqqqhRtmq6zsrKi1HNqTSTGtsmhQ4cwZ84cPPjgg3jsscei2m+6ukiM68aNG/HOO+9ArVZDrVZjzpw5AIC0tDQ8/vjj0b8JChGJcc3OzgYADB06NNAmPT0daWlpKC8vj2LvqSWRGNcVK1bg9OnTePnllzFhwgRMnjwZK1aswKlTp7By5cqY3AeFam1sw/08AkBBQUHg5zErKwsXL15U1Pt8PtTW1vL5KYIY/FBYiYmJSE9Px/Hjx7Fz504sWrSoxbalpaUArvxgFxYWYv/+/Yof4HXr1sFisYT80FPsdWZsAeDgwYOYNWsWFi9ejN/+9rfR7i61UWfG9d1338XevXtRWlqK0tJS/OMf/wAAfPHFF0yLHGedGdepU6cCAI4ePRpoU1tbi0uXLqFPnz7R6zS1qjPj6nQ6IYoiBEEItGm6bppdoPhpaWzz8/ORk5Oj+HkEgGPHjgV+HgsLC1FfX49du3YF6jdu3AhJkgJBMEVAPLMtUOzZbDZ5z5498p49e2QA8jPPPCPv2bNHPnPmjCzLsvz222/LmzZtkk+cOCF/8MEHcp8+feRbb7018PqysjL5ySeflHfu3CmfOnVKXrlypdyvXz95+vTpgTY+n08ePny4PG/ePLm0tFRes2aNnJ6eLi9fvjzm99uTxGJs9+/fL6enp8v33HOPXFFREfjv4sWLMb/fniIW4xps06ZNzPYWZbEa10WLFsnDhg2Tt27dKu/fv1++4YYb5KFDh8oejyem99tTxGJcDx8+LOt0Onnp0qXyoUOH5AMHDsj33HOPnJiYKF+4cCHm99xTdHZsZVmW//SnP8kWi0V+55135OPHj8uPPfaYrNfr5bKyskCb+fPny2PGjJG3b98ub9myRR44cKB81113xfReuzsGPz1M00NN8H+LFy+WZVmW//znP8u5ubmyRqORe/fuLT/22GOK9NTl5eXy9OnT5ZSUFFmn08kDBgyQf/zjH8sNDQ2Kzzl9+rS8YMEC2WAwyGlpafKjjz4qe73eWN5qjxOLsX388cfDfkafPn1ifLc9R6x+ZsN9JoOf6InVuDY0NMjf/va35aSkJDklJUW+5ZZbFCmSKbJiNa6ffvqpPHXqVDkxMVFOTk6WZ8+eLRcXF8fyVnuczo5tk6eeekrOzc2VjUajXFhYKH/xxReK+pqaGvmuu+6SzWazbLFY5Pvvv1+22WyxuMUeQ5DlZrvsiIiIiIiIuinu+SEiIiIioh6BwQ8REREREfUIDH6IiIiIiKhHYPBDREREREQ9AoMfIiIiIiLqERj8EBERERFRj8Dgh4iIiIiIegQGP0RERERE1CMw+CEiIiIioh6BwQ8REREREfUIDH6IiIiIiKhHYPBDREREREQ9wv8P4ASimN82WucAAAAASUVORK5CYII=", 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7K2VmZqKwsDBlPg8RERERRZeSwU9n4JOfnw+73c4b2zgRQsDtdqfEd9r5WWprawEARUVFwzwjIiIiIhpsKRf8hEKhcOCTk5Mz3NNJKUIIhEIhWK3WpA9+AMBmswEAamtrkZ+fzyVwRERERCku5RoedNb42O32YZ4JJYPOnxPWhhERERGlvpQLfjqlQmaCBh9/ToiIiIhGjpQNfoiIiIiIiLpj8EMDdt999+HYY48d7mngtNNOw/e+973hngYRERERJSgGPwmkuroad9xxByZMmACr1YqCggLMmzcPTz75JNxu93BPr99WrlwJSZLi1no83ucjIiIiopEh5bq9Jav9+/dj3rx5yMzMxMMPP4wZM2bAYrFg27Zt+Otf/4qSkhJceOGFEV8bCARgMpmGeMbx5/f7YTabh3saRERERJSiUj7zowgFrUH3sP1RhNKned56660wGo3YtGkTLrvsMkydOhXjxo3DRRddhLfffhsXXHBB+FhJkvDkk0/iwgsvhMPhwEMPPQQAePLJJzF+/HiYzWZMnjwZ//73v8OvKS8vhyRJ2LJlS3isubkZkiRh5cqVALoyKitWrMCsWbNgt9tx0kknYdeuXaq5PvLIIygoKEB6ejpuuukmeL3eqJ+rvLwcX/va1wAAWVlZkCQJ119/PYCOZWq33347vve97yE3Nxdnn312r/Ps6XwAoCgKfvjDHyI7OxuFhYW47777+vT9ExEREVHqS/nMT3vIi2/teWrY3v8vE78Np7HnttsNDQ14//338fDDD8PhcEQ8RtuV7L777sMjjzyCRx99FEajEa+//jruuOMOPProo1iwYAGWLl2KG264AaNGjQoHC33105/+FL/73e+Ql5eHb3/727jxxhuxevVqAMBrr72G+++/H48//jhOPvlk/Pvf/8Yf//hHjBs3LuK5SktL8eqrr2LRokXYtWsXnE5neH8dAHjmmWdwyy23hM/fm76c76677sL69euxdu1aXH/99Zg3bx7OPPPMmL4DIiIiIko9KR/8JIO9e/dCCIHJkyerxnNzc8NZldtuuw2/+tWvws9dddVVuOGGG8KPr7zySlx//fW49dZbAQB33XUX1q1bh9/+9rcxBz8PPfQQ5s+fDwD48Y9/jIULF8Lr9cJiseDxxx/HjTfeiJtuugkA8OCDD2L58uVRsz8GgwHZ2dkAgPz8fGRmZqqenzhxIn7961+HH5eXl/c4t97Od/TRR+PnP/95+Nx//vOfsWLFCgY/RERERJT6y96S2YYNG7BlyxZMnz4dPp9P9dysWbNUj3fu3Il58+apxubNm4edO3fG/L5HH310+P8XFRUBAGprawEAu3fvxpw5c1THz507N+b36DRz5sx+vzaS7nMHOubfOXciIiKiVNcQaMNTle/hsSNLUe7lPZAWMz8JYMKECZAkSVdb07mUrPuyrk7RlsdFI8sdca4QIjwWCAQiHtu9eULncjtF6VvtUqy0nyOWeUaibfwgSdKgzZ2IiIgo0fytahm2usoBANtdh/C7cdfDadTfS45UKR/8pBms+MvEbw/r+/cmJycHZ555Jv785z/jO9/5TsyBDQBMnToVq1evxuLFi8Njq1evxrRp0wAAeXl5AICqqiocd9xxAKBqKtBXkyZNwvr161Xvs27duh5f09nBLRQK9Xr+vswzlvMRERERjRQhoeBz18Hw4/aQB+83bcHX8/q/SifVpHzwI0tyrw0HEsETTzyBefPmYdasWbjvvvtw9NFHQ5ZlbNy4EV9++WWvy8PuvvtuXHbZZTjuuOOwYMECvPXWW3jttdewfPlyAB3ZoxNPPBGPPPIIxo4di9raWtx7770xz/PWW2/Ft7/9bcyePRvz5s3Dc889hy+++CJqwwMAKCsrgyRJWLp0Kc477zzYbDakpaVFPLYv84zlfEREREQjRVvIAwGhGvtf02c4P2cmrDK3EwFY85Mwxo8fj88++wwLFizAPffcg2OOOQazZs3Cn/70J/zgBz/AL37xix5ff/HFF+Oxxx7Db3/7W0yfPh1/+ctfsGTJEpx22mnhY/75z38iGAxi5syZ+N73vocHH3ww5nkuWrQI9957L374wx9i5syZOHjwIG655ZYeX1NSUoL7778fP/7xj1FQUIDbb7+9x+N7m2es5yMiIiIaCVqCbt2YK+TFh81fDMNsEpMkuhdXJInW1lZkZGSgpaUFTqdT9ZzX68WBAwcwduxYWK29LzmjvhNCwOVyweFw6FpvJyv+vHQIBoNYv3495syZA6Mx5RPCIwava2ridU1NvK6payiv7TbXQTxc8apuPMfkxKPjb4BRMgzq+w+XnmIDLWZ+iIiIiIhSQFso8tYjDYFWrG3dFfG5kYbBDxERERFRCmiNsOyt01sNm5CEC77ijsEPEREREVEKaA15oj53yFePLV+1wB7JGPwQEREREaWAth4yPwDwVsPGIZpJ4mLwQ0RERESUArSZnzxThurxTvdh7PFUDeWUEg6DHyIiIiKiFNAaUmd+zsw6BllG9V6IIz37w+CHiIiIiCgFtAbVmZ8cUzrOzT5eNbapbR9agq6hnFZCiTn4OXLkCK655hrk5OTAZrNhxowZ2LRpU/h5IQR+9rOfoaioCDabDQsWLMCePXtU52hsbMTVV18Np9OJzMxM3HTTTWhvbx/4pyEiIiIiGqG0y97SDVackTkDJqlrfyEBgUp/01BPLWHEFPw0NTVh3rx5MJlMePfdd7Fjxw787ne/Q1ZWVviYX//61/jjH/+Ip556CuvXr4fD4cDZZ58Nr7er7/jVV1+NL774AsuWLcPSpUvx0Ucf4Zvf/Gb8PhURERER0QiiCAUuzT4/ToMddoMFmUaHarw9yn5AI0FM28z+6le/QmlpKZYsWRIeGzt2bPj/CyHw6KOP4t5778VFF10EAPjXv/6FgoICvPHGG7jiiiuwc+dO/O9//8PGjRsxa9YsAMCf/vQnnHfeefjtb3+L4uLieHwu6sH111+P5uZmvPHGGwCA0047DcceeyweffTRfp8zHucgIiIiov5pC3kgoN7Hx2m0AQDSDTbUBVpUx45UMQU/b775Js4++2x84xvfwKpVq1BSUoJbb70VN998MwDgwIEDqK6uxoIFC8KvycjIwJw5c7B27VpcccUVWLt2LTIzM8OBDwAsWLAAsixj/fr1uOSSS3Tv6/P54PP5wo9bW1sBAMFgEMFgUHVsMBiEECL8J5nccMMNeOaZZwAAJpMJo0ePxrXXXouf/OQnMBpjulR90vn9vPrqqzCZTL1+X0IIfPTRR1i4cCEaGxuRmZkZfq6v50g0nT8nkX6WRpJQKAQhBEKh0HBPheKI1zU18bqmJl7X1DVU17bJ1667D7MJE4LBIByyRfVci9+VUvc9sXyWmO6o9+/fjyeffBJ33XUXfvKTn2Djxo347ne/C7PZjMWLF6O6uhoAUFBQoHpdQUFB+Lnq6mrk5+erJ2E0Ijs7O3yM1i9/+Uvcf//9uvFNmzbB4VCn8SRJgt1uh9vtTrp/QAKBAM4880w8+eST8Pl8eP/993HXXXdBCIEf/OAHqmP9fj/MZnO/3ycYDMLl6ih2s1gsABB+HI0QAoqihI81mUzh5/p6jkTj8/ng9/vx+eefJ13gFk+KoqCtrQ0bNmyALLMPSqrgdU1NvK6pidc1dQ3VtT0oNcFl6roPswoTNm/oqMtvNTbAJXc9t731S+TvSa775J7Ecv8ZU/CjKApmzZqFhx9+GABw3HHHYfv27XjqqaewePHi2GYZg3vuuQd33XVX+HFraytKS0sxa9YsOJ1O1bFerxcVFRWw2+2wWq0QigLhHr4bcsnugNTHH3STyQS73Y5x48YBAKZOnYp33nkH7733HsrLy9Hc3IxZs2bhiSeegMViwf79+3Ho0CH84Ac/wPvvvw9ZlnHKKafg0UcfxZgxYwB0/Lbh7rvvxpIlS2AwGHDjjTfCaDTCaDSGA8evfe1rOOaYY8JL1nw+H372s5/h+eefR21tLUpLS/HjH/8Yp59+Oi644AIAQGlpKQBg8eLFWLJkie4cTU1N+N73voe33noLPp8P8+fPx2OPPYaJEycCAJ5++mnceeedeOGFF3DnnXfi0KFDOPnkk/HPf/4TRUVF8fjq+8RgMMBsNmPChAmwWq1D9r6JJhQKYePGjZg9ezYMBsNwT4fihNc1NfG6piZe19Q1VNdWtO2Go2pX+HGhOQtzxswBAOyu9aK8uS38XI6zAHMK5wzaXIZa56qwvogp+CkqKsK0adNUY1OnTsWrr74KACgsLAQA1NTUqG5ga2pqcOyxx4aPqa2tVZ0jGAyisbEx/Hoti8USziyoJv/VTbx2TJKk8B/hcaPpZ3fpXjtUsn/xB0hp6TG9RpKk8P+32WxoaGgAAKxYsQJOpxPLli0D0PG9nXPOOZg7dy4+/vhjGI1GPPjggzj33HPx+eefw2w24/e//z2eeeYZ/POf/8TUqVPxu9/9Dq+//jpOP/101ft0fl9AR0Czdu1a/PGPf8QxxxyDAwcOoL6+HqNHj8azzz6La665Brt27YLT6YTNZgu/rvs5brjhBuzZswdvvvkmnE4nfvSjH2HhwoXYsWMHTCYTJEmC2+3G7373O/z73/+GLMu45pprcPfdd+O5557r/xceo845R/pZGmkkSYLBYBjx30Oq4XVNTbyuqYnXNXUNxbV1Cb/q3i7DaA+/X4bZoXrOJfwp9XMWy2eJ6VPPmzcPu3btUo3t3r0bZWVlADqaHxQWFmLFihXhYKe1tRXr16/HLbfcAgCYO3cumpubsXnzZsycORMA8MEHH0BRFMyZkzoR6EAJIbBixQq89957+M53voO6ujo4HA78/e9/Dy93e/bZZ6EoCv7+97+Hf6CXLFmCzMxMrFy5EmeddRYeffRR3HPPPbj00ksBAE899RTee++9qO+7e/duvPTSS1i2bFm4dqszEyWEQHZ2NgAgPz9fVfPTXWfQs3r1apx00kkAgOeeew6lpaV444038I1vfANAx/K7p556CuPHjwcA3H777XjggQcG8rURERERjUjaJgZOoz38/9MNth6PHUliCn7uvPNOnHTSSXj44Ydx2WWXYcOGDfjrX/+Kv/71rwA6otrvfe97ePDBBzFx4kSMHTsW/+///T8UFxfj4osvBtCRKTrnnHNw880346mnnkIgEMDtt9+OK664gp3eACxduhRpaWkIBAJQFAVXXXUV7rvvPtx2222YMWOGqs5n69at2Lt3L9LT1Zklr9eLffv2oaWlBVVVVaqg0mg0YtasWVHrW7Zs2QKDwYD58+f3+zPs3LkTRqNR9b45OTmYPHkydu7cGR6z2+3hwAfoyCxqs4JERERE1LvWkFv12Nkt4EkzqJf2tzP46ZvZs2fj9ddfxz333IMHHngAY8eOxaOPPoqrr746fMwPf/hDuFwufPOb30RzczNOPvlk/O9//1PVUzz33HO4/fbbccYZZ0CWZSxatAh//OMf4/epktjXvvY1PPnkkzCbzSguLlal8bTNHdrb2zFz5syIy8Ty8vL69f42m633g+Kke8MEoCN4HslNB4iIiIj6qzWoyfwYesr8cJ+fPjv//PNx/vnnR31ekiQ88MADPS5fys7Oxn/+859Y37pfJLsD2b/4w5C8V7T3j4XD4cCECRP6dOzxxx+PF198Efn5+brGD52Kioqwfv16nHrqqQA66oQ2b96M448/PuLxM2bMgKIoWLVqlapleafOgKWnTnpTp05FMBjE+vXrw8veGhoasGvXLl3NGBERERENnC7zY4ye+XGFvFCEAlkaeZ0FU/4TS7IMOS192P70tdNbf1x99dXIzc3FRRddhI8//hgHDhzAypUr8d3vfheHDx8GANxxxx145JFH8MYbb+DLL7/Erbfeiubm5qjnHDNmDBYvXowbb7wRb7zxRvicL730EgBg9OjRkCQJS5cuRV1dHdrb23XnmDhxIi666CLcfPPN+OSTT7B161Zcc801KCkpCW9+S0RERETxo6v56Zbt0WZ+BARcig8jUcoHP6nMbrfjo48+wujRo3HppZdi6tSpuOmmm+D1esOZoO9///u49tprsXjxYsydOxfp6ekRN5Lt7sknn8TXv/513HrrrZgyZQpuvvnmcP/04uJi3Hffffjxj3+MgoIC3H777RHPsWTJEsycORPnn38+5s6dCyEE3nnnHd1SNyIiIiIaOO2yt/RuDQ+cBn1ZQ/sIXfomiSQssmhtbUVGRgZaWloi7vNz4MABjB07dkTv2zIYhBBwuVxwONTtEpMZf146dC5TnDNnTkq1vhzpeF1TE69rauJ1TV1DcW0VoeCaLx+DQNdt/S/HXoMx1vzw48Vf/gl+EQg/vr/sCkyyp0azsZ5iAy1mfoiIiIiIklh7yKcKfAD9Urd0I9tdAwx+iIiIiIiSmrbZAaBf6pauaXowUju+MfghIiIiIkpi2iyOXbbAJKuX2HGj0w4MfoiIiIiIklhrUJ350S5xA7jRaScGP0RERERESaxV1+barjuGG512SNngR1GU4Z4CJQH+nBAREVGy02V+IrS21o6N1MxPyvVSNJvNkGUZlZWVyMvLg9lsTpm2zMNNCAGfzweDwZD036kQAn6/H3V1dZBlGWazebinRERERNQvusxPn5a9jczMT8oFP7IsY+zYsaiqqkJlZeVwTyeldAYMqRRQ2u12jB49GrKcsklQIiIiSnHa5gWRNjVlw4MOKRf8AB3Zn9GjRyMYDCIUCg33dFJGMBjE559/jgkTJqTEBmwGgwFGozFlAjkiIiIamVqD6kAmwxip5ked+dG+ZqRI/jvYKCRJgslkgslkGu6ppIxgMAghBKxWa0oEP0RERESpoCXUe81Pmq7mxwshxIj7JTDX+hARERERJTFtw4OI3d40dUAKFHgU/6DOKxEx+CEiIiIiSlKKUPQ1P31oeACMzLofBj9EREREREnKrfghIFRjkTI/VskEk6QuWxiJe/0w+CEiIiIiSlLaJW9A5G5vkiRFaHfNzA8RERERESUJ7R4/VtkMkxy5MRXbXTP4ISIiIqIRSAiBKn8TGgJtwz2VAWkNaZsd6LM+nbSZn5EY/LBfMRERERGNKIpQ8FTV+/i4ZQeMkgE3F52JUzOmDfe0+kW7X48zwh4/nXTBT5A1P0REREREKe1/TZ/h45YdAICgCOHVurXDPKP+iyXzo32ONT9ERERERCms0teIF2pXq8ZqAy0ICWWYZjQwbZrMT6QNTjtpNzrV1guNBAx+iIiIiGhEUISCJ6veQ0AEdc8laxZEG8BoNzNVP6ft9sZlb0REREREKWlp42bs9VRFfC5Z97zRL3uLXvOjzQox+CEiIiIiSkGHfPV4uW5N1OeTtfOZdtlbRo8ND9jqmsEPEREREaW0oAjhicr/IShCUY/Rdk1LFi0xNDyIlPkRQgzKvBIVgx8iIiIiSmn/rd+Acm9tj8ckY82PEEKXvemp4UG6ptV1QAThFYFBmVuiYvBDRERERCnLFfLivw0bVWMl5hxMt49WjSVjzY9b8em61PW0z0+kwGik1f0w+CEiIiKilLXPW6Pq7iZBwi3FZyPblKY6LhnrXyK1qu5p2ZtNNkPW3P5ra4ZSHYMfIiIiIkpZzUGX6vEoSw7G2wqRplkClozBjzZwMUsmWGRT1OMlSdJ97mRc7jcQDH6IiIiIKGW1aIKfTKMDgH4JWDIGP9o21z11euvkNGo/N5e9ERERERGlhKa+Bj9JuPxLt8FpD0veOqVCxmsgGPwQERERUcpqCUbOjqTChp/az6bN6kSSCp97IBj8EBEREVHKagq2qx5nfZX50S//Sr4MiHbOPTU76DTSNzpl8ENEREREKUuX+TFEXvbmVnw9boKaiLQbszoNvdf8aPf6YfBDRERERJQitN3eump+rLpjk20JmLbhQV+WvTHzQ0RERESUggJKEG7FpxrrDH4cEYKfZAsEtPNN70fmJ9kCvoFi8ENEREREKUnb6Q0AMr9qeGCUDLDLFtVzyRYINAbU9UyZfWh1rW/xnVyfeaAY/BARERFRSmoJqYMfk2RUBTzpmmVi2hqaROZXgrplb7kmZ6+vS4UW3wPB4IeIiIiIUlJzhDbXkiSFHyfzRqcNwTbdWI4pvdfXaQM+vwjArwTjNq9Ex+CHiIiIiFJSS5RmB52SufNZfaBV9dhhsMImm3t9nXaTUyC5PvdAMfghIiIiopSkzfzog58kzvwE1JmfXGPvWR8AcMgWSJBUY8n0uQeKwQ8RERERpSRdm2tNN7RkDn7qNcFPTh/qfQBAlmRdp7tk+twDxeCHiIiIiFKStttbRm+ZnyQq/tcue8vtQ71PJ+3St2TrcjcQDH6IiIiIKCX1WvNjTN62z9rMTyzBjzboY/BDRERERJTkmjWtoLNSqOanPqjO/PR12RuQ3J97oBj8EBEREVHKEULoMj/aZW/a5V/JEgQoQtFtcNrXhgdApC53zPwQERERESUtl+JDUIRUY5nGnhseeBU/Akmw501byIOAUM+zLxucdkrmWqeBYvBDRERERClH2+kNADI03d6cmiAASI4siLbexyDJusCuJ/qGBwx+iIiIiIiSljb4cRisMMlG3Zh2z5tkCAS0nd6yjemQpb7f1idzo4eBYvBDRERERClHW++jbXYAdGRM7AaLaiwZAoGBdHoDkrfWKR4Y/BARERFRytHt8WPQBz9AcnY+qw9qNziNLfjRt7pO/M8cLwx+iIiIiCjltATVba6j1cQkY/DToFn2lhdDswNA/5k9ih8BTXOIVMXgh4iIiIhSTnOo5zbXnZIx+NEue8uJoc01oG91DQCuJFjuFw8MfoiIiIgo5WgbHmRGDX6Sb88bbcODWNpcA0BaxC53iR/0xQODHyIiIiICAGx3VeD3h9/EkuoP0J4EQUBP9MFPlGVv2s5nCb7njU8J6AKVWGt+DJIMhyboa9Bkk1JVTMHPfffdB0mSVH+mTJkSft7r9eK2225DTk4O0tLSsGjRItTU1KjOUVFRgYULF8JutyM/Px933303gsHE30yKiIiIKJVVeOvwq0OvY2PbXrzftAUv1H4y3FMakGZdzU/kzI92r59Ez4BEClJi7fYGAEXmLNXjSn9jv+eUTIy9H6I2ffp0LF++vOsExq5T3HnnnXj77bfx8ssvIyMjA7fffjsuvfRSrF69GgAQCoWwcOFCFBYWYs2aNaiqqsJ1110Hk8mEhx9+OA4fh4iIiIj648W61Qh2K3rf0n5gGGczMAER0nUwixb8aJeAJXrwo+30lmawwSqbYz5PkTkLez1V4cdVvqYBzy0ZxBz8GI1GFBYW6sZbWlrwj3/8A//5z39w+umnAwCWLFmCqVOnYt26dTjxxBPx/vvvY8eOHVi+fDkKCgpw7LHH4he/+AV+9KMf4b777oPZHPuFIyIiIqKB+dJ9BJ+271eNJUPtSzStmqwP0FOr6+Ta80Zf7xN71geIlPkZGcFPzDU/e/bsQXFxMcaNG4err74aFRUVAIDNmzcjEAhgwYIF4WOnTJmC0aNHY+3atQCAtWvXYsaMGSgoKAgfc/bZZ6O1tRVffPHFQD8LEREREcVICIHnaz/WjftFAAElOUsTtPU+BklGmmYz007J1u1toJ3eOhWbs1WPq0ZI8BNT5mfOnDl4+umnMXnyZFRVVeH+++/HKaecgu3bt6O6uhpmsxmZmZmq1xQUFKC6uhoAUF1drQp8Op/vfC4an88Hn88Xftza2hHxBoNB1gsNoVAoBCEEQqGR0Qd+JOG1TU28rqmJ1zU1Ded1/az9AHa5j0R8rtnfjixj2hDPaOAafK0QQoQfOw02KCEFChTdsXaYVcd6Q354/F6Y5JgXSEUU72tb52tWzTfb4OjX/XC+wak6T2OgDW0+N2yG5FuJFcvnj+mqnnvuueH/f/TRR2POnDkoKyvDSy+9BJtN3zIvXn75y1/i/vvv141v2rQJDkfkFCbFn6IoaGtrw4YNGyDLbBSYSnhtUxOva2ridU1Nw3VdFQgsMW2AS3JFfP6TT9cjXyRf8LNFPgKXseszpQsD1q9fH/FYDwJwmdWff9XG1UiHfi+c/oj3td1h3AeX3DXf5pZarD8Q+bP1JAgFbrMbAl0B0HubV6FIxNY2OxG4XJF/fiMZUEibmZmJSZMmYe/evTjzzDPh9/vR3Nysyv7U1NSEa4QKCwuxYcMG1Tk6u8FFqiPqdM899+Cuu+4KP25tbUVpaSlmzZoFpzP5LlCyCoVC2LhxI2bPng2DwTDc06E44rVNTbyuqYnXNTUN13Vd3fol3NUCDkT+ZfKEUZMx1T5qyOYTL5UN6+FoOBR+PMExGnNK5kQ8VhEK/rHnU1UQMHHaNJRZ8uIyl3hf2xcPfAlHwB9+PKfoOMxJn9ivc716YBdqAy3hx3mFpZjjnNLDKxJT56qwvhhQ8NPe3o59+/bh2muvxcyZM2EymbBixQosWrQIALBr1y5UVFRg7ty5AIC5c+fioYceQm1tLfLz8wEAy5Ytg9PpxLRp06K+j8VigcWiX6dpNBpV3eZo8EmSBIPBwO89BfHapiZe19TE65qahvq6BkQIrzauhyRJUY9xI5CUP2dtwqv6XFnm9B4/R5rRpuoO54Y/rp87XtdWEQqaQu2qz5Zvzez3eYst2agLdgUONaHWpLzescw5ptzbD37wA6xatQrl5eVYs2YNLrnkEhgMBlx55ZXIyMjATTfdhLvuugsffvghNm/ejBtuuAFz587FiSeeCAA466yzMG3aNFx77bXYunUr3nvvPdx777247bbbIgY3RERERDQ4Pmjahrpuv/UHAJOkvolM9OL/aPQbnPZcJqHv+JaYne5aQm5VO3Kg/93egI7gp7uqEbDXT0yh3eHDh3HllVeioaEBeXl5OPnkk7Fu3Trk5XWkBf/whz9AlmUsWrQIPp8PZ599Np544onw6w0GA5YuXYpbbrkFc+fOhcPhwOLFi/HAAw/E91MRERERUVQ+JYDX6tepxibaimGXzdjqKg+PtSdoENAb/Qan9h6PTzfYUIWubmdtwcQM+rSd3gySjAxDz5+tJ8WadtcjYa+fmIKfF154ocfnrVYrHn/8cTz++ONRjykrK8M777wTy9sSERERURx94T6E1pA6QLgy/2R80LxNNeZK2uBHnfmJtsdPJ227a+0GqYmiIUKba1nqfxOFSO2uFaEM6JyJLnU/GRERERFFpP0N/zhrIabaRyEtyfa8iUQIgRZN8JPV27I3Y3J8bv0GpwNr/FVkUWd+/CKIhmD7gM6Z6Bj8EBEREY0wtZpan+KvboKTpfalJx7FD79Q7/vSW81PWpJ8bn3w0/96HwDINDhgldX7+lT5Urvuh8EPERER0QijbXSQb8oAoA8CEnX5V0+0S94AIKOXmh9nkmS8tDU/OQPM/EiSpFv6VulP7bofBj9EREREI0ydJoOQFw5+tLUviZkB6UmLppbJJpthkU09vkZb85OowU9DUB38DDTzAwBF2qYHDH6IiIiIKFUIIVDrVwc/BeZomZ/kC36atM0OelnyBkQKfhLzc2szP7nGgQc/xZq6n8oUb3fN4IeIiIhoBGkNueEXAdVY3lfLp/Q1Px4IIYZsbvEQa7MDIELwk4Ctrr2KX7cMcaANDwB9x7dK1vwQERERUaqo1Sx5kyEj25gGQL/sTUDAo/iHbG7xoM389NbsANB3e/OLAHxKIMrRw0Ob9QGAnEFY9tYYbIc3ya55LBj8EBEREY0g2nqfXFPXXjHaZW9A4ta/RKPN/PRn2RuQeEv+tHv8pBtsvdYy9UWROQsSJNVYtb95wOdNVAx+iIiIiEaQWr+m09tX9T4AYJVMMEoG1fOJFgT0pjmobniQaei50xsA2GWzLgBItKBP2+Y6HlkfADDLRt25UrnjG4MfIiIiohFE2+a6s9Mb0NH6WL/nTWIFAb3pT+ZHlmRd9qc1wT63vtPbwOt9OmmXvqVy3Q+DHyIiIqIRRLvBaZ7mJjrZO77pa356z/wA+qVvifa5dZ3e4hj8lGiaHqRyu2sGP0REREQjiLbmJ79b5gdI/CCgJyGh6DJVfWl4AADpRk3GK8E6vmkzdvFoc92paAS1u2bwQ0RERDRCKELR1Y7km1Mn89McdEFA3Zq7L62uAX2nu0Ra7vel+wi+dB9RjQ3msrcqf1PStTjvKwY/RERERD0QQsAV8iIoQsM9lQFrCroQEopqTJv5SeQgoDc1mi5lZskIZx8aHgCRNjpNjM/tV4L4S9X7qjGzZMR0R2nc3kO7149PCaAx2B638ycS43BPgIiIiCjRNAXbsd1Vgc9dB7HdVYHmoAu5Jid+MOoilFnzhnt6/aat94kUHCRz5kdbq1JgzoQkSVGOVou0wWsieKV+Lao1n+sbeSdFbEveX9nGNFhkk2pvo0p/Y9w6yiUSBj9EREREX1nbuguv16/HIV+97rn6QCveaFiPO0rOH4aZxYe2zXWeKUMXHCRqENAX1YFm1WPtcq6eJGLmZ5+nGksbNqnGxlsLcV728XF9H0mSUGTOQrmnBtlNbmQ2e9DauA4+azVEMAAEg5DSnTBPPwaSwdD7CRMYgx8iIiIiAAe9dfjjkbd7PKbcWzdEsxkc2mYHeWZ93Yh22VsyZX60y96672HUG13wM8wNDwIihL9Uva+qYTJIMr5dfHZ4U9p4mtQkMPudLSg93AwAcBr3oU2T+TGOHov0G26FIbPvQWWiYc0PEREREYDN7ft6PaYh0JbUheDaZW/aeh8guZe9VWuCn5gyP8bEyvz8t36DLgN5ae6JGGXJiev7KG2taH/xXzh5yTvhwAcAAiKoOzZYcQAtv3sAgT1fxnUOQ4nBDxERERGAI5qNHa2yGUc7xqjGAiKI1pB7CGcVX/oNTiNlfrTBz/Av/+oLRSi6zM9Alr21hjxQNM0hhsohXz3eaFivGiuz5OHCnNlxew+hKPB8+B6aHvoJvOs+gllSL2cLKJEbfCjtbWh98vfwfPheUv4igMEPEREREYDDvgbV46vyT8GPSi+GBHVNjHazyWRS59cse4uQ+dEGAR7FnxSd7pqCLvg12YpIma1oso1pqsdBEULLMAW6bzdsVnXlkyDhW8VnwSjFr97G9frzcL35MoSvI7NnkrqqYbxWI+oyLZALi2EsHQPZqf4ehVDgevNltP/rLxDe5MkMAqz5ISIiIkJIKLqNHUdZciBLMnJM6aq9ceoDbRhvKxzqKQ5YQIR07YsLItTEaBseAIAr5EVGH/fLGS6R2lxrA5qeZBkdMEoGVaBX529FVgzniJc9nirV44U5MzHWWhC38wcO7of3kw9VYybJgIBJxoaZZdg0sxQhowEzxt+AInMWFLcb7c/9Hf4dn6te49uyCaGqSqTfeBsM+fGb32Bi5oeIiIhGvBp/sy67UWLuqK3QLg1rCKqzJ8miIdCq2wA00rI3R4Tgpy0J6n4G0uYaAGRJ1m0cqq2RGgp+Jaj7LCekT4jb+YUQcL/xom7cOmsu/rP4FKyfMwYhY0eGqTnoAgDIdjvSb7od9nMuBDSZ0GBNJXyfrteeLmEx+CEiIqIR75BmyZvTYIfzqwJ47V4nybrsrVbT6c0uWyIGOkbJAJtsVo0Nd/F/XwykzXUnbTA4HMHPEX+DLkgdZcmN2/n9WzYhUK5u7uG44OtIv+b/YMlSv09jt591SZZhP/tCOP/vO5BtXXtDmSdPh+2s5Gn/zuCHiIiIRrwjfnXw072jVq5Rk/lJ1uBHt8ePPuvTKRnbXWuXvRWYM2M+h7ZGSPudDYUKr7rDW74pQxeM9pcIBOBe+qpqzJCTB+spZwAAsk3qJX7aZZIAYJ5+NDLuuhfGohIYsnORdu3NkOTkCSlY80NEREQjnrbZQffgJ1UyP9pObz3tgZNmsKqOdyVB8KNtc13Yn+BH850Mx7XWtrcujWPWx/vRcoQa1ee3X/B1SCYTAOjqmxq/WvamZcjNR8YdP4HS0gzZMfQ1UQORPGEaERER0SDRBj+l3TM/uuAnOWt+dBuc9tAJTdvuOtGXvQ20zXWnRKj5qdAEP6Ot8Ql+lLZWuJepN/E1jZ8E89HHhx9rG0Q09hD8SRZL0jQ56I7BDxEREY1oIaHoCsxLVMGP+oa4NeSGX9FvAJnotEu4emoDrW13negNDwba5jraa+oDraqW00NBF/xY8uJyXve7b4TbWndyXHSZqimENvPTFCXzk8wY/BAREdGIVh2h01tPy94AoCGYfEvfdJkfc081P9qNThM7+NG3uTbF1Oa6U74m0BUQQ1rj1Rp0o0UTcMRj2Vuw6gh86z5RjVlmzYWxdIxqLMukbmfeFKHmJ9kx+CEiIqIRTbvkzWmwqzIfNtms64qWbEvfvIofrZoNO2PJ/LQn+LI3beauMMY2153SDTZYZJNqTBs0DiZt1sckGftVu6TlfvNliO6bpppMsC+8VHdctlEd6DcF26EMceZrsDH4ISIiohFN2+kt0m/aczU3hcnW8S1S4X5P3d60wV6iZ360ba77GzBIkqSrhdI2ihhM2mYHJZZsGKSB3a4HD5XD/+V21Zjt9HNhyNTXRGVpNrINCSXh671ixeCHiIiIRjR9p7ds3TE5mkBhKLMB8aCt93Ea7LoMR3fpuoYHiR38xKPNdSddu+shDH709T4DX/LmWf6O6rGc5oTta2dHPDbT6ICk2cQ01ep+GPwQERHRiKbd4DTShpLajm/JlvnR3sD31OYaiFTzk9i//a+KQ5vrTtq6n1r/0AW6h7zxbXMdrKmC7/PPVGPW+QsgWSwRjzdIMjKMdtVYpL1+khmDHyIiIhqxgiKEKn+jaqwkQuZH3+46uYOfnpa8AZFqfrwQQsR9XvGgCAW1cWhz3UnbCGKolr0pQtEF4gPN/Hg/+B+ArusmWW2wzjutx9do634aAwx+iIiIiFJCjb9F18o40m/btcve6oPJtexNu0yvtzbQ2sxPUITgFYG4zyseIrW5LjBl9vt8+pqfobnWtYEW+DXf8Whr/9tch5oa4Nu0VjVmO/l0yDZ7lFd00Nb9pFrHNwY/RERENGJp630yjA7djT+gb3jQGEiuLlh1/oFlfoDEbXoQqc219gY+FtrvpinYjsAQ7OukrfdJN9iQYeg5UOmJ98P3IZRuHd6MJlhPPaPX12WbNBudMvghIiIiSg2Hfdoai5yIx2n3+gmIYFJ1wdJlfnqp+bHJZl3he6LW/cSrzXWnSFmxocj+VESo9+nv51Da2+Bd95FqzHLiyZDTew56gQgbnXLZGxEREVFqOKxpcz3KHDn4yTI6IGtum+qSpO6nPeSFW/Gpxnpb9iZJUsS6n0QUrzbXnewGi67V91B0fNO2uR7IkjfvR8shAl1L6CRZjtrhTUsX/KRYtzfjcE+AiIiIaLgc9mmbHUQOfmRJRo4pXVX83hBoxQRb4aDOLx60G7JKkJCtyWRFkmawqjZGTdR21/Fsc90p35SBA90+75BkfuLU5lrxeuD5+APVmPn4OTBk9+18fVn29ptD/0WG0Y5SSw5GWXIw0VYEq2zu13yHGoMfIiIiGpEidXobFSX4ATo6vnUPfpKl45v25jXDaIdJMvT6umRpd61tcz2QTm+d8kxOHPDWhB8PdubHpwR0QVx/21z71qyC8Kqvle2Mc/v8+mxN5qc95EFACcIkG7967MWn7ftUx/xm3OIe/+4kEi57IyIiohGp2t+s6/TW0w2ctu6nPpgkwY+mZkO7rCmatCRY9hapzfVAl70B+poo7Sax8XbY1wDRvSU1pIib7fYmVFsD9/tLVWPmGcfBWFjc53Nogx9AHUBrm4QYJDku3/lQYfBDREREI9IRzU1cZpROb51yjepi8YYhaoE8UM2amo2+Bj/pmu8iEZe9xbvNdSdtTdRgZ/m09T755oyYl5EJvw9tTz8B4VNfJ/uC82I6j002wyyZVGPd6360TUKKzdkw9iGTmCgY/BAREdGIpN1QsrdlO9rMz1Dt/zJQ2n1asvvYBlrf8CDxlr3Fu811J22768Fe9jbQeh8hBNpf+jeCVUdU45bZJ8E4emxM55Ikqce6n1j/3iQaBj9EREQ0Ih3Rdnrr5YYzVxP8NCRpzU+WqW+ZH23Hs0Rc9hbvNtedtBudtoc88Cj+AZ83Gm3mJ9Z6H9/aj+DbvE41ZiwsQdrXr+7XfHQbnQa6Bz8Dm+twY/BDREREI5K2dmGUuecai1xNNqAt5IFPCUQ5OnFo92mJVNMRib7hQeIFP/Fuc90pP8ImsINZ96Pd42e0pe9troMVB+B67XnVmGSxIv2GWyGZLf2aT7Z2U9+vAmghhC7zE21vrETF4IeIiIhGnI5Ob+qsQW+/wdYuewOSI/uj3ael7zU/6mVvibip62C0uQYAk2xEpib7UTdIS99agi5VS3EAGG3tWzZFaW9D29NPQYTUdU9pV94AQ35Bv+eUZdJkfr4KflpDbt3yR2Z+iIiIiBJcpE5vJb1017LJZt1SsETv+BYQId2NdV9rYrQNDxIx8zMYba47aZe+DVbdj7bexywZUdDLJrRCCPi2bELzr36OUJM6E2ObfyYsx8wc0Jy02cHO4CfSXLX1UYmO+/wQERHRiKPNGGQaHbrAJpJcYzpc3YIA7QaiiUbb6Q3oe82PdtmbO+SDIhTIUmL87nyw2lx3yjdlYI+nMvy4zj8411pbQzPKktvjd6y0NKH9lefg375F95xp7ATYL1g04Dlpg5/Odum6paK9zDURMfghIiKiEadBk7HR1vNEk2Ny4qCvLvw40Tc61db7mCQj0uTegzxAv8+PgEB7yAen0RblFUOryt88KG2uO+WbnZBDCo79/Agm7q1DobIDLdnrIVmskGw2SBYbjKPHwDLrREimyG2p20Ne7Gw/hBZEz5pp632i1dAIRYFv/SdwvfmybhNTAJAzspC++FuQDAO/vdcujWwKuiCEiBD8xL4X0XBj8ENEREQjjrZWJ8eor+eJJE/X8S2xMz/aep9Mo6PP3dC0y94AwKV44URiBD+ftu9XPc40OuLS5rpT2YF6LH59A7KaOgINk+yCbuXbuo/gWf4O7Bd+HeajZ6q+24ZAG+4/+BJq/c1wm90odY/HDOcY3fvs9VSrHkeq9wkc2AvXGy8iWHEg4lzN049B2jeugZwRn2V/2lbXARFEu+KN0OY6uep9AAY/RERENAJp2z9rb/aiydFkiBI986P7nH1sdgB0FP2bJRP8oqujXVvQE9e6moH4TBP8HJ82Li5trkMNdXC98SKKP98A2deVYQmKUOTjG+vR9vRTMI2fBMclV8BYMhoA8M/qD8JNEgQEXmtYjxnOMRBeLxR3O+T0DLRKfl3L9Um2kq5zNzXCvfRV+D5dH/G95bR0OC69CuZjZ8Xls3fKiBBENgTaIrS5Tq5ObwCDHyIiIurFZ+0HsLFtLybZinBqxrSkW+MfiTbzo93DJ5pk2+tHu8GptotXb9INVjQEu4KfRGl60B7y4ku3ekPP49PGDfi83g2r4Xr5WYhgACbJoHpOCNFjzVNg3240//YXMJWNRZ3kRZmnAiUGCYoswdTmRpbvC9QpqyD5O/YLkgxGNBZlY15mKw6XZKAuLx3ZHoES/2F4GrciVFsN3+Z1EIHI7dQtM0+E45IrIDv6HtD2lUkywGmwq5pl7PVUw6vZ6yjZOr0BDH6IiIioB3s8Vfj1odcBAB82b8N+bw1uKDg9rr9lHg6Nur1v+hn8BNsSqgmAli74iSHzA3Q0PeheH5Uo7a63th+AgAg/NklGHOUYPaBzBo9UwPXCMxBfdQE0dgt+qgvSsXVGCa7JOQkFihXC60Vg3y4EDuzVnEXAX74Prb4GjP0qUyQAKEoIsmyA22wKN9YQoSCUA/swJ+jGnI0dr7YZzHCZ9/U4T0NeARyXXAHz1BkD+ry9yTalqYKfba6DquftsiWmTGKiYPBDREREUa1t3aV6vKxpK9IMVlyWN2+YZjRwQoh+LwfLMaqXvQVFCC0hd8xBxVBpCvRvj59O6UYb4Ot6nCiZn0/b1bUvRzlKYZFN/T6fEAKu/74cDnw6BR02rJhbhh1TCyFkCVUl0zDGOTn8Gv/nm+H+78uqdtNNwXaEoiyR8yh+VVdBjyaTYpMjN04AAMlqg/3sC2E9+WuQjIN/C59tTEM5asOPt7sqVM+PsuQk5S9BGPwQERFRVOXeWt3Y6/Xr4ZCtWJgzsL1EhktbyIOApktYpA1MI8k02mGQZNUeQfWBtoQNfho1Xe2yY2wIoG13nQjBT1CEsEUT/ByfNn5A5wzs2IbAnp2qMcvME7Fqtg1fKDXhsbpuDS4kSYLlmFkwTzsang/fh2f5O/D53GgJqvdV6s4d8gFfxWiKUBBQ1D+H1gjBjyTJsMw9FfZzL4Kc1ref03jQ/ky7FZ/qcTIueQMY/BAREVEUQgjdpoadnq1dBYfBgtMyjxriWQ2cNusjQUJmH4MCWZKRY0xXbXjZEGjDRFtRXOcYL9pub9l9DPI6adtdtyfAsrfd7krdjfhxaWP7fT4RCsL15kuqMTkjC2mXXYvM+pVAS1fwU+vXb3Qqmcywn3U+zHNPwT8/eQoNbbUwBBUYQwqMIQEpFEILFASz0+FKs+I7U67AqMxR+HzXaqzf+jZGHWlGYU0rjApgkU2QM7JgyM2DIScPcm4+zEcfD2PB0P989dY5b1QSNjsAGPwQERFRFI3BdtWGnlp/rVoGu8GCE9InDuGsBk7bpCDL6IAhhpqdHJM6+EnUjU49il9XoN7XIK+Tdk+gtiHK/Bzw1uCf1R/AE/Lj8vx5mJ0+IfzcZk2XtzHW/D5n7iLxrvkIoVp1u2nH+ZdCMluQb85Qjdf1cK1XBsvx4RgTgK5ubYty52Jl83YcbK6Cw9HRZnyr3YXR6U58NsqC1Y6OJg2GYAgzUYCZU66EZOr/8r146i1QTtbMT2JW5xEREdGwO+itUz2WoF7fLyDwpyPvoEJzXKLTbnAaazZEuyFqonZ8025wCvT+23ytdOPwLHv7W9Vy7PVU4Yi/AX84/BZ2ug+Hn9Pu7zOQLm+K2w3P//6rGjOOKoP5+DkAgHyTOvip9jdFPE9r0I3naz9RjRWas3BRzmwcbS9TjX/21ZK97p8pZDRgbNGUhAl8gN5/VpKxzTUwwODnkUcegSRJ+N73vhce83q9uO2225CTk4O0tDQsWrQINTU1qtdVVFRg4cKFsNvtyM/Px913341gMAgiIiJKHAd96qBmsr0YF+TMVo0FRQjvNH46lNMaMH2nt9jqdbQd33rKBgwn7ZI3u2yJWFPSk+FY9uZTAjjg7bp3FBD485F30Br0oMrfpAtABhL8eJa/DcWt/p4cF10GSe64RS42Z6ueqwu0wqfoW0+vad2lW4p3Y+HpMMlGHOsYoxrf5a5Ejb9Zt2fONPuo/n6MQdHT3wunwQ6n0T6Es4mffgc/GzduxF/+8hccffTRqvE777wTb731Fl5++WWsWrUKlZWVuPTSS8PPh0IhLFy4EH6/H2vWrMEzzzyDp59+Gj/72c/6/ymIiIgo7rTNDsosebgy72ScmjFNNa4NkhKdNvMT65KpHE1b7ERd9qZrdtDHjVy7S9cEP9qAajBoa7I6x56ofBeb2tRtoDOMDoy15vfrfUL1tfB+tEI1Zjn6eJgmTA4/LraoN3QVEKiKkP3R/l2ZlT4BMxwdGZ+p9lEwdLvlVqDglfq1quPNkgnjbAX9+hyDpaeMaLIueQP6Gfy0t7fj6quvxt/+9jdkZXX9ULS0tOAf//gHfv/73+P000/HzJkzsWTJEqxZswbr1q0DALz//vvYsWMHnn32WRx77LE499xz8Ytf/AKPP/44/H5/tLckIiKiIaZtdlBmzYckSThFE/xU+ZsghECy0C5T0wYzvcmLoQ5kODVrApVY630AoECz7Ks15B70pW/aeXfa6irHq5qg4fi0cf3eY8m99FWIUNfKI8lggP38RapjrLIZeZrv4LCvAVpH/I2qx1NsXXU/NtmMUUqm6vnVLV+qHk+2F6v2FUoEDtkCkxS5PcAoS3bE8WTQr4YHt912GxYuXIgFCxbgwQcfDI9v3rwZgUAACxYsCI9NmTIFo0ePxtq1a3HiiSdi7dq1mDFjBgoKuqLbs88+G7fccgu++OILHHfccbr38/l88Pm6UomtrR3/yASDQS6XG0KhUAhCCIRCkXvXU/LitU1NvK6paaiuq1cJoNrXpNpIssSYhWAwiHzZqQp2vCE/6n0tCdvuWave36aaf6Zsj+l+Ikuyq17vCnnR4mtX7d8Sq8G4rnW+VtU8M2L8nACQI6dBhqzat+aQu25Qu9vVeVuiBtPekPoX5cfayvp1Lxg8sBe+LZtUY+aTToPIytGdr9iUhVp/c/hxhacOQUdXkw8hBA5561VzLjRmhM8TCoUwTslGnagCvvr71P3vFQBMthQn5D1tpsGuau7RqfirfwsSRSxziTn4eeGFF/Dpp59i48aNuueqq6thNpuRmZmpGi8oKEB1dXX4mO6BT+fznc9F8stf/hL333+/bnzTpk1wOGL/LQb1j6IoaGtrw4YNGyDL7JWRSnhtUxOva2oaqut6RGpBu6lr+ZEECZVb96EO5VAg4DN7EUTXDfHyTz/BaJEV6VQJRUCgwlyFELr26Tm88wAgGnt4lVoQClxmN9DtBnbF5k9QIPrfcWwwrusO4x645K4sSmtrPdYfXB/zecymEOqlrvN8uH0dGpXiuMwxko3yQbiMvS+vM0CG+4tqrEeMyy6FgvylL8Pk6noPYbZgd2Y+xHr99xM0tMFl6Dp2U9sXKNvXlaVpgRcNZvVSuNrtB7EeHXVLiqIg32WBy+BCtD1Bg82NWL839msz2IJGt+pnqFPT7iqsF9H3MxpqLlffl2PGFPwcOnQId9xxB5YtWwartf+/3YjVPffcg7vuuiv8uLW1FaWlpZg1axacTmcPr6R4CoVC2LhxI2bPng2DIbFSszQwvLapidc1NQ3VdV3RvA2O2q5fMBabs3HymJPCjyce3KdaFpeTX4I5mTMGbT7x0hbywLpPfZN52ti5Mdf9vLh/h6o2pbh4LGYNYKPNwbiu/6s4CIe366bw2PzpmJN5TMznWVdZj43te8OP07JyMSdvTlzmGMnuWi8czVXhxxbZFLHJwDGOMTi55CTdeG/8m9fD7XUB3X6Bbrv4cpSePD/i8b6WdGyv6fpZV8wWzBnT9fm3usrhOLKl61yyGQvGnwrpq0gnFAoBGzegLLNBV28GAGbJiAsnLIApwZa9AcDGqkY0tu3WjZ87fj7sBsswzCiyzlVhfRFT8LN582bU1tbi+OOPD4+FQiF89NFH+POf/4z33nsPfr8fzc3NquxPTU0NCgsLAQCFhYXYsGGD6ryd3eA6j9GyWCywWPRfsNFohNHIrYqGkiRJMBgM/N5TEK9tauJ1TU1DcV0PBxrCN28AMNZWoHq/Iks2Dvm7ah/qQm1J8XPWEvSoPpcECbnWjJj2+QE66n6aQl2BRaPSPuDPH+/r2qy4VZ811+Ls17lHWXOxydXVaKAq2Dyo11o777Oyj8U+T7WqNTQAzHZOiHkewu+D99030D0FY8grgP2U0yEZIp+rzJ6vmk9toAVCBkxyx/GVwWbV86OsuTBpWlbLkoxjHGPwYet23fknO0pgMyVOINFdjtmp+mxAR42c05JYK69i+TmI6W/6GWecgW3btmHLli3hP7NmzcLVV18d/v8mkwkrVnR1zti1axcqKiowd+5cAMDcuXOxbds21NZ2dcVYtmwZnE4npk2bpntPIiIiGnoHdc0O8lSPtS2AK6Psf5JoBrrBaSf95peJtdePEAJNmq5p/a3JKtEUtx/x9X2JYH9oO8rlGp24rfhcVec5k2TE8f3ItHlWLYfSov5ZdVz4jaiBDwAUaz6/gFD9vB/RNEAYFWX/G23L605TE6zFdXeR2l1H+3zJIqZwOT09HUcddZRqzOFwICcnJzx+00034a677kJ2djacTie+853vYO7cuTjxxBMBAGeddRamTZuGa6+9Fr/+9a9RXV2Ne++9F7fddlvE7A4RERENLUUoqNC0rx6taW1baM5UPY62+WOiGegGp53yNBud1vr1ReHDqS3kQUgoqrFY9zPqpA1+6gOt8CtBmOXByf5oW11nmRzIMaXjntGX4u9VK+BSvLgsb17MrbuV1hZ4lr+jGjNNnALT9J6XAtpkM3JMTjR06+p32NcQ/oWAtvtbtM0/p9pHwSgZEBQh3XiiyorwHSdzm2ugn93eevKHP/wBsixj0aJF8Pl8OPvss/HEE0+EnzcYDFi6dCluueUWzJ07Fw6HA4sXL8YDDzwQ76kQERFRP9QGWnQ1FtrMT5FZ3dygJtCMkFD6lUUZSgPd4LSTtv1xou31ow0gJEjI6OemlNpr3ZH5aMSYfu6v0xNFKGgOaq9RR4A61lqAh8Ze1e9zu999A8LffSNSqWND02hdCLoZZc5WBT9HvlryKYTQtbkuMUcOfmyyGVPsJdjuqgiPmSQjJlgjl30kghGf+Ylk5cqVqsdWqxWPP/44Hn/88aivKSsrwzvvvBP1eSIiIho+B73qrI/TYEemQb3Gv1BzQxwSCuoDrSjQZIQSzUA3OO2kzfzUBTraM/flRnooaJeOpRts/d5HxiqbkWtyqgK8I77BCX4iZayy+rE/kVbwSAW86z5RjVlPOAnGktF9ev0oSw62usrDjzuX/tUH2+BV/Lpjozkubawq+JlsLw7XDiWiSMFPsmd+EvvXM0RERDTkDmqWvJVZ83Q39ekGq25fm6okWPo20A1OO2mDH4/ih0vxRTl66GnrfWJdIqZVoqnx0mY74kUbtEmQ+rU5a3dCUeB64yV0b00umS2wn3dxn8+hDWgOfVUTp13yZpPNPWYTT8+cEc6kGSQZl+XN6/MchkOOKV31/acbbFGX9SWLxA01iYiIaFgc9GqaHVjydMdIkoQicxb2erpaElf5m3HsYE9ugLTLwfobFOSY0iFBUm1WWRdoQdoANjqNp6Y4Le/rVGzJ1mQ+GqIfPADaZYmZ/WxI0UkIAdcrzyKw90vVuO30cyBn9H1fKm3dU7W/GQEliMOaxiCjLLk9Zv+sshm/Gncd9nqqMMqSo2rikIgMkoxvFp2Ff1avgABwfcFpCZ2p6ovknj0RERHFnTbzM9oaeZlLoTlTFfwketMDIYTu5rq/mR+jZEC2KV1VB1Lrb8VYa0EPrxo62gxKfzu9ddJmfir9jR31MwYjpDjuN6VrdjDAebvffg3etR+pxuSMLNi+dlZM59HW8QgIVAeadZ3v+lIPY5IMCd3kQOu4tLH404T/G+5pxA2DHyIiIgprD3lVN/RA5MwPoC+ET/Rlb20hLwIiqBrrb80PAORrOoDVBRKn45u+zfXAlo6VWHIgKQIllS0Yd6AeEw6sR4P3Y0hGEwzFo2AsKYVxVBkMJaNhHFXaY+vonuiDn/7P27PiXXhWvKsakwwGpF11AyRzbB2G7QYLso1pqvkd9jXgkK7Ndbb2pZRgGPwQERFRWIWm2YFRMuj2OemkbXqQ6MFPo6bZwUDrSXJ1TQ8Sp+Obfnlf/4O8YNUR5C1/C7ds/gRWb1fwGLDaYQoCwYoDCFYcCI/LGVlIu+xamKcdHfu8A9pW5P3L/HjXrIJr6auqMUmSkXbtN2Ge1L99JUssOarv9ZCvIdz1rdOoKJ3eKHGw4QERERGFaZe8jbLkRO0SVqwJfhoCbQgowYjHJoJ4bXDaKT+B211rl/f1N4MSaqhHyx8fgfh0E+w+dRe2aNdaaWlC69/+CNdbr0CEYvt5aB7gcj0RCMDzwf/Q/vKzuuccl18HyzEzYzpfd9olbVvby3Ut4UuSvBnASMDMDxEREYVp21xHW/IGQNfWWkCgJtCSsPuAxDMbAugzP7UJsuwtKEJoC3lUY/3JcAkh0P7iMxDejnOZJQO83dpQdywhjL58zPPB/xA8sBdp130Lhsy+NRfQXaM+Bj9CUeD/dD3c7/4XocZ63fOOi6+Adc7JfTpXNNqf6/3eatVju2wZcGMJGnwMfoiIiCisr80OgI62vlnGNFV9SaW/MWGDH23mZ6A3qvkRlr0lwl4/LUG3qgsd0LVRaCx8az9CYM/O8GOTbIRXCaA2Lw37x+Yie8ZsfCNjFkKHKxA8fBDBQwcRqq9VnSNwYC9afnMfHJddB/OM4yDJPWfaYu3GJxQFgS+3w/326whWHop4jP3sC2Gbv6DH8/RFb0vaSiw5w37tqXcMfoiIiAhAR8ZAu29JmaXnjSwLzZmq4Kfa3zwYU4uLeG1w2inPrF725lMCaAt54TQOb/tibQBhlAxIj7EFd6ixHq43X1aNSRmZ+PsFx6A1o+PzjbcaYR47DfiqhkYIAe8nH8L93xchQqHw6xS3C21PPwnZmQHLcSfAMmsuDCWlukDBrwThCnlVY5GWvSluNwK7d8C/43MEdm6H0h59uaHt9HNgO/uCmD57NNp211rJvv/NSMHgh4iIiAB0NCwIipBqrMwafdkb0NHxbaf7cPhxpW9wNr+Mh3hnfrKNaRH3+hn24EdX75MWU0ZCCAHXi/+C8KkDkdCib6DVti38+Ii/UZXpkiQJtlNOh6lsLNqe+Ytu+ZnS2gLPqmXwrFoGQ0ERTGXjIDnSIaenQ0pLR7NFgjXoh9dmDr+m8xoJRYF/22fwrv4Qwb27IYS6/kjLNHYC7Bd8HaaxE/r8uXvjMFh1mc7ueguOKDEw+CEiIiIAQIVmc9McY3qvm3ZqO74lcuZHmxEZaObHIMnINTlVLa7rAq0Ybysc0HkHSts0INZ6H9/6T+DfvUM1Zp1zMvJnzAH2dgU/XsWPxmC77ns0jh6LjO//DO0vLIF/22cR3yNUU4VQTZVqLKj48e1AM8rLsrFjaiGOjC+CLSDg+WQ5vB8tj1jLo2UsKIb9gkUwTTt6UJaglViyowY/oyzRl4hS4mDwQ0RERACAmkCz6nG0FteqY5Kk3XU8NzjtLk8X/Ax/0wNtS+9YMlyhpka43nhRNSZnZMF+0WVwGG2wyCZVh7NKf2PEIFK225F+w63wbVwD78crEDxc0et7B4UCWREYd6AB4w40QFj3odG2N9xwoSeGgiLYTj8Hlllze60rGohR5hxsd0X+LGxznRwY/BAREREAoNavvnEv0LRyjkSb+WkNueEO+WA3xLaJ5GCL9wannfJ0Hd+Gv911k7ZddB/3yhFCwPWSfrlb2uXXQbbZAQDF5mwc8NaEnzvia8QMR1nE80mSBOsJ82A9YR6C1ZXwb14P36fro2ZwQpollza/AiFFDnwkowmmCZNgmnY0zFNnwJDbc21avERrZW2XLQPeSJaGBoMfIiIiAqBv1Zxv7j34yTdn6OpeqvxNw770SyvSBqcZRvuAz6sNfur8w5/50S7Lyu7jTblvw2r4v9yuGrOeMA/mqTPCj0ssmuDH37caL2NhMYwLL4HtvIsRLN+HwL5dEK2tUNrboLS3Qbja4D+yB+i2bY4xwh5MxlFlsM0/s6NznGXoA+xonQxHsdNb0mDwQ0RERAD0WYt8U2avrzFJBuSZnKrAqToBgx9ts4NMoyPq5q2x0AaI9Zr3GQ5Ngdg3Cg01N+mXuzkzYb/octVYsVm9FPKIpjtgbyRJgmnshIiNCJ498DqaP12DqV/WoPRwEwzh6yPBfNSxsJ12JozjJg5rkBEt+CllvU/SYPBDREQUIyEENrfvw0FvHWanT8DoXjqiJYOACKFRc+Ne0IfMD9DR8a178FOVgE0P4t3soJN2o9O6QMuw7/Wjzfz0FvyEl7tpamvSLrsOsl2dHdMGP5VxrPGqk3zYPa0IX0wrQnqbFzfW5WOUpQiWY2bBkFcQt/cZiDSDFRlGB1o0SwvZ6S15MPghIiKK0cqWL/DXqvcBAO80foqHxl6lq31JNvWBVt3GmPl9qPkBOoKfra7y8OPqBGx6EO82152035FfBNEaciNjkOo/GgJtMEhy1A5uXsUPt+JTjfW2Uahv4xr4d25TjVlmnwTz9KN1x2pv8luCLrhCXjhi3Ecoku61Sm3pVkhT5sPunDjg88bbKHOOLvhJ1I19SW/w2mEQERGlqA+au24U3YoPbzZsGsbZxIe22YHDYO1z0wJt4JeImZ94b3DaKcvogEFTmzJYTQ/eatiE7+z9O27Z8xe8Ub8h4jE1EWqOemp1HWpuguv1F1RjsjMDjosvj3h8oTkTsub28Ugc9nYSQuhrlfrYqGGoRcryMPhJHgx+iIiIYiCE0G3k+UnLTrQG3cM0o/jQNjso6EO9T6ciizb46dj8MpFo21zHK/MjSzJyjdqlb/EPfjyKH6/UrQ1n516s+wTl3lrdccubtqoeOw122GSz7jjgq+VuL/9bv9ztG9dCtkcOmIySAYXmTNVYZR+bHnS+58t1a/Cj/f/GMzUrEfpqs9K2kEe3wW68rlG8lWoCHYfBikwDO70lCwY/REREMWgLeXXLigIiiBXN26K8IjloMz/5ZmeUI/WKNJkfj+JHayixgkFt5ic7TpkfAMgzD37Ht0pfI/wioBp7ofYT1ePGQDtWtnyhGpuXMSXqOf2b18G/43PVmGXmiTAfdWyPc9FmPmLJ/HzSuhOv1a9Dha8O/2v8FP9t6MhgaWuy4tWNbzBMd4yGhK6arhmO0ez0lkQY/BAREcUgWj3L+01bEND85jqZ6Npc97HeBwByjGkwSeoy4ngWwvemNehGe8gb9flIG5zmxmGD0/C5dE0P4p/5ibR57FZXOXa4DoUfv924WZU9MUoGnJ89K+L5gofK0f7qf1RjcpoTjkuv7HUuJZrNPI/4+97xbWWzOjh7r7Hj7412byKn0R6XbnyDocichf8rWoBRlhwclzYWV+efOtxTohiw4QEREVEMIt2EAkBz0IUNrbsxL2PqEM8oPmo0dTqxBD+yJKPAnIHD3doe1/ibMdU+Kl7Ti+qVurV4tX4tTJIRV+WfgnOyj9MdE2mD03jWk+SbMmAMhHD8lkMYW96IXOlLtGRsAoxGSEYTJLMFxvGTYJ0zD5K5f3vTRAsmn6/7BA/Yr0C74sXyJnUWZ37G9IifM1C+D61/eTRCd7foy926K9Ysc+xroNsadGOn+7B6LOTGxra98IQ0TRoSdMlbp9MzZ+D0zBm9H0gJh8EPERFRDKp7KOZ/p/EznOScknRLYIQQ/drgtLsic5Yq+BmKzE9L0IXX6tcB6Fh6+EzNhzBKBizIUncpO+hT18ZIkHpsAhCrUQfrsfjFDcho7cg+GeV2BBoV1TG+rZvgeX8pbGcthPXEUyGZTDG9R1WUupq9nipsbt+H/d5a1bI4CRIuyNFnfQL796D1r49B+NSZMsvME2GeoQ8cIynStLuu9bcgIEIw9ZKp2dS+T9dREOioU5pmL1WN9WVvIqL+YPBDREQUg2iZHwDY763GHk8VJtmLh3BGA9eueOFR/KqxWBoeAPqOb0PR7vqQr0F3M/3P6hWwGyw4yTkZALDPU43HjrytOiZeG5wq7W1wvfEiijZ+DHQLJjqL+PXHt8L12vPwfvA/2M66AObjZkO22vr0XlW+6N/n87WfoFmzbGxexhQUaBoTBHbvROvf/wQRUF9r86RpSLvs2j7NAwCKNddaQKDG39xrx7MNrXsiju90H0ZQ850laqc3Sn4MfoiIiGLQ2039O42fJl3wo212IEGKuRW09oa4pyAxXuoj1NYICDxR+S7sshkOgwW/rHhNF9gdlzZ2wO/t27oJrpf+DcXt0mU8hBAICUXXArtTqLkJ7S/9C3jpX5AsVsgZmZCcGchqc8FdfRBGux2S1dbxx+6AYczYHr/PSN3WLso5AUIIKI31CFaUI1hxAN5PPoQIqpsmmKfOQPoNt8aUibIbLHAa7KqmFtX+ph6DH1fIi+3uiqjP7/FUqh4n+rI3Sl4MfoiIiPpICKHbw+a4tLH4rP1A+PGGtj2oC7Qiz9T3bmnDrUaz5C3P5Ix64x6NNstQ42+BIhTIMZ4nFvWajUs7hYSCPxx+C7Ikw6sJfKbaR+GagvkDel//7h1oe/ovwFdZJ4MkA5IECIEjxRn4YlohrsyZhyI5HQgGENj7JfxffhHxXMLnRai2Gqipgt3lgr/6EPyaZZNBCJxXGMK26cU4MCYbikFGntGJQF01iqpbUVDbBrvbD0NIgUERKJKdSLf8DU01VVDcrojvCwDmo45F+nXfinkJHtDR8a3V3RX89Bbsftq+P2pGLJKsQdoklojBDxERUR81Btt17Yavyj8VO91HwjfZAgLvN21Jqg5Q+jbXsdX7APrMT0AE0RBsH9QgMFLmp5NfBKEtL5nhKMP3R10Iixz7zX4nxe1C+3P/hPbkwmLB8pNGY/v0YghZQlXxFIz7qvmF7YxzEdi3G+5330Bg3+6Y3zOgBDDuQBPGHWiA225GU246jmsxo7alKuLxxRaBgNxzq3HLMbOQdu3/QTL071aw0Jylal7QW7vrDW17VY/HWgtQ7q2NWAMEMPNDg4etromIiPpI2xHNLJlQbM7CqRnTVOMfNG3TLbVKZAPZ4LRTpM00B3vpm3beTkP0fWGOTRuLu0ddNKDARwjRsdSttVk1bjn6eHz8zYuwbUYJhCx9NTd1YGYaPwnO2+6G81t3wjR2QkzvG1C6OtXZ3X6MPdwKh1+BNcLmpTaDudfPaJl9EtKuvbnfgQ+g39upp2vtUfzY2l6uGjsr61gclzYu6mviuQ8TUXfM/BAREfWRtoNZoTkTsiTjnOzj8H7TlvC4W/FhU9tenKIJihJVPDI/kiShyJyN/d7q8FiVrwlHO8oGPL9otMveriuYjzWtu/Bp+37V+My08bijZCFM8sBue/yb18G3dZNqzDxpGtIWfxsZNR8AzV1d5bSBGdDxHZmnTId5ynSIgB9KSzOU5iYoLc0INNajevs2ZBfkQ/L7IDweKF4PQtWVCGg+Z2eNUbYpDZWajEukLnZyWjqMo8pgHD0WpinTYw6+Iomlxmtre7mq1bgMGbPSxyPL6MCn7fsivobL3miwMPghIiLqI23mp/CrOpcicxaOcYzBVld5+LndnqrkCX4GsMFpd0XmTFXwM5gd3xShoEETFBSYs3BHyfn47eH/YpvrIADgJOcU3FJ89oC7u4Ua6tH+ynOqMdnuQNpVN0CSO/Y56q63ZWCSyQxDbj4MufkAAEMwiBZ7Juxz5sBo7Lo9E14v3ln2GJyffo7iqo5skvmrIM4im2DNyMH2HAmN2XaMSS/BUbnHAQYjYDBAdmbAOHoM5MzsuLdfL7ao2123hTxoD3mRZrDqjt3Qpu7yNt1RijSDFTMco1FgykRNoFn1vFkywS73bz8kot4w+CEiIuojbean+9Kf6Y5SVfCzz1ONZBAUIV3tTH+DH22768Fc9tYUdEGBuoA+z5QOs2zEj0svwQ73YZglY1w67wlFQftzf9ftjeO47FrIGR2fudSSq3rukK8+Lg0fJKsV66dko2HCTOQ0uDDuQD3OckxF+vjZMI4ei6yMDFi8NQhBwSRbccyNKvorz+SEDFl1Dar8jZhoU3/fASWoaggCACekTwTQsTnuGVlH4z+1H6mezzalJd1eWZQ8WPNDRETUR/rMT9fN/nhroeq5g946+LvVaiSqhkCbrui8P8vegI7MT3eDGfzUaWtqJGO45keWZBzlGB23luOeD/6HwAF1wb71hHmwHNO1iag2+PEq/qjd6CKpC7Rgt1yHtpBHd56GYMd5GnIc2DirDGnnXQrLMTNhyMqGQTZgkr0YU+2jhizwAQCjZNBluyoj7EX0ueugquOeBAmz0seHH5+WOR0mSf27eC55o8HE4IeIiKgPFKHolud0v9kfayuAhK7fVitQUO6tRaLTtrm2y5aIS5f6Qpv5qQu0IiBC/Z5bT7TZqlxT+qBkC4JHKuB59w3VmCE7F/ZLrlCNZRvT4NB8b4d89b2evz7Qiqcq38NdB57Ba8bP8f0Dz6gCu2pNwA3omw0MlyKzeulbpGBXu+Rtsr1YVZeUbrBhrnOS6hht23SieGLwQ0RE1Af1gTYENTfy3W9CbbIZJZpNHvd6I7ciTiTxaHbQSXtTLiB0548XbeYndxBaaotQEO3PPw2hdC3tkiQZaVffBNlqUx0rSRJGa7I/FT0EP20hD56tWYW79j2NVS1fhLNvbsWHN+s3hI/TLrXMMTkH1LEunnprehAUIWzWNJ/oXPLW3aLcueGsnVEy4MzMY+I8U6IurPkhIiLqA+2NnV22IN2gvgEeby3A4W43vPs8NUMyt4HQZrP6W+8DAHaDBU6DHa2hrj1mqv1NKNEUx8eDttnBYOwn5PngPQSPVKjGbGecC9M4/Q080LH0rfveN5EyP0IIvNv0KV6tWwe34ot4ng1te3F94ekwSDKqNI0TtAHHcCqyqOdS6VfPdYfrEFwhdZ1UpOAn35yBh8dejT2eKky0FSGHba5pEDHzQ0RE1Afa4KfQnKVbZjXBVqR6nAxND+KZ+QGAYt0N8eDU/Wg71MU78xOsroTnvbdUY8aCYtjOOj/qa7R1PxVeffCzsuUL/LtmVdTABwBaQ27scB8CoP+5S5Qlb4B+LjX+FiiiK0umzfqMsxZGDWxyTOk40TmJgQ8NOgY/REREfaBt2xzpJnSCTd30oCbQjNagR3dcItEuH+vPBqc9vX6w2l1ra37imfkRioL255dAhLoaVkiSDMeVN0AyRV9ypl32VulvVG1QCgBrW3fpXidD1tULrWvd/dU51N9fsTn+WbT+0mahAiKoavKw3aXOmnVvdEA0XBj8EBER9UFVlD1+uiu15MKs6VzVfd+bRKTb42fAmZ/ei+AHSgihW/YWz8yPd9UyBCvU7Zmtp50FU9nYHl9XalUHPwICR7otBQsJBXs86jqwmWnj8fvx1+OCrFmq8Q1texEUIX3mx5I4mR+nwa7bj6dz6VtDoE23DG4wN7wl6isGP0RERH3Ql8yPQZIx1lqgGtubwEvf2kNeXU1GwQBqfgB9UBipW9lAtYbc8At1RiVemZ9QbQ3c77yhGjPkFcB+7oW9vtYmm5Gn+f661/0c8tWr2j4DwM1FC1BgzsSc9Amq8faQB5+0fKk7PpFqfiRJihrsarM+dtmCsdb8IZsbUTQMfoiIiHoRECHd8rBImR8AGK9Z+rbXk7gd3+o0WR8J0oBrLrTLspqC7fBobuAHqk6T9ZEhx2VvGKEoaH/xaYhgoNuohLQrrodkMvfpHD3V/exyV6qeKzBnIuOreeeZMlAk1AHcGw3rVY/NkhHZxrQ+zWOoaP8edC7T2+5WBz9HOUYPeMNXonjgTyEREVEvav0tuo1AoxWea4Offd4aCCEiHjvctM0Ock1OGCXDgM5ZYMpQ7XcE6DeHHShtvU+OKT0uN9but15BYL96XxrbKadH7e4WSU/trnd5jqiem2xTb8I6NaTOGmq/tyJzVsIFENpgt8rXBCEEtrn0wQ9RIkisv0FEREQJSLvkzWmww26wRDx2glUd/LSHPLq6mkShq/cZ4JI3ADDJRl32KN4d3/R7/Ay8Q5h39Up4Vr6vGjNk58K+8NKYzjNaU/fTuexNCIFdbk3wYy9RPZ6i9LwsLJHqfTrp9/ppxGF/A1qCLtU4gx9KFAx+iIiIehFLu+E8kzO8YWOnRK37qYlzm+tO2u8n3h3f4t3pzf/ldrhe/Y9qTJJlpF11IyRL5CA3Gu2yt6ZgO9pCHtQH29AYbFc9N0mT+XHCiolWdbv07ooSqNNbJ+21bgy2Y1PbPtVYrsmJwgF2ESSKFwY/REREvdAW7Uer9wE6isDH29TLl/YlaMe3wcj8APob4nh3fNMGPwPp9BasPIy2p5+C6LY/DQA4Ll8M0/hJMZ+vyJwFk6bj3yFvPXZrsj5pBhtKIgQzcyJsAtqpJIGaHXQqNGfpljmuaPpc9fgox2jdnlhEw4XBDxERUS+0LXt722hSu9lpomZ+4r3BaadCXeanOS7n7aRd9tbfzI/S0oy2vz0G4VN3vLMvWAjrCfP6dU6DJKNE0wGtwlePLz3qZgeTbMURA4IT0ibqgolOiZj5MctGXfDZEFQ3pJjBFteUQBj8EBER9UJbeK69udcar6n7KffWIihC8Z7WgISEEmGD03hlfjJVjyt9jXFt+jDQzI/weuFZuQzNf3gIoWZ1Vspy3GzYzr1oQPPTLn075KvHbk2nt8l29ZK3TtmmtKjPJWLND9D7LwOm20cN0UyIemfs/RAiIqKRy6v4dbUa2pt7LW3Ht4AI4pCvXrcH0HBqCLRBgXqp12DV/LgVH9pCXjiNtgGf2xXy6lpn9zXzo7S3wfvxCng/+RCK26V73jRmPNKuvBGSPLDfDWuDn13uSl32UNvprbsT0yfjS80yuSxjGmxy39ptD7UicyY+13+dAIAyS164nTdRImDwQ0RE1INIS7Z6y/ykGawoNGepCv33eqqHLPjZ0n4AbzZshNNgx9nZx2Kq5jfvilCwsmW7aswqm5EmW+Py/rkmJwySjFC3Oppqf1Ncgh9ttkqChOxeur0Fj1TA+8lK+DavhQgEIh5jyMlD+k23QzKZBjxHbbvrI/4G1WOjZMC4Hn4W5jgn4pmaD1Xt1XvLrgynnpbjcckbJRoGP0RERD3QBj85xnSY5d7/8zneWqgKfvZ5qnFm1jHxnp5Oa9CDx468De9X2ZH1bbtxXNpYXJF3MkZb81Drb8FTVe9hp/uw6nUFpoy4FaUbJBkFpkxVtqPK34RJUZZzRbPHU4UKbx2OcoxGwVfZNu2StyyjA6YIexOJQAD+rZvhXf0hAuX7dM93Zxo/CWlX3gg5beAtswF9u2utcdYCmHr4Gco0OjDFXqK6RtqW0omkuIfleGxxTYmGwQ8REVEPdG2u+1h3Md5WiNWtO8OPh6rj2xfuinDg0+mz9gPY0l6OWenjsc2lfx4AZqVPiOs8Cs1ZqPQ3whgIIb+uHe3tXyCQawFkuWNZmckMQ14BJEPkTVXfbfwM/65ZCQEBp8GO+8ZcjiJzFuoC6mL6HM2SNxEIwPvxCng+fA9Ku/pYLfP0Y2A741yYxsb3s2caHEgz2NAe8kR8vi9B4IU5s1XBz2mZR8VtfvGm3ei0k1EyRK1fIhouDH6IiIh6oA1++rpfyQRN3c8RXyPcIV/UzVHj5ZCvIeK4gMDGtr26cQkSFubMxKW5c+I6j1FSGowby3HCpgqY/SHYDV+gxazeRFROc8J60nxYT5oPOSMzPL6l/UA48AGA1pAb/6n9GN8fdWHUPX6EosC/ZSPcS19DqCnydwB07N9jnnkibKefA2Ph4NyYS5KEUkuOLrvWaYqtJOJ4d8emjcUPSy/BF64KzEwfr6sjSyRZRgfMkgl+oV5SONFWBGuC1inRyMXgh4iIqAdHfOpC9WJLTp9eN8aSp6p7ERDY760Z9GVAh3z1fT42z5SBW4rP1tUEDYRQFPg2rcWs/76E5oauDmeRut0p7a1wv/8WPMvfhvmYWbCe/DXU2CU8ffA1ZIb8kBUBUyCEtHYfQu2HUW6uQ3H1l7ikrRYuuwWtTgsmlzjhq98Ez4r/IXioPOq8ZGcmrHNPhXXuKZAzBn8JWZklL2rw09flf8eljcVxaWPjOa1BIUsyisyZOOirU42z3ocSEYMfIiKiKBSh4LAmkzLK0re9VkyyEWWWfOzvttxtOIKfGY4y7PFU6Za6zc+YjusKTotrJiqwdxdcrz+PYOVh2BQ/mrs/10Orb6Eo8H22AZ5P16HO14iroxzbJh9ELgScSleGIdfUiDbjuqjnNk2YAuvJX4P5qGMgGYbutqc0St1PsTkb6YaBN35INEWWLAY/lBQY/BAREUVRH2jTLeUpMfct8wMA46zq4KfcWxu3uUXiVfy6jUuvyDsZOaY0vFG/AWtadyHDaMfX8+bihPSJcX1v/xefo+0ff4b4KtNlktS3GG6rESGLGUYhAYoCEdR2XROoCbT0uB9SpFoloxS5LbVp3ETYL7wMprLhyZxo2113mmzvfclbMtJ2fLPLFoyz5g/TbIiiY/BDREQUxRHN3ix22YKsGPYsKdPc/A128HPE16hqjyxBQoklGxbZhMWFX8N1BafFraNbd8HKw2j791/DgQ/Q0fFNkiSUl2bio3njUZefjp+O/no48xWsPATvxx/Ct2ktRDCAhkA7vCF1cGOWTQgJBaEeAiKjptObIa8QjgsWwXTUsYPyWftqlCUHEiTV9QCAST3s75PMpmg+13Fp4yBHCUyJhhODHyIioii0S95KLDkx3VCP0QQ/1f5meBX/oBWBa5e8FZgzYZG79q0ZjGBAaWtF29/+COHzqsaNhSVYM2sK1hZJwFfvW+1vCgc/xuJSpF1+HeznX4odK19B9aq3kdGtl4FBklFozoAbQew3e9GWZkFbmgXt6RZ4LUakt/vgbPViHEYBzc0w5OTBetKpsJx4ypAub4vGJpuRb8pATaBZNT4lRbufzXCU4aysY7Gq5QuUWnJxRf684Z4SUUQx/evw5JNP4sknn0R5eTkAYPr06fjZz36Gc889FwDg9Xrx/e9/Hy+88AJ8Ph/OPvtsPPHEEygo6NrIq6KiArfccgs+/PBDpKWlYfHixfjlL38Jo3H4/6EiIiLq7oiu3qfvS94AoFTz238BgQpvfcz73fSVttNbaYzzjZUIBND2jz8j1KzOkFmOOwFp1/wfUPUu0Lor6vwAQHak4fWjHNgxbi7M/iAEAIPBhJ+OuQz5jmIoEPjz/n+p9gzq5DTYcdmkb8f9c8VLqTVXFfw4DXYU9LFbYLKRJAk3FJ6OGwpPH+6pEPUopnzkqFGj8Mgjj2Dz5s3YtGkTTj/9dFx00UX44osvAAB33nkn3nrrLbz88stYtWoVKisrcemll4ZfHwqFsHDhQvj9fqxZswbPPPMMnn76afzsZz+L76ciIqIh51H8eKluNR6vfBe73ZW9vyAJ9LfZQSeLbEKJ5jXaovB40mZ+otWdxIMQAu3PL0Hg4H7VuGnMeKRdeQMkWda9/35vje48ilDC436zEQGzEVeVnI4JaSWQJAkGScZleZGzCLmaPX4SzRhLnurxFHvJsC7FI6IYg58LLrgA5513HiZOnIhJkybhoYceQlpaGtatW4eWlhb84x//wO9//3ucfvrpmDlzJpYsWYI1a9Zg3bqOLizvv/8+duzYgWeffRbHHnsszj33XPziF7/A448/Dr9fX8RIRETJ44XaT/B6/Xp80rIT9x18EV+4Dg33lAZECKGr+RkVQ7ODTmWaG+CD3tQIfjzvvwXfZxtUY4asHKTfeBskU8dSO+1eR+XeWl3Xt0p/k66Rwaz08arHJ6RPwDirfp+bXFN6v+c/FE7LPAoOgxVAxzK+hdkzh3lGRNTvSrRQKIQXXngBLpcLc+fOxebNmxEIBLBgwYLwMVOmTMHo0aOxdu1aAMDatWsxY8YM1TK4s88+G62treHsERERJR8hBNa17u56DIE/HHkLNf7m4ZvUADUE23Q35SX9WEambXpwYJCaHrSFPGgOulRjgxX8+L/YCvf/3lSNSRYr0m/+LuT0rmzMOGuB6pigCKFCE/xps0HZxjRkGdPU55YkXJl/sm4eeQme+ckxpeO34xbjjpLz8dtxiwdtuSMR9V3MhTbbtm3D3Llz4fV6kZaWhtdffx3Tpk3Dli1bYDabkZmZqTq+oKAA1dUdbT6rq6tVgU/n853PRePz+eDz+cKPW1s7KiKDwSCCwWCsH4H6KRQKQQiBUCh61x1KTry2qWkor2tTsB0tmhvv9qAHv6p4HfeXXg6bIfl2eT/oroUQXZ26bLIZTlhj/u9OqSlbdZ4Kbx18AT8M/eyEFe26lrtrVO9jkozIldPi/t9JpaUZbc/9A+j2XpBk2K++CcgrUL2fBUYUmjJR5W8Kj+1yHUGZqSso2+uqVM17jCU/4pynWIox3VaK7e6KrmPNeQl/H5AGC2bZxwFAj3Plv8Opi9d28MXy70DMwc/kyZOxZcsWtLS04JVXXsHixYuxatWqWE8Tk1/+8pe4//77deObNm2Cw9H3lqM0MIqioK2tDRs2bIAss31lKuG1TU1DeV33SfVwmVy68T0uF/5fw9NYFDwaMpKr1mGDXAGXseszZShGbNiwoYdXROaGHy6z+rv538YPkSvSoryiZ9Gu62b5sGq++SIdmzZs7Nd7RCUU5L7/X1jq1NmrltmnoL3VDaxfr3uJ1RiCS+6a18f7PkVGsKsz3HrjF6rn0erC+kP68wDALGTjgOkwaqU2TFbyIZoasR6Rj002/Hc4dfHaDj6XS//fn2hiDn7MZjMmTJgAAJg5cyY2btyIxx57DJdffjn8fj+am5tV2Z+amhoUFnas0y0sLNT9h6Ompib8XDT33HMP7rrrrvDj1tZWlJaWYtasWXA6EzvlnUpCoRA2btyI2bNnw2Aw9P4CShq8tqlpKK9rdcNGOBoi/zKqGh6UZwVxZZ5+2VIi+7ymDY6Wrs90jHMS5hTO6de5Xt+/G43B9vDjzMISzHFO6de5ol3XL2pcqvkeO4D5RuP94H/wtjYB3X7xaDrqOJQs/mbUQv6WZisO1K4MP/aZjZgzpmNeQRHCU3s3wSG6zreg5ETMcJRFncMCzEdACcIkp1aXWP47nLp4bQdf56qwvhjwvxyKosDn82HmzJkwmUxYsWIFFi1aBADYtWsXKioqMHfuXADA3Llz8dBDD6G2thb5+R1roJctWwan04lp06ZFfQ+LxQKLxaKfvNHIFtlDTJIkGAwGfu8piNc2NQ3Vda0I1PfYxeqd5k9RZs/HqRnR/61PNJWBJtVnGm3L6/f3ONZWiKb2feHHhwINA7omka7r4UCjar5jbPlxve6Bgwfge+/N8J49ACBnZCH9qhsgm0xRXzfZoe5wVhVogk8KwmGw4rC3EUGEVM9PTCuBsZd9eowpuk0h/x1OXby2gyuW7zWm3Ns999yDjz76COXl5di2bRvuuecerFy5EldffTUyMjJw00034a677sKHH36IzZs344YbbsDcuXNx4oknAgDOOussTJs2Dddeey22bt2K9957D/feey9uu+22iMENERElB20Hs/kZ0yFr/hPz96rluoL8RCWEGPAeP92VWTUd3+Lc7loIgcOaTm+j4tjsQPF60P7vv0IoSnhMkmSkX/N/kO09Lz8fbc2DUVL/truzycE+j7reN9+UgbSvuqMREQ2GmIKf2tpaXHfddZg8eTLOOOMMbNy4Ee+99x7OPPNMAMAf/vAHnH/++Vi0aBFOPfVUFBYW4rXXXgu/3mAwYOnSpTAYDJg7dy6uueYaXHfddXjggQfi+6mIiGjIuEM+3S7252Qfhxs1mx0GRBCb2vYhGTQG2+HRdHobUPCjaXd9wKtupjBQDcE23XxHxzH4cb3yHEIN6oDNduZ5ME2Y3OtrTZIBYzQd7/Z+FfRoO72Nt0VfAk9EFA8x5d7+8Y9/9Pi81WrF448/jscffzzqMWVlZXjnnXdieVsiIkpg2iyGDBkl5myMsebjs/YD2NxtuddeTxUWZB091FOMmTbrY5XNyDH2f0+ZsZqbf1fIi8ZgO3LitE/NIc187bIF2cb+NVTQci9/B77N61RjpjHjYTvrgj6fY7y1AHs9VeHH0YIfbWtsIqJ4Y8sJIiIaEO2St1JLTrgY/SjHaNVze7rdACeyw351MFFizu6xpqk3eSYn7LJ6eXd5HPf70W5uOsqSM6D5dvJ8/AHcb7+mGpOsNqRdezOkGAq3J9iKVI/3eqrgV4Ko8KrnzcwPEQ02Bj9ERDQg2pv47vUtEzU3vZX+RrSHvEh0R3yNqscDWfIGdBQ7a+t+BjP4icfmpt4Nq+F67T+68bQrFsOQHdv5tUFNa8iNT9v3Q0G3GiJIuuVxRETxxuCHiIgGRLvsrfsN7GhrHkySeoW1tsg9ER3WLCMrsWQP+Jxlmhv7/jQ98CkBvN+8FWsM5WgJusPjh7zxDX58WzfB9cIzunHHxVfAcsysmM9XaMqEQ9PI4P2mLarHxeZs2OTk2wyXiJILgx8iIuq3gAjpsg7dg59Ixe6JvvSto3OaNvgZWOYHAMZYtJmf2IKfGn8z/l/58/hX7Up8ZNiHew4+h4PeOoSEgiN+daZqtLX/wY9v22do//ffIISiGrefezFs8xf065ySJGG8VZ392ek+rHo8zsZ6HyIafGw2TkRE/XbE14CQ5iZZ22Vsoq0IezyV4cd7Ezz4aQ654FZ8qrFR5oEHP9plb3WBFrhCXl1GJJJtroN47MjbcHVbMtgacuOBgy/h2oLTEBQh9XxjDNaEEAjs+gKeZW8jsH+P7nnb186B7cyFMZ1Ta7ytEJ+7yqM/b2W9DxENPgY/RETUb9rsRZ4pQ3czr6372euthiIUyFJiLj7QZn3Mkgm5cejKVmLJgVEyqAKVg946THOURn2NEALvNH6K52o/goC+NbZb8eEvVe+pxjKNDqQbbH2ak1AU+Ld9Bs/ytxE8XBHxGOtJp8F+waIBN1CY0Eszg3E21vsQ0eBj8ENERP2mr/fJ0x2jDX5cIS+q/c0ojkMdzWDQNjsosWTHJVAzSQaMsuSoGh2U+2qjBj9+JYi/VS/DJy07Y3qfvtT7iIAfvo1r4Vn5PkJ1NVGPs8w8EY5FV8Wlc9z4HtpYy5AxxsLgh4gGH4MfIiLqt4OajmWRunVlG9OQZUxDU7A9PLbHU5WwwY828zPQTm/dlVny1MFPD3U/0QKfmWnjcbjtCGoQuWteT8GP0tYK7+qV8H7yARRXe9Tj5Iws2BecB8tJ8yHJ8cnQZRgdyDNloC7QontutDU33B6diGgw8V8aIiLqFyGEvs21RZ/5kSQJE21F2NDWVUuy11OF+ZnTB32O/XHEP3jBzxhrPla1fBF+rN0jqVNACWJNyy7d+Ndz5+KCzJlYd2g9NqU1YaNrr+6YSMGP4mqHZ9lSeFevgggGos7PkJsP2xnnwjJrLiRj/G8RJtgKIwY/3NyUiIYKgx8iIuqX2kALPIpfNRZtn5YJtkJ18ONNzHbXQggc0nZ6M8cvQ6X9fg77GhBQgrqsxxF/o2oPHAD4/qgLMSt9AoLBIAyQcXvROfhn3YeqYAoAxnZ7DxHww7NqOTwr3oXweqLOy1g8CrYzzoP52Flxy/REMt5aiLWt+qCOwQ8RDRUGP0REQ0wIga2ucjQH3ZjjnJi0e5tol2ylGWzINqZFPHaCpu7noLcOXsUPa4J99taQW9VRDYhv5kfbCU+BgkO+Bl2bZ2378FyTE7PSJ6jGZEnGN4vORLrBhrcbN0NA4ATHBJQGrQhWVyJYvg/u/70JpaUp6nzMU6bDetpZME2aFpe6nt5oNzvtbZyIKN4Y/BARDSFFKHiq6n183LIDAPBO42Y8MvaahO181pNIzQ6i3UCPsxZAghTuWCYgcMBbi6n2UYM+z1gc1jQ7MEtG5JmccTu/3WBBgTkTtd4mZLR4kObyo7b2QxQpWeEgxTR+Mg7lqAMWbdAEIRCqr0XocAUurjiC08ubEWqoh9WzE43izR7nIBkMMB8/B7bTzoSxOHqnucEw1pqv+jkAAJNkjGuASUTUEwY/RERDRAiBZ2pWhgMfoOM3/F+4D2GGo2wYZ9Y/umYHPXTrssgmlFnVxf57PVXDGvwERQj7PNXY7alCjb8ZNYFm3ZK34jh1euskAgGctqUW2Z9sgM3TUXuTbvwSrm4BlmfVckwT7QiMScPuifmoKUjH1HYfvIdWIVRdiUDVYRTv2I42kxH4Ktg0ffWnN5bj58B+3iUw5PR/E9SBsMgmjLbkqgLnMmsejJJhWOZDRCMPgx8ioiHySv1avN+0RTe+x1OVlMGPrtlBhDbX3U20Fales2eINzsVQqDK34Rtrgp87jqIHe5D8GpqlrRK4rC5KfDVfjqfbYT77dcwo64CjYGupgM+Jag7XvF5MO1LF6Z92dGGOs/8Bdo7908SApLfB5j6/p9w08QpcFzwdRhLxwzoc8TDRHuxKvjRLokkIhpMDH6IiIbAu42f4rX6dRGf2+eJvs9KomoNetAYVLdKjtbsoNNEWxGWNW0NP97tqYIQYkhqTQJKEH848hY+az8Q0+um97ABaZ/fe+8uuP77EoKHDwIAzLI6R+MXQQgISOj4HhShICTUzQ7MUuz/uZasNhhHlcF2+tkwTTlqSL7nvjg36zisbtkJj+KHXbbgrKxjhntKRDSCMPghIhpkH7XswL9qVkZ9fu8QBgHxctCnzvqYJCOKzFk9vkb7G/6WoAsNwTbkxrGmJppVLTtiCnwkSDjJORmnZEwd0Pt6Pl4B12vPq8Ys3QIZv9mA9jQLigrHICO7CEpzI9r2qLu3QQLMcuRlYXKaE8aysTCWjYOxpBSyMxNSWjpkRxokU18Wwg29Yks2fj/+Buz31mC8tQAZRsdwT4mIRhAGP0REg2hL+wE8Vflej8e0htyoD7bFtbB+sGmXvI225MLQS21MoSkTDoNV1U1tj6dqSIKfne7DUZ8rteRinLUABeZMFJozUWDq+F+7wTKg9/R8/IEu8AE6urQ1lObjvZNKUV3Y8dlvKlyABVlHAwA2VXyCTWtew6Q9tSipbIFZMkIymGDIL4ChoAhSXgEqmlpRfOY5MOXlJ1XQ3CnT6MDxaeOGexpENAIx+CEiGiRCCCyp/kDV2QoALs87GUsbN6mCgH2e6qQKfg5omx30suQN6NrsdEu3DMweTxXmOifHfX5a+73qpYUzHGU4JWMqZjjKkDkImQfv6g/heu0/unFDXiEcFyzC7qwjqG7bHR7vHkyWm7zYcswobDlmFCzeAE42j8ExU74OydCR/QkGg/CsXw85OycpAx8iouGUfL1ViYiSxB5PFWo1u9mfnzMLF+XMxgSrel+TfZ7E3PQzEiEEdrmPqMb6EvwAHXU/3e0dgs/tDvlQ7Ve3jr48bx5OyZg2OIHPmlVof+U53bj97AuR+aP7YJ5xHMZq9rXZ123T14pue/z4rCbkFYwNBz5ERDQwDH6IiAaJdif7InMWrso7BZIk6TZ13Osd2s5nA1EbaNE1O5hiL+nTa7V1P+XeWgQidDuLJ+1+RDJk/b45ceJd+xHaX/63btx+3iWwn3MhJEPHgovxVv2mpgElCCEEDmvabZdah6ctNRFRKmLwQ0Q0CBShYG23ZU0AcJJzSniZkjb42e+p1XX4SlQ7NPUzToMdJebsPr12grUw3NUMAAIiiHJNcBJvBzRL3kotOTDJ8V/17duyCe0vRQh8zrkQ9jMXqsa0mbKQUFDhq0dDsA1uxad6brACNSKikYjBDxHRIPjSfQQtQZdq7ETnpPD/1y5784sADnVb7pTIdrgPqR5PtY/qc+2J3WBBiUUdKO3xVMZtbpH0pz4pVsHqSrQ/vwTQ1HfZz7oA9rMv1B1vN1hQqOmOd8Bbq1ryBgA22YwcY3rc50tENFIx+CEiGgRrNEveyix5GGXp2jDTabQjz5ShOiYZ6n6EENipqfeZ5hgV0zkm2YpVj7/UnC/etJ3pxtkKohzZP8LrRdvTT0L41Rkb+4KFsJ2jD3zC89AsfTvgrcEhrzr4GWXJZVMDIqI4YvBDRBRnQRHC+rY9qrETI3Q0m6Arek/8zU7rAq1oCLSqxqbZY9sIVFsf9KX7CIQQUY4eGK/ixxFfo2psbBwzP0IItL/0DEI16pot6wnzYDvv4h4DF+089ntrdNk/LnkjIoovBj9ERHH2hesQ2kMe1djcbkveOmmDn72exG96oK33STfY+lzv02myTR38tIU8OOJvjHL0wBz01qlajUuQUGaJX/Dj/eRD+D7bqBozFpfC8fWre83YaOu+Krz1ugC4lMEPEVFcMfghIoozbZe38dZCFJgzdceN19T9HPY1wKP4B3NqA7ZzAPU+nfJMTl0dy2AtfdPW+5RYcmCOU7ODQPk+uP/7ompMstqQfuOtkEzmXl9fZslTPVag6Fpyj2anNyKiuGLwQ0QURwEliI1te1VjJ2VE3sRzrLVA1flMQOjqUxKNrt4nxiVvQMdmp5GWvg0Gbae3cXFa8qa0taLt6acgQiHVePrVN8GQkxflVWp2gwVFmqYHWsz8EBHFF4MfIqI4+tx1UNeqeE66fskbAJhlI8qs6hvlRF76VhdoRZ1m09apfdzfR2uKXd0k4UvNcrp40Xd6G3izg2DVEbQ8+jCUFnWWxrbgPJiPOjamc2mbHnSXbUxDmsHanykSEVEUDH6IiOJI2+Vtir0EOaborYq1S9/2JnDHt52aAMVhsKo62MVCm/lpCLahTtNIYaD8SlC3YehAMz/+HZ+j5bFfItSobkxgmjAF9nMuivl8PXWeY9aHiCj+GPwQEcWJTwlgc/s+1djcCF3eutN1fEvg4GeHS13vM80+CrLUv/+MlJizkWawqcbinf056KvVNTsYbe3bkjQtIQQ8q5aj7e9/hvB5Vc8ZsnORft03IRkMMZ93bA+Zn1LW+xARxR2DHyJKGK6QF42B9uGeRr991n4APiUQfixBwpz0iT2+ZoKtSPW4IdiGpmBifgfazM9Ue2z7+3QX77qf9pBXt6msdslbsTkbNrn3RgRawepKtP/nn3C98QKEUFTPGUePRcYd90BOd8Y+aXRsuNq97qs7trkmIoq/+LS8ISIaoA+bt+NvVcsAABfnnoDL8uYN84xit7J5u+rxdEcpMoyOHl9TbM6CVTbD263L2z5PNWalTxiUOfZXQ6ANtbp6n/4HPwAwxVaCTd2aQ/Q3+Hm/aQuerv4QAHBJ7hx8PXcuJEnCfo+62cGYGJa8KW43/J+th3f9agQPlUc8xnLcCUi78vo+dXaLxiabUWTOQmWEVt9c9kZEFH8Mfoho2B3y1ePvVcvDS5TeqN+A0zNnINfUv9+mD4cv3Uew1VWuGuttyRsAyJKMcdYC7OjWQnqfpybhgp9I9T4DzUxoMz+V/ka0BF29BozdHfTW4enqD8M/O6/Vr4PTYMfZ2cfqOueNs/Uc/AghENy3G951H8O/dTNEMBD1WPs5F8J21gUxt/mOZKy1QBf8SJBi3j+JiIh6x+CHiIaVIhT8vWo5FHQtJxIQ+NJ9BCdnJEfwI4TA87Ufq8acBnufgh+gY7PL7sHPXm/idXzTbm46xVbS73qfTmOs+bDIJtVSwS/dlZjj7HmpYCchBJ6tXaWq6wGAZ2o+RJ7ZiUOaZgfR6msUVzt8G9fCu3YVQrU911xJRhPSrroRluNm92mOfTHOlo/VrTtVY8XmbJjitB8RERF14b+sRDSsVrXswG5PpW58j6cKJ2dMHYYZASGhoDnoQprBCots6vX4z1wHdJ/hktw5fa4vidT0QBHKgIOLeNJmfqY5BrbkDQAMkoxJtmJscx0Mj+3yHOlz8LO5fR+2uyp04wICvz/8piqgBtSbigohENy/B961H/Wa5QEA2ZkBy6y5sJ50Ggw58V2OFiko628XPSIi6hmDHyIaNq1BN56r/Sjic5ECoqFQ7W/CwxWvhfezyTA6UGDKQL45A0XmLJzsnIp8c0b4eEUoeKH2E9U58kwZOCNzRp/fU9vu2qP4UelvGpIb4EO+euxyH8FUeylKLJGXWTUG2lHtV+9p05/NTSOZYi/BrqZ9GHegAeP216PYvQMtznWAJAGSDMgSZJsdpsnTYT7qWMjOju8+oATxbE3knx2gI4DtrtCcBbvBAsXtgm/DGnjXfYRQTc8ZNslggPmoY2E5YR5Mk6f3q5tbX3Q2PeiewRrNTm9ERIOCwQ8RDZtnaz+CK+SN+NxBbx08ir9f3bkG4q2GTaqNPFuCLrQEXeFg7LX6dfi/wgU4LfMoAB37+hzyqfd8+Ube3JiWLOWY0pFtTENjty5vX7gODXrws9dTjfsOvoCQUGCQZHyneGHErIs262OXLQOu91Hcbvi3f4bjN69C8fZ1MIS6bvx9VgFZU0vj+/xT4OVnYRo3AeYZx+OTHD+UxkMoEAKyIiAJgTKfBUptDbKa3chuciOr2QOzP4iQQUaayY5G+xYInxciFOpxboa8QlhPOhWWWXMhp0XfoylebLIZpZZcVPjqwmM9tcAmIqL+Y/BDRMPiC9chfNyyI+rzAgL7PTWY7ohPhqGvettkNCQU/KXqfRz2NeCyvHl4qW6N6vlRllzMc06J+X2nOUrxSUtX3cfnroM4O/vYmM8Ti1XN28MZkpBQ8OfKd2AzXIyjHWXhY1whL95p/FT1uin2gdX7eDeshuu15yF8XjiEgEFR1+z4FD9sBkuEVwoE9u+Bd98uZPnqca3oep1FNoW7pvmVoOpVxqACuwQobpf2hGGSwQjz0cfDetJ8GMdPiksjg1hcmjsHfzzyDhQomGArwjHdrgEREcUPgx8iGnIBJYh/VC9XjdlkM5xGO2r8zeGx3Z7KIQ1+QkKJ2HI4krcbN2Nj215VlggArsib16/A4BjHGFXws8N9CAERgkkanKVWAHBE81mDIoTfH34TPx29CBNtxWgPefFwxas44FW3jO7vkjcRCMD12n/gXdfVHEKSJFgkE3yiq+bGqwSiBD8dmoLtEEIdMOWY0iFJEgrMmTjia4SiWfZmjlK7ZcgrgHXuqbDMPmlIsjzRzHFOwu+t+WgMtmOSrTih6r2IiFIJgx8iGnJvNW5ClaaG5PK8k1Hlb8R7/i3hsaGu+6nxNyMo1Euibi8+Dy0hF/Z6qrG2dZfqOe2+NxNtxTg+bVy/3nuGY7TqsVfxY6+nasB76fSke6DZyacE8KtDb+Cukgvwr5qVONhtKRbQUQP1ta+W/MUiVF+LtqefQvCIvkGBVTbDLQVRUZqN8rJsFNpzcVnuXAhFAKEQghX74d+xDcLnhV8Joi3oUb2+e2MKo2RAgTkTVf5GdJbQdAZYnSSDoSPLM3c+jBMmD3mWJ5oCcyYKzJnDPQ0iopTG4IeIhlStvwVv1G9QjY2zFuLMrKOxrm0P3mvaEh7f46ka0q5n2tbIToMd8zK6lrAd7SjD36uX64rpO12Zf3K/b6QzjA6Msear9qbZ2l4+aMGPTwmoaoy6c4W8+EXFyxHneO/or8PeQ1YmEv+2z9D2n39CeD2658xTZyA4tQxPpe2Cz9oVwFw56RRV3ZQIBBDYvQPLP34Wjt1tsLv9CBkkSJKMbEcOJNkE2WqDnFcAa0EhWp0y/iuVo91mxMXZs5BlLQW+qvUxFI+CbO/7XkJERJQ6GPwQ0ZB6pmYlAqKrJkOChP8rOgOyJGOSrUh1rCvkRZW/OWoXsng74lcHP6Waov7TMo9CgTkTvz/8FtpD6hv5Y9PGDjhQOdpRpg5+XOW4AicP6JzRRMr69CTbmIZ7y76BInNWTK9zL38H7rdf041LJjPSLl8My8w5GBPywb/7ADpTNUERwi5PJY7qlg2TTCa0ThqLf8tFwPyun5Nv5J2EObkn6s4/7as/RERE3XFRMRENmU/b9+PT9n2qsbOyjgl3tsoxpiPLmKZ6fs8QLn07rMn8RAq6ptpH4cExV6o6sZkkIy7Pmzfg9z/GMUb1uNxbi5Zg9CL9gajSBD9Ogx2TbSURj801OfHzsstjCnyEEHC99UrEwMdQUISMu+6FZeYcAIDdYNF1N9vaXq57nXbMYbBiYfbMPs+JiIiIwQ8RDYmAEsQz1R+qxpwGO76Rd1L4sSRJmKjJ/uz29LwXSzxpl72VRmk1XWDOxP1lV+CyvHk4+f+3d9/hcVV3/vjf904vGvVqyXLvRe6WDTYuuADGpoaWGOKFxMFpZJONvyEQ2GTZZfMjwC6BDSF0EzoGA8YVG9tCLljGvdtyU7Ha9Hrv7w+hke7MSJbkKbL0fj2Pn4d7zpk7585B8nx8zvmc5KH4bcFN6KPPuuz3H2jMgz4ktffeCId4RkOVr15x3UuXhl8XLFQcBAoA2ZoUPFp4u+Jso0uRJQmOd1+Ha8PqsDrdmIlI+cXvoM7JU5SPNvdRXJc5Toa99tsWh6ECjTNl7TmEloiIqAmDHyKKi09qd4YlCLgr62qYVHpF2WCj8ktxvGZ+/HKgcZN8C73aOGfHqNLhpoxJeDBvPoZFKSOdRlCFZVIL/cIfLaEzPznaVJhUevy2980YbmxcbjbUmI/fF96GDI2l3feVA37Y33gR7pLwA0hNC2+H+fv3Q9Drw+qKQma9znpqcNFnDV4HZAn7QgLBUUwHTUREHcQ9P0QUc5GSHAw05OHq5KFhbUNnfs56amAPuGFWhX9hjqYKb31YIoPQPT/xMMpUqFga+K3jNGRZjnpGsoqQbHu532UZS1Gb8HDhrZBkCQKEDr2v5HLC/sbf4T3wraJcEESY7lgM/cTWlwYOMOTApNIrDr0ts5/C7NRRAIDjrgo4JY/iNSMZ/BARUQdx5oeIYu61CEkOfpgzM2IWtz66LGgE5b/LHIvD0rdzHuWsT7LaFPOAK5JRZuUX+ga/IyzddDSEJjzI0qQorkVBbHfgI0sS3Ns2of4/fhce+KjUMC/+UZuBT9P7he55KrM3L30LnQHL12UgXZO4c3mIiOjKxOCHiGJqt/0kdoUkObg2dXSre2Q0ohp9Q+rise/nrOei4rq1/T6xlqNJQaZGub9mb5SXvkVKc53byfNlvEcOoOHPj8H+7uuQ7DZFnaDRIumBn0E3un1JCYpC9v3s/+6gV6Ax811LoznrQ0REncBlb0QUUx9eLFVcW1RG3N4iyUEkgwx5igNO47Hv52xImut8bWKCH0EQMNpUiHX13yKlzok+5bXw+T+Gw3IakKXGP5IEwWSGdngRVL0KOrwkLlKa6/YerinLMgIXzsF3eD+8+7+F7/jhiO1EgxFJ9/8Mmr4D2t2v0D08bsmLw85z6KPPwnFXRZttiYiI2oPBDxHFjEfyhS1ZuzPrqrAkB6EGGnOBFqvQjrkqYn7YaXia6/gHP3IgAP+p45jyzVHk7y5FWq2zsUIQ4NSdDwtynKs/hjo7D9rxk6AbOwmqtPbtUQpNdpCmNreaNU32+RCoOA//2dPwHT8C35EDkGzWiG2baEcUwbToe1ClZ7bZLlSy2oR++hyccDcHOnvsp2APuCF/dwYQ0JhafEiMDn8lIqLujcEPEcXMSXeV4kurAAGTLYMu+bpBBmXGN7fkRbnnYlTSSUfSmOlNmQAg3svevHt3w/7+CkgNdUiXZdjdzuZKWYZL8sKo0oW9zl95Hv5PP4Tz0w+hzu0FwWyBaDJBMJog6w1IOnceHq8Dfr0egkYDaLSwCuchqGTIYmMw1XLWR3K74Nv/LXzHDsF/5jQCFecgBwLtegZ1Xj5MC78HzaDwRBbtNdrcRxH8lDlOwi65FW2GGvOhFfnXFxERdRz/9iCimAldqlSgywg7xyaSFLUJmZpkVLdIjX3UdSFmwU+kTG/5cQp+5IAfzk/eh2vT2mCZKAjQiRp4JF+wrLXgpyX/hXMAzrW4uQyLwwHXoT1Ai1mjAp8VP9b4cbIwDSf6ZqBwUB+4S7fAu2cXfEcOQg74w2/eBtFsgfG6RdBNugqCeHmzc0WmPvjw4tfB67OeGtSFHPQ62swlb0RE1DkMfogoZo65lUveBhhy2v3aQYY8RfBzxHUe16aOjlrfWgpd8paqNl9yaV40BOpqYH/1/+A7fSKszqjSwSP54DRqcbZXMgxaE+ZkjG0MYmQZvqOHEKiriXDXS/NJARhcPgw7VIlhhyqRtu407GpTh+6hSsuAZtAwaAYPg3boSAi6tgOz9oqU8rrlfwPAqJCscERERO3F4IeIYua4q1Jx3b8jwY8xF1utB4PXR52xy/gWGvzEY9bHu/9b2Fe8BMnpCKtT5/aCZlA/rEg+icqspODytOkDbg2md5YlCf5Tx+HZWQLvnl0R79OalmnHAUAtqC75GlVmNtS9CqDpPxiawcMgZmRF/ewhoDnl9TbroYj1aWozemnTov6+RETUMzD4IaKYaPA7FDM3ANBf37GZn5YqffWo8dlicrbLmZA017EMfmS/H85PP4DryzVhdYJaA9Oi70E3ZToskGE7+gLkFrMeh5xnMfW7g2EFUYSm30Bo+g2EfNOd8J08CqmuFrLTAdnphOS0I2CzwV1+GinpaRACgcbkBS4HAueVQWnouUqCStU4qzNoKNT5hVD1KoBoMMbg04isyNx68DPK3CcmQRcREfUMDH6IKCZCZ310oqZDSQSa9ge5JW+wbHPDAdyUMSlqfWwSesBprIKfQFUlbK//H/xny8PqVOmZSLp3KdT5vQE0JocYbMjDN/bmJXGHnOeCwU9LgkYD7aBhYeV+vx81paUYMGkS1OrGX/fl7mr8fc+L6HfyIvqdrEHB2TpoBFXjPYaOhHbUOGiGjYxrsBOqrTTWTHFNRESXg8EPEcXEMbcy2UE/fXaHUlWrBBGTkgZiU8P+YNnG+n1YmD4hqimvfREyvUX7jB9ZluHZsQ2O91dA9nrC6nWjx8H0vcVhAccQYy9l8OM6F/rSVl3w1mFHw1GcFs9itDQW5u9+3V/w1sNq0aNsdD7KRucjS9LjqZzbISYlQdBGZ9/O5YqU8hpoDAhHMvghIqLL0KFvEE888QQmTJiApKQkZGVlYdGiRTh8WHnAndvtxoMPPoj09HSYzWbccsstqKxU/gtweXk5rr/+ehiNRmRlZeHXv/41/P6OZRcioq4tNNNbR/b7NJmZMlJxXe1rwH7nmcvqV6gKbx0kxC7Tm2S3wf76i7C/9XJY4COoNTDdfBfMi38ccaZliLGX4vqspwa2gKvV9/LJAZRYD+OPp9/DQ8dfxorqr7BGfRgvVHwRbFPlq1e8Jt2cAVV6RpcJfJqMNvcJK+tvyIE5DokoiIio++pQ8LNp0yY8+OCD+Prrr7F27Vr4fD7MmTMHDkfzRttf/vKX+OSTT/Duu+9i06ZNOH/+PG6++eZgfSAQwPXXXw+v14tt27bh1VdfxSuvvIJHHnkkek9FRAklyzKOh/yr/YAO7PdpMtCQGxaIbKjfe1l9C3UmJNlBmtp8yZTS7SG73XCu/hh1f1wOz+7tYfWq7Fwk//L/wXD1zFb3sPTRZ0MrKA8fPewMn/1xBNx4q+orLDv6Ip499yn2O5XL6nbaj+Oo6zyA8ANOc7SpHXmsuBkTIfjhkjciIrpcHVr2tnr1asX1K6+8gqysLOzatQvTpk1DQ0MDXnrpJaxYsQIzZ84EALz88ssYOnQovv76a0yePBlr1qzBgQMHsG7dOmRnZ6OoqAj//u//jn/7t3/DH/7wB2i1lz4DhKi7avA7UO93KsoEALnaVGiuoEMdK3z1YemJOzPzIwgCZqaMwmuVG4NlO2zHYPU7YVFHZ09KeKa3jMu6n+zzwV2yCa61n0Ky2yK20RdPg2nR9y4526IRVBhoyFUEMwed5zA+aUDwOiBL+PfT7+K0p7rNe31csxO/yr8RFSFL/HJbHHDalfTX58CiMsIaaP55KIoQEBEREXXEZX2bamhozOSUltaYdnTXrl3w+XyYPXt2sM2QIUPQu3dvlJSUYPLkySgpKcHIkSORnZ0dbDN37lwsXboU+/fvx5gxY8Lex+PxwONpXi5itVoBNG7m5XK5+AkEApBlGYF2nvZOHfNK1Uasr98LGXJYnVbQ4Bd518fsX76jPbaH7Wchy83PkawyIRmGTv28FpsGYgU2B9Mz++UAvqzbh+tSx0alr2dc1Yq+5mlSOtVPyWGHd/tWeLZ+Cbm+NmIbwWCE4bZ7oB01DgEAaMf7DNLnYJ/jdPD6oOOMon97HeU45a6K+Nqmx5JlYKf1GMqdVajw1CmeN11M6rK/R+/OuBovVKyBBAlTLUPQV5PVZfsaT/xd3D1xXLsvjm3sdeTvhk4HP5Ik4Re/+AWmTp2KESNGAAAqKiqg1WqRkpKiaJudnY2Kiopgm5aBT1N9U10kTzzxBB577LGw8p07d8Jk6tjBfNR5kiTBZrNh+/btEC/zFHdSKhfqsFLzTav1DgD/dfhdLPVNgQaXPpOlo6I9tl+qDsOhal4OmysZsH17+NKv9uqlNmC/2Pz74X37ZqQd8UJAx1Me2+CBF82/JPdojsIhNM8uOBtqUHqitN3309RUw3xwD4wnjwCt/sUmwNl/MKxjJyPg8gOl7b+/T6iDQ9P8We5znMRXF7ZC+92v789UBxWfNQAUSCkYKeVineooAlIADocDggD8794PcUZU7sGsPHgapXLnDkuNNRWAe1EEl+BFRp0Zpafb/7l1Z/xd3D1xXLsvjm3stdyCcymdDn4efPBB7Nu3D1u2bOnsLdpt+fLleOihh4LXVqsVBQUFGD9+PCwWS8zfnxoFAgHs2LEDEyZMgEoV/S/gPdm+yvUwNVw6kLdm6qM249FStMf2s/JTMLmbn+eq9DGYlD6h0/ezOHvhT2ffD157ACTn52OoMf+Sr7UH3NjvPIN9znLsc5aj2mcNaSHAhOa+zigoxoB2LNHzHT0E95pPEDh5rLFAH3kjvnrYKBjmL4Iqt1fE+kspknz49PgxSHJzUoaUQQUYaeoNnxzAS8d3wyQ19//2jKm4MW08ACCpagvePrcZRqMJggCUw654VgCYM2AadKJyXxF1bfxd3D1xXLsvjm3sNa0Ka49OBT/Lli3DqlWrsHnzZuTnN3/5yMnJgdfrRX19vWL2p7KyEjk5OcE2of8C3JQNrqlNKJ1OB50ufG28Wq0Onl1B8SEIAlQqFT/3KPLLAexwHFdsem+a0QhdAvdp3TeYmz4mJl9WLzW2kizBLfkumQzAJ/lR7r2oeJ5B5rzL+n9mRFIhcnVpiv0qm+0HMdLSp9XX7HWcxjvV23DcVaH4HC91QGahKQvqNvZX+U4dh/OzD+E7eqjphhHbafoNhPGGW6DpOyBifXupoUZ/Qw6OuS4Ey456KzAmuR++tZXDJXsVzzQ9dXjws56fNhbvndsCQYj83GlqM0xaw2X1jxKDv4u7J45r98Wxja2OfK4dmnuTZRnLli3Dhx9+iA0bNqBv376K+nHjxkGj0WD9+vXBssOHD6O8vBzFxcUAgOLiYuzduxdVVc1r1NeuXQuLxYJhw8IP6SPq7vY5ysOSAzwzYAlWDP0lHu9zh6LcGnBGPdtZe5z11OBnx17CkiPP4dlzn8Int75uudxzEf6Q+n767FZat09j4oMRirJS61HYQz63JnV+O/6/sx/jmOtCxD1UrSnUZcIgRk664j9XDuuLz6LhmSeaA5/QfqpU0I2dhOSfL4dl2W8uO/BpEpry+pDzLABgm1V51MAgQx7SNUnB62S1ESMDua3eN7uLJjsgIiKKlQ6Fnw8++CBWrFiBlStXIikpKbhHJzk5GQaDAcnJyViyZAkeeughpKWlwWKx4Kc//SmKi4sxefJkAMCcOXMwbNgwfP/738eTTz6JiooKPPzww3jwwQcjzu4QdXehX2AHGvKQqbEE/3ukqRB7W2x4/7hmB2anjIpb9jdJlvC/5z5Djb8xc1mJ9TB66zKxKGNixPbHQs73ydOmwRSFs1mmJQ/D29VbEfhu+ZdP9mNLw0HMSwtPklJiPQyP5OvQ/XO0qViSOyusXLLb4PzsQ7hLvgJaCaRESwr0U6ZDXzwNoiW5Q+/bHkMMvbAKO4PXx1wVcAY82GU/rmg3xTIk7LUTA71xHNaIQWBXTXNNREQUKx369vT8888DAK655hpF+csvv4x7770XAPCXv/wFoijilltugcfjwdy5c/HXv/412FalUmHVqlVYunQpiouLYTKZsHjxYjz++OOX9yREVyCf5McO2zFFWbFlkOL61oxiRfBT73dgff3eiF/6Y6HEejgsjfIHF79GsWVQxJmDY+4LiuvOpLiOJFltwnjzAJTajgTLNtTvxdzUorAlXaXWo4prraDGEGM+Rpp6Y4SpN/J16WHJElSCciJcDgTg3vYlXJ+vhORSph9vIianwjjnBugmToUQw6UMg429IEAIBjA+2Y/3L34Nt+QNthEgYJJlYNhr02DEeHN/7HAcC6vrqmmuiYiIYqVDf1u3TI/aGr1ej+eeew7PPfdcq20KCwvx2WefdeStibql3Y6T4V9gk5TBzyBjHkaYemOfo/msl49rdmBWysiYz/745ADeqd4WodyPlys24N8KbgoLPI6HzPz078Thpq2ZmTpSEfyc8VzEMXcFBhqal3bV+uw48t2Bnk1+nn8Dxpr7tXpfWZIgOWyQrA2Nf+pr4d60Dv6K8ANFAUA0mWGYfT30U6dD0MT+bDKzSo98XTrOeC4Gy1bX7la0GWYsQIo6ctKMBWnjIgY/WZqUqPaTiIioq+OuK6IE+tp6RHE9xNgLaRpzWLubMyYrgp86vx0b6vdhblpRTPu3oW4vqnwNEev2OE6h1HYUk1vMVNkDblwIOUSzPZnT2muEsQCZmmRUt+jT+rpvFcFP6EyaQdRipLE3gMZ/wJHqahC4cB6BinPwXziHQMV5BCovQPZfepmcoNHCMGseDNPnQGglu1usDDXmK4IfCZKiPnTGsKW++uywABrgzA8REfU8DH6IEsQtebHLdkJRNsUyOGLbocZ8DDMW4IDzTLDs45rtmJkyImazPy7Jiw8uft1mm1crN2KUqTCYAe6EW3mGjEZQo7c+M2p9EgURM1JG4MNzm5Ba50Rqgwu+hjOoU5VDrK0FZBmSUIHpWi9sZh0cJi3GIhOe3W/AWXEe/orzkD2RkyRcim7MRBhvvA2qlMTskxls7IU1dWUR60SImBhhyVtLC9InKIIftaBiwgMiIupxGPwQJcg39pPwys2zDQIETExq/QvsrZnFePx0c/BT67fjy4b9uDZ1dEz693ntN7AGlHtd5qWNxera5sNY6/0OvFO9FffmzAQQvuStUJ8JjRCdMw0klxPevbsxdedW9Nq3BYLUvAy3XlOFJLUBAVlChrsaGS1el61NgfsS6bnbos4rgOnmO6Hp3/rMSjwMMbR+TtBIU28kqdpOWT3S2BvFlsEo+S7BxpzUIp7vQ0REPQ6DH6IE2dagTJc8wtQbFrWx1fZDjY2Heh78Ls0xAHxauwuzU0Zd8uyajrL6nfikZqeirMjcFz/Imo4LnlrscZwKlq+p2wOTSo/jrgocaNE3ABhwmft9ZEmCd18ZPDtL4Du4D7LfBxGASdDCCU9zfwMuJKkNcAY8itcLgtBq6urWiOYkiJZkiClp0I4aC92EKRC6wIncaRozsjUpqPTVh9VNSQ7P8hZKEAT8JG8eZqaMhAihXQfEEhERdTcMfogSwBnwKAIIoPUlby3dnDEZfyp/L3hd6a3HaU81+uizotq/j2q2hyViuCPzKgiCgB/mzMK/nngVPtkPoPEg1taWx11OpjfJYYf9jRfhPbQ/rC5JpQx0vJIPXskPh6Rc0mYUdREDQ0FvgDq3F1Q5eVDl5kOdkwcxMxtiUhIEVdf9tTjE2AuVDfWKMrWgwnhz/3a9Xi2oMMLUOwY9IyIiujJ03b/libqxHbZjioNAVYKICUmXPhBzeIQN/9ttR6Ma/Fzw1mFt3R5F2dTkISj8bu9OljYZN2dMxtvVW9q8j17UYpSpsFN98JefhO3l5xGor41Yb1TpIIoq1Jk1qE8xoD7ZgHO5/XDEUwmz3QOzwwOz3YPRUhrU5swWgU4vqHJ6QUxOifpsWTwMMfbCpgZlMFhk7hvcc0VERERtY/BD1EkHHGfwj4oN8COAe7KmYXw7ghegMePYVqtyydtoU992HQQqCAImJQ3EqtrmJWlfW4/gtowpUfky7wi48d9nPwoLzG7LmKJod336OGy1HsRZT014HyFggCEXd2RObXMZXySyLMPz9VdwvL8CcsAffm+VCpohI6AbOwmnspx4z/ZNSIvmpVxaQY35g37c4WVvXdkQY/i+n+J2zBgSERFRIwY/RJ0gyRKev/AFLvqsAIBnzn2Kv/S/DxkaS5uv80l+vFSxXnFoKQBMaSNNcaiJlgGK4OeCtw7nvLXI16V34AnCSZDxXMUXYamqr00tQpY2WVGmEVT4ea8b8OSZj1Dta0Cq2oxRpkKMNvfBSFMhzO0I5ELJPi8c770J9/atYXVikgXG+YugHT0OorHxLJtpPivet+0OHvwZqsjct1sFPgCQrUnBAEMujrkaD5JNU5sx1tw3wb0iIiK6cjD4IeqEcs/FYOADAH45gJU1O7AkZ1arr6nx2fCXs5/guFuZEU0najA2qX17NoDGQ0PT1GbU+u3BslLrEeRnFnfgCcJtVB3DAUeNYgapUJeJ2zOnRGyfr0vH0/3vg0f2Qy9oLmvmyX+uHPbX/w5/5fmwOk2/gUj6wY8gJqcoyjM1FowyFYbtnWrSVua8K1VT0oIPLn4NnxTAwowJ0HezAI+IiCiWGPwQdcJB57mwsi/r92FR+kSka5LC6o44z+Opc5+gwe8Iq1ucPaNDMxSiIGKiZZAi5XSp7ShuuYzgZ1PDfuxQlcMEU7DMojLiVwUL2+ybKIgwCJ3/8i1LEtyb1sL56YcRl7kZps+GccGtrSYhmJk6MmLwoxZUGNNNZ0Rytal4MG9+ortBRER0RUp8/laiK9ChkJTOQOPsz8c1O8LKv2o4gMfL3wkLfHSiBr/otQAzUkZ0+P0nhuwvOuO5GLZcrb0OO8/hH1UbFWUqQcRD+QuQeYllfJcjUF8H6wtPwfHxu2GBj6DVIekHP4Jp0R1tZl8ba+4Hiyp8X9EoUx8mASAiIqIwnPmhLu2spwZbGw7BFnApygU0plGeljwMohDfGF6WZRyKMPMDABvr92Fh+kSkacwAGpMiPH/+i7B9KVmaZPxrwUIU6DIi3eaSBhvykKw2KQKqUutRLMqY2KH7fGM7gecvfIFAiwQHAPAvObMxOMLm+miQZRneXV/D8eE/ITnDZ8LUeQUwf/9+qHPyLnkvtaDCNSnDw4LOSZbut+SNiIiILh+DH+qy6vx2PHb6HdhDAp8m6+q/xTlPLe7OnhbXflX46mENOCPW+WQ/PqnZgcU5M3DRZ8Uz5z4NC3xGmgrxs17XdyopQBNREDExaYAiJXWp7Ui7gx9HwI3XKzeFpU0GgOvSxuGaTsxGtUegqgL2d9+A79ihCLUCDDPmwjh/IQSNpt33nJkyEp/U7Ax+zhpBjbHmflHqMREREXUnXPZGXdaGur2tBj5NPq3dheOuijbbRFtrsz5N1tfvRZW3AU+d/SQsSJqTWoR/K7jpsgKfJqEb+k+5q1Dprb/k6/bYT+E3J16LGPiMMvXB3VlXX3bfQsk+L5yff4T6J/8QMfARk1OR/JNfwXTjrR0KfAAgW5uC2zOnQoAAESLuzpoWlc+XiIiIuh/O/FCXJMsySqxHLt0OMv5esQ5/7HMXVHFa/ha632eQIQ/H3RUIyBKAxtmfh0+tCFuqN9bcD4uzr4naMr2hxnwkqQyK99luO4YF6eMjtpdlGW9Wbcantbsi1veXMvDTnPlR65/kdMB39FDjn/1lCNRH3pOkGzMBplvvCaaw7oxFGRMxM2UkAMCiNnT6PkRERNS9MfihLumM5yLOeZUHaF6dPAxGUYtqnxXf2E8Ey0+5q7Cmrgzz08bGpW+hmd6mWIagQJeB9fXfBstCA58cbSp+kjcvqvuTVIKICUkDsKF+b7Bsu+1oq8FPie1IxMBHL2pxT8bV0NXZYFBdRuY2jwe+E0e+C3gOwn/2DNDKGTwAoEpJg+mWu6AdUdTp92yJQQ8RERFdCoMf6pJCZ33SNRb8OHcOREGETw7g/518A2c9zcHRO9XbMDFpYMQ009FU47Oh2tegKBti7IUx5r7YWL8PEqSw1+hFLX6VfyNMMViKNTFpoCL4Oea6gBqfLeLnsK7F/qAmI02FeCD3WqQIRpSitMPv7y8/Ce+BvfAdOQB/+UnIgcAlXyMIIvTXzIFx7gIIOmZkIyIiovhh8ENdjizL2GZV7gspThoUnDXRCCr8MGcWHj/9TrDeLXnxWuWX+GX+gpj2LXS/j1HUoUCXDlEQMT1lODa2CESaLM2bi3xdekz6M9xUAJNKD0fAHSzbbjsaNgtW7bPiYMhyvdszp2JR+kQIggC/P/yMndbIAT+8e3bBtWkd/OUnO9RfTZ/+MN32fajz8jv0OiIiIqJoYPBDXc4JdyWqQmZXii2DFddDjfmYnjxcsWl/u+0ovrGfiGmmr0MuZfAzxNgrGJQtTJ+AL+v3KbK73ZQxKSwxQTSpBRXGm/srPocv6/djXuoYCIIQLNvaoAwmjaION6SNU7S5FMlmhfvrr+DeshGStb5drxE0Gmj6DoRm0FBoBg6BqqBPh96TiIiIKJoY/FCXU2I9rLjO1qagrz4rrN3d2dOwy35CkRHulYqNGN6vADqxYxnD2it05mdIi7NwsrUp+EH2NXi1svHA0OnJw3FrRnFM+tHSZMsgRfBT7qnGHscpFJn7AmicSdvScFDxmmLLYGjEtn/85YAf/lMn4Du0H97D++E/cxpt7eEBAEEUoS7sB83AIdAMHAp1Yb8OZ28jIiIiihUGP9SlSLIUFvwUJw2OOFuQpDLg7qxp+L8LXwTLqn0NWFmzA7dnTol632wBF856LirKhoQcBDovbQzGJfWHR/LFbKlbqFGmQuRqU3HB25xN7eOaHcHg57SnOix5xNTkIeE38vvhP34E3vKT8J84Ct/JY5A97vB2IVQpqdAWTWic3ek7EIKeaaaJiIioa2LwQ13KEdcF1PrtirIpyYNbaQ1MTx6GzQ37FftZvqzfh9syiqO+vOqI87ziWiuo0UefHdYuU2OJ6vteiiiIWJA+AX+7sCZYdtB5FsdcFRhgyMFXIbM+GRoLBuly4K84D/+ZUwicOQ3v6RPodWg/7AYD0M7PTdN3APTTZ0M7YgwElSqqz0REREQUCwx+qEsJnfXJ16WjQJfRantBEPCD7Guw/OQbwbI6vx3lnoso1GdGtW+hS94GGvKgEbrGl/6pSYPx+bF10FRWIaPGgfRaBy42PIJUpxqD3JXIVwM+rQpejRr5+gzU1/8Mss/bfANZBqTwTHWhBI0W2tHjYJg2G+qCwhg+EREREVH0MfihLiMgS/g6JMV1aKKDSAp1mcjUJCtSUJfZT0Y9+AnNlha65C0RZI8Hnm9K4d6yAT8qP4xan3LWrEGTBNHnhdkDwNFYlq3TQr7Efp+W1HkF0AweDu2Q4VD3HcA9PERERHTFYvBDXcYB5xlYA05FWXuCH0EQMMbcF2vqyoJlZY6TWJgxMWp9c0lenHRXKcoGG/Oidv+OClysgnvLRni2b4XkavzMklRG1PsdkOTmpAQ1PpvidVpR03aiA0GEulcBNP0GQN13IDT9B0G0JMfkGYiIiIjijcEPdRmhsz599FnI1aa267VFIcHPYed5OALuqB0sutdxWnGAqQgRAw25Ubl3R0hOB5wr34F7+zaEZl4TBQFJKiMa/I5WX28O+TzE5FSoCwoh5hXglM2BXtctgMYc24NiiYiIiBKFwc8VqNR6FJsbDsAteRXlKkHEUGM+bkyfANV3Z89cKXxyAKW2o4qyKe2Y9WkyzJgPjaCGT248rFOGjL2Ocky2DLqsfvnlAD6u2YH3q79WlPczZEMvai/r3h3l2bMLjvfehGS3ttomOSkDB0xaXEwzoCbNhJo0E/xqEVpvAFpfAD/PuBYmSYSYlg51fiHEpMbkDH6/H57SUgh6Q7weh4iIiCjuGPxcYfbYT+GZc6sUB2m2tNdxGtaAC4uzr4lvxy7TQccZOALKtMqTktofuOhEDYYZ87HHcSpYVmY/eVnBzxnPRfz1/GqcClnuBgAjjL07fd+OkqwNcLz/JjzfftNqG+3QkdBfPROawcNRXbkeG+r3hrUZZeqD9N4zY9lVIiIioi6Nwc8VJCBLeKNqU6uBT5MvanfjKssQ9DfkxKlnl2+n/bjiur8+B1naju01KTL3VQY/jlOQZAliB2fBArKET2p24v2LJfDLgbD6Ql0m5qeN7dA9O8uzqxSOD1ZAcoYvZRN0eugnXw391GugymxOuX1D+nhsrN8X9v/JVZHO9iEiIiLqQRj8XEE21u/DWU/NJdvJkPH3inX4Y5+7rojlb7IsY5dNGfyMTxrQ4fsUmfvg1crm6wa/A6c91egb4Sye1pz11OCF81/guLsirE6AgIXpE3FzxqS2kwZEgeR0wvHeG/Ds3h6xXjtsFEy3fR+qlPA9UbnaVExIGoDtLZYRagVNpz5TIiIiou6Ewc8VwiV58W71NkVZvi4ds1JGAQBOuauwqWF/sO6Uuwpr6vZgftqYuPaziSzL7T5k9IS7Muxg0/FJ/Tv8njnaVORoU1HhrQuW7bafbFfwI8kSPq39Bu9WbwvuG2qplzYdS/PmxmU2zXfsMOxvvoRAfW1YnWg0wXTzXdCOndjm53tTxiTssh9HQG5M0nBNynAY4rxHiYiIiKirYfBzhVhVszMsDfTi7BkYYWrce+KTAzjurlDMDL1TvRUTkwYgXRO/7F07bcfwWuUmqAQRt2RMxlXJQy/5ml0hS95ytKnopU3r1PsXmfpgdYvgp8x+CjdnTG7zNRe8dXj+/Bc46jofVidAwPXp43B7xpSYz/bIfj+cX3wM17rPEZrJDQB0YybAdNOdwSQFbemjz8Kv8hdiQ/1e5GpTcWtGcQx6TERERHRlYfBzBaj12bGqZpeirMjcNxj4AIBGUOGHObPw+Ol3gmVuyYvXKr/EL/MXxKWf9X4Hnj33WXDm5Lnzn+O4uxL3ZE1rc/ndztAlb+b+7Z41ClVk7ovVdbuD18dcF2ALuJCkipzF7IznIh459c+wzHlAYxC2NHcuBsX4PB9ZkuD9Zjucaz5BoLoyrF40GGG67fvQjZnQofuOMffFGHPfaHWTiIiI6IrH4OcK8O7FbfDKvuC1AAF3Z00LazfUmI/pycMVy9+2245it/1kXL4Ef1FbFrZkbHXtNzjjvoif9boeFnV4AFLprccZz0VF2bhOLHlrMsyYD62ghrdFyutv7acxtZXN/p/VfhMW+AgQMD9tLL6XORXaGM72yJIE7+4dcH7xCQLV4XuMAEDTfzDMdy+BKrVzM2FERERE1Kzr74bv4U67q7Gpfr+ibEbKCOTr0iO2vytrGswhsxwvV2yAR/JFbB8tHsmHtfV7Itbtd5bj4VMrIqaMDl3yZlEZMegyDg/ViGoMNxUoysocJyO2lWUZ39pPKcqyNSn4feFt+H729JgFPrIkwfPNdtQ/+Shsb7wYMfARVCqYFtwKy09+xcCHiIiIKEoY/HRxK6q+UqQs1oka3JrZ+v4Ni9oQNitU7WvAJzU7Y9ZHANjccCDsnJ7QPjx66p9hWd1Cl7yNMfftcGrqUEUhs1x77I0pr0Od89aGJVr414KFGGrMv6z3b01T0NPw5B9ge/1vCFReiNhOnV+I5J8vh2HmPAgif0SJiIiIooXfrLqwvY7T+LbFuTUAsCBtPFLV5jZfNz15WNgX+DV1ZfBJ4VnMokGSJXxWqzyAs68+O6yfXtmPZ899hvOexixmtoALh5znFG0mRCEdc5FJGfzYAi6ciDDrFPrZpqnNnU600BZZkuAp2xkMevyV4YkVAEDdqzcsS5Yh+aGHoS7oE/V+EBEREfV03PMTByXWw3ivugR6UYtiyyBMTxne6gb8JrIs459VWxVlKWoTrk8fd8n3EwQB92bPwL+dfD1YZgu4sM16GNNThnfuIdqw235SkV4aAO7Mugr5unT85ewqRRY1r+zD/5z/DI8X3oFvbCcUs1paQa1I4tBZWdpk5GnTcN7bnCq6zH4SA0LSVO+xn1ZcjzL36XSihUgkhx2e0i1wb/0SgdqLrbZT5xXAOO9GaEYURfX9iYiIiEiJwU+MVXjr8Nz5z4PnrZxwV+Cd6m2YmDQQs1JHYoihV8QvvNttx3Ai5KDNWzKKoW/nWS299ZkYYeqNfY7yYNkXdWWYljws6l+wP61VZqLrrcvECGNvCIKA3/e+Fc9f+AIl1sPB+lPuKrxTvQ2VvnrF60aZ+0AnaqLSpyJzX5yvbQ5+tlgP4paMycFn90p+HHSeVbxmtKlPVN7bf+YU3Fs2wvPNdsj+1vdaqfPyYZh7I7Qjiri8jYiIiCgOGPzE2MqaHcHAp4lP9mOr9SC2Wg+ity4TS/Pmoo8+K1gfkCW8Xb1F8ZocbSpmpIzo0HvPTS1SBD8n3ZU45q7AwMtIKBDqhKsyLIi4Lm1sMMjQiGr8KHcOTrurFTMxq2p3Qi2oFK8bZ+58lrdQk5IG4rMWQVmltx4HnWcx7LtkCIecZxWZ6QQIlz3rJNmscHz4Fjy7d7TZTp3TC4Z5N0I7cgyDHiIiIqI44jevGKrx2fBVw4E225R7qvHvp99VHE66ueEALoQsI/te5tQ2z8qJZKy5HzI0ygMxv6gt69A9LuWzkFmfFLUJU0LSSutEDX7a67qwYMcvB4L/LUDAWHO/qPVroCE3LCPehvp9wf/e41AueetvyIFZpe/Ue8myDM/OEtT/5+/bDHzUeQVIWvxjJP/6UehGj2PgQ0RERBRn/PYVQ6tqdipmfVSCGBYAAIBT8uCJ8vdR47PBJ/nxXvU2RX1ffTYmdiIRgCiImJM6WlH2te0wGvyODt8rkhqfDdtaLGcDgDmpRdBEeMY++izclXV1q/cabMyLeA5QZwmCEDZTtt12FPbvMtJ9GxL8jDIVdup9AnU1sP3tGdjefAmSM/xzFVQq6MZOQvLPfovkf30EuqLxDHqIiIiIEoTL3mKkwe/Ahvq9irJZKaNwS0YxNjfsx7r6b1HprQ/W1frt+M8zH2BC0oCw9Mt3ZF3V6fTP16SMwLvVJcElXgFZwvr6vbg5Y3Kn7tfSu9XbQhIWaHBtSLDV0rzUMdhjP4U9IVnWAGC8+fKzvIW6yjIMK6q+CgagPtmPrQ2HMD6pP86GHKzakf0+ktMB36F98O7bA+/+PZC9nrA2osEI/Yy50E++GmKSJcJdiIiIiCjeGPzEyGe138DbYk+JCBE3pI+HRW3ADenjcW3qaPyx/D0cczWf9XLWU6NY/gYAw429MdLY+b0oSSoDplgGY1ND80Gp6+q+xY3pEyLOQrXXN/YTinsCwDUpw9tcOiYIApbmzcVvTrwOa8CpqBufFL39Pk0sagMmJA3A19YjwbIN9XuhCzm81Cjq0D8kE1yoQH0dvHt2wbv3G/hPHIMc4dygJrrR42C6+S6IluTLewAiIiIiiiquv4kBR8CNNXV7FGVXJw9FZov9NzpRg98ULEKuNrXNe92RddVlZ2ebm1akuK7z28MOF+0Ie8CNFy+sVZTpRS0WpI+/5GuT1SYszZurKOuvz0G2NqXT/WnLjJSRiutyT3VYdroRpt4R91MF6uvg2rQODc/+J+oe+zUcH/0TvuNHWg18xCQLku77CZLuXcrAh4iIiKgL4sxPDHxRVwa35A1eCxBwY/qEsHZJKgOW974Fj5x6C/UR9uFMTBoYdjZNZ/TVZ2OgIU9x3s6aujJMtgzq1P1eq9oU1t/vZ08PS67QmiJzXyzNm4cPL5YiSWXAA7nXdqof7THCWIAMjQUXfdZgWejsWtN+H1mWEai8AO++Mvj2lcF3+kS73kMQROgmToHxxtsgGk3R6zwRERERRRWDnyhzSV58XrtbUTbJMhB5urSI7TM1Fvy24GY8dvptuEICptsyp0StX3NTixTBz0HnWZx2V6NQnxmx/VHXBZxwVWKgIRf9DNnB8sNiFbbZTihmo0ab+mBGcsfScE9LHoZpycM6+BQdJwoiZqSMwLvV26DyS8i70IDMajtUkgT5u2cYmX0CDsdBePfvQeBiVbvuK6g10AwaCu3w0dAMGwVVStszeERERESUeAx+osgnB/BqxUbYAy5F+cL0iW2+rlCfiX/NX4gnznwQTP88N7UoLFXz5ZhoGYjkKpMi09uq2p14MG9+WNvttqP4y9lPgtd99FmYlTIKw/W98IXqMAQ0H0RqFHW4P/faqB+cGg2yJMF/+gSuPnQW2FWG3IoGqP3KJWsaUQ1RtwquVu7RkqA3QDuiCLpRY6EZNAyCThebjhMRERFRTDD4iZJ6vwN/OfsJjrSYXQGAMea+igNMWzPMVID/7Pt9rK//FtmaFFybOiqq/dMIKsxOGYX3L5YEy7Y2HMLNGZMV+458cgCvV25SvPaUuwovVayDLMtwCl6YWgQ/i3NmIF2TFNW+Xi5ZluH99hs4P/0AgepKCAAGeZ1wBcL36hhEbZv3CgY8RROgGTQUgkbTZnsiIiIi6roY/ETBcVcFnjr7cViKahEibskobvd9eunS8IPsa6Lcu2ZzUouwqnYnPJIPACBDxkcXS7E0b16wzZf1+xT7Y9oy1twfV1uGxqSvneU7eQzOle+E7ddJUunhCoSnpI4U/IjJqdAOHw3tyCJo+g9mwENERETUTTD4uUybGw7g7xfWBc/RaaIV1Phx3txLplCOJ4vagDmpRfikZkew7KuGg7g5YzKytSnwSn58eLG0XfcyqfS4P3d2l1nuFqiuhOOT9+DduztivUmlgyiICEBCVWYSHCYtRAjonzwSakEABBHqgkJoRxRBlV/YZZ6LiIiIiKKHwU8nBWQJb1Ztxue134TVpWss+Nf8G9u13C3ebkgbhy9qy+CVm2d/PrxYih/nzcW6+j2oC5m9uivrahxynsdu+wnFgaZLcmYhRd01Mpt5dpXC/s5rEQ8bBQBVdi60g4ejMsODd8wV8OgbZ3JGmHrjtt63xrOrRERERJRADH46aZv1UMTAZ6gxH7/odQMsamMCenVpFrURc1JHY1XtzmDZ5oYDuC5tLFZe3KFoO8LUGwvSJ2BBOlDjs2FD7bfYYduHRbnTUGwZHO+uh5F9Pjg+fAvuks0R61XpmTDecAu0o8dBEARc5Xfi81Mr4PFZG7PpZUQvmx4RERERdX0MfjrpKstQ7LQdx3bb0WDZ3NQi3JM9HWpBlcCeXdoN6eOwpq4M3u+W6smQ8afy92ENOBXtbm+Rajtdk4Sb0ich71jj+UOJFqiuhO2VF+A/fyasTjSaYJi7APop10BQN/8vblEb8Z9978Fh53kU6jO7XKIGIiIiIootBj+dJAgCfpw3FxdO1eGCtw5LcmbhmpSOnXWTKMlqE2anjsZntbuCZaGBzxhzXww05MW7a+3i2bML9rdehuxxh9XpJ06FceH3IBojz7yZVHqMTeoX6y4SERERURfE4OcyGEQtfpV/I6wBZ5cNFFqzIH081tbtCUvU0CSaB6xGiyxJcH3xMZxrVoXVCRotTLfdA/2ErtdvIiIiIuoaxI6+YPPmzViwYAHy8vIgCAI++ugjRb0sy3jkkUeQm5sLg8GA2bNn4+jRo4o2tbW1uPvuu2GxWJCSkoIlS5bAbldutL9SZGtTrrjABwBS1CbMbuUsoYlJA9FXnx3nHrVNdrthe+X5iIGPKjsXyQ89zMCHiIiIiNrU4eDH4XBg9OjReO655yLWP/nkk3j22WfxwgsvoLS0FCaTCXPnzoXb3bxE6e6778b+/fuxdu1arFq1Cps3b8YDDzzQ+aegTlmQPh4aQTn5J0DArZntP5soHgI1F9Hw7BMR01jrxk1Gyi8fhjrnygtAiYiIiCi+Orzsbf78+Zg/f37EOlmW8fTTT+Phhx/GwoULAQCvvfYasrOz8dFHH+GOO+7AwYMHsXr1auzYsQPjx48HAPzP//wPrrvuOvz5z39GXh6/xMZLqtqMWSkjsbquOagotgxGgS4jgb1S8h07DNsrz0NyKGcGBUGE8aY7oL9qBs/kISIiIqJ26fDMT1tOnjyJiooKzJ49O1iWnJyMSZMmoaSkBABQUlKClJSUYOADALNnz4Yoiigtbd8BmxQ9t2VOwaDvlu0V6jLxg+zpCe5RI8nphP3d19Hw3J/DAh/RaELSj38Bw9UzGfgQERERUbtFNeFBRUUFACA7W7lfJDs7O1hXUVGBrCzl4Z9qtRppaWnBNqE8Hg88nuYDLK1WKwDA7/fD74+8YZ/aRwsVfp9/K+wBN0yiDgKEVj/TQCAAWZYRCARi1h9ZluHbswuulW9DtlnD6sWsXJh++CDEjEyOfRTFY2wp/jiu3RPHtXviuHZfHNvY68h3wisi29sTTzyBxx57LKx8586dMJlMCehRzyRJEmw2G7Zv3w5RjOqkIQBAXV+L5B1boD93OmK9O78PaotnQT5+Ajh+Iurv35PFemwpMTiu3RPHtXviuHZfHNvYczgc7W4b1eAnJycHAFBZWYnc3NxgeWVlJYqKioJtqqqqFK/z+/2ora0Nvj7U8uXL8dBDDwWvrVYrCgoKMH78eFgslmg+ArUhEAhgx44dmDBhAlSq6BzkKgcC8B3cC+/WjfAfPdRYGBrQarTQz70RummzMIC/NGIiFmNLicdx7Z44rt0Tx7X74tjGXtOqsPaIavDTt29f5OTkYP369cFgx2q1orS0FEuXLgUAFBcXo76+Hrt27cK4ceMAABs2bIAkSZg0aVLE++p0Ouh0uvDOq9VQq6+IyatuQxAEqFSqy/7cJacT7m1fwrP1SwTqa5tuHtZOO3QkTLfcDVV610nC0F1Fa2ypa+G4dk8c1+6J49p9cWxjqyOfa4dHwG6349ixY8HrkydPoqysDGlpaejduzd+8Ytf4I9//CMGDhyIvn374ve//z3y8vKwaNEiAMDQoUMxb9483H///XjhhRfg8/mwbNky3HHHHcz01gPIPh/cW7+Ea+0qSM7WpyhFswWmm++Etmg8kxoQERERUVR0OPjZuXMnZsyYEbxuWo62ePFivPLKK/jNb34Dh8OBBx54APX19bjqqquwevVq6PX64GvefPNNLFu2DLNmzYIoirjlllvw7LPPRuFxqKuSZRnesp1wrnofgdqLrbYTTWboi6dDP2MuRKMxjj0kIiIiou6uw8HPNddcA1mWW60XBAGPP/44Hn/88VbbpKWlYcWKFR19a7pC+c+chv29N+AvP9lqG3VBH+ivngld0QQIGk0ce0dEREREPQUXHlLMyIEAXOs+g2vNJ5AlKWIb3ehx0M+YB01h3zj3joiIiIh6GgY/FBOBqkrY3vx7q7M9mj79YbzxNmj6Dohzz4iIiIiop2LwQ1ElyzI8276EY+W7kH3esHpVRhaMN9wC7aixTGRARERERHHF4IeiQpYkeMt2wrX+c/jPnwmrFwQRhmuvg+HaGyAwzSMRERERJQC/hdJlkX0+eHaWwLVhNQIXqyK2UWVmw3z3EmgK+8W5d0REREREzRj8UKfIbjfcX2+Ga+MaSNb6Vtvpr5oB0w23QohwSC0RERERUTwx+KEOEd1uuNd8Au/WL9s8pFSVmQPTzXdAO2REHHtHRERERNQ6Bj/ULpLDDteaVchZ8yncOi3QSrICdX4hDLOvg3bkGAiiGOdeEhERERG1jsEPtUn2euDavA6udZ9Ddrsg+H2AThvWTtN/MAyzr4Nm8DBmcSMiIiKiLonBD0UkBwLwbN8K5+qVkKwNrbbTDh/dGPT06R/H3hERERERdRyDH1II1NfBs2MbPKVbEKipjtxIEKEbNwmGWfOhzu0V3w4SEREREXUSgx+C7PPBu38PPKVb4Dt8ALIstdrW1W8Qcu9bCl1Obhx7SERERER0+Rj89HDevbvh+OAtBOpr22ynHTwc2vkLcfjsBQzMyIxT74iIiIiIoofBTw8VqKuB44O34N1X1mY7dX5vGBfcCu2gYfD7/cDZC/HpIBERERFRlDH46WHkQADuzevgXP0xZK8nYhtBp4duzEToJk6Buk9/Zm8jIiIiom6BwU8PEqi9CNvLf4X/bHnEek2f/tBNmQ7dqHEQdLo4946IiIiIKLYY/PQQvpPHYPvHXyHZrWF1ojkJpoW3QztuMmd5iIiIiKjbYvDTA3h2lsD+z1chB/xhdfriaTDecAtEoykBPSMiIiIiih8GP92YLElwfv4RXOs+C6tTZebAfOe90PQdkICeERERERHFH4Ofbkr2+2F/40V49uwKq9MOGQHzDx6AaDAmoGdERERERInB4KcbkiUJ9jf+HjHwMUyfDeOC2yCoVAnoGRERERFR4jD46WZkSYLj7Vfh2bNTUS6IIky33A39lOkJ6hkRERERUWIx+OlGZFmG8+N34N6+VVEuaHWwLFkGzaChCeoZEREREVHiiYnuAEWP64tP4Nq0TlEmqDUMfIiIiIiIwOCn23B9uQbOLz5WlAmCiKTFP2LgQ0REREQEBj/dgmfn13CsfCekVID5niXQjihKRJeIiIiIiLocBj9XON+xw7D/8+WwcvNt90A3dlICekRERERE1DUx+LmC+SvOw/aP5yAHAopy04JbmdWNiIiIiCgEg58rlGRtgO1vz0ByORXlhmmzYZg5L0G9IiIiIiLquhj8XIFktxvWF59FoK5GUa4dOQbGhbcnqFdERERERF0bg58rjBwIwPb63+A/e1pRru7dF0n3/AsEkUNKRERERBQJvylfQWRJgv2tl+E98K2iXJWeCcu//BSCVpegnhERERERdX0Mfq4QsizD8cFb8Oz6WlEuGk2wPPBziEmWBPWMiIiIiOjKwODnCuH87EO4t25UlAlqDZKWLIMqKydBvSIiIiIiunIw+LkCONd9Cte6zxRlgkqFpB/+BJp+AxPUKyIiIiKiKwuDny7O9dUGOD/9UFEmCCLM99wP7dCRCeoVEREREdGVh8FPF+b6agMcH6wIKzd97wfQFY1PQI+IiIiIiK5c6kR3gCJzfbkGjpXvhJWbFt0B/aSrEtAjIiIiIqIrG4OfLsi57jM4P/0grNw4fxEM02cnoEdERERERFc+Bj9diCzLcK35BM7VH4fVGa+7CcZrr09Ar4iIiIiIugcGP12I6/OP4Fz7aVi5acGtMMycl4AeERERERF1Hwx+ugjXxi8iBz6L7uBSNyIiIiKiKGDw0wV4dpXC8fG7YeXmW++GfuqMBPSIiIiIiKj7YfCTYN7D+2F/6x9h5ebbfwB98bQE9IiIiIiIqHviOT8J5D9zGraXn4ccCCjKTQtuZeBDRERERBRlDH4SJFBTDevfnoHscSvKDdNmQz9jboJ6RURERETUfTH4SQDJ6YT1/56GZLcqynVF42FceDsEQUhQz4iIiIiIui8GP3EmSxLsr/8NgepKRblmwBCY71oCQeSQEBERERHFAr9px5nr85XwHtqnKFPnFSDphz+BoNEkqFdERERERN0fg5848pTthHOd8iwf0ZyEpH/5KUSDMUG9IiIiIiLqGRj8xIn//BnYVyhTWguiiKR7l0KVmpagXhERERER9RwMfuJActhhe+k5yD6votx0853Q9B+UoF4REREREfUsDH5iTJYk2F79PwRqLyrK9ZOvhm7KNYnpFBERERFRD8TgJ8ZcX3wM39GDijJNYT+Ybr6LKa2JiIiIiOKIwU8MeQ/tg3NNSIIDSzKS7lvKzG5ERERERHHG4CdGAnW1sL/+IgA5WCYIjQkOxOTUxHWMiIiIiKiHYvATA3LAD/tr/wfJ6VCUGxfcAk3fAQnqFRERERFRz5bQ4Oe5555Dnz59oNfrMWnSJGzfvj2R3YkKWZbh/OQ9+E4dV5RrR4yB/po5CeoVERERERGpE/XGb7/9Nh566CG88MILmDRpEp5++mnMnTsXhw8fRlZWVqK61SlywA//8aPwHtgD7749CNRUK+pV6Zkw33kfExwQERERESVQwoKfp556Cvfffz/uu+8+AMALL7yATz/9FP/4xz/w29/+NlHdajfZ44F3Xxm8+8vgPbgPstsVsZ2g1iBp8Y8hGo1x7iEREREREbWUkODH6/Vi165dWL58ebBMFEXMnj0bJSUlYe09Hg88Hk/w2mq1AgD8fj/8fn/sOxyB5LDD9vrfLtlOf+NtQG6vhPUzmgKBAGRZRiAQSHRXKMo4tt0Tx7V74rh2TxzX7otjG3sd+Z6dkODn4sWLCAQCyM7OVpRnZ2fj0KFDYe2feOIJPPbYY2HlO3fuhMlkilk/LyVLb4KmpipinWQ0oWHMZDgFLVBaGueexYYkSbDZbNi+fTtEkbkyuhOObffEce2eOK7dE8e1++LYxp7D4bh0o+8kbNlbRyxfvhwPPfRQ8NpqtaKgoADjx4+HxWJJWL/c1otwf/Fx8FqVXwjNsJFQDxsNVa8C9O5me3wCgQB27NiBCRMmQKVSJbo7FEUc2+6J49o9cVy7J45r98Wxjb2mVWHtkZDgJyMjAyqVCpWVlYryyspK5OTkhLXX6XTQ6XRh5Wq1Gmp14uI3/eixkM6ehnb4aGiGjYIqpfuf3yMIAlQqVUI/d4oNjm33xHHtnjiu3RPHtfvi2MZWRz7XhMy9abVajBs3DuvXrw+WSZKE9evXo7i4OBFd6hR1XgEs9/8M+inTe0TgQ0RERER0JUtY+PnQQw9h8eLFGD9+PCZOnIinn34aDocjmP2NiIiIiIgomhIW/Hzve99DdXU1HnnkEVRUVKCoqAirV68OS4JAREREREQUDQldeLhs2TIsW7YskV0gIiIiIqIegvn2iIiIiIioR2DwQ0REREREPQKDHyIiIiIi6hEY/BARERERUY/A4IeIiIiIiHoEBj9ERERERNQjMPghIiIiIqIegcEPERERERH1CAx+iIiIiIioR2DwQ0REREREPQKDHyIiIiIi6hEY/BARERERUY/A4IeIiIiIiHoEdaI70BmyLAMArFZrgnvSs/j9fjgcDlitVqjVV+T/OtQKjm33xHHtnjiu3RPHtfvi2MZeU0zQFCO05YocAZvNBgAoKChIcE+IiIiIiKgrsNlsSE5ObrONILcnROpiJEnC+fPnkZSUBEEQEt2dHsNqtaKgoABnzpyBxWJJdHcoiji23RPHtXviuHZPHNfui2Mbe7Isw2azIS8vD6LY9q6eK3LmRxRF5OfnJ7obPZbFYuEPbzfFse2eOK7dE8e1e+K4dl8c29i61IxPEyY8ICIiIiKiHoHBDxERERER9QgMfqjddDodHn30Ueh0ukR3haKMY9s9cVy7J45r98Rx7b44tl3LFZnwgIiIiIiIqKM480NERERERD0Cgx8iIiIiIuoRGPwQEREREVGPwOCHiIiIiIh6BAY/PczmzZuxYMEC5OXlQRAEfPTRR4r6yspK3HvvvcjLy4PRaMS8efNw9OhRRZtrrrkGgiAo/vz4xz9WtCkvL8f1118Po9GIrKws/PrXv4bf74/14/Vo8RjbPXv24M4770RBQQEMBgOGDh2KZ555Jh6P12PF62e2SU1NDfLz8yEIAurr62P0VBTPcX3llVcwatQo6PV6ZGVl4cEHH4zlo/Vo8RrXHTt2YNasWUhJSUFqairmzp2LPXv2xPrxerRojC0AlJSUYObMmTCZTLBYLJg2bRpcLlewvra2FnfffTcsFgtSUlKwZMkS2O32WD9ej8Lgp4dxOBwYPXo0nnvuubA6WZaxaNEinDhxAitXrsTu3btRWFiI2bNnw+FwKNref//9uHDhQvDPk08+GawLBAK4/vrr4fV6sW3bNrz66qt45ZVX8Mgjj8T8+XqyeIztrl27kJWVhTfeeAP79+/H7373Oyxfvhz/+7//G/Pn66niMa4tLVmyBKNGjYrJs1CzeI3rU089hd/97nf47W9/i/3792PdunWYO3duTJ+tJ4vHuNrtdsybNw+9e/dGaWkptmzZgqSkJMydOxc+ny/mz9hTRWNsS0pKMG/ePMyZMwfbt2/Hjh07sGzZMohi89fxu+++G/v378fatWuxatUqbN68GQ888EBcnrHHkKnHAiB/+OGHwevDhw/LAOR9+/YFywKBgJyZmSm/+OKLwbLp06fLP//5z1u972effSaLoihXVFQEy55//nnZYrHIHo8nqs9AkcVqbCP5yU9+Is+YMeNyu0ztEOtx/etf/ypPnz5dXr9+vQxArquri2LvqTWxGtfa2lrZYDDI69ati0W36RJiNa47duyQAcjl5eXBsm+//VYGIB89ejSqz0CRdXZsJ02aJD/88MOt3vfAgQMyAHnHjh3Bss8//1wWBEE+d+5cdB+iB+PMDwV5PB4AgF6vD5aJogidToctW7Yo2r755pvIyMjAiBEjsHz5cjidzmBdSUkJRo4ciezs7GDZ3LlzYbVasX///hg/BUUSrbGNpKGhAWlpadHvNF1SNMf1wIEDePzxx/Haa68p/hWS4i9a47p27VpIkoRz585h6NChyM/Px+23344zZ87E50FIIVrjOnjwYKSnp+Oll16C1+uFy+XCSy+9hKFDh6JPnz5xeRZSas/YVlVVobS0FFlZWZgyZQqys7Mxffp0xdiXlJQgJSUF48ePD5bNnj0boiiitLQ0Tk/T/fFvOAoaMmQIevfujeXLl6Ourg5erxf/9V//hbNnz+LChQvBdnfddRfeeOMNbNy4EcuXL8frr7+Oe+65J1hfUVGhCHwABK8rKiri8zCkEK2xDbVt2za8/fbbnJJPkGiNq8fjwZ133on//u//Ru/evRPxKNRCtMb1xIkTkCQJ//Ef/4Gnn34a7733Hmpra3HttdfC6/Um4tF6tGiNa1JSEr788ku88cYbMBgMMJvNWL16NT7//HOo1epEPFqP156xPXHiBADgD3/4A+6//36sXr0aY8eOxaxZs4J7gyoqKpCVlaW4t1qtRlpaGr8/RRF/SihIo9Hggw8+wJIlS5CWlgaVSoXZs2dj/vz5kGU52K7lF92RI0ciNzcXs2bNwvHjx9G/f/9EdJ0uIRZju2/fPixcuBCPPvoo5syZE7dnoWbRGtfly5dj6NChbQa6FD/RGldJkuDz+fDss88Gf0bfeust5OTkYOPGjdz7E2fRGleXy4UlS5Zg6tSpeOuttxAIBPDnP/8Z119/PXbs2AGDwZCIx+vR2jO2kiQBAH70ox/hvvvuAwCMGTMG69evxz/+8Q888cQTCet/T8OZH1IYN24cysrKUF9fjwsXLmD16tWoqalBv379Wn3NpEmTAADHjh0DAOTk5KCyslLRpuk6JycnRj2nS4nG2DY5cOAAZs2ahQceeAAPP/xwTPtNbYvGuG7YsAHvvvsu1Go11Go1Zs2aBQDIyMjAo48+GvuHoDDRGNfc3FwAwLBhw4JtMjMzkZGRgfLy8hj2nloTjXFdsWIFTp06hZdffhkTJkzA5MmTsWLFCpw8eRIrV66My3NQuEuNbaSfRwAYOnRo8OcxJycHVVVVinq/34/a2lp+f4oiBj8UUXJyMjIzM3H06FHs3LkTCxcubLVtWVkZgOYf7OLiYuzdu1fxA7x27VpYLJawH3qKv8sZWwDYv38/ZsyYgcWLF+NPf/pTrLtL7XQ54/r+++9jz549KCsrQ1lZGf7+978DAL766iumRU6wyxnXqVOnAgAOHz4cbFNbW4uLFy+isLAwdp2mS7qccXU6nRBFEYIgBNs0XTfNLlDitDa2ffr0QV5enuLnEQCOHDkS/HksLi5GfX09du3aFazfsGEDJEkKBsEUBYnMtkDxZ7PZ5N27d8u7d++WAchPPfWUvHv3bvn06dOyLMvyO++8I2/cuFE+fvy4/NFHH8mFhYXyzTffHHz9sWPH5Mcff1zeuXOnfPLkSXnlypVyv3795GnTpgXb+P1+ecSIEfKcOXPksrIyefXq1XJmZqa8fPnyuD9vTxKPsd27d6+cmZkp33PPPfKFCxeCf6qqquL+vD1FPMY11MaNG5ntLcbiNa4LFy6Uhw8fLm/dulXeu3evfMMNN8jDhg2TvV5vXJ+3p4jHuB48eFDW6XTy0qVL5QMHDsj79u2T77nnHjk5OVk+f/583J+5p7jcsZVlWf7LX/4iWywW+d1335WPHj0qP/zww7Jer5ePHTsWbDNv3jx5zJgxcmlpqbxlyxZ54MCB8p133hnXZ+3uGPz0ME1fakL/LF68WJZlWX7mmWfk/Px8WaPRyL1795YffvhhRXrq8vJyedq0aXJaWpqs0+nkAQMGyL/+9a/lhoYGxfucOnVKnj9/vmwwGOSMjAz5V7/6lezz+eL5qD1OPMb20UcfjfgehYWFcX7aniNeP7OR3pPBT+zEa1wbGhrkH/7wh3JKSoqclpYm33TTTYoUyRRd8RrXNWvWyFOnTpWTk5Pl1NRUeebMmXJJSUk8H7XHudyxbfLEE0/I+fn5stFolIuLi+WvvvpKUV9TUyPfeeedstlsli0Wi3zffffJNpstHo/YYwiy3GKXHRERERERUTfFPT9ERERERNQjMPghIiIiIqIegcEPERERERH1CAx+iIiIiIioR2DwQ0REREREPQKDHyIiIiIi6hEY/BARERERUY/A4IeIiIiIiHoEBj9ERERERNQjMPghIiIiIqIegcEPERERERH1CAx+iIiIiIioR/j/AcCndWzbnevcAAAAAElFTkSuQmCC", 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" ] @@ -188,17 +188,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:21.454708Z", - "iopub.status.busy": "2023-05-05T10:38:21.454470Z", - "iopub.status.idle": "2023-05-05T10:38:21.604696Z", - "shell.execute_reply": "2023-05-05T10:38:21.604141Z" + "iopub.execute_input": "2023-12-04T17:50:28.874466Z", + "iopub.status.busy": "2023-12-04T17:50:28.874327Z", + "iopub.status.idle": "2023-12-04T17:50:29.001707Z", + "shell.execute_reply": "2023-12-04T17:50:29.001422Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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ajQkf/ACAyWQCANTX1yM3N5dK4AghhBBCklzSNTzoWONjNpsHeCYkEXT8nNDaMEIIIYSQ5Jd0wU+HZMhMkL5HPyeEEEIIIYNH0gY/hBBCCCGEENIVBT+k1x544AFMmjRpoKeBM844A3feeedAT4MQQgghhMQpCn7iSG1tLe644w6MGDECRqMReXl5mDFjBp577jm4XK6Bnl6PrVy5EhzHxaz1eKxfjxBCCCGEDA5J1+0tUR06dAgzZsxAeno6Hn30UUyYMAEGgwHbt2/Hiy++iKKiIlx00UVBn+v3+6HT6fp5xrHn8/mg1+sHehqEEEIIISRJJX3mR2ISbIJrwP6RmBTRPG+99VZotVps3LgRl19+OcaOHYthw4Zh3rx5+Oyzz3DhhRcGzuU4Ds899xwuuugiWCwWPPLIIwCA5557DsOHD4der8fo0aPx2muvBZ5z5MgRcByHLVu2BMZaW1vBcRxWrlwJoDOjsnz5cpSXl8NsNmP69OnYu3evbK6PP/448vLykJqaiptuugkejyfk5zpy5AjOPPNMAEBGRgY4jsP1118PoL1M7fbbb8edd96J7OxszJkzp9t5hns9AJAkCb/5zW+QmZmJ/Px8PPDAAxF9/4QQQgghJPklfebHIXrw8/3PD9j7vzDyF7Bqw7fdbmpqwtKlS/Hoo4/CYrEEPUfZleyBBx7A448/jieffBJarRYffvgh7rjjDjz55JOYPXs2Fi9ejBtuuAHFxcWBYCFSf/jDH/CPf/wDOTk5+MUvfoEbb7wRq1atAgB88MEHePDBB/HMM8/g1FNPxWuvvYann34aw4YNC/paJSUleP/99zF//nzs3bsXVqs1sL8OALz66qu45ZZbAq/fnUhe7+6778a6deuwZs0aXH/99ZgxYwbOPvvsqL4DQgghhBCSfJI++EkEBw4cAGMMo0ePlo1nZ2cHsiq33XYb/vrXvwYeu/rqq3HDDTcEjq+66ipcf/31uPXWWwEAd999N9auXYu///3vUQc/jzzyCGbOnAkA+N3vfoe5c+fC4/HAYDDgmWeewY033oibbroJAPDwww9j2bJlIbM/Go0GmZmZAIDc3Fykp6fLHh85ciT+9re/BY6PHDkSdm7dvd7EiRPx5z//OfDa//rXv7B8+XIKfgghhBBCSPKXvSWy9evXY8uWLRg/fjy8Xq/ssfLyctnx7t27MWPGDNnYjBkzsHv37qjfd+LEiYE/FxQUAADq6+sBAPv27cPUqVNl51dUVET9Hh0mT57c4+cG03XuQPv8O+ZOCCGEEJLsmvx2PF+9BE8dX4wjHroHUqLMTxwYMWIEOI5Tra3pKCXrWtbVIVR5XCg83x7nMsYCY36/P+i5XZsndJTbSVJka5eipfwc0cwzGGXjB47j+mzuhBBCCCHx5t81X2Gr8wgAYIezCv8Ydj2sWvW95GCV9MFPisaIF0b+YkDfvztZWVk4++yz8a9//Qu//OUvow5sAGDs2LFYtWoVFixYEBhbtWoVxo0bBwDIyckBANTU1OCkk04CAFlTgUiNGjUK69atk73P2rVrwz6no4ObKIrdvn4k84zm9QghhBBCBguRSdjmPBo4dohuLG3Zgstyel6lk2ySPvjhOb7bhgPx4Nlnn8WMGTNQXl6OBx54ABMnTgTP89iwYQP27NnTbXnYvffei8svvxwnnXQSZs+ejU8//RQffPABli1bBqA9ezRt2jQ8/vjjGDp0KOrr63H//fdHPc9bb70Vv/jFLzBlyhTMmDEDixYtws6dO0M2PACAsrIycByHxYsX4/zzz4fJZEJKSkrQcyOZZzSvRwghhBAyWNhFNxiYbOzLls24IGsyjDxtJwLQmp+4MXz4cGzevBmzZ8/GfffdhxNPPBHl5eX45z//iXvuuQd/+ctfwj7/4osvxlNPPYW///3vGD9+PF544QUsXLgQZ5xxRuCc//znPxAEAZMnT8add96Jhx9+OOp5zp8/H/fffz9+85vfYPLkyTh69ChuueWWsM8pKirCgw8+iN/97nfIy8vD7bffHvb87uYZ7esRQgghhAwGbYJLNeYUPVjRunMAZhOfONZ1cUWCsNlsSEtLQ1tbG6xWq+wxj8eDw4cPY+jQoTAauy85I5FjjMHpdMJisahabycq+nlpJwgC1q1bh6lTp0KrTfqE8KBB1zU50XVNTnRdk1d/XtvtzqN4tPJ91XiWzoonh98ALafp0/cfKOFiAyXK/BBCCCGEEJIE7GLwrUea/Dasse0N+thgQ8EPIYQQQgghScAWpOytw6dNG5GABV8xR8EPIYQQQgghScAmukM+VuVtxJYfW2APZhT8EEIIIYQQkgTsYTI/APBp04Z+mkn8ouCHEEIIIYSQJKDM/OTo0mTHu13HsN9d059TijsU/BBCCCGEEJIEbKI883N2xonI0Mr3Qhzs2R8KfgghhBBCCEkCNkGe+cnSpeK8zJNlYxvtB9EmOPtzWnEl6uDn+PHjuPbaa5GVlQWTyYQJEyZg48aNgccZY/jTn/6EgoICmEwmzJ49G/v375e9RnNzM6655hpYrVakp6fjpptugsPh6P2nIYQQQgghZJBSlr2laow4K30CdFzn/kIMDNW+lv6eWtyIKvhpaWnBjBkzoNPp8MUXX2DXrl34xz/+gYyMjMA5f/vb3/D000/j+eefx7p162CxWDBnzhx4PJ19x6+55hrs3LkTX331FRYvXoxvv/0WP/vZz2L3qQghhBBCCBlEJCbBqdjnx6oxw6wxIF1rkY07QuwHNBhEtc3sX//6V5SUlGDhwoWBsaFDhwb+zBjDk08+ifvvvx/z5s0DAPz3v/9FXl4ePvroI1x55ZXYvXs3vvzyS2zYsAHl5eUAgH/+8584//zz8fe//x2FhYWx+FwkjOuvvx6tra346KOPAABnnHEGJk2ahCeffLLHrxmL1yCEEEIIIT1jF91gkO/jY9WaAACpGhMa/G2ycwerqIKfTz75BHPmzMFPfvITfPPNNygqKsKtt96Km2++GQBw+PBh1NbWYvbs2YHnpKWlYerUqVizZg2uvPJKrFmzBunp6YHABwBmz54Nnuexbt06XHLJJar39Xq98Hq9gWObzQYAEAQBgiDIzhUEAYyxwD+J5IYbbsCrr74KANDpdCgtLcV1112H3//+99Bqo7pUEen4ft5//33odLpuvy/GGL799lvMnTsXzc3NSE9PDzwW6WvEm46fk2A/S4OJKIpgjEEUxYGeCokhuq7Jia5rcqLrmrz669q2eB2q+zAT00EQBFh4g+yxNp8zqe57ovksUd1RHzp0CM899xzuvvtu/P73v8eGDRvwq1/9Cnq9HgsWLEBtbS0AIC8vT/a8vLy8wGO1tbXIzc2VT0KrRWZmZuAcpcceewwPPviganzjxo2wWORpPI7jYDab4XK5Eu4vEL/fj7PPPhvPPfccvF4vli5dirvvvhuMMdxzzz2yc30+H/R6fY/fRxAEOJ3ti90MBgMABI5DYYxBkqTAuTqdLvBYpK8Rb7xeL3w+H7Zt25ZwgVssSZIEu92O9evXg+epD0qyoOuanOi6Jie6rsmrv67tUa4FTl3nfZiR6bBpffu6fJu2CU6+87Edtj3I3Z9Y98nhRHP/GVXwI0kSysvL8eijjwIATjrpJOzYsQPPP/88FixYEN0so3Dffffh7rvvDhzbbDaUlJSgvLwcVqtVdq7H40FlZSXMZjOMRiOYJIG5Bu6GnDNbwEX4g67T6WA2mzFs2DAAwNixY/H5559jyZIlOHLkCFpbW1FeXo5nn30WBoMBhw4dQlVVFe655x4sXboUPM/jtNNOw5NPPokhQ4YAaP9tw7333ouFCxdCo9HgxhtvhFarhVarDQSOZ555Jk488cRAyZrX68Wf/vQnvPnmm6ivr0dJSQl+97vfYdasWbjwwgsBACUlJQCABQsWYOHCharXaGlpwZ133olPP/0UXq8XM2fOxFNPPYWRI0cCAF555RXcddddeOutt3DXXXehqqoKp556Kv7zn/+goKAgFl99RDQaDfR6PUaMGAGj0dhv7xtvRFHEhg0bMGXKFGg0moGeDokRuq7Jia5rcqLrmrz669oy+z5YavYGjvP1GZg6ZCoAYF+9B0da7YHHsqx5mJo/tc/m0t86qsIiEVXwU1BQgHHjxsnGxo4di/fffx8AkJ+fDwCoq6uT3cDW1dVh0qRJgXPq6+tlryEIApqbmwPPVzIYDIHMgmzyP97EK8c4jgv8w9wutPzpbtVz+0vmX54Al5Ia1XM4jgv82WQyoampCQCwfPlyWK1WfPXVVwDav7dzzz0XFRUV+O6776DVavHwww/jvPPOw7Zt26DX6/F///d/ePXVV/Gf//wHY8eOxT/+8Q98+OGHmDVrlux9Or4voD2gWbNmDZ5++mmceOKJOHz4MBobG1FaWorXX38d1157Lfbu3Qur1QqTyRR4XtfXuOGGG7B//3588sknsFqt+O1vf4u5c+di165d0Ol04DgOLpcL//jHP/Daa6+B53lce+21uPfee7Fo0aKef+FR6phzsJ+lwYbjOGg0mkH/PSQbuq7Jia5rcqLrmrz649o6mU92b5emNQfeL01vkT3mZL6k+jmL5rNE9alnzJiBvXv3ysb27duHsrIyAO3ND/Lz87F8+fJAsGOz2bBu3TrccsstAICKigq0trZi06ZNmDx5MgDg66+/hiRJmDo1eSLQ3mKMYfny5ViyZAl++ctfoqGhARaLBS+99FKg3O3111+HJEl46aWXAj/QCxcuRHp6OlauXIlzzjkHTz75JO677z5ceumlAIDnn38eS5YsCfm++/btwzvvvIOvvvoqsHarIxPFGENmZiYAIDc3V7bmp6uOoGfVqlWYPn06AGDRokUoKSnBRx99hJ/85CcA2svvnn/+eQwfPhwAcPvtt+Ohhx7qzddGCCGEEDIoKZsYWLXmwJ9TNaaw5w4mUQU/d911F6ZPn45HH30Ul19+OdavX48XX3wRL774IoD2qPbOO+/Eww8/jJEjR2Lo0KH44x//iMLCQlx88cUA2jNF5557Lm6++WY8//zz8Pv9uP3223HllVdSpzcAixcvRkpKCvx+PyRJwtVXX40HHngAt912GyZMmCBb57N161YcOHAAqanyzJLH48HBgwfR1taGmpoaWVCp1WpRXl4ecn3Lli1boNFoMHPmzB5/ht27d0Or1creNysrC6NHj8bu3bsDY2azORD4AO2ZRWVWkBBCCCGEdM8mumTH1i4BT4pGXtrvoOAnMlOmTMGHH36I++67Dw899BCGDh2KJ598Etdcc03gnN/85jdwOp342c9+htbWVpx66qn48ssvZespFi1ahNtvvx1nnXUWeJ7H/Pnz8fTTT8fuUyWwM888E8899xz0ej0KCwtlaTxlcweHw4HJkycHLRPLycnp0fubTKbuT4qRrg0TgPbgeTA3HSCEEEII6SmboMj8aMJlfmifn4hdcMEFuOCCC0I+znEcHnroobDlS5mZmXjjjTeifese4cwWZP7liX55r1DvHw2LxYIRI0ZEdO7JJ5+Mt99+G7m5uarGDx0KCgqwbt06nH766QDa1wlt2rQJJ598ctDzJ0yYAEmS8M0338halnfoCFjCddIbO3YsBEHAunXrAmVvTU1N2Lt3r2rNGCGEEEII6T1V5kcbOvPjFD2QmASeG3ydBZP+E3M8Dz4ldcD+ibTTW09cc801yM7Oxrx58/Ddd9/h8OHDWLlyJX71q1/h2LFjAIA77rgDjz/+OD766CPs2bMHt956K1pbW0O+5pAhQ7BgwQLceOON+OijjwKv+c477wAASktLwXEcFi9ejIaGBjgcDtVrjBw5EvPmzcPNN9+M77//Hlu3bsW1116LoqKiwOa3hBBCCCEkdlRrfrpke5SZHwYGp+TFYJT0wU8yM5vN+Pbbb1FaWopLL70UY8eOxU033QSPxxPIBP3617/GddddhwULFqCiogKpqalBN5Lt6rnnnsNll12GW2+9FWPGjMHNN98c6J9eWFiIBx54AL/73e+Ql5eH22+/PehrLFy4EJMnT8YFF1yAiooKMMbw+eefq0rdCCGEEEJI7ynL3lK7NDywatTLGhyDtPSNYwm4yMJmsyEtLQ1tbW1B9/k5fPgwhg4dOqj3bekLjDE4nU5YLPJ2iYmMfl7adZQpTp06NalaXw52dF2TE13X5ETXNXn1x7WVmIRr9zwFhs7b+seGXoshxtzA8YI9/4SP+QPHD5ZdiVHm5Gg2Fi42UKLMDyGEEEIIIQnMIXplgQ+gLnVL1VK7a4CCH0IIIYQQQhKastkBoC51S1U0PRisHd8o+CGEEEIIISSBKbM4Zt4AHS8vsaONTttR8EMIIYQQQkgCswnyzI+yxA2gjU47UPBDCCGEEEJIArOp2lybVefQRqftkjb4kSRpoKdAEgD9nBBCCCEk0akyP0FaWyvHBmvmJ+l6Ker1evA8j+rqauTk5ECv1ydNW+aBxhiD1+uFRqNJ+O+UMQafz4eGhgbwPA+9Xj/QUyKEEEII6RFV5ieisrfBmflJuuCH53kMHToUNTU1qK6uHujpJJWOgCGZAkqz2YzS0lLwfNImQQkhhBCS5JTNC4JtakoND9olXfADtGd/SktLIQgCRFEc6OkkDUEQsG3bNowYMSIpNmDTaDTQarVJE8gRQgghZHCyCfJAJk0bbM2PPPOjfM5gkfh3sCFwHAedTgedTjfQU0kagiCAMQaj0ZgUwQ8hhBBCSDJoE7tf85OiWvPjAWNs0P0SmGp9CCGEEEIISWDKhgdBu70p1gFJkOCWfH06r3hEwQ8hhBBCCCEJSmKSes1PBA0PgMG57oeCH0IIIYQQQhKUS/KBgcnGgmV+jJwOOk6+bGEw7vVDwQ8hhBBCCCEJSlnyBgTv9sZxXJB215T5IYQQQgghhCQI5R4/Rl4PHR+8MRW1u6bghxBCCCGEDEKMMdT4WtDktw/0VHrFJiqbHaizPh2UmZ/BGPxQv2JCCCGEEDKoSEzC8zVL8V3bLmg5DW4uOBunp40b6Gn1iHK/HmuQPX46qIIfgdb8EEIIIYQQktS+bNmM79p2AQAEJuL9hjUDPKOeiybzo3yM1vwQQgghhBCSxKq9zXirfpVsrN7fBpFJAzSj3rErMj/BNjjtoNzoVLleaDCg4IcQQgghhAwKEpPwXM0S+JmgeixRsyDKAEa5man8MWW3Nyp7I4QQQgghJCktbt6EA+6aoI8l6p436rK30Gt+lFkhCn4IIYQQQghJQlXeRrzbsDrk44na+UxZ9pYWtuEBtbqm4IcQQgghhCQ1gYl4tvpLCEwMeY6ya1qiaIui4UGwzA9jrE/mFa8o+CGEEEIIIUnt48b1OOKpD3tOIq75YYypsjfhGh6kKlpd+5kAD/P3ydziFQU/hBBCCCEkaTlFDz5u2iAbK9JnYby5VDaWiGt+XJJX1aUu3D4/wQKjwbbuh4IfQgghhBCStA566mTd3ThwuKVwDjJ1KbLzEnH9S7BW1eHK3ky8Hrzi9l+5ZijZUfBDCCGEEEKSVqvglB0XG7Iw3JSPFEUJWCIGP8rARc/pYOB1Ic/nOE71uROx3K83KPghhBBCCCFJq00R/KRrLQDUJWCJGPwo21yH6/TWwapVfm4qeyOEEEIIISQptEQa/CRg+Zdqg9MwJW8dkiHj1RsU/BBCCCGEkKTVJgTPjiTDhp/Kz6bM6gSTDJ+7Nyj4IYQQQgghSatFcMiOM37M/KjLvxIvA6Kcc7hmBx0G+0anFPwQQgghhJCkpcr8aIKXvbkkb9hNUOORcmNWq6b7NT/KvX4o+CGEEEIIISRJKLu9da75MarOTbQSMGXDg0jK3ijzQwghhBBCSBLySwJcklc21hH8WIIEP4kWCCjnm9qDzE+iBXy9RcEPIYQQQghJSspObwCQ/mPDAy2ngZk3yB5LtECg2S9fz5QeQatrdYvvxPrMvUXBDyGEEEIISUptojz40XFaWcCTqigTU66hiWc+SVCVvWXrrN0+LxlafPcGBT+EEEIIISQptQZpc81xXOA4kTc6bRLsqrEsXWq3z1MGfD7mh08SYjaveEfBDyGEEEIISUptIZoddEjkzmeNfpvs2KIxwsTru32ecpNTILE+d29R8EMIIYQQQpKSMvOjDn4SOPPjl2d+srXdZ30AwMIbwIGTjSXS5+4tCn4IIYQQQkhSUrW5VnRDS+Tgp1ER/GRFsN4HAHiOV3W6S6TP3VsU/BBCCCGEkKSk7PaW1l3mJ4EW/yvL3rIjWO/TQVn6lmhd7nqDgh9CCCGEEJKUul3zo03cts/KzE80wY8y6KPghxBCCCGEkATXqmgFnZFEa34aBXnmJ9KyNyCxP3dvUfBDCCGEEEKSDmNMlflRlr0py78SJQiQmKTa4DTShgdAsC53lPkhhBBCCCEkYTklLwQmysbSteEbHngkH/wJsOeNXXTDz+TzjGSD0w6JvNaptyj4IYQQQgghSUfZ6Q0A0hTd3qyKIABIjCyIcr2PhuNVgV046oYHFPwQQgghhBCSsJTBj0VjhI7XqsaUe94kQiCg7PSWqU0Fz0V+W5/IjR56i4IfQgghhBCSdJTrfZTNDoD2jIlZY5CNJUIg0JtOb0DirnWKBQp+CCGEEEJI0lHt8aNRBz9AYnY+axSUG5xGF/yoW13H/2eOFQp+CCGEEEJI0mkT5G2uQ62JScTgp0lR9pYTRbMDQP2Z3ZIPfkVziGRFwQ8hhBBCCEk6rWL4NtcdEjH4UZa9ZUXR5hpQt7oGAGcClPvFAgU/hBBCCCEk6SgbHqSHDH4Sb88bZcODaNpcA0BK0C538R/0xQIFP4QQQgghBACww1mJ/zv2CRbWfg1HAgQB4aiDnxBlb8rOZ3G+541X8qsClWjX/Gg4HhZF0NekyCYlq6iCnwceeAAcx8n+GTNmTOBxj8eD2267DVlZWUhJScH8+fNRV1cne43KykrMnTsXZrMZubm5uPfeeyEI8b+ZFCGEEEJIMqv0NOCvVR9ig/0AlrZswVv13w/0lHqlVbXmJ3jmR7nXT7xnQIIFKdF2ewOAAn2G7Lja19zjOSUSbfenyI0fPx7Lli3rfAFt50vcdddd+Oyzz/Duu+8iLS0Nt99+Oy699FKsWrUKACCKIubOnYv8/HysXr0aNTU1+OlPfwqdTodHH300Bh+HEEIIIYT0xNsNqyB0WfS+xXF4AGfTO34mqjqYhQp+lCVg8R78KDu9pWhMMPL6qF+nQJ+BA+6awHGNt6XXc0sEUQc/Wq0W+fn5qvG2tja8/PLLeOONNzBr1iwAwMKFCzF27FisXbsW06ZNw9KlS7Fr1y4sW7YMeXl5mDRpEv7yl7/gt7/9LR544AHo9dFfOEIIIYQQ0jt7XMfxg+OQbCwR1r6EYlNkfYBwra4Ta88b9Xqf6LM+QLDMTwuYIMC/bxfE+jpAEsEkCZAkQBTBW9NgmDwNnFHdLCGRRB387N+/H4WFhTAajaioqMBjjz2G0tJSbNq0CX6/H7Nnzw6cO2bMGJSWlmLNmjWYNm0a1qxZgwkTJiAvLy9wzpw5c3DLLbdg586dOOmkk2LzqQghhBBCSEQYY3iz/jvVuI/54ZcE6PiobxcHnHK9j4bjkaLYzLRDonV7622ntw6F+kzZMTt0AK1vPACxvjbkc9zfLIP1f34JTW5eyHPiXVQ/zVOnTsUrr7yC0aNHo6amBg8++CBOO+007NixA7W1tdDr9UhPT5c9Jy8vD7W17V9ibW2tLPDpeLzjsVC8Xi+8Xm/g2GZrj3gFQaD1Qv1IFEUwxiCKg6MP/GBC1zY50XVNTnRdk9NAXtfNjsPY6zoe9LFWnwMZ2pR+nlHvNXltYIwFjq0aEyRRggRJda4Zetm5HtEHt88Ts6Av1te2wdsqm2+mxtKj++FcjRWMMRg8fsz8/iBO2FULvyEHPMeFfI5YX4OW//sLLNf+D3RjTujR/PtCNJ8/qqt63nnnBf48ceJETJ06FWVlZXjnnXdgMqlb5sXKY489hgcffFA1vnHjRlgswVOYJPYkSYLdbsf69evB89QoMJnQtU1OdF2TE13X5DRQ11UCw0Ldejg5Z9DHv/9hHXJZ4gU/W/jjcGo7P1Mq02DdunVBz3XDD6de/vm/2bAKqYhNeVesr+0u7UE4+c75trbVY93h4J8tHIGJGFpVidlrj8Di9kMC0OayQwdN+Cc6nXA89Ve0Ta6A44STgTDBUn9xOoP//AbTq5A2PT0do0aNwoEDB3D22WfD5/OhtbVVlv2pq6sLrBHKz8/H+vXrZa/R0Q0u2DqiDvfddx/uvvvuwLHNZkNJSQnKy8thtUbX15z0nCiK2LBhA6ZMmQKNppt/MUhCoWubnOi6Jie6rslpoK7rKtseuGoZLAj+y+QRxaMx1lzcb/OJleqmdbA0VQWOR1hKMbVoatBzJSbh5f0/gKEzmzJy3DiUGXJiMpdYX9u3D++Bxe8LHE8tOAlTU0dG/TqeFUtg/fYo/EwC+PZ56XR6pGiM4HMLwGdmArwGHM9DrK+FpCiHS9mzFTqTAebLrwOnG9h1+x1VYZHoVfDjcDhw8OBBXHfddZg8eTJ0Oh2WL1+O+fPnAwD27t2LyspKVFRUAAAqKirwyCOPoL6+Hrm5uQCAr776ClarFePGjQv5PgaDAQaDuk5Tq9XKus2RvsdxHDQaDX3vSYiubXKi65qc6Lomp/6+rn4m4v3mdeDC/ObeBX9C/pzZmUf2uTL0qWE/R4rWJOsO54Ivpp87VtdWYhJaRIfss+Ua06N+XbGlGd4ln0DHayB0Kcfz6jXIm3cVjKfOAtclS8V8XjjeegXezRtkr+PfsgHOxjqk3ng7NBnyNUT9KZrPH1Xu7Z577sE333yDI0eOYPXq1bjkkkug0Whw1VVXIS0tDTfddBPuvvturFixAps2bcINN9yAiooKTJs2DQBwzjnnYNy4cbjuuuuwdetWLFmyBPfffz9uu+22oMENIYQQQgjpG1+3bEeDv002puPkN5Hxvvg/FPUGp+GXSag7vsVnp7s20SVrRw70rNub++svwERRdr0PDMvGiv+ZC9Pps2WBDwBwegNSrvsZzHMvASAPloVjlfCsWhH1HAZKVGHisWPHcNVVV6GpqQk5OTk49dRTsXbtWuTktKcFn3jiCfA8j/nz58Pr9WLOnDl49tlnA8/XaDRYvHgxbrnlFlRUVMBisWDBggV46KGHYvupCCGEEEJISF7Jjw8a18rGRpoKYeb12Oo8EhhzxGkQ0B31BqfmsOenakyoQec+N3YhPoM+Zac3DccjTRP+sylJbS3wrm3fwFbPtZe7bZtQiGWzRqPM4Av5PI7jYJ49F9qCYthf+zeYt/1nQzd8NMznzYtqDgMpquDnrbfeCvu40WjEM888g2eeeSbkOWVlZfj888+jeVtCCCGEEBJDO11VsInyAOGq3FPxdet22ZgzYYMfeeYn1B4/HZTtrpUbpMaLpiBtrnkuuiYK7q+XgAl+AICO10LUcFg7pQwAUONrgcSksK+pH38i0u78Pez/eQYQBKRe/wtwmsQpjUycmRJCCCGEkJio8bbIjocZ8zHWXIz19gOy8UQse2OMoU0R/GR0V/amTYy9ftQbnEbX+EuytcGz+pvAsY7TYse4AjhS28v+fExAk+BATjevq80vRNqdv4dkt4FP6dk+QwOFemQSQgghhAwy9Yq1PoWGDACJs/YlHLfkg4/J933pbs1PSoJ8bnXwE13g4V7RmfUBAI1Gi22njJCdU+Ntjui1eLMF2ryCqN4/HlDwQwghhBAyyCgbHeTq0gCog4B4Lf8KR1nyBgBp3az5sWoSJfOjKHuLIvMj2W2qxgTGU2YgNbtINlbtk2cFkw0FP4QQQgghg0yDIoOQEwh+lGtf4jMDEk6bYi2TidfDwOvCPke55ideg58mQR78RJP5ca9YCubvzPpwHA/T7Lko0GfIzquh4IcQQgghhCQLxhjqffLgJ08fKvOTeMFPi7LZQTclb0Cw4Cc+P7cy85OtjSz4kRx2VdbHMKUCmqzsQMljh2pfZGVviYqCH0IIIYSQQcQmuuBjftlYxwJ39ZofNxhj/Ta3WIi22QEQJPiJw1bXHsmnKkOMtOGB+5uvwHzewHF71ud8AEChXr45aXWEa34SFQU/hBBCCCGDSL2i5I0Hj0xtCgB12RsDg1sKvfdLPFJmfrprdgCou735mB9eyR/i7IGhzPoAQFYEZW/M64Xne3nWRz95KjQ5eQCgKntrFhzwJNg1jwYFP4QQQgghg4hyvU+2rnOvGGXZGxC/619CUWZ+elL2BsRfyZ9yj59UjanbtUwA4P1hHZin6zXkYD57buCoQJ8BDpzsObW+1t5MNa5R8EMIIYQQMojU+xSd3n5c7wMARk4HLaeRPR5vQUB3WgV5w4N0TfhObwBg5vWqACDegj5lm+uIsj6MqbM+4yZAk5vfecxrVa+VzB3fKPghhBBCCBlElG2uOzq9AQDHcUH2vImvIKA7Pcn88Byvyv7Y4uxzqzu9db/eRzhyEEJ1lWzMeOqZqvOUpW/JvO6Hgh9CCCGEkEFEucFpjuImOtE7vqnX/HSf+QHUpW/x9rlVnd4iCH6UWR9NVg50o8erzitSND1I5nbXFPwQQgghhAwiyjU/uV0yP0D8BwHhiExSZaoiaXgAAKlaRcYrzjq+KTN23bW5luw2+LZulI0Zp88Ex6tv/wsGUbtrCn4IIYQQQgYJiUmqtSO5+uTJ/LQKTjDIW3NH0uoaUHe6i6dyvz2u49jjOi4b6y7z41n7HZgoBo45rQ6GqacGPTfYRqeJ1uI8UhT8EEIIIYSEwRiDU/RAYGL3J8e5FsEJkUmyMWXmJ56DgO7UKbqU6TktrBE0PACCbXQaH5/bJwl4oWapbEzPaTHeUhLyOUwU4V29UjZmOPkU8JaUoOcr9/rxSn40C46eTTjOaQd6AoQQQggh8aZFcGCHsxLbnEexw1mJVsGJbJ0V9xTPQ5kxZ6Cn12PK9T7BgoNEzvwo16rk6dPBcVyIs+WCbfAaD95rXINaxef6Sc70oG3JO/h3bYPYKn+OccYZIc/P1KbAwOtkextV+5oj6iiXaCj4IYQQQgj50RrbXnzYuA5V3kbVY41+Gz5qWoc7ii4YgJnFhrLNdY4uTRUcxGsQEIlaf6vsWFnOFU48Zn4OumuxuEm+bme4MR/nZ54c9nnKRgfakiHQlg4NeT7HcSjQZ+CIpz4wVuNrwQRLWQ9mHd+o7I0QQgghBMBRTwOePv5Z0MCnwxFPQz/OKPaUzQ5y9Op1I8qyt0TK/CjL3rruYdQdVfAzwA0P/EzECzVLZWuYNByPXxTOCWxKG4xYXwffvl2ysWDtrZWUpW/J2u6agh9CCCGEEACbHAe7PafJb0/oheDKsjfleh8gscveahXBT1SZH218ZX4+blyvCsQvzZ6GYkNW2Od5VsmzPrzZAsNJU7p9P9VeP0na7pqCH0IIIYQQAMcVv+k28npMtAyRjfmZAJvo6sdZxZZ6g9NgmR9l8DPw5V+RkJikyvz0puzNJrohKZpD9JcqbyM+alonGysz5OCirPBBjHC8Ep5VK2VjhqmngdPpu33PQsPg2OuHgh9CCCGEEADHvE2y46tzT8NvSy4GB/maGOVmk4mkwacoewuS+VEGAW7JlxCd7loEJ3xMkI0Fy2yFkqmVd0ITmIi2AQp0P2vaJOvKx4HDzwvPgZbThHwO83ph/++LYGLnd8BxPIzTT4/oPQsVgWKT3w6fJIQ4O3FR8EMIIYSQQU9kkmpjx2JDFniOV3W8StTgx89EVfvivCBrYpQNDwDAmQClb8HaXCsDmnAytBZVcKEMFvvLfneN7Hhu1mQMNeaFfY7zo7cg1tfKxoxnnA1Ndm5E75mnT5cdMzA0CYn5sx4OBT+EEEIIGfTqfK2q7EaRvn1thbI0rEkYmBvi3mry21QbgAYre7MECX7sCRD89KbNNQDwHK/aOFS5Rqo/+CRB9VlOSR0R9jneLRvhWfudbExbXAbz+ZdE/L4mXg8jLy+PaxWcET8/UVDwQwghhJBBr0pR8mbVmGH9cQF8smR+6hWd3sy8IWigo+U0MCluggd68X8ketPmuoMyGByI4Oe4r0kVpBYbskOeLzY3wvH2q7IxTm9A6nU/A6eNblcbZaasOUF/1sOh4IcQQgghg95xnzz46dpRK1uryPwk6A2heo8fddanQyK2u1aWvSnLuCKhXCOk/M76Q6VH3uEtV5emCkY7MFGE47V/g3nkwanlsmugyQ1fJhdMpk4R/CjKJJMBbXJKCCGEkEFP2eyga/CTLJkfZae3cHvgpGiMsvMTYc2Pss11fk+CH8V30ui3w39gL3zbN4N53OCt6eCsaeDT0sFb06AtLAanN/Ri1mrK9tYlIbI+Yn0dHO+9Dv8ReYt2w+RpMJRX9Oi9M5SZnyQse6PghxBCCCGDnjL4Kema+VEFP4m55ke1wWmYTmjKdtfxXvbW2zbXHbqu+TG7fBi7dAXajnwZ8nzOYIT5nAtgnDkbnCY2t9WViuCn1CgPfpjfD/fXX8K97HMwwS97TJOVA8tl10S11qmrwVD2RsEPIYQQQgY1kUmqBeZFsuBHXh5mE13wSQL0fGLdRilLuMK1gVa2u473hge9bXPd9TmcxHDCrhqc9v1BGL0CYApdPsa8Hjg/fQ/eTeuQcsVPoS0dGvV7KqmCH0NO4M/+A3vheOc1iA21yqeB43mkXHczeKNJ9ViklJmfFsr8EEIIIYQkl9ognd7Clb0BQJNg71FmYSCpMj/6cGt+lBudxnfwo25zrYuqzXWHbLsfl7+/GUXVnYGiwMSw++sAgFBdhbYnH4PxtFkwzbkIvNkc9XsDgE1woU0RcJQYsiHU1cD12Qfwbd8c9Hl8WgZSrroeurJhPXrfDhk6i+y4hdb8EEIIIYQkF2XJm1VjlmU+TLweFo1Rtu6l0W9LqODHI/lgU2zYGU3mxxHnZW/KzF1+lG2uAYB5PJBe+BeKa2yyXmsCE6E3p0I/qRzM6YBka4Nka4PY3AR0OZMxCe5vl8H97TJwBiN4axp4axpgTkFm9XE4tq8DJwhgPh8gitDk5EF/wonQjZ0A3tIeqCmzPulOAakffoK29avBumx62oHjeBhPnwXzuReDM6o790UrUysP9FsEByQmgeeSp0caBT+EEEIIGdSUnd6CLTDP1qbKgp9E6/gWrElDuG5vyhbY8Z75Uba57kmzA8+qFZCam6DlNPB3KaFrPWE0Cq/8JfhU+fflP3oIzrdfhVBzXPVazOuB2OCB2FAHMAaT0wmhsRboEpAJ1VXwbt0IjuOhHTocutHj4XRV4vSGA9B7RZg8fow5aoNPG/yzaEuGIOXyn0JbXBr1Zw0lQyvP/IhMgl10I00xnsgo+CGEEELIoKbu9JapOidLZ8VRb0PgWFlCFu+U632sGjMMvC7k+amqhgfxHfz0ts0183rhXrEEAKDjNPBDQJvViGWzRuHkyWfghFR1oKgrG4a0X/8R7hVL4V7yqar5QMTvzST4D+2H/9B+pPltKBc6s2ymIPsw8WYLTOfOg3HGGeD42GZk0rUWcOBk+wy1CE4KfgghhBBCkoVyg9NgG0oqO74lWuZHuVlnuDbXQLA1P/Fe9tYqO4428+NZtQKSs319S8f6nk/PH4/6PCuKfaEDXU6jhXn2+TBMKofzw7fg370jaHlapHySvGlD16YanE4P0xlnw3jmHPCmnq0p6o6G45GmNaO1y7qjZsGBIcjtk/cbCBT8EEIIIWTQEpiIGl+zbKwoSOZH3e46sYOfcCVvQLA1Px4wxnrcQrkvSUxCfS/aXDOfF+4VSwPHWk6DQ0OzUJ/X/h0p90cKRpOdC+vNv2pfN2RrDawLkuw2CLZWVFdWInvUaGiMJnB6PSD44duzE/69O8H8nRkjZcc6PacDx/EwVJwG8zkXgk9Lj/hz9VSmNlUe/PiTq+kBBT+EEEIIGbTqfG0QFb+pD7bmJ0sRLDQKiVX2pizT664NtDLzIzARHuaHidPHfG69FazNdZ4uPeLne1Z/C8nR+f3oOA3WnjIkcBxNiSNnNEJjzIcmNz8wJggC7OvWwTh1KrTazltv44wzwfw++Pfthm/nVjjqqnDQJcBj0MJn0MJj0OLqYecjfcIUaLJygr1dn1Cu+0m2jm8U/BBCCCFk0FKu90nTWlQ3/kB7w4Oumv2J1QWrwde7zA/Qnv0x8fEX/ARrc628gQ+F+X1wf/2FbIwfMw61+YbAcYvggF8SoOuDfZ04nR768SdCP/5E7LLvx0fHPg08lqoxIXPkef2ebcvUKTY6TbLgJzH+jSWEEEII6QPHFK2FS7rs79OVcq8fPxNgj/N1MF2pMj/drPkx8XpwkN90x+u6n960ufas+RaSXf7dpM25WHVefzS4qPQofxazB6TMULXRaZKVvVHwQwghhJBB65iizXWxPnjwk6G1gFfcNjUkyLofh+iBS/LKxrore+M4Lui6n3jU0zbXzO+He/mXsjH9qHFIHT5W1epbuWaqL1QpAvFSY/+VunWlCn4Um64mOip7I4QQQsigdcyrbHYQPPjhOR5ZulTZ4vcmvw0jTPlBz48njYqsBQcOmYpMVjApGqNsY9R4bXfd0zbXnrXfQbLJn2s69yIA7cHh4S6ft18yP8rgJ8jas/4QSdnb/1Z9jDStGSWGLBQbsjDSVABjHJZEBkPBDyGEEEIGpWCd3opDBD9Ae8e3rsFPonR8U968pmnN0P3YzjmcRGl3rWxzHUmnt/asj3ytj27kWOiGjgDQvibqsKcu8FhfZ368kl8VxAVrvNEfMhWZH4folq15coge/OA4KDvnf4ctCPvvTjyhsjdCCCGEDEq1vlZVp7dwN3DKdT+NQoIEP4o1G8qyplBSEqDsLVib60jK3rzrv4fUJl8rZD7ngsCflWuilJvExtoxb5NsY1EOXNDNdvuDMvgB5AG0skmIhuOj3ldpIFHwQwghhJBB6bjiJi49RKe3DtlaeYe0pn4ohYqFVsWajUiDn1TFdxGPZW89aXPNBAHuZZ/LxnTDR0M3YnTgWLkmqq+zfMr1Prn6tAErIzPxeug5nWys67ofZZOQQn1mYGPYREDBDyGEEEIGpSpF8NNd2Y4y89Mf60BiQblPS2aEbaDVDQ/ir+ytJ22uvetXQWxVZH3mXCg7VrYC7+uyt3hZ7wO0N7sIt+4n2n9v4g0FP4QQQggZlI4rO711c8OZrQh+mhJ0zU+GLrLMj7LjWTyWvUXb5pqJQbI+w0ZC2yXrAwA5isyPQ3TDLfl6OdvQlJmfgVrv00G10am/a/ATX3ONFgU/hBBCCBmUlGsXivXh11hkK7IBdtENr+SP+bxiTblPS7A1HcGoGx7EX/ATbZtr74Y1EFvk190050JVwJQbZBPYvlz3o9zjp9QwMG2uO2QqN/X9MYBmjKkyP6H2xopXFPwQQgghZNBp7/Qmzxp09xtsZdkbkBjZH+U+LZGv+ZGXvcXjpq7RtLlmogD3V5/JxnRDhkM3cqzqXB2vRboi+9HQR6VvbYJT1lIcAEqNA5z50SkyPz8GPzbRpSp/pMwPIYQQQkicC9bpraib7lomXq8qBYv3jm9+JqpurLtbE9NB2fAgHjM/0bS59m5aB7FZnmExzbkoZJmcsvStr9b9KNf76Dkt8rrZhLavKbODHcFPsLkq10fFOwp+CCGEEDLoKDMG6VqLKrAJJltRDqTcQDTeKDu9AZGv+VGWvblELyRFwDiQomlzzUQR7qWLZWO6smHQjR4X8vWVHd8afH1zrZVraIoN2eC5gb1FVwY/He3SVaWicTDXaNEmp4QQQggZdJoUGRvlep5QsnRWHPU2BI7jfaNT5XofHadFCt99kAeo9/lhYHCIXqTyBgiVhyHWVoO3pEA7YjR4kzlmc45Uja814jbXvs3rITY1yMaCrfXpKlev7PgWffDjED3Y7ahCG0JnzZTrfeJhDY2yNLJFcIIxFiT4GZi9iHqDgh9CCCGEDDrKtTpZWvV6nmByVB3f4jvzo1zvk661hL3h76pr2ZvB48eQymY4178MYf8BSM7OoIrjeGiHtGdRdKPHQ1s6FBzf99mAHxyHZMfpWkvQkj7m8cClyPpoS4ZAN+aEsK+vyvxEWfbW5LfjwaPvoN7XCpfehRLXcEywDlGdd8BdKzse6PU+AFStrv1MgEPyBGlzPfBzjRYFP4QQQggZdJTtn5U3e6FkKTJE8Z75UX3OCJsdAO2L/vXQYsrqfZiyqRK8xCAZaiHx8g0wGZPgP3wA/sMHgC8/gSYrBynX3ATd0BEx+QyhbFYEPyenDFMFdkwUYH/1OYgNdbJxczdZH0CdDWzw28AYizh4/E/t14GAiYHhg6Z1quCnTXCqWq6PMhVF9Pp9KS1IENnktwdpcz3wWapoJVaRHiGEEEL63WbHYbxY8xVWtu6IqzUfvaHM/Cj38Akl0fb6UW5wquzi1Z1xlTZM3XAUvMQAQNUkIhixqQG2Z/4O78Y1Ub1XNByiB3tcx2VjJ6cMkx0zSYLjzVfg27NTNq4tGQLduIndvocy8+ORfLBH2PRho/0AfnAclI3t9VTDJXplY7sVn8HI6zHUmBvRe/QlHaeBVSMvZTzgroVHsddRonV6Ayj4IYQQQkgY+901+FvVh1jRuh0v1CzFK3UrwBgb6Gn1WrNq75seBj+CPa4DQlXwE0Xmh4kiKr7fJxuT0PlZNZnZ4DTBi4iYKMC+6GU4P/sATIr997PVcRgMnT+HOk6LEyylsnNci9+Hd9Na2RhvtiD12v+JKHuTpUsFB/l5kZS+uSUfXqldoRqXmIQdrkrZ2G7XMdnxaFMhNHHSQECZDd3uPCo7NvOGqDKJ8YLK3gghhBAS0hrbXtnxVy1bkaIx4vKcGQM0o95jjPW4HCxLKy+FEpiINtEVVVDRn1r8PdvjBwC8a79DRrNTtlTfNrQEeeXnQD9uIjRZ2WBeL/yH9sG/dyd8u7arysvcyz6HVF+LlKtvAmcw9OajyPzgOCw7PsFSAkOXcjz311/CvWKJ7BxOp0fqz+6AJjc/ovfQcDyydVZZwFPvb8NwU/jnv9ewRtVQo8MWxxGckjoycLzLVSV7fJylJKK59YdMbQqOoD5wvMMpD9yKDVkRlwDGEwp+CCGEEBLSEU+9auzDxnWw8EbMzZo8ADPqPbvohl/RJSzYBqbBpGvN0HC8rPyr0W+P2+CnWXETnhnhHj+Sxw3Xl59A06VIqC43Fc6r5mJi/mmBMc5ggH7sBOjHToD5wp/A9fE7cH+3XPZa3m0/QKyvg+nci6CfcFKvmyEITMQWRfBzcsrw9nm7XPB88xVcSz+VPc7xPFJvuAW6MnlpXHdyFMFPQzcNLo546vFF8w8hH9/qOBxYN2QTXKruaWPNA7/ep4PyZ9olyUv2ErHkDaDghxBCCCEhMMZUmxp2eL3+G1g0BpyRHr5jVjxSZn04cEiPMCjgOR5Z2lTZhpdNfjtGmgpiOsdYUXZ7y4wwyPMs/xKSwwa+y2/2vzltOMYwb8jncBoNLJdeBU1eAZzvvwHWJUAUao/D/spz0OTkwXTmHBjKK8DpdCFfK5x9rmrVjfgkZMP12Ydwf7cczKtel2O58nrox06I+r2UG3jW+0KXvUlMwsu1y2XleBw42XGz4ECVtxGlxhzVeh8Dr8NQY17Uc+wr3W2GW5yAzQ4ACn4IIYQQEkKz4IAzzALvF2u+glljkJXxJAJlk4IMrSWqdRZZOnnwE68bnboln2qBeiRBntjSDPfKpQAQyPwcHJaFY8UZKIpgwb9xxhngc3LheOV5SG6X/LUb6uB4579wffERDCefAm1xGTSFJdDk5cvWDx321OE/tV/DLfpwRe4MTEnt7By36ccubyaXD/l1Npx83Adu/1/g8vuDzsdy4WUwTpne7byDydUr212HvtZft27HAXeNbOzS7GlY2boDTnQGoVudR1BqzFGVvI0yFULLaXo0z77QXaBMmR9CCCGEJJWjHvmmkMrfYjMw/PP453hkyNUoNeb09/R6TLkeI9JsSAdlC+R47fim3OAU6P63+QDg+uwDMKE9kOA5HhLP4dsZ7WVljgi7nelHjUPanb+H/T/PQqirVj0u2W1wf7MscMxptNAUFEI/ZgKMp56Bfzcvw2FP+/qhJ459ij+W/QSjRSt8WzchZdN7uOl4PdJs7XNJ11rAQrQqN59zIUyzzo1ozsEoO77V+lqCnmcTXHiz/nvZWL4+A/OypqDZZ8fR1s6gaLPjMC7MmqJqdjDOHD/rfYDuf1YSsc010Mtub48//jg4jsOdd94ZGPN4PLjtttuQlZWFlJQUzJ8/H3V18sVvlZWVmDt3LsxmM3Jzc3HvvfdCEAQQQgghJH4c9cqDn9HmQlyYNUU2JjARn4dZ4xCP1J3eoluvo+z41t06kIGiLHkz8wYYeX3Y5whVR2Qd0jQch20TCtGS2X4j7BDdEb+/Jjcfaff+GanX3QxtYfgbeyYKEI5VwrXsMzQ99FuM+nQlspqc0AgSRu6vw95nHkTjn+9Gy3v/RfHuykDgAwBmjbKRAgfDieVIv+dPMJ83L+L5BlOoz5QdN/ht8ErqDNNq215VKd6N+bOg47WYZBkiG9/rqkadr1W1Z844c3Gv5hpr4f69sGrMsGrNIR+PZz3O/GzYsAEvvPACJk6U90m/66678Nlnn+Hdd99FWloabr/9dlx66aVYtWoVAEAURcydOxf5+flYvXo1ampq8NOf/hQ6nQ6PPvpo7z4NIYQQQmJG2eygzJCDq3JORZvgxLdtuwLjyiAp3ikzP5E2Owicr2iLHa9lb6pmB91s5MoYg/Pjd2VjnMGENacMCRwrA6rucBoNDCdPhf6kU+DfuxPu5V/Cf2BP2OcIoh/jdzVi/K5a+PQa6H0iAKBeo4dJEbxpOD7Q5Y3jeOjLp8F01nnQ5sVmDVahIUN2zMBQ42vBEMVePMp/V8pTR2CCpQwAMNZcLGscIUHCe43yPZD0nA7DTPGz3gcInxFN1JI3oIfBj8PhwDXXXIN///vfePjhhwPjbW1tePnll/HGG29g1qxZAICFCxdi7NixWLt2LaZNm4alS5di165dWLZsGfLy8jBp0iT85S9/wW9/+1s88MAD0OvD/0aCEEIIIf1D2eygzJgLjuNwWto4WfBT42sJdLBKBMoyNWUw052cKNaBDKRWRaDS3Xof3/bN8B+UtzbXzT4XbnNni2Ob6IJD9CBFY4xqLhzHQT/mBOjHnACh8jB8O7dBOF4JsfoYxBZ5xzORiYE/dwQ+AOAWffAosi4GgwW6YSOhHTYSxmmnQ5MV25tyI69Hji5N1vHtmLdJFfwc9zXLjseYOru2mXg9iqV0NKIzM7SqTR4AjjbH13ofALDwBug4raozIgAUGzKDPCMx9Cj4ue222zB37lzMnj1bFvxs2rQJfr8fs2fPDoyNGTMGpaWlWLNmDaZNm4Y1a9ZgwoQJyMvrjG7nzJmDW265BTt37sRJJ52kej+v1wuvt/MHxmZr/0tGEAQql+tHoiiCMQZRFLs/mSQUurbJia5rcuqv6+qR/Kj1tsjW+BRpMyAIAnJ5q2yjU4/oQ6O3LW7bPSs1+uyy+afz5qjuJzI4s+z5TtGDNq8DligDgq764ro2eG2yeaaF+ZzM74Pzo7eBLudz6ZnIPO088Ef/LQtIqlwNvetuV1gCfWEJOn7dLbmcEKuPwb91I3wb1kAQ3Ai1ja5fw+HgsBxUFaejJs+K6yZcAYu1vRkCA/rkvrBQl4F6X2vguNLdAMHS2eSDMYYqT6Psu87XpgXmIooihkmZaGA1P84Ssn+vAGC0oTAu72nTNWZZc48OhT/+XRAvoplL1MHPW2+9hR9++AEbNmxQPVZbWwu9Xo/09HTZeF5eHmprawPndA18Oh7veCyYxx57DA8++KBqfOPGjbBYImtNSXpPkiTY7XasX78efC979JP4Qtc2OdF1TU79dV2Pc21w6DrXxnDgUL31IBpwBBIYvHoPBHTeEC/74XuUsoxgLxVXGBgq9TUQ0dmG+djuwwBrDvMsOQESnHoX0OUGdvmm75HHossgddUX13WXdj+cfGf2x2ZrxLqj64Kem7p1A6xVR2VjzeWnwr15C/Q6EY1c5+us2LEWzVJhTOYoUzwCfFYhGvauhHXPdhi9P97QchyOFqRi24gc7BmaCZ++PUOiAQ/X7nqsQ1OYF+09QWOHU9P5+Tfad6LsYGeWpg0eNOnljRDqdxzFOrSveZckCblOA5waJ0IlR4XWZqw7EPzaDCRB65L9DHVo2VeDdcwV5BkDw+mMvBwzquCnqqoKd9xxB7766isYjT3/7Ua07rvvPtx9992BY5vNhpKSEpSXl8NqtYZ5JoklURSxYcMGTJkyBRpNfKVmSe/QtU1OdF2TU39d1+Wt22Gp7/wFY6E+E6cO6WwXPPLoQVlZXFZuEaamR7+PSn+zi24YD8pvMs8YWhH1up+3D+2S7RdUWDgU5T9utNkTfXFdv6w8Coun86ZwUu54TE0/UXWe1NoC20evA11+oawZNhJFV1wLjuOwtroRGxwHAo+lZGRjas7UmMwxmNfG8Xi3IQ0jDjZA5xdxfGg+mlLavxPdj/8AwImWITi1qGctrKPhbUvFjrrOn3VJb8DUIZ2ff6vzCCzHtwSOTbwes4efHigDFUUR2LAeZelNqvVmAKDntLhoxGzo4qzsDQA21DSj2b5PNX7e8JlBGk0MnI6qsEhEFfxs2rQJ9fX1OPnkkwNjoiji22+/xb/+9S8sWbIEPp8Pra2tsuxPXV0d8vPzAQD5+flYv3697HU7usF1nKNkMBhgMKi/YK1WC62WunX3J47joNFo6HtPQnRtkxNd1+TUH9f1mL9JtoZnqClP9n4FhkxU+Tp/494g2hPi56xNcMs+FwcO2ca0qPb5AdrX/bSInYFFs+To9eeP9XVtlVyyz5ptsAZ9bfvnHwKCHx1pCY7jkTr/amh/3IS02JiNjc6DgfNrhNY+vdatkguCXos9Y9tL6y7IKsdBd62qNfQU64h++ZkrM+fKvsd6fxsYD+j49veuFlpljxcbs6FTbODKczxOtAzBCtsO1euPthTBpIufQKKrLL1VtZYvS5sKqyG+Kq+i+TmI6t/0s846C9u3b8eWLVsC/5SXl+Oaa64J/Fmn02H58uWB5+zduxeVlZWoqKgAAFRUVGD79u2or+/sivHVV1/BarVi3Lhx0UyHEEIIIX3kqKrZgXwfH2UL4OoQ+5/Em95ucNpBvfllfO31wxhDiyBv6R1sTZb/4D54N8t/KW2oOA3aotLAcZFicftxb+Qlgj2h7CiXrbXitsLzkKoxBcZ0nBYn9yLTFo1CxednYLKf9+NeedldcYj9b5QtrzuMjbMW110Fa3cd6vMliqjC5dTUVJxwwgmyMYvFgqysrMD4TTfdhLvvvhuZmZmwWq345S9/iYqKCkybNg0AcM4552DcuHG47rrr8Le//Q21tbW4//77cdtttwXN7hBCCCGkf0lMQqWifXWporVtvj5ddhxq88d409sNTjvkKDY6bXQ1w/XZh/Dt2gZOrweXYgVvtYJPTQNvTYNu1FhosnNDvFrs2UU3RCbJxpQ3skyS4PzgTdkYbzLDfN7FsjFl8NPot8EnCdDzfZN1aVYGbToLsnSpuK/0UrxUsxxOyYPLc2Z027o7Vky8Hlk6K5q6dPU75m0K/ELgmCL4CbX551hzMbScBgITVePxKiPId5zIba6BXuzzE8oTTzwBnucxf/58eL1ezJkzB88++2zgcY1Gg8WLF+OWW25BRUUFLBYLFixYgIceeijWUyGEEEJID9T721QbOSozPwV6eXODOn8rRCb1KIvSn3q7wWmHHF1n5iejxYXJSz6Byxb6torjeBhPPROm8y8GbzSFPC9WlAEEBw5pik0pvWu/g1BdJRsznTcPfIo8IFRe6/bMR7Oq3XMsSExCq6C8Ru3zGWrMwyNDr475e0aiWJ8pC36O/1jyyRhTtbku0gcPfky8HmPMRdjh7GwdruO0GGEMvuwjHgz6zE8wK1eulB0bjUY888wzeOaZZ0I+p6ysDJ9//nlv35oQQgghfeCoR571sWrMSNfIa/zzFTfEIpPQ6LchT5ERije93eC0Q47OCjCGcbtrcdbK/dALEhAmGGBMgvu75fBu+wGWS6+CfsJJfbovkrJ0LFVjku0jIzbWw/XZB7JztHmFME4/Q/VaRl6PbJ1VtpnrcW/fBD/BMlYZ3exP1B+KDVnY6jwSOO4o/WsU7PBIPtW5oZyUMlQW/Iw2FwbWDsWjYMFPomd+4vvXM4QQQgjpd0cVJW9lxhzVjXqqxqja16YmAUrfervBaYdsSY/zlu7GuV/tgc7fvkePpLhpD0Zqa4F94bOwv/wvCFVHwcS+2StFud6na4mY2NIE27P/gOSSB0jmS68EF6LTXJFijZcy2xEryqCNA9ft5qz9QRnQVP24Jk5Z8mbi9WGzibPSJwQyaRqOx+U5M2I809jK0qXKvv9UjSlkWV+iiN9QkxBCCCED4qhH0ezAkKM6h+M4FOgzcMBdExir8bViUl9PrpeU5WA9WTfCPB4Ynn8eYw/WycYFJsKcXwzDjDPAnA5ItlZIdhv8e3aCCfIyQt/OrfDt3ApOq4OmsBh8USnMLg+Eglzw+UXgzfIStWi1hCjvk9paYHv2HxBb5DfthhMnQz8qdOOpQkOmIvPRN3vrKMsS03vYkCLWlOuean2t8EsCjikagxQbssNm9Iy8Hn8d9lMccNeg2JAla+IQjzQcj58VnIP/1C4HA3B93hlxnamKRGLPnhBCCCExp8z8lBqDl7nk69NlwU+8Nz1gjKlurnuS+XF//QXE6mPQcBqIXRavt5VPQsEVt4LTyxs4iY31cL63CL69O9VzEvwQKg8DRw8hw+mEY8tagOPAp6RCk5MHTW4+dOMmQn/CJHBRbH6qzKBkaFMgOexoe+7/IDbWyx7TFpbAcvlPw76eMvNT3UeZH1Wzgx6uyYo15ToeBoZaf6uq810k62F0nCaumxwonZQyFP8c8T8DPY2YoeCHEEIIIQEO0SNb2A0Ez/wA6oXw8V72Zhc98DN5mVm0a37E1ha4Vy4F0H4TKzIRXr0GX80egykzTsNYvbpzrSY7F6k/vxO+zevh/PBtSI7uN2SUHHZIDjv8hw/As+57aPIKYJ59PvQnnaIqTWN+PyCKgMEQyDp0lL1xEoPF5UOxsxW2Ff8Hsa5G9lxtXiGst9wN3hy+tKxIcVNf42vpkwYX6uBn4EveAMCsMSBTmyKb3zFvE6pUba4zlU8lcYaCH0IIIYQEVCqaHWg5jWqfkw7KpgfxHvw0K5od9GQ9ifuLj9qDDQBajofEc3j7spPRmJOCIf7QQQ3HcTCcPBW6MSfAvXQxfNs3Q2xuDHm+klhXA/uil6H58hPoT5oCZmuD2NQAsbEBUlvLj+/BgzOZwJnMmCLWYpLbiVS7BxqRIVu/A4KixEqTkwfrrb9WdXcLRln2JTIJ9f42VQDcW81+ZSvy+Mj8AO0BYNfgp8rbFOj61qE4RKc3Ej8o+CGEEEJIgLLkrdiQJesS1lWh4sa3yW+HXxLidk1Abzc4FY5XwrN+deBYy2mwfVwBGnPab9AbwwQ/HXizBZaLr4Dl4isgOR0Qjh2FUHUE/qOHIezcDjAJAAv5fLGpAe5lwTvmMiaBuZyAy4lUT7OsAYNW0eNKk5kN6y2/Bm9NU75MUKkaE1I1JthFd2Cs2tsc8+CnNUi5XrwoNmRhu/No4Hir44iqJbwyQ0biT3z+7UQIIYSQAaFscx2q5A2Aqq01A0Odvy1u9wFRNzuIvOSNMQbnx++ia2CiMZqwZtrQwHG9vy2q+fCWFOhHj4d+9HgIgoAd69ahrLwcfFsLxPpaiPW18G5aC6H6WFSvC6g7z2m6BLB8Wgast94DTUZ0JVpFhkzscR0PHB/3NWMyhkc5t/BU1yjOgp+uDnlqZcdm3hBX8yXBUfBDCCGEkIBImx0A7W19M7QpsrbK1b7muA1+lJmfaG5U/Xt2wL9/t2xMOmMWXObO76vBbwNjrFf793AaDTS5+dDktm98aTxzDvw7t8K1dDGEqiMRvYZynxwA0OoM0GTmQFtcAvPc+dBkRb9XS5E+Sx789EHHt1h04+sr3ZW0FRmy+nTvJhIbFPwQQgghBEB7q2blviVlhvAbWebr02XBT62vtS+mFhM93eCUiSJcH78rG+PTMmA98zyg8r+BMa/kh130wKqNXftijuOgP2ESdONPhH/fLng3rQOz28BnZkGTlQM+OxearGxwRjOY2wXmdqGqtQpLji6FoOPRZjXBmWbB85N+DT6KbnHBFBrkJW7KTme95ZMEOEWPbCyeyt6U656UEn3/m8GCgh9CCCGEAGhvWCB0ad0MtG9wGk6BPgO7XZ1lWdUxviGOpZ5mfrzrV0Goq5aNmc+/GDpzJjhwYF1K4Rr8bTENfjpwHBcoketOg02DnSkFgeMcXVqvAx9A3e75uK+515murpQbswLxVfZm0RhVmc6uuguOSHwY+F2jCCGEEBIXKhWbm2ZpU5GiMYZ9jrLjWzxnfpQlVZFkfpjHA9fnH8nGtIUlMJRXQMPxyNZZZY81RND0oK8pmwZE29EuFOXNvUfyqb7T3lC+lpHXw6xRtw4fSOECnGJD9KWEpP9R8EMIIYQQAECdv1V2HKrFteycBGl33dMNTt3fLlPty2Oe95PAhqM5quAnuqYHfUHZ0jtW2ZMsbSoMvE42FsvNTpXXJ1ZBWyyFW/dDba4TAwU/hBBCCAEA1PvkN+55uu7bICszPzbRBZfojem8YqEnG5xKLifcX38pG9OPOQH6UeMCx8rgpz4OMj8tynbRMWoawHEcCvXygDiW636U5WTxVPLWIVQrazNviJsNWUl4FPwQQgghBIC6VXOuvvvgJ1efBg7yNR/xmP0JtsFpmtYc9jnur78E88oX4JsvuFR2rMr8+AY+86MOImJ3U64s+zoey8xPHHd66xCqk2ExdXpLGBT8EEIIIQSAOmuRq0vv9jk6TqMKAGrjMPhRNjtI11pCbt4KAJKtDZ5vl8nGDCdNgbaoVDamDBAbFe8zEFr8fbdRqDrzE7t218qgLZ46vXUIFfyU0HqfhEHBDyGEEBIlxhg22g/g/YY1qFRsCpqo/ExEs+LGPS+CzA/Q3vGtq5o4bHoQbbMD97LPwPz+wDHH8TCfe7HqPHXDgzYwxlTn9ae+DCKUwU91DANd5ZqfeCx7S9EYkRYkk0ad3hIHBT+EEEJIlFa27cQ/jn2C9xrX4MGj78RlpiNajX6brGUzAORGsOYHUAc/8fh9RNPmWmxuhGf1N7IxwynTocnNU52r/I58TIBNdPVipuE1+e2qbm5deSQfXJJ8zVUsy8eUN/ltglO1N09PqdYqxWHwAwRvbBCvG/sSNQp+CCGEkCh93bo98GeX5MUnTRsHcDaxoWx2YNEYI24zrGx6EI+Zn2g2OHUv+RRM7NzviNNoYZpzYdBzM7QWaDj57VRfNT34tGkjfnngJdyy/wV81Lg+6Dl1QdYcxbJrWr4+Hbzi9jEWTQ8YY+q1SnG45gcInuWh4CdxUPBDCCGERIExptrI8/u23bAJfffb/v6gbHaQF8F6nw4FBmXw0zzgpV9KkZZUifW18G5YIxszTp8JTUbwm1ue45Gt7fu9ftySD+81rAlk595u+B5HPPWq85a1bJUdWzVmmHh9zOah5TTI16fLxqJpd80Yw7sNq/HbQ6/h1bqVEJkEALCLbtUGu/FY9gYAJYpAx6IxIl1Dnd4SBQU/hBBCSBTsokdVVuRnApZ3yQYlImXmJ1dvDXGmmrLszS35+rT0qyeUmZ/MEJkf15cfg/14Qw4AnE4P09lzw752jr7vO75Ve5vhY37Z2Fv138uOm/0OrGzbKRubkTYm5nNRdXyLIvPzvW03Pmhci0pvA75s/gEfN7VnsJRrsiLpxjdQxltKZR0OJ1hKqdNbAqHghxBCCIlCqPUsS1u2wK/4zXUiUbW5jnC9DwBkaVOg47SysVguhO+OTXDBEWbdSbANTrODbHDqP3wA3s0bZGPGmbPBp4YPBNVND2Kf+QnWPnyr8wh2OasCx581b5JlT7ScBhdklsd8LkWKNS/HfZF3fFvZKg/OljS3/3ujXO9j1ZrDduMbSAX6DPxPwWwUG7JwUspQXJN7+kBPiURB2/0phBBCCOkQag+bVsGJ9bZ9mJE2tp9nFBt1inU60QQ/PMcjT5+GY13aHtf5WjHWXByr6YX0XsMavN+4BjpOi6tzT8O5mSepzgm2walyPYnkccPx+kuyMc5ogunMOd3OQfld9UXwEyqYfLPhezxkvhIOyYNlLdtkj81MG98n62YKFWWOkQa6NsGF3a5j8jHRhQ32A3ArNsaN15K3DrPSJ2BW+oSBngbpAcr8EEIIIVGoDbOY//PmzXG31iUSjLEebXDalbL0rT8yP22CEx80rgXQXnr4at0KVQAAAEe98rUxHDhVEwDne4sgNjfKxsxnzwVv7n4th3KfI+V3GQs1IdbVHHDXYJPjIL5o3iwri+PA4cKs2Gd9AKBA0e663tcWUdZzo+OgqqMg0L5OKVE6vZHER5kfQgghJAqhMj8AcMhTi/3uGowyF/bjjHrPIXnglnyysWgaHgDqjm/90e66ytukupn+T+1ymDUGTLeOBgAcdNfiqeOfyc5RbnDq3bQO3k1rZefoho6AcebZEc1DGfw0+e2QmASei93vmGu8ob/PN+u/V7W/npE2BnmKxgSxUqi41gwMdb7WbjuerbftDzq+23UMQpd1VkD8dnojiY8yP4QQQkgUurup/7z5h36aSewomx1w4LrdBFRJeUMcLkiMlcYg5WUMDM9Wf4EtjsPY767GI5XvqfahOSllaODPYlMjHO++JnucMxiRcs3/gNNEtuYkR1H25mcC2mLY8EFiUtjvs9rXrGrCMS/rlJi9v5JZY4BVI29G0N2/F07Rgx2uypCP73dXy47jveyNJC4KfgghhJAIMcZUe9h0vZEGgPX2/X2y5qMv1SnKtHJ0VtXeNd1RZhnqfG2QFL/Nj7VGxcalHUQm4Yljn+LRyg9UGa2x5mJcmzcTAMBEEY7X/w3mlQdHKZdfB01WdsTzSA+yOL/BF7ufgWbBAZ9izZIy4OrqlNSRfb7vjLLjW3fB7g+OQ4G21pHIiOHeRIR0RcEPIYQQEqH2m1B5u+Grc0+Hscs+KgwMS1u29PPMekfd5jq69T6AOvPjZwKaFO2LYy1Y5qeDjwnwKAKfCZYy/LbkEph4PZjHA+dHb8F/5KDsHEN5BQwnT41qHjzHB+n4Frt1P8r1U0ZejxvyZ4U8vy+zPh2UZY7dtbtebz8gOx5qzJO1i1aizA/pKxT8EEIIIRFSdkTTczoU6jNweto42fjXLdtVGYd41psNTjsE20yzr0vflPNWlmJ1NSllKO4tngedywPXFx+j5S+/hef7FbJzNFk5SJl/TY/mom56ELvMj3JT3UJ9JiZZhgTtpjfRMgTDTHkxe+9QlA0uwl1rt+TDVscR2dg5GZNwUsqwkM8JtQ8TIb1FwQ8hhBASIeVv4PP16eA5XtVe2SV5sVHxm+54FovMD8dxqi5g4Rbpx4Ky7O2neTNxcpAb6skpw3GHcRp8H72Llod+A9fSTyG55A0COI5HynU3gzMaezQXZbvrWHZ8UwYWBfoMcByHK3NOVZ17cXbfZ32A6NZ4bXUckbUa58GjPHU4zsk4MeRzqOyN9BXq9kYIIYRESJn5yf9xnUuBPgMnWoZgq/NI4LF97hqcpsgIxavebHDaVYE+HYc8tYHjWl8L/EcPQzi4F+A4cJYU8JZUcCkp4FOs4DOzwHGhS5/CkZiEJkXwk6fPwB1FF+Dvxz7GdudRcBLDhQ0pOO/bQ3DsWQwEabPcjoP5kiuhKwudiehOniJg7K4MLBrVijbXHfvsjDIX4pyMSYEyy1PTxvbL3krtc5AHunbRDYfoQYpGHTyut8u7vI23lCBFY8QESynydOmo87fKHtdzOph5Q8znTAhAwQ8hhBASMWXmp2vpz3hLiSz4OeiuRSIQmKhaO9PT4KfrOhCTy4f85YvRdjB0BkSTkw/zuRdBP6kcHB9dMUqL4IQE+QL6HF0q9LwWv8meg8PbP4Rx/UaYbE4IIV4DAPTjT4TprPOgGzoiqvdXKjHIGyRUeRtj1u5amUHr+nO3IO8MzLCOgQgJo0z912I9R2cFD152DWp8zRipmINfErDZcVg2dkrqSADta6XOypiIN+q/lT2eqUvpcVBMSHco+CGEEEIipM78dN6EDjfmyx476mmATxKg5+P7P7VNfrtqr5yelL0B7ZkfMIbR++sxa+V+pHokwBi6a5rYUAv7ay9Cu+JLmOfOh270uIhvepUd9XScFqkwwLNqBVxffIwMZ+hmCxzPQ3/yVJhmnQttQVFE79cdZfDjkXxo9Nsj/i4b/G3YxzdgnOhGhrZzvYtH8qFJkGe4ugY/PMcPyL5SWk6DPH2arNyt2tuiCn62OY/KGk9w4FCeOjxwfEb6eLzbsFpWFkclb6QvxfffyIQQQkickJikKs8p6NLeeaipvXtVRyAhQcIRT33cb3iqbHNt5g1BS5cike/hMW/xdgw/1AQAENDe/S5cVy8AEI5VwvbCE9CNGAPdqLHgjCbwJhM4ownQ6wGfD8znBfO2/wNJhMPsgZXzwJZqADgOE4+5YfvoIQh11SHfhzOaYJw6A8bTZ0OTGXkr60hkalNg0RhlewpVeRu7DX4a/Ta817AG37TuhEPrwHeHj+Gvw38aaKBQqwi4AXWzgYFSoM+UBT/B1v0oS95GmwuR3iW4SdWYUGEdhW/bdgXG+mpzVkIACn4IIYSQiDT67RCYKBvrehNq4vUoMmThmLcxMHbAUxP3wU8smh0AgNjcCOszz2N4fZNsXJBEGCxWaIrLwJwOSE4HmNMBJvhVr+E/sAf+A3sier90wYn/8TvgMWjhSDGgpNUPIURQoC0qhfHUM2E4+RRw+r5ZS8JxHEoN2djtOhYYq/Q2YnKXLEdXdtGNjxvXY2nLVviZEAiaXZIXnzSux00FswGoSy2zdFYYeF2ffIZoFeoz0HVLX2XwIzARmxyHZGMdJW9dzc+uwBbHEdhEF7ScBmenh26EQEhvUfBDCCGEREB5Y2fmDUjVmGRjw415suDnoLuuX+bWG8psVk/W+zDG4Hz7v+DsdvAcL9vc1DF2JPKuug18WrrsfN+2H+D67EOIDT1bG9URiBq9AoxeAVqtSXWO/oRJMM0+H9rSof2yhqREEfxUdflZ6MAYwxctP+D9hrVwSd6gr7PefgDX58+ChuNRo2pzHR9ZHwAoMMjnomzMsMtZJcuEAcGDn1x9Gh4deg32u2sw0lSALGpzTfoQBT+EEEJIBJTBT/6P7Ya7GmEqwDdtOwPHidD0IBaZH+/6VfDtay9b0nMaeJgEl1mPr88YifLp52Bkl8AHaM+SGE6cDP0Jk+DduAauLz6G1BZdW2xlFk7LaTr/XFgCy8VXQDdyTNSfpTeU634qPergZ2XbTrxW903Y17GJLuxyVWGCpSxom+t4oZxLna9N1uRBmfUZZswPGdhk6VIp6CH9goIfQgghJAK1EdyEjjDJmx7U+VthE9ywBslKxAtl44BoNziV2lrh+vidwLGW08KewuP1q8rhNutRrMgsdcVpNDBOPRWGk6fCu2E1/If2g3ncYG7Xj//vBvP7wOkN7f8YDIDeAEginIc2gnehy/tqwKdaYZ57KQxTpkfdPS4WShXBT7WvGX5JgK5L04s1tr2q5/HgYdTo4ETn3kNrbfswwVKmKnsrVOylNJCUWSg/E2RNHnY4K2WPl4coASSkP1HwQwghhESgJsQeP12VGLKh57TwdelcdchTi0kpQ/t4dj2n2uMniswPYwyO9xZBcndGIXpeg2WzRsBt1gMIv/llB06ng3H6TBinz4z4fZ/d8zQMdidyGhzIbnLgoqKZyJgxt8eblMZCiaKzHQPDcV8zhhhzAQAik7DfXSM7Z3LKcFyXNxNrWvfiZduSwPh6+wHckD9LnfkxxE/mx6oxw8wbZOV71b5m5OrT0OS3q8rgJlrK+nuKhKj0/69FCCGEkAQUSeZHw/EYasyTjR2I49I3h+hRrcnIi2LNj2/rJvh2bJaNCZMm4fDQziAgWLey3rKJLvggwp5qxKFh2Vg/ZQjSTz9nQAMfoL3pRY7i++u67qfK2yhr+wwANxfMRp4+HVNT5fsMOUQ3vm/bozo/ntb8cByn2uy0I1hTZn3MvAFDfwwCCRlIFPwQQggh3fAzUVUeFizzAwDDFaVvBxS/6Y8nDYqsDwcu4nUXktMB5/tvyMb4lFRYLrlSNtYiOOBW3MD3VoNfvu8NDz5u9oYJt+5nr0vehjtPn460H+edo0tDAbPKHv+oaZ3sWM9pkalNieV0e03570FHmd4Olzz4OcFSGpMNXwnpLfopJIQQQrpR72tTbQQaauG5Mvg56KkDYyzouQNN2ewgW2eVNQ4Ix/nhW5Ac8oDQcslVyEsvUu3ro9wctrcaFYFoli41bm6slet+Krtkfva6j8seG63YEHSsKM8aKr+3An1G3HzODso1SDXeFjDGsN2pDn4IiQfx9W8QIYQQEoeUJW9WjRlmTfD9YkYY5cGPQ3Sr1tXEC9V6nwhL3nz7dsG7aa1sTD/+ROhPmgIdr1Vlj5SL9ntLmYXLjqMuYaWKdT8dZW+MMex1KYIfc5HseIwUviwsntb7dFCW4dX4mnHM14Q2wSkbp+CHxAsKfgghhJBuRNNuOEdnhVVjlo3F67qfuh60uWaSJOvuBgCc0YSUn1wbaP2t/H6UwWNvKTM/OTpriDP7n7LsrUVwwC660SjY0Sw4ZI+NUmR+rDBipLEg5GsXxFGntw7Ka90sOLDRflA2lq2zIj/KLoKE9BUKfgghhJBuKBfth1rvA7QvAh9ukpcvHfTEZ/DTk8yPd+MaCNXHZGOWCy8Dn9Z5E6y8IY6k41s0lMFPdhwFPwX6DOg4eTPdKk8j9imyPikaE4qCBDNTg2wC2qEojpoddMjXZ6jKHJe3bJMdn2Ap7ZdNZgmJBAU/hBBCSDeULXu722hyhEn+2/t4zfxEu8Ep83nh+uxD2Zg2vwiGaafJxvJVmZ/Wnk8yCGXZWzxlfjQcjyJFB7RKbyP2uOXNDkaZCoMGBKekjFQFEx3iMfOj57Wq4LNJkDekmEAtrkkcoeCHEEII6YZy4bny5l5puGLdzxFPPQQmxnpavSIyKcgGp+GDH/c3yyDZWmVj5osuU20oWqDsAOZtjmnTh3jO/ADq0rcqbyP2KTq9jTbLS946ZOpSQj4Wj2t+gO5/GTDeXNxPMyGkexT8EEIIIWF4JJ9qrYby5l5J2fHNzwTZfi/xoMlvhwRJNhYu8yPZbXAv/0I2phs5FroxJ6jOVd4MuyQv7Ir9hHrKKXpUrbPjKfMDqIOfva5q1fVXdnrralrqaNVYhjYFJl4fmwnGWLh/H8oMOYF23oTEAwp+CCGEkDCClWx1l/lJ0RhV5/Rn6dsWx2E8dPQdPHlsMXa7jqkel5iElW07ZGNGXo8UPvQmoa4vPwHzdg1gOFjm/SRo6Va2zgqNoiVzrJoeKLNVHDhkxlG3N0Dd7vq4r0nWKl3LaTBMsRluV1Ot6tK37rIrAylcOR6VvJF4Q8EPIYQQEoYy+MnSpkLPa4Of3IWy9O1gPwU/NsGNp45/ht2uY1hn34eHjr6Dv1V9iEpPA4D2dT4PV76HDxvlG2jm6dJCLkoX6mrgXfOtbMwwpQLaouDtizUcjzxFd6+eND3Y767B8pZtsrJDZclbhtYCXYR7E/UXZbtrpWHGPOjC/Aylay0Yo2iDrWwpHU8Kw5TjUYtrEm+6/9ubEEIIGcRUba4jXHcx3JSPVbbdgeP+6vi201UJj6IsbLPjMLY4jqA8dTi2O9WPA0B56oiQr+n69D0w1lkix2l1MJ9/Sdh55OszZI0iom168EXzZrxWtxIMDFaNGQ8MuQIF+gw0+OWL6bPirOQNANI1FqRoTHCI7qCPjwqxpqeri7KmyLJ2Z6SrywvjhXKj0w5aThNy/RIhA4UyP4QQQkgYyuAn0v1KRijW/Rz3NsMlemM1rZCqvE1BxxkYNtgPqAIfDhwuyCrHpdlTgz7Pt3s7fDu3ysZMZ5wDTXr4IFCZqVB2zAtni+NwIPABAJvowhv13wGI7z1+OnAchxJDVsjHx5iKQj7WYVLKUPym5BLMzZyMP5VdrlpHFk8ytBboOZ1qfKSpAMY4XadEBi/K/BBCCCFhHPfKb9oLw9zUdjXEkAMNx0P8MWPCwHDIU9fnZUDRNFbI0aXhlsI5GBuiG5fY0gzH6y/JxviUVBjPOrfb11buhRRp5ueYtwlPH/9MtkYGADbaD2C/uzohgh+gfaF/sPVWQGSZHwA4KWUoTkoZGstp9Qme41GgT8dRb4NsnNb7kHhEmR9CCCEkBIlJOKbIpBQbIttrRcdrUWbIlY0d8tTFbG6hKIOfCZayoL99n5k2Ho8PvTZk4MNEAY7/vgDJ5ZSNm8+7GLzR1O08lAv0a32tkJgU4ux2DtGDv1d9rOrm1uHN+u9VDQ/irc11h5IQ634K9ZlI1XT//SWaYOWgFPyQeESZH0IIISSERr8dPuaXjRXpI8v8AMAwYy4OdVnrc8RTH7O5BeORfKqNS6/MORVZuhR81Lgeq217kaY147KcCpySOjLsa7k+fR/+IwdlY/oTToKh4vSI5qLsdudjfrQITmSF6MwmMBFPHV+MOn9ryNfc7Tqm6oKWHWed3joo2113GG3uvuQtESk7vpl5A4YZc0OcTcjAoeCHEEIICeG4Yp2KmTcgI4o9S8oUN399Hfwc9zbLysU4cCgyZMLA67Ag/0z8NO+MkB3duvJu+wHub76SjWmycpBy1Q0RPR9oXwdi4HXwSp3BY42vJWTw81rdN9jhrJSNDTXmwSa40CR0NjlQlsPFa+an2JAFDpxqvqPC7O+TyMYoPtdJKcPAc1RgROIP/VQSQgghIShL3ooMWRHf/APAEEXwU+trDdppLVaUJW95+nQY+M6F6JHMXWxqgOPNhbIxTqNF6oJfgDebI54Lx3FBSt+Ct7teZ9uPpS1bZGPpWgt+XXwRLsupCPs+8brmx8TrkatTbxo7Jkm7n02wlOGcjEkw8DqMMBXgytwZAz0lQoKKKvh57rnnMHHiRFitVlitVlRUVOCLLzp3e/Z4PLjtttuQlZWFlJQUzJ8/H3V18vrmyspKzJ07F2azGbm5ubj33nshCEJsPg0hhBASQ8dV630iL3kDgJIff/vfgYGh0hN5Q4JoKTu9hes4psQkCd6tm2B7/gkwj7xFs+WSK6EtiX79hjL4CdWJThn46Dgtfl08D1m6VJyWNi5kK2WrxiwL7uKNct2PVWNW7X+ULDiOww35s/DK6F/iL0OuituMHCFRBT/FxcV4/PHHsWnTJmzcuBGzZs3CvHnzsHPnTgDAXXfdhU8//RTvvvsuvvnmG1RXV+PSSy8NPF8URcydOxc+nw+rV6/Gq6++ildeeQV/+tOfYvupCCGE9Du35MM7DavwTPUX2OeqHujpxERPmx10MPA6FCmeo+yIFUvKzE+odSdddQQ9bX9/EPZXnoPYKC/NM5w8FYbpM3s0H+X7B2v4IDFJNX5d3sxAq3ANx+PynOBZhHi/wR5iyJEdjzEXRZU5JITEXlRrfi688ELZ8SOPPILnnnsOa9euRXFxMV5++WW88cYbmDVrFgBg4cKFGDt2LNauXYtp06Zh6dKl2LVrF5YtW4a8vDxMmjQJf/nLX/Db3/4WDzzwAPR66gVPCCGJ6q367wO/wV/Vtgd/KL0M4y0lAzupXmCMqdb8FEfR7KBDmSFHFkQd9cRH8MMkCb7tP8C95FMINceDnqPJzUfKT67r8Q27cq+jI556+JkIHacJjFX7WlSlgOWpw2XHp6SOwDBjvqx5BBC/zQ46nJF+Ar5o2Qyn6IGG4zE3c/JAT4mQQa/Ha35EUcRbb70Fp9OJiooKbNq0CX6/H7Nnzw6cM2bMGJSWlmLNmjUAgDVr1mDChAnIy8sLnDNnzhzYbLZA9ogQQkjiYYxhrW1f5zEYnjj+Keoi3NslHjUJdtVNeVGUZW+AuunB4T5qemAX3WgV5G2pgwU/7ZmejWj73wdhf+X5kIGPtrAY1p/dAc5o7PGchhnzZMcCE1GpCP6UWZ9MbQoytCmyMY7jcFXuqarXj9f1Ph2ydKn4+7AFuKPoAvx92IKI9/chhPSdqLu9bd++HRUVFfB4PEhJScGHH36IcePGYcuWLdDr9UhPT5edn5eXh9ra9t/U1NbWygKfjsc7HgvF6/XC6+3cFdtma+/xLwgCrRfqR6IogjEGURQHeiokxujaJqf+vK4tggNtihtvh+DGXys/xIMlV8CkSbzM/lFXPRjr7NRl4vWwwhj1f3dKdJmy16n0NMDr90HTw05Yoa7rEVed7H10nBbZfEpgvkyS4N++GZ6vFkOqDV2WyOcVwHj2BdBNPBmM53v131kDtMjXpaOmS6ODvc7jKNN1BmUHnNWyeQ8x5AZ9zzGGQow3lWCHq7Mj3BB9TtzfB6TAgHLzMAAIO1f6ezh50bXte9H8PRB18DN69Ghs2bIFbW1teO+997BgwQJ888030b5MVB577DE8+OCDqvGNGzfCYom85SjpHUmSYLfbsX79evA8NQpMJnRtk1N/XteDXCOcOqdqfL/TiT82vYL5wkTwSKy1Duv5Sji1nZ8pTdJi/fr1Ub+OCz449fLv5ssNK5DNUkI8I7xQ13UTf0w231yWio3rNwAA9PU1SF/3LXRNobNOQnombCeeAveQ4YBXBDZs6NH8lIxaEU6+c17fHfwBaYIncLxOu1P2OGxOrKtaF/S1ypGJw7pjqOfsGC3lgrU0Yx2Cn5to6O/h5EXXtu85ner//oQSdfCj1+sxYsQIAMDkyZOxYcMGPPXUU7jiiivg8/nQ2toqy/7U1dUhP7+95jc/P1/1H46ObnAd5wRz33334e677w4c22w2lJSUoLy8HFZrfKe8k4koitiwYQOmTJkCjUbT/RNIwqBrm5z687rWNm2ApSn4L6Nq4caRDAFX5ajLluLZtjo7LG2dn+lE6yhMzZ/ao9f68NA+NAuOwHF6fhGmWsf06LVCXdeddU7ZfCdZR2GKeQzci9+H/4cfA4QgvzDk8wpgPOdC6CacBK4PbszaWo04XL8ycOzVazF1SPv3KDARzx/YCAvrnNfsommYYAndWW42ZsIvCdDxybVVIf09nLzo2va9jqqwSPT6bw5JkuD1ejF58mTodDosX74c8+fPBwDs3bsXlZWVqKho79FfUVGBRx55BPX19cjNba+B/uqrr2C1WjFu3LiQ72EwGGAwGNST12qh1SbXX37xjuM4aDQa+t6TEF3b5NRf17XS3xh2UfznrT+gzJyL09NC/10fb6r9LbLPVGrK6fH3ONSUjxbHwfYDxtBycAf89mNgHjeYxw3J4wFzu8HpdNCPmwD9ieXgdKFbOCuvKxMENDUfQ6bNBb1fhN4notx1BPa1X4B5PUCQa6PNL4JpzoXQTzy5T4KeDqMt8g5nNf4WeDkBFo0RxzzNECDKHh+ZUgStJvz3rE3SPdrp7+HkRde2b0XzvUZ1Be677z6cd955KC0thd1uxxtvvIGVK1diyZIlSEtLw0033YS7774bmZmZsFqt+OUvf4mKigpMmzYNAHDOOedg3LhxuO666/C3v/0NtbW1uP/++3HbbbcFDW4IIYQkBmUHs5lp4/Fd225IkAJjL9Usw0RLGdK18V+uzBjr9R4/XZUZc/CD4yBS7R7M/novhlethlOxB04H7+b14D9+F8YZZ8A4fSZ4a+dGmczvg9jYAGPlIXjsTWD1NRCrj0NoqMXZrlrZ2pk8fTqYRv3fVk1OPsxzL4Z+Qt8GPR1KjTnQchoIrHO9wyFPHSZYynDQLV/vm6tLQ4qm5w0WCCGkO1EFP/X19fjpT3+KmpoapKWlYeLEiViyZAnOPvtsAMATTzwBnucxf/58eL1ezJkzB88++2zg+RqNBosXL8Ytt9yCiooKWCwWLFiwAA899FBsPxUhhJB+4xK9qPO3ysbOzTwJI00FeKl2WWDMzwRstB/E7IyJ/TzD6DULDrgVnd56FfzosnDituM4bdVB6H0ifN00O5AcNriWfAL3ss+gHTYKzOWE1NoMyekAGEOW0wmPxRLI6AhMlAU+AGBQlIVxBiPM586D8bQzwXWTWYklHafBEGMuDrhrAmMH3LWYYClTdXobbgpdAk8IIbEQ1d9+L7/8ctjHjUYjnnnmGTzzzDMhzykrK8Pnn38ezdsSQgiJY8pNO3nwKNJnYogxF5sdh7Gpo9wLwAF3TUIEP8qsj5HXI0vbsz1lxPo6lL35Ls7a3dkKXGISRCZCw4Wv/2eiCP/+3d2+h0+SdzriOU722sZTZsB8wXzwqQOzTna4MU8V/ADqNtfK1tiEEBJrVHhICCGkV5QlbyWGrMBi9BMspbLgZ3+XG+B4dswnD36K9Jk92ujTf2AvbC8+Ba3fB57jIHXJzrjNRmSNOhGcyQTOYARnNMF/YC/8B/dG/z6sM/gRtDx0BjM06dnQFJXAdNb50JUNjfo1Y2mEqQBLftwAF2gPgn2SgEqPfFNWyvwQQvoaBT+EEEJ65Yhi084yY07gzyNNBbLHqn3NcIieuF/XcdzbLDvuScmb2NoC+6vPg/nby+f0nA4e1v7n7eMLkDnvclxceqbqeUJ1FTzffQ3vxrVggj/oazO9AZqhI6ErKoG2oAhfao/ga20tvAYtJA2Ps9InorxgdtDnDgRlUGMTXfjBcUi2JowDhyGKDWEJISTWKPghhBDSK8qyt643sKXGHOg4rSwzcdBdixNThvTX9HrkmKLsrciQGdXzmSDA8erzkBz2wJie16IuhcdXZ41GZWkmpvD2oM/VFpYg5YoFMF8wH75tP0BqawWflg4+PQNCmhXfsuPYXnUYN066ECnG9jK2HYdeg9vbuZFsiSE76GsPlHxdOiwaI5xi5/4+S7tkggCgUJ8JE594m+ESQhILBT+EEEJ6zM9EVHnlpUtdg5+Oxe773dWBsf3umrgOfhhjQYKf6DI/rk/ehf/IQdmYNGo0/jszBX59+396jyjKBZV4SwqMFacHjut8rfjHsU9Q6WmAU+PE/qOL8Ieyy1BsyMJxnzxTVWqMr+CH4zgMN+Zjm/NIYGy365jsnGEmWu9DCOl7tM0sIYSQHjvubYLIJNlYqSLroCx9OxDn635aRSdcklc2VqyPPPjx/rAe7u+Wy8Y0mdkwXXtjIPABgAZ/mywTEs5251H84cgbskDTJrrw0NF38F3bblkbaaB3nen6SnfreYYbab0PIaTvUfBDCCGkx5TZixxdGiyK9Tyq4MdTC0kRMMUTZdZHz+mQrYus05tQWw3H26/KxjitDqk33IqijBJoFd3dlM0ilBhj+KxpEx6r/CBooOSSvHihZolsLF1rQarGFNF8+9OIboKfYSZa70MI6XsU/BBCCOkx9XqfHNU5yuDHKXpQ62vty2n1irLZQZEhE3w3+/IAAPN4YH/lOTCfPGtkmX81tMWl0HEaVUbmiFfeLKIrnyTg2Zov8Xr9N2BgIc9Tirf1Ph2Gh2ljzYPHEAMFP4SQvkfBDyGEkB47quj0FqxbV6Y2BRnaFNlYPLe8VmZ+Ii0hc376HsQ6+ecynjIDxmmnBY7LDPLgMNy6n3/XfoXv29R7/ExOGY5hUug5xWvwk6a1IEeXFvSxUmN2oD06IYT0JQp+CCGE9AhjTN3m2qDO/HAcl1Drfo77og9+hOOV8K75VjamLSyB5bJrZGPK4DBU2ZtfErC6Tb3fz2XZFbij4HzMFybilJSRQZ8br8EPELr0jTY3JYT0Fwp+CCGE9Ei9vw1uyScbC7VPi/Km94Cnts/m1RuMMVQpO73pw7e5ZozB+eFbYF3WMXE6HVJvuAWcTt66Wfn9HPM2wS8JUDrua5btgQMAvy6+CPNzKsBzPDTgcXvBuZiZNl713KFxvFdOqKYGFPwQQvoLBT+EENLPGGPY4jiMla07VcFDIlGWbKVoTMhUlLd1GKHI/Bz1NMATh5/dJrpUjQW6y/z4tmyE/+A+2ZjprPOhyVYHIcpOeBIkVbAFQNU+PFtnRXnqCNkYz/H4WcHZuCCzHBw4AMApqSNlm8zGm1Ad37rrBEcIIbFCBbaEENKPJCbh+Zql+K5tFwDg8+ZNeHzotREtqI83wZodcBwX9Nxhxjxw4AIL9xkYDnvqMdZc3OfzjMYxRbMDPadFjs4a8nzm88L1yTuyMU1mNkyz5gQ936wxIE+fjrouDR8Oe+pUe9xUKoIfZdDUged4XJN3Os5MPwFOyYsRcd4ueqgxV/ZzAAA6ThuXrbkJIcmJgh9CCOknjDG8WrcyEPgA7b/h3+mqwgRL2QDOrGdUzQ7CdOsy8DqUGXNka4QOuGsGNPgRmIiD7lrsc9egzteKOn+rKgtT2E2nN/fyLyG2tsjGzBf9RFXu1tVQY64i+FF3fKvyyIOf7tbxFBrCl+bFCwOvQ6khWxY4lxlzVC3ACSGkr1DwQwgh/eS9xjVY2rJFNb7fXZOQwY+q2UE35VYjTQWy5/R3xzfGGGp8LdjurMQ251HsclV1W3pXFGZzU7G5Ee6vv5SN6UaMgX7iyWFfc5gxD2ttnWVyQYMfb3TBTyIZaS6UBT/KkkhCCOlLFPwQQkg/+KL5B3zQuDboYwfddf08m96zCW40Cw7ZWKhmBx1GmgrwVcvWwPE+dw0YYyFL5WLJLwl44vin2Ow4HNXzxltKQj7m+uRdMMEfOOY4HpZLruz28ww1KkvcGuBnInQ/Zj8cokf13ZYakyf4OS/jJKxq2w235IOZN+CcjBMHekqEkEGEgh9CCOlj37btwn/rVoZ8/EA/BgGxclSxOaeO06JAnxH2Ocrf8LcJTjQJdmSHWVMTK9+07Yoq8OHAYbp1NE5LGxv0cd+enfBu3SQbM8yYCW1h92V8ym5sAhNxzNsYCIqUWR8Nx3f73SaSQkMm/m/4DTjkqcNwYx7StJaBnhIhZBCh4IcQQvrQFsdhPF+9JOw5NtGFRsEedmF9vFGWvJUasqHppmlDvi4dFo1R1k1tv7umX4Kf3a5jIR8rMWRjmDEPefp05OvTkadr/3+zxhD0fP/hA7C/8pxsjDdbYD7v4ojmYtEYVU0PDrrrQgY/hfrMpFsTk6614OSUYQM9DULIIETBDyGE9BHGGBbWfi3rbAUAV+ScisXNG2VBwEF3bUIFP8p1Kt2VvAGdm51u6ZKB2e+uQYV1dMznp3TIIy8tnGApw2lpYzHBUob0KDIP/qOHYXvhSTCvvB22+fyLwZsjf51hxjxZ8NM1mFQ2OwjV6Y0QQkj0Eq+3KiGEJIj97hrU+9tkYxdklWNe1hRVS+KD7vjc9DMYxhj2uo7LxiIJfoD2dT9dHeiHz+0Svaj1yTuyXZEzA6eljYsq8BGqjsD2/P+pAh/9CZNgqJgZ1ZyU634Odtn0VdnmOpmaHRBCyECj4IcQQvrIGtte2XGBPgNX55wGjuNUmzoe8PRv57PeqPe3qRbkjzEXRfRc5bqfI556+CUhZnMLRrkfEQ8+6myKcLwStuefAPO4ZeP6sROQ+tOfg+Oj+8/pcEXwU+VthF8SwBjDMUW77ZIkanZACCEDjcreCCGkD0hMwhr7PtnYdOuYQFMDZfBzyF0PkUndrpuJB7sU62esGjOK9JHtMzPCmC/b5NLPBBzxNqgyQrF0WFHyVmLIgo7v/j9/jDEIRw7Cu34VvD+sB/N5ZY/rx4xH6g23gtPpop6TMlMmMgmV3kakac1wSfL3obI3QgiJHQp+CCGkD+xxHUeb4JSNTbOOCvxZWfbmY35UeRsjLh8bSLtcVbLjsebiiDvVmTUGFBkyZdmN/e7qPg5+olufJLW1wLNhNbzrV0NsCN6GXDdyLFJvvK1HgQ/Q/j3k6zNk5XiHPfXI1KXIzjPxemRpU3v0HoQQQtQo+CGEkD6wWlHyVmbIQbGhc8NMq9aMHF0aGrqsCTroro374Icxht2K9T7jLN23d+5qlKlQFvzscR3H+ZmT219fkqIuIeuOsjPdMFOe6hwmSfDv3QnP6m/g37kNjEkhX083fDSs/3M7OJ2+V/MaZsxTBD91siYYAFBsyE6oFuiEEBLvKPghhJAYE5iIdfb9srFpQTqajTDly4MfTx3O6vPZ9U6D34Ymv002Ns4ceiPQYMaYi/B163YAACcx2PZug2NVK3w7tkBy2MBbUsCnpoFLtYK3pkGTlQ39ieUR7aGj5JF8OO5tlo113WdHsrXBs+57eNd+B7G5Ufl0BQ6GyVOR8pPrwOmDt8GOxlBjLlbb9gSOD3nq4JX8snOo5I0QQmKLgh9CCImxnc4qOET5wviKLiVvHUaY8mVNEQ6447/pgXK9T6rGFPF6nw6jTUXIabDjhJ01GHWgARanD3bDgcA6HMnpgOR0ALWdGSbX0sXQDR8F46mzoJ8wCZwmsv98HfU0yFqNc+BQqsuGb98ueFd/A9/2zWBS6CwPAGhy8mA4ZToM5dOhSY/dZqPKdV+VnkZ4FMEPdXojhJDYouCHEEJiTNnlbbgxH3n6dNV5wxXrfo55m+CWfDDxvSun6ku7e7HeB2gvm0tZ+R2u/2gzREkMjHskf7dNCPwH98F/cB94azoMk6eBz8gEb7GAM6eAN5vBGMDsbZDsNkh2G5jDBqfjGGb4K+E1aOHVa1AomuD64AGIjfVh34vTG2A4+RQYTpkB7ZDhfVJ6VmbIkR1LkFQtuUup0xshhMQUBT+EEBJDfknABvsB2dj0tOCbeA415sk6nzEwHPHUY6w5+vKu/qJa7xNFyRtjDK5P34d7xZcwcjo40Rn8uCUfUmGK6HUkWyvcK76M6FyTrw1Tu6yjSdEYIerTQp6vLSyBcfpM6CdPBW+MbD49ZdYYUKDPQI0i4OmKMj+EEBJbFPwQQkgMbXMeVbUqnpqqLnkDAD2vRZkxR7Yg/4C7Jm6Dnwa/TbZGCQDGRri/D5MkON97HZ413wJo72LWdXF/dZYRQ2fOh27kGEhuF5jdBsnWBrGlCb4tmyA5bKFeOiwvk+8hZODV3dk4nQ6Gk06BYfoZ0JYO6dcGA8OMeSGDn0xtClI0xn6bCyGEDAYU/BBCSAwpu7yNMRchSxe6VfFwY74i+Knts7n11m7Feh+LxijrYBcKEwU43lgI7w/rAmPGH4OQDZNLsWNcPloyLThxxAzk6Kzq58+7Ar5tm+D57mv4jxyMeL6MMdUGqvouwY82vwiG6TPbS+jM5ohfN5aGmfKwqkvTg64o60MIIbFHwQ8hhMSIV/Jjk0N+c14RpMtbVyNM+Vjeui1wfDCOg59dTvl6n3HmYvDdbMrK/H7YX30evp1bZeM6jQ4rzz0RP4zubJawx3UMOWnjVK/BabUwnDwVhpOnQjhWCe/G1RCbGsGcDjCXC5LLAeZyAuDAp6aCS00Dn2pFixHYZtsJvVeAwSfA6BUwzDwEhuIhMEw7rc/W8kRjqFHddrtDCa33IYSQmKPghxASN5yiB15JUG30mCg2Ow7LWhVz4DA1dWTY54xQbO7ZJNjRIjiQoY2/70CZ+emuPI9JEhyLXlIFPpxGg5TrfgY+6zjQZX3UHtdxnBYk+OlKW1wKbXGpatwuuCFBQprWEhhb27IFy2o7Mz9F+ixcNHxB2Nfvb0OMubJ1X11Rm2tCCIk9Cn4IIXFhResO/LvmKwDAxdmn4PKcGQM8o+itbN0hOx5vKZHdjAdTqM+AkdfDI/kCYwfdtShPHdEnc+ypJr8d9ar1PuGDH/cXH8O7dZNsjNPpkHrjbdCPOQFjmoCNiuCnJ5a2bMErtSsAAJdkT8Vl2RXgOA6H3HWy8+JxA1kTr0eBPgPVvmbVY1T2RgghsRfbbbQJIaQHqryNeKlmGdiP//uocT0a/T1b4D5Q9riOY6vziGysu5I3AOA5HsMUpU8HFTft8SDYep9wmQnvxjVwLftMNsYZjLD+4m7ox5wAoH09VFfVvma0Cc6o5nXU04BXalcEfnY+aFyLpS3tmaaua6kAYJgp/oIfIHjpGwcu6v2TCCGEdI+CH0LIgJKYhJdqlkFC50aTDKzHWYCBwBjDm/XfycasGnNEwQ+g3uzygCf+NjtVbm46xlQUcr2P//ABON56VTbGcTxSb7wVumGdZYBDjLmq7mt7XNURz4kxhtfrv1GVjL1atwI/OA6hytskGw+3vmYgBQvKCvWZ3e57RAghJHoU/BBCBtQ3bbuwz62+4d3vHrgAQGQSmvx22fqdcDY7D6s+wyXZUyPerHSEIvg56K6FxKQQZw8MZeZnnCV4yZvY1Aj7f54BE+Vd1izzr4J+lHw9j4bjMcpUKBvb64486N3kOIgdzkrVOAPD/x37RBZQA+pNReNFsKAski56hBBCoke/ViKEDBib4MKi+m+DPhYsIOoPtb4WPFr5QWA/mzStBXm6NOTq01Cgz8Cp1rHI7bJJpsQkvFX/vew1cnRpOCt9QsTvOdwoD37ckg/V3mbkN7rg270dzO0Cn5IK3poGLtUKPtUKTWYOOGPv94Cp8jZir+s4xppLUGQIXmbV7HegVrEXTbDNTSWXC/aX/gnJYZeNm06bBeOMM4O+9hhzEbY7jwaOlUFWKH5JwOt1wX92gPYAtqt8fQbMGkNEr93fgjU9KKVOb4QQ0ico+CGEDJjX67+VbXTZ1VFPA9ySL+LsSax82rRRtpFnm+BEm+AMBGMfNK7F/+TPxhnp7etWVtv2osrbKHuNn+RURFWylKVLRaY2BS0+Owpq2zDyQAOcb/wRrY7QmSeO56GfNAWms+dCm18Y8rxwDrhr8cDRtyAyCRqOxy8L52KqVd2dThmQmHmDar2Pb8cWON5bBKlNHiTpx4yHed4VIeegbJpw1NMAl+jtNlD5smUL6vytsrFCfWbQxgEAMDQOmx10MPF6lBiyUeltCIzFa4keIYQkOip7I4QMiJ3OKnzXtivk4wxM1a2rP3S3yajIJLxQsxSv130DnyTgnYbVsseLDdmYYR0T9ftWNOtxw3/X4sp3N2Py5mMQmhvCns8kCd4f1qH1r3+G/b8vQKiJfo3UN607AhkSkUn4V/Xn2NYlCwO0tx//vPkH2dgYc+d6H8lhh/2/L8L28r9UgY82rxApP/05OI0m5ByGG/Oh6bJ2iIF1m/VrE5z4sHGtbGyEqQAPD706ZGlbvAcTl2ZPBf/jf5JHmApwoqVsgGdE/r+9O4+Purr3x//6fGZfMpnsC0lI2Pd9hwICshQRq611a92utlbvraW3t1++9Wr1tvW2tz+31tb7ba1LLW211g0VZVNQIgQEZIewBchGlslMJrN/zu+PmEk+M5OQkEkyJK/n48FDP+dzPp85nzkZmHfOOe9DRP0TR36IqNcFlCCer9ykKjPJeti0ZlT5HeGy455yjLVET6/qKSGhtDtyEOnduj0ocZWqRokA4KaMuZfc+DNSoPQY5r76EWrcraNgHsUPAQEJl9qEU8C3twS+vSXQj5kA3dAR0OQVQJs3GNB3PHpyIeJZgyKEJ86/jZ8U3IDhplw0hrz4RdnrOO1VB6FjTHkIVVfCf+wwPBvegtIUnaFNttqQdM+/QjaZO2yDXtZiqDFbFfAcayrHJGtRu9e8drEYnjapwQHg9qyrYJL1+GH+ajx0eh2coSbV+UQe+QGAmbYReMKYibpgI0aYcrv8M0RERJ3D4IeIet07dbtREbGG5JsZ81Dhr8MH/n3hst5e91PldyAoQqqyB3K/ioaQG6WeShQ7j6nORe57M9yUiynWIV16zcDJ43D+4RmYQ+ovu0II+JQALBmDoC0ohNLYCOFyQml0Rq2paeE//AX8h78IH0vJKUiTtfBUn4MuKweajCzIGVmQk+2QJEkVaLbwKQH88tybWDNoFV6u+ghlnmrYnR7YHR5kXmxEYVUTZtSfQb2nKeraFvoRY2D55rehSe3cupVR5kGqvj7qaX/dz1nvRWxxHFCVzUseHU4akaGzYU3eKvxX2WvhUS2TrI9KJ56IsvR2ZOntfd0MIqJ+jcEPEfWqan8D3qzZpSobYszG1SkT8JnrBD6o3xcuP+GpgCKUXvsteGRqZJvGjLnJrVPYJlgG44+Vm6IW07e4OXMeJOlSIzWtAqdL4fzDMxB+HzSSDL2shV8JojbVjOPDMzF42mJcO2511D2VJje8H2+E5+NNEL7Ya6YAQDjqYHS74aurhq/NPWSLFZrJ0yBnVAIZ1nC5pAik1zSi6OwZHKjYhUWOJiQ3eCErzQvxNZKMHH0KJFlEvRYAyGYLzKtvhGH6nC69D6PMeXi7tiR8XOqpREAJxlw39ZfqbarEAHpJh5sy5qnqjDQPwpq8a/Gnyi0IihC+nbUwYZMdEBFR72LwQ0S96qWqjxAQrWmQJUj4l5zFkCUZI0w5qrrukBcVfke7Wcji7YJfHfzkRyzqX2gfhyy9HU+cfweNIY/q3CRrUdTi/Y4EzpyE83+fUgUvJtmAQ0V2vLtiLBSNjApjI1bHCCJkswXmFdfBuGApvNs3wfvxJigdjMREUtyN8G7bhG/7alGZlYTjwzORVutG0dk6mJv8Ma/RSDJyDKnQSbHX7xgmToXl+lsg25Jjnu/ISFOuKttZUIRwzFOOcZYCVb2agFOVGQ4AVqdPR5ouKeqeU6xDMGVY10bhiIio/2PwQ0S95vPGU/i88aSqbGnKxPBi9DRtElK0VtQHG8PnT3jKey34OR8x8hPrdUeb8/Czwpvx6/NvhevrJC2+mTG3068TOHsKzueejBq10Y2biHfn6aBomke6znir0RB0I1lriXkf2WyGedm1MM5fAv/+PQiWnUbwfBlCFRcggh3vUdQSgGZXuZBd5YIsydBJGvhi1NVKGuQYUqCNCHxkmx26omEwzJgD/ZgJnXz6aGaNAUXGLJzytiab2N94Jir42d94RnVs0RixMnXqZb8uERENPAx+iKhXBJQgXqrcqiqzacz4Rsac8LEkSRhuysEu14lw2XFPRTitdE+LnPaW385Gk1l6Ox4dfBM+qN+Hcn8drkoeh8JOLqgPlB6D84+/iQp89GMmYNAd34H+1B/gbbOY/4C7DPOSR3d4T9lkhnHWV4BZXwEAiFAQoapK+MtOo6JkJ1IsJojaGoQuVkIEmoOiyLVNekmLLH0yKvz18CutI3MtgY/eYoOcngltQSF0RcOhLRoK2Z7apeltHZloLVQFP/vcp3Er5qvqRGaim2AZDIOsi8vrExHRwMDgh4h6xTt1u6MSBNyS+RVYNOqNOkeac1XBz4leSnoQFCFURGQ/G9RO8AM0j1Z8LX1ml17Df/QQXH/6bTgAaaEfPR5Jd9wHSafDGHO+anTsC/fZSwY/kSSNFtrcPCAzG86QDMvMmdBqtRCKAqWmGr6SYlzY/hbgaB1h08kayJKMbH0KTlsUfJFnhG7wEKwasRypOUMgW6wdvGL3TbIUqtJXn/fVoibgRLrOBqA5E99Bd5nqmglMB01ERF3E4IeIelysJAfDTbn4Sowv9cMj1v2c99WiMeSFNSJIirdKvyMqkUHkmp/u8B/cB9eLz0GEgqpy/ahxSLrze5B0zSMYEyyDo4IfIURcRlgkWYYmMxvmlV/D22N88B75AmMPVyK5wQN9eiEsU5ZAP2ocsjKyMEMokCDFbWTnUoaZsmHRGFWb3u5rPIMlKc3T6U56KtGkqCfljWfwQ0REXcTgh4h63Msxkhzclb0oZha3QkMmdJJWVb/UU9Hhvi/xcMGnHvVJ1lriFnD5Pt+Fxlf+CBERXOnHTUbSt+8NBz4AMME6GGizrU5D0I2zvoudnlbXWZVBJ+qK0nGqqDnAe3DQKphsw8Pne3ufGVmSMdFSiB3Oo+GyfY2nw8FP5JS3PEN6zEQHREREHeEuakTUo/Y2nsaeiCQHV6dMbPfLvE7WRm1IedxT0WPta3HeV6M6bm+9T1eIUAhNG9+NGfgYJs9A0h3fUQU+AJCtsyNDp86YFpnhrLt8SgB1bZJKAEBOAuwvM8laqDo+1HQOgS/XJu13n1Gdm8hRHyIiugwMfoioR71Rs1N1bNOYcWObJAexjDDlqo57Y93P+Yg013n67gU/wfLzaHjqF2h6742owMc4cx6st/0LJE304LskSVFf7CNHPbor1uamibC5ZuQaHq/ix7GmC2gMeXHSU9lhXSIios7gtDci6jE+JYDSiFGbmzPnRSU5iDTcnAO0mYVW6qns8c1Oo9NcX17wI0JBeDa9D8/G9RChUNR501cWwXzdTZDk9p9lgrUQmxxfhI+PNl2AV/HDKOsvq02RKiKCn1StNSGypiVrLRhizI5Ked0Y8qo2NtVJWozqwp5KRERELRj8EFGPOe2tVn1plSBhlm3EJa+LHPnxKn6U+Wrivu6lRXOmt3pVWVenvQlFgf/gPng+eAfB8nNR5yVJhmnpNTAtW3XJJAJjzflRm34eabqAyXFa91QdcKiOE2HUp0WslNeNijot+GhzHvQy//kiIqKu478eRNRjIqcq5RvSOzV6YddakKFLxsU2qbFPeCp6LPiJlektr5PBjwgE4NtdDM/WDxC6WBWzjiYrB9ab74Ru8JBO3dOsMWCEKRfHPBfCZV80nolb8BM58pOtT4nLfeMhVsrr+qBbVWeilVPeiIjo8jD4IaIeU+pVT3kbZsru9LUjTLmq4Oe4pxxXp0yMW9vaipzylqK1XnJqnggE4N2+GZ6PPoTicsasI0kyjIuXw7x0VVRig0uZYB2sCn4iF/x3R2XEKFciJDtoESvlddv/B4AJlsJebhUREfUXTHhARD3mpEc9EjK0K8GPWb3fz4mmnsv4Fhn8XGrUJ1B6DI7/+Snc7/yj3cBHm5WL5AfXwrLy+i4HPgAwMeILfoW/HrUBV5fvE0tkwoNMnT0u942HlpTX7UnVWjFIn9p7DSIion6FIz9E1CMagm7VyA0ADDV2beSnraqAA7UBV4/s7XIuIs11e8GP0uRG01uvwrvr03bvpc3Nh2nRcugnTYOk0Vx2m4qMmVEjIEebzmNujI1huyJR01y3Ncmq3u+nrQnWwl7beJWIiPofBj9E1CMiR30Msq5LSQRa1gd5FX+4bFvDYXwtfWbc2tgicoPTyOBHCAH/3l1wv/F3KI2xR3p0w0fBtGgFdCPHxOXLuSzJGGnKxeeNp8JlR5sudDv4SdQ01211lMaaKa6JiKg7OO2NiHpEqVed7GCIMatLqao1koyZScNVZVsdB6FEJCborkCMTG9t9/hR3I1ofOk5uP78h5iBj7agCMkP/gTJ3/t36EeNjeuoxCjzINXx0TZrgC6lwl+P9+o/x+fyeXiVQJtyh6peoqS5bqsl5XUkCRLGM/ghIqJu6FLw8/jjj2P69OlISkpCZmYmrrvuOhw7dkxVx+v14v7770daWhqsVituuOEGVFWpfwNcVlaGlStXwmw2IzMzEz/60Y8QDAa7/zRElDAiM711Zb1Pi0X28arji4EGHGqKTiPdHZX+eiiInenNf/QQHL96BL79e6KukwxGWK6/BcnfXwvd4PhkYYsUGfyc99XCFfK0Wz8gQih2HsPPzv4Da06+gHUXt+ND7TE8V/lBuE4ip7lua6K1MKpsqCkb1kskoiAiIupIl4Kfjz/+GPfffz8+++wzbNy4EYFAAEuXLoXb3ZqG9Ac/+AHeeecdvPbaa/j4449RXl6O66+/Pnw+FAph5cqV8Pv92LFjB1566SW8+OKLePjhh+P3VETUp4QQOBkx8jOsC+t9Wgw35URNQdviONCttkU6F5HsIFVrhUmR4P7nOjj/90kozoaoa/RjJ8L+48dg+sqiDjcr7a5CYxb0knpU5lhT9OiPO+TFX6u344ETf8AzF97FoaYy1fndjSdxwlMOILHTXLc1OUbwwylvRETUXV1a87NhwwbV8YsvvojMzEzs2bMH8+fPR0NDA55//nmsW7cOixYtAgC88MILGD16ND777DPMmjULH374IQ4fPoxNmzYhKysLkyZNwn/913/hxz/+MX76059Cr4/PDuZEV6KGoBuOYJOqTAKQo0+B7gra1LEy4IhKT3w5Iz+SJGGRfQJertoKAJBDCmr2f4bajyugczVC+HwQfj+Ev/m/stEEbdFQaIuGQTd0BOTU9PA0NBEKQTS5obgaoDS6IJxOKI0uKJX7sbT6OMweP8xNfmT7tagL7oUIBqLaI5stsHzjW9BPnNori+51kgbDTTmqYOZI0wVMSxoWPg4JBf919jWc9V3s8F5v1+7GD/OuTeg0120NNWbDpjHDGWr9PEyKERARERF1Rbe+TTU0NP9GNDW1Oe3onj17EAgEsGTJknCdUaNGoaCgAMXFxZg1axaKi4sxfvx4ZGVlhessW7YM9913Hw4dOoTJkydHvY7P54PP5wsfO53N8+6DwSCny/WiUCgEIQRCoVBfN6VferF6KzY7DkBARJ3TSzo8mLuyx37zHe++PdZ4HkK0PkeyxoJkmC7r8zrLPAybK9/BiCPlGHmiGiZPAA7tSSRrzVF1FQDBygtA8TYAgGSzQzKZIBpdEE1uQES/tymBBowNtf79YtOaIbTRa2C0I8fCfOO3ISfbe/UzMMKYjYPus+HjI+5zqvfxgLsMZ7zVMa9teVwhgN3OUpQ1VaPSV6/qmzQ5KWH/Hr01/St4rvJDKFAw1zYKRbrMhG1rb+Lfxf0T+7X/Yt/2vK7823DZwY+iKHjwwQcxd+5cjBs3DgBQWVkJvV4Pu92uqpuVlYXKyspwnbaBT8v5lnOxPP7443j00Uejynfv3g2LxXK5j0BdpCgKXC4Xdu3aBbkHp/oMRGVSPd7Sfd7ueTeAXx57DfcF5kCHy0+f3J549+1HmmNwa1qnw+YoJuzatavL99HVVCH1k834VsN5eNA8EqMAqPe7oPFFBzJR2kzJbaFAqAJMj+RVrflRfEE0+tpcp9HAMW0e3KPGA0fVaxx7Q0Cqh1vX2p6D7tPYXvEp9F/+9f2e5ojqvQaAfMWO8UoONmlOIKSE4Ha7IUnAbw+8gXOyeg1m1ZGz2CnUU/8ShQbAHZgEj+RHer0VO8/u7OsmJQT+Xdw/sV/7L/Ztz3PH+Pe+PZcd/Nx///04ePAgPvnkk8u9RaetXbsWa9asCR87nU7k5+dj2rRpsNlsPf761CwUCqGkpATTp0+Hphv7l1C0g1WbYWm4dCDvzDDiqylT4v768e7b98rOwOJtfZ55aZMxM216l+4RPHcWjW+vAwJeGIzJqoxsCgCNXg9TJ7KUhYSAR/HDq/jhUfwIiMjfvEmQ2wSUSXoLjF/eV1M0DOav34a8rBz0lUlKAO+eLFVlubOPyMd4SwECIoTnT+6FRWl9r29Mn4trU6cBAJKqP8HfL2yD2WyBJAFlaIQF6p+zpcPmJ1y2N+oY/y7un9iv/Rf7tue1zArrjMsKfh544AGsX78e27ZtQ15eXrg8Ozsbfr8fDodDNfpTVVWF7OzscJ3I3wC3ZINrqRPJYDDAYDBEN16rhVZ75ayD6A8kSYJGo+H7HkdBEUKJ+6RqDYmEL9epREyBe7f+cyxLm9wjX1Yv1beKUOBVAjBroj+LbQWUIMr8NarnGWHN7dLPTLCqAk3P/wbw+wBJgkmjh07WIKg0By7lOTZUjxmDJTkzAb0eksEASatH6GIVgqdPIHDqBNzOWtQHGuFTItbuSIDXqEOTWY8mkw5us775/806eEx6/Ouom2BMSoFsT4XG3vfJALTQYqgpG6WeinDZCX8lJicPwReuMniEX/VeL0gZG36vV6ROwT8ufAJJQsw1SqlaKyx6U88/BMUd/y7un9iv/Rf7tmd15X3tUg8IIfCv//qveOONN/DRRx+hqEid3nXq1KnQ6XTYvHkzbrjhBgDAsWPHUFZWhtmzZwMAZs+ejZ///Oeorq5GZmYmAGDjxo2w2WwYM2ZMV5pD1C8cdJdFJQd4etjdyNDZcMJTjofP/C1c7gw1YYvjAFakxn/0pyPnfbX477J/ojbowmzbSNyXuxw6KfZvr8p8NQhGjK4MMWbFrBtLqK4Gzt8/AcXdqCo3pmdjY5EOR0ZlwWE3QydpsXD47OjUxwuvRl3AhV/veRaplfWAEF8GOq0BjpBjJysYbMhA0pBpnW5rbxllHqQKfo42nQcA7HCqp+GNMOUiTZcUPk7WmjE+lIMTcMS8b6KmuSYiIuopXZp4eP/99+OVV17BunXrkJSUhMrKSlRWVsLjad53Ijk5GXfffTfWrFmDrVu3Ys+ePbjzzjsxe/ZszJo1CwCwdOlSjBkzBt/61rewf/9+fPDBB3jooYdw//33xxzdIervIr/ADjflIkNnC/9/5KaOb9eWIKD03qJvRSj47YX3UBt0AQCKncfwbm30vjctSiP298nVp8LSyb1ZFJcTzueehNKgzkimGz4aOWv/G7tmD4XD3pzoICCC+KThSMz7fOY6jupkPY6OzMLRUdkoK0hFTYYVTRZDu4FPtj4Fd+cs7lQ7e9sok3q/n1JPJZpCPuxpPKkqn2MbFXXtjFAB5Hb+qk/UNNdEREQ9pUsjP7///e8BAAsXLlSVv/DCC7jjjjsAAE8++SRkWcYNN9wAn8+HZcuW4Xe/+124rkajwfr163Hfffdh9uzZsFgsuP322/HYY49170mIrkABJYgSV6mqbLZthOr46+mzcaBNti9H0I3NjgNYnhqdGbEnFDuPRaVR/mfNZ5htGxFz5KDUW6E67myKa6WpCc7nnkToonpBvragCLa77odkNGKadRh2uo6Hz21xHMCylElRU7p2Ok+ojvWSFqPMeRhvKcA4SwHyDGnhqYUtNFLiLkIdaR4ECVJ4GmRABPF6zWfwKv5wHQkSZtqGR12bCjOmWYeixF0adS5R01wTERH1lC5Pe7sUo9GIZ599Fs8++2y7dQYPHoz33nuvKy9N1C/tdZ+O/gKbpA5+RphzMc5SgIPu1r1e3q4twWL7+B7f+ycgQnj14o4Y5UG8ULkFP87/WlTgcTJi5GdoJzY3VRpdcP6/pxEsP6cq12YPgu07D0IyNo8cLUoZrwp+zvlqUOqtxHBTa0KCukAjjn+5oWeL7+ddgynWIZdsR6KyaozIM6ThnK8mXLahbq+qzhhzPuza2EkzVqVOjRn8ZOrscW0nERFRokvcX3USDQCfOY+rjkeZByFVZ42qd336LNVxfbARWxwHe7RtALCl/gCqAw0xz+13n8FOl3qEpTHkVWVlA4Bhlxj5CTnq0fDbXyF47oyqXJOWAdt3fwDZ3PqFfpw5Hxm6ZFW9zfVfqI4jR9JMsh7jzQUdtuFKMNqcpzpum54biB4xbKvImIVxluj3gCM/REQ00DD4IeojXsWPPa5TqrI5tpEx644252GMOV9V9nbtrh5d++NR/PhnzWcd1nmpaiua2mwQesqrnrKmk7QoMGa0e32ougrOpx9HqEo9VU62JcN23xrIyXZ1uSTjKvs4VVmx87iqDW1HhgBgatLQHh8h6w0jzYPaPSdDxowYU97aWhWRalwraZjwgIiIBhwGP0R95PPG0/CL1jTMEiTMSGr/C+zXM2arjuuCjfio4VCPte/9us/hDDWpypZHZJlzBN149eKn4ePIKW+DjRntZoULnjuLht/8N0KOOlW5xp6K5Pt/BE1a7KBpQfJY1XodvwiEk0Y0BN042nRBVb+j9/RKEpn0oK3xlgIkaTpOWT3eXIDZbYLrpSmTuL8PERENOFf+r0OJrlA7Go6qjsdZCmDTmtutP9qch9HmPBz5Ms0xALxbtwdL7BNi7uHSHc5gE96p3a0qm2QtwrczF6DCV4f97jPh8g/r98OiMeKkpxKH27QNAIa1s94ncPwInH96FsKnTvGtycyG7btroElJbbdtqTorpliHqDKdbXEcwJKUCShxnVTtjWSQdZhoKbzU414RUnVWZOnsqAo4os7NSY7O8hZJkiR8L3c5FtnHQ4YUNY2OiIhoIODID1EfaAr5VAEE0P6Ut7Yi1/5U+R1Rmdji4c3aXVGJGG7KmAdJknBX9mLopNbfmwgI/LPmM+x3n0FAqKfhRWZ6E0LA89GHcD73ZFTgo80vRPK//rjDwKfFopTxquPT3iqc8VZjV8QapCnWIdD3gylvLUbFmPqmlTSYZh3aqeu1kgbjLAUYY8mPe8BMRER0JWDwQ9QHSlylqo1ANZKM6UnDLnnd2BgL/iO/8HdXhb8eG+v3q8rmJo/C4C/X7mTqk6OCsFiMsh4T2uxRJHw+NP75/8H91qsQQr1YXzd8FJK/9++QrUmRt4lpkqUQqVp1Yoi3anepMuIB/WfKW4tYwc8kaxHMGu6RRkRE1BkMfogu02H3Ofz7yZfw4Mk/YbcrOo1we4QQ+NSpnvI20VLUqY1AJUnCzIgv9J85j3cqDX1nuENe/M+5N6MCs2+kz1HVW5k2FXmGtNhthIThplz8KG91eBpfqKYaDU//Ar69JVH19eMnw3bP98PprDtDlmQsjEh88JnzuGrKm17SYqK1sNP3vBLECn5md2LEkIiIiJr1n/kgRL1IEQp+X/EBagJOAMDTF97Fk0PvRLrO1uF1ASWI5ys3qzYtBYA5HaQpjjTDNgzr61rX41T463HBX9duMNJZCgSerfwgKlX11SmTkKlXjzbpJA2+P+ga/Orcm7gYaECK1ooJlsGYaC3EOFM+zPUuBI+dgftcMYLnziBYdhoiEEAk87JrYVp6DSS567+HWWgfhzdqdqoCnrYmWYtgkvVdvm8iy9LZMcyUg1JPc3a8VK0VU6xFfdwqIiKiKweDH6LLUOarCQc+ABAUIbxVW4K7sxe3e01twIUnz7+Dk151RjSDrMOUpM6t2QCaNw1N1VpRF2wMl+10HkdeRDa4rtqqKcVhd61qLchgQwZuzJgTs36eIQ1PDrkD3tpKaM5dQOhIGYLn34H/3Fn4vJ4OX0sympB0279AP3biZbc3Q2fDBMvgqLVTLfrblDegNWnBP2s+Q0AJYXX6dBj7WYBHRETUkxj8EF2GIxHplAHgI8dBXJc2A2m66HUrx5vK8cSFd9AQdEeduz3rqi6NUMiSjBm2EdhQ93m4bKfrBG7oRvDzccMhlGjKYEHrhqI2jRk/zF8ds21Kkxu+z7bD++lHCNXVdOm1tFm5SLrrfmgysy67vS0WpYyPGfxoJQ0m99MRkRx9Cu7PXdHXzSAiIroiMfghugxHI1I6A82jP2/XluDO7EWq8u0Nh/G/FR8iFLHI3yDrcF/Ocsy8xOaUscxIGqYKfs75alDhr0eOPqXL9zrWdAF/qt6qKtNIMtbkrUJGxDS+YPk5eLdvgW/PZzGnsXVEkmTop82C9fpburS+pyNTrENg05ij9iOaYClkEgAiIiKKwuCHEtp5Xy0+bTgKV0g9jUpCcxrl+cljIEu9m7dDCBG1kWaLrY6DWJ02A6m65kxkh93n8PvyD6LWpWTqkvHv+auRb0i/rDaMNOUiWWtRjSTtdJ7AdekzunSfz12n8PuKDxBqk+AAAP4lewlGmgdBeL0InDmJwMljCBw/gmDZ6U7fW7baoM0fDG1+YfN/C4og25IvfWEXaCUNFtrH4u1adSKFywkoiYiIqP9j8EMJqz7YiEfPvorGUOz1I5scX+CCrw63Zs3v1XZVBhxRIw0tAiKId2pLcHv2VagJOPH0hXejAp/xlsH4t0ErYe1Edrf2yJKMGUnDsLFuH/IuODD+UAVyzpegVpMKtKzZkSRIsgw5JQ3aoqHQFQ2DtmgYNClpcIe8+HPVx9hWfxC6QAg2rx9p1U5kBVyYF8rCtH0lcFS+hdD5sxCK0nFjAMgWa3OQkzcYmoJCaPMLISfbe2UvmUX28Xindnf4fdZJWkyxDunx1yUiIqIrD4MfSlhb6g+0G/i0eLduD2bZRkRtptmT2hv1abHZcQArUqfgqQvro4KkpSmT8O2shdB0c7RKaajHV/aUI3/7Ttgdre9RwGiEVtKEjwWa1+cEL5TB+0nz1Da3xYCzgVqM8/owxR+EJL6sp4Rg1ZqQbaiHF5cmyTL0E6fB+JVF0BYO7bNNM7P0dtyYMRevXvwUEiTcmjm/W4ElERER9V8MfighCSFQ7Dx+6XoQ+GPlJvys8JZuBxSdFbneZ4QpFye9leE1PQERxENn1kVN1ZtiHYLbsxZ2a5qeCATQtP51eLdvQYpQ4PT60HZcxh3yIllraff6uoALDTVVMMU4Z4AOmbpLT0uTrTYY5y6EcfZ8yMn2Lj9DT7gufQYW2ccDAGzaWE9HRERExOCHEtQ5Xw0u+GtVZV9JHgOzrMfFgBOfN54Kl5/xVuPD+n1YkTqlV9oWmeltjm0U8g3p2Oz4IlwWGfhk61Pwvdzl3Qp8QtWVcL30vwiWnwuXWTQGuIKtr+UO+doNftwhLxqC0dP1JElCutYKyatAjjF6I8kytAVF0A0bBe3QEdANGwlJm3h/dTDoISIioktJvG8wREDUqE+azobv5iyFLMkIiBD+7+lXcN7XGhy9enEHZiQNj5lmOp5qAy5cDDSoykaZB2GytQhbHQehIHp9jFHW44d518LSjalY3pIdcP/jLxB+n6rcIhvhgge1aRYcGJuDimwb/m/+DUjWmgEAwu9r3mT09EmcPVoM+NX3NWn0SNfZoIUMl9YPOTsX2rQMyKlp0KSkQZObD13RMEgGZk4jIiKiKx+DH0o4QgjscB5Vlc1OGhEeNdFJGtyVvRiPnX01fN6r+PFy1Uf4Qd6qHm1b5Hofs2xAviENsiRjgX0stjoORF1zX+4y5BnSLuv1hNeLxtf/At/u4qhzklaH5Blz8VxOOU5lGsOJDkrSgliROixcTz9yLC4GnPj/jgeQ4mhCal0TFI2E+VnTsCh7OmSzGYpOj2P79iN/1ixoE3BUh4iIiCgeejdHMFEnnPJWoTpidGW2baTqeLQ5DwuSx6rKdrlOqKbD9YSjHnXwM8o8KByUrU6bDgnqaWNfS5+JGUmXl3Y5dLEKDU/9Imbgo83KRfIP/xO2m+9E/ohprRneAHzkOAQh1BnmPm04CiFLqEu1oHRYBiqHDsKiyddCNygfmpQ0SEaT6h5ERERE/RGDH0o4xc5jquMsvR1FxsyoerdmzYdVo17n8WLlVviUrm2+2RWRIz+jzIPC/5+lt+PbWQvDxwuSx+Lr6bMv63X8xw+j4alfIFhVHnXOOGs+ktf8BNrsXADALNsI1fky30Xsd58JHwsh8EnDEVWd2baR0Mkc4SEiIqKBhd9+KKEoQokKfmYnjYyZRjlJY8KtmfPxvxUfhMsuBhrwVm0JbsyYE/e2uUIenPfVqMraBj8AsDx1MqYmDYVPCVzWVDchBLzbN6PpzVchhHr9kGQwwnrjt2GYot7IdIJlMHL0Kajw14fL3q4twSRrEQDgrO9iVPKIucmjutw2IiIioisdR34ooRz3VKAu2Kgqm5M8sp3awILkMRhtzlOVfeQ4GDXtKy5ta1KPwuglLQqNWVH1MnS2ywt8AgG4//Yi3G/8LSrw0eYMgv3fH44KfIDmDU9XpU1XlR1pOo9STyUAYHvEqE+6zoaRptwut4+IiIjoSsfghxJK5KhPniEN+Yb0dutLkqSaagYA9cFGlEWM0MRD5JS34aZc6NpsKHq5RCgI765P4fifR+Hd9WnUef34yUj+t7XQpEdP/WsxzzYKKVqrquyd2hIoQsGnEckj5tlGdyvlNhEREdGVitPeKGGEhILPIlJcRyY6iGWwIQMZumRVCup9jacx2JgR1/YdidjcNHLKW1eJQAC+XZ/Cs2UDQnWxgzXzsmthWnoNJLnjYEUna7EidTLWVW8Pl5W4SrGx/gs0BN2qupzyRkRERAMVf/1LCeNw0zk4Q+pNODsT/EiShMlfrm9psc99Oq5t8yh+nPZWq8pGmi9v6pjS4EDTh+tR/7O1aPzHKzEDH0mnR9Id98G8/NpLBj4tFtsnwCy37scjIPBy1UeqOkXGrMtOu01ERER0pePIDyWMyFGfQmMmcvQpnbp2krUIH9bvCx8fayqHO+Tt1saibR1wn1VtYCpDxnBTTqevF4qCQOlR+HZ8DP+BvRBK9GaoLbR5BbDedAe0gwq61EazxoAlKRPwdm1JuCxy09V5HPUhIiKiAYzBzxVop/MEtjUchlfxq8o1kozR5jxcmzYdmitsTUdAhLDTdUJVNqcToz4txpjzoJO0CIgggOZRjwPusqg00F0VFCG8XVuC1y9+piofYsqCUdZ3eK0IhRA8cxL+w1/Af2AvQherOqyvGzwEpqXXQDd6fMzsdp2xPHUy3qv7HEERijonQcIcG4MfIiIiGrgY/Fxh9jeewdMX1kMgdjazA+6zcIY8uD0iCUCiO+I+B3fIqyqbmdT5wMUg6zDGnKfa32Zf4+luBT/nfDX4XfkGnImY7gYA48yxR2VEKAT/F5/Df3AfAkcPQmlyx6zXlm7YKJivXgnt8FGXHfS0SNFaMT95DLY4DkSdG28ZDLvW0q37ExEREV3JGPxcQUJCwSvVH7cb+LT4oG4v5tlGYagpu5da1n27G0+qjocas5GpT+7SPSZZi9TBj/sMFKF0ObNZSCh4p3Y3Xq8pjjmCMtiQgRWpU6LKA6XH4H59HYKVF6LORZJ0OhimzIRxzgJoC4ouWb8rrkmbhq2Og1E/J5zyRkRERAMdg58ryFbHQZz31V6ynoDAHys34WeFt1wR09+EENjjUgc/05KGdfk+k6yFeKnNzDJPUwPKjpUgz5IFOTkFkjXpkskDzvtq8Vz5BzjprYw6J0HC6rQZuD59JnRy60cnVF+HprdfhW/f7ku2UZuVC8PcBTBMnQ3ZbO78w3VBjj4F05OGYVebaYR6SXdZ7ykRERFRf8Lg5wrhUfx47eIOVVmeIQ2L7RMAAGe81fi44VD43BlvNT6s348VqZN7tZ0thBCdnsJ1ylsVtbHptKShXX7NbH0K8oUV1qOlGFFajcKzdQjKe+H4cqqXJMuQbXbI9hRoC4qgnzQN2sFDIMkyFKHg3brP8drFHeF1Q20N0qfhvtxl4dE0xd2IUGU5AieOwrNlA0TAH3VNC21uHnRjJkA/ZgK0hUO7PbWtM76WPhN7Gk8i9OVmqQvtY2G6xBolIiIiov6Owc8VYn3t7qg00LdnXYVxlua1JwERwklvpWpk6NWLn2JG0jCk6ZJ6rZ27XaV4uepjaCQZN6TPwrzk0Ze8Zk/ElLdsfQoG6VO79Lr+44fh/XgT7jpQDJe/NZBqEr7wOhehKAg56hBy1CFw5iQ82zZBY0+Bd/w4/C2nEUflBlhCCrRBBRpFgS4QgqUpgHnSIMyACdLO99BQX4dQZTmURmeH7dHm5sM4dyF0o8dDk9K1Z4mHQmMmfpi3GlscB5CjT8HX02f3ehuIiIiIEg2DnytAXaAR62v3qMomWYvCgQ8A6CQN7spejMfOvhou8yp+vFz1EX6Qt6pX2ukIuvHMhffCIyfPlr+Pk94q3JY5v8Ppd7sjp7xZOz86ojTUw/3m38NTzizQwdXmvE8JdLjux1N3EeUbX8c8ITAv4pxW1iBTlwyDXIP2x3XUZLMF5q9eB8PsBZ3en6enTLYWRe1/RERERDSQMfi5ArxWswN+EQgfS5Bwa+b8qHqjzXlYkDxWNf1tl+sE9jae7pUvwR/U7YuaMrah7nOc89bg3wathE1rirqmyu/AOZ96k8+pnZjyJkIheLdvQdOGtyB8rVnijLIOkiRBiObF/oosoUkEYJUMMe/jDDWF67aVrDUjRWvtwhQ1CcY582H+6tcgW6ydvIaIiIiIehODnwR31nsRHzsOqcquso9DniEtZv1bMudjT+MpNIY84bIXKrdgzJBvwyDrmr/oCxH3UQmfEsBGx/6Y5w41leGhM+uwJm8VCo2ZqnORU95sGjNGXGLz0MCZk3C/9gqC5eeizkmSBIPOiEN5ZhwflolTRWmYnjEe9yXNhtLgQMhRj1D5efj27kKouhJNIZ/qeq2kQYbedsk9fABA0mihycyCNr8QxnmLoM0ffMlriIiIiKjvMPhJcOuqt6tSFhtkHb6e0f76DZvWhFsz5+OP597H2CMVGHqqBuamAEpRghzFCOH1QIQUaLNyoBs9DvrR46EdMhyStns/CtsaDkft09PWxUADHjnzN/zboJWqkZ3IKW+TrUXtTlETAT+a3nsTno82AjHSfctmC8wrrkPlMDPeqv80XL6/6SwwaDm0yc2JDjBhCkzLVuHC2YPYsfl/MeJENVIczcFitt4OnayDpNNC0ukhWayQbcmtf5KSIadnQpszCHJaBiSNpitvExERERH1IQY/CeyA+yy+aLNvDQCsSp2GFG3706pEIICZByphXr8PUkNDuNwruREypIencQWryhGsKofnow8hGYzQDR8Fw+QZ0E+c2uUv9IpQ8F7d56qyImMWHEE36ttkcfOLIJ658B4eL7oVuYZUuEIeHG1S74kzvZ10zIHTpWj86wsIXayKed44Yy7Mq74O2ZqEif4GoE3w4wp5cMpbjWFt9j2SJAlf2Pz4dM4QfDpnCPT+IOxaK54ceQ8kjbZXMrIRERERUe9i8NMLip3H8I+LxTDKesy2jcAC+1gkaaLXv7QlhMDfqj9Vldm1FqxMmxq7fsAPb/E2eDZvgOJ0IE/Rom1YoQgFbsULa4zXFT4v/Af3wX9wHzQbsmBa8lUYps7qdBC0t/E0Kv31qrKbM+chz5CGJ8+vxwlPebjcLwL4Tfl7eGzwTfjcdUo1qqWXtKokDgAg/L7m0Z6PNyHWaI82ZxAs3/gWdEWtQVOmPhm5+lSU++vCZfsaT6uCHwDY33i2tV16LcbYh0HW6jr1zERERER05WHw08Mq/fV4tvz98H4rp7yVePXiDsxIGo7FKeMxyjQo5ijDLlcpTkVstHlD+uyYa1ECpceaR0XqWhMH6GUtjBo9vKHWPGUNQU/M4Ket0MUqNP71BXg+XA/T1SthmDIDkq7j9S/v1qkz0RUYMjDOXABJkvCfBV/H7ys+QLHzWPj8GW81Xr24A1UBh+q6CdbC5nVJgQACxw7Bt283/Af3qRIatJA0WpiWXwvTVUshaaJ/jCdZi1Be1xr8fOI8ghvSZ4Xfa78SxJGm86prJloKO3xOIiIiIrqyMfjpYW/VloQDnxYBEcSnziP41HkEBYYM3Je7TJUIICQU/P3iJ6prsvUpuMo+TlUmfD641/8D3k+2xnztZI0ZJ3LMODIqC16DDn69BvcOWY0CjR2BY4fhP3IAwXNnYl4bqr2Ixr+9CPffX4YmJxfavMGQcvOgr2uACASAL9cInfJURQURX02dEg4ydLIW96YthPfEEQSrK2Bz+ZDk8sLs+hwTG/2YIAT8ei38eg1GpDjgNJchUHoMwuuJalMLbUERrDffCW12brt1ZiYNx3ttgrIqvwNHms5jjCUfAHC06bwqM50EKWrUiYiIiIj6FwY/Pag24ML2hsMd1inzXcR/nX0NjxbeFM7gtq3hMCoippF9M2Ouaq+cQOkxNP7tRYRqL8a8r274aGQtXYmt+AQ1gdYNOd831+CBQdOhGzIc5hWrobic8B85AO/2LQiePxt1HyEUBMvPI1h+HhACGW43Gj75ENpBBdAVDsEeax3sSU3w65t/lGxaM2bJufAfPYTgyeMInDyGYNlp3Bbw4YK/DoiRVrpFet15+KXyds9LWh3MK1bDuODqS07JG27KQZ4hTbXp6xbHwXDws9+tftahpmxYNcYO70lEREREVzYGPz1ofe1u1aiPRpIhQUJQhFT1mhQfHi97HY8V3gybxoR/XNyhOl9kzMKMpGEQQiB45iS8Oz6Gb3dxzNfUFQ2DedXXw2tglta6sK56e/j8Z65j+FZwPpK1FgCAnGSDccZcGKbPQeDwATR98Ha7o0FhoRCC587AV3YShd4a3NXmVIrOCpf2i6hL9LIWaVoragOuqHNA8/487WV5AyToR42BefU3OxztUV0hSbjKPg5/rvo4XLbLdQKNoatg1RjxRUTwM8HCNNVERERE/R2Dnx7SEHRji+OAqmyxfQJuSJ+NbQ2HsMnxBar8jvC5er8Lf9j1PGbValHoOolMix5NJh3cFj1uyV8A7+YN8O36tN1sZ5JOB/M1N8A4b5FqD5+F9nF47WJxeIpXSCjY7DiA69Nnqa+XJOjHToBuzHgEjh6EZ/P7CJw8gVhJBlrUBdxR97B1sKbIpjWjSfHB02YdUgtzjFEXXdEw6CfPgGHCFMjJ9nbv2555tjFYV709HIAGRBCfNhzFtKShOB+xsSrX+xARERH1fwx+esh7dZ/D32ZNiQwZ16RNg01rwjVp03B1ykT84vTf4TtxFMNOXcTQU7WwNjZvuLmwzX2Msh7ZhjI0dfBauqJhsN58JzQZWVHnkjQmzLGNxMcNrRulbqr/AtemTYdWip46JkkS9KPHQz96PITXi2D5OQTPnUXw/FkEyk4Dp5v35WkK+VQbqba8VnujN5IkQ5OTi2z7WLyrnEa1VQOnzQhFlmDwBXFv2gJYgjKE1ws52Q7duEnQ2FM6eOpLs2lNmJ40DJ85j4fLtjgOwCCrf+zNsgFDIzLBEREREVH/w+CnB7hDXnxYv19V9pXk0cjQ2QA0p7EWxZ/g/nc/Q7njPAJKKNZtAACpuvb39JG0OphXXg/j/MWq0Z5Iy1InqYKf+mAjdrtOYpZtRIfPIRmN0A0ZDt2Q4QCAYDCIo59sx6gMG97f+zfYL2iQU+mEyROAJElI1ppbr5VlaAuKoB06ArqhI6ArGg7J2Dy6M7HxNH557o1w3aHGbGQXreywLZfrKvt4VfBT5rsYlZ1unKVAtZ6KiIiIiPonBj894IP6ffAqrVO7JEi4Nm06AEAoCpre/Bs827dARnMWt3JfXVRGOAAwawwwyNH7zmgysmCcOQ+G6XMg25Iv2Z4iYxaGm3JV++18WL/vksFPLEKnxyu2ShRPzQGm5gBCwOAL4o7MhchMHhNOaCAZTZB0sffMmWQtwn25y/FGzU4kaUy4N+fqLrejs8aZ85Gus6mSPrRNggBwvQ8RERHRQMHgJ848ih/v1+1Vlc20DUeuIRUi4Ifrz3+A/0Drea2kaQ6A/HUQQqA+xYRGiwFmTwCDlFTA2xxESQYjDJOmwTBzHrSFQ2PuDdSRZSmTVMHPkabzOOu9iMHGjJj1T3gqcMpTheGmHAwxtU6nOyZXY4frVOvrSxJGpQ3HgtwZXWrT/OQxmJ88pkvPcDlkScZV9nF4LSKJRFsTrYU93g4iIiIi6nsMfuIoIEJ4qXJr1FqY1WkzoDS64PrjbxA4eyrqOnPhcNhHDMYzKWW4mNI8NWx5ymTkZl8FEQxCeJogWawdTm27lBm24UiutqAh2JqkYH3dbtyfuyKq7i7XCTx5/p3wcaExE4vtEzDWOAgfaI5BQuuIjlk24J6cq7scjPWmBclj8Y+LxRAxkjfk6lOR/uV0RCIiIiLq3xj8xIkj6MaT59/BcY96n5rJ1iLkOYJoeP5xhGqqVeckjQbWm++CYepM2AH82FeHzY4vkKWz4+qUCc11tFpISd3/cq6TNFhin4DXa1pTZH/acBTXp89Cjr41sUBAhFTpoQHgjLcaz1dughACTZIfljbBz+3ZVyFNl9Tt9vWkNF0SJloLsa/xdNQ5TnkjIiIiGji4yjsOTnoq8ZPTf4kKfHKqGnHThlI4fvlIdOBjNMH23TUwTJ0ZLhtkSMW3sxZiWeqkDva8uXxLUyap1hAJCLxZs1NV5yPHQdX6mI5MsQ7FV2yj49rGnnKVfVzM8gmc8kZEREQ0YHDkp5u2NRzGHys2hffRAYDccgfm7SzDlMpgzP1rNPYUJH3nB53esDNebFoTlqZMwju1JeGy7Q1HcH36LGTp7fArQbwREQy1x6Ix4p6cJQk93a2tKdYhsGnMcIZak4ZrJQ1Gmwf1YauIiIiIqDdx5OcyhYSCl6s+wu/LN6gCn6Ena3DLP7/AzKrmACGSNjcfyQ/+314PfFpckzoVekk9+tMS8Gxy7Ed9sFFV/5bMr2CKdSgkqIOcu7MXw6619HyD40QrabDAPlZVNso8CEZZ30ctIiIiIqLexpGfy7TDeRTv130eVb7guAv5utSoaWuS0QTTvEUwLfkqJIOht5oZxaY1Y2nKRKyv2x0u29ZwGF9NnYK3akpUdcdZCrAqbTpWpQG1ARe21H2BEtdBXJczH7NtI3u76d12TepUFDuPoSbghAQJ30if09dNIiIiIqJexODnMs2zjcZu10nscp0Ily1LmYRpigshqTXbm2y1wbTwahjmLoRsNPVFU6NckzYVH9bvg//LESsBgZ+Xva6aEgYAN2a0BgdpuiR8LW0mckuBGUnDe7W98WLTmvHfRbfhWFM5BhszEj5RAxERERHFF6e9XSZJkvDd3GXIN6RDK2nwnZyluCN7EeBRp7m23vgtmBavSJjABwCStRYsSZmoKosMfCZbizDc1DdT83qSRWPElKQhDHyIiIiIBiCO/HSDSdbjh3nXwhlqCgcKSpNbVUcymfuiaZe0Km0aNtbvV61XausbGZwSRkRERET9S5dHfrZt24ZVq1YhNzcXkiThzTffVJ0XQuDhhx9GTk4OTCYTlixZghMnTqjq1NXV4dZbb4XNZoPdbsfdd9+Nxkb1QvsrRZbeHg58hKJAeNUjP5I5MZMC2LUWLPlyL6FIM5KGo8iY1cstIiIiIiLqWV0OftxuNyZOnIhnn3025vlf/epXeOaZZ/Dcc89h586dsFgsWLZsGbxeb7jOrbfeikOHDmHjxo1Yv349tm3bhnvvvffynyJBRAY+QOIGP0Dz6I9OUg/+SZDw9YzZfdQiIiIiIqKe0+VpbytWrMCKFStinhNC4KmnnsJDDz2E1atXAwBefvllZGVl4c0338RNN92EI0eOYMOGDSgpKcG0adMAAL/5zW/w1a9+Fb/+9a+Rm3vlrjMREVPeAEBO0GlvAJCitWKxfTw21O8Nl822jUS+Ib0PW0VERERE1DPimvDg9OnTqKysxJIlS8JlycnJmDlzJoqLiwEAxcXFsNvt4cAHAJYsWQJZlrFzZ+c22ExUkcGPpNEA+sTeR+YbGXMw4stpe4MNGfh21oI+bhERERERUc+Ia8KDyspKAEBWlnq9SFZWVvhcZWUlMjMz1Y3QapGamhquE8nn88Hn84WPnU4nACAYDCIYjL1gvy8EGl2AEK0FRjNCoVDfNagT9NDgP/O+jsaQFxbZAAlSu+9pKBSCECLhn4m6jn3bP7Ff+yf2a//Efu2/2Lc9ryvxwBWR7e3xxx/Ho48+GlW+e/duWCyJs6bGdPoEUt2toz9BjQ5Hr/DRrLYURYHL5cKuXbsgy8yS3p+wb/sn9mv/xH7tn9iv/Rf7tue53dFLT9oT1+AnOzsbAFBVVYWcnJxweVVVFSZNmhSuU11drbouGAyirq4ufH2ktWvXYs2aNeFjp9OJ/Px8TJs2DTabLZ6P0C0+xQdPm2BMk5ePwpkz+7BF8RUKhVBSUoLp06dDo9H0dXMojti3/RP7tX9iv/ZP7Nf+i33b81pmhXVGXIOfoqIiZGdnY/PmzeFgx+l0YufOnbjvvvsAALNnz4bD4cCePXswdepUAMCWLVugKApmthMoGAwGGAyG6MZrtdBqE2fwyu/zApIUPtaYLQnVvniQJAkajabfPRexb/sr9mv/xH7tn9iv/Rf7tmd15X3tcg80NjaitLQ0fHz69Gns27cPqampKCgowIMPPoif/exnGD58OIqKivCf//mfyM3NxXXXXQcAGD16NJYvX4577rkHzz33HAKBAB544AHcdNNNV3SmNwAQTU2q40ROc01ERERENNB0OfjZvXs3rrrqqvBxy3S022+/HS+++CL+4z/+A263G/feey8cDgfmzZuHDRs2wGg0hq/5y1/+ggceeACLFy+GLMu44YYb8Mwzz8ThcfqW8EQGP4mb5pqIiIiIaKDpcvCzcOFCiLYZzSJIkoTHHnsMjz32WLt1UlNTsW7duq6+dMITHvViq0Te44eIiIiIaKBhyok4UjjtjYiIiIgoYTH4iaPIkR+JIz9ERERERAmDwU8cMeEBEREREVHiYvATR5EJD7jmh4iIiIgocTD4iROhKFA8HlUZs70RERERESUOBj9xIrweAOoseJKJ096IiIiIiBIFg584iZzyBnDaGxERERFRImHwEyeiKSLTmywDBkMftYaIiIiIiCIx+IkTxROd6U2SpD5qDRERERERRWLwEyeR0964xw8RERERUWJh8BMnUXv8MPghIiIiIkooDH7iJGqPH25wSkRERESUUBj8xIkSmfCAIz9ERERERAmFwU+cRGV748gPEREREVFCYfATJ1HT3jjyQ0RERESUUBj8xElUwgMzgx8iIiIiokTC4CdOYu3zQ0REREREiYPBT5xErfnhtDciIiIiooTC4CdOmOqaiIiIiCixMfiJA6EoEB6PqkwymfqoNUREREREFAuDnzgQPi+EUFRlXPNDRERERJRYGPzEQWSmNwCQTQx+iIiIiIgSCYOfOIhc7yNJMmAw9FFriIiIiIgoFgY/cRCV6c1shiRJfdQaIiIiIiKKhcFPHHCPHyIiIiKixMfgJw64xw8RERERUeJj8BMH0Xv8MPghIiIiIko0DH7iIGraG0d+iIiIiIgSDoOfOIie9sY1P0REREREiYbBTxxET3tj8ENERERElGgY/MRB5CanksnURy0hIiIiIqL2MPiJAyVqnx+O/BARERERJRoGP3EQOe2NCQ+IiIiIiBIPg584iEx4wDU/RERERESJh8FPNwkhIDweVZnEfX6IiIiIiBIOg59uEj4vhFBUZZz2RkRERESUeBj8dFPklDeACQ+IiIiIiBIRg59uikp2IMmQ9IY+ag0REREREbWHwU83xdrjR5L5thIRERERJRp+S++mqD1+uN6HiIiIiCghMfjppqhpb1zvQ0RERESUkBj8dFPUHj8c+SEiIiIiSkgMfrqJe/wQEREREV0ZGPx0E9f8EBERERFdGRj8dJPwRAQ/XPNDRERERJSQGPx0U2TCA5nBDxERERFRQmLw001K1D4/nPZGRERERJSIGPx0U3SqawY/RERERESJiMFPN0WmuubIDxERERFRYmLw0w1CCIiIaW+yiWt+iIiIiIgSEYOf7vD5IISiKmK2NyIiIiKixMTgpxuUiDTXAKe9ERERERElKgY/3RC53geQIBmNfdIWIiIiIiLqGIOfbhAej+pYNpshyXxLiYiIiIgSEb+pd4PCTG9ERERERFcMBj/dELXHD4MfIiIiIqKExeCnG7jHDxERERHRlaNPg59nn30WhYWFMBqNmDlzJnbt2tWXzemyqD1+mOaaiIiIiChh9Vnw8/e//x1r1qzBI488gs8//xwTJ07EsmXLUF1d3VdN6jIlctqbmSM/RERERESJqs+CnyeeeAL33HMP7rzzTowZMwbPPfcczGYz/vSnP/VVk7qM096IiIiIiK4c2r54Ub/fjz179mDt2rXhMlmWsWTJEhQXF0fV9/l88Pl84WOn0wkACAaDCAaDPd/gdoSaGgEhwsfCYOzT9vS0UCgEIQRCoVBfN4XijH3bP7Ff+yf2a//Efu2/2Lc9ryvfv/sk+KmpqUEoFEJWVpaqPCsrC0ePHo2q//jjj+PRRx+NKt+9ezcslr5bZ5Nx9iz07tbRn/PnL8C9c2eftaenKYoCl8uFXbt2QeZ+Rv0K+7Z/Yr/2T+zX/on92n+xb3ue2+2+dKUv9Unw01Vr167FmjVrwsdOpxP5+fmYNm0abDZbn7XLufVdKJ7W4Ctz4iToJ07ts/b0tFAohJKSEkyfPh0ajaavm0NxxL7tn9iv/RP7tX9iv/Zf7Nue1zIrrDP6JPhJT0+HRqNBVVWVqryqqgrZ2dlR9Q0GAwwGQ1S5VquFVtt38VvSrXdBaXRBNLkhPE3QFxRC04ft6Q2SJEGj0fTp+049g33bP7Ff+yf2a//Efu2/2Lc9qyvva5/0gF6vx9SpU7F582Zcd911AJqHBDdv3owHHnigL5p0WXRFw/q6CURERERE1El9Fn6uWbMGt99+O6ZNm4YZM2bgqaeegtvtxp133tlXTSIiIiIion6sz4Kfb37zm7h48SIefvhhVFZWYtKkSdiwYUNUEgQiIiIiIqJ46NOJhw888MAVNc2NiIiIiIiuXMy3R0REREREAwKDHyIiIiIiGhAY/BARERER0YDA4IeIiIiIiAYEBj9ERERERDQgMPghIiIiIqIBgcEPERERERENCAx+iIiIiIhoQGDwQ0REREREAwKDHyIiIiIiGhAY/BARERER0YDA4IeIiIiIiAYEBj9ERERERDQgaPu6AZdDCAEAcDqdfdySgSUYDMLtdsPpdEKrvSJ/dKgd7Nv+if3aP7Ff+yf2a//Fvu15LTFBS4zQkSuyB1wuFwAgPz+/j1tCRERERESJwOVyITk5ucM6kuhMiJRgFEVBeXk5kpKSIElSXzdnwHA6ncjPz8e5c+dgs9n6ujkUR+zb/on92j+xX/sn9mv/xb7teUIIuFwu5ObmQpY7XtVzRY78yLKMvLy8vm7GgGWz2fjh7afYt/0T+7V/Yr/2T+zX/ot927MuNeLTggkPiIiIiIhoQGDwQ0REREREAwKDH+o0g8GARx55BAaDoa+bQnHGvu2f2K/9E/u1f2K/9l/s28RyRSY8ICIiIiIi6iqO/BARERER0YDA4IeIiIiIiAYEBj9ERERERDQgMPghIiIiIqIBgcHPALNt2zasWrUKubm5kCQJb775pup8VVUV7rjjDuTm5sJsNmP58uU4ceKEqs7ChQshSZLqz3e/+11VnbKyMqxcuRJmsxmZmZn40Y9+hGAw2NOPN6D1Rt/u378fN998M/Lz82EymTB69Gg8/fTTvfF4A1ZvfWZb1NbWIi8vD5IkweFw9NBTUW/264svvogJEybAaDQiMzMT999/f08+2oDWW/1aUlKCxYsXw263IyUlBcuWLcP+/ft7+vEGtHj0LQAUFxdj0aJFsFgssNlsmD9/PjweT/h8XV0dbr31VthsNtjtdtx9991obGzs6ccbUBj8DDButxsTJ07Es88+G3VOCIHrrrsOp06dwltvvYW9e/di8ODBWLJkCdxut6ruPffcg4qKivCfX/3qV+FzoVAIK1euhN/vx44dO/DSSy/hxRdfxMMPP9zjzzeQ9Ubf7tmzB5mZmXjllVdw6NAh/OQnP8HatWvx29/+tsefb6DqjX5t6+6778aECRN65FmoVW/16xNPPIGf/OQn+D//5//g0KFD2LRpE5YtW9ajzzaQ9Ua/NjY2Yvny5SgoKMDOnTvxySefICkpCcuWLUMgEOjxZxyo4tG3xcXFWL58OZYuXYpdu3ahpKQEDzzwAGS59ev4rbfeikOHDmHjxo1Yv349tm3bhnvvvbdXnnHAEDRgARBvvPFG+PjYsWMCgDh48GC4LBQKiYyMDPGHP/whXLZgwQLx/e9/v937vvfee0KWZVFZWRku+/3vfy9sNpvw+XxxfQaKraf6Npbvfe974qqrrupuk6kTerpff/e734kFCxaIzZs3CwCivr4+jq2n9vRUv9bV1QmTySQ2bdrUE82mS+ipfi0pKREARFlZWbjsiy++EADEiRMn4voMFNvl9u3MmTPFQw891O59Dx8+LACIkpKScNn7778vJEkSFy5ciO9DDGAc+aEwn88HADAajeEyWZZhMBjwySefqOr+5S9/QXp6OsaNG4e1a9eiqakpfK64uBjjx49HVlZWuGzZsmVwOp04dOhQDz8FxRKvvo2loaEBqamp8W80XVI8+/Xw4cN47LHH8PLLL6t+C0m9L179unHjRiiKggsXLmD06NHIy8vDjTfeiHPnzvXOg5BKvPp15MiRSEtLw/PPPw+/3w+Px4Pnn38eo0ePRmFhYa88C6l1pm+rq6uxc+dOZGZmYs6cOcjKysKCBQtUfV9cXAy73Y5p06aFy5YsWQJZlrFz585eepr+j//CUdioUaNQUFCAtWvXor6+Hn6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" ] @@ -234,17 +234,17 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:21.607270Z", - "iopub.status.busy": "2023-05-05T10:38:21.607028Z", - "iopub.status.idle": "2023-05-05T10:38:21.770511Z", - "shell.execute_reply": "2023-05-05T10:38:21.769579Z" + "iopub.execute_input": "2023-12-04T17:50:29.004048Z", + "iopub.status.busy": "2023-12-04T17:50:29.003933Z", + "iopub.status.idle": "2023-12-04T17:50:29.151033Z", + "shell.execute_reply": "2023-12-04T17:50:29.150715Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -290,10 +290,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:21.772974Z", - "iopub.status.busy": "2023-05-05T10:38:21.772799Z", - "iopub.status.idle": "2023-05-05T10:38:21.789288Z", - "shell.execute_reply": "2023-05-05T10:38:21.788782Z" + "iopub.execute_input": "2023-12-04T17:50:29.152764Z", + "iopub.status.busy": "2023-12-04T17:50:29.152637Z", + "iopub.status.idle": "2023-12-04T17:50:29.164461Z", + "shell.execute_reply": "2023-12-04T17:50:29.164031Z" }, "tags": [] }, @@ -339,17 +339,17 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:21.792046Z", - "iopub.status.busy": "2023-05-05T10:38:21.791679Z", - "iopub.status.idle": "2023-05-05T10:38:21.952822Z", - "shell.execute_reply": "2023-05-05T10:38:21.952102Z" + "iopub.execute_input": "2023-12-04T17:50:29.166517Z", + "iopub.status.busy": "2023-12-04T17:50:29.166407Z", + "iopub.status.idle": "2023-12-04T17:50:29.307795Z", + "shell.execute_reply": "2023-12-04T17:50:29.307515Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -396,17 +396,17 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:21.955401Z", - "iopub.status.busy": "2023-05-05T10:38:21.955230Z", - "iopub.status.idle": "2023-05-05T10:38:22.118916Z", - "shell.execute_reply": "2023-05-05T10:38:22.118261Z" + "iopub.execute_input": "2023-12-04T17:50:29.309825Z", + "iopub.status.busy": "2023-12-04T17:50:29.309706Z", + "iopub.status.idle": "2023-12-04T17:50:29.490676Z", + "shell.execute_reply": "2023-12-04T17:50:29.490112Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -456,17 +456,17 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:22.121784Z", - "iopub.status.busy": "2023-05-05T10:38:22.121542Z", - "iopub.status.idle": "2023-05-05T10:38:22.287535Z", - "shell.execute_reply": "2023-05-05T10:38:22.286448Z" + "iopub.execute_input": "2023-12-04T17:50:29.492524Z", + "iopub.status.busy": "2023-12-04T17:50:29.492402Z", + "iopub.status.idle": "2023-12-04T17:50:29.634856Z", + "shell.execute_reply": "2023-12-04T17:50:29.634576Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -493,13 +493,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:38:22.310579Z", - "iopub.status.busy": "2023-05-05T10:38:22.310453Z", - "iopub.status.idle": "2023-05-05T10:38:22.333099Z", - "shell.execute_reply": "2023-05-05T10:38:22.332312Z" + "iopub.execute_input": "2023-12-04T17:50:29.636941Z", + "iopub.status.busy": "2023-12-04T17:50:29.636775Z", + "iopub.status.idle": "2023-12-04T17:50:29.650448Z", + "shell.execute_reply": "2023-12-04T17:50:29.649972Z" } }, "outputs": [ @@ -570,6 +570,7 @@ " padding: 1em;\n", " border-style: solid;\n", " background: white;\n", + " max-width: max-content;\n", "}\n", "\n", ".river-pipeline {\n", @@ -696,7 +697,7 @@ ")" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -722,7 +723,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" }, "vscode": { "interpreter": { diff --git a/docs/examples/content-personalization.ipynb b/docs/examples/content-personalization.ipynb index 7d85819855..78dc55b340 100644 --- a/docs/examples/content-personalization.ipynb +++ b/docs/examples/content-personalization.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -15,6 +17,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -25,13 +28,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:09.647679Z", - "iopub.status.busy": "2023-01-29T19:27:09.647451Z", - "iopub.status.idle": "2023-01-29T19:27:09.659176Z", - "shell.execute_reply": "2023-01-29T19:27:09.658611Z" + "iopub.execute_input": "2023-12-04T17:50:31.083670Z", + "iopub.status.busy": "2023-12-04T17:50:31.083311Z", + "iopub.status.idle": "2023-12-04T17:50:31.114735Z", + "shell.execute_reply": "2023-12-04T17:50:31.114428Z" } }, "outputs": [ @@ -41,7 +44,7 @@ "1" ] }, - "execution_count": 18, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -73,6 +76,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -81,13 +85,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:09.661875Z", - "iopub.status.busy": "2023-01-29T19:27:09.661752Z", - "iopub.status.idle": "2023-01-29T19:27:09.856254Z", - "shell.execute_reply": "2023-01-29T19:27:09.855688Z" + "iopub.execute_input": "2023-12-04T17:50:31.117540Z", + "iopub.status.busy": "2023-12-04T17:50:31.117363Z", + "iopub.status.idle": "2023-12-04T17:50:31.329078Z", + "shell.execute_reply": "2023-12-04T17:50:31.328797Z" } }, "outputs": [], @@ -136,6 +140,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -148,28 +153,25 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:09.859123Z", - "iopub.status.busy": "2023-01-29T19:27:09.858934Z", - "iopub.status.idle": "2023-01-29T19:27:10.228676Z", - "shell.execute_reply": "2023-01-29T19:27:10.228100Z" + "iopub.execute_input": "2023-12-04T17:50:31.332143Z", + "iopub.status.busy": "2023-12-04T17:50:31.331942Z", + "iopub.status.idle": "2023-12-04T17:50:31.623109Z", + "shell.execute_reply": "2023-12-04T17:50:31.622233Z" } }, "outputs": [ { - "ename": "TypeError", - "evalue": "string indices must be integers", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[15], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mriver\u001b[39;00m \u001b[39mimport\u001b[39;00m reco\n\u001b[1;32m 3\u001b[0m model \u001b[39m=\u001b[39m reco\u001b[39m.\u001b[39mRandomNormal(seed\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m simulate(\u001b[39m5_000\u001b[39;49m, get_reward, model, seed\u001b[39m=\u001b[39;49m\u001b[39m42\u001b[39;49m)\n", - "Cell \u001b[0;32mIn[14], line 34\u001b[0m, in \u001b[0;36msimulate\u001b[0;34m(n, reward_func, model, seed)\u001b[0m\n\u001b[1;32m 31\u001b[0m item \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mrank(user, items\u001b[39m=\u001b[39mitems, x\u001b[39m=\u001b[39mcontext)[\u001b[39m0\u001b[39m]\n\u001b[1;32m 33\u001b[0m \u001b[39m# Measure the reward\u001b[39;00m\n\u001b[0;32m---> 34\u001b[0m clicked \u001b[39m=\u001b[39m reward_func(user, item)\n\u001b[1;32m 35\u001b[0m n_clicks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m clicked\n\u001b[1;32m 36\u001b[0m ctr\u001b[39m.\u001b[39mappend(n_clicks \u001b[39m/\u001b[39m (i \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m))\n", - "Cell \u001b[0;32mIn[13], line 6\u001b[0m, in \u001b[0;36mget_reward\u001b[0;34m(context, item)\u001b[0m\n\u001b[1;32m 3\u001b[0m USER_LIKED_ARTICLE \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m 4\u001b[0m USER_DISLIKED_ARTICLE \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m----> 6\u001b[0m \u001b[39mif\u001b[39;00m context[\u001b[39m'\u001b[39;49m\u001b[39muser\u001b[39;49m\u001b[39m'\u001b[39;49m] \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mTom\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m 7\u001b[0m \u001b[39mif\u001b[39;00m context[\u001b[39m'\u001b[39m\u001b[39mtime_of_day\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mmorning\u001b[39m\u001b[39m'\u001b[39m \u001b[39mand\u001b[39;00m item \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mpolitics\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m 8\u001b[0m \u001b[39mreturn\u001b[39;00m USER_LIKED_ARTICLE\n", - "\u001b[0;31mTypeError\u001b[0m: string indices must be integers" - ] + "data": { + "image/png": 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", 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" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -224,6 +230,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -235,43 +242,29 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:10.337936Z", - "iopub.status.busy": "2023-01-29T19:27:10.337756Z", - "iopub.status.idle": "2023-01-29T19:27:10.354889Z", - "shell.execute_reply": "2023-01-29T19:27:10.354032Z" + "iopub.execute_input": "2023-12-04T17:50:31.723155Z", + "iopub.status.busy": "2023-12-04T17:50:31.723022Z", + "iopub.status.idle": "2023-12-04T17:50:31.733220Z", + "shell.execute_reply": "2023-12-04T17:50:31.732873Z" } }, "outputs": [ { "data": { - "text/html": [ - "
\n",
-       "defaultdict(Zeros (), {\n",
-       "    'music': -0.013333333333333336,\n",
-       "    'camping': -0.01,\n",
-       "    'health': -0.0078000000000000005,\n",
-       "    'sports': -0.0063106666666666675,\n",
-       "    'food': -0.0052320723809523816,\n",
-       "    'finance': -0.00441314521904762,\n",
-       "    'politics': 0.01650162758658716\n",
-       "})\n",
-       "
\n" - ], "text/plain": [ - "\n", - "\u001b[1;35mdefaultdict\u001b[0m\u001b[1m(\u001b[0mZeros \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m{\u001b[0m\n", - " \u001b[32m'music'\u001b[0m: \u001b[1;36m-0.013333333333333336\u001b[0m,\n", - " \u001b[32m'camping'\u001b[0m: \u001b[1;36m-0.01\u001b[0m,\n", - " \u001b[32m'health'\u001b[0m: \u001b[1;36m-0.0078000000000000005\u001b[0m,\n", - " \u001b[32m'sports'\u001b[0m: \u001b[1;36m-0.0063106666666666675\u001b[0m,\n", - " \u001b[32m'food'\u001b[0m: \u001b[1;36m-0.0052320723809523816\u001b[0m,\n", - " \u001b[32m'finance'\u001b[0m: \u001b[1;36m-0.00441314521904762\u001b[0m,\n", - " \u001b[32m'politics'\u001b[0m: \u001b[1;36m0.01650162758658716\u001b[0m\n", - "\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n" + "defaultdict(Zeros (),\n", + " {'politics': 0.06389451550325113,\n", + " 'music': -0.04041254194187752,\n", + " 'camping': -0.040319730234734,\n", + " 'health': -0.03581829597317823,\n", + " 'food': -0.037778771188204816,\n", + " 'finance': -0.04029646665611086,\n", + " 'sports': -0.03661678982763635})" ] }, + "execution_count": 5, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -279,6 +272,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -290,16 +284,19 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:10.359874Z", - "iopub.status.busy": "2023-01-29T19:27:10.359636Z", - "iopub.status.idle": "2023-01-29T19:27:10.457116Z", - "shell.execute_reply": "2023-01-29T19:27:10.456237Z" + "iopub.execute_input": "2023-12-04T17:50:31.735061Z", + "iopub.status.busy": "2023-12-04T17:50:31.734953Z", + "iopub.status.idle": "2023-12-04T17:50:31.826428Z", + "shell.execute_reply": "2023-12-04T17:50:31.826129Z" } }, "outputs": [ { "data": { - "image/png": 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" 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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -308,7 +305,7 @@ "source": [ "simulate(\n", " 5_000,\n", - " reward_func=lambda user, item: (\n", + " reward_func=lambda user, item, context: (\n", " item in {'music', 'politics'} if user == \"Tom\" else\n", " item in {'food', 'sports'}\n", " ),\n", @@ -318,6 +315,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -329,16 +327,19 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:10.461719Z", - "iopub.status.busy": "2023-01-29T19:27:10.461138Z", - "iopub.status.idle": "2023-01-29T19:27:10.607872Z", - "shell.execute_reply": "2023-01-29T19:27:10.607254Z" + "iopub.execute_input": "2023-12-04T17:50:31.827897Z", + "iopub.status.busy": "2023-12-04T17:50:31.827802Z", + "iopub.status.idle": "2023-12-04T17:50:31.959947Z", + "shell.execute_reply": "2023-12-04T17:50:31.959498Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" 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", 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" + ] }, "metadata": {}, "output_type": "display_data" @@ -516,6 +523,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -529,16 +537,19 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:10.819127Z", - "iopub.status.busy": "2023-01-29T19:27:10.818923Z", - "iopub.status.idle": "2023-01-29T19:27:10.968474Z", - "shell.execute_reply": "2023-01-29T19:27:10.967995Z" + "iopub.execute_input": "2023-12-04T17:50:32.164572Z", + "iopub.status.busy": "2023-12-04T17:50:32.164472Z", + "iopub.status.idle": "2023-12-04T17:50:32.301735Z", + "shell.execute_reply": "2023-12-04T17:50:32.301435Z" } }, "outputs": [ { "data": { - "image/png": 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" + "image/png": 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 preferencepreference
itemcampingfinancefoodhealthmusicpoliticssportscampingfinancefoodhealthmusicpoliticssports
user
Anna@afternoon0.0000000.000000-0.030868-0.0195620.000000-0.0033850.000000Anna@afternoon-0.0181050.0328650.069222-0.0590410.1683531.0000000.195960
Anna@morning0.0000000.000000-0.008942-0.050784-0.0531850.000000-0.035998Anna@morning-0.1175770.0811310.076300-0.1363990.1544830.2218901.000000
Tom@afternoon0.124429-0.0320770.1370960.0893321.0000000.174578-0.179105Tom@afternoon0.057220-0.027115-0.074671-0.2330711.0000000.1636070.141781
Tom@morning-0.000000-0.024397-0.017108-0.016825-0.118900-0.014694-0.000848Tom@morning-0.028562-0.0054280.061163-0.0501070.0634831.0000000.125515
\n" + ], + "text/plain": [ + "" ] }, "execution_count": 13, @@ -684,7 +691,7 @@ " for user in model.u_latents\n", " for item in model.i_latents\n", " )\n", - " .pivot('user', 'item')\n", + " .pivot(index='user', columns='item')\n", " .style.highlight_max(color='lightgreen', axis='columns')\n", ")" ] @@ -709,7 +716,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/debugging-a-pipeline.ipynb b/docs/examples/debugging-a-pipeline.ipynb index 3097c016b1..300790048c 100644 --- a/docs/examples/debugging-a-pipeline.ipynb +++ b/docs/examples/debugging-a-pipeline.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:12.328598Z", - "iopub.status.busy": "2023-01-29T19:27:12.328456Z", - "iopub.status.idle": "2023-01-29T19:27:21.535869Z", - "shell.execute_reply": "2023-01-29T19:27:21.535304Z" + "iopub.execute_input": "2023-12-04T17:50:33.792436Z", + "iopub.status.busy": "2023-12-04T17:50:33.792067Z", + "iopub.status.idle": "2023-12-04T17:50:43.261751Z", + "shell.execute_reply": "2023-12-04T17:50:43.261453Z" }, "tags": [] }, @@ -33,12 +33,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "30000 MAE: 2.220942\n", - "60000 MAE: 2.270271\n", - "90000 MAE: 2.301302\n", - "120000 MAE: 2.275876\n", - "150000 MAE: 2.275224\n", - "180000 MAE: 2.289347\n" + "30000 MAE: 13.328051\n", + "60000 MAE: 7.824087\n", + "90000 MAE: 6.003909\n", + "120000 MAE: 5.052855\n", + "150000 MAE: 4.496826\n", + "180000 MAE: 4.140702\n" ] } ], @@ -87,7 +87,7 @@ " # Answer\n", " else:\n", " metric.update(y, questions[i])\n", - " model = model.learn_one(x, y)\n", + " model.learn_one(x, y)\n", " \n", " if i >= 30000 and i % 30000 == 0:\n", " print(i, metric)" @@ -105,10 +105,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:21.538389Z", - "iopub.status.busy": "2023-01-29T19:27:21.538204Z", - "iopub.status.idle": "2023-01-29T19:27:21.555741Z", - "shell.execute_reply": "2023-01-29T19:27:21.555167Z" + "iopub.execute_input": "2023-12-04T17:50:43.263530Z", + "iopub.status.busy": "2023-12-04T17:50:43.263338Z", + "iopub.status.idle": "2023-12-04T17:50:43.278572Z", + "shell.execute_reply": "2023-12-04T17:50:43.278309Z" }, "tags": [] }, @@ -124,33 +124,29 @@ " 'day': x['moment'].weekday()\n", " }\n", "\n", - "
['clouds', 'humidity', 'pressure', 'temperature', 'wind']
(\n", + "
['clouds', [...]
Select (\n", " clouds\n", " humidity\n", " pressure\n", " temperature\n", " wind\n", ")\n", - "\n", - "
y_mean_by_station_and_hour
(\n", + "
y_mean_by_station_and_hour
TargetAgg (\n", " by=['station', 'hour']\n", " how=Mean ()\n", " target_name=\"y\"\n", ")\n", - "\n", - "
y_ewm_0.5_by_station
(\n", + "
y_ewm_0.5_by_station
TargetAgg (\n", " by=['station']\n", " how=EWMean (\n", " fading_factor=0.5\n", " )\n", " target_name=\"y\"\n", ")\n", - "\n", - "
StandardScaler
(\n", + "
StandardScaler
StandardScaler (\n", " with_std=True\n", ")\n", - "\n", - "
LinearRegression
(\n", + "
LinearRegression
LinearRegression (\n", " optimizer=SGD (\n", " lr=Constant (\n", " learning_rate=0.01\n", @@ -166,19 +162,19 @@ " clip_gradient=1e+12\n", " initializer=Zeros ()\n", ")\n", - "\n", "
" + ], + "text/plain": [ + "Pipeline (\n", + " FuncTransformer (\n", + " func=\"add_time_features\"\n", + " ),\n", + " TransformerUnion (\n", + " Select (\n", + " clouds\n", + " humidity\n", + " pressure\n", + " temperature\n", + " wind\n", + " ),\n", + " TargetAgg (\n", + " by=['station', 'hour']\n", + " how=Mean ()\n", + " target_name=\"y\"\n", + " ),\n", + " TargetAgg (\n", + " by=['station']\n", + " how=EWMean (\n", + " fading_factor=0.5\n", + " )\n", + " target_name=\"y\"\n", + " )\n", + " ),\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " LinearRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " )\n", + " loss=Squared ()\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + ")" ] }, "execution_count": 2, @@ -280,10 +331,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:21.558294Z", - "iopub.status.busy": "2023-01-29T19:27:21.558145Z", - "iopub.status.idle": "2023-01-29T19:27:21.571901Z", - "shell.execute_reply": "2023-01-29T19:27:21.571426Z" + "iopub.execute_input": "2023-12-04T17:50:43.280147Z", + "iopub.status.busy": "2023-12-04T17:50:43.280048Z", + "iopub.status.idle": "2023-12-04T17:50:43.290636Z", + "shell.execute_reply": "2023-12-04T17:50:43.290346Z" }, "tags": [] }, @@ -349,22 +400,22 @@ "pressure: 0.04916 (float)\n", "temperature: -0.51938 (float)\n", "wind: -0.69426 (float)\n", - "y_ewm_0.5_by_station: 0.19214 (float)\n", - "y_mean_by_station_and_hour: -0.26013 (float)\n", + "y_ewm_0.5_by_station: 0.19640 (float)\n", + "y_mean_by_station_and_hour: -0.27110 (float)\n", "\n", "4. LinearRegression\n", "-------------------\n", "Name Value Weight Contribution \n", - " Intercept 1.00000 9.22316 9.22316 \n", - " y_ewm_0.5_by_station 0.19214 9.26418 1.78000 \n", - " humidity 1.16366 1.01252 1.17823 \n", - " temperature -0.51938 -0.42112 0.21872 \n", - " wind -0.69426 -0.04088 0.02838 \n", - " pressure 0.04916 0.18137 0.00892 \n", - "y_mean_by_station_and_hour -0.26013 0.19801 -0.05151 \n", - " clouds 1.54778 -0.32697 -0.50608 \n", + " Intercept 1.00000 9.19960 9.19960 \n", + " y_ewm_0.5_by_station 0.19640 9.19349 1.80562 \n", + " humidity 1.16366 1.01680 1.18320 \n", + " temperature -0.51938 -0.41575 0.21593 \n", + " wind -0.69426 -0.03810 0.02645 \n", + " pressure 0.04916 0.18321 0.00901 \n", + "y_mean_by_station_and_hour -0.27110 0.19553 -0.05301 \n", + " clouds 1.54778 -0.32838 -0.50827 \n", "\n", - "Prediction: 11.87982\n" + "Prediction: 11.87854\n" ] } ], diff --git a/docs/examples/imbalanced-learning.ipynb b/docs/examples/imbalanced-learning.ipynb index 305898a4bd..60c2680908 100644 --- a/docs/examples/imbalanced-learning.ipynb +++ b/docs/examples/imbalanced-learning.ipynb @@ -21,10 +21,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:22.871223Z", - "iopub.status.busy": "2023-01-29T19:27:22.871035Z", - "iopub.status.idle": "2023-01-29T19:27:25.447782Z", - "shell.execute_reply": "2023-01-29T19:27:25.447185Z" + "iopub.execute_input": "2023-12-04T17:50:44.731164Z", + "iopub.status.busy": "2023-12-04T17:50:44.730834Z", + "iopub.status.idle": "2023-12-04T17:50:47.316542Z", + "shell.execute_reply": "2023-12-04T17:50:47.316218Z" }, "tags": [] }, @@ -69,26 +69,23 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:27:25.450558Z", - "iopub.status.busy": "2023-01-29T19:27:25.450384Z", - "iopub.status.idle": "2023-01-29T19:27:43.258204Z", - "shell.execute_reply": "2023-01-29T19:27:43.257676Z" + "iopub.execute_input": "2023-12-04T17:50:47.318216Z", + "iopub.status.busy": "2023-12-04T17:50:47.318074Z", + "iopub.status.idle": "2023-12-04T17:51:05.643165Z", + "shell.execute_reply": "2023-12-04T17:51:05.642866Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
ROCAUC: 89.11%\n",
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ROCAUC: 94.75%\n",
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ROCAUC: 96.52%\n",
-       "
\n" - ], "text/plain": [ - "ROCAUC: \u001b[1;36m96.52\u001b[0m%\n" + "ROCAUC: 96.52%" ] }, + "execution_count": 9, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb index 566a81283b..42f763a496 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-1.ipynb @@ -92,10 +92,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:47.626421Z", - "iopub.status.busy": "2021-09-02T16:04:47.625342Z", - "iopub.status.idle": "2021-09-02T16:04:49.382871Z", - "shell.execute_reply": "2021-09-02T16:04:49.383355Z" + "iopub.execute_input": "2023-12-04T17:55:55.085940Z", + "iopub.status.busy": "2023-12-04T17:55:55.085155Z", + "iopub.status.idle": "2023-12-04T17:55:55.493970Z", + "shell.execute_reply": "2023-12-04T17:55:55.493604Z" } }, "outputs": [ @@ -104,8 +104,8 @@ "output_type": "stream", "text": [ "x = {\n", - " \"user\": 259,\n", - " \"item\": 255,\n", + " \"user\": \"259\",\n", + " \"item\": \"255\",\n", " \"timestamp\": 874731910000000000,\n", " \"title\": \"My Best Friend's Wedding (1997)\",\n", " \"release_date\": 866764800000000000,\n", @@ -142,10 +142,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:49.388868Z", - "iopub.status.busy": "2021-09-02T16:04:49.388279Z", - "iopub.status.idle": "2021-09-02T16:04:49.389859Z", - "shell.execute_reply": "2021-09-02T16:04:49.390339Z" + "iopub.execute_input": "2023-12-04T17:55:55.512554Z", + "iopub.status.busy": "2023-12-04T17:55:55.512388Z", + "iopub.status.idle": "2023-12-04T17:55:55.533215Z", + "shell.execute_reply": "2023-12-04T17:55:55.532985Z" } }, "outputs": [], @@ -178,10 +178,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:49.394825Z", - "iopub.status.busy": "2021-09-02T16:04:49.394246Z", - "iopub.status.idle": "2021-09-02T16:04:50.550082Z", - "shell.execute_reply": "2021-09-02T16:04:50.550612Z" + "iopub.execute_input": "2023-12-04T17:55:55.534732Z", + "iopub.status.busy": "2023-12-04T17:55:55.534655Z", + "iopub.status.idle": "2023-12-04T17:55:56.611865Z", + "shell.execute_reply": "2023-12-04T17:55:56.611600Z" } }, "outputs": [ @@ -189,10 +189,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.934259, RMSE: 1.124469 – 00:00:00 – 514 B\n", - "[50,000] MAE: 0.923893, RMSE: 1.105 – 00:00:01 – 514 B\n", - "[75,000] MAE: 0.937359, RMSE: 1.123696 – 00:00:01 – 514 B\n", - "[100,000] MAE: 0.942162, RMSE: 1.125783 – 00:00:02 – 514 B\n" + "[25,000] MAE: 0.934259\n", + "RMSE: 1.124469 – 00:00:00 – 898 B\n", + "[50,000] MAE: 0.923893\n", + "RMSE: 1.105 – 00:00:00 – 898 B\n", + "[75,000] MAE: 0.937359\n", + "RMSE: 1.123696 – 00:00:00 – 898 B\n", + "[100,000] MAE: 0.942162\n", + "RMSE: 1.125783 – 00:00:01 – 898 B\n" ] } ], @@ -238,13 +242,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:50.555652Z", - "iopub.status.busy": "2021-09-02T16:04:50.554908Z", - "iopub.status.idle": "2021-09-02T16:04:54.278018Z", - "shell.execute_reply": "2021-09-02T16:04:54.278496Z" + "iopub.execute_input": "2023-12-04T17:55:56.613421Z", + "iopub.status.busy": "2023-12-04T17:55:56.613340Z", + "iopub.status.idle": "2023-12-04T17:55:57.984509Z", + "shell.execute_reply": "2023-12-04T17:55:57.984259Z" } }, "outputs": [ @@ -252,10 +256,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761844, RMSE: 0.960972 – 0:00:00.864336 – 132.26 KB\n", - "[50,000] MAE: 0.753292, RMSE: 0.951223 – 0:00:01.737809 – 191.78 KB\n", - "[75,000] MAE: 0.754177, RMSE: 0.953376 – 0:00:02.598330 – 225.88 KB\n", - "[100,000] MAE: 0.754651, RMSE: 0.954148 – 0:00:03.464756 – 240.29 KB\n" + "[25,000] MAE: 0.761844\n", + "RMSE: 0.960972 – 00:00:00 – 161.03 KB\n", + "[50,000] MAE: 0.753292\n", + "RMSE: 0.951223 – 00:00:00 – 216.34 KB\n", + "[75,000] MAE: 0.754177\n", + "RMSE: 0.953376 – 00:00:01 – 254.81 KB\n", + "[100,000] MAE: 0.754651\n", + "RMSE: 0.954148 – 00:00:01 – 278.41 KB\n" ] } ], @@ -312,10 +320,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:04:54.283903Z", - "iopub.status.busy": "2021-09-02T16:04:54.283307Z", - "iopub.status.idle": "2021-09-02T16:05:01.252564Z", - "shell.execute_reply": "2021-09-02T16:05:01.253150Z" + "iopub.execute_input": "2023-12-04T17:55:57.986142Z", + "iopub.status.busy": "2023-12-04T17:55:57.986057Z", + "iopub.status.idle": "2023-12-04T17:56:00.090208Z", + "shell.execute_reply": "2023-12-04T17:56:00.089941Z" } }, "outputs": [ @@ -323,10 +331,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 1.070136, RMSE: 1.397014 – 0:00:01.705144 – 938.07 KB\n", - "[50,000] MAE: 0.99174, RMSE: 1.290666 – 0:00:03.466905 – 1.13 MB\n", - "[75,000] MAE: 0.961072, RMSE: 1.250842 – 0:00:05.205363 – 1.33 MB\n", - "[100,000] MAE: 0.944883, RMSE: 1.227688 – 0:00:06.934770 – 1.5 MB\n" + "[25,000] MAE: 1.070136\n", + "RMSE: 1.397014 – 00:00:00 – 557.99 KB\n", + "[50,000] MAE: 0.99174\n", + "RMSE: 1.290666 – 00:00:01 – 690.31 KB\n", + "[75,000] MAE: 0.961072\n", + "RMSE: 1.250842 – 00:00:01 – 813.07 KB\n", + "[100,000] MAE: 0.944883\n", + "RMSE: 1.227688 – 00:00:02 – 914.17 KB\n" ] } ], @@ -380,10 +392,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:01.259368Z", - "iopub.status.busy": "2021-09-02T16:05:01.258783Z", - "iopub.status.idle": "2021-09-02T16:05:08.962142Z", - "shell.execute_reply": "2021-09-02T16:05:08.962611Z" + "iopub.execute_input": "2023-12-04T17:56:00.091813Z", + "iopub.status.busy": "2023-12-04T17:56:00.091738Z", + "iopub.status.idle": "2023-12-04T17:56:02.472397Z", + "shell.execute_reply": "2023-12-04T17:56:02.472157Z" } }, "outputs": [ @@ -391,10 +403,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761818, RMSE: 0.961057 – 0:00:01.917323 – 1.01 MB\n", - "[50,000] MAE: 0.751667, RMSE: 0.949443 – 0:00:03.825794 – 1.28 MB\n", - "[75,000] MAE: 0.749653, RMSE: 0.948723 – 0:00:05.737369 – 1.51 MB\n", - "[100,000] MAE: 0.748559, RMSE: 0.947854 – 0:00:07.666314 – 1.69 MB\n" + "[25,000] MAE: 0.761818\n", + "RMSE: 0.961057 – 00:00:00 – 643.81 KB\n", + "[50,000] MAE: 0.751667\n", + "RMSE: 0.949443 – 00:00:01 – 817.72 KB\n", + "[75,000] MAE: 0.749653\n", + "RMSE: 0.948723 – 00:00:01 – 964.02 KB\n", + "[100,000] MAE: 0.748559\n", + "RMSE: 0.947854 – 00:00:02 – 1.05 MB\n" ] } ], @@ -453,7 +469,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb index 0e5e5db67a..f641f0bd94 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-2.ipynb @@ -81,10 +81,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:11.275107Z", - "iopub.status.busy": "2021-09-02T16:05:11.274331Z", - "iopub.status.idle": "2021-09-02T16:05:12.222291Z", - "shell.execute_reply": "2021-09-02T16:05:12.222707Z" + "iopub.execute_input": "2023-12-04T17:56:03.715589Z", + "iopub.status.busy": "2023-12-04T17:56:03.715073Z", + "iopub.status.idle": "2023-12-04T17:56:04.126649Z", + "shell.execute_reply": "2023-12-04T17:56:04.126227Z" } }, "outputs": [], @@ -111,10 +111,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:12.230617Z", - "iopub.status.busy": "2021-09-02T16:05:12.229997Z", - "iopub.status.idle": "2021-09-02T16:05:29.301168Z", - "shell.execute_reply": "2021-09-02T16:05:29.301749Z" + "iopub.execute_input": "2023-12-04T17:56:04.128590Z", + "iopub.status.busy": "2023-12-04T17:56:04.128429Z", + "iopub.status.idle": "2023-12-04T17:56:09.513255Z", + "shell.execute_reply": "2023-12-04T17:56:09.512930Z" } }, "outputs": [ @@ -122,12 +122,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading https://maxhalford.github.io/files/datasets/ml_100k.zip (1.83 MB)\n", - "Uncompressing into /Users/max/river_data/MovieLens100K\n", - "[25,000] MAE: 0.761778, RMSE: 0.960803 – 00:00:02 – 818.86 KB\n", - "[50,000] MAE: 0.751986, RMSE: 0.949941 – 00:00:04 – 948.77 KB\n", - "[75,000] MAE: 0.750044, RMSE: 0.948911 – 00:00:05 – 1.07 MB\n", - "[100,000] MAE: 0.748609, RMSE: 0.947994 – 00:00:07 – 1.19 MB\n" + "[25,000] MAE: 0.761778\n", + "RMSE: 0.960803 – 00:00:01 – 778.29 KB\n", + "[50,000] MAE: 0.751986\n", + "RMSE: 0.949941 – 00:00:02 – 908.2 KB\n", + "[75,000] MAE: 0.750044\n", + "RMSE: 0.948911 – 00:00:03 – 1.03 MB\n", + "[100,000] MAE: 0.748609\n", + "RMSE: 0.947994 – 00:00:05 – 1.15 MB\n" ] } ], @@ -191,10 +193,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:29.306371Z", - "iopub.status.busy": "2021-09-02T16:05:29.305547Z", - "iopub.status.idle": "2021-09-02T16:05:29.308166Z", - "shell.execute_reply": "2021-09-02T16:05:29.308976Z" + "iopub.execute_input": "2023-12-04T17:56:09.515335Z", + "iopub.status.busy": "2023-12-04T17:56:09.515220Z", + "iopub.status.idle": "2023-12-04T17:56:09.527171Z", + "shell.execute_reply": "2023-12-04T17:56:09.526776Z" } }, "outputs": [ @@ -247,10 +249,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:29.313500Z", - "iopub.status.busy": "2021-09-02T16:05:29.312843Z", - "iopub.status.idle": "2021-09-02T16:05:29.314659Z", - "shell.execute_reply": "2021-09-02T16:05:29.315075Z" + "iopub.execute_input": "2023-12-04T17:56:09.528913Z", + "iopub.status.busy": "2023-12-04T17:56:09.528771Z", + "iopub.status.idle": "2023-12-04T17:56:09.538807Z", + "shell.execute_reply": "2023-12-04T17:56:09.538396Z" } }, "outputs": [], @@ -274,10 +276,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:29.319543Z", - "iopub.status.busy": "2021-09-02T16:05:29.318952Z", - "iopub.status.idle": "2021-09-02T16:05:29.320561Z", - "shell.execute_reply": "2021-09-02T16:05:29.320961Z" + "iopub.execute_input": "2023-12-04T17:56:09.540871Z", + "iopub.status.busy": "2023-12-04T17:56:09.540745Z", + "iopub.status.idle": "2023-12-04T17:56:09.551743Z", + "shell.execute_reply": "2023-12-04T17:56:09.551414Z" } }, "outputs": [], @@ -305,10 +307,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:05:29.327666Z", - "iopub.status.busy": "2021-09-02T16:05:29.327057Z", - "iopub.status.idle": "2021-09-02T16:06:11.277539Z", - "shell.execute_reply": "2021-09-02T16:06:11.278025Z" + "iopub.execute_input": "2023-12-04T17:56:09.553540Z", + "iopub.status.busy": "2023-12-04T17:56:09.553426Z", + "iopub.status.idle": "2023-12-04T17:56:25.748558Z", + "shell.execute_reply": "2023-12-04T17:56:25.748307Z" } }, "outputs": [ @@ -316,10 +318,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.759838, RMSE: 0.961281 – 00:00:04 – 935.54 KB\n", - "[50,000] MAE: 0.751307, RMSE: 0.951391 – 00:00:09 – 1.06 MB\n", - "[75,000] MAE: 0.750361, RMSE: 0.951393 – 00:00:14 – 1.22 MB\n", - "[100,000] MAE: 0.749994, RMSE: 0.951435 – 00:00:20 – 1.37 MB\n" + "[25,000] MAE: 0.759838\n", + "RMSE: 0.961281 – 00:00:03 – 895.78 KB\n", + "[50,000] MAE: 0.751307\n", + "RMSE: 0.951391 – 00:00:08 – 1.02 MB\n", + "[75,000] MAE: 0.750361\n", + "RMSE: 0.951393 – 00:00:12 – 1.18 MB\n", + "[100,000] MAE: 0.749994\n", + "RMSE: 0.951435 – 00:00:16 – 1.33 MB\n" ] } ], @@ -388,10 +394,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:06:11.285040Z", - "iopub.status.busy": "2021-09-02T16:06:11.284442Z", - "iopub.status.idle": "2021-09-02T16:09:36.626866Z", - "shell.execute_reply": "2021-09-02T16:09:36.627349Z" + "iopub.execute_input": "2023-12-04T17:56:25.750135Z", + "iopub.status.busy": "2023-12-04T17:56:25.750035Z", + "iopub.status.idle": "2023-12-04T17:57:29.483921Z", + "shell.execute_reply": "2023-12-04T17:57:29.483635Z" } }, "outputs": [ @@ -399,10 +405,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761297, RMSE: 0.962054 – 0:00:51.632190 – 2.61 MB\n", - "[50,000] MAE: 0.751865, RMSE: 0.951499 – 0:01:42.890329 – 3.08 MB\n", - "[75,000] MAE: 0.750853, RMSE: 0.951526 – 0:02:34.207244 – 3.6 MB\n", - "[100,000] MAE: 0.750607, RMSE: 0.951982 – 0:03:25.248686 – 4.07 MB\n" + "[25,000] MAE: 0.761297\n", + "RMSE: 0.962054 – 00:00:15 – 1.67 MB\n", + "[50,000] MAE: 0.751865\n", + "RMSE: 0.951499 – 00:00:31 – 1.97 MB\n", + "[75,000] MAE: 0.750853\n", + "RMSE: 0.951526 – 00:00:47 – 2.3 MB\n", + "[100,000] MAE: 0.750607\n", + "RMSE: 0.951982 – 00:01:03 – 2.6 MB\n" ] } ], @@ -473,10 +483,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:09:36.635563Z", - "iopub.status.busy": "2021-09-02T16:09:36.634918Z", - "iopub.status.idle": "2021-09-02T16:10:39.510781Z", - "shell.execute_reply": "2021-09-02T16:10:39.511270Z" + "iopub.execute_input": "2023-12-04T17:57:29.485700Z", + "iopub.status.busy": "2023-12-04T17:57:29.485595Z", + "iopub.status.idle": "2023-12-04T17:57:54.281600Z", + "shell.execute_reply": "2023-12-04T17:57:54.281345Z" } }, "outputs": [ @@ -484,10 +494,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.757718, RMSE: 0.958158 – 0:00:15.781740 – 3.04 MB\n", - "[50,000] MAE: 0.749502, RMSE: 0.948065 – 0:00:31.431484 – 3.59 MB\n", - "[75,000] MAE: 0.749275, RMSE: 0.948918 – 0:00:47.079510 – 4.19 MB\n", - "[100,000] MAE: 0.749542, RMSE: 0.949769 – 0:01:02.776969 – 4.75 MB\n" + "[25,000] MAE: 0.757718\n", + "RMSE: 0.958158 – 00:00:06 – 2.04 MB\n", + "[50,000] MAE: 0.749502\n", + "RMSE: 0.948065 – 00:00:12 – 2.41 MB\n", + "[75,000] MAE: 0.749275\n", + "RMSE: 0.948918 – 00:00:18 – 2.82 MB\n", + "[100,000] MAE: 0.749542\n", + "RMSE: 0.949769 – 00:00:24 – 3.19 MB\n" ] } ], @@ -553,10 +567,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2021-09-02T16:10:39.519856Z", - "iopub.status.busy": "2021-09-02T16:10:39.519214Z", - "iopub.status.idle": "2021-09-02T16:12:05.385426Z", - "shell.execute_reply": "2021-09-02T16:12:05.386017Z" + "iopub.execute_input": "2023-12-04T17:57:54.283331Z", + "iopub.status.busy": "2023-12-04T17:57:54.283223Z", + "iopub.status.idle": "2023-12-04T17:58:24.755886Z", + "shell.execute_reply": "2023-12-04T17:58:24.755394Z" } }, "outputs": [ @@ -564,10 +578,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[25,000] MAE: 0.761539, RMSE: 0.962241 – 0:00:20.963815 – 1.18 MB\n", - "[50,000] MAE: 0.754089, RMSE: 0.953181 – 0:00:42.057991 – 1.38 MB\n", - "[75,000] MAE: 0.754806, RMSE: 0.954979 – 0:01:04.051777 – 1.6 MB\n", - "[100,000] MAE: 0.755404, RMSE: 0.95604 – 0:01:25.823651 – 1.79 MB\n" + "[25,000] MAE: 0.761539\n", + "RMSE: 0.962241 – 00:00:07 – 792.94 KB\n", + "[50,000] MAE: 0.754089\n", + "RMSE: 0.953181 – 00:00:15 – 922.85 KB\n", + "[75,000] MAE: 0.754806\n", + "RMSE: 0.954979 – 00:00:22 – 1.04 MB\n", + "[100,000] MAE: 0.755404\n", + "RMSE: 0.95604 – 00:00:30 – 1.17 MB\n" ] } ], @@ -617,7 +635,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb b/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb index be82de7caa..037a839cea 100644 --- a/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb +++ b/docs/examples/matrix-factorization-for-recommender-systems/part-3.ipynb @@ -31,7 +31,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/examples/quantile-regression-uncertainty.ipynb b/docs/examples/quantile-regression-uncertainty.ipynb index 4ca53a2757..e459e7bb9f 100644 --- a/docs/examples/quantile-regression-uncertainty.ipynb +++ b/docs/examples/quantile-regression-uncertainty.ipynb @@ -12,10 +12,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:29:30.584608Z", - "iopub.status.busy": "2023-01-29T19:29:30.584421Z", - "iopub.status.idle": "2023-01-29T19:29:30.805677Z", - "shell.execute_reply": "2023-01-29T19:29:30.805298Z" + "iopub.execute_input": "2023-12-04T17:52:47.886294Z", + "iopub.status.busy": "2023-12-04T17:52:47.885937Z", + "iopub.status.idle": "2023-12-04T17:52:48.106622Z", + "shell.execute_reply": "2023-12-04T17:52:48.106311Z" } }, "outputs": [], @@ -39,17 +39,20 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-01-29T19:29:30.808090Z", - "iopub.status.busy": "2023-01-29T19:29:30.807933Z", - "iopub.status.idle": "2023-01-29T19:29:31.647342Z", - "shell.execute_reply": "2023-01-29T19:29:31.646891Z" + "iopub.execute_input": "2023-12-04T17:52:48.108477Z", + "iopub.status.busy": "2023-12-04T17:52:48.108356Z", + "iopub.status.idle": "2023-12-04T17:52:48.657556Z", + "shell.execute_reply": "2023-12-04T17:52:48.656135Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": 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\n" 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LowYWqa54e9KkSdq+fbs6dOggqa4WYfr06VHPv/zyywM7qvuXO05UkP/QQw9p2bJlGjJkiLp166bdu3fr5Zdf1sqVK3XLLbeoZ8+eEa+ZMWOGcnJy9P3vfz/qe0arNVy1apUkaejQoSGLBixevFgDBw7Ufffdp/vvv1+S1LVrV91+++36/e9/r9raWvXr109z5szRkiVLNGPGjEANyemnnx4Rqvz3e9JJJ4W0oz73+cc//lETJ07UokWLdN5550mqW2Xr1Vdf1aWXXqrdu3dH9EFwsJwwYYJmz56t73//+7rjjjtUUlKiKVOmqLa2NvD/ovz8/Kif05w5c/Tuu++GPFdRUaHBgwdrz549uuuuu/SPf/wj5DXHHntsSIjyT3vu1q2b1q9fr8mTJ6tNmzYh08lOOOEEnXDCCRHXl+rqcepTLwoAzVWzDC2rV6+WbduB3yL5VVdXq23bthHnv/LKK6qoqEj4gwUAuOEf//iH9u7dG3ck5tJLL9Xjjz+uF154Qbfeequkuv+mXXvttVHPX79+fSC0JGvYsGFat26dnn32We3YsUO5ubnq06ePpk6dGvW/j+Xl5frHP/4RKERvLH/NTvCogiQ98sgjat26tf7yl79o2rRpOu644zR9+vS4m3TGU9/7DOcPXf/7v/8bdfW24NDSsWNHLV26VHfeeaeefPJJ1dbWasCAAZo+fbpOPfXUerd9165d2rRpkyRF/UXbmDFjQkLLqaeeqqlTpwb2kBkxYoQmTpwYCL4A0FJYxoRVRzZFIywrZPWwWbNmadSoUfr0008j9gQoLCxUp06dQo5dcMEFKi4ujjo8DwBIj5///Od6/vnn9dVXX0Vd5hgAgIZqliMtp512mmzb1vbt2xPWqKxfv16LFi3Sq6++mqbWAQCiWbRokX71q18RWAAAKddkoWX//v2BfQykuvCxatUqtWnTRscff7xGjRql0aNH6/HHH9dpp52mHTt2aOHCherTp0/IXgfPPvusOnfuHLKLMAAg/VauXNnUTQAAZKgmmx7mL9gMN2bMGE2bNk21tbV68MEH9de//lVff/212rVrp29/+9uaOHGiTjnlFEl1uzh3795do0eP1kMPPZTuWwAAAACQBs2ipgUAAAAAYvE0dQMAAAAAIB5CCwAAAIBmLe2F+I7j6JtvvlFRUZEsy0r35QEAAAA0E8YYVVRUqEuXLvJ4Yo+npD20fPPNNyorK0v3ZQEAAAA0U5s2bVLXrl1jPp/20FJUVCSprmHFxcXpvnyL5fP59N5776lv377KymqW2/OgAejXzES/Zi76NjPRr5mJfk2P8vJylZWVBTJCLGnvAf+UsOLiYkJLGvl8PhUUFKi4uJhvvAxCv2Ym+jVz0beZiX7NTPRreiUqG6EQHwAAAECzRmgBAAAA0KwRWgAAAAA0a0zQAwAAOELYtq3a2tqmbkaL4PP5ZFmWqqqqqGlphFatWsnr9Tb6fegBAACAZs4Yo61bt2rv3r1N3ZQWwxij/Px8bdy4kb0FG6m0tFSdOnVq1OdIaAEAAGjm/IGlQ4cOys/P54foNDDG6MCBA3zejeD/DLdv3y5J6ty5c4Pfi9ACAADQjNm2HQgsbdu2bermtBjGGNm2rdzcXEJLI+Tl5UmStm/frg4dOjR4qhiF+AAAAM2Yv4YlPz+/iVsCNIz/725j6rEILQAAAEcAftuPI1Uq/u4SWgAAAAA0a4QWAAAAHLEWL14sy7JYWS3DEVoAAACQUpZlxf26//77G/S+5513nm6//faUthVHBlYPAwAAQEpt2bIl8OdZs2bp17/+tdasWRM4VlhYGPizf5UuNnBEPPUaabn//vsjkvIJJ5zgVtsAAAAQxjGOyn0HmuzLMU7CNnbq1CnwVVJSIsuyAo+/+OILFRUV6fXXX9cZZ5yhnJwcLV26VGPHjtVll10W8j633367zjvvPEnS2LFj9eabb2rSpEmBn0M3bNgQOPf9999X3759lZ+fr7POOiskJOHIV+9Ie9JJJ+mf//zn4TcgFQMAAKTNfrtKP147pcmu/5fjfqLirMYvv3zPPffoscce0zHHHKPWrVsnPH/SpEn68ssvdfLJJ+uBBx6QJLVv3z4QXH75y1/q8ccfV/v27fWTn/xE119/vZYtW9bodqJ5qHfiyMrKUqdOndxoCwAAAFqIBx54QBdeeGHS55eUlCg7O1v5+flRfxZ96KGHdO6550qqC0TDhg1TVVWVcnNzU9ZmNJ16F+KvXbtWXbp00THHHKNRo0Zp48aNbrQLAAAAGaxv374pfb8+ffoE/ty5c2dJdbuwIzPUa6Slf//+mjZtmnr16qUtW7Zo4sSJOvvss/XJJ5+oqKgo6muqq6tVXV0deFxeXi5J8vl88vl8jWg66sO27UChGzIH/ZqZ6NfMRd9mJrf71efzyRgT8tWU6tsG/7nh/87Pzw95H8uyIt67pqYm5DXRru//c1ZWVkS7/H3TEM3l884E/s8x2s//yeaBeoWWoUOHBv7cp08f9e/fX927d9fs2bN1ww03RH3Nww8/rIkTJ0Ycf++991RQUFCfyzcZ5+BBWV6vrOzspm5KgzmOo4qKCr377rvyeFjpOlPQr5mJfs1c9G1mcrtfLctSfn6+Dhw4INu2ZRlHj3e5NuXXSbo9VbYqrcqkz/f/8rqysu41VVVVgcetWrUKnFdaWqrVq1cHzpOkDz74QK1atQoc83q9qq6uDjkn2vsdPHhQknTgwIGQc+vDGCPHcVRZWZmSHd1bsurqatXU1Ojjjz+OCIHJ9k+jquhLS0t1/PHH66uvvop5zoQJE3THHXcEHpeXl6usrEx9+/ZVcXFxYy6fNjUffyBPUbGyju7Z1E1pMNu2tXLlSvXr109er7epm4MUoV8zE/2auejbzOR2v1ZVVWnjxo3Kz88P1GcUKfoMl+YoJydHkgK/rPbfQ0FBQcgvsAcPHqxJkybp5Zdf1oABAzR9+nR9/vnnOu200wLnHXPMMfrggw+0Y8cOFRYWqk2bNlHfLy8vT1LdaE5Df0lujFFlZaUKCgoILY3k9XqVnZ2tnj17RtQY+WdhJdKo0LJ//36tW7dO114bO+3n5OQE/rKGXDgr64hZeczn2PI4R/764ZZlyev1HvH3gVD0a2aiXzMXfZuZ3OzXrKyskO0mjjT+Nkf7d/D9DBkyRL/61a909913q6qqStdff71Gjx6t1atXB8676667NGbMGJ100kk6ePCg1q9fH/X9Yl2jIW0/Uj/35sT/GUb7+T/Z75l6fWfdeeeduvTSS9W9e3d98803uu++++T1ejVy5Mj6vM2RxxiZoLocAAAAJGfs2LEaO3Zs4PF5550Xs05k4sSJUcsK/I4//ngtX7485FiPHj0i3u9b3/oWtSgZpl6hZfPmzRo5cqR27dql9u3b67vf/a7eeecdtW/f3q32NQ+OkamuaupWAAAAAC1SvULLCy+84FY7mjfjyFSz0hkAAADQFFi6JEmmppphRgAAAKAJEFqSYYxk2xL7ygAAAABpR2hJgjFGxrZlfLVN3RQAAACgxSG0JIORFgAAAKDJEFqSYRzJtmUILQAAAEDaEVqS4RjJsSWmhwEAAABpR2hJhjEyto+RFgAAAKAJEFqSQk0LAABAczV27FhddtllgcfnnXeebr/99ka958CBA3X33Xc3rmFIGUJLMoyRsR1WDwMAAKiHsWPHyrIsWZal7Oxs9ezZUw888IB8Lv8i+O9//7t+85vfJHXu4sWLZVmW9u7dG3L85Zdf1r333utC69AQWU3dgCOBMZJsHyMtAAAA9TRkyBBNnTpV1dXVeu2113TzzTerVatWmjBhQsh5NTU1ys7OTsk127Rpk5L3qKysTEFrkAqMtCTDOJKMnJqqpm4JAABo4YzjyNlf0WRfxnHq1d6cnBx16tRJ3bt314033qhBgwbp1VdfDUzpeuihh9SlSxf16tVLkrRp0yaNGDFCpaWlatOmjYYPH64NGzYE3s+2bd1xxx0qLS1V27Zt9fOf/1zGmJBrhk8Pq66u1t13362ysjLl5OSoZ8+eeuaZZ7RhwwYNHDhQktS6dWtZlqWxY8dKipwetmfPHo0ePVqtW7dWfn6+hg4dqrVr1waenzZtmkpLSzV//nz17t1bhYWFGjJkiLZs2VKvzwvRMdKSQN03gZEsj1RV3dTNAQAALZw5UKndv/ppk12/zW+elFVY1ODX5+XladeuXZKkhQsXqri4WAsWLJAk1dbWavDgwRowYICWLFmirKwsPfjggxoyZIg+/vhjZWdn6/HHH9e0adP07LPPqnfv3nr88cf1yiuv6Pzzz495zdGjR2v58uX6wx/+oFNPPVXr16/Xzp07VVZWppdfflnf//73tWbNGhUXFysvLy/qe4wdO1Zr167Vq6++quLiYt199926+OKL9dlnn6lVq1aSpAMHDuixxx7T3/72N3k8Hl1zzTW68847NWPGjAZ/XqhDaEmGkeT1ytQQWgAAABrCGKOFCxdq/vz5uuWWW7Rjxw4VFBTo6aefDkwLmz59uhzH0dNPPy3LsiRJU6dOVWlpqRYvXqyLLrpITz31lCZMmKArrrhCkjRlyhTNnz8/5nW//PJLzZ49WwsWLNCgQYMkScccc0zgef9Usg4dOqi0tDTqe/jDyrJly3TWWWdJkmbMmKGysjLNmTNHP/jBDyTVha4pU6bo2GOPlSSNHz9eDzzwQEM/MgQhtCRyaLjRysqSqWZ6GAAAQH3MmzdPhYWFqq2tleM4uvrqq3X//ffr5ptv1imnnBJSx/LRRx/pq6++UlFR6EhOVVWV1q1bp3379mnLli3q379/4LmsrCz17ds3YoqY36pVq+T1enXuuec2+B4+//xzZWVlhVy3bdu26tWrlz7//PPAsfz8/EBgkaTOnTtr+/btDb4uDiO0JGLMoZGWLBmfT8b2yfLysQEAACRj4MCBmjx5srKzs9WlSxdlZR3+OaqgoCDk3P379+uMM86IOp2qffv2Dbp+rOlebvBPE/OzLCtmmEL98NN3QodqWrxeGduWfLZEaAEAAE3Eyi9Qm9882aTXr4+CggL17NkzqXNPP/10zZo1Sx06dFBxcXHUczp37qwVK1bonHPOkST5fD69//77Ov3006Oef8opp8hxHL355puB6WHB/CM9tm3HbFfv3r3l8/m0YsWKwPSwXbt2ac2aNTrxxBOTujc0DquHJWLq/mF5syTHZq8WAADQpCyPR57Coib7sjzu/fg4atQotWvXTsOHD9eSJUu0fv16LV68WLfeeqs2b94sSbrtttv0yCOPaM6cOfriiy900003ReyxEqxHjx4aM2aMrr/+es2ZMyfwnrNnz5Ykde/eXZZlad68edqxY4f2798f8R7HHXechg8frnHjxmnp0qX66KOPdM011+ioo47S8OHDXfksEIrQkgwjKcsr2bZEaAEAAHBFfn6+3nrrLXXr1k1XXHGFevfurRtuuEFVVVWBkZef/exnuvbaazVmzBgNGDBARUVFuvzyy+O+7+TJk3XllVfqpptu0gknnKBx48YF9mA56qijNHHiRN1zzz3q2LGjxo8fH/U9pk6dqjPOOEOXXHKJBgwYIGOMXnvttYgpYXCHZdI80a68vFwlJSXat29fzGG/5sTU1urgov+TPJbM/grlnn2+vG0bNqeyKfmHNPv37x8ylxRHNvo1M9GvmYu+zUxu92tVVZXWr1+vo48+Wrm5uSl/f0RnjFFlZaUKCgoCK5mhYeL9HU42GzDSktChTOfxyBgjU8tICwAAAJBOhJZEAptLWrIkyfY1cYMAAACAloXQkohRYKk6I8n4CC0AAABAOhFaEgkq+bEkielhAAAAQFoRWhIw/poWy5KxJKe2pmkbBAAAALQwhJZkHBptsTxeqaa6iRsDAAAAtCyElkSMqfuyLMmbJVNFaAEAAADSidCSiAn8Q/J6ZaqrmrI1AAAAQItDaEkoqBDf65Xx1cg4ThO2BwAAAGhZCC2JmMOF+PJ6JZ/DXi0AAKBZMNXVcir3p+XLVDffKfJjx47VZZddFnh83nnn6fbbb2/Uew4cOFB333134xqWwOLFi2VZlvbu3evqddxmWZbmzJnj6jWyXH33TGBM3d6SsmS8Xpnqaplan6xW2U3dMgAA0IKZ6mpVrVgiZ//+tFzPU1io3P5ny8rJSer8sWPH6rnnnpMktWrVSt26ddPo0aP1i1/8QllZ7v4I+ve//12tWrVK6tzFixdr4MCB2rNnj0pLSwPHX375ZdXUuLtq7FlnnaUtW7aopKQk6deMHTtWe/fudT0kNDeElmQYI1l1q4cZx5Z87NUCAACalvHVytm/X1Z2tqzs5IJEg69VUy1n/34ZX23SoUWShgwZoqlTp6q6ulqvvfaabr75ZrVq1UoTJkyIOLempkbZ2an5pXCbNm1S8h6VlZUpaE1s2dnZ6tSpk6vXiCWVn3c6MD0sKUbSoelhtiPD9DAAANBMWNk5snJz3f1qYCjKyclRp06d1L17d914440aNGiQXn31VUmHp3Q99NBD6tKli3r16iVJ2rRpk0aMGKHS0lK1adNGw4cP14YNGwLvadu27rjjDpWWlqpt27b6+c9/LhO0GbgUOT2surpad999t8rKypSTk6OePXvqmWee0YYNGzRw4EBJUuvWrWVZlsaOHSspcnrYnj17NHr0aLVu3Vr5+fkaOnSo1q5dG3h+2rRpKi0t1fz589W7d28VFhZqyJAh2rJlS8zPJ3x6WKL3uP/++/Xcc89p7ty5sixLlmVp8eLFSX1u0T7vX/ziF+rfv39Eu0499VQ98MADkqSVK1fqwgsvVLt27VRSUqJzzz1XH3zwQcx7cguhJZFATYskj7eunqWW0AIAAFBfeXl5IVOuFi5cqDVr1mjBggWaN2+eamtrNXjwYBUVFWnJkiVatmxZ4Ad3/+sef/xxTZs2Tc8++6yWLl2q3bt365VXXol73dGjR+v555/XH/7wB33++ef6y1/+osLCQpWVlenll1+WJK1Zs0ZbtmzRpEmTor7H2LFj9d577+nVV1/V8uXLZYzRxRdfrNrawzNwDhw4oMcee0x/+9vf9NZbb2njxo2688476/UZxXuPO++8UyNGjAgEmS1btuiss85K6nOL9nmPGjVK7777rtatWxc459NPP9XHH3+sq6++WpJUUVGhMWPGaOnSpXrnnXd03HHH6eKLL1ZFRUW97quxmB6WSCC516VZIyPD9DAAAICkGWO0cOFCzZ8/X7fcckvgeEFBgZ5++unANKXp06fLcRw9/fTTsixLkjR16lSVlpZq8eLFuuiii/TUU09pwoQJuuKKKyRJU6ZM0fz582Ne+8svv9Ts2bO1YMECDRo0SJJ0zDHHBJ73TyXr0KFDSE1LsLVr1+rVV1/VsmXLdNZZZ0mSZsyYobKyMs2ZM0c/+MEPJEm1tbWaMmWKjj32WEnS+PHjAyMWyYr3HoWFhcrLy1N1dXXItLJkPjcp8vOW6kZVZs6cqV/96leB++rfv7969uwpSTr//PND2vff//3fKi0t1ZtvvqlLLrmkXvfWGIy0JHKoEP8wi5oWAACAJMybN0+FhYXKzc3V0KFDddVVV+n+++8PPH/KKaeE/AD90Ucf6auvvlJRUZEKCwtVWFioNm3aqKqqSuvWrdO+ffu0ZcuWkClNWVlZ6tu3b8w2rFq1Sl6vV+eee26D7+Pzzz9XVlZWyHXbtm2rXr166fPPPw8cy8/PD4QNSercubO2b99er2s15D0SfW5+4Z+3JI0aNUozZ86UVBcun3/+eY0aNSrw/LZt2zRu3Dgdd9xxKikpUXFxsfbv36+NGzfW674ai5GWhIwOLR8mqe5fxrabskEAAABHhIEDB2ry5MnKzs5Wly5dIlYNKygoCHm8f/9+nXHGGZoxY0bEe7Vv375BbcjLy2vQ6xoifMUyy7Ii6m3ceI9kP7fwz1uSRo4cqbvvvlsffPCBDh48qE2bNumqq64KPD9mzBjt2rVLkyZNUvfu3ZWTk6MBAwa4vrJaOEJLMsyhQnxJRkaqZaQFAAAgkYKCgsA0o2ScfvrpmjVrljp06KDi4uKo53Tu3FkrVqzQOeecI0ny+Xx6//33dfrpp0c9/5RTTpHjOHrzzTcD08OC+Uce7Di/lO7du7d8Pp9WrFgRmB62a9curVmzRieeeGLS95cK2dnZEW1N5nOLpWvXrjr33HM1Y8YMHTx4UBdeeKE6dOgQeH7ZsmX685//rIsvvlhSXcH/zp07G38j9cT0sATMoYEW/0iLPF451VVN2CIAAIDMNGrUKLVr107Dhw/XkiVLtH79ei1evFi33nqrNm/eLEm67bbb9Mgjj2jOnDn64osvdNNNN8XdnLFHjx4aM2aMrr/+es2ZMyfwnrNnz5Ykde/eXZZlad68edqxY4f2R9n35rjjjtPw4cM1btw4LV26VB999JGuueYaHXXUURo+fLgrn0W8+/n444+1Zs0a7dy5U7W1tUl9bvGMGjVKL7zwgl588cWQqWFS3b3/7W9/0+eff64VK1Zo1KhRaR298iO0JBJUiC9JljdLhtACAACaCVNTLVNV5e5XTXVa7iU/P19vvfWWunXrpiuuuEK9e/fWDTfcoKqqqsAIws9+9jNde+21GjNmjAYMGKCioiJdfvnlcd938uTJuvLKK3XTTTfphBNO0Lhx4wJ7sBx11FGaOHGi7rnnHnXs2FHjx4+P+h5Tp07VGWecoUsuuUQDBgyQMUavvfZa0ptYpsq4cePUq1cv9e3bV+3bt9eyZcuS+tziufLKK7Vr1y4dOHBAl112WchzzzzzjPbs2aPTTz9d1157rW699daQkZh0sUx9J9o1Unl5uUpKSrRv3756D181BXvPLlUtWSRP+/ayvFmyd+2Up7hEed85r6mbVi/+Ic3+/fu7vgst0od+zUz0a+aibzOT2/1aVVWl9evX6+ijj1Zubm7guKmuVtWKJXKijAy4wVNYqNz+Z9drc8kjmTFGlZWVKigoCKzIhYaJ9XdYSj4b8F/MRALzww7xemTSXHgEAAAQzsrJUW7/s9O2FYOV1arFBBY0P4SWRIxCCvEtb5Zk18r4fLL4LRkAAGhCVk4OQQItAjUtCZm60OIfFfR6ZWyHDSYBAACANCG0JM0/0uKVfL66LwAAAACuI7Qk4l+nIGikRY4tQ2gBAABplOa1k4CUScXfXUJLIsYELXssyeOVbFtiehgAAEgD/5K6Bw4caOKWAA3j/7vbmOWhqSRPJJBXDk0P83hkjGGkBQAApIXX61Vpaam2b98uqW4vE5bgdZ8xRtXV1fJ6vXzeDWSM0YEDB7R9+3aVlpbK6/U2+L0ILQkZGZmQv6yWMVItIy0AACA9OnXqJEmB4AL3GWNUU1Oj7OxsQksjlZaWBv4ONxShJYFoc/CMZcnYjLQAAID0sCxLnTt3VocOHVTLL07Twufz6eOPP1bPnj3ZDLYRWrVq1agRFj96IJGgPVr8LInVwwAAQNp5vd6U/ACIxHw+n4wxys3NJbQ0AxTiJyV0tMV4LDnV1U3UFgAAAKBlIbQkJWykxeOVaqqaqC0AAABAy0JoSSTautJerwwjLQAAAEBaEFoSMUbh08MILQAAAED6EFoSilKI782Sqa2Vse2maRIAAADQghBakhCxMrfXKzm2xLLHAAAAgOsILYlEq2nxeGRsnwzLHgMAAACuI7QkYhQRXCxvlmQ7Eps7AQAAAK4jtCRijGSFTRDzeiVGWgAAAIC0ILQkZMLXDqubHkZNCwAAAJAWhJZEotS0WJYlyZLxMT0MAAAAcBuhJYFodfiB52oZaQEAAADcRmhpKGPqlj0GAAAA4CpCS0LRh1qs8OJ8AAAAAK4gtCRiTOw5Yo6T3rYAAAAALRChJZFoSx4HPwcAAADAVYSWBoqyEDIAAAAAFxBaEjFGMatXGGkBAAAAXEdoSSRmMLEILQAAAEAaEFqSES2cWJKhEB8AAABwHaElkZiF+JaMIbQAAAAAbiO0NJRlSQ7TwwAAAAC3NSq0PPLII7IsS7fffnuKmtMMGRN7pTDHTm9bAAAAgBaowaFl5cqV+stf/qI+ffqksj3NT6xie4tCfAAAACAdGhRa9u/fr1GjRul//ud/1Lp161S3qRmKrGmxJImaFgAAAMB1DQotN998s4YNG6ZBgwaluj3NjpGJllkky0NNCwAAAJAGWfV9wQsvvKAPPvhAK1euTOr86upqVVdXBx6Xl5dLknw+n3w+X30vn3a2z5YtyQqbCmZLsnz2EXEPkmTbtowxsm3qcDIJ/ZqZ6NfMRd9mJvo1M9Gv6ZHsz9L1Ci2bNm3SbbfdpgULFig3Nzep1zz88MOaOHFixPH33ntPBQUF9bl8k3Aq98tRtqzd5WFPeKWdu+VdsaJpGlZPjuOooqJC7777rjweFo3LFPRrZqJfMxd9m5no18xEv6ZHZWVlUudZxiRfTT5nzhxdfvnl8nq9gWO2bcuyLHk8HlVXV4c8J0UfaSkrK9OuXbtUXFyc7KWbTM3nn6j232uU1emokOP2ju3ytu+onDP6N1HL6se2ba1cuVL9+vWL6CMcuejXzES/Zi76NjPRr5mJfk2P8vJytW3bVvv27YubDeo10nLBBRdo9erVIceuu+46nXDCCbr77rujdmhOTo5ycnIiL5yVpayses9OSzvbIzmy5A3fYNJjySsdEffgZ1mWvF7vEdVmJEa/Zib6NXPRt5mJfs1M9Kv7kv1s69UDRUVFOvnkk0OOFRQUqG3bthHHM0bMYnuL1cMAAACANGCCXjLCR1mkQ5mF0AIAAAC4rdFjXYsXL05BM5oxY6KueFyXWljyGAAAAHAbIy2JxJoCZjE9DAAAAEgHQksijok5PUxMDwMAAABcR2hJxBgp6gQxi9ACAAAApAGhJRFjYmYWSloAAAAA9xFaEjDGUbTUYlkealoAAACANCC0NIqRYbgFAAAAcBWhJZGY08MsyRFzxAAAAACXEVoScWIV4kuSYYoYAAAA4DJCSyLGiT3SIiMx0AIAAAC4itCSSKwlj5keBgAAAKQFoSWRWDUtdU+KoRYAAADAXYSWBOoiSfSRFmMMIy0AAACAywgtiSQKJQ6hBQAAAHAToSWRuIX4kmF6GAAAAOAqQksisZY8tizJcZgeBgAAALiM0JJIokJ8MgsAAADgKkJLIsZRzJGWuhPS2RoAAACgxSG0JCNqZrHq8orjpL05AAAAQEtCaEmgbnZY1Ep86lkAAACANCC0JGJijKRYUl1NC8EFAAAAcBOhJQ5jDu14b8UaaRHTwwAAAACXEVriMXFWB7Ms1T3JSAsAAADgJkJLIkbRR1osSY5hdhgAAADgMkJLPCbeSIoVdA4AAAAAtxBa4ooTSPyF+EwPAwAAAFxFaInHHPpHzEJ8IzmEFgAAAMBNhJZ4EhXix50+BgAAACAVCC1xmfiF+BI1LQAAAIDLCC3xJCrEZ3oYAAAA4DpCSwLGRK9psSxLhkJ8AAAAwHWElngS5hGrLtQAAAAAcA2hJZ5kAgmhBQAAAHAVoSUO4x9qibrkMQAAAIB0ILTEY0yCkRQjOU7amgMAAAC0RISWRGIU4texRCE+AAAA4C5CSzwm8I+oLImaFgAAAMBlhJa4kqhpIbMAAAAAriK0xHNoFMVS9NBSNxBDTQsAAADgJkJLPMbUJZN4i4cxPQwAAABwFaElkQShhMwCAAAAuIvQEo8xSmKoJU2NAQAAAFomQksy4mUW9mkBAAAAXEVoicdf0xI3tQAAAABwE6ElHn/BSozMwj4tAAAAgPsILXH5a1rinUJoAQAAANxEaImjrg4/QSE+oQUAAABwFaElngT7tBgZQgsAAADgMkJLXP5AwkgLAAAA0FQILQmZ2JnFsupGWwAAAAC4htASjzEJalosGdtOZ4sAAACAFofQEk+iQRRLksNICwAAAOAmQktch0ZaYpa0WJKcNLYHAAAAaHkILfGYBIX4lsVICwAAAOAyQksyYmUWSTKMtAAAAABuIrTEk6gQ37Ikm9ACAAAAuInQEs+hmV+WFXv1MPZpAQAAANxFaInLxN+HxSK0AAAAAG4jtMRhEgUSSzLUtAAAAACuIrTEE3djSdU9R00LAAAA4CpCS2NYYvUwAAAAwGWElsagpgUAAABwHaElHmOkeIX4shLXvQAAAABoFEJLPEkU4sux09IUAAAAoKUitMSVRCG+w0gLAAAA4CZCSzzGxI0sdYX4hBYAAADATYSWeBLlEcti9TAAAADAZYSWeBKMoliyZJgeBgAAALiK0JJA3EhiWZIcVhADAAAAXERoiSvRksf+UwgtAAAAgFsILfGYBKuHWRahBQAAAHAZoSWOhFnEspTUaAwAAACABiO0xJVEGDGGzAIAAAC4iNDSGIHpYSx7DAAAALilXqFl8uTJ6tOnj4qLi1VcXKwBAwbo9ddfd6ttTc+Y+HPE/NPDqGkBAAAAXFOv0NK1a1c98sgjev/99/Xee+/p/PPP1/Dhw/Xpp5+61b6mZcyhYBLvFKaHAQAAAG7Kqs/Jl156acjjhx56SJMnT9Y777yjk046KaUNOyL4Aw0jLQAAAIBr6hVagtm2rRdffFGVlZUaMGBAzPOqq6tVXV0deFxeXi5J8vl88vl8Db18Wti2LUeSHSOUOMbIOI5qbZ88R8C9GGNk23ZTNwUpRL9mJvo1c9G3mYl+zUz0a3okmwcsU8/t3FevXq0BAwaoqqpKhYWFmjlzpi6++OKY599///2aOHFixPH58+eroKCgPpdOO+fgAZn9FVJObowTbMl25CltLcvrTW/j6slxHFVUVKioqEgeD+svZAr6NTPRr5mLvs1M9Gtmol/To7KyUoMHD9a+fftUXFwc87x6h5aamhpt3LhR+/bt00svvaSnn35ab775pk488cSo50cbaSkrK9OuXbviNqw58P17rWo+/UjeLl2jPu9UHZSprFTu2efLk5ef5tbVj23bWrlypfr16ydvMw9YSB79mpno18xF32Ym+jUz0a/pUV5errZt2yYMLfWeHpadna2ePXtKks444wytXLlSkyZN0l/+8peo5+fk5CgnJyfywllZyspq8Oy0tDAej7ySvDGK8T2WR46kLI9HnmZ+L5JkWZa8Xm+z/9xRP/RrZqJfMxd9m5no18xEv7ov2c+20WNdjuOEjKRkGhNvaTBLiZdFBgAAANAo9YqNEyZM0NChQ9WtWzdVVFRo5syZWrx4sebPn+9W+5pWojDCPi0AAACA6+oVWrZv367Ro0dry5YtKikpUZ8+fTR//nxdeOGFbrWvaSUMI1bdHi2EFgAAAMA19QotzzzzjFvtaJbqpobF2VzSqjuL0AIAAAC4h/Xb4nFM3MxSN9JipHh1LwAAAAAahdAShzGOEo60GMk4hBYAAADALYSWROKNtASWQia0AAAAAG4htMSTcATFkoxDTQsAAADgIkJLXMlMDzMMtAAAAAAuIrTEk2ikxT89zDjutwUAAABooQgt8RgTVLcSjXX4PAAAAACuILTEY5wEdfhW3QpjZBYAAADANYSWeJIOI6QWAAAAwC2ElngSTg+TJEvGoaYFAAAAcAuhJR5jFH+jFgAAAABuI7TEY0wSmcVQiA8AAAC4iNAShzEJ9mmR6p4ntAAAAACuIbTEk8RIS93ThBYAAADALYSWhJKoaUm0CSUAAACABiO0xOMkU9MipocBAAAALiK0xGMSL2Vsgv4JAAAAIPUILfEkueSxYXoYAAAA4BpCSxwmqSWPJUZaAAAAAPcQWuJJZqTFsE8LAAAA4CZCSzzGSWKbFvZpAQAAANxEaEkofmpJavYYAAAAgAbLauoGNGfm4EFVL39Lzv5yterZS9mn95dlRYkpTuJVxgAAAAA0DCMtcdR89rF8mzbIVJSr5sOVsr/eFP1EpocBAAAAriG0xOHs2hHy2N6yOeIcw8phAAAAgKsILXEYO7QQ39m3N/qJTA8DAAAAXENoicfxKTi1OOX7opxkMdoCAAAAuIjQEk/YCIpTvq9uw8lgliSH0AIAAAC4hdASgzEmctqXr1bm4IHQY5YlY5geBgAAALiF0BKLMXU1LWEi61osaloAAAAAFxFaYok20iLJRKtrIbQAAAAAriG0xGSkKNO+IorxLYt9WgAAAAAXEVriiTKC4pTvDXlsSVHDDQAAAIDUILTEYiTj2BGHI0daPKweBgAAALiI0BJLjJoWp3xv6LLHlqhpAQAAAFxEaIkpemhRba1M1cGgAyx5DAAAALiJ0BJLjJEWSTLByx5TiA8AAAC4itASh3GculASJqQY3xI1LQAAAICLCC0xGNuJOYISWoxvSVEK9gEAAACkBqElFp8v5lMhocVSaGE+AAAAgJQitMRgnDihJbimRdS0AAAAAG4itMRgEoy0BEZXLIvNJQEAAAAXEVpi8cWpU6mtObzsMfu0AAAAAK4itMRixx5pkSQTqGuxCC0AAACAiwgtMZgEK4L5i/Ety6KkBQAAAHARoSWWQzUtkbu01Ans1UJNCwAAAOAqQksM8QrxpfAVxAzLHgMAAAAuIbTEYic3PUyWJTli2WMAAADAJYSWWGyfJKNYE8Sc8r1BoyuGKWIAAACASwgtMQSmh8UqaqmpkamuqhtpkanLNwAAAABSjtASi88XEkSsnBzJCv24zL59TA8DAAAAXEZoiSV875WsVvIUFYee4l9BTEYMtQAAAADuILTEYOza0AOWR1ZJScghp7xupMUYw0gLAAAA4BJCSwzGF7p6mOXxyFNcGnLs8EiLJIfQAgAAALiB0BJL+JLHUUPLoZoWSYbpYQAAAIArCC0xGCdsc0mPR57i0OlhpnzvoUJ8h+lhAAAAgEsILbHYYYX4Ho88JaUhh0x1tUx1teqWPCa0AAAAAG4gtMRih460WF6vrMKiyGWP91cc+gOhBQAAAHADoSUGYzsKWcbY46krxi8qCjnPqdh3aMVjQgsAAADgBkJLLD7/ksd1hfby1H1UVnhdS2WlZMKmkgEAAABIGUJLLFFWD5Mkq1VOyOHAfi6MtAAAAACuILTEYPyh5dBAi+Xx1v0hyxt6os+umx7mMNoCAAAAuIHQEovtCylpCYy0eLNCz3NsHSpqSVfLAAAAgBaF0BJLlCWPJUlZoaHF2LbkGGaHAQAAAC4htMRgnNCaFv/0MCsstMh3aGlkUgsAAADgCkJLDMYXuk9LYKTFG1rTYhz/0siEFgAAAMANhJZY7OihJaKmxfbVjbI4hBYAAADADYSWWMJXA/OPsISFFuOzD00NI7QAAAAAbiC0xGDs8JqWQyMt4TUtNjUtAAAAgJsILbHEqmmJFlqYHgYAAAC4htASS9jqYbEK8WXbMhTiAwAAAK4htMQQPj0sViF+3XmWDNPDAAAAAFfUK7Q8/PDD6tevn4qKitShQwdddtllWrNmjVtta1rh+7RYMaaH+Wrr/k1oAQAAAFxRr9Dy5ptv6uabb9Y777yjBQsWqLa2VhdddJEqKyvdal/TscNWD/NvLhlt9TAAAAAArslKfMphb7zxRsjjadOmqUOHDnr//fd1zjnnpLRhTS1iepjXP9ISXtPikzFO5BLJAAAAAFKiXqEl3L59+yRJbdq0iXlOdXW1qqurA4/Ly8slST6fT77wFbqaEePzhZTWO5ZHtjFyPN6IknvHkWzbjlxxrBmxbVvGmLp2ImPQr5mJfs1c9G1mol8zE/2aHsnmAcs0sILccRx973vf0969e7V06dKY591///2aOHFixPH58+eroKCgIZdOi/ZzZip751bpUC3Lrn7fVWWP4+Q9UKmj/jE75NxNQ74vtWsvT05uUzQ1KY7jqKKiQkVFRfJ4WH8hU9CvmYl+zVz0bWaiXzMT/ZoelZWVGjx4sPbt26fi4uKY5zV4pOXmm2/WJ598EjewSNKECRN0xx13BB6Xl5errKxMffv2jduwprbv/16RzxhZreqmg7UuKVRWm2KZ/GwdCJsi1sc+qNxjj5H3qLKmaGpSbNvWypUr1a9fP3nDl23GEYt+zUz0a+aibzMT/ZqZ6Nf08M/CSqRBoWX8+PGaN2+e3nrrLXXt2jXuuTk5OcrJyYm8cFaWssJX4mpGLMeRJck69Njj8cprWTKtsgLHAufatrweq1nfjyRZliWv19vs24n6oV8zE/2auejbzES/Zib61X3Jfrb16gFjjG655Ra98sorWrx4sY4++ugGNe5IYJLdXFKSbB9LHgMAAAAuqVdoufnmmzVz5kzNnTtXRUVF2rp1qySppKREeXl5rjSwyYQVXVmHwopleeqWPw4ONbZDZgEAAABcUq+qosmTJ2vfvn0677zz1Llz58DXrFmz3Gpf0wlfKSK4ACuspqVuVIbUAgAAALih3tPDWoqY08NUt8GkUc3h52ybfVoAAAAAl7B+Wyx2aAixrKCPyhuW9QgsAAAAgGsILbGEj7QEFeBb4ascUIgPAAAAuIbQEoOJW9MSHlpsQgsAAADgEkJLFMaYyClfITUtYYX4hBYAAADANYSWaMJHWSRZnqCgElbTYhxCCwAAAOAWQks0Pl/kseCRFqaHAQAAAGlDaIkiYrljKbSmJcrqYcawghgAAADgBkJLNFGmh8XdXNK2ZVj2GAAAAHAFoSWKupXDQqd7Bde0WBEjLbbkMD0MAAAAcAOhJRr/SIsVdCzukseOJEZaAAAAADcQWqKx7fCBlrAlj6OsHsZICwAAAOAKQksUCQvxw2pa6kIOIy0AAACAGwgt0QSWPD40P8zyyLIOzxWLrGlxDk0RAwAAAJBqhJYoIgrxPWEfU3hoYZ8WAAAAwDWElmjCp4d5Qz8mK8qSx4QWAAAAwB2Elmh8vpCBluDljiVF2VzSjl4HAwAAAKDRCC1RJJweFrHkMauHAQAAAG4htERjh42ahIWWqEses3oYAAAA4ApCSxTG9oU8tpIZaaGmBQAAAHAFoSWaiJGW0JoWyxtZiG8ILQAAAIArCC3R2HZISUvCJY8dO2J0BgAAAEBqEFqiqAsgsQvxrfDpYUZBG1ICAAAASCVCSzT+6WHWoX8lqmmRCC0AAACASwgtUZhATcuh1BJR0xIWWizJ1Na63zAAAACgBSK0RJOwpiVss0lZMr4at1sFAAAAtEiElmjCd7ePmB4WHlok+XysIAYAAAC4gNASRd30sMMBJLymxbI8EVPGDHu1AAAAAK4gtESTYJ8WSZGjLT4foQUAAABwAaEliog9V8KnhymyGD9imWQAAAAAKUFoiSZipCXKxxS+gpjPR2YBAAAAXEBoiSZs9TDLG2WkJXyvFp8tGcflhgEAAAAtD6ElCuMLm+oVraYleNljy6qbHkZNCwAAAJByhJZoEi15rMiRFsP0MAAAAMAVhJYoTFhNS/iSx5Iia1pY8hgAAABwBaElGp+vbtDEOvQ4yvSwyJoWnww1LQAAAEDKEVqicfyjJodSS9SRlvDNJZkeBgAAALiB0BJF+PSwpJY8Zp8WAAAAwBWElijCN5eMVtMSPD3M0qGgQ00LAAAAkHKElmh8oaEl6pLHUWpaCC0AAABA6hFaovElseRx8PQwy2L1MAAAAMAlhJYojBM+0pJMIT6hBQAAAHADoSWaJPZpscJCi2ymhwEAAABuILREk8zqYVmtoryG0AIAAACkGqElioglj8NHVSRZWZHTw4xDaAEAAABSjdASTcTqYcns08JICwAAAOAGQksU4SMt0WpaIkZfqGkBAAAAXEFoica/Ephl1T2OurlkaE0Lq4cBAAAA7iC0RGMns7lk0DHLYqQFAAAAcAmhJYqIQvxEm0tKku0QWgAAAAAXEFqiSaqmJTS0GMfH6mEAAACACwgtURgnfKQl2pLHYSMtRpJd616jAAAAgBaK0BLGGJPc5pJR9m5xampcahUAAADQchFawoWPsijG9LDwkRZLUi0jLQAAAECqEVrChY+ySFFHVSIK8Y1kfIQWAAAAINUILWGM73Bosfx/iDrSEmUZZKaHAQAAAClHaAnnRNkkMtqSx5YnokDf1BJaAAAAgFQjtIQL2Viybqwlak2LFDltjJoWAAAAIOUILWEOTw8zh+eHxQgt4cseG0ILAAAAkHKElnDRCvFjjrSEFeNTiA8AAACkHKElTGBjyeCyliRHWpwaQgsAAACQaoSWcCE1LZIsq67oPprgmhZLjLQAAAAALiC0hAufHhZrapgkKzi0GMn4WD0MAAAASDVCSxhTj9CirFahjynEBwAAAFKO0BLODt2nxfJE2UTS/1xW+D4thBYAAAAg1QgtYUx4TUu8kZaI1cN80c8DAAAA0GCElnD1mR7mZZ8WAAAAwG2ElnARoSXe9DD2aQEAAADcRmgJc7gQv66uxYo70kJNCwAAAOA2Qku4kJEWK/6Sx2EjLYaRFgAAACDlCC1hQgrxLSVf02KJJY8BAAAAFxBawoUveeyNU9MSPj0sfOUxAAAAAI1GaAnnnx7mzy1Jby5pydTWuNUqAAAAoMWqd2h56623dOmll6pLly6yLEtz5sxxoVlNx9RryeOwUZhaRloAAACAVKt3aKmsrNSpp56qP/3pT260p+lFbC6Z/JLHFOIDAAAAqZeV+JRQQ4cO1dChQ91oS7MQPtJSnyWP5WOkBQAAAEg1alrC1WN6mBVS08JICwAAAOCGeo+01Fd1dbWqq6sDj8vLyyVJPp9PvmY4MmHX1sgYE6jDN5ZHdtBqYsEcr1fBz5ja5nlPkmTbtowxssNDGY5o9Gtmol8zF32bmejXzES/pkeyPzu7HloefvhhTZw4MeL4e++9p4KCArcvX28l//63CquqJcuSfLYqa2q1a3d51HOz9x9UJ5//L7IlU1WlL1esSF9j68FxHFVUVOjdd9+VJ96UNxxR6NfMRL9mLvo2M9GvmYl+TY/KysqkznM9tEyYMEF33HFH4HF5ebnKysrUt29fFRcXu335eju49T+q+vITOZW1slp5VZyfpx5torfTMdU6mHWorqXWJ1nSmWeeKcuy0tji5Ni2rZUrV6pfv37yxtl7BkcW+jUz0a+Zi77NTPRrZqJf08M/CysR10NLTk6OcnJyIi+claWsLNcvX2+WjCwp8OXxeuWNEUKsVtnyP2OsutdmKXJVsebCsix5vd5m+bmj4ejXzES/Zi76NjPRr5mJfnVfsp9tvXtg//79+uqrrwKP169fr1WrVqlNmzbq1q1bfd+u+fE1Yp8WY2Rqa2W1ahX9fAAAAAD1Vu/Q8t5772ngwIGBx/6pX2PGjNG0adNS1rAm49QjtERLhqwgBgAAAKRUvUPLeeedJxNjNa1MUJ99Wqwo8xtNLaEFAAAASCWWQggXvuxavMKrKCMtprYmxQ0CAAAAWjZCS5jwkZa4m0tantDnjZEILQAAAEBKEVrCObYUvGVkonW5vaGjLUwPAwAAAFKL0BLOthVcsmN54q/LHVje2LJkJBkK8QEAAICUIrSEMeE1LfUZaTFGqmF6GAAAAJBKhJZw9VnyWJEriDmEFgAAACClCC1hIpc8jj89LGIFMQrxAQAAgJQitISr5/Sw8JEWU1Od6hYBAAAALRqhJVw9ljyWJGW1CnnI6mEAAABAahFawpj61rRkhU0fo6YFAAAASClCSzg7dJ8WK9FIiydsnxaWPAYAAABSitASJrwQXwkL8Q89b1kseQwAAAC4gNASzraDB1qSmB4WXtNCaAEAAABSidASzq7v5pLhq4cRWgAAAIBUIrSEqe8+LVYWNS0AAACAmwgt4eq75HHYSAubSwIAAACpRWgJYoyJLMT3Jtpcsm6kxfK/B/u0AAAAAClFaAnmOKqrwjeHU0jCzSXDpodR0wIAAACkFKElWPjGkkpc0yKvP7RYkozESAsAAACQUoSWIIGpYfVa8vhQaDk0MsP0MAAAACC1CC3BfL7IY/UpxDeSWD0MAAAASClCS7BAEb6Rf+ik3kses3oYAAAAkFKEliAmSk1L4pGW8NDCSAsAAACQSoSWYNGmhyW55LEfoQUAAABILUJLkIYU4isrbPqYr1bGmOjnAgAAAKg3QkswJ7imRZIsWVai1cNahR4wJqg2BgAAAEBjEVqChU8PSzA1rO4c/0jLoX1aRDE+AAAAkEqEliAmfIQk0dQw6XBosYKOUdcCAAAApAyhJVhYTYuVRGgJWfLYSDKGYnwAAAAghQgtQSKWPE6wR4skKTi0HBptYXoYAAAAkDqElmB2WCF+MiMtlufwef76fUILAAAAkDKElmDBNS2WpAQrhwV4Q0dkTLT9XgAAAAA0CKElSGghviUrmdXDFLbssTFMDwMAAABSiNASzD40QuKf5pVMTYsUMtJijJFqCC0AAABAqhBagjRoyWMFjbRYkhxHpro6tQ0DAAAAWjBCS7AGFOJLkpVf4P+T5Niy9+xMedMAAACAlorQEswOLaBPZp8WSfIUFR16gSU5jpxdu1LdMgAAAKDFIrQEiZwellxNi1VUfOgPlowxsnduS3HLAAAAgJaL0BKsgTUtnsK60GJZHsk4cnbtSHXLAAAAgBaL0BLEBFYPq6tpSXbJY09gpKXutfbuXTKO40ILAQAAgJaH0BIsBdPDZCTV1sop35fatgEAAAAtFKElWHhosZJcPSwvL2KvFmc3K4gBAAAAqUBoCRJSiG9JSnJ6mGV55Cn0ryCmuhXECC0AAABAShBagh2qaTlU0ZL0ksdS8BQxT91eLbtZ9hgAAABIBUJLkMBIi/FvLplcTYt0eAUxWZaM48hmBTEAAAAgJQgtwRq45LEUvILYoQ0md25PYcMAAACAlovQEsy2/XPD6jRgephlWZKMbEILAAAAkBKEliAmbKSlPjUtwdPDZIycPbsj3g8AAABA/RFagjm2QoZa6jXSUnT4gZFk23L27UlZ0wAAAICWitASxPh8oQfqUYhv5eZJWVmH38s4clhBDAAAAGg0QkuwRhTiW5YVOkXMcWSzVwsAAADQaISWYE7Da1qk4L1aLMlmpAUAAABIBUJLEOMLH2lJfnqYFLrssXFsVhADAAAAUoDQEswOr2mp38cTvoIYoQUAAABoPEJLkJAlii0rEFqMbcsYJ+Hr/SuIWf5lj3fucKWdAAAAQEuSlfiUFsQ5tLmkMZJlyfLWTQ+zt2yWjORp116evPyYLw+eHiZj5JTvlfH5ZGXxMQMAAAANxUhLMF/kPi3GsWV5vMrq1kNm7x7ZO3fIGBP15YHQ4mfbcvbudq+9AAAAQAtAaAkSsYO9xyNTVSXl5in7pFOV0/fbsnJy5Hy9WaamJuL1Vk6ulJ19+P1Y9hgAAABoNOYtBYuy5LGpOihPfoGsvHxl5RfIU9JatV98qtqN6+Vp3UaegsKQ13gKi+Xs3nloipgjZxehBQAAAGgMRlqCGF/46mFemaoqedq2qyuul+QpLFL2aX3VqteJcvZETv0KqWuxbfZqAQAAABqJ0BIsyvQwOY68RSUhhy1vlrI6HSUrq5VMbeg0MStkrxZH9i5WEAMAAAAag9ASLGx6mGRkeb2yCooiTvWUtpanpFROeXnocfZqAQAAAFKK0BIkuBDfkmRqa6XcvIi6FUmyvF55j+oqc/BAyHFPxF4thBYAAACgMQgtwWy7bo+WQ0xtrTyFRbJyc6Oe7m3bXlZ2tkx1deBY8PQwycgp31cXfgAAAAA0CKElWHhNi69WnjZtY57uKWktT2lrORWHp4iF7NVyaKNKZw/F+AAAAEBDEVoOMY4jYxz/I0mWLFnyFhbHfI3l8SirS5lM1cHDx1pl1+3XEvS+7NUCAAAANByhxS9slMXISK2yZRVG1rME87ZtJysnp24TykNCpog57NUCAAAANAahxS84tBhJjiMrLz9qEX4wq7hUntZt5VTsCxwLWUHMsRlpAQAAABqB0HKICV/u2DjyFBfLys6J+zrLspTVpatMVZXMoSJ+/wpi7NUCAAAANB6hxc/nC31sjDzt2if1Um+bdrLy8gO1LSHTw4yRs4NljwEAAICGIrQcYsJXDpMlb1FJUq+1iorlbdtW5tAqYv4VxPx7tTDSAgAAADQcocUvfHqYZYUuXxyHZVnyduoq1VTLGCOrMGykpXxfyF4uAAAAAJLXoNDypz/9ST169FBubq769++vd999N9XtSjvjnx7m31zS45FVXJr0671t20l5+TIHKg/XtBx6H1N1QJVvzJVTW5O6BgMAAAAtRL1Dy6xZs3THHXfovvvu0wcffKBTTz1VgwcP1vbtR3jdRtj0MMvjkZWdnfTLPYVFyurURWbPbllZrWTl5de9T1aWZHl04JUXtPvnN2v/nFmyd2wLFO0DAAAAiC+rvi944oknNG7cOF133XWSpClTpugf//iHnn32Wd1zzz0pb6AbKiv3aus3a0OOebbvULZTKxlbjuXIauXVuqpt9Xpfc1SJqnYYWfu/VmHbYmVtrqh7wqu6EZe9O1Q170Xt++erUm6eTOvWclq3lmndRqa4WMrNlcnJkXKyZbJzpFatJCv4ClboBa2wxzFPtWQ7tvbu/Err13rk9da729FM2bZPe3euo18zDP2auejbzES/ZqaW0K+l7bqoTZujmroZSbFMPX7lX1NTo/z8fL300ku67LLLAsfHjBmjvXv3au7cuQnfo7y8XCUlJdq3b5+Ki5OrGUm1Lz5epIo/PxH1OSMjOY5qs7P0x5vPq/d7m6oqOQcPqMvual2+4EvlHawNO8FIsuoChyXJsmSFhxEX1G09Y8vj8abhakgX+jUz0a+Zi77NTPRrZmoJ/Vp78cU663s3Nmkbks0G9YqNO3fulG3b6tixY8jxjh076osvvoj6murqalUHFaGXl9etsOXz+eQLX2Y4TWzbUSCpGSf0yUMZzvF4GjaFKztbqqnRN+08evqqU9X7q106ec0Oddqxv+55/+iIceq+G2TJWHX/VtC/XGOC7h2Zg37NTPRr5qJvMxP9mpkytV8tj4xjmuzncb9kr+/6WNfDDz+siRMnRhx/7733VFBQ4Pblo9q1ZZ06+FcLi/G3cEtpriorKxv0/kaWZKQDHktvH99Obx/fTh13HdC3vtyhk9ftUl61rZB0YoIa4vJ3hUMtTUaiXzMT/Zq56NvMRL9mpoztV8vWtu3btGLFiiZtRrI/b9crtLRr105er1fbtoXWemzbtk2dOnWK+poJEybojjvuCDwuLy9XWVmZ+vbt22TTw9Z+WqO9b8Yust/VtlDLLjxJrQsbGqqMzP4KmdraQDF/TcdsrWhXpHf7lamwVio+aKtkf42KK6pVvL9aeQdrlF1rq1WtrVY1trJrbXntw6NAVsT3i4nzXLQmGdmOI6/Hk7FDnC2SkWzHltfjdX+UDulDv2Yu+jYz0a+ZqQX0a8fOndW/f/8mbYN/FlYi9Qot2dnZOuOMM7Rw4cJATYvjOFq4cKHGjx8f9TU5OTnKycmJvHBWlrKymqaoqfepA6X/Ghj3nEsaeQ1753ZVvbNUVmGhPPkFsvfskqqqlHXscco+/kRZ2ZGfiZt8Pp9WrFihM/v3b7LPHann79d+9GtGoV8zF32bmejXzES/pkeyn229e+COO+7QmDFj1LdvX5155pl66qmnVFlZGVhNDHU8bdsrq9vRqv3qCzn79srKzVPOaf3k7dpdloc9PQEAAIBk1Tu0XHXVVdqxY4d+/etfa+vWrfrWt76lN954I6I4v6WzLEutjukpe/tWWTk5yj6pj7xt2jV1swAAAIAjToPGusaPHx9zOhgO8xQWKeeM/rLy8uTJzWvq5gAAAABHJCbouczbuk1TNwEAAAA4olFcAQAAAKBZI7QAAAAAaNYILQAAAACaNUILAAAAgGaN0AIAAACgWSO0AAAAAGjWCC0AAAAAmjVCCwAAAIBmjdACAAAAoFkjtAAAAABo1ggtAAAAAJo1QgsAAACAZo3QAgAAAKBZI7QAAAAAaNYILQAAAACaNUILAAAAgGaN0AIAAACgWctK9wWNMZKk8vLydF+6RfP5fKqsrFR5ebmystLe7XAJ/ZqZ6NfMRd9mJvo1M9Gv6eHPBP6MEEvae6CiokKSVFZWlu5LAwAAAGiGKioqVFJSEvN5yySKNSnmOI6++eYbFRUVybKsdF66RSsvL1dZWZk2bdqk4uLipm4OUoR+zUz0a+aibzMT/ZqZ6Nf0MMaooqJCXbp0kccTu3Il7SMtHo9HXbt2TfdlcUhxcTHfeBmIfs1M9Gvmom8zE/2amehX98UbYfGjEB8AAABAs0ZoAQAAANCsEVpaiJycHN13333Kyclp6qYghejXzES/Zi76NjPRr5mJfm1e0l6IDwAAAAD1wUgLAAAAgGaN0AIAAACgWSO0AAAAAGjWCC0AAAAAmjVCyxHkrbfe0qWXXqouXbrIsizNmTMn5Plt27Zp7Nix6tKli/Lz8zVkyBCtXbs25JzzzjtPlmWFfP3kJz8JOWfjxo0aNmyY8vPz1aFDB911113y+Xxu316LlY5+/eijjzRy5EiVlZUpLy9PvXv31qRJk9Jxey1Wur5f/Xbt2qWuXbvKsizt3bvXpbtCOvt12rRp6tOnj3Jzc9WhQwfdfPPNbt5ai5euvl25cqUuuOAClZaWqnXr1ho8eLA++ugjt2+vxUpFv0rS8uXLdf7556ugoEDFxcU655xzdPDgwcDzu3fv1qhRo1RcXKzS0lLdcMMN2r9/v9u316IQWo4glZWVOvXUU/WnP/0p4jljjC677DL9+9//1ty5c/Xhhx+qe/fuGjRokCorK0POHTdunLZs2RL4evTRRwPP2batYcOGqaamRm+//baee+45TZs2Tb/+9a9dv7+WKh39+v7776tDhw6aPn26Pv30U/3yl7/UhAkT9Mc//tH1+2up0tGvwW644Qb16dPHlXvBYenq1yeeeEK//OUvdc899+jTTz/VP//5Tw0ePNjVe2vp0tG3+/fv15AhQ9StWzetWLFCS5cuVVFRkQYPHqza2lrX77ElSkW/Ll++XEOGDNFFF12kd999VytXrtT48ePl8Rz+MXrUqFH69NNPtWDBAs2bN09vvfWWfvSjH6XlHlsMgyOSJPPKK68EHq9Zs8ZIMp988kngmG3bpn379uZ//ud/AsfOPfdcc9ttt8V839dee814PB6zdevWwLHJkyeb4uJiU11dndJ7QCS3+jWam266yQwcOLCxTUYS3O7XP//5z+bcc881CxcuNJLMnj17Uth6xOJWv+7evdvk5eWZf/7zn240G0lwq29XrlxpJJmNGzcGjn388cdGklm7dm1K7wGRGtqv/fv3N/fee2/M9/3ss8+MJLNy5crAsddff91YlmW+/vrr1N5EC8ZIS4aorq6WJOXm5gaOeTwe5eTkaOnSpSHnzpgxQ+3atdPJJ5+sCRMm6MCBA4Hnli9frlNOOUUdO3YMHBs8eLDKy8v16aefunwXCJeqfo1m3759atOmTeobjYRS2a+fffaZHnjgAf31r38N+a0f0i9V/bpgwQI5jqOvv/5avXv3VteuXTVixAht2rQpPTeCCKnq2169eqlt27Z65plnVFNTo4MHD+qZZ55R79691aNHj7TcCw5Lpl+3b9+uFStWqEOHDjrrrLPUsWNHnXvuuSH9vnz5cpWWlqpv376BY4MGDZLH49GKFSvSdDeZj//DZYgTTjhB3bp104QJE7Rnzx7V1NTod7/7nTZv3qwtW7YEzrv66qs1ffp0LVq0SBMmTNDf/vY3XXPNNYHnt27dGhJYJAUeb926NT03g4BU9Wu4t99+W7NmzWLouomkql+rq6s1cuRI/f73v1e3bt2a4lYQJFX9+u9//1uO4+i3v/2tnnrqKb300kvavXu3LrzwQtXU1DTFrbV4qerboqIiLV68WNOnT1deXp4KCwv1xhtv6PXXX1dWVlZT3FqLlky//vvf/5Yk3X///Ro3bpzeeOMNnX766brgggsCtS9bt25Vhw4dQt47KytLbdq04WenFOI7JEO0atVKf//733XDDTeoTZs28nq9GjRokIYOHSpjTOC84B9STznlFHXu3FkXXHCB1q1bp2OPPbYpmo443OjXTz75RMOHD9d9992niy66KG33gsNS1a8TJkxQ79694wZUpE+q+tVxHNXW1uoPf/hD4Hv0+eefV6dOnbRo0SJqW5pAqvr24MGDuuGGG/Sd73xHzz//vGzb1mOPPaZhw4Zp5cqVysvLa4rba7GS6VfHcSRJP/7xj3XddddJkk477TQtXLhQzz77rB5++OEma39Lw0hLBjnjjDO0atUq7d27V1u2bNEbb7yhXbt26Zhjjon5mv79+0uSvvrqK0lSp06dtG3btpBz/I87derkUssRTyr61e+zzz7TBRdcoB/96Ee69957XW034ktFv/7rX//Siy++qKysLGVlZemCCy6QJLVr10733Xef+zeBCKno186dO0uSTjzxxMA57du3V7t27bRx40YXW494UtG3M2fO1IYNGzR16lT169dP3/72tzVz5kytX79ec+fOTct9IFSifo32/ShJvXv3Dnw/durUSdu3bw953ufzaffu3fzslEKElgxUUlKi9u3ba+3atXrvvfc0fPjwmOeuWrVK0uFvygEDBmj16tUh33wLFixQcXFxxDcs0qsx/SpJn376qQYOHKgxY8booYcecru5SFJj+vXll1/WRx99pFWrVmnVqlV6+umnJUlLlixhedwm1ph+/c53viNJWrNmTeCc3bt3a+fOnerevbt7jUZSGtO3Bw4ckMfjkWVZgXP8j/2/0UfTiNWvPXr0UJcuXUK+HyXpyy+/DHw/DhgwQHv37tX7778feP5f//qXHMcJBFekQFOuAoD6qaioMB9++KH58MMPjSTzxBNPmA8//ND85z//McYYM3v2bLNo0SKzbt06M2fOHNO9e3dzxRVXBF7/1VdfmQceeMC89957Zv369Wbu3LnmmGOOMeecc07gHJ/PZ04++WRz0UUXmVWrVpk33njDtG/f3kyYMCHt99tSpKNfV69ebdq3b2+uueYas2XLlsDX9u3b036/LUU6+jXcokWLWD3MZenq1+HDh5uTTjrJLFu2zKxevdpccskl5sQTTzQ1NTVpvd+WJB19+/nnn5ucnBxz4403ms8++8x88skn5pprrjElJSXmm2++Sfs9twSN7VdjjHnyySdNcXGxefHFF83atWvNvffea3Jzc81XX30VOGfIkCHmtNNOMytWrDBLly41xx13nBk5cmRa7zXTEVqOIP4fSMK/xowZY4wxZtKkSaZr166mVatWplu3bubee+8NWaZ448aN5pxzzjFt2rQxOTk5pmfPnuauu+4y+/btC7nOhg0bzNChQ01eXp5p166d+dnPfmZqa2vTeastSjr69b777ot6je7du6f5bluOdH2/RrsmocU96erXffv2meuvv96UlpaaNm3amMsvvzxkmVykXrr69v/+7//Md77zHVNSUmJat25tzj//fLN8+fJ03mqL0th+9Xv44YdN165dTX5+vhkwYIBZsmRJyPO7du0yI0eONIWFhaa4uNhcd911pqKiIh232GJYxgRVkAEAAABAM0NNCwAAAIBmjdACAAAAoFkjtAAAAABo1ggtAAAAAJo1QgsAAACAZo3QAgAAAKBZI7QAAAAAaNYILQAAAACaNUILAAAAgGaN0AIAAACgWSO0AAAAAGjWCC0AAAAAmrX/D/REKaJdW53NAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" diff --git a/docs/examples/sentence-classification.ipynb b/docs/examples/sentence-classification.ipynb index b5710f8f9a..a456ad5299 100644 --- a/docs/examples/sentence-classification.ipynb +++ b/docs/examples/sentence-classification.ipynb @@ -19,54 +19,36 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T10:54:17.554992Z", - "iopub.status.busy": "2022-10-26T10:54:17.553914Z", - "iopub.status.idle": "2022-10-26T10:54:18.187632Z", - "shell.execute_reply": "2022-10-26T10:54:18.188333Z" + "iopub.execute_input": "2023-12-04T17:52:50.005002Z", + "iopub.status.busy": "2023-12-04T17:52:50.004369Z", + "iopub.status.idle": "2023-12-04T17:52:50.415357Z", + "shell.execute_reply": "2023-12-04T17:52:50.415077Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
\n",
-       "SMS Spam Collection dataset.\n",
-       "\n",
-       "The data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent\n",
-       "13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.\n",
-       "\n",
-       "      Name  SMSSpam                                                                          \n",
-       "      Task  Binary classification                                                            \n",
-       "   Samples  5,574                                                                            \n",
-       "  Features  1                                                                                \n",
-       "    Sparse  False                                                                            \n",
-       "      Path  /Users/max.halford/river_data/SMSSpam/SMSSpamCollection                          \n",
-       "       URL  https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\n",
-       "      Size  466.71 KB                                                                        \n",
-       "Downloaded  True                                                                             \n",
-       "
\n" - ], "text/plain": [ - "\n", "SMS Spam Collection dataset.\n", "\n", - "The data contains \u001b[1;36m5\u001b[0m,\u001b[1;36m574\u001b[0m items and \u001b[1;36m1\u001b[0m feature \u001b[1m(\u001b[0mi.e. SMS body\u001b[1m)\u001b[0m. Spam messages represent\n", - "\u001b[1;36m13.4\u001b[0m% of the dataset. The goal is to predict whether an SMS is a spam or not.\n", - "\n", - " Name SMSSpam \n", - " Task Binary classification \n", - " Samples \u001b[1;36m5\u001b[0m,\u001b[1;36m574\u001b[0m \n", - " Features \u001b[1;36m1\u001b[0m \n", - " Sparse \u001b[3;91mFalse\u001b[0m \n", - " Path \u001b[35m/Users/max.halford/river_data/SMSSpam/\u001b[0m\u001b[95mSMSSpamCollection\u001b[0m \n", - " URL \u001b[4;94mhttps://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\u001b[0m\n", - " Size \u001b[1;36m466.71\u001b[0m KB \n", - "Downloaded \u001b[3;92mTrue\u001b[0m \n" + "The data contains 5,574 items and 1 feature (i.e. SMS body). Spam messages represent\n", + "13.4% of the dataset. The goal is to predict whether an SMS is a spam or not.\n", + "\n", + " Name SMSSpam \n", + " Task Binary classification \n", + " Samples 5,574 \n", + " Features 1 \n", + " Sparse False \n", + " Path /Users/max/river_data/SMSSpam/SMSSpamCollection \n", + " URL https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip\n", + " Size 466.71 KB \n", + "Downloaded True " ] }, + "execution_count": 1, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -80,10 +62,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T10:54:18.193109Z", - "iopub.status.busy": "2022-10-26T10:54:18.192345Z", - "iopub.status.idle": "2022-10-26T10:54:18.222366Z", - "shell.execute_reply": "2022-10-26T10:54:18.222796Z" + "iopub.execute_input": "2023-12-04T17:52:50.416917Z", + "iopub.status.busy": "2023-12-04T17:52:50.416799Z", + "iopub.status.idle": "2023-12-04T17:52:50.427410Z", + "shell.execute_reply": "2023-12-04T17:52:50.427185Z" }, "tags": [] }, @@ -93,21 +75,9 @@ "output_type": "stream", "text": [ "{'body': 'Go until jurong point, crazy.. Available only in bugis n great world '\n", - " 'la e buffet... Cine there got amore wat...\\n'}\n" + " 'la e buffet... Cine there got amore wat...\\n'}\n", + "Spam: False\n" ] - }, - { - "data": { - "text/html": [ - "
Spam: False\n",
-       "
\n" - ], - "text/plain": [ - "Spam: \u001b[3;91mFalse\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -133,26 +103,23 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T10:54:18.227903Z", - "iopub.status.busy": "2022-10-26T10:54:18.227159Z", - "iopub.status.idle": "2022-10-26T10:55:05.710003Z", - "shell.execute_reply": "2022-10-26T10:55:05.710491Z" + "iopub.execute_input": "2023-12-04T17:52:50.428788Z", + "iopub.status.busy": "2023-12-04T17:52:50.428710Z", + "iopub.status.idle": "2023-12-04T17:53:07.741457Z", + "shell.execute_reply": "2023-12-04T17:53:07.741048Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
ROCAUC: 93.00%\n",
-       "
\n" - ], "text/plain": [ - "ROCAUC: \u001b[1;36m93.00\u001b[0m%\n" + "ROCAUC: 93.00%" ] }, + "execution_count": 3, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -195,32 +162,25 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T10:55:05.714445Z", - "iopub.status.busy": "2022-10-26T10:55:05.713771Z", - "iopub.status.idle": "2022-10-26T10:55:05.741641Z", - "shell.execute_reply": "2022-10-26T10:55:05.742035Z" + "iopub.execute_input": "2023-12-04T17:53:07.743587Z", + "iopub.status.busy": "2023-12-04T17:53:07.743450Z", + "iopub.status.idle": "2023-12-04T17:53:07.754659Z", + "shell.execute_reply": "2023-12-04T17:53:07.754387Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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-       "        False   True  \n",
-       "False   4,809     17  \n",
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y_mean_by_store_id
(\n", + "
y_mean_by_store_id
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~['area_name', 'date', 'genre_name', 'latitude', 'longitude', 'store_id']
(\n", + "
~['area_name', [...]
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" + ], + "text/plain": [ + "Pipeline (\n", + " TransformerUnion (\n", + " FuncTransformer (\n", + " func=\"get_date_features\"\n", + " ),\n", + " TargetAgg (\n", + " by=['store_id']\n", + " how=Rolling (\n", + " obj=Mean ()\n", + " window_size=7\n", + " )\n", + " target_name=\"y\"\n", + " ),\n", + " TargetAgg (\n", + " by=['store_id']\n", + " how=Rolling (\n", + " obj=Mean ()\n", + " window_size=14\n", + " )\n", + " target_name=\"y\"\n", + " ),\n", + " TargetAgg (\n", + " by=['store_id']\n", + " how=Rolling (\n", + " obj=Mean ()\n", + " window_size=21\n", + " )\n", + " target_name=\"y\"\n", + " )\n", + " ),\n", + " Discard (\n", + " area_name\n", + " date\n", + " genre_name\n", + " latitude\n", + " longitude\n", + " store_id\n", + " ),\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " LinearRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " )\n", + " loss=Squared ()\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + ")" ] }, "execution_count": 11, @@ -782,7 +777,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/docs/faq/index.md b/docs/faq/index.md index 9c6a08f2b9..7218ed5149 100644 --- a/docs/faq/index.md +++ b/docs/faq/index.md @@ -29,7 +29,7 @@ Python encourages a coding style called [EAFP](https://docs.python.org/2/glossar Reinforcement learning works in an online manner because of the nature of the task. Reinforcement learning can be therefore be seen as a subcase of online machine learning. However, we prefer not to support it because there are already many existing opensource libraries dedicated to it. -## What are the differences between scikit-learn's online learning algorithm which have a partial_fit method and their equivalents in river? +## What are the differences between scikit-learn's online learning algorithm which have a partial_fit method and their equivalents in River? The algorithms from `sklearn` that support incremental learning are mostly meant for mini-batch learning. In a pure streaming context where the observations arrive one by one, then River is much faster than `sklearn`. This is mostly because `sklearn` incurs a lot of overhead by performing data checks. Also, sklearn assumes that you're always using the same number of features. This is not the case with River because it use dictionaries which allows you to drop and add features as you wish. diff --git a/docs/introduction/basic-concepts.md b/docs/introduction/basic-concepts.md index 025399d3c9..669ab4a7d6 100644 --- a/docs/introduction/basic-concepts.md +++ b/docs/introduction/basic-concepts.md @@ -22,13 +22,13 @@ The challenge for machine learning is to ensure models you train offline on proa ## Online processing -Online processing is the act of processing a data stream one element at a time. In the case of machine learning, that means training a model by teaching it one sample at a time. This is completely opposite to the traditional way of doing machine learning, which is to train a model on a whole batch data at a time. +Online processing is the act of processing a data stream one element at a time. In the case of machine learning, that means training a model by teaching it one sample at a time. This is completely opposite to the traditional way of doing machine learning, which is to train a model on whole batches of data at a time. An online model is therefore a stateful, dynamic object. It keeps learning and doesn't have to revisit past data. It's a different way of doing things, and therefore has its own set of pros and cons. ## Tasks -Machine learning encompasses many different tasks: classification, regression, anomaly detection, time series forecasting, etc. The ideology behind River is to be a generic machine learning which allows to perform these tasks in a streaming manner. Indeed, many batch machine learning algorithms have online equivalents. +Machine learning encompasses many different tasks: classification, regression, anomaly detection, time series forecasting, etc. The ideology behind River is to be a generic machine learning approach which allows these tasks to be performed in a streaming manner. Indeed, many batch machine learning algorithms have online equivalents. Note that River also supports some more basic tasks. For instance, you might just want to calculate a running average of a data stream. These are usually smaller parts of a whole stream processing pipeline. @@ -36,13 +36,13 @@ Note that River also supports some more basic tasks. For instance, you might jus River is a Python library. It is composed of a bunch of classes which implement various online processing algorithms. Most of these classes are machine learning models which can process a single sample, be it for learning or for inference. -We made the choice to use dictionaries as the basic building block. First of all, online processing is different to batch processing, in that vectorization doesn't bring any speedup. Therefore numeric processing libraries such as numpy and PyTorch actually bring too much overhead. Using native Python data structures is faster. +We made the choice to use dictionaries as the basic building block. First of all, online processing is different to batch processing, in that vectorization doesn't bring any speed-up. Therefore numeric processing libraries such as NumPy and PyTorch actually bring too much overhead. Using native Python data structures is faster. -Dictionaries are therefore a perfect fit. They're native to Python and have excellent support in the standard library. They allow naming each feature. They can hold any kind of data type. They allow transparent support of JSON payloads, allowing seemless integration with web apps. +Dictionaries are therefore a perfect fit. They're native to Python and have excellent support in the standard library. They allow the naming of each feature. They can hold any kind of data type. They allow transparent support of JSON payloads, allowing seamless integration with web apps. ## Datasets -In production, you're almost always going to face data streams which you have to react to. Such as users visiting your website. The advantage of online machine learning is that you can design models which make predictions as well as learn from this data stream as it flows. +In production, you're almost always going to face data streams which you have to react to, such as users visiting your website. The advantage of online machine learning is that you can design models that make predictions as well as learn from this data stream as it flows. But of course, when you're developping a model, you don't usually have access to a real-time feed on which to evaluate your model. You usually have an offline dataset which you want to evaluate your model on. River provides some datasets which can be read in online manner, one sample at a time. It is however crucial to keep in mind that the goal is to reproduce a production scenario as closely as possible, in order to ensure your model will perform just as well in production. @@ -58,4 +58,4 @@ This is what makes online machine learning powerful. By replaying datasets in th The main reason why an offline model might not perform as expected in production is because of concept drift. But this is true for all machine learning models, be they offline or online. -The advantage of online models over offline models is that they can cope with drift. Indeed, because they can keep learning, they usually adapt to concept drift in a seemless manner. As opposed to batch models which have to be retrained from scratch. +The advantage of online models over offline models is that they can cope with drift. Indeed, because they can keep learning, they usually adapt to concept drift in a seamless manner. As opposed to batch models which have to be retrained from scratch. diff --git a/docs/introduction/getting-started/binary-classification.ipynb b/docs/introduction/getting-started/binary-classification.ipynb index d58014261a..6867d14186 100644 --- a/docs/introduction/getting-started/binary-classification.ipynb +++ b/docs/introduction/getting-started/binary-classification.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -21,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:13.491348Z", - "iopub.status.busy": "2023-05-05T10:11:13.491157Z", - "iopub.status.idle": "2023-05-05T10:11:14.135580Z", - "shell.execute_reply": "2023-05-05T10:11:14.135257Z" + "iopub.execute_input": "2023-12-04T17:47:13.264755Z", + "iopub.status.busy": "2023-12-04T17:47:13.264536Z", + "iopub.status.idle": "2023-12-04T17:47:13.668821Z", + "shell.execute_reply": "2023-12-04T17:47:13.668532Z" } }, "outputs": [ @@ -56,6 +58,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -67,10 +70,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.150653Z", - "iopub.status.busy": "2023-05-05T10:11:14.150484Z", - "iopub.status.idle": "2023-05-05T10:11:14.166766Z", - "shell.execute_reply": "2023-05-05T10:11:14.166473Z" + "iopub.execute_input": "2023-12-04T17:47:13.683729Z", + "iopub.status.busy": "2023-12-04T17:47:13.683574Z", + "iopub.status.idle": "2023-12-04T17:47:13.695685Z", + "shell.execute_reply": "2023-12-04T17:47:13.695411Z" } }, "outputs": [], @@ -80,6 +83,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -91,10 +95,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.168845Z", - "iopub.status.busy": "2023-05-05T10:11:14.168695Z", - "iopub.status.idle": "2023-05-05T10:11:14.181753Z", - "shell.execute_reply": "2023-05-05T10:11:14.181370Z" + "iopub.execute_input": "2023-12-04T17:47:13.697178Z", + "iopub.status.busy": "2023-12-04T17:47:13.697104Z", + "iopub.status.idle": "2023-12-04T17:47:13.705637Z", + "shell.execute_reply": "2023-12-04T17:47:13.705411Z" }, "tags": [] }, @@ -128,10 +132,10 @@ "execution_count": 4, "metadata": { "execution": { - 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"iopub.execute_input": "2023-05-05T10:11:14.224123Z", - "iopub.status.busy": "2023-05-05T10:11:14.223995Z", - "iopub.status.idle": "2023-05-05T10:11:14.236012Z", - "shell.execute_reply": "2023-05-05T10:11:14.235664Z" + "iopub.execute_input": "2023-12-04T17:47:13.733503Z", + "iopub.status.busy": "2023-12-04T17:47:13.733414Z", + "iopub.status.idle": "2023-12-04T17:47:13.741526Z", + "shell.execute_reply": "2023-12-04T17:47:13.741290Z" } }, "outputs": [], "source": [ - "model = model.learn_one(x, y)" + "model.learn_one(x, y)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -224,10 +231,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.238006Z", - "iopub.status.busy": "2023-05-05T10:11:14.237847Z", - "iopub.status.idle": "2023-05-05T10:11:14.251868Z", - "shell.execute_reply": "2023-05-05T10:11:14.251490Z" + "iopub.execute_input": "2023-12-04T17:47:13.742950Z", + "iopub.status.busy": "2023-12-04T17:47:13.742870Z", + "iopub.status.idle": "2023-12-04T17:47:13.751313Z", + "shell.execute_reply": "2023-12-04T17:47:13.751031Z" } }, "outputs": [ @@ -247,6 +254,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -258,10 +266,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.253862Z", - "iopub.status.busy": "2023-05-05T10:11:14.253717Z", - "iopub.status.idle": "2023-05-05T10:11:14.266336Z", - "shell.execute_reply": "2023-05-05T10:11:14.265953Z" + "iopub.execute_input": "2023-12-04T17:47:13.753009Z", + "iopub.status.busy": "2023-12-04T17:47:13.752897Z", + "iopub.status.idle": "2023-12-04T17:47:13.761857Z", + "shell.execute_reply": "2023-12-04T17:47:13.761633Z" } }, "outputs": [ @@ -281,6 +289,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -292,10 +301,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.268352Z", - "iopub.status.busy": "2023-05-05T10:11:14.268210Z", - "iopub.status.idle": "2023-05-05T10:11:14.315175Z", - "shell.execute_reply": "2023-05-05T10:11:14.314795Z" + "iopub.execute_input": "2023-12-04T17:47:13.763372Z", + "iopub.status.busy": "2023-12-04T17:47:13.763296Z", + "iopub.status.idle": "2023-12-04T17:47:13.794339Z", + "shell.execute_reply": "2023-12-04T17:47:13.794075Z" }, "tags": [] }, @@ -327,6 +336,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -338,10 +348,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.317346Z", - "iopub.status.busy": "2023-05-05T10:11:14.317194Z", - "iopub.status.idle": "2023-05-05T10:11:14.365770Z", - "shell.execute_reply": "2023-05-05T10:11:14.365345Z" + "iopub.execute_input": "2023-12-04T17:47:13.795893Z", + "iopub.status.busy": "2023-12-04T17:47:13.795799Z", + "iopub.status.idle": "2023-12-04T17:47:13.827938Z", + "shell.execute_reply": "2023-12-04T17:47:13.827710Z" } }, "outputs": [ @@ -366,6 +376,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -377,10 +388,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.367819Z", - "iopub.status.busy": "2023-05-05T10:11:14.367651Z", - "iopub.status.idle": "2023-05-05T10:11:14.389332Z", - "shell.execute_reply": "2023-05-05T10:11:14.389052Z" + "iopub.execute_input": "2023-12-04T17:47:13.829351Z", + "iopub.status.busy": "2023-12-04T17:47:13.829280Z", + "iopub.status.idle": "2023-12-04T17:47:13.843109Z", + "shell.execute_reply": "2023-12-04T17:47:13.842876Z" }, "tags": [] }, @@ -415,6 +426,7 @@ " padding: 1em;\n", " border-style: solid;\n", " background: white;\n", + " max-width: max-content;\n", "}\n", "\n", ".river-pipeline {\n", @@ -486,6 +498,10 @@ " background-color: white !important;\n", "}\n", "\n", + ".river-wrapper > .river-details {\n", + " margin-bottom: 1em;\n", + "}\n", + "\n", ".river-estimator-name {\n", " display: inline;\n", " margin: 0;\n", @@ -554,10 +570,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-05-05T10:11:14.391330Z", - "iopub.status.busy": "2023-05-05T10:11:14.391065Z", - "iopub.status.idle": "2023-05-05T10:11:14.456114Z", - "shell.execute_reply": "2023-05-05T10:11:14.455726Z" + "iopub.execute_input": "2023-12-04T17:47:13.844434Z", + "iopub.status.busy": "2023-12-04T17:47:13.844357Z", + "iopub.status.idle": "2023-12-04T17:47:13.889821Z", + "shell.execute_reply": "2023-12-04T17:47:13.889600Z" }, "tags": [] }, @@ -565,7 +581,7 @@ { "data": { "text/plain": [ - "ROCAUC: 95.04%" + "ROCAUC: 95.07%" ] }, "execution_count": 12, @@ -577,13 +593,6 @@ "metric = metrics.ROCAUC()\n", "evaluate.progressive_val_score(dataset, model, metric)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That concludes the getting started introduction to binary classification! You can now move on to the [next steps](/introduction/next-steps)." - ] } ], "metadata": { @@ -602,7 +611,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" }, "vscode": { "interpreter": { diff --git a/docs/introduction/getting-started/concept-drift-detection.ipynb b/docs/introduction/getting-started/concept-drift-detection.ipynb index f70483d842..922936ec1e 100644 --- a/docs/introduction/getting-started/concept-drift-detection.ipynb +++ b/docs/introduction/getting-started/concept-drift-detection.ipynb @@ -20,9 +20,9 @@ "\n", "Concept drifts might happen in the electricity demand across the year, in the stock market, in buying preferences, and in the likelihood of a new movie's success, among others.\n", "\n", - "Let us consider the movie example: two movies made at different epochs can have similar features such as famous actors/directors, storyline, production budget, marketing campaigns, etc., yet it is not certain that both will be similarly successful. What the target audience *considers* is worth watching (and their money) is constantly changing, and production companies must adapt accordingly to avoid \"box office flops\".\n", + "Let us consider the movie example: two movies made at different epochs can have similar features such as famous actors/directors, storyline, production budget, marketing campaigns, etc., yet it is not certain that both will be similarly successful. What the target audience *considers* is worth watching (and their money worth spending) is constantly changing, and production companies must adapt accordingly to avoid \"box office flops\".\n", "\n", - "Prior to the pandemics, the usage of hand sanitizers and facial masks was not widespread. When the cases of COVID-19 started increasing, there was a lack of such products for the final consumer. Imagine a batch-learning model deciding how much of each product a supermarket should stock during those times. What a mess!\n", + "Prior to the pandemic, the usage of hand sanitizers and facial masks was not widespread. When the cases of COVID-19 started increasing, there was a lack of such products for the end consumer. Imagine a batch-learning model deciding how much of each product a supermarket should stock during those times. What a mess!\n", "\n", "## Impact of drift on learning\n", "\n", @@ -45,10 +45,10 @@ "metadata": { "collapsed": false, "execution": { - "iopub.execute_input": "2022-05-28T14:07:39.576810Z", - "iopub.status.busy": "2022-05-28T14:07:39.576050Z", - "iopub.status.idle": "2022-05-28T14:07:40.450245Z", - "shell.execute_reply": "2022-05-28T14:07:40.450673Z" + "iopub.execute_input": "2023-12-04T17:47:15.186479Z", + "iopub.status.busy": "2023-12-04T17:47:15.185478Z", + "iopub.status.idle": "2023-12-04T17:47:15.510637Z", + "shell.execute_reply": "2023-12-04T17:47:15.510311Z" }, "jupyter": { "outputs_hidden": false @@ -61,14 +61,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -211,7 +207,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.0" }, "vscode": { "interpreter": { diff --git a/docs/introduction/getting-started/concept-drift-detection_files/concept-drift-detection_1_0.png b/docs/introduction/getting-started/concept-drift-detection_files/concept-drift-detection_1_0.png index 5e18095256..89c8061930 100644 Binary files a/docs/introduction/getting-started/concept-drift-detection_files/concept-drift-detection_1_0.png and b/docs/introduction/getting-started/concept-drift-detection_files/concept-drift-detection_1_0.png differ diff --git a/docs/introduction/getting-started/concept-drift-detection_files/concept-drift-detection_3_1.png b/docs/introduction/getting-started/concept-drift-detection_files/concept-drift-detection_3_1.png index 9f918ca70d..7e8897c146 100644 Binary files a/docs/introduction/getting-started/concept-drift-detection_files/concept-drift-detection_3_1.png and b/docs/introduction/getting-started/concept-drift-detection_files/concept-drift-detection_3_1.png differ diff --git a/docs/introduction/getting-started/multiclass-classification.ipynb b/docs/introduction/getting-started/multiclass-classification.ipynb index c8ab0d7f83..19415ff54f 100644 --- a/docs/introduction/getting-started/multiclass-classification.ipynb +++ b/docs/introduction/getting-started/multiclass-classification.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -19,7 +21,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:17.633774Z", + "iopub.status.busy": "2023-12-04T17:47:17.633578Z", + "iopub.status.idle": "2023-12-04T17:47:18.059839Z", + "shell.execute_reply": "2023-12-04T17:47:18.059524Z" + } + }, "outputs": [ { "data": { @@ -29,12 +38,13 @@ "This dataset contains features that describe image segments into 7 classes: brickface, sky,\n", "foliage, cement, window, path, and grass.\n", "\n", - " Name ImageSegments \n", - " Task Multi-class classification \n", - " Samples 2,310 \n", - "Features 18 \n", - " Sparse False \n", - " Path /Users/max.halford/projects/river/river/datasets/segment.csv.zip" + " Name ImageSegments \n", + " Task Multi-class classification \n", + " Samples 2,310 \n", + "Features 18 \n", + " Classes 7 \n", + " Sparse False \n", + " Path /Users/max/projects/online-ml/river/river/datasets/segment.csv.zip" ] }, "execution_count": 1, @@ -50,6 +60,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -59,7 +70,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:18.062123Z", + "iopub.status.busy": "2023-12-04T17:47:18.061924Z", + "iopub.status.idle": "2023-12-04T17:47:18.085783Z", + "shell.execute_reply": "2023-12-04T17:47:18.085481Z" + } + }, "outputs": [], "source": [ "for x, y in dataset:\n", @@ -67,6 +85,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -76,7 +95,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:18.087624Z", + "iopub.status.busy": "2023-12-04T17:47:18.087509Z", + "iopub.status.idle": "2023-12-04T17:47:18.098210Z", + "shell.execute_reply": "2023-12-04T17:47:18.097952Z" + } + }, "outputs": [ { "data": { @@ -114,7 +140,14 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:18.099585Z", + "iopub.status.busy": "2023-12-04T17:47:18.099501Z", + "iopub.status.idle": "2023-12-04T17:47:18.108487Z", + "shell.execute_reply": "2023-12-04T17:47:18.108239Z" + } + }, "outputs": [ { "data": { @@ -132,6 +165,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -140,8 +174,15 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:18.109928Z", + "iopub.status.busy": "2023-12-04T17:47:18.109851Z", + "iopub.status.idle": "2023-12-04T17:47:18.140712Z", + "shell.execute_reply": "2023-12-04T17:47:18.140335Z" + } + }, "outputs": [ { "data": { @@ -149,7 +190,7 @@ "{}" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -162,6 +203,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -172,8 +214,15 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:18.142820Z", + "iopub.status.busy": "2023-12-04T17:47:18.142689Z", + "iopub.status.idle": "2023-12-04T17:47:18.152525Z", + "shell.execute_reply": "2023-12-04T17:47:18.152285Z" + } + }, "outputs": [ { "name": "stdout", @@ -188,6 +237,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -196,8 +246,15 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:18.153876Z", + "iopub.status.busy": "2023-12-04T17:47:18.153798Z", + "iopub.status.idle": "2023-12-04T17:47:18.165027Z", + "shell.execute_reply": "2023-12-04T17:47:18.164772Z" + } + }, "outputs": [ { "data": { @@ -205,7 +262,7 @@ "{'path': 1.0}" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -216,6 +273,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -226,30 +284,37 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:18.166512Z", + "iopub.status.busy": "2023-12-04T17:47:18.166430Z", + "iopub.status.idle": "2023-12-04T17:47:18.610839Z", + "shell.execute_reply": "2023-12-04T17:47:18.610498Z" + } + }, "outputs": [ { "data": { "text/plain": [ " Precision Recall F1 Support \n", " \n", - "brickface 77.13% 84.85% 80.81% 33 \n", - " cement 78.92% 83.94% 81.35% 33 \n", - " foliage 67.00% 20.30% 31.16% 33 \n", - " grass 100.00% 96.97% 98.46% 33 \n", - " path 90.39% 91.49% 90.94% 329 \n", - " sky 99.08% 98.18% 98.63% 33 \n", - " window 43.50% 67.88% 53.02% 33 \n", + "brickface 77.13% 84.85% 80.81% 330 \n", + " cement 78.92% 83.94% 81.35% 330 \n", + " foliage 65.69% 20.30% 31.02% 330 \n", + " grass 100.00% 96.97% 98.46% 330 \n", + " path 90.63% 91.19% 90.91% 329 \n", + " sky 99.08% 98.18% 98.63% 330 \n", + " window 43.50% 67.88% 53.02% 330 \n", " \n", - " Macro 79.43% 77.66% 76.34% \n", - " Micro 77.65% 77.65% 77.65% \n", - " Weighted 79.43% 77.65% 76.33% \n", + " Macro 79.28% 77.62% 76.31% \n", + " Micro 77.61% 77.61% 77.61% \n", + " Weighted 79.27% 77.61% 76.31% \n", "\n", - " 77.65% accuracy " + " 77.61% accuracy " ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -271,6 +336,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -279,30 +345,37 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:18.613061Z", + "iopub.status.busy": "2023-12-04T17:47:18.612927Z", + "iopub.status.idle": "2023-12-04T17:47:19.085072Z", + "shell.execute_reply": "2023-12-04T17:47:19.084781Z" + } + }, "outputs": [ { "data": { "text/plain": [ " Precision Recall F1 Support \n", " \n", - "brickface 77.13% 84.85% 80.81% 33 \n", - " cement 78.92% 83.94% 81.35% 33 \n", - " foliage 67.00% 20.30% 31.16% 33 \n", - " grass 100.00% 96.97% 98.46% 33 \n", - " path 90.39% 91.49% 90.94% 329 \n", - " sky 99.08% 98.18% 98.63% 33 \n", - " window 43.50% 67.88% 53.02% 33 \n", + "brickface 77.13% 84.85% 80.81% 330 \n", + " cement 78.92% 83.94% 81.35% 330 \n", + " foliage 65.69% 20.30% 31.02% 330 \n", + " grass 100.00% 96.97% 98.46% 330 \n", + " path 90.63% 91.19% 90.91% 329 \n", + " sky 99.08% 98.18% 98.63% 330 \n", + " window 43.50% 67.88% 53.02% 330 \n", " \n", - " Macro 79.43% 77.66% 76.34% \n", - " Micro 77.65% 77.65% 77.65% \n", - " Weighted 79.43% 77.65% 76.33% \n", + " Macro 79.28% 77.62% 76.31% \n", + " Micro 77.61% 77.61% 77.61% \n", + " Weighted 79.27% 77.61% 76.31% \n", "\n", - " 77.65% accuracy " + " 77.61% accuracy " ] }, - "execution_count": 15, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -315,13 +388,6 @@ "\n", "evaluate.progressive_val_score(dataset, model, metric)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That concludes the getting started introduction to multiclass classification! You can now move on to the [next steps](/introduction/next-steps)." - ] } ], "metadata": { @@ -343,9 +409,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "orig_nbformat": 4 + "version": "3.11.0" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/docs/introduction/getting-started/regression.ipynb b/docs/introduction/getting-started/regression.ipynb index ae5d4abc06..673e3e2d2b 100644 --- a/docs/introduction/getting-started/regression.ipynb +++ b/docs/introduction/getting-started/regression.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -8,6 +9,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -17,7 +19,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:20.479638Z", + "iopub.status.busy": "2023-12-04T17:47:20.479153Z", + "iopub.status.idle": "2023-12-04T17:47:20.897979Z", + "shell.execute_reply": "2023-12-04T17:47:20.897633Z" + } + }, "outputs": [ { "data": { @@ -29,12 +38,12 @@ "5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of\n", "this task is to see if we can reproduce FiveThirtyEight's model.\n", "\n", - " Name TrumpApproval \n", - " Task Regression \n", - " Samples 1,001 \n", - "Features 6 \n", - " Sparse False \n", - " Path /Users/max.halford/projects/river/river/datasets/trump_approval.csv.gz" + " Name TrumpApproval \n", + " Task Regression \n", + " Samples 1,001 \n", + "Features 6 \n", + " Sparse False \n", + " Path /Users/max/projects/online-ml/river/river/datasets/trump_approval.csv.gz" ] }, "execution_count": 1, @@ -50,6 +59,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -59,7 +69,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:20.899687Z", + "iopub.status.busy": "2023-12-04T17:47:20.899562Z", + "iopub.status.idle": "2023-12-04T17:47:20.910834Z", + "shell.execute_reply": "2023-12-04T17:47:20.910553Z" + } + }, "outputs": [], "source": [ "for x, y in dataset:\n", @@ -67,6 +84,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -76,7 +94,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:20.912303Z", + "iopub.status.busy": "2023-12-04T17:47:20.912228Z", + "iopub.status.idle": "2023-12-04T17:47:20.921120Z", + "shell.execute_reply": "2023-12-04T17:47:20.920869Z" + } + }, "outputs": [ { "data": { @@ -100,6 +125,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -108,8 +134,15 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:20.922589Z", + "iopub.status.busy": "2023-12-04T17:47:20.922495Z", + "iopub.status.idle": "2023-12-04T17:47:20.934349Z", + "shell.execute_reply": "2023-12-04T17:47:20.933989Z" + } + }, "outputs": [ { "data": { @@ -117,7 +150,7 @@ "0.0" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -130,6 +163,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -140,14 +174,22 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:20.935928Z", + "iopub.status.busy": "2023-12-04T17:47:20.935816Z", + "iopub.status.idle": "2023-12-04T17:47:20.944466Z", + "shell.execute_reply": "2023-12-04T17:47:20.944074Z" + } + }, "outputs": [], "source": [ - "model = model.learn_one(x, y)" + "model.learn_one(x, y)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -156,8 +198,15 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:20.945980Z", + "iopub.status.busy": "2023-12-04T17:47:20.945898Z", + "iopub.status.idle": "2023-12-04T17:47:20.954845Z", + "shell.execute_reply": "2023-12-04T17:47:20.954612Z" + } + }, "outputs": [ { "data": { @@ -165,7 +214,7 @@ "43.75505" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -175,6 +224,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -183,16 +233,23 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:20.956655Z", + "iopub.status.busy": "2023-12-04T17:47:20.956493Z", + "iopub.status.idle": "2023-12-04T17:47:22.658069Z", + "shell.execute_reply": "2023-12-04T17:47:22.657771Z" + } + }, "outputs": [ { "data": { "text/plain": [ - "MAE: 0.31039" + "MAE: 0.310353" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -213,6 +270,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -221,16 +279,23 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:22.659592Z", + "iopub.status.busy": "2023-12-04T17:47:22.659489Z", + "iopub.status.idle": "2023-12-04T17:47:24.299285Z", + "shell.execute_reply": "2023-12-04T17:47:24.299054Z" + } + }, "outputs": [ { "data": { "text/plain": [ - "MAE: 0.31039" + "MAE: 0.310353" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -243,13 +308,6 @@ "\n", "evaluate.progressive_val_score(dataset, model, metric)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That concludes the getting started introduction to regression! You can now move on to the [next steps](/introduction/next-steps)." - ] } ], "metadata": { @@ -271,9 +329,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "orig_nbformat": 4 + "version": "3.11.0" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/docs/introduction/installation.md b/docs/introduction/installation.md index 84310696f4..a6ee6adc3c 100644 --- a/docs/introduction/installation.md +++ b/docs/introduction/installation.md @@ -10,12 +10,9 @@ You can install the latest development version from GitHub, as so: ```sh pip install git+https://github.com/online-ml/river --upgrade +pip install git+ssh://git@github.com/online-ml/river.git --upgrade # using SSH ``` -Or, through SSH: - -```sh -pip install git+ssh://git@github.com/online-ml/river.git --upgrade -``` +This method requires having Cython and Rust installed on your machine. Feel welcome to [open an issue on GitHub](https://github.com/online-ml/river/issues/new) if you are having any trouble. diff --git a/docs/introduction/why-use-river.md b/docs/introduction/why-use-river.md index af110b006f..9c5f2744d1 100644 --- a/docs/introduction/why-use-river.md +++ b/docs/introduction/why-use-river.md @@ -10,8 +10,8 @@ In the streaming setting, data can evolve. Adaptive methods are specifically des ## General purpose -River supports different machine learning tasks, including regression, classification, and unsupervised learning. It can also be used for adhoc tasks, such as computing online metrics, as well as concept drift detection. +River supports different machine learning tasks, including regression, classification, and unsupervised learning. It can also be used for ad hoc tasks, such as computing online metrics, as well as concept drift detection. ## User experience -River is not the only library allowing you to do online machine learning. But it might just the simplest one to use in the Python ecosystem. River plays nicely with Python dictionaries, therefore making it easy to use in the context of web applications where JSON payloads are aplenty. +River is not the only library allowing you to do online machine learning. But it might just be the simplest one to use in the Python ecosystem. River plays nicely with Python dictionaries, therefore making it easy to use in the context of web applications where JSON payloads are aplenty. diff --git a/docs/license/.pages b/docs/license/.pages new file mode 100644 index 0000000000..319cebe79a --- /dev/null +++ b/docs/license/.pages @@ -0,0 +1,2 @@ +title: License 📝 + diff --git a/docs/license/license.md b/docs/license/license.md new file mode 100644 index 0000000000..87980bb6fe --- /dev/null +++ b/docs/license/license.md @@ -0,0 +1,3 @@ +# License + +River is free and open-source software licensed under the [3-clause BSD license](https://github.com/online-ml/river/blob/main/LICENSE). \ No newline at end of file diff --git a/docs/overrides/partials/integrations/analytics.html b/docs/overrides/partials/integrations/analytics.html index 9d6104fafd..f0acec71cd 100644 --- a/docs/overrides/partials/integrations/analytics.html +++ b/docs/overrides/partials/integrations/analytics.html @@ -1,5 +1,5 @@ - diff --git a/docs/parse/__main__.py b/docs/parse/__main__.py index d4021ba01e..6615af5d87 100644 --- a/docs/parse/__main__.py +++ b/docs/parse/__main__.py @@ -62,7 +62,7 @@ def snake_to_kebab(text: str) -> str: return text.replace("_", "-") -def find_method_docstring(klass, method: str) -> str | None: +def find_method_docstring(klass, method: str) -> str: """Look through a class' ancestors for the first non-empty method docstring. Since Python 3.5, inspect.getdoc is supposed to do exactly this. However, it doesn't seem to @@ -95,7 +95,7 @@ def find_method_docstring(klass, method: str) -> str | None: return doc -def find_method_signature(klass, method: str) -> inspect.Signature | None: +def find_method_signature(klass, method: str) -> inspect.Signature: """Look through a class' ancestors and fill out the methods signature. A class method has a signature. But it might now always be complete. When a parameter is not diff --git a/docs/recipes/active-learning.ipynb b/docs/recipes/active-learning.ipynb index 5bab814088..15d39fa328 100644 --- a/docs/recipes/active-learning.ipynb +++ b/docs/recipes/active-learning.ipynb @@ -51,7 +51,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:26.242700Z", + "iopub.status.busy": "2023-12-04T17:47:26.242281Z", + "iopub.status.idle": "2023-12-04T17:47:27.079931Z", + "shell.execute_reply": "2023-12-04T17:47:27.079482Z" + } + }, "outputs": [ { "data": { @@ -100,14 +107,21 @@ }, { "cell_type": "code", - "execution_count": 71, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:27.094899Z", + "iopub.status.busy": "2023-12-04T17:47:27.094694Z", + "iopub.status.idle": "2023-12-04T17:47:27.105156Z", + "shell.execute_reply": "2023-12-04T17:47:27.104823Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1922 / 5574 = 34.48%\n" + "1921 / 5574 = 34.46%\n" ] } ], @@ -125,19 +139,26 @@ }, { "cell_type": "code", - "execution_count": 72, - "metadata": {}, + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-04T17:47:27.106972Z", + "iopub.status.busy": "2023-12-04T17:47:27.106834Z", + "iopub.status.idle": "2023-12-04T17:47:27.553244Z", + "shell.execute_reply": "2023-12-04T17:47:27.552984Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1,000] Accuracy: 86.32% – 661 samples used\n", - "[2,000] Accuracy: 86.44% – 1,057 samples used\n", - "[3,000] Accuracy: 86.52% – 1,339 samples used\n", - "[4,000] Accuracy: 86.62% – 1,569 samples used\n", - "[5,000] Accuracy: 86.57% – 1,791 samples used\n", - "[5,574] Accuracy: 86.60% – 1,922 samples used\n" + "[1,000] Accuracy: 84.80% – 661 samples used\n", + "[2,000] Accuracy: 86.00% – 1,057 samples used\n", + "[3,000] Accuracy: 86.37% – 1,339 samples used\n", + "[4,000] Accuracy: 86.65% – 1,568 samples used\n", + "[5,000] Accuracy: 86.54% – 1,790 samples used\n", + "[5,574] Accuracy: 86.60% – 1,921 samples used\n" ] }, { @@ -146,7 +167,7 @@ "Accuracy: 86.60%" ] }, - "execution_count": 72, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -221,9 +242,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.0" }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "14b46bd212fa4dd89e3980db6ba7efbb9fe535833e1e483b914b71733e0a56d2" diff --git a/docs/recipes/bandits-101.ipynb b/docs/recipes/bandits-101.ipynb index 0ba41e3084..4d36c117f5 100644 --- a/docs/recipes/bandits-101.ipynb +++ b/docs/recipes/bandits-101.ipynb @@ -23,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:01:56.055080Z", - "iopub.status.busy": "2022-10-26T11:01:56.054056Z", - "iopub.status.idle": "2022-10-26T11:01:56.177551Z", - "shell.execute_reply": "2022-10-26T11:01:56.177957Z" + "iopub.execute_input": "2023-12-04T17:47:28.911688Z", + "iopub.status.busy": "2023-12-04T17:47:28.909441Z", + "iopub.status.idle": "2023-12-04T17:47:28.986844Z", + "shell.execute_reply": "2023-12-04T17:47:28.986570Z" } }, "outputs": [], @@ -35,7 +35,7 @@ "\n", "for k in gym.envs.registry:\n", " if k.startswith('river_bandits'):\n", - " print(k)" + " print(k)\n" ] }, { @@ -48,13 +48,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:01:56.183214Z", - "iopub.status.busy": "2022-10-26T11:01:56.182610Z", - "iopub.status.idle": "2022-10-26T11:01:56.562107Z", - "shell.execute_reply": "2022-10-26T11:01:56.562518Z" + "iopub.execute_input": "2023-12-04T17:47:28.988524Z", + "iopub.status.busy": "2023-12-04T17:47:28.988444Z", + "iopub.status.idle": "2023-12-04T17:47:29.404060Z", + "shell.execute_reply": "2023-12-04T17:47:29.403703Z" } }, "outputs": [], @@ -81,7 +81,7 @@ " env=env,\n", " reward_stat=stats.Mean(),\n", " n_episodes=(n_episodes := 2000),\n", - ")" + ")\n" ] }, { @@ -94,13 +94,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:01:56.566620Z", - "iopub.status.busy": "2022-10-26T11:01:56.565967Z", - "iopub.status.idle": "2022-10-26T11:03:32.171796Z", - "shell.execute_reply": "2022-10-26T11:03:32.172475Z" + "iopub.execute_input": "2023-12-04T17:47:29.405926Z", + "iopub.status.busy": "2023-12-04T17:47:29.405781Z", + "iopub.status.idle": "2023-12-04T17:47:59.938410Z", + "shell.execute_reply": "2023-12-04T17:47:59.938118Z" } }, "outputs": [ @@ -108,9 +108,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/6000000 [00:00episode\n", " step\n", " policy_idx\n", - " action\n", + " arm\n", " reward\n", " reward_stat\n", " \n", @@ -148,60 +148,60 @@ " 441\n", " 632\n", " 0\n", - " 4\n", - " 1.215703\n", - " 1.798879\n", + " 2\n", + " 0.226086\n", + " 0.499848\n", " \n", " \n", " 3566176\n", " 1188\n", " 725\n", " 1\n", - " 8\n", - " -0.427939\n", - " 0.757612\n", + " 6\n", + " 2.363962\n", + " 0.935468\n", " \n", " \n", " 1109043\n", " 369\n", " 681\n", " 0\n", - " 7\n", - " 0.256075\n", - " 0.908808\n", + " 5\n", + " 2.780757\n", + " 1.467402\n", " \n", " \n", " 4286042\n", " 1428\n", " 680\n", " 2\n", - " 2\n", - " 2.794259\n", - " 1.435460\n", + " 1\n", + " 2.039255\n", + " 1.603312\n", " \n", " \n", " 5395174\n", " 1798\n", " 391\n", " 1\n", - " 1\n", - " -0.206970\n", - " 0.709420\n", + " 8\n", + " 1.625523\n", + " 1.232745\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " episode step policy_idx action reward reward_stat\n", - "1324896 441 632 0 4 1.215703 1.798879\n", - "3566176 1188 725 1 8 -0.427939 0.757612\n", - "1109043 369 681 0 7 0.256075 0.908808\n", - "4286042 1428 680 2 2 2.794259 1.435460\n", - "5395174 1798 391 1 1 -0.206970 0.709420" + " episode step policy_idx arm reward reward_stat\n", + "1324896 441 632 0 2 0.226086 0.499848\n", + "3566176 1188 725 1 6 2.363962 0.935468\n", + "1109043 369 681 0 5 2.780757 1.467402\n", + "4286042 1428 680 2 1 2.039255 1.603312\n", + "5395174 1798 391 1 8 1.625523 1.232745" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -214,7 +214,7 @@ " env._max_episode_steps\n", " )\n", "))\n", - "trace_df.sample(5, random_state=42)" + "trace_df.sample(5, random_state=42)\n" ] }, { @@ -227,13 +227,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:03:32.178419Z", - "iopub.status.busy": "2022-10-26T11:03:32.177746Z", - "iopub.status.idle": "2022-10-26T11:03:34.135656Z", - "shell.execute_reply": "2022-10-26T11:03:34.136110Z" + "iopub.execute_input": "2023-12-04T17:47:59.940573Z", + "iopub.status.busy": "2023-12-04T17:47:59.940435Z", + "iopub.status.idle": "2023-12-04T17:48:00.781440Z", + "shell.execute_reply": "2023-12-04T17:48:00.770622Z" } }, "outputs": [ @@ -243,13 +243,13 @@ "" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -265,20 +265,14 @@ " 2: 'ε = 0 (greedy)'\n", "}\n", "\n", - "colors = {\n", - " 'ε = 0.1': 'tab:blue',\n", - " 'ε = 0.01': 'tab:red',\n", - " 'ε = 0 (greedy)': 'tab:green'\n", - "}\n", - "\n", "(\n", " trace_df\n", " .assign(policy=trace_df.policy_idx.map(policy_names))\n", " .groupby(['step', 'policy'])\n", " ['reward'].mean()\n", " .unstack()\n", - " .plot(color=colors)\n", - ")" + " .plot()\n", + ")\n" ] }, { @@ -303,13 +297,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:03:34.143839Z", - "iopub.status.busy": "2022-10-26T11:03:34.143054Z", - "iopub.status.idle": "2022-10-26T11:05:04.817389Z", - "shell.execute_reply": "2022-10-26T11:05:04.817907Z" + "iopub.execute_input": "2023-12-04T17:48:00.839669Z", + "iopub.status.busy": "2023-12-04T17:48:00.837348Z", + "iopub.status.idle": "2023-12-04T17:48:29.796813Z", + "shell.execute_reply": "2023-12-04T17:48:29.796482Z" } }, "outputs": [ @@ -317,9 +311,9 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/6000000 [00:00" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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-       "Higgs dataset.\n",
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-       "      Name  Higgs                                                                       \n",
-       "      Task  Binary classification                                                       \n",
-       "   Samples  11,000,000                                                                  \n",
-       "  Features  28                                                                          \n",
-       "    Sparse  False                                                                       \n",
-       "      Path  /Users/max.halford/river_data/Higgs/HIGGS.csv.gz                            \n",
-       "       URL  https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\n",
-       "      Size  2.62 GB                                                                     \n",
-       "Downloaded  True                                                                        \n",
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\n" - ], "text/plain": [ - "\n", "Higgs dataset.\n", "\n", - "The data has been produced using Monte Carlo simulations. The first \u001b[1;36m21\u001b[0m features \u001b[1m(\u001b[0mcolumns \u001b[1;36m2\u001b[0m-\u001b[1;36m22\u001b[0m\u001b[1m)\u001b[0m\n", + "The data has been produced using Monte Carlo simulations. The first 21 features (columns 2-22)\n", "are kinematic properties measured by the particle detectors in the accelerator. The last seven\n", - "features are functions of the first \u001b[1;36m21\u001b[0m features; these are high-level features derived by\n", + "features are functions of the first 21 features; these are high-level features derived by\n", "physicists to help discriminate between the two classes.\n", "\n", " Name Higgs \n", " Task Binary classification \n", - " Samples \u001b[1;36m11\u001b[0m,\u001b[1;36m000\u001b[0m,\u001b[1;36m000\u001b[0m \n", - " Features \u001b[1;36m28\u001b[0m \n", - " Sparse \u001b[3;91mFalse\u001b[0m \n", - " Path \u001b[35m/Users/max.halford/river_data/Higgs/\u001b[0m\u001b[95mHIGGS.csv.gz\u001b[0m \n", - " URL \u001b[4;94mhttps://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\u001b[0m\n", - " Size \u001b[1;36m2.62\u001b[0m GB \n", - "Downloaded \u001b[3;92mTrue\u001b[0m \n" + " Samples 11,000,000 \n", + " Features 28 \n", + " Sparse False \n", + " Path /Users/max/river_data/Higgs/HIGGS.csv.gz \n", + " URL https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\n", + " Size 2.62 GB \n", + "Downloaded True " ] }, + "execution_count": 2, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -127,6 +103,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -138,10 +115,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:24:34.941699Z", - "iopub.status.busy": "2022-10-26T11:24:34.941102Z", - "iopub.status.idle": "2022-10-26T11:24:40.222841Z", - "shell.execute_reply": "2022-10-26T11:24:40.221937Z" + "iopub.execute_input": "2023-12-04T17:48:51.239810Z", + "iopub.status.busy": "2023-12-04T17:48:51.239681Z", + "iopub.status.idle": "2023-12-04T17:48:53.685265Z", + "shell.execute_reply": "2023-12-04T17:48:53.683815Z" }, "tags": [] }, @@ -166,6 +143,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -177,6 +155,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -188,312 +167,45 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:24:40.232378Z", - "iopub.status.busy": "2022-10-26T11:24:40.231237Z", - "iopub.status.idle": "2022-10-26T11:24:41.221217Z", - "shell.execute_reply": "2022-10-26T11:24:41.222030Z" + "iopub.execute_input": "2023-12-04T17:48:53.690630Z", + "iopub.status.busy": "2023-12-04T17:48:53.690343Z", + "iopub.status.idle": "2023-12-04T17:48:53.852127Z", + "shell.execute_reply": "2023-12-04T17:48:53.850613Z" }, "tags": [] }, "outputs": [ { - "data": { - "text/html": [ - "
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-       "Phishing websites.\n",
-       "\n",
-       "This dataset contains features from web pages that are classified as phishing or not.\n",
-       "\n",
-       "    Name  Phishing                                                        \n",
-       "    Task  Binary classification                                           \n",
-       " Samples  1,250                                                           \n",
-       "Features  9                                                               \n",
-       "  Sparse  False                                                           \n",
-       "    Path  /Users/max.halford/projects/river/river/datasets/phishing.csv.gz\n",
-       "
\n" - ], "text/plain": [ - "\n", "Phishing websites.\n", "\n", "This dataset contains features from web pages that are classified as phishing or not.\n", "\n", - " Name Phishing \n", - " Task Binary classification \n", - " Samples \u001b[1;36m1\u001b[0m,\u001b[1;36m250\u001b[0m \n", - "Features \u001b[1;36m9\u001b[0m \n", - " Sparse \u001b[3;91mFalse\u001b[0m \n", - " Path \u001b[35m/Users/max.halford/projects/river/river/datasets/\u001b[0m\u001b[95mphishing.csv.gz\u001b[0m\n" + " Name Phishing \n", + " Task Binary classification \n", + " Samples 1,250 \n", + "Features 9 \n", + " Sparse False \n", + " Path /Users/mastelini/Documents/river/river/datasets/phishing.csv.gz" ] }, + "execution_count": 2, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -159,12 +132,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "execution": { - "iopub.execute_input": "2022-10-26T11:24:49.067834Z", - "iopub.status.busy": "2022-10-26T11:24:49.066753Z", - "iopub.status.idle": "2022-10-26T11:24:49.306295Z", - "shell.execute_reply": "2022-10-26T11:24:49.307072Z" - }, "tags": [] }, "outputs": [ @@ -172,60 +139,161 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 174 ms, sys: 5.16 ms, total: 180 ms\n", - "Wall time: 192 ms\n" + "CPU times: user 64.8 ms, sys: 2.75 ms, total: 67.6 ms\n", + "Wall time: 68.1 ms\n" ] }, { "data": { "text/html": [ - "
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" + ], + "text/plain": [ + " parent is_leaf depth \\\n", + "node \n", + "0 False 0 \n", + "1 0 True 1 \n", + "2 0 False 1 \n", + "3 2 True 2 \n", + "4 2 True 2 \n", + "\n", + " stats \\\n", + "node \n", + "0 {True: 260.0, False: 390.0} \n", + "1 {True: 443.4163997711022, False: 59.8769131081... \n", + "2 {True: 71.58360022889781, False: 404.123086891... \n", + "3 {False: 31.426538522574834, True: 33.0} \n", + "4 {False: 250.57346147742516, True: 6.0} \n", + "\n", + " feature \n", + "node \n", + "0 empty_server_form_handler \n", + "1 NaN \n", + "2 popup_window \n", + "3 NaN \n", + "4 NaN " ] }, "execution_count": 5, @@ -421,12 +487,6 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "execution": { - "iopub.execute_input": "2022-10-26T11:24:49.429279Z", - "iopub.status.busy": "2022-10-26T11:24:49.427994Z", - "iopub.status.idle": "2022-10-26T11:24:50.745215Z", - "shell.execute_reply": "2022-10-26T11:24:50.745715Z" - }, "scrolled": true, "tags": [] }, @@ -437,82 +497,85 @@ "\n", "\n", - "\n", "\n", - "\n", + "\n", "\n", - "\n", + "\n", "\n", "\n", "0\n", - "\n", - "empty_server_form_handler\n", + "\n", + "empty_server_form_handler\n", "\n", "\n", "\n", "1\n", - "\n", - "Class True:\n", - "\tP(False) = 0.1\n", - "\tP(True) = 0.9\n", - "samples: 503\n", + "\n", + "Class True:\n", + "\tP(False) = 0.1\n", + "\tP(True) = 0.9\n", + "samples: 503\n", "\n", "\n", "\n", "0->1\n", - "\n", - "\n", - "≤ 0.5455\n", + "\n", + "\n", + "≤ 0.5455\n", "\n", "\n", "\n", "2\n", - "\n", - "popup_window\n", + "\n", + "popup_window\n", "\n", "\n", "\n", "0->2\n", - "\n", - "\n", - "> 0.5455\n", + "\n", + "\n", + "> 0.5455\n", "\n", "\n", "\n", "3\n", - "\n", - "Class True:\n", - "\tP(False) = 0.5\n", - "\tP(True) = 0.5\n", - "samples: 64\n", + "\n", + "Class True:\n", + "\tP(False) = 0.5\n", + "\tP(True) = 0.5\n", + "samples: 64\n", "\n", "\n", "\n", "2->3\n", - "\n", - "\n", - "≤ 0.0909\n", + "\n", + "\n", + "≤ 0.0909\n", "\n", "\n", "\n", "4\n", - "\n", - "Class False:\n", - "\tP(False) = 1.0\n", - "\tP(True) = 0.0\n", - "samples: 256\n", + "\n", + "Class False:\n", + "\tP(False) = 1.0\n", + "\tP(True) = 0.0\n", + "samples: 256\n", "\n", "\n", "\n", "2->4\n", - "\n", - "\n", - "> 0.0909\n", + "\n", + "\n", + "> 0.0909\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" ] }, "execution_count": 6, @@ -537,55 +600,27 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "execution": { - "iopub.execute_input": "2022-10-26T11:24:50.750591Z", - "iopub.status.busy": "2022-10-26T11:24:50.749801Z", - "iopub.status.idle": "2022-10-26T11:24:50.792987Z", - "shell.execute_reply": "2022-10-26T11:24:50.793363Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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-       "        'is_popular': 0.5,\n",
-       "        'long_url': 1.0,\n",
-       "        'age_of_domain': 1,\n",
-       "        'ip_in_url': 1\n",
-       "    },\n",
-       "    True\n",
-       ")\n",
-       "
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empty_server_form_handler ≤ 0.5454545454545454\n",
-       "Class True:\n",
-       "        P(False) = 0.1\n",
-       "        P(True) = 0.9\n",
-       "\n",
-       "
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- }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" @@ -776,48 +773,32 @@ "source": [ "In our example we use the `EBSTSplitter`, which is going to discussed later. For now, is enough to know that it is a mechanism to evaluate split candidates in the trees.\n", "\n", - "As we can see, our tree uses almost 10 MB to keep its structure. Let's say we wanted to limit our memory usage to 5 MB. How could we do that?\n", + "As we can see, our tree uses almost 10 MiB to keep its structure. Let's say we wanted to limit our memory usage to 5 MiB. How could we do that?\n", "\n", "Note that we are using a illustration case here. In real applications, data may be unbounded, so the trees might grow indefinitely.\n", "\n", "HTs expose some parameters related to memory management. The user can refer to the documentation for more details on that matter. Here, we are going to focus on two parameters:\n", "\n", - "- `max_size`: determines the maximum amount of memory (in MB) that the HT can use.\n", + "- `max_size`: determines the maximum amount of memory (in MiB) that the HT can use.\n", "- `memory_estimate_period`: intervals after which the memory-management is triggered.\n", "\n", - "We are going to limit our HTR to 5 MB and perform memory checks at intervals of 500 instances." + "We are going to limit our HTR to 5 MiB and perform memory checks at intervals of 500 instances." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { - "execution": { - "iopub.execute_input": "2022-10-26T11:25:24.678552Z", - "iopub.status.busy": "2022-10-26T11:25:24.677145Z", - "iopub.status.idle": "2022-10-26T11:26:03.562172Z", - "shell.execute_reply": "2022-10-26T11:26:03.562703Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
<Figure size 3000x1500 with 3 Axes>\n",
-       "
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kTZ48ecBzZuR/Pl7s5+3DNeZLg78mAADKjffrhUbiNWH4OA9eBQAGFggEbGWv1zvkP6T7fL4BzwkAAAq1tbXpiiuusG07//zztWjRogGPyx9n88fhg8mvH4vFFIlE5PF4StpOX8f09ztCua4JAIBS2rJli7785S9ny1/84hd15JFHluTcxY6NhmGourraFobraxwu52cC5bomAABKIZlMFoTKr7nmGr3yyiuSUuPSOeeco7POOktTp05VMBjUK6+8ol/+8pfavXt39pgVK1bo0ksv1YMPPthnO4z5hRjzAQCVctVVV+kd73iHrrnmGq1bt05S6kkq+U9TyTV79mzdcMMNuvzyy20zr+cr1+ft4XBYiUSiqLbKNeYPpS0AAMqN9+uFRuI1YfgwczqAouX/8M59dNZgVVdXD3hOAABgl0wm9dGPflQ7d+7Mbqurq9Mdd9xx0GOLHbvzx+2+zlmKdvpqa7BvhIfrmgAAKKXPfOYzCgaDkqQjjzxSX/rSl0p27nKNw6NpvB9KWwAAFKuzs9M2y5ckvfzyy5KkxsZGPf300/r973+vK6+8Uuecc44uvvhi3XrrrdqwYYOWL19uO+6hhx7SL37xiz7bYcwvri0AAErt3e9+t1544QVdd911cjgcA9adPn26rrvuOi1fvnzAYLpUuTH/UNpizAcAgPfrxbTF7whjA+F0AEXr6emxld1u95DPkT8raTgcLqpPAACMdddff73+9Kc/2bb99Kc/1bRp0w56bLFjd1+zifc1dpfzd4RyXRMAAKXys5/9TCtWrJCUmi3kzjvvPKSxsj/lGodH03g/lLYAAChWf3/0dDgc+sMf/qDTTjutz/1+v1+//OUv9d73vte2/Zvf/GZB2F1izC+2LQAASu0nP/mJ5syZo9tuu61gBvJ827dv19VXX62ZM2fqnnvuGbBupcb8Q2mLMR8AAN6vF9MWvyOMDYTTARQt/+6kaDQ65HNEIpEBzwkAAHrdcccd+t73vmfbdsMNN+jiiy8e1PHFjt3543Zf5yxFO3211d/vCOW6JgAASmHPnj267rrrsuVPf/rT/QbUDlW5xuHRNN4PpS0AAIrV3xjz6U9/WieffPKAx5qmqR//+Me2GVQ3bNigp59++qDtMOYPrS0AAEolFovpwx/+sK666irt2bNHkjRu3Dh99atf1Zo1a9Te3q5oNKrdu3fr97//vT74wQ/KMAxJUltbmy677DJdf/31/Z6/UmP+obTFmA8AAO/Xi2mL3xHGBsLpAIrm9/tt5b7upj6Y/LuT8s8JAABS7rvvPl177bW2bZdeeqluvfXWQZ+j2LG7r7uK+xq7y/k7QrmuCQCAUvjsZz+rjo4OSdLEiRP1ne98p+RtlGscHk3j/VDaAgCgWP2NMZdffvmgjp89e7bOPPNM27a+wumM+cW1BQBAqVx11VV68MEHs+VFixZp3bp1uvnmm3XSSSepvr5eLpdLkyZN0gc+8AE99NBDeuSRR2xBqdtuu0333ntvn+ev1Jh/KG0x5gMAwPv1Ytrid4SxgXA6gKLl//AOhUJ9Pl50IMFgcMBzAgAA6bHHHtMnPvEJ2zh7wQUX6O67787OsDIY+eNs/jh8MPn1nU5nn3caF9tOX8cM9o3wcF0TAADFuv/++/Xwww9nyz/4wQ9UX19f8naKHRstyzqkD4mH8zOBcl0TAAClUF1dLYfDYdtWU1Oj448/ftDnOOOMM2zlF198saAOY34hxnwAQLk99dRT+tnPfpYtjx8/Xo899pgmTpw44HHnnnuufvjDH9q2XX/99YOaTGW4Pm/v63eYYj9vH64xfyhtAQBQbrxfLzQSrwnDh3A6gKI1NTXZAnGxWEz79+8f0jl27dplK48fP74kfQMAYKxYuXKlLrzwQsXj8ey2ZcuW6de//nXBB8UHkz/O7ty5c0jH54/bzc3Ng2on/7hDaau/3xHKdU0AABQr9xHdZ599ti666KJhaafYsXHfvn223ztM01RTU1NBvXJ+JlCuawIAoFTyx665c+fKNAf/p7l3vOMdtnJfYyxjfiHGfABAud1xxx228rXXXjvoz5gvvfRSHXHEEdlya2urHnrooYJ6xY6PkrR79+4Bz5mR3/diP28frjFfGvw1AQBQbrxfLzQSrwnDh3A6gKJVV1dr+vTptm3bt28f0jny6x955JFF9wsAgLHi+eef17nnnmt7XNUpp5yihx9+WG63e8jny//j9nCN27Nnz5bT6cyWw+GwDhw4MCxtleuaAAAoVkdHR3b9D3/4gwzDOOhr6dKltnNs27atoM6rr75qq1PqsXHGjBl9PlWknJ8JlOuaAAAolfnz59vKtbW1Qzo+v357e3tBHcb8g7fDmA8AGE6WZemvf/2rbdsHPvCBQR9vmqbOPvts27ZVq1YV1Ct2fNy/f7/tbwxut1uzZ8/us265Pm8v5zUBAFBuvF8/eDsj4ZowfAinAyiJ/B/gb7755pCOX79+/YDnAwDgcPX666/r/e9/vwKBQHbb8ccfrz/+8Y/y+XyHdM5yjdsul0tz5sw55LYikYg2b948qLb4XQQAALtyjo3laovxHgAw2ixYsMBWjkQiQzo+N2wlSV6vt6AOY/6htwMAQCm0t7ers7PTtm3WrFlDOkd+/b6eQpo/nm3atEnRaHTQbeSPj3PmzLFNLjNQW+Ua84fzmgAAKDferx96O+VuC8ODcDqAkjjuuONs5dWrVw/62D179mjr1q3ZssvlKvjQHgCAw9GGDRu0bNky28xo8+fP1+OPP666urpDPm/+uP3CCy/YHp91MM8+++yA5xto31B+R3jppZdsf7ifNGlSv4/bKuc1AQAwGhx11FFyuVzZ8tatW7Vnz55BH1+u8X4onwmU85oAACiFE044wVbet2/fkI7Pf2R1Y2NjQR3G/EKM+QCAcurr5rOhBqRzxz1JSiQSBXUmTpyoiRMn2tp96aWXBt1Gucb8eDyuNWvWDKqtcl4TAADlxvv1QiPxmjB8CKcDKIlzzjnHVl6xYoUsyxrUsX/5y19s5aVLl8rv95esbwAAjEbbtm3TmWeeaftD9KxZs/TEE0+oubm5qHMfeeSRthnNg8HgoN/MBYNB/d///V+2bBhGwe8BufL3PfHEE4PuZ37dgR6FWs5rAgCgGI8++qieeOKJIb1uu+022zkmTJhQUGfu3Lm2OjU1NTr99NNt2wY7DluWpRUrVti2DTQOl+szgXJeEwAApXD22WfLNHv/FLdlyxa1tbUN+vj8cFb+47Mlxvx8jPkAgHLr6+ax3bt3D+kc+TOl9/c3gLPPPttWHq7P2/PbWb16tYLB4KDaefbZZxUKhbLlI444QkccccSg2xquawIAoNx4v243Uq8Jw4dwOoCSOOWUU9TU1JQtb968WU899dSgjv3Zz35mK5933nml7BoAAKPOnj179J73vEc7d+7MbpsyZYqefPJJTZkypSRtnHvuubZy/njcn9/+9rcKBALZ8oknnqjJkyf3W/+ss86yzRLz1FNPafPmzQdtx7Is/fznP7dtO9jvCOW6JgAAinHGGWfozDPPHNJr4cKFtnNUVVUV1Onrg9VDHRtXrlypLVu2ZMsTJkzQySef3G/9cn4mUK5rAgCgFMaPH69TTz3Vtu2hhx4a1LHxeFwPP/ywbduSJUv6rMuY34sxHwBQbm63W5MmTbJt++tf/zqkczz55JO2cu5ELLnyx8d77713UCGtTZs26emnn86WXS6XzjrrrH7rT5s2Tccff3y2HAgE9Lvf/e6g7UjFj/nDdU0AAFQC79d7jeRrwvAgnA6gJEzT1KWXXmrbdvPNNx/0jeOTTz6pv/3tb9lyTU2NLrroouHoIgAAo0JbW5uWLVumTZs2Zbc1NzfriSee0KxZs0rWzqc+9SkZhpEt/+Y3v9H69esHPKanp0e33nqrbdtll1024DHjxo3T+eefny1blqWvf/3rB+3fPffcY3vU1owZM3TmmWcOeEy5rgkAgNHikksukc/ny5ZXrVp10D+QW5alm2++2bbtk5/8pG3W13zl/EygXNcEAECpXHHFFbbyf/zHfygSiRz0uLvuukt79+7Nlmtra/W+972vz7qM+SmM+QCASnnPe95jK3//+99XPB4f1LFPP/207cmefZ0v433ve5+mTp2aLW/dulX33nvvQdv4+te/bhuvP/ShD6murm7AY/I/J7/11lvV09Mz4DHr16/Xb3/722y5r98d8pXzmgAAKDfer6eM9GvCMLEAoEQOHDhg+f1+S1L29a1vfavf+jt37rRmzpxpq//lL3+5jD0GAGBk6erqsk466STb2FhfX2+98sorw9LexRdfbGvrpJNOsjo7O/usm0wmrSuuuMJWf/bs2VY0Gj1oO+vWrbNM07Qde9999w1Yv76+3lb/7rvvHlHXBABAOa1cudI2Xs2YMWPQx9544422Y2fNmmXt2rWr3/q33HKLrX5dXZ3V2tp60HbK+ZlAua4JAIBSSCQS1tFHH20biz7xiU9YiUSi32Oee+65gnH1C1/4woDtMOYz5gMAKufPf/6zbQySZF1++eUDjveWZVlvv/22NXnyZNtx8+bNs+LxeL/H/PjHP7bVb2hosNatW9dv/V/96le2+g6Hw9qwYcNBrykSiVjTp0+3HXvllVdayWSyz/qdnZ3WiSeeaKv/0Y9+9KDtlPOaAAAYrGI+k8/H+/XRcU0oPcLpAErqm9/8ZsEb76uuuso2ACUSCevhhx8ueDM7efJkq729vXKdBwCgwpYsWVIwjv7bv/2b9cQTTwz51dbWdtD23n77bcvr9draO/bYY62VK1fa6m3YsMG64IILCvr2u9/9btDX9pnPfMZ2rGma1le+8hVbP6PRqHXvvfdaDQ0NtrrHHHOMFYvFBtVOOa8JAIByKeaD8NbWVmvixIkFxz/66KO2Pyjv2LGj4KYtSdZ3vvOdQbdVrs8EynlNAACUwooVKyzDMGzj0Zlnnmm9+OKLtnodHR3Wd7/73YI/vB5xxBF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l2OrSSacC1+3r4ZaD/dx5dIA7Dw9w44FesulUq0uTJEmSJEm7VLsFzT8HTAB91eMfZ3NB8x+r2S8An9pEX5IkSZIkSZIkSZLWaHGl8bFCEggfKyyGxYtcKKw8XgyOj80UqbQ+M76q7lyamw70ccvBPm4+0MctB/u57ooeOrPpVpcmSZIkSZK0pK2C5jHGSgjhL4Bvqza9MoTwphjjr663rxDCvwe+Alh8K+qvY4ylBpUqSZIkSZIkSZIk7SpzpfJSKHwxPD46Pc/oTCkJjdeFyccKJYrlSqvL3rThnhw3H+yvCZX3ceWePOlUaHVpkiRJkiRJl9VWQfOqnwG+FQjVxy+FEAaBn4oxlle7OISQBt5Cshp6rPZRAd62VQVLkiRJkiRJkiRJO934TJFnRgo8M1Lg2ZECT48UODE6w4VqeHymuOpHdTvelXu6ueVgskL5Yqh8b28HIRgqlyRJkiRJO0/bBc1jjA+HEH4T+NckQfEU8J+AbwshvAP4yxjjo/XXhRBuBF4NfD9wDUnAPFYf74wxfrFJt7CqkLwTdRVwG3AYGADmgTHgCeCzMca5Bo/ZC7wUuB7oA2aB54BPxhhPNXIsSZIkSZIkSZIkbU+F+YUkSH6hwDPnCzxzYTlYPjaze34cOJsOXLevtxoq7+Pmg/3cdKCX3s5sq0uTJEmSJElqmLYLmlf9EHAL8HKWVyU/Bvwy8MshhAIwAkwDPcAwkK9eu7icwOJ1Hwb+TbMKv5TqquyvBf4J8JUkNV9KKYTwAeDtMcaPbHLcq4G3Aq8Hchc5JYYQPgK8Ocb40c2MJUmSJEmSJEmSpNabXyhz/MIMT1cD5M/UPM5Nzbe6vC2XCjDYnWOgO8tQPsdAd46h7hyD+RzX7M1zy8E+rtvXSy6TanWpkiRJkiRJW6otg+YxxoUQwtcB7yIJZ8fqU4sh8p7q46KX15z7f4E3xhgXtqbStQkh/Hfge7h40PtisiT3/doQwu8B/zbGOLmBcV8P/A7QfbnTgFcCHw4h/CLw4zHGeJnzJUmSJEmSJEmS1GKVCKPzcG42cOqzJzk9fXwpTH5yfJZ2+bQnmw4MdOcY7M4y2J1LHvnkeClEns9Wz0kC5b2dGVKpsHrnkiRJkiRJba4tg+YA1WD1N4UQvg/4ceDo4lOXuSxUH88CPx1j/F9bWuTafSkXD5mXgdPAWZJw+ZVAf9053wncGEL4qhjj9FoHDCG8DvgDoH4phvPACWAfcIjl8H4AfhToAN601nEkSZIkSZIkSZK09c5PzXPfiXHuPT7GZ58Z4f4TaYqV6sc8jz7d2uLWKJMKDOYXVxdPguKD3bkVgfHFMHnSlqWnI0MIhsYlSZIkSZI2om2D5otijP8zhPDbwDcB/xh4OXANK+99AXgS+BjwV8B7Y4yVZte6RuPAu4EPAB+LMU4tPhFCSJPc31ur20X3kKzu/i1rGSCEcIxkJfPakPn9wJtijB+qOe8G4GdJ/m0X/XAI4WMxxv+39luSJEmSJEmSJElSoxQXKjxyepIvHB/j3uPj3HtijBOjs3VntT58PdCdrYbGF8PhWYbyHUuB8aF8riZYnqOv09C4JEmSJElSM7V90BwgxlgG3lN9ABBC6AV6ganasPY29izw08C7Y4z17wQCS/f54RDCVwDvAL635ulvDiF8RW1Q/DLeBuRrjj8LvKq6SnzteI+FEL4F+I26sX4xhPC+GOPCGsaSJEmSJEmSJEnSBsUYOT0xlwTKj49x74lxHjg5QXGhdWsqdWXTXDWc55rhPFcNd3NwoIs91TD5np5k29+VJZOu/2FdSZIkSZIkbSe7Imh+MdVw+U4ImAO8GfhgjLG4lpNjjOUQwg8AdwMvrnnqe4DLBs1DCLcA31rTVAS+qz5kXjNWDCH8O+ArgOuqzceANwC/tZZ6JUmSJEmSJEmStDZzpTIPnJzg3uNjfOG5ZLXys5PzTa8jl05xdE83V+3Jc83ePFftyXP1cPK4oq/DlcclSZIkSZLawK4Nmu8kMcYPbOCacgjhF4E/rmn+mjVc+t1A7fIRfxhjfGSVseZCCD8PvLOm+XswaC5JkiRJkiRJkrRhMUaeuzDDvSfGqiuWj/PI6UkWKrEp46cCHB7sXgqQXz2cX1qp/OBAF+mUYXJJkiRJkqR2ZtC8vX2s7nhPCKE7xjhzmWu+vu74nRc964X+CPh1IF89/pIQwsEY46k1Xi9JkiRJkiRJkrSrzJXKjM0UGS0sP8YKRS4Uijx8apJ7T4wzWljTD95uyhW9Oa7Z28vVe/NcvWc5UH50qJtcJrV6B5IkSZIkSWpLuypoHkIYAm4ChoB+kpW7/zrGeLalhW2dsYu09QMXDZqHEG4Arq1pKgCfXMtAMcZCCOGTwFcvdge8Blc1lyRJkiRJkiRJu0ClEpmcK3GhJixeux2dWRkkHysUKRTLTa/z0EAXtx3I0zV7lqt6Ige64Z+86uX09fU1vRZJkiRJkiRtb20fNA8h7AN+EPhm4MaLnPLVwAuC5iGENwBHqoenYoy/vWVFbp1DF2m7cJnz76w7/kyMcWEd432C5aD5xfqTJEmSJEmSJEna9sqVyMRsifGZImMzyXZ8psRYdTs6UxciLxQZmylSia2ufKWubJrbD/dz19FB7jo6wF1HBtjX18nk5CQf+tCZVpcnSZIkSZKkba6tg+YhhP8IvBXIkaywXe9yb/f1AG+pnlMOIbx/B658/vK64+dijJf7fcWb6o4fXud49efX9ydJkiRJkiRJktQ0MUam5xcYnyktBcXHZopMzJYYKywGx4uMz5ZWBMon50rEbRYaX4trhvPceXQgCZYfGeDG/b1k0qlWlyVJkiRJkqQdqi2D5iGENPAe4BtIAub1bwVGLh48r/VO4G1AH5AGvh341cZWuuW+u+74L1Y5/4a64xPrHK/+/Pr+JEmSJEmSJEmSGibGyPNjs3zx+Qm+eHKcZ84XqmHy5dD4wnZbZrxBejsz3HlkYGm18jsPDzCYz7W6LEmSJEmSJLWRtgyaA/8deG11fzFUfi/wN8Dx6vOXFWOcCSG8H/iOatOr2UFB8xDCq4F/VNf8rlUu21d3/Pw6hz1Zd7x3nddfVAhh3wb6OlZ7MD09zeTkZCPK0QYUCoXLHkuSpJ3LeV6SpPblPC9J2o7OTxd56PQUD52a4sHT0zx8eoqx2YVWl7XlAnDt3m5uP9TH7Yd6uf1gL1cPd5MKNesqleeYnJxbU3/O85IktTfnekmS2pfz/O4zPT3d0vFD3Im/+3cZIYSXAR9leRXzEeBfxhj/suacSs3zXx1j/PtL9PXNJCujA8wBAzHG4pYU3kAhhCHgfuBwTfOfxRi/cZXrPg3cU9P0jTHGP1vnuBfqmjtjjPNr7eMS/b4FePNm+vj1X/91jh49upkuJEmSJEmSJElSExVKcKIQOD4Nx6cDxwuBieJqP1i7cwUi3RnIZ6AnC73ZyOF85KpeOJqPdLbr8lGSJEmSJEm6pOPHj/NDP/RDtU23xhgfatb47fiW1Fur2wBMAq+IMT66wb4+XbPfAdwAPLCJ2rZcCCEF/G9WhswngB+6+BUr9NQdr23Zi2Wzl+hzU0FzSZIkSZIkSZLU3ubK8Pw0HC+EJFQ+Hbgwv7ND5dkQyWeT0HhPprqfgZ7s8n4+G6tt0J2B1M6+ZUmSJEmSJLWZtgqahxAGgZezvFr5T28iZE6M8fkQwhgwWG26kW0eNAd+CfjaurbvizGeWMO1nXXH6129/WKB8q519iFJkiRJkiRJktpYqQInC8urlJ+YDpydhcj2TVmnQySfYWnF8e5MTALkWcgv7teFyHPpVlctSZIkSZIkbU5bBc2BlwGLb9uVgd9uQJ/nWA6a72tAf1smhPBDwL+va/7FGOMfrbGL+hXMc+ssoWMNfW7EO4D3rPOaY8B7Fw/uuecebrrppgaUoo0oFAp85jOfWTq+5557yOfzLaxIkiQ1ivO8JEnty3lektQIhfkFnhyZ4YlzBR4+Pc2Dp6d48vwMC5W4+sVbIAD9XRn6u7L0d2UY6MrS15lhYHG/uh3oytBX3Q50ZenKpghh+wbh18t5XpKk9uZcL0lS+3Ke330eeeSRlo7fbkHzg9VtBJ6OMY43oM+Jmv3eBvS3JUII3w68va75XcCPraOb6brj+hXOV3Ox1cvr+1y3GOM5ksD/mtW/2dvT00NfX99mS1GD5PN5//eQJKlNOc9LktS+nOclSZezUK7wzEiBR89M8diZqWR7dpITo7NNq+Ga4Ty3He7nyqFuBrpzDOazDHTlGOjOMtidbPs6s6RS7RMYbxTneUmS2ptzvSRJ7ct5vv319PS0dPx2C5oP1eyPNqjP2lW6Sw3qs6FCCP8U+F1Y8ZuS/w/4nhjjepYEqQ+Fr/drLvXnL8QYG7GiuSRJkiRJkiRJ2gZijJyZnFsKlC+Gyp86N02xXGlaHYcGurj9cD+3Hx7gjsP93HKon/6ubNPGlyRJkiRJknaDdguab8Xq4/tq9kca1GfDhBC+AngPK/+3/CDwbTHG8jq7q181/PA6rz9Ud3x+nddLkiRJkiRJkqRtYmquxONnp1auUn5mionZ5q7LM9zTwR3VUPnth/u57XA/wz0dq18oSZIkSZIkaVPaLWi+GGwOwJUhhFSMccPLZ4QQjgAHappObaa4RgshfCnwPqCzpvmTwDfGGIsb6PKxuuOj67y+/vxHN1CDJEmSJEmSJElqolK5wtPnCzx6ZnLFKuUnx2ebXktfZ4Y7jgxw26HqauVH+tnf10kIYfWLJUmSJEmSJDVUuwXN76/Z7wZeCnxsE/29rma/DPzDJvpqqBDC7cBfAj01zfcCr44xFjbYbX0w/OZ1Xn/TKv1JkiRJkiRJkqQWmi2WeeTMJA+dnODBk5M8dHqCx89MUyxveN2eDevOpbn1YP/SKuV3HB7gyj3dhsolSZIkSZKkbaKtguYxxsdDCM8AV1Wb/j0bDJqHEPqANwGx2vTZGOPUpotsgBDCDcAHgcGa5keAr4kxTmyi6/vqjr8khJCJMS6s8fqXrtKfJEmSJEmSJElqkonZEg+fmuShUxM8dGqSB09O8NT5aSpx9WsbLZ9Lc/3+3qVg+R1HBji2t4d0ylC5JEmSJEmStF21VdC86veAN1f3vz6E8F0xxt9dTwchhHS1n0PVpgi8o3ElblwI4Urgb4F9Nc3PAF8dYzy/mb5jjI+GEJ4CjlWb8sBLgI+uoa488OW13QF/vpl6JEmSJEmSJEnS2oxMz/PgySRQ/tCpZLXy46MzTa8jnQpcPZznxv293Li/lxv293Hj/l4ODXSRMlQuSZIkSZIk7SjtGDT/ZeD7gb1AAH47hLAP+JUYY3m1i0MINwL/E3gZy6uZPw68e2vKXbsQwgHg74DDNc0nga+KMZ5s0DDvI1nJfdEbWUPQHPhWoKfm+HMxxlMNqkmSJEmSJEmSJAExRk5NzPHQyQkePDXJQ9Vw+ZnJuabXsr+vkxuWAuXJ49p9PXRk0k2vRZIkSZIkSVLjtV3QPMZYCCF8D/CnQApIAz8P/EAI4Q+Az1dPDSRB8heFEIaAa4GvrD5C9QEwC3x7jLEFPyS5rFrjB1lebRzgPMlK5s80cKj/Bfwwy/f/z0IIPx9jfOQytXUCP1bX/M4G1iRJkiRJkiRJ0q4SY2SuVOH0xCwPnZrkwVMTPHxqkgdPTjA2U2pqLT0dmaUg+Y37e7nhimR/oDvX1DokSZIkSZIkNVfbBc0BYox/HkL4N8A7qk0BuBL40bpTA0kIvb5tMVReAt4QY7x3q2pdixBCL/BXwC01zePAP75cAHwjYowPhhD+mGSFcoAc8LshhFfFGCcvUlsA3g5cV9P8NElgXZIkSZIkSZKkXS3GyGypzPhMibGZIhMzJcZnS4zPlBifTY7HZorV41L1+SJjMyWKC5Wm1ppJBY7t7VkZKt/fy6GBLpKPAyRJkiRJkiTtJm0ZNAeIMf5mCOEp4PeB/SyHx2uD5IvHS5dVHwE4C7wuxvjxJpS7mvcBX1LX9ivAcAjhVevs6/MxxrFVzvnPwNcB3dXjLwE+GkL44RjjhxdPCiFcD/wc8E111/9YjLG5y6lIkiRJkiRJktQEMUbGZko8MzLNmYl5xmerIfG6sPjYTHFpv1hubmB8LfK5NLcc7Ofmg33ceqifmw/0ce2+HnKZVKtLkyRJkiRJkrRNtG3QHCDG+HchhJuA7wd+EDhYfepSy24EYIxkhe5fu9gK3i3yyou0vXWDfX0F8OHLnRBjfDKE8Ebg3Sz/W90BfCiEcB44DuwDDvPCf8v/FmN8zwZrkyRJkiRJkiRpW5ieX+DZkQLP1DyeHinw7EiBidmdtdbKYHeWWw72c8uhPm492M8tB/u4ak+eVMpVyiVJkiRJkiRdWlsHzQFijBPAz4cQfpEkLP1y4CZgDzAAzAAjwDPAh4DPxBgXWlPt9hFj/MOQ/A7mO4Gumqf2Vh8X88vAj2x1bZIkSZIkSZIkNcL8QpkTozM8fX5loPyZkQLnpuZbXd6GXNHXkYTJDyWB8lsP9XOwv5PkLX9JkiRJkiRJWru2D5ovijFWgHurD61BjPEPQgifBt4GvA7IXuLUjwL/Jcb4kaYVJ0mSJEmSJEnSGpQrkVPjs0urkS+uTP7MyDQnx2apxFZXuHFHh7q59VBfslr5wWS7t7ej1WVJkiRJkiRJahO7Jmi+k8UYW7bMSIzxaeA7QgjfD7wMuA7oBeaA48AnYownW1WfJEmSJEmSJGl3KpUrjM0UGZ8pMVooMj5TZLRQYmymyGihyInRGZ4ZKfDchRmK5Uqry92UVIBr9vZwa3WF8lsO9nPzwT76uy61PowkSZIkSZIkbZ5Bc61JjHES+ItW1yFJkiRJkiRJaj/zC+WlwPjYTJGxamB8rFBkbKa6X3tcKDI1v9DqsjestyPDQD7LQFeOge4s/V1ZBrqXjwe6cwwstnVnOTjQRXfOj3QkSZIkSZIkNZfvSkqSJEmSJEmSpIYoLlQYny0yOVtifCZ5TMyWGJ8tMTFTZGK2tBQcr12JvFAst7r0DenOpRnu6agJi+cY7M4y0JWlf0VYvBog78rS15Ulm061unRJkiRJkiRJWpVBcyCEcA/wJuBlwDAwBnwe+M0Y4/tbWZskSZIkSZIkSc0UY2R6fmEpJD4xWxsYLzIxs/J4fKaUBMtnS8zs0MD45eQyKa4c6ubq4TxX781zzXCeq/Yk+3t7OgghtLpESZIkSZIkSdoSbRc0DyH8Y+A/1zR9T4zx8cuc/8PALwOh+gDYD7waeHUI4Q+A74wxVramYkmSJEmSJEmSWuPU+CxfOD7GF54b594TYxy/MMP4bIlyJba6tKZKBTg8WA2T1z0ODnSRThkmlyRJkiRJkrT7tF3QHPhukpXJI/DAKiHzVwH/leWAef075wH4NmAK+P7GlypJkiRJkiRJUnPML5R58OQk9x4fWwqXn5mca3VZTXVFX0ddkLyHq4fzHBnqoiOTbnV5kiRJkiRJkrSttGPQ/Ctr9v9wlXN/hSRMHlle0fws0Ad01bR/bwjh92OMn2x8uZIkSZIkSZIkNd7piVm+8Nx4Eio/PsZDJycpltv3xzvzuTSD+RyD3TkG8zmGe3JcvSfP1XvzXLUnCZbnO9rxYxFJkiRJkiRJ2hpt9Y5qCOEaYLim6S8vc+5XALeyvIr5Z4FvjzE+FULIAj8I/HLN828CDJpLkiRJkiRJkrad+YUyD52a5AvPjXHv8SRcfnpi565W3tuZWQqMD3ZnGerOMdCdYyifrW5zDHRnGaoGywe6s65ILkmSJEmSJEkN1lZBc+D6mv0S8OBlzv1n1W0AisC3xBhPAMQYS8CvhhCOAT9QPe81IYSuGONsg2uWJEmSJEmSJGldzk7O8YXnxqqrlY/zwMkJigvbb7XyXCbFQFeWge4sA105+rqy9HdlVwTGB7uzNaHyJDSeTadaXbokSZIkSZIk7XrtFjS/srqNwHMxxvJlzv3HLK9W/r7FkHmdX2M5aN4B3AH8QyMKlSRJkiRJkiRpLWaLZR49M7m0Uvm9x8c5Od7cNVH6OjP0V8PiA91Z+rqyKwLk/V3Z6vPZFed1Zl1lXJIkSZIkSZJ2qnYLmvfW7E9c6qQQwlGSUPpi0PzPLnZejPGJEMJ5YG+16UYMmkuSJEmSJEmStkCMkdMTczxyepJHz0zx8OlJHjk9ybMjBSpx9es345rhPHcdHeSuowMcHuxioDsJjw90JaHydCpsbQGSJEmSJEmSpG2n3YLmHTX7l1vN/CXVbSAJm//9Zc49wXLQfHDjpUmSJEmSJEmSlJgrlXny3PRSmHwxXD4+U9rysfO5NHccGeDuo4PcfeUAdx0ZZDCf2/JxJUmSJEmSJEk7S7sFzQs1+/2XOe8VNftPxxjPXObcYs1+94aqkiRJkiRJkiTtSjFGzk/NVwPlU0uh8qdHCpS3epnyqquH89x1tBosPzrIDft7XaFckiRJkiRJkrSqdguaX6huA3BVCCETY1y4yHlfU91G4KOr9DlQsz+zufIkSZIkSZIkSe2quFDhyXPTS2HyR85M8ujpKS4Uiqtf3CDduTR3HB7g7iuTYPldRwcZcrVySZIkSZIkSdIGtFvQ/MGa/Q7gVcBf1Z4QQvhy4CqSkDnAh1fpc3/N/ujmypMkSZIkSZIktYO5UpmHTk1y/4lxHjw5wcOnJ3nq/DSlcnNWKV901Z7uJFB+5SB3Hx3ghit6yaRTTa1BkiRJkiRJktSe2i1o/gAwRrIKeQB+OoTwoRjjPEAIIQ28rXpuABaAv7lUZyGEo6xc0fzpxpcsSZIkSZIkSdrOFsoVnjg3zRefH+e+ExN88flxHjszxUKluaHy7lya2w71c/eVg9XVygcY7uloag2SJEmSJEmSpN2jrYLmMcaFEMIfAD9AsmL5XcAXQgjvAkrA64EvZXk187+OMZ69TJcvqTt+uLEVS5IkSZIkSZK2kxgjJ0Znue/5cb54Ypz7nx/nwZOTzJbKTa3j0EAXNx3o5aYDfUuPK4e6SaVCU+uQJEmSJEmSJO1ebRU0r3ob8B1AX/X4JuDn684JQAV46yp9fXPN/pMxxgsNqVCSJEmSJEmStC2cn5rni8+Pc/+Jce5/PlmtfGym1LTxOzIpbtjfy437a0Ll+/vo7842rQZJkiRJkiRJki6m7YLmMcazIYTXA+8DOlhevXzR4nIv/yXG+NlL9RNCGAC+tub6Dze2UkmSJEmSJElSM03PL/DA8xPc//x4NVw+wcnx2aaNf0Vfx4oVym8+0MtVe/Jk0qmm1SBJkiRJkiRJ0lq1XdAcIMb4wRDCi4FfBL6alff5BPCWGOMfrNLNvwa6F7sE3t/wQiVJkiRJkiRJDVWpREZnioxMzzMyVeSZkWnuO5GsVP7k+Wli/dIkWyCbDly3b3GF8uWVyofyua0fXJIkSZIkSZKkBmnLoDlAjPEh4DUhhD7gaqALOBVjPL7GLp4D3lRz/MEGlyhJkiRJkiRJWoNyJTJWEx4/Pz3HyFRyfH56nvNT84xMJ8ejhSLlShPS5FX5XJpbD/Vz++F+bj6YBMqP7e0h6yrlkiRJkiRJkqQdrm2D5otijJPA/Ru4brUVzyVJkiRJkiRJG1QbHk+C4vPL4fGpJEC+GB6/MD1PE7Pjl5RNB2460Mcdhwe4/XA/dx4Z4Jq9PaRTodWlSZIkSZIkSZLUcG0fNJckSZIkSZIkbb1KJTIxW+JCociF6sriI4Uio9NFLhTmV7RfmC4yNlPcFuHxSwkBrhnOc8eRAe48MsDthwe46UAvHZl0q0uTJEmSJEmSJKkpDJpLkiRJkiRJkl4gxsjk7EJNSDwJjCfB8eLKQHk1OF7ezsnxVRzs7+T2wwPccWSAOw73c+vhfvo6s60uS5IkSZIkSZKkljFoLkmSJEmSJEni+IUZPv7kCJ94coR7j49xfnqeUnnnBscvp78ruxQov+PwALcf6Wdfb2ery5IkSZIkSZIkaVsxaC5JkiRJkiRJu9Boocgnn0qC5R9/coQTo7OtLmlLDHZnOba3hzuODHD74X7uPDLA0aFuQgitLk2SJEmSJEmSpG1txwTNQwh/BPxEjPGpVtcCEEK4FviZGOO3troWSZIkSZIkSVrNbLHMZ58dXQqWP3RqstUlbdhgd5a9vR0M9yw/kuMcw70d7K0eD+VzZNOpVpcrSZIkSZIkSdKOtGOC5sDrgNeGEH4X+KUY4xOtKCKEcB3wI8B3srP+/SRJkiRJkiTtIgvlCg+cnOATT47wiScv8PnnxiiWK60u65Lqw+PL+4bHJUmSJEmSJElqhZ0WlM4AbwS+O4TwXuA3YowfbMbAIYSvBn4A+DogVB+xGWNLkiRJkiRJ0mpijDw9UkhWLH9ihE89fYGpuYWW1ZPPpdnTkwTDh3tyDOVz7OnpYE8+x56eHEP5ZN/wuCRJkiRJkiRJ29NOCpq/E3gDkCIJeb+WZIXz54D/A/y/GOO9jRwwhHA38E3AtwNXLjZXt5VqTZIkSZIkSZLUEuem5vjkkxf4+JMjfOLJEU5PzG3ZWN259Iqw+FA1ML4nn2NPvoOhnhzD1e2efI7ObHrLapEkSZIkSZIkSVtvxwTNY4z/KoTwP4G3Ay9hOfB9FfATwE+EEE4CHwI+AnwOeCTGWFpL/yGEHHAz8CLgHwFfARxafLru9E8APxxj/PxG70eSJEmSJEmS1mOuVGZkep7Hz07x8Scu8IknR3js7FRDxwgBbjnYx0uvHebFVw6xr7ejGibvoCtncFySJEmSJEmSpN1kxwTNAWKMnwNeFkJ4NfBW4O6apwNwGPjn1QdAubri+fPAaWAamK2e2wn0Ageq1x0F0nX9AcSats8D/yXG+JcNvC1JkiRJkiRJu1BxocLYTJGR6XkuTBcZLVT3C0VGp4tcKMwzUm2/MD1PoVjekjqODnXz0muHedm1w3z5sT0M5XNbMo4kSZIkSZIkSdpZdlTQfFGM8S+AvwghvAr4d8DXkgTDF0PhiyHxDHAMuGaVLutXLI/Vx2L7B4C3xxj/bpOlS5IkSZIkSWpT5UpkbKYmMD6dBMRHC0VGCsv7F6aT5yfnFlpS52B3lpdUg+UvPTbM0T3dLalDkiRJkiRJkiRtbzsyaL4oxvi3wN+GEI4C3wF8G3Br7Slr7aq6DTXbh4B3A/8nxni8AeVKkiRJkiRJ2iFK5WS18fGZEqOFIuMzRUYLJcZmiowViozNlJK2mnMm50rEtb4j2USd2RT3XL2Hlx7bw0uvHebmA32kUvVrb0iSJEmSJEmSJK20o4Pmi6pB8J8Dfq4aOv8a4OXAi4AbgNQqXVSAx4DPAx8D/tpwuSRJkiRJktQe5kplxmdWhsRHZ4qMF5aD4rXPjRWKTM23ZrXxRkgFuP3wQLJi+bXD3H3lAB2ZdKvLkiRJkiRJkiRJO0xbBM1rVQPiv1V9EELIAUeBI0A/sPg7sDPAOHACOBFjLDa9WEmSJEmSJElrEmNken6B8ZkSE7Ol5e1ssaYt2R+fLTExs/zc/EKl1eVvuWv25peC5V92zR76u7KtLkmSJEmSJEmSJO1wbRc0r1cNkD9ZfUiSJEmSJEnaJgrzCzxxbprnLhSSgHg1HD5RDYuPzxRrQuMlypXY6pK3jb29Hbz02B5eWg2XHxzoanVJkiRJkiRJkiSpzbR90FySJEmSJElSay0Gyp84O8UT56Z5/OwUT5yd5uT4bKtL2zZCgMHuHHvyOfb05NjT05Hs5zuS43y1rbrf35UlhNDqsiVJkiRJkiRJUhszaC5JkiRJkiSpIRYD5Y+fneJJA+X0dWaWA+MrwuO1ofFkO9idI50yOC5JkiRJkiRJkrYPg+aSJEmSJEmS1mU3BsrzuTQD3TmG8jkGurMM5ZNw+GB3jsF8NnmuO3luuKeDoXyOXCbV6rIlSZIkSZIkSZI2zKC5JEmSJEmSpIuaL8OZGTg9G/jC3z3Nc+PFtgiU93ZmqgHxHIPd2WpAPMdQNTC+GB4frAmWd2TSrS5bkiRJkiRJkiSpqQyaS5IkSZIkSQJgrlTm88+N8amnLvCxx8/ywMk0FULy5FMnW1tcnRCgrzNLf1eWge7FbY6BSxwPdGfp60rC49m0K41LkiRJkiRJkiStxqC5JEmSJEmStEsVFyrc//w4n3zyAp96eoQvPDdOsVypOSM0pY6ubJqDA50rwuH93VkGunJLIfH64HhvZ5Z0qjn1SZIkSZIkSZIk7UYGzSVJkiRJkqRdYqFc4aFTk3zyqQt88qkRPvfsGLOlctPG78qmue6KHq7d18P1V/Ry/RU9XLevl0MDXaQMjUuSJEmSJEmSJG0rBs0lSZIkSZKkNlWpRB49M8Wnnr7Ap54a4dNPjzI1v7Dl4xoolyRJkiRJkiRJ2vkMmkuSJEmSJEltIsbIU+cLfOqpkWq4/AJjM6UtG89AuSRJkiRJkiRJUvsyaC5JkiRJkiTtYCdGZ/jkUyN88qkkWH5uar7hY+RSkeuu6OWGA/0GyiVJkiRJkiRJknYJg+aSJEmSJEnSDjFXKvPYmSkeODnB/SfG+dTTF3h+bLbh4/R3ZXnxkT4Giue4tj+yvwu+6ivv3Nlc3wABAABJREFUoq+vr+FjSZIkSZIkSZIkaXsyaC5JkiRJkiRtQ4uh8i+enODB5yd44OQEj5+dYqESGz5WPpfmnquHeMmxYb782B5uOtBHYXqKD33obMPHkiRJkiRJkiRJ0s5g0FySJEmSJElqsblSmUerK5VvdagcoCOT4sVXDfKSY8N82TV7uP1wP9l0akvGkiRJkiRJkiRJ0s5k0FySJEmSJElqomaHygGy6cCdRwb48mPDvOTYHu48MkBnNr1l40mSJEmSJEmSJGnnM2guSZIkSZIkbZH6UPkXT07wxBaHygFSAW47PMCXX7OHlxzbw4uvGqQ751uBkiRJkiRJkiRJWjs/XZIkSZIkSZI2qLhQYWR6nvNT1Ud1//mxGR44OdmUUPmimw708ZJje/jya/ZwzzVD9HVmmzKuJEmSJEmSJEmS2pNBc0mSJEmSJKlGjJGJ2dJSePxcXYj83NTc0nNjM6WW1NiZTXHzgT5uPzzAPVcP8WXX7GEon2tJLZIkSZIkSZIkSWpPuyZoHkIIwF3ATcAQ0A+kgN+LMT7bwtIkSZIkSZLUBDPFBS5MF5cC4ytC5ItB8sk5RqaLFMuVVpe7pCub5uaDfdx2qJ9bD/Vz26F+ju3Nk0mnWl2aJEmSJEmSJEmS2ljbB81DCHcA/wH4BqDnIqd8HHj2Itf9CHBj9fB4jPEtW1SiJEmSJEmSNqC4UGG0UORCYZ4L08vbkekiF6bnuVCo3RaZLZVbXfKq6kPltx/u59jeHtKp0OrSJEmSJEmSJEmStMu0bdA8hJADfhX414tNFzktXqaLM8DPV8+JIYR3ufK5JEmSJEnS1qlUIuOzJS5Mzydh8aUAeTUwviJMPs/k3EKrS96UrmyaWw72La1SfpuhckmSJEmSJEmSJG0jbRk0DyF0A38H3EMSMK8PlEcuHjyv9W7gl4C91ePvAH6mgWVKkiRJkiS1rRgjM8Uyo4Vi8pgpMra4XygyNlPdFkqMVvfHZ4pULrcswA5mqFySJEmSJEmSJEk7TVsGzYE/AL6U5YB5BP4M+BvgBPDnq3UQY1wIIfwp8L3Vpq/FoLkkSZIkSdqliguVmnB4ssp47fHoTInRwjyjhVL1uEhxodLqspsqmw7s7elgb2/yODLUnYTKD/VzjaFySZIkSZIkSZIk7TBtFzQPIXwd8HUsh8yfAL45xvhgzTnwwlXOL+b9JEHzANwTQuiKMc42tmJJkiRJkqTtZaFc4fGz09x3Ypz7Toxx34lxnjg3TWzT1cZXM9CdZW9PB/v6OlYEyff1di7t7+3pYKA7u/i+kyRJkiRJkiRJkrTjtV3QHPjJ6jYAZ4BXxhhPb7Cvz9bsp4GbgC9sojZJkiRJkqRt5+zkHPceH+PeE+Pcd3ycB05OMFMst7qsLZXLpNhXExJ/QXC8t4N9vR3s6cnRkUm3ulxJkiRJkiRJkiSp6doqaB5CuAJ4Ecurlf/kJkLmxBjPhRDOA3urTTdg0FySJEmSJO1gs8UyD5yc4N7jY9UVy8c5PTHX6rI2LQQY6s6xpyfHnnwHQz05hvM59vR0LLUN9yTHQ/kcfZ0ZVx+XJEmSJEmSJEmSLqOtgubAS0lWMgcoAX/YgD5HWA6aDzegP0mSJEmSpKaoVCJPj0xz7/HxpdXKHzs7RbkSV794G+jtyCQh8Z4O9lRD48M9uaX9PTVB8sHuHOmUwXFJkiRJkiRJkiSpUdotaL6/uo3AkzHGQgP6nKzZ72lAf5IkSZIkSVviwvT80irl9x4f5/7nx5maW2h1WQCkU4HB7hxD+Wx1u/xYPB7M5xjqzjHcmxx3ZNKtLluSJEmSJEmSJEnatdotaN5fsz95ybPWJ1+zP9ugPiVJkiRJkjYsxsj5qXkePTPFY2emeODkBPedGOf46EzTaujrzFw6LF4NjNfu93ZmSLniuCRJkiRJkiRJkrRjtFvQfKxmv/+SZ63P/pr9Cw3qU5IkSZIkaU2m5ko8fnaKR89M8fiZZPvY2SnGZ0pbPnYuneLmg33ceWSAu44OcMP+XvbkOxjozpJNp7Z8fEmSJEmSJEmSJEmt025B87PVbQCuDiHkYozFjXYWQrgOGK5pOrGZ4iRJkiRJki6luFDhqfPTS6Hyx6qPk+PN+4G1o0Pd3HV0gDuPJI+bD/bRkUk3bXxJkiRJkiRJkiRJ20e7Bc0/V7OfA74S+KtN9PcdNftF4B820ZckSZIkSRKVSuTk+Gw1TD7JY2eneezMJE+fL7BQiU2ro7czk6xUfmSAO48OcMfhAfb0dDRtfEmSJEmSJEmSJEnbW1sFzWOMJ0IIDwM3VZt+lA0GzUMIB4B/Cyx+wvvxGOPc5quUJEmSJEm7wWyxzNhMkWdHCjx2Nlmd/NEzUzxxdopCsdzUWtKpwI37e5dWKr/r6CDXDOdJpUJT65AkSZIkSZIkSZK0c7RV0Lzqt4BfJQmI/6MQwn+KMf7MejoIIfQC/xcYrDZF4O2NLFKSJEmSJO0M8wtlxmdKjM+UGJspVveLjM8mxxM1z03MLp8zv1BpWc0H+ju56+hANVg+yG2H+unKpVtWjyRJkiRJkiRJkqSdpx2D5u8A/h1wJRCAt4YQDgI/EWOcWO3iEMLXkITKr2d5NfPPxhg/sDXlSpIkSZKkZooxcnpijsfOTHF+ep6JxQD5bDVAPlNibKbExEyRsZkSs6Xmrj6+Xvt6O7hhfy83H+zjriOD3HV0gCv6OltdliRJkiRJkiRJkqQdru2C5jHGUgjh24C/BzpJwub/GvjOEML7gc9XTw0kQfLXhBDuBq4FvhI4VvNcAEaBb2vqTUiSJEmSpIaIMXJ8dIYHT07y4KkJHjw5wUOnJhktFFtd2rr1dGS4/ooebtjfx437e7n+il5u2N/LUD7X6tIkSZIkSZIkSZIktaG2C5oDxBg/HUL4Z8AfkoTNAfLAt1YfiwLww3XHsBwynwC+Jcb4zJYWLEmSJEmSNq1ciTwzUuChaqB8MVw+NbfQ6tLWJZsOHNvbww3VMPmN+5NA+aGBLkIIq3cgSZIkSZIkSZIkSQ3QlkFzgBjj+0MI95CEzW8hCY8vPV2zXxsujzVtDwHfHGN8fKtrlSRJkiRJ67NQrvDk+ekkTH5ygodOJSuVzxTLrS5tXY4MdXFDdWXyG/b3ccMVvVw9nCeXSbW6NEmSJEmSJEmSJEm7XNsGzQFijA+FEO4EXgf8O+AeloPl9RbbHwR+Cfg/McbKlhcpSZIkSZIua36hzBNnp3nw5AQPnJzgwVOTPHp6kvmFnfOyfSifWwqU37i/l+urq5X3dLT1WzOSJEmSJEmSJEmSdrC2/zQzxlgmWdX8D0MIQ8DLgJuAPcAAMAOMAM8AH4oxnmpRqZIkSZIk7VrzC2VGC0UuTBc5Pz3P86MzyWrlpyZ4/OwUpXJcvZMm6c6lGezO0d+VZTCfZaArx0B3loHu7HJ792JbjqF8jsHuLCFc6rvvkiRJkiRJkiRJkrT9tH3QvFaMcRR4X/UhSZIkSZK2SIyRybkFRqbnuTBd5ML0PCPT84xMF7lQmGdkKtkuBsun5hZaUucVfR1LofDacPhANSzev6I9S39Xlo5MuiW1SpIkSZIkSZIkSVIz7aqguSRJkiRJ2pypuRLPXZhZDpAXkvD4iuNqiHw7rUIeAlwznOfWQ/3cerCfWw71ccuBfvq7s60uTZIkSZIkSZIkSZK2JYPmkiRJkiTpskam5/mrB8/wgS+e5tPPXKCyffLjF5VOBa7b18MtB/u59VAftx7q5+YDfeQ7fBtEkiRJkiRJkiRJktbKT1glSZIkSdILLIbL/+KB0/zD09s3XJ5NB27Y31tdpbyf2w71c+P+Xjqz6VaXJkmSJEmSJEmSJEk7mkFzSZIkSZIEbP9weUcmxU0H+rj1UB+3HernloP9XH9FL7lMqtWlSZIkSZIkSZIkSVLbafugeQghDbwYuAs4CvQBXUBYZ1cxxvjGBpcnSZIkSVJLXZie568eOsMHvrh9wuXpVGAon+OqPd3ceqifWw/2c+uhfo7tzZNJGyqXJEmSJEmSJEmSpGZo26B5CKEf+M/AvwD2brY7IAIGzSVJkiRJO95iuPwvHjjNp55qTrg8n0uzp6eD4Z7c0na4p4M9+cXj5ecGurKkUuv9frgkSZIkSZIkSZIkqZHaMmgeQvhy4E9JAua1n0xvg3XZJEmSJElqvkaHy1MBhvLVsHhPjj35jqX9pRB5NUg+3NNBVy7dmBuRJEmSJEmSJEmSJDVF2wXNQwi3AH8N9FSbIsthc5dDkyRJkiTtGhem5/nrh87ygQdO8Q9Pj1LeZLr8muE8r77tAK++7QA37O8l7arjkiRJkiRJkiRJktS22i5oDryDJGS++Ol5AP4GeC/wAHABmGlNaZIkSZIkba3FcPlfPHCaTz19YdPh8quH87ymGi6/6UAvIRgulyRJkiRJkiRJkqTdoK2C5iGEY8DLWV7FfAz45hjjh1tZlyRJkiRJjbRQrnBuap7TE7OcGp9b2j5+dopPP7P5lcsNl0uSJEmSJEmSJEmS2ipoDry0ug0kYfPvM2QuSZIkSdpJKpXIyPQ8pybmOFMbJJ+Y4/T4LKcn5jg7Occms+QvcPVwnlfftp/X3HbQcLkkSZIkSZIkSZIkqe2C5vtr9seAP2lVIZIkSZIk1YsxMjZT4lQ1MF67Ivnp8TlOTcxydnKOUrnBKfJLuGpPN6+5PVm5/OYDfYbLJUmSJEmSJEmSJElL2i1oXq5uI/B0jLE5n8xLkiRJkna1+YUyI9NFzk/NMzI1z8j0fLI/Pb/Ufn56ntMTs8yVKi2t1XC5JEmSJEmSJEmSJGkt2i1ofrxmv7NlVUiSJEmSdrzF8PjlguOL7VNzC60u97Ku2tPNq29LwuW3HDRcLkmSJEmSJEmSJElaXbsFzT9Jspp5AK4KIWRijNv7035JkiRJ0pYrVyJTcyXGZ0qMz5aYmC0xPlOsbkuMFopJcHwxQD41z+Q2D4+v5so93bzGcLkkSZIkSZIkSZIkaYPaKmgeYzwZQvhb4KuBPPBq4H2trUqSJEmS1CjFhQoTsyUmZotJaHymGhqfLTExU2R8tiZMXnM8OVcixlZXvzW6c2kO9HdycKCLA/2dXLknzyuu32u4XJIkSZIkSZIkSZK0KW0VNK/6UeCVJPf2CyGEv4sxFlpbkiRJkiRpLUYLRZ44O8WT56d58tw0T50vMDI1v7QCeaFYbnWJTZXLpDjQ35kEyfu7ODDQyYH+Lg4ubvu76OvKGCiXJEmSJEmSJEmSJDVc2wXNY4z3hRB+APhN4Hrgz0MI3xpjPNfi0iRJkiRJQIyRM5NzPHlumifOTieh8up2tFBsdXlNk0kFrujrXAqNHxiohslrVicfyucMkUuSJEmSJEmSJEmSWqLtguYAMcZ3hhBmgN8CXgE8FEL478CfAA/G2K4/mC5JkiRJ20e5EjkxOsOT55IQ+WKo/Klz00zPL7S6vC2TTQeGezoY7ulgb28Hwz25peP9i6uTD3Qx3NNBOmWIXJIkSZIkSZIkSZK0PbVl0BwgxvgHIYRPA+8HbgJ+svoohRBGgbn1dxmPNbhMSZIkSdrxigsVnr1QSILkS6HyKZ4eKVBcqLS6vIbIpgN78iuD48l+B8O9Hezt6WBvb9Le35V1FXJJkiRJkiRJkiRJ0o7XtkHzEMJLgV8BbgQisPgpfw7Yv4EuXQVdkiRJ0q42PlPkqfMFnj4/zdMjBZ6qhsqfuzBDubKzXjKlU4H+riwDXVn6u5PtYmi8diXyvdV9w+OSJEmSJEmSJEmSpN2mLYPmIYQfBH4VSJEEzCObC4qbJpAkSZK0K5TKFY6PzvD0YqD8fIGnqsHy0UKx1eW9QC6TYrA7y0BXjv7u7FJ4fKA7y0B3LjmuPj+w+Hx3lp6OjMFxSZIkSZIkSZIkSZIuo+2C5iGE1wC/xsqA+WJ6oAhMADOtqU6SJEmSWi/GyGihyNMjtWHyZP/46AwL22B18v6uLNfu6+G6fT1cuSfPUD5LfzUsXhsc78ymW12qJEmSJEmSJEmSJEltqe2C5sDbWQ6ZB+Ac8CvA+4DHY4yV1pUmSZIkSc1TXKjw3IVqiHxkenmV8pEC4zOlVpcHwL7eDq7d17MUKj+2r4fr9vUy3JNzxXFJkiRJkiRJkiRJklqorYLmIYQvA46RhMwBvgh8VYzxQuuqkiRJkqSNiTEyUywzPb/A1NwC0/MLTM8tMD1fqjteYKpmf3pugXNTc5wYm6W8DVYnBzg82MV1S4HyXo5V9/u7sq0uTZIkSZIkSZIkSZIkXURbBc2Bu6vbxRXNv8eQuSRJkqRmKS5UmC2VmS2WmS2VmSku1OyXmatuC/OXDolPzZWYnkvaCvMLbJOc+JpkUoEr93QvhckXVyo/treHrly61eVJkiRJkiRJkiRJkqR1aLegeX/N/skY4+daVokkSZKkbWWhnITA50oV5krlFYHwuepjMRA+WyyvCIi/MCy+sCI4vtjXwk5KhW9CPpfmmr09HNub55q9PUsrlV+5J08uk2p1eZIkSZIkSZIkSZIkqQHaLWh+vrqNwKlWFiJJkiSpMc5PzfPI6UmeH5tlprjA/EJlKfg9WyozVywzt1Cuaau8oG2uVKZU3h0h8EYJAQ4PdnHNcLIi+TV781yzN8+xvT3s6+0ghNDqEiVJkiRJkiRJkiRJ0hZqt6D5iZr9vpZVIUmSJGndSuUKT58v8MjpSR45PcnDpyd55PQUI9PzrS6trfV2ZpLVyYfzHNvXwzXDySrlV+7ppjObbnV5kiRJkiRJkiRJkiSpRdotaP5xYBroAY6FEPpijJMtrkmSJElSnfGZ4lKQfDFY/sTZaYrlSqtLa0upAEeHurlm73KQ/NjeZDvck3N1ckmSJEmSJEmSJEmS9AJtFTSPMRZCCO8B3kByb98J/P9aW5UkSZK0e5UrkWcvLK9SvhgsPz0x1+rSdqwQoKcjQ29Hhp7ODD0dGXo6s8lxTVtvZ4bDg90c25vn6J5uOjKuTi5JkiRJkiRJkiRJktaurYLmVT8JfD0wBPxUCOGDMcbHWlyTJEmS1PbmFuALJyY4PjnKI6cnefj0FI+dmWSu5CrlqQDduQxduTRd2TTduTS9NSHxxWB4T01YvLcjQ29ndkVwvKcjQ1c2TSrlCuSSJEmSJEmSJEmSJGlrtV3QPMZ4KoTw9cAHgEHgQyGE74oxfrDFpUmSJEltYWK2xFPnp3nq3DSPnBzlM4+mOD0TuDAf4LNfbHV5G5JLp+jMpujMpleEwZP9JCDevfhc/X71/K5cpmZ/ZR+5dIoQDIdLkiRJkiRJkiRJkqSdo+2C5iGEo8BJ4PXAbwJXAX8VQvgk8B7g88B5YG69fccYjzeuUkmSJGn7qlQipyZmeep8gafOTSfB8vPTPHW+wPmp+bqzU02paW9vB8M9HXQtBsKzaToXg93ZNJ3Z1Iq2pXOqwe/OTGopAN5Z155JN+ceJEmSJEmSJEmSJEmSdoq2C5oDzwKx5jgCAXhJ9bFRkfb895IkSdIuNlcq8+yFAk+em+apc4WlQPnT5wvMlsotqSmTCly7r4ebDvRx04Febj7Qz40Hehnu6WhJPZIkSZIkSZIkSZIkSbtROwenA8uB81jTJkmSJO06o4UiT52frgbKl1cnPzE2Q4yrX79VBruz1UB531Kw/Np9PXRk0q0rSpIkSZIkSZIkSZIkSW0dNAeD5ZIkSWpzM8UFRqaKnJ+e5/zUPCPTNY+pIuem5nhmpMDYTKmldaYCXD2cXwqU31zdXtHXQQj+2S5JkiRJkiRJkiRJkrTdtGPQ/HdbXYAkSZK0UTFGCsUyIzWh8fPTxeUQ+VJ7kZHpeWaK5VaX/AK9nRlu2p+sTr4YLL9hfy+dWVcplyRJkiRJkiRJkiRJ2inaLmgeY3xDq2uQJEnS7lWpRGZLZQrFBWaLZQrzZWaKC8wUl7eFYpmZ+QWm5xeWAuO1q5HPlSqtvo016c6luWqoi66FSa7oihzshm/+qi/jxiN7XaVckiRJkiRJkiRJkiRph2u7oLkkSZK0EcWFChcKSeD7/NQ84zOlFcHw2eLCUkA8CY3XhMlrQuWzpe23wvhmXdHXwbG9PdVHnmP7erh2Xw/7+zqZmpriQx/60NK5hwY6DZlLkiRJkiRJkiRJkiS1AYPmkiRJalvlSuRCYZ6RqSLna1YNXwyTL+1PJ8Hy3SyTClw1nE+C5Iuh8n1JsLy3M9vq8iRJkiRJkiRJkiRJktRkBs0lSZK0o8QYGZ8prQiJr9hOzTMyXeT81DyjhXkqsdUVby+9nRmu3dezYoXya/f1cGSom2w61eryJEmSJEmSJEmSJEmStE0YNJckSdKWiTEyV6pQKC4wM19OtsUFpufLzMwvUCiWmSkuUJgvU5hfqDuvzPR8cv5SW3VrePziOjIphns6GO7tYG9PjuGeDvb2dnBFXyfH9vZw7b4ehntyhBBaXaokSZIkSZIkSZIkSZK2OYPmkiRJWrNyJXJhep6zk/OcnZzj7NQc5ybnOTc1x9nJZDXx+sC4ofDN6cqmGe5NQuOLj709uWqYPAmVJ+05ejoyhsglSZIkSZIkSZIkSZLUEAbNJUmSRKUSuVAocnZybik0nuzPc25y+Xhket7g+AalU4HuXJruXJp8LkNXdTuYzy6HyGtWIl9cjTzf4Z/skiRJkiRJkiRJkiRJar4dk1oJIfx9XVOMMX7VGs5rlIuOJ0mStF1UKpFiucJcqcz8QoX5UoX5her+QpnJuYWl0PhimHzx+Pz0PGUT5Euy6cBQPke+I1MTCk/TncskQfGO5bauXCZ5riNDdzZNd0dyXvJcEibv7kiTS6dcbVySJEmSJEmSJEmSJEk7xo4JmgOvBBbTT6Fm/3LnNcrlxpMkSbvcQrlCsVyhtBCZL5cpLlQolSPFhUryKC9vS7XHdc8tBsPnVgTEK8yXysxVt0ttC+XlIHkpaSuWK63+p9jWUgGG8skq4Xt7OxjuySX71ZXDl7a9HfR3ZQ2FS5IkSZIkSZIkSZIkaVfbSUFzSZKkLRVj5NzUPI+dmUoeZ6d4dqTATLFMqbwcCi+Vq8Hu6r4LgbfWQHd2KSQ+XBMW39vTwXBNgHwonyOdMjwuSZIkSZIkSZIkSZIkrcVOC5qHS+xf7jxJkqQXmJgp8djZJEz+eDVU/tiZKSZmS60ubVdIpwL5XJp8RyZ55NJ05zLkOxa31bbqNjmv+lwuQ19Xhr29HezJd5DLpFp9O5IkSZIkSZIkSZIkSVLb2TFB8xjjmhJEaz1PkiTtDnOlMk+cnU4C5WenePRMEiw/MznX6tJ2vEwqsK+3g319nVzR18EVfZ3s6+2gvzu3FBzv6cjQ3ZEmn8vQnUsvHefSKULwu4GSJEmSJEmSJEmSJEnSdrVjguaLQgj/pbobgd+PMT7bwnIkSdI2sVCu8OyFAo+dmeaxM5PVYPk0z14oEGOrq9tZ0qnA3p4OruhbDpHv6+1cPq7uD3bnSKUMi0uSJEmSJEmSJEmSJEntaMcFzYG3kITMAT4BPNuySnaZEMIx4B7gMJADxoBHgU/GGF0WVpLUEDFG5hcqzBbLzC2UmS2WmS2VmStVmCuVV7Sfn57n8TNTPHZ2mqfOTVMsV1pd/raWCjDck6w8fkVfB3t7l1ciXw6TdzKUz5E2QC5JkiRJkiRJkiRJkiTtajsxaA4QWA6ba4uFEF4L/CRw9yVOmQ4hvAv4qRjjSLPqkiRtD6VyhbGZIqOF5cdYocjU/AJzxTJz1dB4EhZPHovB8dniyrbF9naVSQVymRTZdIpcJkWubtuZTdGRSdORSdFR3V/RlknRkU2v3Gaqz2dTdFa3S23Vfjqr5+bSKUIwQC5JkiRJkiRJkiRJkiRpdTs1aG7IvAlCCB3AO4HvWOXUHuAHgW8NIXxLjPGjW16cJGlLxBiZml9grFDkQjUwvrgdnSkyOl1kbKamrVBkcm6h1WVviX29Hdywv5cbrujlir7OJBD+gpB4IJdOV9vDyvB4/bnpFClXCZckSZIkSZIkSZIkSZK0Q+zUoLm2WAghBfwR8A11T5WB48AEcDXQX/PcXuAvQwivijF+qimFStIuEGNkoRIplSuUysl2obx4vNxWKleS8xYqlKrbhUqFYjmyUHNucaHCxGwpWX18ZjkwPlpIQuSl8u76PldvZ4Yb9/dy/RW9S9vrr+hlMJ9rdWmSJEmSJEmSJEmSJEmS1DIGzXUp/5EXhsx/A3hbjPEULIXRvwF4O3C0ek438MchhFtjjBNNqlWStp0YIzPFMhOzJSbnSkzMlJiYXX5Mzi0wOVvXNltiam6BUrlCsRomX6hUdl3we6t0ZFJLIfIb9vdUg+V9XNHXQQiuNC5JkiRJkiRJkiRJkiRJtQya6wVCCHuA/1TX/OMxxp+vbYgxVoA/DSF8Bvg4cFX1qcPAvwfevMWlStKmXW618GLd/lw1OL4yMF5iYnZhRVh8MUC+UDEg3grpVODq4Tw3LIXKk8fRoW7SKQPlkiRJkiRJkiRJkiRJkrQWBs11MT8C9NYcfxT4hUudHGM8GUL4HuBva5rfFEL49RjjhS2qUdI2NlNcYLRQZLRQZHymxEIlCWyXK0moe3lbWT4uX6J9xfMXaa+evxgSrw2ML4bGizX79c+7WvjO05VN05VL05lJ0ZVLc9WePNfv710Klh/bl6cjk251mZIkSZIkSZIkSZIkSZK0oxk01wohhBTwhrrmt8QYL5vEjDH+XQjhY8DLq029wOuB/9H4KiU1U7kSGZ8pLgXHx2aKXCgUGSvUbWeKjE4XGZ0pMleqtLpstVBvZ4ahfI7+rmxNKLy6zabpzKaS9mz1OLe4v9zeUd0m16SWzu3IpAjBVcklSZIkSZIkSZIkSZIkaasZNFe9lwB7a46fBj68xmvfyXLQHOC1GDSXtoVKJTJTKjNTXGC2WGam+piaKy0FyJdC5NMrw+TjsyUu/1UTtbNsOjCUzzHYnWNPT7Idytc9unMM5nPsyecY6M6Ry6RaXbYkSZIkSZIkSZIkSZIkaZMMmqvea+qOP7jaaua159YdvzKEkI8xFhpQl7RtxRipRKjESCVGYoRYewzEysrjxfPqt7XXzS9UmCmWq8HwBWZLZQrzNWHx0vJzM0vh8ZVB8uSaBeYXXGF8t8mmA9l0ikwqkMukyKRSZDOBbCpFJh3o6cgwlO9gKJ9dConXhsn35DsYzGfp6ci4grgkSZIkSZIkSZIkSZIk7UI7PWj+X0MIY00aK8YYv6pJY7XSnXXHn1zrhTHGUyGEZ4Grqk054Gbgs40oTGqESiVSKC4wNbf4KDE1v7w/Xds+t1B9rrR0/vR88ihXlkPl0lp1ZdP0dWXo78ouPfq6svR1rjzuyKSWg+Lp5f2VwfHltmw6kEmnyKWTEHkmFQyHS5IkSZIkSZIkSZIkSZI2ZScHzQNwexPH2i1x0pvqjh9e5/UPsxw0X+zPoLm21PvuP8VYoVgXDl8OiE/X7hcXDIdrU3o7Mkk4vCtLf31ovDNLf/elAuQZOjLpVpcvSZIkSZIkSZIkSZIkSdKa7OSguRoshNAFHK1rPrHOburPv2HjFUlr87Y/f5jzU/OtLkOXkArQkUmTSQXS1dW206lAJpWqbpPjdCqQSQfSqVTNObXb1EX6SNrTKcikUuSqK4HX72czKXI1+9nF1cAX9zMXXy18aT+TrBaeTSc1S5IkSZIkSZIkSZIkSZLU7nZ60Ny0X2MNs/LftAScW2cfJ+uO922qIiCEsA/Yu87LjtUeTE9PMzk5udlStEGFQuGyx5vVnU01tD9dXncuzWB3lsGuDAPd2ep+sh3ozjLUnWWgK5O0d2fp7cyQCjvpP9ex+qgkhxWgCMVkI0mqs9XzvCRJah3neUmS2pfzvCRJ7c25XpKk9uU8v/tMT0+3dPydHDSPwC8DD7e6kDbSU3c8E2OM6+yj/r9a9X1uxA8Ab95MB5/5zGc4c+ZMA0pRI3zmM59paH9xPo3fO9mYFJHuLPRkoCcLPZlIvnqcz0Z6spDPQE82Vtsgm1oALrKC/Hz1MQbjJI9nmngvkqTtodHzvCRJ2j6c5yVJal/O85IktTfnekmS2pfzfPs7fvx4S8ffyUFzgL+OMf59q4toI/Wh8LkN9DG7Sp9Sw3VmIu0cNA9EcinIpVnadqQgl47V7QbbU5BNwY5abFySJEmSJEmSJEmSJEmSJDXFTg+aq7E6646LG+ijfpnjrg3WIq1ZV3pz16dDpDOd9NOZgc50rNmvtqcjXdXjjjRkUkm0PQAhxJp9Xrhf00aAVE0bQKpmP1Sfh2QMw+CSJEmSJEmSJEmSJEmSJKkVDJqrVv0K5rkN9NGxSp8b8Q7gPeu85hjw3sWDe+65h5tuuqkBpWgjCoXCip/ouOeee8jn8w3r//OVp5l4YpSezgy9HWl6OjL0XGJ7sec7MimCSW5JkjZkq+d5SZLUOs7zkiS1L+d5SZLam3O9JEnty3l+93nkkUdaOr5Bc9WarjuuX+F8LepXMK/vc91ijOeAc+u5pj403NPTQ19f32ZLUYPk8/mG/u/x1m+8s2F9SZKkzWn0PC9JkrYP53lJktqX87wkSe3NuV6SpPblPN/+enp6Wjp+qqWja7upD4V3h/Uv81z/1ZhNB80lSZIkSZIkSZIkSZIkSZIkNZdBc9UaAWLNcRbYt84+DtUdr2slckmSJEmSJEmSJEmSJEmSJEmtZ9BcS2KMs8Dxuuaj6+ym/vxHN16RJEmSJEmSJEmSJEmSJEmSpFYwaK569cHwm9d5/U2r9CdJkiRJkiRJkiRJkiRJkiRpmzNornr31R2/ZK0XhhAOAFfVNJWAhzdfkiRJkiRJkiRJkiRJkiRJkqRm2qlB81B9qPH+vO74VSGEtf5b/+O64w/FGKcbUJMkSZIkSZIkSZIkSZIkSZKkJsq0uoANuLpm/0zLqmhfnwRGgOHq8TXAK4EPreHaN9Ydv7dxZUmSJEmSJEmSJEmSJEmSJElqlh23onmM8bmax3yr62k3McYK8K665jevtqp5COGrgJfXNE0Bf9zY6iRJkiRJkiRJkiRJkiRJkiQ1w44LmqspfgGYrjl+BfCjlzo5hHAI+O265l+LMY5sQW2SJEmSJEmSJEmSJEmSJEmStlim1QVo+4kxjoQQfhb42ZrmnwshHAV+OsZ4CiCEkAK+Hvg14GjNuaeA/9qsei8hV3vw5JNPtqoOAdPT0xw/fnzp+JFHHqGnp6eFFUmSpEZxnpckqX05z0uS1L6c5yVJam/O9ZIktS/n+d3nIvnX3MXO2yohxtjM8bRDVEPk7wX+ad1TZeA5YAK4Ghioe34W+OoY4ye2usbLCSF8PUn9kiRJkiRJkiRJkiRJkiRJUjv4hhjj+5o1WKpZA2lniTFWgNcBf1j3VBq4BriLF4bMLwCvbnXIXJIkSZIkSZIkSZIkSZIkSdLmGDTXJcUY52KM3wZ8C3DfZU4tAO8Abo4xfrgJpUmSJEmSJEmSJEmSJEmSJEnaQiHG2OoatEOEEK4FvhQ4BOSAceAR4BMxxrkWlvYCIYR+4BU1TSeAYovKERwD3ltz/A3AUy2qRZIkNZbzvCRJ7ct5XpKk9uU8L0lSe3OulySpfTnP7z454EjN8UdijBPNGjzTrIG088UYnwSebHUda1H9P9H7Wl2HEiGE+qanYowPtaIWSZLUWM7zkiS1L+d5SZLal/O8JEntzblekqT25Ty/a93bqoFTrRpYkiRJkiRJkiRJkiRJkiRJkrQ9GTSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCgbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCgbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStEKm1QVI2hXOAz9VdyxJktqD87wkSe3LeV6SpPblPC9JUntzrpckqX05z6upQoyx1TVIkiRJkiRJkiRJkiRJkiRJkraRVKsLkCRJkiRJkiRJkiRJkiRJkiRtLwbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCgbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCgbNJUmSJEmSJEmSJEmSJEmSJEkrGDSXJEmSJEmSJEmSJEmSJEmSJK1g0FySJEmSJEmSJEmSJEmSJEmStIJBc0mSJEmSJEmSJEmSJEmSJEnSCplWFyCp/YUQjgH3AIeBHDAGPAp8MsY418raJEnabkIIAbgKuI1k7hwA5knmzyeAzzZ6/gwh9AIvBa4H+oBZ4DmSufpUg8e6BXgRcABIAxeAB4FPxxgXGjhO0+5JkqTtKoTQCbwEuBEYBIrA8yTz7tMNHqspr/2beU+SJDVCCOEG4A6SObKb5PXpWeBx4P4Y4/wm+naulySpyUIIHcBdwE0kc1UXMAmcA74APBljjA0YJwN8KXArsAcoA6eBz8cYH9ps/3VjHQK+HLiS5ft5HPh4jHG6geM07Z4kSWoWX5tveixzhTtAaMDft5J0USGE1wI/Cdx9iVOmgXcBPxVjHGlSWZIkbTshhEHgtcA/Ab4SGL7M6SXgA8DbY4wf2eS4VwNvBV5P8qKtXgQ+Arw5xvjRTYwTgDcAP0oS/L6YC8D/AH4+xljYxFhNuSdJkhothPAHwD+ra34uxnjVBvraC7wZ+JdA/hKnfR54W4zxvevtv26s19KE1/7NvCdJkjar+uXnfwt8D3D1ZU4tAp8B/m+M8dfW0b9zvSRJTRZCeBHwJuBbgI7LnHoSeCfwazHG0Q2M0wP8GPD9wNAlTnsM+AXgXZsJtYcQXgG8BXjlJU4pAn8E/JcY47ObGKdp9yRJUvULVPeQfLnpHuDFQG/NKRt63/0i4/jafBPMFe4sBs0lNVz1W9zvBL5jjZecB77FsJckaTcKIfx3kg+eLxaKXs3vAf82xji5gXFfD/wOyWpqq4nALwI/vt43eEMIA8AfA1+9xkueBr5+I6uXNOueJElqtBDC1wHvu8hT637DO4TwSuA9XP6La7V+D/hXMcbiOsdp2mv/Zt2TJEmNEEL4p8BvA1es47KzMcb9a+z/lTjXO9dLkpomhJACfhb4j0BqHZeeBf5ljPGv1jHWbcB7ufwX1Wr9NfCtMcaJddS1uDjML5Dc01oUgO+KMf7JesapjtWUe5Ik7W4hhJcC/4EkXH5wldM3HTT3tTmwA+5JjWPQXFJDVV9o/z/gG+qeKgPHgQmSF5H9dc/PAK+KMX5qy4uUJGkbCSF8DnjRRZ5a/MnIs0CW5Ccr6+dPSFY++6r1/HxlCOF1wB/ywjfFzwMngH3AISDUPf/2GOOb1jFOF/Bhkm+K1yoCzwLzwDW88NvQ54GXxBifXMdYTbknSZIaLYTQDzxEMk/VW9cb3iGElwF/Q/Iz17XGgWdIfuLyCJCue/7/kbxRu6Y3Cpv52r9Z9yRJUiOEEN4E/Fde+NpzDjgFjJDMaQdY+cHtmoLmzvUrONdLkpoihPBbJIvF1JsBngJmgT0k73XX/w1QBF4bY/zLNYxzA/BxXhjumiZZoKULuIrk84JanwK+MsY4t9oYNWP9N+AH65oj8DzJe+pHL1JHGXhdjPFP1zFO0+5JkrS7hRB+GPjVNZ6+qaC5r81X2Lb3pMZaz7ctJWkt/iMvnAx+AzgaY7wmxngXyc9hfRPJBLGoG/jj6gfskiTtVuPAO4DXAIMxxiMxxhfHGO8geaP6K4CP1V1zD8lPRq1JCOEYyarfta8F7id503ZfjPFFMcYjwE0kL/Jq/XAI4ZvWcT+/wsqQeQV4G7A/xnhDjPF2kr8L3gCM1Zy3l+TvgvoXqtvhniRJarRfYjlkXthoJyGEQZKfs659I/g54LXAUIzx7hjj1SQf4P7Pusu/ieTnv9eqKa/9m3xPkiRtSgjhjSSvg2sDZn8JfC0wEGM8FmP80hjj7THGvSTz/78A/oQkhLZa/871KznXS5K2XAjhW3hhyPxhkvfw+6vz+pfGGK8l+TWTN7NyXs8Bv1ud8y43ToYXriA6CnwXyZx4R4zxemA/8DMk77Uv+nKSX+9c6z29nheGzP8EuCHGeLT6fvpe4FXAF2vOSVfv5ao1jtO0e5IkaRVrXrBtNb423xn3pMZzRXNJDRNC2EPyLabemuYfjzH+/CXOP0TyDearaprfGmN885YVKUnSNlNd0XwP8NPAu2OMs6ucnyYJo39v3VNfGWP80BrGezfwbTVNnyX59u/kRc4NJC/sasd6Crgxxriwyjg3Ag+y8lvN3x5j/INLnH8Lyd8FAzXN3x1j/J3LjVO9tin3JElSo1V/ivLvSQJpFeDHWPlB6ppXVgkh/Czw4zVNzwAvizGeusT5P0HyQe6iCeDqGOPYxc6vua5pr/2bdU+SJG1WCOFa4AGgs9pUAr7rUq+BL3L94BrmYOd653pJUpOFEB4Abq1p+hzwyhjjJb8oHkL4SuCvgUxN80/EGH/uMtd8LyuDW2Mkc+LDlzj/24H/U9O0ANwcY3ziUmNUr8sBj7Fy3v4N4AcuthJpNcz1t8CLa5p/L8b4XZcbp3ptU+5JkiRYsaL5FPB5ks+LP1PdXg3Ufo6+4RXNfW2+M+5JjWfQXFLDhBB+AfiRmqaPkrzQvuR/aEIIX0Xy4nTRFMnkc2FrqpQkaXsJIbwG+GCMcdXVy2quSQP/wMo3d98dY/yOVa67hWQFksWVv4vAnTHGRy5zTWf1mutqmr83xvhbq4z1R8Dra5p+P8b4natc80bgt2uangOuizGWLnNN0+5JkqRGCiF0kQTSjlWbfg34MzbwhncIYS/JT0731DS/Ksb4d5e5JgAfBv5RTfPPxhj/0ypjNeW1fzPvSZKkzQoh/D3Jr5Aten2M8T0N7N+5Hud6SVJzhRCuIVmkpNY9McbPruHa3wC+r6bpUzHGl1zi3BzwJHCkpvmNMcb/tcoYvw/885qmtXxG8P0kC9ksegK4PcY4d5lrbgbuJVmdHaAM3BpjfPQy1zTtniRJgqVfwO4AHo0xVuqeeyUNCJr72nzpmm19T9oaqdVPkaTVhRBSwBvqmt9yuckAoDoxfaymqZeVoTRJktpajPED6wmZV68p88KfjfyaNVz63ax8DfCHlwtkV8eaA+q/RVz/U6ErVH9e65tquwHesob6fockXL7oSpKf57ycptyTJElb4G0sh8yPA/95E339M1a+EfzRy70RDFB9vf5Tdc3fXX2T+KKa/Nq/KfckSdJmhRC+gZUh8/c0MmRe5VyPc70kqeluqDt+fi0h86o/qTu+9jLnfg0rA9nPkrxXvpq3kLz3vuh11RXIL6f+ffCfu1zIHKC6Avkf1TSleeHfC/WaeU+SJBFjfCrG+HB9yLzBfG3OjrgnbQGD5pIa5SXA3prjp0m+vbQW76w7fm0D6pEkqd19rO54Twihe5Vrvr7uuH4OvpQ/Amp/CvRLQggHL3P+a1j5s6AfjjE+vdog1Rf+9W82v3aVy5p1T5IkNUwI4UuAH65p+jcxxulNdPkNdcdrnQ8/RPJTlYv2A192mfOb+dq/WfckSdJmfW/dcf2HrY3gXL/MuV6S1CxDdccn1nHt8brjgcucWz8n/s5qoStIAnXAR2qassCrL3V+COEwcHdN0zTwx6uNU1U/T9fXXK8p9yRJUpP52nzZdr4nbQGD5pIa5TV1xx9cy4vFxXPrjl8ZQsg3oCZJktrZ2EXaLrmyRwjhBlaumlIAPrmWgWKM9ecGXjj316p/7m/WMk5V/d8F//RSJzb5niRJaogQQpbkjdF0tek9McY/30R/Paz8iUpY49xbfd3+t3XNl5x7adJr/ybfkyRJGxZCOMTKXxi7L8b4UIPHcK6v4VwvSWqiibrjrnVcW3/uyGXObcr76RcZ5xPV98nX4hPATM3xDSGE69Yx1lbdkyRJTeFr85W26z1p6xg0l9Qod9YdrynkBRBjPEXyc1mLcsDNmy9JkqS2dugibRcuc/6ddcefiTEurGO8T6zS3+WeW/PfBcDngfma44MhhL2XOLd+nK28J0mSGuXHgduq++PAD22yv1tIVvha9EyM8cw6rm/KHL/O1/7NvCdJkjbjn7D85TFIVvRqNOf6F3KulyQ1w311xzetI9R0T93xZy52UgjhCpIVQBfNA19Y4xjQvHl+gRfew0XHavI9SZLULL42f6HteE/aIgbNJTXKTXXHD6/z+vrz6/uTJEkrvbzu+LkYY/Ey5zdlrq6u0nptXfOax4oxzgNPrWWsi7T794ckaVsLIdwM/Keaph9d5xu3F9PM+bBZYznHS5J2ii+pO75/cSeEcFcI4ddDCPeHEMZCCDMhhGdDCB8MIfx/1dXQ18K5fuPjSJK0YTHG51kZgupgDV8WDyF0AD9c1/zOS5xeP4c9ucr7/PXq58RrQwiZNY7VrHl+K+9JkqRm8bX5xsdp9ljaAgbNJW1aCKELOFrXfGKd3dSff8PGK5IkaVf47rrjv1jl/Pq5davm6muA2jd9Z2OMl/tZ0M2M1ax7kiRp00IIKZIPlnPVpo8Bv9WArhs9H14ZQuisP6nJr/2bck+SJDVAfdD86RBCTwjhnSQrd/5b4HZgAOgCrgReBfwS8EQI4WerX9i+HOf61cdxrpckbZUfBSo1x28NIXzXpU4OIQwA/5eV4af3xxjff4lLNjUnxhjPA3M1TTng6q0Y6yLnb8k8v857kiSpWXxtvvo42+GetEUMmktqhGEg1ByXgHPr7ONk3fG+TVUkSVIbCyG8GvhHdc3vWuWy+rn1+XUOWz9X713jOPXXbWSsS/1d0Kx7kiSpEX4I+LLqfhH43hhjbEC/m50PzwILNccpYM9Fzmvma/9m3ZMkSZtV/4teFeCjvPDL4RfTBfw48BchhN7LnOdc/0LO9ZKkpogxfhz4QWDx9XsGeFcI4TMhhB8LIXxjCOGfhBD+eQjhv5H8Wuc/renig8C3XWaIzc6JAKdW6XNR/fvfm30/favmeVj7PUmS1Cy+Nn+h7XhP2iL+vIykRuipO57ZwIflhVX6lCRJQAhhCPifdc1/FmP8zCqX1s+t9XPvaurPz4YQOmKM8w0e52LXXOrvgmbdkyRJmxJCuBr46Zqmn4sxPtqg7jc1H8YYYwhhFqgNuF1s7m3ma/9m3ZMkSRtW/bWS+oD4rwN3Vfcj8Ockv0D2PJCvPvcvgIM117yK5Mvj33yJoZzr6zjXS5KaKcb4P0IIj5HM87dUm7+EF/6ySa2ngV8EfivGWLnMeU15P726kmh6k2M163379YwlSVKz+Nq8zja9J20RVzSX1Aj1//Geu+hZlze7Sp+SJO161Q+x/zdwuKZ5gmSF1NVsdr6un6sv1mcjxrnYWGt98btV9yRJ0mb9Jkm4DOBR4Gcb2Hez5t6dNMevZyxJkjaqn5UrcgHcXd1eAF4RY/z6GONvxBj/PMb4RzHGHyP5eed31133TSGE77zEOM71mxtLkqRNizH+PUmw/JeB8iqnH6+e9+5VQubQunl+I2M5z0uSdjNfm298LP82aAMGzSU1QmfdcXEDfdSvHNq1wVokSWpnvwR8bV3b98UYT6zh2s3O1xdb5fti83Uz/y5o1j1JkrRhIYQ3kqxUCsnKpt8bY9zI/HgpzZp7d9Icv56xJEnaqEt9qFkGXhNj/NjFnowxTpOsav43dU/9RAihPrgOzvWbHUuSpE0LIfxr4Cng/+OFK4PXOwq8A3g2hPDdq5zbqnl+I2M5z0uSdjNfm298LP82aAMGzSU1Qv03jXIb6KNjlT4lSdrVQgg/BPz7uuZfjDH+0Rq72Ox8XT9XX6zPRoxzsbEu9XdBs+5JkqQNCSEcIFnFbNFvXyp0tgnNmnt30hy/nrEkSdqoS80tvx1j/PTlLqyubvr9QO0qpzcAr1jDOM716xtLkqQNCyFkQwj/F/gfwIFq8yjwVuAeYJBkHjsIfD3wpyRfMgcYAt4ZQvilywzRqnl+I2M5z0uSdjNfm298LP82aAMGzSU1wnTd8cW+Eb2a+m8a1fcpSdKuFUL4duDtdc3vAn5sHd1sdr6+2LeCLzZfN/PvgmbdkyRJG/XfgYHq/hngR7ZgjGbNvTtpjl/PWJIkbdSl5pbfWsvFMf7/2bvrOKuq9Y/j3zXN0N3d3R02BlgIIogKiIGBdb3q76rXe+1uQhFBQBEDUFFs6e7u7pzus35/zOCdGc4+Z/LMmZnP+/Wal5y91t772U7setaz7B5Jv2Va7C7RnHN97vYFAEBujJN0U7rPKyS1tNb+21q70lp7zlqbZK09aq393lo7QNINypj89A9jzAiH7RfUeT4n++I8DwAozrg3z/m+uDYoAkg0B5AXMv/xDneY4tOTkl62CQBAsWSM6S9piqT059ZvJY2y1lr3a7mV+dya+dzrTeb+ydZadyOFc7sfd+tk9eY3v44JAIBsM8YMknRjukUPWWvP5cOucnU+TLt/z8nD4Py89/fVMQEAkGPW2jhJKZkWR0lam43NzM/0uZObPpzrM+FcDwDwBWPMxZLuTLfohKT+1tpjntaz1n4n6f5Mi183xmSl8Em+PE93uG7J7fP0/Hpun519AQDgK9ybZ+Knx4R8QqI5gLxwSv+bAkySgiVVyeY2amb6fCJXEQEAUAQYYy6R9JWkoHSLf5U0xFqb+aGwN5nPrbWyuX7mc/XJLO4n83o52ZfTdYGvjgkAgJxIPzX2XGvtzHzaT27Ph1WV8VrDpdT7/Mx8ee/vq2MCACC3Mp+zdllrXdlYf3umz+7OrZzrL8S5HgDgC2MyfX7HWpvVZ8iTJe1I97mipAFu+uX2nChJNbxs87zMsef2eXp+neelrB8TAAC+wr35hfzxmJBPSDQHkGtpI6APZFpcJ5ubydx/W84jAgCg8DPGdJX0nTJOHbVE0o3W2sQcbDLzy+v8OlfvkZSc7nMJY0zlfNqXr44JAICcKJfu3/2MMdbbl6Q/M22jrpt+7TL1yevz4X53M3z4+N7fJ8cEAEAe2Jrpc2Q218/cv7ybPpzrve+Hcz0AIE+lVdm8NNPi77O6ftrAs7mZFvdx0zVX50RjTBVlfIeQqNRn9O746nm6L48JAABf4d7c+3784ZiQT0g0B5BXMv8Bb5HN9Zt72R4AAMWGMaaNpJ8klUq3eK2ka6y1MTncrE/O1dbaJEm7c7ovY0yopAZZ2Zeb5Vx/AACKI1+eD321L87xAIDCYkumz6HZXD8s0+dYN3041+d8PwAA5FR5SWUzLdubzW1k7u9u9s/M57CGxpiQbOwj8zlxt7U22W3PgjvP5+cxAQDgK9yb53w/vt4X8gGJ5gDyyrpMn3tkdUVjTHVJ9dItStKFD+gBACgWjDFNJf2qjFXMtkq60lobkYtNr8v0ubMxJshdRwc9vWzPU1uWrwskdVTGF/NHrbVOU19l3k9+HhMAAP5qs1Lvo8+rl3afnVU+Ocdn897fl8cEAEBurMn0uWo21888VfRpN30411+Icz0AIL+5GzyW3WTnpEyfAzN3sNYek3Qs0347ZmMfvjrPB0nqkpV9+fiYAADwFe7NL+SPx4R8QqI5gLzyQ6bPl6dNKZYVfTN9/tNaG50HMQEAUKgYY+pK+k0ZXzTvlXSFtfZkbrZtrd2mjJXGSyqLN3DGmJKSuqffnC4896eXue2KrOzHoa/jdKQ+PiYAALLreqWe17Lz9Y9M2zjups+u9B2stVGSFmRaL0vn3rT79sszLfY0FbhP7v19fEwAAOTGXEmudJ/rG2MqZGP9zElXmaet5lyfCed6AICPuBv8VSOb28hcwdzpGf/cTJ/z5Xm6m/30SHtOnhU9JYWn+7zDWrsjG/vKr2MCAMAnuDfPyF+PCfmHRHMAeWWJpFPpPjeQdHEW170z0+c5eREQAACFSdpI3N8l1Uq3+LCky6y1h/NoN99l+pz5HOxksKRS6T6vstYe8dD/R2Ws7nKxMaaBt52k3UwOz7TY23WBr44JAIBssdbOt9b+lp0vSaszbSbeTT93D1Bzej68RFL9dJ+PS1ruob8v7/19dUwAAORY2gxcizMtHpCVddMqg96YafFfDt051/8P53oAQL6z1iZKOppp8aXZ3MxlmT7vdtvrwnPiiKwkXhljGkq6KN2iJKU+m3fLWntQ0tp0i0pJutnbftLk9jyfL8cEAICPcW/+P/58TMgHJJoDyBPWWpekyZkW/9vbDaMx5jJJvdMtipI0M2+jAwDAv6VVO/tVUsN0i08qtZL53jz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- }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" @@ -850,39 +831,23 @@ "\n", "HTs monitor the incoming feature values to perform split attempts. To do so, they rely on a class of algorithms called *Attribute Observers* (AO) or *Splitters* (spoiler alert!). Each leaf node in an HT keeps one AO per incoming feature. After pre-determined intervals (`grace_period` parameter), leaves query their AOs for split candidates. Well, there are costs to monitor input features (mainly the numerical ones). In fact, AOs correspond to one of the most time and memory-consuming portions of the HTs. To manage memory usage, an HT firstly determines its least promising leaves, w.r.t. how likely they will be split. Then, these leaves' AOs are removed, and the tree nodes are said to be \"deactivated.\" That's it! The deactivated leaves do not perform split attempts anymore, but they continue to be updated to provide responses. They will be kept as leaves as long as there are not available resources to enable tree growth. These leaves can be activated again (meaning that new AOs will be created for them) if there is available memory, so don't worry!\n", "\n", - "**Hint:** another indirect way to bound memory usage is to limit the tree depth. By default, the trees can grow indefinitely, but the `max_depth` parameter can control this behavior." + "**Hint:** another indirect way to bound memory usage is to limit the tree depth. By default, the trees can grow until they get close to the maximum recursion limit enabled in the system, but the `max_depth` parameter can control this behavior." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { - "execution": { - "iopub.execute_input": "2022-10-26T11:26:03.569294Z", - "iopub.status.busy": "2022-10-26T11:26:03.568507Z", - "iopub.status.idle": "2022-10-26T11:26:25.165695Z", - "shell.execute_reply": "2022-10-26T11:26:25.166233Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
<Figure size 3000x1500 with 3 Axes>\n",
-       "
\n" - ], + "image/png": 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\n" - }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" @@ -948,32 +913,16 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "execution": { - "iopub.execute_input": "2022-10-26T11:26:25.174881Z", - "iopub.status.busy": "2022-10-26T11:26:25.173304Z", - "iopub.status.idle": "2022-10-26T11:27:28.373180Z", - "shell.execute_reply": "2022-10-26T11:27:28.373721Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
<Figure size 3000x1500 with 3 Axes>\n",
-       "
\n" - ], + "image/png": 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4O+3IMRoNdjtG/Du2xctrW2qp86utPqDWuoDa6gJuu6XO7waw3RB3foDb61HAZyvgtWXxQ3kAS1UuLFschC4IQ6clJ+98Ojcn73xxcLr4fPHaY8aK+umJ5o8X4M6rN/98OsnOwgCAyrHs0dCzG4bOD1DnwtG+Mv3sPI+/6JqiNQrC1tNYf6I13XAzf4cCAAAAAGAmJgq1TikUO4nA7Lhh2vz7TGZ+Xqg3d39Hmc1Tcuu4/dx14zzjmPWKQsS5sfzzBaHi4mvKvI/59RTfrzikPJX3olwQutT7Ue78mGcuUW+pPxPj1Tren52J/lyVrXmhe0h613Hv0nWnXVfpSlCFFk34O+dzn/ucJMmyLL3jHe/Q+vXrK1vQErJz505t27ZN+/btUyKRUHNzs4455hideeaZCgaDlS4PADAJmf9YViaMnD1KhX3j5PqZscx/SOfNcfL7mf+cNtlzuR2qR0PRo+OTDVMXjzu5na+z944l09mQ9tjwdjRR1E+mCVJjxixLagn5M2Hu+sJAd1t9Ybupxieb3bABzIVJ7xBdLnA8nX6JwHS6OEA9XuC6qF/u2oJgdbLS7zQAAFNgTSPIXDTuDeQFqP3Zl6+o7Ssz7p/ctaXqsyf3G4AAAAAAANNXMjhZIlhZKhA73m60jhk/1FocqJxs4LVcbaXqKBe+LRWSda/Pv/84IdH8+RMGeksEdCcK0zrGGfdc8XszXg2lAssF56YQ9h3vvSn7PJP4MzXeMyyqEC2wSPG/L8yVRRf+/uxnP+vuhHjWWWcR/p4Hd955pz7/+c/riSeeKHm+rq5O7373u/WZz3xGra2t81wdACweybSjzr6odnSHtfNwOHuMaH//iNKOkw1RZ+aabKg699+AuYD1aDs3bvLa+fMLx3PrEYTGUuGxLfk9tvze7MuT2VE71/d57ILzNT5PUZjb77aX1frl9RCMABa0ksHocXaHLmjnBZ+Ld2UuOX8S144JYeeCzsUB6vHmFgWm2SEaAFCN7FxQOS+YXLDbc4mx/DCzxz9x284LTrsh6fHaxdeNtxu1p9LvIAAAALBkFewmWhT4nM1dZMe9vkR4cqJdZEuFMSORiLYnt7vPZe23FOwLTlybGSckWyLsWSrgmx8EnWjX3nLh2lLh0rIh3ikEVY1MYWh5Gl+nqfwZmNRuxnn98f4s5b8fAICljc8DzJVFF/6Wsv+xy69xnHPxeFxXXXWVbrnllnHnhcNhfeMb39Btt92mO+64Q+ecc848VQgAC1M4ntLOgoB35rinN6qUQ/oa1cNrW6oPegvC1D6PLZ/XyhzdsWzfW9T35ILYY+d7c+3sWvlB7YDXlt/jGQ12Z9cI5I152H0bKJQLR09qx+gy/XSyaKfmUmHpZF5geaJgdVGQeszO0QSjAQBVzrKzL08mRJw7um1vUT87Znkyuza77ew8y867xpvt51+Tm5v/8uQFmr1Fc3xjr/GUGLM92bl5/fFC1blgtu3J/JodAAAALBizEoYdZ/6kdn2dYZh22sHeEuvnapPGD9NOZnfayez+Ol7dZWvIC9RO+hmL3n/lhXFL7aA71T8HU95JOG+d8e6lotBwVdtW6QIAAACw0C3K8DfB77nnOI7e+ta36q677ioY93g8Wrt2rRobG7Vr1y4NDg665w4fPqyLLrpI9913n175ylfOd8kAMK+MMeoejheFvCPa0R1W11Cs0uUB0+L32moJ+bUs+8q0A2qpKx7zqyUUUEONl/8uQ3VxnMJwcUEwebr9yQaki8LWZYPW+UHq8cLTRX3C0QCwdOXCxbkgsOWRsSwlU2kZy5aRLX8gIDsXFrbsTLDYsgvDyW7bGg0aj5lj5a3hkWS59yy8Lr8eu/CaMeetEvM9RTWWWa/UmgXP5ykxr9S1+fcsfC8L5owZy7umYCzX5r+lAQCodiWDniUClpPaaXQKO6OOCVWOF7Sd4c6s5UKsBfcvFQKdYBfccjvKTup9mWQgWdKYdSYVVJ1MQLlMncXvQangc/H7M+ZrM82v1XjB5CURpgUAAAAqgJ2/MVcWZfgbc++f/umfxgS/P/CBD+hTn/qUVq5cKSkTEL/rrrt0zTXXqLOzU5IUjUb1lre8RU8//bQaGxvnvW4AmG2ptKM9fVHt7A5rx+GwdnZHtONwWC91hzUcT1W6PGBcNT5PJqg9JrwdGA1512XGWuoCCvk9hLkxOcZkQsWJ6NgA8kS7O6fzw9FFxzHn89css07JMHW5e5RaI28uf/EGUK0sW5I1GsyVNRo8LTiXF9wtnjNmbDJzS9ynZGi4XOA2bz2rVDB3gnVyz172OXIvlTk33vOUCy2PU/O4gepSc0oEmksGiMuFpnPB6bH/fTc8NKStW7e6/S1btqihoWEu/xQCADCrphOwnGzItCCcO1GgdYpB0+IQbLm54wVZ8+8fjUa1Pb7dDWwO7BpQMBCcci3TCdSOed/y35dpfj1KBqSLvgbjrT/pZ5zg6zVRMLdU2JYwLQAAAACgFP5eiLlC+Btj9Pb26otf/GLB2Je//GV9/OMfLxizbVtvfOMbdfrpp+vss8/W7t27JUn79u3TP//zP+uGG26Yr5IBLCHGGA2OJHVgIKaecFzxlKNEylEinc4cU05mLO24/URRPz7OuVw7s25a0URaKYf/EKs2HttSyO9RKOBVrd+jWn/mGAp4VeP3KOC15bEs2ZYl27bksZXp25Z7tK3xxzPHzMuyMueLxz22Ja9tyWPb8tqZ671544V9u+ia0nNsW5mjJYLclWJMUaC51Cs9QX8yc/L67o7QRQHs4jB0yf54u0yPnqtPJ/WGZFyWScuWI/2x0m80AJRgeSSPT7K9mfCr7ZXs4r53bN/jG/98yX6Je3i8o/38dvG5Kfc9RWHmEiHu8QLdAABMwkQhUGmCXUdz/SkGb2cjnDlba5d6psmEeku9B5PeEXaSu/DORZh2vK/3TMO0U6lzOjseo4w/VroAAAAAAJh7ljI/g7eV2dzDlp3pZ7+Pblu2O8eSNaaff23ufO5ad37e3NxYqbVza5WbX1BDmXpKrjnJZxmv1vHOW5mHL1l7qfenXA2lzhXXXvzsuXPTeh9y52fpWafydSle310v73x4OKyHHnrIrXXLuVvU2MAGupgbhL8xxj/+4z9qeHjY7Z9zzjn62Mc+Vnb+qlWr9L3vfU+vfe1r3bEbb7xR/+f//B+1tLTMaa0Aqk8smdaBgREdGIjpwOCIDg7EMv3BER0YGNHBwZiiiXSly8Q0ebIhacsabdvZ4LJtWbKz4/kBadsaDUzblqWgz3ZD2yG/V7WB7DEb4B7tj4a7i+f7PTbB6EpwnEns+Fxud+j84HNR310jPXpNrp8u6o97Pu+VTo2932TnVunu0ZYkT6WLADA9lj1+qNgNSxf3i16eEmNlw9NF/YLzRWHsUvdz5/vK3L9MrXy+A1igprzr6gQ71E4l8FkyADqZAG+uX7R76UTPMpldYCcTHC53bkyIdRq78Jarq2TQdYq74k7mecd7n6aze26pr3OpPxvjvY+EaQEAAAAAxaHP4kDtmDBkXvhRmmTgtsQcSSUDtKWClZMNZk76XIlnKAi2FtdU6tx065lEqDT/fS0IwY7zPo1Zr8z6Gi/YWyZAW27ctor+zOTVVOo9dN/rCd7D4utztQELVdqblt/yu32v7ZXH5qfsmBuEv1HAcRzdfPPNBWOf/exnJ/zgPO+88/SqV71KDz/8sCRpeHhYP/3pT/W//tf/mrNaASw+qbSj7uF4NsydCXUfHBjR/oGYDg5mgt19kUSly5xztiWtWVarI9rqtKm9ThtbQ2qo8UnK/sZ59//lWm47M24VzBk9l/lXiuXm5ebkdoK2Lcm2LXduLlxtZ0PZmd2uS1yTPyd7fS6wbeUC3Xlhbk92HdsaDXljAgU7RucHjsvsyDxheDpVdO04/YJwc5mw83j9Me2imp1U1YaiASxy+cHogl2b88LHY9r5AeXi9kQB6jIh6AnnlwpBT3IHaduu9LsMzJnJhDInDEnOdKfYSYZp53OX2lL9/HYsFtOLsRczdcloz3N75Av4xgRqJ/OM5cLJUw0Tj/f8+fee1M62MwzVlqtlMs9RcidbQrUAAAAAsOSNG7iUpXQ67bZ9Pp9s2y4ZhJ1sWLNcoFLKC7dOsINrqfBmqRDmeGHaUuu6/zeFoOlEoddSu8zmP2u5c6WeY8zc8XaLLdGXVf5ekw02j3tugrXHe+Zy4etcHwAALB6Ev1Hgscce0+HDh93+xo0bde65507q2quuusoNf0vSnXfeSfgbWETSjlEy7SjlGKXSjpJpo5TjKJXOjGfOZ8aS6cycVO6a/PHsMRpP6eBgTPuzu3UfGBjRoaGYnCX0M++gz9bG1kzAOxf0PqI9pPUtIQV9/Mu+GTFmdBfngh2kS/XzA9PF/aLrygWpJwpPuztTjzd3guvSScmwqz2ABcjylAg554eMi9rjhqPLXVccuC51vmg3aU9ecHqi3aGL6ykIai+dYPRMd52VVDIgOVGYMz8A6a4hU3Cf6YRxy9UzqYDoFHaJLfcsE96zRCB1snMnu0tt8Xs+5uucnVNw3xJj5WorFaYd789Lqa9zybDtFILP+V9ndqmdQ9srXQAAAAAATF2pQOyUA6m53W2LA5LjhGmlcXaGneHur+PuDFui/okCoFPZGXbS1+fVmn/fcfuTqaPEvcs+V1GItuSzTOL9ntSfkaI/E+OGeUsEnCezS+3Q0JC2bt3q9rds2aKGhoZZ+d8JAAAAqhPhbxS4++67C/rnn3/+pP913/nnn1/Qf+CBBxSJRBQKhWatPgClxVNpDUST6osk1B9NuO2BaEL90aT6s+P90aQGogkNx1J5QW+jpOPIkJuYtpaQX0e01emI9jod0RZyw96rmmoW3m7Xud2c04nR4HM6URSKTozTThZdV6JdKtRcvCP0mDB0Xjg7f7frMTtZ580DgHliJDnZo/uyJEeWGzt0LI+MxycnG0Y2tleO7ZXx+GTs0XPG9mb6tle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- }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" @@ -1022,32 +971,16 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "execution": { - "iopub.execute_input": "2022-10-26T11:27:28.378822Z", - "iopub.status.busy": "2022-10-26T11:27:28.378261Z", - "iopub.status.idle": "2022-10-26T11:28:28.621256Z", - "shell.execute_reply": "2022-10-26T11:28:28.621813Z" - }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
<Figure size 3000x1500 with 3 Axes>\n",
-       "
\n" - ], + "image/png": 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WmI7ddNNNHts4r7MtW7YsV5/O6/bhw4d96se53Zn0VdZzhEDNCQAAf3r11Vf166+/SpLCw8P1zjvvyGKxVPi6FV0XmzRpotDQUEfZbrcrIyPDpV4g3w8I1JwAAPAn50/5slqtGjx4sMsN4+7k5uZqypQpGjVqlE6ePFlmPdZ9V6z7AIBAGzRokF5//XXHa/qioiLddNNN6tu3r5599lnNnDlTP/zwg/7v//5Pd999t9q1a6c5c+Y42o8cOVKff/55mdev6NooSc2bN/d4zRLO741X9L32ylrvJd/nBABAoPFa3VVVnBMqV6j3KgBqk6ysLFM5Ojq63L8Yj4mJ8XhNAADg6ujRo7rttttMx8aMGaO+fft6bOe8zjqvw9441y8sLFR+fr4iIiL82o+7NmU9RwjUnAAA8JeUlBQ9+uijjvLDDz+szp07++XaFV0XLRaLoqKiTKE2d2twIN8PCNScAADwF7vd7hIQv+eee7R27VpJxWvTJZdcolGjRqlly5bKzs7W2rVr9cknn2jfvn2ONvPnz9dNN92kr7/+2m0/rPuuWPcBAMFwxx13qFOnTrrnnnu0adMmScWfbuL8CSeltW3bVg888IBuvfVW047ozgL1Xntubq5sNluF+grUel+evgAACDReq7uqinNC5WJHcwAmzv8Yl/6IKl9FRUV5vCYAADCz2+26/vrrlZ6e7jgWHx+vV1991Wvbiq7dzuu2u2v6ox93ffn6wray5gQAgL/87W9/U3Z2tiSpc+fOmjhxot+uHag1uDqt9eXpCwAAfzh+/LhpBy5J+v333yVJDRo00OLFi/XNN9/o9ttv1yWXXKKrr75azz77rLZu3aprr73W1G7GjBn6+OOP3fbDul+xvgAA8KfzzjtPq1ev1v3336+QkBCPdVu3bq37779f1157rceQuRS89f5M+mK9BwCA1+oV6YvnCDUHQXMAJnl5eaZyeHh4ua/hvFtobm5uhcYEAEBNN2HCBH3//femY2+//bZatWrltW1F1253u3y7W7sD+RwhUHMCAMAf3n//fc2fP19S8S4e77zzzhmtk2UJ1Bpcndb68vQFAIA/lPVLzJCQEM2dO1fnnnuu2/OxsbH65JNPdP7555uOT5482SW4LrHuV7QvAAD86a233lK7du30wgsvuOwM7iwtLU133nmn2rRpo6lTp3qsG6z1/kz6Yr0HAIDX6hXpi+cINQdBcwAmzncOFRQUlPsa+fn5Hq8JAABOe/XVV/Xiiy+ajj3wwAO6+uqrfWpf0bXbed12d01/9OOur7KeIwRqTgAAVNT+/ft1//33O8p//etfywyanalArcHVaa0vT18AAPhDWevMX//6V/Xr189jW6vVqjfffNO0u+nWrVu1ePFir/2w7pevLwAA/KGwsFBXXHGF7rjjDu3fv1+SVL9+fT3++ONatWqVjh07poKCAu3bt0/ffPONxo4dK4vFIkk6evSoxo8frwkTJpR5/WCt92fSF+s9AAC8Vq9IXzxHqDkImgMwiY2NNZXd3ensjfOdQ87XBAAAxT777DP94x//MB276aab9Oyzz/p8jYqu3e7u+HW3dgfyOUKg5gQAQEXdddddyszMlCQ1bdpU//nPf/zeR6DW4Oq01penLwAA/KGsdebWW2/1qX3btm01YsQI0zF3QXPW/Yr1BQCAP9xxxx36+uuvHeW+fftq06ZNmjRpkvr06aO6desqLCxMzZo106WXXqoZM2Zo1qxZptDTCy+8oA8++MDt9YO13p9JX6z3AADwWr0iffEcoeYgaA7AxPkf45ycHLcf4elJdna2x2sCAABpzpw5uvHGG03r7Lhx4/Tee+85dj/xhfM667wOe+NcPzQ01O1dwBXtx10bX1/YVtacAACoiGnTpmnmzJmO8iuvvKK6dev6vZ+KrouGYZzRG76V+X5AoOYEAIC/REVFKSQkxHQsLi5OZ599ts/XGDJkiKm8Zs0alzqs+65Y9wEAgbRo0SK9//77jnLjxo01Z84cNW3a1GO7yy67TP/73/9MxyZMmODTpiiV9V67u+cvFX2vvbLW+/L0BQBAoPFa3VVVnBMqF0FzACYNGzY0hdsKCwt16NChcl1j7969pnLjxo39MjYAAGqKhQsX6sorr1RRUZHj2MiRI/X555+7vPHrjfM6m56eXq72zut2o0aNfOrHud2Z9FXWc4RAzQkAgIoo/THYF198sa666qpK6aei6+LBgwdNzzmsVqsaNmzoUi+Q7wcEak4AAPiT8/rVvn17Wa2+/5qtU6dOprK7dZZ13xXrPgAgkF599VVT+R//+IfP7y/fdNNN6tixo6OckZGhGTNmuNSr6NooSfv27fN4zRLOY6/oe+2Vtd5Lvs8JAIBA47W6q6o4J1QuguYATKKiotS6dWvTsbS0tHJdw7l+586dKzwuAABqipUrV+qyyy4zfSzUgAEDNHPmTIWHh5f7es6/qK6sdbtt27YKDQ11lHNzc3X48OFK6StQcwIAoCIyMzMdj+fOnSuLxeL1a9iwYaZr7N6926XOunXrTHX8vS4mJCS4/aSPQL4fEKg5AQDgT126dDGV69SpU672zvWPHTvmUod133s/rPsAgMpiGIZ+/vln07FLL73U5/ZWq1UXX3yx6diSJUtc6lV0bTx06JDp9wvh4eFq27at27qBeq89kHMCACDQeK3uvZ+qMCdULoLmAFw4/4O8efPmcrVPTk72eD0AAGqr9evX66KLLlJWVpbj2Nlnn63vvvtOMTExZ3TNQK3bYWFhateu3Rn3lZ+fr127dvnUF89FAAA4LZDrYqD6Yq0HAFRHSUlJpnJ+fn652pcOT0lSdHS0Sx3W/TPvBwCAijp27JiOHz9uOpaYmFiuazjXd/fJoM5r2c6dO1VQUOBzH85rY7t27UybxHjqK1DrfWXOCQCAQOO1+pn3E+i+UHkImgNw0bNnT1N5xYoVPrfdv3+/UlNTHeWwsDCXN+ABAKiNtm7dqpEjR5p2LOvSpYt+/PFHxcfHn/F1ndft1atXmz6mypvly5d7vJ6nc+V5jvDbb7+ZfgnfrFmzMj/WKpBzAgCgquvatavCwsIc5dTUVO3fv9/n9oFa68vzfkAg5wQAgL+cc845pvLBgwfL1d75o6EbNGjgUod13xXrPgAgUNzdRFbesHPpNU+SbDabS52mTZuqadOmpn5/++03n/sI1HpfVFSkVatW+dRXIOcEAECg8VrdVVWcEyoXQXMALi655BJTef78+TIMw6e2P/30k6k8bNgwxcbG+m1sAABUR7t379aIESNMv1ROTEzUvHnz1KhRowpdu3PnzqadxrOzs31+cZadna1ffvnFUbZYLC7PA0pzPjdv3jyfx+lc19NHjgZyTgAAnKnZs2dr3rx55fp64YUXTNdo0qSJS5327dub6sTFxWnw4MGmY76uwYZhaP78+aZjntbgQL0fEMg5AQDgLxdffLGs1tO/VktJSdHRo0d9bu8ctnL+mGqJdd8Z6z4AIJDc3QS2b9++cl3DeQfzst7/v/jii03lynqv3bmfFStWKDs726d+li9frpycHEe5Y8eO6tixo899VdacAAAINF6rm1XVOaFyETQH4GLAgAFq2LCho7xr1y4tWrTIp7bvv/++qTx69Gh/Dg0AgGpn//79Gj58uNLT0x3HWrRooQULFqhFixZ+6eOyyy4zlZ3X47J8+eWXysrKcpR79+6t5s2bl1l/1KhRph1cFi1apF27dnntxzAMffjhh6Zj3p4jBGpOAACcqSFDhmjEiBHl+urVq5fpGpGRkS513L1Jeqbr4sKFC5WSkuIoN2nSRP369SuzfiDfDwjUnAAA8JfGjRtr4MCBpmMzZszwqW1RUZFmzpxpOjZ06FC3dVn3T2PdBwAEUnh4uJo1a2Y69vPPP5frGgsWLDCVS2+oUprz2vjBBx/4FLjauXOnFi9e7CiHhYVp1KhRZdZv1aqVzj77bEc5KytLX331ldd+pIqv95U1JwAAgoHX6qdV5Tmh8hA0B+DCarXqpptuMh2bNGmS1xeCCxYs0NKlSx3luLg4XXXVVZUxRAAAqoWjR49q5MiR2rlzp+NYo0aNNG/ePCUmJvqtn1tuuUUWi8VR/uKLL5ScnOyxTV5enp599lnTsfHjx3tsU79+fY0ZM8ZRNgxDTz75pNfxTZ061fSRVgkJCRoxYoTHNoGaEwAA1cE111yjmJgYR3nJkiVef9ltGIYmTZpkOnbzzTebdmJ1Fsj3AwI1JwAA/Om2224zlZ9//nnl5+d7bffuu+/qwIEDjnKdOnV0wQUXuK3Lul+MdR8AEAzDhw83lV9++WUVFRX51Hbx4sWmT9t0d70SF1xwgVq2bOkop6am6oMPPvDax5NPPmlaqy+//HLFx8d7bOP8Hvmzzz6rvLw8j22Sk5P15ZdfOsrunjc4C+ScAAAINF6rF6vqc0IlMgDAjcOHDxuxsbGGJMfXlClTyqyfnp5utGnTxlT/0UcfDeCIAQCoWk6cOGH06dPHtDbWrVvXWLt2baX0d/XVV5v66tOnj3H8+HG3de12u3HbbbeZ6rdt29YoKCjw2s+mTZsMq9VqavvZZ595rF+3bl1T/ffee69KzQkAgEBZuHChaa1KSEjwue2DDz5oapuYmGjs3bu3zPrPPPOMqX58fLyRkZHhtZ9Avh8QqDkBAOAvNpvN6N69u2k9uvHGGw2bzVZmm19//dVlbX3ooYc89sO6z7oPAAiOH374wbT+SDJuvfVWj2u9YRjG9u3bjebNm5vadejQwSgqKiqzzZtvvmmqX69ePWPTpk1l1v/0009N9UNCQoytW7d6nVN+fr7RunVrU9vbb7/dsNvtbusfP37c6N27t6n+9ddf77WfQM4JAABfVeQ9eWe8Vq8ec0LlIGgOoEyTJ092eSF9xx13mBYUm81mzJw50+XFafPmzY1jx44Fb/AAAATZ0KFDXdbRp556ypg3b165v44ePeq1v+3btxvR0dGm/nr06GEsXLjQVG/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- }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" @@ -1073,7 +1006,7 @@ " splitter=tree.splitter.QOSplitter()\n", " )\n", " ),\n", - " \n", + "\n", " }\n", ")" ] @@ -1093,7 +1026,7 @@ "hash": "b27b2a9f272874e098660428b28f5afa65c7850d82ff592660d49a141d883cd1" }, "kernelspec": { - "display_name": "Python 3.9.12 ('river')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1107,7 +1040,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_12_0.svg b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_12_0.svg index 9fbbfcd04f..8fc11bc88c 100644 --- a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_12_0.svg +++ b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_12_0.svg @@ -1,79 +1,79 @@ - - + - + 0 - -empty_server_form_handler + +empty_server_form_handler 1 - -Class True: - P(False) = 0.1 - P(True) = 0.9 -samples: 503 + +Class True: + P(False) = 0.1 + P(True) = 0.9 +samples: 503 0->1 - - -≤ 0.5455 + + +≤ 0.5455 2 - -popup_window + +popup_window 0->2 - - -> 0.5455 + + +> 0.5455 3 - -Class True: - P(False) = 0.5 - P(True) = 0.5 -samples: 64 + +Class True: + P(False) = 0.5 + P(True) = 0.5 +samples: 64 2->3 - - -≤ 0.0909 + + +≤ 0.0909 4 - -Class False: - P(False) = 1.0 - P(True) = 0.0 -samples: 256 + +Class False: + P(False) = 1.0 + P(True) = 0.0 +samples: 256 2->4 - - -> 0.0909 + + +> 0.0909 diff --git a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_21_0.png b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_21_0.png index bb6894318e..181fe901aa 100644 Binary files a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_21_0.png and b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_21_0.png differ diff --git a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_23_0.png b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_23_0.png index 108073ac38..14ac24af82 100644 Binary files a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_23_0.png and b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_23_0.png differ diff --git a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_25_0.png b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_25_0.png index 81d313c764..b718a64a37 100644 Binary files a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_25_0.png and b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_25_0.png differ diff --git a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_27_0.png b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_27_0.png index 3720938efd..ba23e63deb 100644 Binary files a/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_27_0.png and b/docs/recipes/on-hoeffding-trees_files/on-hoeffding-trees_27_0.png differ diff --git a/docs/recipes/pipelines.ipynb b/docs/recipes/pipelines.ipynb index ff7965fdfb..bb2a552261 100644 --- a/docs/recipes/pipelines.ipynb +++ b/docs/recipes/pipelines.ipynb @@ -13,7 +13,9 @@ "source": [ "Pipelines are an integral part of River. We encourage their usage and apply them in many of their examples.\n", "\n", - "The `compose.Pipeline` contains all the logic for building and applying pipelines. A pipeline is essentially a list of estimators that are applied in sequence. The only requirement is that the first `n - 1` steps be transformers. The last step can be a regressor, a classifier, a clusterer, a transformer, etc. Here is an example:" + "The `compose.Pipeline` contains all the logic for building and applying pipelines. A pipeline is essentially a list of estimators that are applied in sequence. The only requirement is that the first `n - 1` steps be transformers. The last step can be a regressor, a classifier, a clusterer, a transformer, etc.\n", + "\n", + "Here is an example:" ] }, { @@ -21,10 +23,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.404078Z", - "iopub.status.busy": "2022-10-26T11:28:32.403407Z", - "iopub.status.idle": "2022-10-26T11:28:32.658166Z", - "shell.execute_reply": "2022-10-26T11:28:32.657437Z" + "iopub.execute_input": "2023-12-04T17:49:52.864965Z", + "iopub.status.busy": "2023-12-04T17:49:52.864389Z", + "iopub.status.idle": "2023-12-04T17:49:53.144412Z", + "shell.execute_reply": "2023-12-04T17:49:53.144042Z" }, "tags": [] }, @@ -54,10 +56,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.666984Z", - "iopub.status.busy": "2022-10-26T11:28:32.666054Z", - "iopub.status.idle": "2022-10-26T11:28:32.694462Z", - "shell.execute_reply": "2022-10-26T11:28:32.694987Z" + "iopub.execute_input": "2023-12-04T17:49:53.146332Z", + "iopub.status.busy": "2023-12-04T17:49:53.146215Z", + "iopub.status.idle": "2023-12-04T17:49:53.154883Z", + "shell.execute_reply": "2023-12-04T17:49:53.154651Z" }, "tags": [] }, @@ -82,10 +84,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.700650Z", - "iopub.status.busy": "2022-10-26T11:28:32.699995Z", - "iopub.status.idle": "2022-10-26T11:28:32.727495Z", - "shell.execute_reply": "2022-10-26T11:28:32.726548Z" + "iopub.execute_input": "2023-12-04T17:49:53.156607Z", + "iopub.status.busy": "2023-12-04T17:49:53.156514Z", + "iopub.status.idle": "2023-12-04T17:49:53.165628Z", + "shell.execute_reply": "2023-12-04T17:49:53.165348Z" }, "tags": [] }, @@ -100,7 +102,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A pipeline has a `draw` method that can be used to visualize it:" + "A pipeline, as any River estimator, has a `_repr_html_` method, which can be used to visualize it in Jupyter-like notebooks:" ] }, { @@ -108,10 +110,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.734230Z", - "iopub.status.busy": "2022-10-26T11:28:32.733546Z", - "iopub.status.idle": "2022-10-26T11:28:32.768994Z", - "shell.execute_reply": "2022-10-26T11:28:32.768518Z" + "iopub.execute_input": "2023-12-04T17:49:53.167122Z", + "iopub.status.busy": "2023-12-04T17:49:53.167045Z", + "iopub.status.idle": "2023-12-04T17:49:53.180439Z", + "shell.execute_reply": "2023-12-04T17:49:53.180202Z" }, "tags": [] }, @@ -119,18 +121,16 @@ { "data": { "text/html": [ - "
StandardScaler
(\n", + "
StandardScaler
StandardScaler (\n", " with_std=True\n", ")\n", - "\n", - "
PolynomialExtender
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PolynomialExtender
PolynomialExtender (\n", " degree=2\n", " interaction_only=False\n", " include_bias=False\n", " bias_name=\"bias\"\n", ")\n", - "\n", - "
LinearRegression
(\n", + "
LinearRegression
LinearRegression (\n", " optimizer=SGD (\n", " lr=Constant (\n", " learning_rate=0.01\n", @@ -146,19 +146,19 @@ " clip_gradient=1e+12\n", " initializer=Zeros ()\n", ")\n", - "\n", "
" + ], + "text/plain": [ + "Pipeline (\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " PolynomialExtender (\n", + " degree=2\n", + " interaction_only=False\n", + " include_bias=False\n", + " bias_name=\"bias\"\n", + " ),\n", + " LinearRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " )\n", + " loss=Squared ()\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + ")" ] }, "execution_count": 4, @@ -252,7 +289,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`compose.Pipeline` inherits from `base.Estimator`, which means that it has a `learn_one` method. You would expect `learn_one` to update each estimator, but **that's not actually what happens**. Instead, the transformers are updated when `predict_one` (or `predict_proba_one` for that matter) is called. Indeed, in online machine learning, we can update the unsupervised parts of our model when a sample arrives. We don't have to wait for the ground truth to arrive in order to update unsupervised estimators that don't depend on it. In other words, in a pipeline, `learn_one` updates the supervised parts, whilst `predict_one` updates the unsupervised parts. It's important to be aware of this behavior, as it is quite different to what is done in other libraries that rely on batch machine learning.\n", + "`compose.Pipeline` implements a `learn_one` method which in sequence calls the `learn_one` of each component and a `predict_one` (resp `predict_proba_one`) method which calls `transform_one` on the first `n - 1` steps and `predict_one` (resp `predict_proba_one`) on the last step.\n", "\n", "Here is a small example to illustrate the previous point:" ] @@ -262,48 +299,29 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:32.773807Z", - "iopub.status.busy": "2022-10-26T11:28:32.772796Z", - "iopub.status.idle": "2022-10-26T11:28:33.624482Z", - "shell.execute_reply": "2022-10-26T11:28:33.625033Z" + "iopub.execute_input": "2023-12-04T17:49:53.181786Z", + "iopub.status.busy": "2023-12-04T17:49:53.181717Z", + "iopub.status.idle": "2023-12-04T17:49:53.319962Z", + "shell.execute_reply": "2023-12-04T17:49:53.319706Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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StandardScaler
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StandardScaler
StandardScaler (\n", " with_std=True\n", ")\n", - "\n", - "
PolynomialExtender
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PolynomialExtender (\n", " degree=2\n", " interaction_only=False\n", " include_bias=False\n", " bias_name=\"bias\"\n", ")\n", - "\n", - "
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" + ], + "text/plain": [ + "Pipeline (\n", + " StandardScaler (\n", + " with_std=True\n", + " ),\n", + " TransformerUnion (\n", + " PolynomialExtender (\n", + " degree=2\n", + " interaction_only=False\n", + " include_bias=False\n", + " bias_name=\"bias\"\n", + " ),\n", + " RBFSampler (\n", + " gamma=1.\n", + " n_components=100\n", + " seed=None\n", + " )\n", + " ),\n", + " LinearRegression (\n", + " optimizer=SGD (\n", + " lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " )\n", + " loss=Squared ()\n", + " l2=0.\n", + " l1=0.\n", + " intercept_init=0.\n", + " intercept_lr=Constant (\n", + " learning_rate=0.01\n", + " )\n", + " clip_gradient=1e+12\n", + " initializer=Zeros ()\n", + " )\n", + ")" ] }, "execution_count": 9, @@ -680,10 +676,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:33.796169Z", - "iopub.status.busy": "2022-10-26T11:28:33.795419Z", - "iopub.status.idle": "2022-10-26T11:28:33.826187Z", - "shell.execute_reply": "2022-10-26T11:28:33.827125Z" + "iopub.execute_input": "2023-12-04T17:49:53.371204Z", + "iopub.status.busy": "2023-12-04T17:49:53.371072Z", + "iopub.status.idle": "2023-12-04T17:49:53.381553Z", + "shell.execute_reply": "2023-12-04T17:49:53.381303Z" }, "tags": [] }, @@ -703,7 +699,282 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Pipelines provide the benefit of removing a lot of cruft by taking care of tedious details for you. They also enable to clearly define what steps your model is made of. Finally, having your model in a single object means that you can move it around more easily. Note that you can include user-defined functions in a pipeline by using a `compose.FuncTransformer`." + "Pipelines provide the benefit of removing a lot of cruft by taking care of tedious details for you. They also enable to clearly define what steps your model is made of.\n", + "\n", + "Finally, having your model in a single object means that you can move it around more easily.\n", + "\n", + "Note that you can include user-defined functions in a pipeline by using a `compose.FuncTransformer`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning during predict\n", + "\n", + "In online machine learning, we can update the unsupervised parts of our model when a sample arrives. We don't _really_ have to wait for the ground truth to arrive in order to update unsupervised estimators that don't depend on it.\n", + "\n", + "In other words, in a pipeline, `learn_one` updates the supervised parts, whilst `predict_one` (or `predict_proba_one` for that matter) **can** update the unsupervised parts, which often yields better results. \n", + "\n", + "In river, we can achieve this behavior using a dedicated context manager: `compose.learn_during_predict`.\n", + "\n", + "Here is the same example as before, with the only difference of activating the such learning during predict behavior:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model = (\n", + " preprocessing.StandardScaler() |\n", + " feature_extraction.PolynomialExtender() |\n", + " linear_model.LinearRegression()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model.predict_one(x)=0.00, y=43.76\n", + "model['StandardScaler'].means = defaultdict(, {'ordinal_date': 736389.0, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 44.104692, 'you_gov': 43.636914000000004})\n", + "model.predict_one(x)=0.00, y=43.71\n", + "model['StandardScaler'].means = defaultdict(, {'ordinal_date': 736389.5, 'gallup': 43.843213, 'ipsos': 46.19925042857143, 'morning_consult': 48.318749, 'rasmussen': 45.104692, 'you_gov': 42.636914000000004})\n" + ] + } + ], + "source": [ + "with compose.learn_during_predict():\n", + " for (x, y) in dataset.take(2):\n", + "\n", + " print(f\"{model.predict_one(x)=:.2f}, {y=:.2f}\")\n", + " print(f\"{model['StandardScaler'].means = }\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calling `predict_one` within this context will update each transformer of the pipeline. For instance here we can see that the mean of each feature of the standard scaler step have been updated.\n", + "\n", + "On the other hand, the supervised part of our pipeline, the linear regression, has not been updated or learned anything yet. Hence the prediction on any sample will be nil because each weight is still equal to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, {})" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict_one(x), model[\"LinearRegression\"].weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Comparison\n", + "\n", + "One may wonder what is the advantage of learning during predict. Let's compare the performance of a pipeline with and without learning during predict, in two scenarios: one in which the flow of data stays the same, we just update " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from contextlib import nullcontext\n", + "from river import metrics\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def score_pipeline(learn_during_predict: bool, n_learning_samples: int | None = None) -> float:\n", + " \"\"\"Scores a pipeline on the TrumpApproval dataset.\n", + "\n", + " Parameters\n", + " ----------\n", + " learn_during_predict : bool\n", + " Whether or not to learn the unsupervided components during the prediction step.\n", + " If False it will only learn when `learn_one` is explicitly called.\n", + " n_learning_samples : int | None \n", + " Number of samples used to `learn_one`.\n", + "\n", + " Return\n", + " ------\n", + " MAE : float\n", + " Mean absolute error of the pipeline on the dataset\n", + " \"\"\"\n", + "\n", + " dataset = datasets.TrumpApproval()\n", + "\n", + " model = (\n", + " preprocessing.StandardScaler() |\n", + " linear_model.LinearRegression()\n", + " )\n", + "\n", + " metric = metrics.MAE()\n", + "\n", + " ctx = compose.learn_during_predict if learn_during_predict else nullcontext\n", + " n_learning_samples = n_learning_samples or dataset.n_samples\n", + "\n", + " with ctx():\n", + " for _idx, (x, y) in enumerate(dataset):\n", + " y_pred = model.predict_one(x)\n", + "\n", + " metric.update(y, y_pred)\n", + " \n", + " if _idx < n_learning_samples:\n", + " model.learn_one(x, y)\n", + "\n", + " return metric.get()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "max_samples = datasets.TrumpApproval().n_samples\n", + "\n", + "results = [\n", + " {\n", + " \"learn_during_predict\": learn_during_predict,\n", + " \"pct_learning_samples\": round(100*n_learning_samples/max_samples, 0),\n", + " \"mae\": score_pipeline(learn_during_predict=learn_during_predict, n_learning_samples=n_learning_samples)\n", + " }\n", + " for learn_during_predict in (True, False)\n", + " for n_learning_samples in range(max_samples, max_samples//10, -(max_samples//10))\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(pd.DataFrame(results)\n", + " .pivot(columns=\"learn_during_predict\", index=\"pct_learning_samples\", values=\"mae\")\n", + " .sort_index(ascending=False)\n", + " .style.format_index('{0}%')\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the resulting table above, the scores are comparable only in the case in which the percentage of learning samples above 90%. After that the score starts to degrade quite fast as the percentage of learning samples decreases, and it is very remarkable (one order of magnitude or more) when less than 50% of the samples are used for learning.\n", + "\n", + "Although a simple case, this examplify how powerful it can be to learn unsupervised components during predict." ] } ], @@ -723,7 +994,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/docs/recipes/reading-data.ipynb b/docs/recipes/reading-data.ipynb index 9ea6d628fd..f88da6ee96 100644 --- a/docs/recipes/reading-data.ipynb +++ b/docs/recipes/reading-data.ipynb @@ -25,54 +25,36 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:36.098680Z", - "iopub.status.busy": "2022-10-26T11:28:36.097765Z", - "iopub.status.idle": "2022-10-26T11:28:36.706759Z", - "shell.execute_reply": "2022-10-26T11:28:36.707258Z" + "iopub.execute_input": "2023-12-04T17:49:54.824345Z", + "iopub.status.busy": "2023-12-04T17:49:54.823858Z", + "iopub.status.idle": "2023-12-04T17:49:55.245273Z", + "shell.execute_reply": "2023-12-04T17:49:55.244942Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
\n",
-       "Bike sharing station information from the city of Toulouse.\n",
-       "\n",
-       "The goal is to predict the number of bikes in 5 different bike stations from the city of\n",
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-       "\n",
-       "      Name  Bikes                                                         \n",
-       "      Task  Regression                                                    \n",
-       "   Samples  182,470                                                       \n",
-       "  Features  8                                                             \n",
-       "    Sparse  False                                                         \n",
-       "      Path  /Users/max.halford/river_data/Bikes/toulouse_bikes.csv        \n",
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-       "      Size  12.52 MB                                                      \n",
-       "Downloaded  True                                                          \n",
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\n" - ], "text/plain": [ - "\n", "Bike sharing station information from the city of Toulouse.\n", "\n", - "The goal is to predict the number of bikes in \u001b[1;36m5\u001b[0m different bike stations from the city of\n", + "The goal is to predict the number of bikes in 5 different bike stations from the city of\n", "Toulouse.\n", "\n", " Name Bikes \n", " Task Regression \n", - " Samples \u001b[1;36m182\u001b[0m,\u001b[1;36m470\u001b[0m \n", - " Features \u001b[1;36m8\u001b[0m \n", - " Sparse \u001b[3;91mFalse\u001b[0m \n", - " Path \u001b[35m/Users/max.halford/river_data/Bikes/\u001b[0m\u001b[95mtoulouse_bikes.csv\u001b[0m \n", - " URL \u001b[4;94mhttps://maxhalford.github.io/files/datasets/toulouse_bikes.zip\u001b[0m\n", - " Size \u001b[1;36m12.52\u001b[0m MB \n", - "Downloaded \u001b[3;92mTrue\u001b[0m \n" + " Samples 182,470 \n", + " Features 8 \n", + " Sparse False \n", + " Path /Users/max/river_data/Bikes/toulouse_bikes.csv \n", + " URL https://maxhalford.github.io/files/datasets/toulouse_bikes.zip\n", + " Size 12.52 MB \n", + "Downloaded True " ] }, + "execution_count": 1, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -101,46 +83,30 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2022-10-26T11:28:36.711683Z", - "iopub.status.busy": "2022-10-26T11:28:36.710959Z", - "iopub.status.idle": "2022-10-26T11:28:36.745781Z", - "shell.execute_reply": "2022-10-26T11:28:36.745114Z" + "iopub.execute_input": "2023-12-04T17:49:55.246985Z", + "iopub.status.busy": "2023-12-04T17:49:55.246871Z", + "iopub.status.idle": "2023-12-04T17:49:55.257529Z", + "shell.execute_reply": "2023-12-04T17:49:55.257231Z" }, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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Instead, default predictions will be produced, which is consistent with the rest of River. +- `compat.SKL2RiverRegressor` and `compat.SKL2RiverClassifier` didn't check whether features were ordered in the same way at each method call. They now store the list of feature names at the first function call, and align subsequent inputs in the same order. + +## compose + +- `compose.TransformerProduct` will now preserve the density of sparse columns. +- Added a `transform_many` method to `compose.FuncTransformer`, allowing it to be used in mini-batch pipelines. +- The `compose.pure_inference_mode` now works with mini-batching. + +## neighbors + +- Added `neighbors.SWINN` to power-up approximate nearest neighbor search. SWINN uses graphs to speed up nearest neighbor search in large sliding windows of data. +- Renamed `neighbors.NearestNeighbors` to `neighbors.LazySearch`. +- Standardize and create base classes for generic nearest neighbor search utilities. +- The user can now select the nearest neighbor search engine to use in `neighbors.KNNClassifier` and `neighbors.KNNRegressor`. + +## preprocessing + +- Rename `sparse` parameter to `drop_zeros` in `preprocessing.OneHotEncoder`. +- The `transform_many` method of `preprocessing.OneHotEncoder` will now return a sparse dataframe, rather than a dense one, which will consume much less memory. + +## proba + +- Added a `cdf` method to `proba.Beta`. + +## tree + +- Expose the `min_branch_fraction` parameter to avoid splits where most of the data goes to a single branch. Affects + classification trees. +- Added the `max_share_to_split` parameter to Hoeffding Tree classifiers. This parameters avoids splitting when the majority + class has most of the data. + +## utils + +- Fixed `utils.math.minkowski_distance`. diff --git a/docs/releases/0.19.0.md b/docs/releases/0.19.0.md new file mode 100644 index 0000000000..0c41605df4 --- /dev/null +++ b/docs/releases/0.19.0.md @@ -0,0 +1,57 @@ +# 0.19.0 - 2023-08-02 + +Calling `learn_one` in a pipeline will now update each part of the pipeline in turn. Before the unsupervised parts of the pipeline were updated during `predict_one`. This is more intuitive for new users. The old behavior, which yields better results, can be restored by calling `learn_one` with the new `compose.learn_during_predict` context manager. + +## bandit + +- Added a `bandit.datasets` submodule, which is meant to contain contextual bandit datasets. +- Added `bandit.base.ContextualPolicy`. +- Added `bandit.datasets.NewsArticles`. +- Added `bandit.LinUCBDisjoint`, which is River's first contextual bandit policy. +- Added `bandit.RandomPolicy`. + +## compose + +- Removed the `compose.warm_up_mode` context manager. +- Removed the `compose.pure_inference_mode` context manager. +- The last step of a pipeline will be correctly updated if it is unsupervised, which wasn't the case before. +- Fixed an edge-case where `compose.TransformerProduct` would not work when chained more than twice. + +## drift + +- Added a `datasets` submodule, which contains datasets that are useful for concept drift experiments. +- Fix bugs in `drift.binary.HDDM_A` and `drift.binary.HDDM_W`. + +## linear_model + +- Added a `predict_many` method to `linear_model.BayesianLinearRegression`. +- Added a `smoothing` parameter to `linear_model.BayesianLinearRegression`, which allows it to cope with concept drift. + +## forest + +- Fixed issue with `forest.ARFClassifier` which couldn't be passed a `CrossEntropy` metric. +- Fixed a bug in `forest.AMFClassifier` which slightly improves predictive accurary. +- Added `forest.AMFRegressor`. + +## multioutput + +- Added `metrics.multioutput.SampleAverage`, which is equivalent to using `average='samples'` in scikit-learn. + +## preprocessing + +- Added `preprocessing.OrdinalEncoder`, to map string features to integers. +- The `transform_many` method of `preprocessing.StandardScaler` now uses the dtype of the input for the output. + +## proba + +- Added `proba.MultivariateGaussian`. + +## stream + +- `stream.iter_arff` now supports sparse data. +- `stream.iter_arff` now supports multi-output targets. +- `stream.iter_arff` now supports missing values indicated with question marks. + +## utils + +- Added `utils.random.exponential` to retrieve random samples following an exponential distribution. diff --git a/docs/releases/0.20.0.md b/docs/releases/0.20.0.md new file mode 100644 index 0000000000..53bfa71c5e --- /dev/null +++ b/docs/releases/0.20.0.md @@ -0,0 +1,64 @@ +# 0.20.0 - 2023-11-09 + +- River's mini-batch methods now support pandas v2. In particular, River conforms to pandas' new sparse API. +- Dropped support for Python 3.8. +- Added support for Python 3.12. + +## anomaly + +- Added `anomaly.LocalOutlierFactor`, which is an online version of the LOF algorithm for anomaly detection that matches the scikit-learn implementation. + - Made `score_one` method of `anomaly.LocalOutlierFactor` stateless + - Defined default score for uninitialized detector +- Implementation of the `anomaly.StandardAbsoluteDeviation` algorithm, which is a uni-variate anomaly detection algorithm, based on the implementation in [PySAD](https://github.com/selimfirat/pysad/blob/master/pysad/models/knn_cad.py) (Python Streaming Anomaly Detection) + +## covariance + +- Added `_from_state` method to `covariance.EmpiricalCovariance` to warm start from previous knowledge. + +## clustering + +- Add fixes to `cluster.DBSTREAM` algorithm, including: + - Addition of the `-` sign before the `fading_factor` in accordance with the algorithm 2 proposed by Hashler and Bolanos (2016) to allow clusters with low weights to be removed. + - The new `micro_cluster` is added with the key derived from the maximum key of the existing micro clusters. If the set of micro clusters is still empty (`len = 0`), a new micro cluster is added with key 0. + - `cluster_is_up_to_date` is set to `True` at the end of the `self._recluster()` function. + - Shared density graph update timestamps are initialized with the current timestamp value + - `neighbour_neighbours` are appended correctly to the `seed_set` when generating cluster labels + - When building weighted adjacency matrix the algorithm accounts for possibly orphaned entries in shared density graph + +## datasets + +- Added `datasets.WebTraffic`, which is a dataset that counts the occurrences of events on a website. It is a multi-output regression dataset with two outputs. + +## drift + +- Add `drift.NoDrift` to allow disabling the drift detection capabilities of models. This detector does nothing and always returns `False` when queried whether or not a concept drift was detected. + +## evaluate + +- Added a `yield_predictions` parameter to `evaluate.iter_progressive_val_score`, which allows including predictions in the output. + +## forest + +- Simplify inner the structures of `forest.ARFClassifier` and `forest.ARFRegressor` by removing redundant class hierarchy. Simplify how concept drift logging can be accessed in individual trees and in the forest as a whole. + +## metrics + +- `metrics.ConfusionMatrix` may now be used with `evaluate.progressive_val_score` and `evaluate.iter_progressive_val_score`. + +## proba + +- Added `_from_state` method to `proba.MultivariateGaussian` to warm start from previous knowledge. + +## tree + +- Fix a bug in `tree.splitter.NominalSplitterClassif` that generated a mismatch between the number of existing tree branches and the number of tracked branches. +- Fix a bug in `tree.ExtremelyFastDecisionTreeClassifier` where the split re-evaluation failed when the current branch's feature was not available as a split option. The fix also enables the tree to pre-prune a leaf via the tie-breaking mechanism. + +## stats + +- Implementation of the incremental Kolmogorov-Smirnov statistics (at `stats.KolmogorovSmirnov`), with the option to calculate either the original KS or Kuiper's test. + +## utils + +- Removed `utils.dict2numpy` and `utils.numpy2dict` functions. They were not used anywhere in the library. +- `utils.TimeRolling` now works correctly if two samples with the same timestamp are added in a row. diff --git a/docs/releases/0.20.1.md b/docs/releases/0.20.1.md new file mode 100644 index 0000000000..d16524c7d4 --- /dev/null +++ b/docs/releases/0.20.1.md @@ -0,0 +1,3 @@ +# 0.20.1 - 2023-11-09 + +Dummy release to make wheels available. No actual difference with v0.20.0. diff --git a/docs/releases/0.21.0.md b/docs/releases/0.21.0.md new file mode 100644 index 0000000000..cde0682aa9 --- /dev/null +++ b/docs/releases/0.21.0.md @@ -0,0 +1,9 @@ +# 0.21.0 - 2023-12-04 + +- The `learn_one` and `learn_many` methods of each estimator don't not return anything anymore. This is to emphasize that the estimators are stateful. +- The `update` and `revert` method of classes that have also cease to return anything. +- `sample_weight` has been renamed to `w`. + +## covariance + +- Fixed an issue where `update_many` would reset `covariance.EmpiricalCovariance` each time it was called. diff --git a/docs/releases/0.21.1.md b/docs/releases/0.21.1.md new file mode 100644 index 0000000000..185e950999 --- /dev/null +++ b/docs/releases/0.21.1.md @@ -0,0 +1,16 @@ +# 0.21.1 - 2024-03-28 + +This release should fix some of the installation issues when building the River wheel from scratch. + +## anomaly + +- Added `PredictiveAnomalyDetection`, a semi-supervised technique that employs a predictive model for anomaly detection. + +## drift + +- Added `FHDDM` drift detector. +- Added a `iter_polars` function to iterate over the rows of a polars DataFrame. + +## neighbors + +- Simplified `neighbors.SWINN` to avoid recursion limit and pickling issues. diff --git a/docs/releases/0.21.2.md b/docs/releases/0.21.2.md new file mode 100644 index 0000000000..fb65bc062b --- /dev/null +++ b/docs/releases/0.21.2.md @@ -0,0 +1,11 @@ +# 0.21.2 - 2024-07-08 + +This release makes Polars an optional dependency instead of a required one. + +## cluster + +- Added `ODAC` (Online Divisive-Agglomerative Clustering) for clustering time series. + +## forest + +- Fix error in `forest.ARFClassifer` and `forest.ARFRegressor` where the algorithms would crash in case the number of features available for learning went below the value of the `max_features` parameter (#1560). diff --git a/docs/releases/0.4.1.md b/docs/releases/0.4.1.md index c9bd6c8694..d2be0a7cd0 100644 --- a/docs/releases/0.4.1.md +++ b/docs/releases/0.4.1.md @@ -19,7 +19,7 @@ ## ensemble -- Removed `ensemble.HedgeBinaryClassifier` because it's performance was subpar. +- Removed `ensemble.HedgeBinaryClassifier` because its performance was subpar. - Removed `ensemble.GroupRegressor`, as this should be a special case of `ensemble.StackingRegressor`. ## feature_extraction diff --git a/docs/releases/0.7.1.md b/docs/releases/0.7.1.md index 4a8ff96308..bc9215b716 100644 --- a/docs/releases/0.7.1.md +++ b/docs/releases/0.7.1.md @@ -2,6 +2,10 @@ Fixed an issue where scikit-learn was imported in `sam_knn.py` but wasn't specified as a dependency. +## expert + +- Each expert model will now raise a `NotEnoughModels` exception if only a single model is passed. + ## stream - Added `drop_nones` parameter to `stream.iter_csv`. diff --git a/docs/releases/0.7.2.md b/docs/releases/0.7.2.md deleted file mode 100644 index 248a57fa70..0000000000 --- a/docs/releases/0.7.2.md +++ /dev/null @@ -1,5 +0,0 @@ -# 0.7.2 - -## expert - -- Each expert model will now raise a `NotEnoughModels` exception if only a single model is passed. diff --git a/docs/releases/unreleased.md b/docs/releases/unreleased.md index 02d4b74ad1..4ed6dd48f1 100644 --- a/docs/releases/unreleased.md +++ b/docs/releases/unreleased.md @@ -1,24 +1,28 @@ # Unreleased -## bandit +- The units used in River have been corrected to be based on powers of 2 (KiB, MiB). This only changes the display, the behaviour is unchanged. +- The methods `learn_one`, `learn_many`, `update`, `revert`, and `append` now return `None`. -- Added `bandit.BayesUCB`. -- Added `bandit.evaluate_offline`, for evaluating bandits on historical (logged) data. +## cluster -## compat +- Update the description of `cluster.ODAC`. +- Change `draw` in `cluster.ODAC` to draw the hierarchical cluster's structure as a Graphviz graph. +- Add `render_ascii` in `cluster.ODAC` to render the hierarchical cluster's structure in text format. +- Work with `stats.Var` in `cluster.ODAC` when cluster has only one time series. -- The `predict_many` method scikit-learn models wrapped with `compat.convert_sklearn_to_river` raised an exception if the model had been fitted on any data yet. Instead, default predictions will be produced, which is consistent with the rest of River. +## drift -## compose +- Make `drift.ADWIN` comply with the reference MOA implementation. -- `compose.TransformerProduct` will now preserve the density of sparse columns. -- Added a `transform_many` method to `compose.FuncTransformer`, allowing it to be used in mini-batch pipelines. +## stats -## preprocessing +- Removed the unexported class `stats.CentralMoments`. -- Rename `sparse` parameter to `drop_zeros` in `preprocessing.OneHotEncoder`. -- The `transform_many` method of `preprocessing.OneHotEncoder` will now return a sparse dataframe, rather than a dense one, which will consume much less memory. +## tree -## proba +- Instead of letting trees grow indefinitely, setting the `max_depth` parameter to `None` will stop the trees from growing when they reach the system recursion limit. +- Added `tree.LASTClassifier` (Local Adaptive Streaming Tree Classifier). -- Added a `cdf` method to `proba.Beta`. +## stream + +- `stream.iter_arff` now supports blank values (treated as missing values). diff --git a/mkdocs.yml b/mkdocs.yml index c38f10b0b3..3ab98f8626 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -43,6 +43,7 @@ theme: - navigation.tabs - navigation.instant - navigation.indexes + - navigation.prune # Extras extra: @@ -84,8 +85,7 @@ plugins: extra_javascript: - javascripts/config.js - - https://polyfill.io/v3/polyfill.min.js?features=es6 - - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + - https://cdn.jsdelivr.net/npm/mathjax@3.2/es5/tex-mml-chtml.js - https://cdn.jsdelivr.net/npm/vega@5 - https://cdn.jsdelivr.net/npm/vega-lite@5 - https://cdn.jsdelivr.net/npm/vega-embed@6 diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000000..968c69b1e4 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,5144 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "alabaster" +version = "0.7.16" 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a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,99 @@ [build-system] -requires = ["cython", "numpy", "setuptools", "wheel", "setuptools-rust"] +requires = ["poetry-core>=1.0.0", "cython", "numpy", "setuptools", "wheel", "setuptools-rust"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry] +name = "river" +version = "0.21.2" +description = "Online machine learning in Python" +readme = "README.md" +homepage = "https://riverml.xyz/" +repository = "https://github.com/online-ml/river/" +authors = ["Max Halford "] + +include = [ + "**/*.cpp", + "**/*.pyx", + "**/*.pxd", + "river/datasets/*.csv", + "river/datasets/*.gz", + "river/datasets/*.zip", + "river/stream/*.zip", + "Cargo.toml", + "rust_src/**/*" +] + +[tool.poetry.build] +generate-setup-file = true +script = "build.py" + +[tool.poetry.dependencies] +python = "^3.9" +numpy = "^1.23.0" +scipy = "^1.12.1" +pandas = "^2.1" + +[tool.poetry.group.dev.dependencies] +graphviz = "^0.20.1" +gymnasium = "^0.29.0" +matplotlib = "^3.0.2" +mypy = "^1.11.1" +pre-commit = "^3.5.0" +pytest = "^7.4.2" +ruff = "^0.4.10" +scikit-learn = "^1.3.1" +sqlalchemy = "^2.0.22" +sympy = "^1.10.1" +pytest-xdist = {extras = ["psutil"], version = "^3.3.1"} +ipykernel = "^6.26.0" +ipython = "^8.17.2" +rich = "^13.6.0" +jupyter = "^1.0.0" +mike = "^2.0.0" +polars = "^0.20.8" + +[tool.poetry.group.compat] +optional = true + +[tool.poetry.group.compat.dependencies] +scikit-learn = "^1.0.1" +sqlalchemy = "^2.0.0" + +[tool.poetry.group.docs] +optional = true + +[tool.poetry.group.docs.dependencies] +"dominate" = "*" +"flask" = "*" +"ipykernel" = "*" +"jupyter-client" = "*" +"mike" = "*" +"mkdocs" = "*" +"mkdocs-awesome-pages-plugin" = "*" +"mkdocs-charts-plugin" = "*" +"mkdocs-material" = "*" +"nbconvert" = "*" +"numpydoc" = "*" +"python-slugify" = "*" +"spacy" = "*" +"tabulate" = "*" +"watermark" = "*" + +[tool.poetry.group.benchmark] +optional = true + +[tool.poetry.group.benchmark.dependencies] +"dominate" = "2.8.0" +"scikit-learn" = "1.3.1" +"tabulate" = "0.9.0" +"vowpalwabbit" = "9.9.0" +"watermark" = "2.4.3" [tool.pytest.ini_options] addopts = [ "--doctest-modules", "--doctest-glob=README.md", + "--ignore=build.py", "--ignore=benchmarks", "--ignore=docs/scripts", "--verbose", @@ -27,25 +116,38 @@ markers = [ "slow: tests that take a long time to run", ] -[tool.black] -line-length = 100 -target-version = ['py310'] - [tool.ruff] -select = ["E", "F", "I", "UP"] # https://beta.ruff.rs/docs/rules/ line-length = 100 target-version = 'py310' +extend-include = ["*.ipynb"] + +[tool.ruff.lint] +select = [ + # pycodestyle + "E", + # Pyflakes + "F", + # pyupgrade + "UP", + # isort + "I", +] ignore = ["E501"] +fixable = ["ALL"] -[tool.ruff.isort] +[tool.ruff.lint.isort] required-imports = ["from __future__ import annotations"] +[tool.ruff.format] +quote-style = "double" +indent-style = "space" + [tool.mypy] files = "river" +strict = true [[tool.mypy.overrides]] module = [ - "river.*", "mmh3.*", "numpy.*", "sklearn.*", @@ -56,6 +158,65 @@ module = [ "vaex.*", "torch.*", "sqlalchemy.*", - "requests.*" + "requests.*", + "gymnasium.*", + "sympy.*", + "polars.*" ] ignore_missing_imports = true + +[[tool.mypy.overrides]] +# Disable strict mode for all non fully-typed modules +module = [ + "river.base.*", + "river.metrics.*", + "river.utils.*", + "river.stats.*", + "river.optim.*", + "river.datasets.*", + "river.tree.*", + "river.preprocessing.*", + "river.stream.*", + "river.linear_model.*", + "river.evaluate.*", + "river.drift.*", + "river.compose.*", + "river.bandit.*", + "river.cluster.*", + "river.anomaly.*", + "river.time_series.*", + "river.feature_extraction.*", + "river.ensemble.*", + "river.proba.*", + "river.multioutput.*", + "river.naive_bayes.*", + "river.checks.*", + "river.rules.*", + "river.model_selection.*", + "river.forest.*", + "river.neighbors.*", + "river.sketch.*", + "river.facto.*", + "river.covariance.*", + "river.compat.*", + "river.multiclass.*", + "river.reco.*", + "river.imblearn.*", + "river.feature_selection.*", + "river.misc.*", + "river.active.*", + "river.conf.*", + "river.neural_net.*", + "river.test_estimators", + "river.dummy", +] +# The strict option is global, the checks must be disabled one by one +warn_unused_ignores = false +check_untyped_defs = false +allow_subclassing_any = true +allow_any_generics = true +allow_untyped_calls = true +allow_incomplete_defs = true +allow_untyped_defs = true +implicit_reexport = true +warn_return_any = false diff --git a/river/__init__.py b/river/__init__.py index 063932fb67..da617455d5 100644 --- a/river/__init__.py +++ b/river/__init__.py @@ -5,6 +5,7 @@ memory, or simply when it isn't available all at once. river's API is heavily inspired from that of scikit-learn, enough so that users who are familiar with scikit-learn should feel right at home. """ + from __future__ import annotations from .__version__ import __version__ # noqa: F401 diff --git a/river/__version__.py b/river/__version__.py index 29add72136..f31b3c83c6 100644 --- a/river/__version__.py +++ b/river/__version__.py @@ -1,5 +1,3 @@ from __future__ import annotations -VERSION = (0, 17, 0) - -__version__ = ".".join(map(str, VERSION)) # noqa: F401 +__version__ = "0.21.2" diff --git a/river/active/__init__.py b/river/active/__init__.py index 7d3f3451b0..ffa17e759a 100644 --- a/river/active/__init__.py +++ b/river/active/__init__.py @@ -1,4 +1,5 @@ """Online active learning.""" + from __future__ import annotations from . import base diff --git a/river/active/base.py b/river/active/base.py index aa0acc398f..b403259e6f 100644 --- a/river/active/base.py +++ b/river/active/base.py @@ -30,8 +30,7 @@ def _wrapped_model(self): return self.classifier @abc.abstractmethod - def _ask_for_label(self, x, y_pred) -> bool: - ... + def _ask_for_label(self, x, y_pred) -> bool: ... def predict_proba_one(self, x, **kwargs): """Predict the probability of each label for `x` and indicate whether a label is needed. @@ -71,4 +70,3 @@ def predict_one(self, x, **kwargs): def learn_one(self, x, y, **kwargs): self.classifier.learn_one(x, y) - return self diff --git a/river/active/entropy.py b/river/active/entropy.py index 214edb16aa..bd02f80e14 100644 --- a/river/active/entropy.py +++ b/river/active/entropy.py @@ -47,19 +47,19 @@ class EntropySampler(ActiveLearningClassifier): >>> n_samples_used = 0 >>> for x, y in dataset: ... y_pred, ask = model.predict_one(x) - ... metric = metric.update(y, y_pred) + ... metric.update(y, y_pred) ... if ask: ... n_samples_used += 1 - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> metric Accuracy: 86.60% >>> dataset.n_samples, n_samples_used - (5574, 1922) + (5574, 1921) >>> print(f"{n_samples_used / dataset.n_samples:.2%}") - 34.48% + 34.46% """ diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index 932023896e..50725dd575 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -11,12 +11,16 @@ model. """ + from __future__ import annotations from . import base from .filter import QuantileFilter, ThresholdFilter from .gaussian import GaussianScorer from .hst import HalfSpaceTrees +from .lof import LocalOutlierFactor +from .pad import PredictiveAnomalyDetection +from .sad import StandardAbsoluteDeviation from .svm import OneClassSVM __all__ = [ @@ -26,5 +30,8 @@ "HalfSpaceTrees", "OneClassSVM", "QuantileFilter", + "StandardAbsoluteDeviation", "ThresholdFilter", + "LocalOutlierFactor", + "PredictiveAnomalyDetection", ] diff --git a/river/anomaly/base.py b/river/anomaly/base.py index b265269c3e..c52a4bfc67 100644 --- a/river/anomaly/base.py +++ b/river/anomaly/base.py @@ -15,7 +15,7 @@ def _supervised(self): return False @abc.abstractmethod - def learn_one(self, x: dict) -> AnomalyDetector: + def learn_one(self, x: dict) -> None: """Update the model. Parameters @@ -23,10 +23,6 @@ def learn_one(self, x: dict) -> AnomalyDetector: x A dictionary of features. - Returns - ------- - self - """ @abc.abstractmethod @@ -52,7 +48,7 @@ class SupervisedAnomalyDetector(base.Estimator): """A supervised anomaly detector.""" @abc.abstractmethod - def learn_one(self, x: dict, y: base.typing.Target) -> SupervisedAnomalyDetector: + def learn_one(self, x: dict, y: base.typing.Target) -> None: """Update the model. Parameters @@ -60,10 +56,6 @@ def learn_one(self, x: dict, y: base.typing.Target) -> SupervisedAnomalyDetector x A dictionary of features. - Returns - ------- - self - """ @abc.abstractmethod @@ -145,7 +137,7 @@ def score_one(self, *args, **kwargs): """ return self.anomaly_detector.score_one(*args, **kwargs) - def learn_one(self, *args, **learn_kwargs): + def learn_one(self, *args, **learn_kwargs) -> None: """Update the anomaly filter and the underlying anomaly detector. Parameters @@ -153,11 +145,6 @@ def learn_one(self, *args, **learn_kwargs): args Depends on whether the underlying anomaly detector is supervised or not. - Returns - ------- - self - """ if self.protect_anomaly_detector and not self.classify(self.score_one(*args)): self.anomaly_detector.learn_one(*args, **learn_kwargs) - return self diff --git a/river/anomaly/filter.py b/river/anomaly/filter.py index 968168b18b..36cce998a2 100644 --- a/river/anomaly/filter.py +++ b/river/anomaly/filter.py @@ -144,8 +144,8 @@ class QuantileFilter(anomaly.base.AnomalyFilter): >>> for x, y in datasets.CreditCard().take(2000): ... score = model.score_one(x) ... is_anomaly = model['QuantileFilter'].classify(score) - ... model = model.learn_one(x) - ... report = report.update(y, is_anomaly) + ... model.learn_one(x) + ... report.update(y, is_anomaly) >>> report Precision Recall F1 Support @@ -180,7 +180,6 @@ def learn_one(self, *args, **learn_kwargs): if not self.protect_anomaly_detector or not self.classify(score): self.anomaly_detector.learn_one(*args, **learn_kwargs) self.quantile.update(score) - return self @classmethod def _unit_test_params(cls): diff --git a/river/anomaly/gaussian.py b/river/anomaly/gaussian.py index fb9ccfd0da..352d10138b 100644 --- a/river/anomaly/gaussian.py +++ b/river/anomaly/gaussian.py @@ -32,7 +32,7 @@ class GaussianScorer(anomaly.base.SupervisedAnomalyDetector): >>> detector = anomaly.GaussianScorer() >>> for y in (rng.gauss(0, 1) for _ in range(100)): - ... detector = detector.learn_one(None, y) + ... detector.learn_one(None, y) >>> detector.score_one(None, -3) 0.999477... @@ -57,11 +57,10 @@ def __init__(self, window_size=None, grace_period=100): ) self.grace_period = grace_period - def learn_one(self, _, y): + def learn_one(self, x, y): self.gaussian.update(y) - return self - def score_one(self, _, y): + def score_one(self, x, y): if self.gaussian.n_samples < self.grace_period: return 0 return 2 * abs(self.gaussian.cdf(y) - 0.5) diff --git a/river/anomaly/hst.py b/river/anomaly/hst.py index d795348250..8fca772e10 100644 --- a/river/anomaly/hst.py +++ b/river/anomaly/hst.py @@ -138,11 +138,11 @@ class HalfSpaceTrees(anomaly.base.AnomalyDetector): ... ) >>> for x in X[:3]: - ... hst = hst.learn_one({'x': x}) # Warming up + ... hst.learn_one({'x': x}) # Warming up >>> for x in X: ... features = {'x': x} - ... hst = hst.learn_one(features) + ... hst.learn_one(features) ... print(f'Anomaly score for x={x:.3f}: {hst.score_one(features):.3f}') Anomaly score for x=0.500: 0.107 Anomaly score for x=0.450: 0.071 @@ -170,8 +170,8 @@ class HalfSpaceTrees(anomaly.base.AnomalyDetector): >>> for x, y in datasets.CreditCard().take(2500): ... score = model.score_one(x) - ... model = model.learn_one(x) - ... auc = auc.update(y, score) + ... model.learn_one(x) + ... auc.update(y, score) >>> auc ROCAUC: 91.15% @@ -268,8 +268,6 @@ def learn_one(self, x): self._first_window = False self.counter = 0 - return self - def score_one(self, x): if self._first_window: return 0 diff --git a/river/anomaly/lof.py b/river/anomaly/lof.py new file mode 100644 index 0000000000..542d4a882f --- /dev/null +++ b/river/anomaly/lof.py @@ -0,0 +1,495 @@ +from __future__ import annotations + +import copy +import functools + +import pandas as pd + +from river import anomaly, utils +from river.neighbors.base import DistanceFunc + + +def check_equal(x_list: list, y_list: list): + """ + Check if new list of observations (x_list) has any data sample that is equal to any previous data recorded (y_list). + """ + result = [x for x in x_list if not any(x == y for y in y_list)] + return result, len(x_list) - len(result) + + +def expand_objects( + new_particles: list, + x_list: list, + neighborhoods: dict, + rev_neighborhoods: dict, + k_dist: dict, + reach_dist: dict, + dist_dict: dict, + local_reach: dict, + lof: dict, +): + """ + Expand size of dictionaries and lists to take into account new data points. + """ + n = len(x_list) + m = len(new_particles) + x_list.extend(new_particles) + neighborhoods.update({i: [] for i in range(n + m)}) + rev_neighborhoods.update({i: [] for i in range(n + m)}) + k_dist.update({i: float("inf") for i in range(n + m)}) + reach_dist.update({i + n: {} for i in range(m)}) + dist_dict.update({i + n: {} for i in range(m)}) + local_reach.update({i + n: [] for i in range(m)}) + lof.update({i + n: [] for i in range(m)}) + return ( + (n, m), + x_list, + neighborhoods, + rev_neighborhoods, + k_dist, + reach_dist, + dist_dict, + local_reach, + lof, + ) + + +def define_sets(nm, neighborhoods: dict, rev_neighborhoods: dict): + """ + Define sets of points for the incremental LOF algorithm. + """ + # Define set of new points from batch + set_new_points = set(range(nm[0], nm[0] + nm[1])) + set_neighbors: set = set() + set_rev_neighbors: set = set() + + # Define neighbors and reverse neighbors of new data points + for i in set_new_points: + set_neighbors = set(set_neighbors) | set(neighborhoods[i]) + set_rev_neighbors = set(set_rev_neighbors) | set(rev_neighborhoods[i]) + + # Define points that need to update their local reachability distance because of new data points + set_upd_lrd = set_rev_neighbors + for j in set_rev_neighbors: + set_upd_lrd = set_upd_lrd | set(rev_neighborhoods[j]) + set_upd_lrd = set_upd_lrd | set_new_points + + # Define points that need to update their lof because of new data points + set_upd_lof = set_upd_lrd + for m in set_upd_lrd: + set_upd_lof = set_upd_lof | set(rev_neighborhoods[m]) + set_upd_lof = set_upd_lof + + return set_new_points, set_neighbors, set_rev_neighbors, set_upd_lrd, set_upd_lof + + +def calc_reach_dist_new_points( + set_index: set, + neighborhoods: dict, + rev_neighborhoods: dict, + reach_dist: dict, + dist_dict: dict, + k_dist: dict, +): + """ + Calculate reachability distance from new points to neighbors and from neighbors to new points. + """ + for c in set_index: + for j in set(neighborhoods[c]): + reach_dist[c][j] = max(dist_dict[c][j], k_dist[j]) + for j in set(rev_neighborhoods[c]): + reach_dist[j][c] = max(dist_dict[j][c], k_dist[c]) + return reach_dist + + +def calc_reach_dist_other_points( + set_index: set, + rev_neighborhoods: dict, + reach_dist: dict, + dist_dict: dict, + k_dist: dict, +): + """ + Calculate reachability distance from reverse neighbors of reverse neighbors ( RkNN(RkNN(NewPoints)) ) + to reverse neighbors ( RkNN(NewPoints) ). These values change due to the insertion of new points. + """ + for j in set_index: + for i in set(rev_neighborhoods[j]): + reach_dist[i][j] = max(dist_dict[i][j], k_dist[j]) + return reach_dist + + +def calc_local_reach_dist( + set_index: set, neighborhoods: dict, reach_dist: dict, local_reach_dist: dict +): + """ + Calculate local reachability distance of affected points. + """ + for i in set_index: + denominator = sum(reach_dist[i][j] for j in neighborhoods[i]) + local_reach_dist[i] = len(neighborhoods[i]) / denominator if denominator else 0 + return local_reach_dist + + +def calc_lof(set_index: set, neighborhoods: dict, local_reach: dict, lof: dict): + """ + Calculate local outlier factor (LOF) of affected points. + """ + for i in set_index: + denominator = len(neighborhoods[i]) * local_reach[i] + lof[i] = sum(local_reach[j] for j in neighborhoods[i]) / denominator if denominator else 0 + return lof + + +class LocalOutlierFactor(anomaly.base.AnomalyDetector): + """Incremental Local Outlier Factor (Incremental LOF). + + The Incremental Local Outlier Factor (ILOF) is an online version of the Local Outlier Factor (LOF), proposed by + Pokrajac et al. (2017), and is used to identify outliers based on density of local neighbors. + + The algorithm take into account the following elements: + - `NewPoints`: new points; + - `kNN(p)`: the k-nearest neighboors of `p` (the k-closest points to `p`); + - `RkNN(p)`: the reverse-k-nearest neighboors of `p` (points that have `p` as one of their neighboors); + - `set_upd_lrd`: Set of points that need to have the local reachability distance updated; + - `set_upd_lof`: Set of points that need to have the local outlier factor updated. + + This current implementation within `River`, based on the original one in the paper, follows the following steps: + 1) Insert new data points (`NewPoints`) and calculate its distance to existing points; + 2) Update the nreaest neighboors and reverse nearest neighboors of all the points; + 3) Define sets of affected points that required updates; + 4) Calculate the reachability-distance from new point to neighboors (`NewPoints` -> `kNN(NewPoints)`) + and from rev-neighboors to new point (`RkNN(NewPoints)` -> `NewPoints`); + 5) Update the reachability-distance for affected points: `RkNN(RkNN(NewPoints))` -> `RkNN(NewPoints)` + 6) Update local reachability distance of affected points: `lrd(set_upd_lrd)`; + 7) Update local outlier factor: `lof(set_upd_lof)`. + + The incremental LOF algorithm is expected to provide equivalent detection performance as the iterated static + LOF algroithm (applied after insertion of each data record), while requiring significantly less computational time. + Moreover, the insertion of a new data point as well as deletion of an old data point influence only a limited number + of their closest neighbors, which means that the number of updates per such insertion/deletion does not depend + on the total number of instances learned/in the data set. + + Parameters + ---------- + n_neighbors + The number of nearest neighbors to use for density estimation. + distance_func + Distance function to be used. By default, the Euclidean distance is used. + + Attributes + ---------- + x_list + A list of stored observations. + x_batch + A buffer to hold incoming observations until it's time to update the model. + x_scores + A buffer to hold incoming observations until it's time to score them. + dist_dict + A dictionary to hold distances between observations. + neighborhoods + A dictionary to hold neighborhoods for each observation. + rev_neighborhoods + A dictionary to hold reverse neighborhoods for each observation. + k_dist + A dictionary to hold k-distances for each observation. + reach_dist + A dictionary to hold reachability distances for each observation. + lof + A dictionary to hold Local Outlier Factors for each observation. + local_reach + A dictionary to hold local reachability distances for each observation. + + Examples + -------- + + >>> import pandas as pd + >>> from river import anomaly + >>> from river import datasets + + >>> cc_df = pd.DataFrame(datasets.CreditCard()) + + >>> lof = anomaly.LocalOutlierFactor(n_neighbors=20) + + >>> for x, _ in datasets.CreditCard().take(200): + ... lof.learn_one(x) + + >>> lof.learn_many(cc_df[201:401]) + + >>> scores = [] + >>> for x in cc_df[0][401:406]: + ... scores.append(lof.score_one(x)) + + >>> [round(score, 3) for score in scores] + [1.802, 1.936, 1.566, 1.181, 1.272] + + >>> X = [0.5, 0.45, 0.43, 0.44, 0.445, 0.45, 0.0] + >>> lof = anomaly.LocalOutlierFactor() + + >>> for x in X[:3]: + ... lof.learn_one({'x': x}) # Warming up + + >>> for x in X: + ... features = {'x': x} + ... print( + ... f'Anomaly score for x={x:.3f}: {lof.score_one(features):.3f}') + ... lof.learn_one(features) + Anomaly score for x=0.500: 0.000 + Anomaly score for x=0.450: 0.000 + Anomaly score for x=0.430: 0.000 + Anomaly score for x=0.440: 1.020 + Anomaly score for x=0.445: 1.032 + Anomaly score for x=0.450: 0.000 + Anomaly score for x=0.000: 0.980 + + References + ---------- + David Pokrajac, Aleksandar Lazarevic, and Longin Jan Latecki (2007). Incremental Local Outlier Detection for Data + Streams. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007). 504-515. + DOI: 10.1109/CIDM.2007.368917. + + """ + + def __init__( + self, + n_neighbors: int = 10, + distance_func: DistanceFunc | None = None, + ): + self.n_neighbors = n_neighbors + self.x_list: list = [] + self.x_batch: list = [] + self.x_scores: list = [] + self.dist_dict: dict = {} + self.neighborhoods: dict = {} + self.rev_neighborhoods: dict = {} + self.k_dist: dict = {} + self.reach_dist: dict = {} + self.lof: dict = {} + self.local_reach: dict = {} + self.distance_func = distance_func + self.distance = ( + distance_func + if distance_func is not None + else functools.partial(utils.math.minkowski_distance, p=2) + ) + + def learn_many(self, x: pd.DataFrame): + x = x[0].tolist() + self.learn(x) + + def learn_one(self, x: dict): + self.x_batch.append(x) + if len(self.x_list) or len(self.x_batch) > 1: + self.learn(self.x_batch) + self.x_batch = [] + + def learn(self, x_batch: list): + x_batch, equal = check_equal(x_batch, self.x_list) + + # Increase size of objects to accommodate new data + ( + nm, + self.x_list, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.reach_dist, + self.dist_dict, + self.local_reach, + self.lof, + ) = expand_objects( + x_batch, + self.x_list, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.reach_dist, + self.dist_dict, + self.local_reach, + self.lof, + ) + + # Calculate neighborhoods, reverse neighborhoods, k-distances and distances between neighbors + ( + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.dist_dict, + ) = self._initial_calculations( + self.x_list, + nm, + self.neighborhoods, + self.rev_neighborhoods, + self.k_dist, + self.dist_dict, + ) + + # Define sets of particles + ( + set_new_points, + set_neighbors, + set_rev_neighbors, + set_upd_lrd, + set_upd_lof, + ) = define_sets(nm, self.neighborhoods, self.rev_neighborhoods) + + # Calculate new reachability distance of all affected points + self.reach_dist = calc_reach_dist_new_points( + set_new_points, + self.neighborhoods, + self.rev_neighborhoods, + self.reach_dist, + self.dist_dict, + self.k_dist, + ) + self.reach_dist = calc_reach_dist_other_points( + set_rev_neighbors, + self.rev_neighborhoods, + self.reach_dist, + self.dist_dict, + self.k_dist, + ) + + # Calculate new local reachability distance of all affected points + self.local_reach = calc_local_reach_dist( + set_upd_lrd, self.neighborhoods, self.reach_dist, self.local_reach + ) + + # Calculate new Local Outlier Factor of all affected points + self.lof = calc_lof(set_upd_lof, self.neighborhoods, self.local_reach, self.lof) + + def score_one(self, x: dict): + self.x_scores.append(x) + self.x_scores, equal = check_equal(self.x_scores, self.x_list) + + if len(self.x_scores) == 0 or len(self.x_list) == 0: + return 0.0 + + x_list_copy = self.x_list.copy() + + ( + nm, + x_list_copy, + neighborhoods, + rev_neighborhoods, + k_dist, + reach_dist, + dist_dict, + local_reach, + lof, + ) = expand_objects( + self.x_scores, + x_list_copy, + self.neighborhoods.copy(), + self.rev_neighborhoods.copy(), + self.k_dist.copy(), + copy.deepcopy(self.reach_dist), + copy.deepcopy(self.dist_dict), + self.local_reach.copy(), + self.lof.copy(), + ) + + neighborhoods, rev_neighborhoods, k_dist, dist_dict = self._initial_calculations( + x_list_copy, nm, neighborhoods, rev_neighborhoods, k_dist, dist_dict + ) + ( + set_new_points, + set_neighbors, + set_rev_neighbors, + set_upd_lrd, + set_upd_lof, + ) = define_sets(nm, neighborhoods, rev_neighborhoods) + reach_dist = calc_reach_dist_new_points( + set_new_points, neighborhoods, rev_neighborhoods, reach_dist, dist_dict, k_dist + ) + reach_dist = calc_reach_dist_other_points( + set_rev_neighbors, + rev_neighborhoods, + reach_dist, + dist_dict, + k_dist, + ) + local_reach = calc_local_reach_dist(set_upd_lrd, neighborhoods, reach_dist, local_reach) + lof = calc_lof(set_upd_lof, neighborhoods, local_reach, lof) + self.x_scores = [] + + # Use nm[0] as index since upon this configuration nm[1] is expected to be 1. + return lof[nm[0]] + + def _initial_calculations( + self, + x_list: list, + nm: tuple, + neighborhoods: dict, + rev_neighborhoods: dict, + k_distances: dict, + dist_dict: dict, + ): + """ + Perform initial calculations on the incoming data before applying the Incremental LOF algorithm. + Taking the new data, it updates the neighborhoods, reverse neighborhoods, k-distances and distances between particles. + + Parameters + ---------- + x_list + A list of stored observations. + nm + A tuple representing the current size of the dataset. + neighborhoods + A dictionary of particle neighborhoods. + rev_neighborhoods + A dictionary of reverse particle neighborhoods. + k_distances + A dictionary to hold k-distances for each observation. + dist_dict + A dictionary of dictionaries storing distances between particles + + Returns + ------- + neighborhoods + Updated dictionary of particle neighborhoods + rev_neighborhoods + Updated dictionary of reverse particle neighborhoods + k_distances + Updated dictionary to hold k-distances for each observation + dist_dict + Updated dictionary of dictionaries storing distances between particles + + """ + + n = nm[0] + m = nm[1] + k = self.n_neighbors + + # Calculate distances all particles considering new and old ones + new_distances = [ + [i, j, self.distance(x_list[i], x_list[j])] + for i in range(n + m) + for j in range(i) + if i >= n + ] + # Add new distances to distance dictionary + for i in range(len(new_distances)): + dist_dict[new_distances[i][0]][new_distances[i][1]] = new_distances[i][2] + dist_dict[new_distances[i][1]][new_distances[i][0]] = new_distances[i][2] + + # Calculate new k-dist for each particle + for i, inner_dict in enumerate(dist_dict.values()): + k_distances[i] = sorted(inner_dict.values())[min(k, len(inner_dict.values())) - 1] + + # Only keep particles that are neighbors in distance dictionary + dist_dict = { + k: {k2: v2 for k2, v2 in v.items() if v2 <= k_distances[k]} + for k, v in dist_dict.items() + } + + # Define new neighborhoods for particles + for key, value in dist_dict.items(): + neighborhoods[key] = [index for index in value] + + # Define new reverse neighborhoods for particles + for particle_id, neighbor_ids in neighborhoods.items(): + for neighbor_id in neighbor_ids: + rev_neighborhoods[neighbor_id].append(particle_id) + + return neighborhoods, rev_neighborhoods, k_distances, dist_dict diff --git a/river/anomaly/pad.py b/river/anomaly/pad.py new file mode 100644 index 0000000000..0ddd3a403f --- /dev/null +++ b/river/anomaly/pad.py @@ -0,0 +1,165 @@ +from __future__ import annotations + +import math + +from river import anomaly, base, linear_model, preprocessing, stats, time_series + +__all__ = ["PredictiveAnomalyDetection"] + + +class PredictiveAnomalyDetection(anomaly.base.SupervisedAnomalyDetector): + """Predictive Anomaly Detection. + + This semi-supervised technique to anomaly detection employs a predictive model to learn the normal behavior + of a dataset. It forecasts future data points and compares these predictions with actual values to determine + anomalies. An anomaly score is calculated based on the deviation of the prediction from the actual value, with higher + scores indicating a higher probability of an anomaly. + + The actual anomaly score is calculated by comparing the squared-error to a dynamic threshold. If the error is larger + than this threshold, the score will be 1.0; else, the score will be linearly distributed within the range (0.0, 1.0), + with a higher score indicating a higher squared error compared to the threshold. + + Parameters + ---------- + predictive_model + The underlying model that learns the normal behavior of the data and makes predictions on future behavior. + This can be an estimator of any type, depending on the type of problem (e.g. some Forecaster for Time-Series Data). + horizon + When a Forecaster is used as a predictive model, this is the horizon of its forecasts. + n_std + Number of Standard Deviations to calculate the threshold. A larger number of standard deviation will result in + a higher threshold, resulting in the model being less sensitive. + warmup_period + Duration for the model to warm up. Since the model starts with zero knowledge, + the first instances will have very high anomaly scores, resulting in bad predictions (or high error). As such, + a warm-up period is necessary to discard the first seen instances. + While the model is within the warm-up period, no score will be calculated and the score_one method will return 0.0. + + Attributes + ---------- + dynamic_mae : stats.Mean + The running mean of the (squared) errors from the predictions of the model to update the dynamic threshold. + dynamic_se_variance : stats.Var + The running variance of the (squared) errors from the predictions of the model to update the dynamic threshold. + iter : int + The number of iterations (data points) passed. + + Examples + -------- + + >>> from river import datasets + >>> from river import time_series + >>> from river import anomaly + >>> from river import preprocessing + >>> from river import linear_model + >>> from river import optim + + >>> period = 12 + >>> predictive_model = time_series.SNARIMAX( + ... p=period, + ... d=1, + ... q=period, + ... m=period, + ... sd=1, + ... regressor=( + ... preprocessing.StandardScaler() + ... | linear_model.LinearRegression( + ... optimizer=optim.SGD(0.005), + ... ) + ... ), + ... ) + + >>> PAD = anomaly.PredictiveAnomalyDetection( + ... predictive_model, + ... horizon=1, + ... n_std=3.5, + ... warmup_period=15 + ... ) + + >>> scores = [] + + >>> for t, (x, y) in enumerate(datasets.AirlinePassengers()): + ... score = PAD.score_one(None, y) + ... PAD.learn_one(None, y) + ... scores.append(score) + + >>> print(scores[-1]) + 0.05329236123455621 + + References + ---------- + [^1]: Laptev N, Amizadeh S, Flint I. Generic and scalable framework for Automated Time-series Anomaly Detection. + Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2015. + doi:10.1145/2783258.2788611. + """ + + def __init__( + self, + predictive_model: base.Estimator | None = None, + horizon: int = 1, + n_std: float = 3.0, + warmup_period: int = 0, + ): + self.predictive_model = ( + predictive_model + if predictive_model is not None + else preprocessing.MinMaxScaler() | linear_model.LinearRegression() + ) + + self.horizon = horizon + self.n_std = n_std + self.warmup_period = warmup_period + + # Initialize necessary statistical measures + self.dynamic_mae = stats.Mean() + self.dynamic_se_variance = stats.Var() + + # Initialize necessary values for warm-up procedure + self.iter: int = 0 + + # This method is called to make the predictive model learn one example + def learn_one(self, x: dict | None, y: base.typing.Target | float): + self.iter += 1 + + # Check whether the model is a time-series forecasting or regression/classification model + if isinstance(self.predictive_model, time_series.base.Forecaster) and isinstance(y, float): + # When there's no data point as dict of features, the target will be passed + # to the forecaster as an exogenous variable. + if not x: + self.predictive_model.learn_one(y=y) + else: + self.predictive_model.learn_one(y=y, x=x) + else: + self.predictive_model.learn_one(x=x, y=y) + + def score_one(self, x: dict, y: base.typing.Target): + # Return the predicted value of x from the predictive model, first by checking whether + # it is a time-series forecaster. + if isinstance(self.predictive_model, time_series.base.Forecaster): + y_pred = self.predictive_model.forecast(self.horizon)[0] + else: + y_pred = self.predictive_model.predict_one(x) + + # Calculate the squared error + squared_error = (y_pred - y) ** 2 + + # Calculate the threshold + threshold = self.dynamic_mae.get() + ( + self.n_std * math.sqrt(self.dynamic_se_variance.get()) + ) + + # When warmup hyper-parameter is used, the anomaly score is only returned once the warmup period has passed. + # When the warmup period has not passed, the default value of the anomaly score is 0.0 + if self.iter < self.warmup_period: + return 0.0 + + # Update MAE and SEV when the warm-up parameter has passed. + self.dynamic_mae.update(squared_error) + self.dynamic_se_variance.update(squared_error) + + # An error above the threshold will result in a score of 1.0. + # Else, the score will be linearly distributed within the interval (0.0, 1.0) + if squared_error >= threshold: + return 1.0 + else: + return squared_error / threshold diff --git a/river/anomaly/sad.py b/river/anomaly/sad.py new file mode 100644 index 0000000000..9d6ff1e498 --- /dev/null +++ b/river/anomaly/sad.py @@ -0,0 +1,73 @@ +from __future__ import annotations + +from river import anomaly, stats + +__all__ = ["StandardAbsoluteDeviation"] + + +class StandardAbsoluteDeviation(anomaly.base.SupervisedAnomalyDetector): + r"""Standard Absolute Deviation (SAD). + + SAD is the model that calculates the anomaly score by using the deviation from the mean/median, divided by the + standard deviation of all the points seen within the data stream. The idea of this model is based on + the $3 \times \sigma$ rule described in [^1]. + + This implementation is adapted from the [implementation](https://github.com/selimfirat/pysad/blob/master/pysad/models/standard_absolute_deviation.py) + within PySAD (Python Streaming Anomaly Detection) [^2]. + + As a univariate anomaly detection algorithm, this implementation is adapted to `River` in a similar way as that of + the `GaussianScorer` algorithm, with the variable taken into the account at the learning phase and scoring phase + under variable `y`, ignoring `x`. + + Parameters + ---------- + sub_stat + The statistic to be subtracted, then divided by the standard deviation for scoring. + Defaults to `stats.Mean()`. + + References + ---------- + [^1]: Hochenbaum, J., Vallis, O.S., Kejariwal, A., 2017. Automatic Anomaly Detection in the Cloud Via + Statistical Learning. https://doi.org/10.48550/ARXIV.1704.07706. + [^2]: Yilmaz, S.F., Kozat, S.S., 2020. PySAD: A Streaming Anomaly Detection Framework in Python. + https://doi.org/10.48550/ARXIV.2009.02572. + + Examples + -------- + + >>> import random + >>> from river import anomaly + >>> from river import stats + >>> from river import stream + + >>> rng = random.Random(42) + + >>> model = anomaly.StandardAbsoluteDeviation(sub_stat=stats.Mean()) + + >>> for _ in range(150): + ... y = rng.gauss(0, 1) + ... model.learn_one(None, y) + + >>> model.score_one(None, 2) + 2.057... + + >>> model.score_one(None, 0) + 0.084... + + >>> model.score_one(None, 1) + 0.986... + + """ + + def __init__(self, sub_stat: stats.base.Univariate | None = None): + self.variance = stats.Var() + self.sub_stat = sub_stat or stats.Mean() + + def learn_one(self, x, y): + self.variance.update(y) + self.sub_stat.update(y) + + def score_one(self, x, y): + score = (y - self.sub_stat.get()) / (self.variance.get() ** 0.5 + 1e-10) + + return abs(score) diff --git a/river/anomaly/svm.py b/river/anomaly/svm.py index 297ad3ed22..2593211d8e 100644 --- a/river/anomaly/svm.py +++ b/river/anomaly/svm.py @@ -48,8 +48,8 @@ class OneClassSVM(linear_model.base.GLM, anomaly.base.AnomalyDetector): >>> for x, y in datasets.CreditCard().take(2500): ... score = model.score_one(x) ... is_anomaly = model.classify(score) - ... model = model.learn_one(x) - ... auc = auc.update(y, is_anomaly) + ... model.learn_one(x) + ... auc.update(y, is_anomaly) >>> auc ROCAUC: 74.68% @@ -102,10 +102,10 @@ def _get_intercept_update(self, loss_gradient): ) def learn_one(self, x): - return super().learn_one(x, y=1) + super().learn_one(x, y=1) def learn_many(self, X): - return super().learn_many(X, y=pd.Series(True, index=X.index)) + super().learn_many(X, y=pd.Series(True, index=X.index)) def score_one(self, x): return self._raw_dot_one(x) - self.intercept diff --git a/river/anomaly/test_hst.py b/river/anomaly/test_hst.py index ffcafcccc0..341f6697db 100644 --- a/river/anomaly/test_hst.py +++ b/river/anomaly/test_hst.py @@ -24,8 +24,8 @@ def test_missing_features(): >>> for x, y in datasets.CreditCard().take(8000): ... del x[random.choice(features)] ... score = model.score_one(x) - ... model = model.learn_one(x, y) - ... auc = auc.update(y, score) + ... model.learn_one(x, y) + ... auc.update(y, score) >>> auc ROCAUC: 88.68% diff --git a/river/anomaly/test_lof.py b/river/anomaly/test_lof.py new file mode 100644 index 0000000000..0b8767324f --- /dev/null +++ b/river/anomaly/test_lof.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import numpy as np +import pandas as pd +from sklearn import neighbors + +from river import anomaly, datasets + +np.random.seed(42) + + +def test_incremental_lof_scores(): + """ + Test that the incremental LOF algorithm returns similar LOF scores for each observation + compared with the original static LOF algorithm implemented in scikit-learn. + """ + norm_dist = 0.5 * np.random.rand(100, 2) + x_inliers = np.concatenate((norm_dist - 2, norm_dist, norm_dist + 2), axis=0) + x_outliers = np.concatenate( + ( + np.random.uniform(low=-4, high=4, size=(20, 2)), + np.random.uniform(low=-10, high=-5, size=(10, 2)), + np.random.uniform(low=5, high=10, size=(10, 2)), + ), + axis=0, + ) + x_train = np.concatenate((x_inliers, x_outliers), axis=0) + x_train_dict = [{f"feature_{i + 1}": elem[i] for i in range(2)} for elem in x_train] + ground_truth = np.ones(len(x_train), dtype=int) + ground_truth[-len(x_outliers) :] = -1 + df_train = pd.DataFrame({"observations": x_train_dict, "ground_truth": ground_truth}) + x_pred = np.random.uniform(low=-5, high=5, size=(30, 2)) + x_pred_dict = [{f"feature_{i + 1}": elem[i] for i in range(2)} for elem in x_pred] + incremental_lof = anomaly.LocalOutlierFactor(n_neighbors=20) + + for x in df_train["observations"]: + incremental_lof.learn_one(x) + + ilof_scores_train = np.array([ilof_score for ilof_score in incremental_lof.lof.values()]) + + ilof_scores_pred = [] + for x in x_pred_dict: + ilof_scores_pred.append(incremental_lof.score_one(x)) + + lof_sklearn = neighbors.LocalOutlierFactor(n_neighbors=20) + lof_sklearn.fit_predict(x_train) + lof_sklearn_scores_train = -lof_sklearn.negative_outlier_factor_ + + assert np.allclose(ilof_scores_train, lof_sklearn_scores_train, rtol=1e-08, atol=1e-08) + + +def test_batch_lof_scores(): + """ + Test that the incremental LOF algorithm returns similar LOF scores for each batch + with `learn_many` compared with the original static LOF algorithm implemented in scikit-learn, + under different batch sizes. + """ + cc_df = pd.DataFrame(datasets.CreditCard()) + cc_df_np = [np.array(list(x.values())) for x in cc_df[0].to_dict().values()] + + batch_sizes = [20, 50, 100] + + for batch_size in batch_sizes: + ilof_river_batch = anomaly.LocalOutlierFactor(n_neighbors=20) + ilof_river_batch.learn_many(cc_df[0:batch_size]) + ilof_scores_river_batch = np.array([v for v in ilof_river_batch.lof.values()]) + + lof_sklearn_batch = neighbors.LocalOutlierFactor(n_neighbors=20) + lof_sklearn_batch.fit_predict(cc_df_np[0:batch_size]) + lof_scores_sklearn_batch = -lof_sklearn_batch.negative_outlier_factor_ + + assert np.allclose( + ilof_scores_river_batch, lof_scores_sklearn_batch, rtol=1e-02, atol=1e-02 + ) + + +def test_issue_1328(): + lof = anomaly.LocalOutlierFactor() + X = [{"a": 1, "b": 1}, {"a": 1, "b": 1}] + for x in X: + lof.learn_one(x) + + +def test_issue_1331(): + import copy + + from river import anomaly + + lof = anomaly.LocalOutlierFactor() + + X = [{"a": 1, "b": 1}, {"a": 1, "b": 1}] + for x in X: + lof.learn_one(x) + + neighborhoods_ = lof.neighborhoods.copy() + rev_neighborhoods = lof.rev_neighborhoods.copy() + k_dist_ = lof.k_dist.copy() + reach_dist_ = copy.deepcopy(lof.reach_dist) + dist_dict_ = copy.deepcopy(lof.dist_dict) + local_reach_ = lof.local_reach.copy() + lof_ = lof.lof.copy() + + lof.score_one({"a": 0.5, "b": 1}) + + assert neighborhoods_ == lof.neighborhoods + assert rev_neighborhoods == lof.rev_neighborhoods + assert k_dist_ == lof.k_dist + assert reach_dist_ == lof.reach_dist + assert dist_dict_ == lof.dist_dict + assert local_reach_ == lof.local_reach + assert lof_ == lof.lof diff --git a/river/api.py b/river/api.py index 666541999c..921a129580 100644 --- a/river/api.py +++ b/river/api.py @@ -1,4 +1,5 @@ """River API module.""" + from __future__ import annotations from . import ( diff --git a/river/bandit/__init__.py b/river/bandit/__init__.py index 8e482364ec..70b0a98a72 100644 --- a/river/bandit/__init__.py +++ b/river/bandit/__init__.py @@ -5,24 +5,30 @@ (see `model_selection.BanditRegressor`). """ + from __future__ import annotations -from . import base, envs +from . import base, datasets, envs from .bayes_ucb import BayesUCB from .epsilon_greedy import EpsilonGreedy from .evaluate import evaluate, evaluate_offline from .exp3 import Exp3 +from .lin_ucb import LinUCBDisjoint +from .random import RandomPolicy from .thompson import ThompsonSampling from .ucb import UCB __all__ = [ "base", + "datasets", "envs", "evaluate", "evaluate_offline", "BayesUCB", "EpsilonGreedy", "Exp3", + "LinUCBDisjoint", "ThompsonSampling", "UCB", + "RandomPolicy", ] diff --git a/river/bandit/base.py b/river/bandit/base.py index c5fb1207b2..f5df1717b0 100644 --- a/river/bandit/base.py +++ b/river/bandit/base.py @@ -7,7 +7,7 @@ from river import base, compose, metrics, proba, stats, utils -__all__ = ["ArmID", "Policy", "RewardObj"] +__all__ = ["ArmID", "Policy", "ContextualPolicy", "RewardObj"] ArmID = typing.Union[int, str] # noqa: UP007 RewardObj = typing.Union[ # noqa: UP007 @@ -65,8 +65,7 @@ def __post_init__(self): ) @abc.abstractmethod - def _pull(self, arm_ids: list[ArmID]) -> ArmID: - ... + def _pull(self, arm_ids: list[ArmID]) -> ArmID: ... def pull(self, arm_ids: list[ArmID]) -> ArmID: """Pull arm(s). @@ -113,7 +112,6 @@ def update(self, arm_id, *reward_args, **reward_kwargs): self._rewards[arm_id].update(*reward_args, **reward_kwargs) self._counts[arm_id] += 1 self._n += 1 - return self @property def ranking(self) -> list[ArmID]: @@ -137,6 +135,80 @@ def __repr__(self): list(map(str, ranking)), [str(self._rewards[arm_id]) for arm_id in ranking], [f"{self._counts[arm_id]:,d}" for arm_id in ranking], - [f"{self._counts[arm_id] / self._n:.2%}" for arm_id in ranking], + [f"{(self._counts[arm_id] / self._n) if self._n else 0:.2%}" for arm_id in ranking], ], ) + + +class ContextualPolicy(Policy): + """Contextual bandit policy base class. + + Parameters + ---------- + reward_obj + The reward object used to measure the performance of each arm. This can be a metric, a + statistic, or a distribution. + reward_scaler + A reward scaler used to scale the rewards before they are fed to the reward object. This + can be useful to scale the rewards to a (0, 1) range for instance. + burn_in + The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled + during the burn-in phase. This is useful to mitigate selection bias. + + """ + + @abc.abstractmethod + def _pull(self, arm_ids: list[ArmID], context: dict) -> ArmID: # type: ignore[override] + ... + + def pull(self, arm_ids: list[ArmID], context: dict | None = None) -> ArmID: + """Pull arm(s). + + This method is a generator that yields the arm(s) that should be pulled. During the burn-in + phase, all the arms that have not been pulled enough times are yielded. Once the burn-in + phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you + only want to pull one arm at a time during the burn-in phase, simply call + `next(policy.pull(arms))`. + + Parameters + ---------- + arm_ids + The list of arms that can be pulled. + context + The context associated with the arm. Doesn't have to be provided if the policy is not + contextual. + + Returns + ------- + A single arm. + + """ + for arm_id in arm_ids: + if self._counts[arm_id] < self.burn_in: + return arm_id + return self._pull(arm_ids, context=context) # type: ignore[arg-type] + + def update(self, arm_id, context, *reward_args, **reward_kwargs): + """Update an arm's state. + + Parameters + ---------- + arm_id + The arm to update. + context + The context associated with the arm. Doesn't have to be provided if the policy is not + contextual. + reward_kwargs + Keyword arguments to pass to the reward object. + + """ + + if self.reward_scaler: + reward = reward_args[0] + self.reward_scaler._update(y=reward) + reward = self.reward_scaler.func(reward) + reward_args = (reward,) + + self._rewards[arm_id].update(*reward_args, **reward_kwargs) + self._counts[arm_id] += 1 + self._n += 1 diff --git a/river/bandit/bayes_ucb.py b/river/bandit/bayes_ucb.py index 539d62d3d9..c60b6353ea 100644 --- a/river/bandit/bayes_ucb.py +++ b/river/bandit/bayes_ucb.py @@ -28,7 +28,7 @@ class BayesUCB(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import proba >>> from river import stats @@ -45,8 +45,8 @@ class BayesUCB(bandit.base.Policy): >>> while True: ... action = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(action) - ... policy = policy.update(action, reward) - ... metric = metric.update(reward) + ... policy.update(action, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -54,7 +54,7 @@ class BayesUCB(bandit.base.Policy): Sum: 841. Reference - -------- + --------- [^1]: [Kaufmann, Emilie, Olivier Cappé, and Aurélien Garivier. "On Bayesian upper confidence bounds for bandit problems." Artificial intelligence and statistics. PMLR, 2012.](http://proceedings.mlr.press/v22/kaufmann12/kaufmann12.pdf) """ @@ -63,9 +63,9 @@ class BayesUCB(bandit.base.Policy): def __init__(self, reward_obj=None, burn_in=0, seed: int | None = None): super().__init__(reward_obj, burn_in) - self._posteriors: collections.defaultdict[ - bandit.base.ArmID, proba.Beta - ] = collections.defaultdict(proba.Beta) + self._posteriors: collections.defaultdict[bandit.base.ArmID, proba.Beta] = ( + collections.defaultdict(proba.Beta) + ) self.seed = seed self._rng = random.Random(seed) @@ -86,4 +86,3 @@ def update(self, arm_id, *reward_args, **reward_kwargs): super().update(arm_id, *reward_args, **reward_kwargs) reward = reward_args[0] self._posteriors[arm_id].update(reward) - return self diff --git a/river/bandit/datasets/__init__.py b/river/bandit/datasets/__init__.py new file mode 100644 index 0000000000..737daf92c3 --- /dev/null +++ b/river/bandit/datasets/__init__.py @@ -0,0 +1,6 @@ +from __future__ import annotations + +from .base import BanditDataset +from .news import NewsArticles + +__all__ = ["BanditDataset", "NewsArticles"] diff --git a/river/bandit/datasets/base.py b/river/bandit/datasets/base.py new file mode 100644 index 0000000000..da76cd6859 --- /dev/null +++ b/river/bandit/datasets/base.py @@ -0,0 +1,49 @@ +from __future__ import annotations + +import abc + +from river import bandit, datasets + + +class BanditDataset(datasets.base.Dataset): + """Base class for bandit datasets. + + Parameters + ---------- + n_features + Number of features in the dataset. + n_samples + Number of samples in the dataset. + n_classes + Number of classes in the dataset, only applies to classification datasets. + n_outputs + Number of outputs the target is made of, only applies to multi-output datasets. + sparse + Whether the dataset is sparse or not. + + """ + + def __init__( + self, + n_features, + n_samples=None, + n_classes=None, + n_outputs=None, + sparse=False, + ): + super().__init__( + task="BANDIT", + n_features=n_features, + n_samples=n_samples, + n_classes=n_classes, + n_outputs=n_outputs, + sparse=sparse, + ) + + @abc.abstractproperty + def arms(self) -> list[bandit.base.ArmID]: + """The list of arms that can be pulled.""" + + @property + def _repr_content(self): + return {**super()._repr_content, "Arms": f"{len(self.arms):,d}"} diff --git a/river/bandit/datasets/news.py b/river/bandit/datasets/news.py new file mode 100644 index 0000000000..6fd2bb8b0e --- /dev/null +++ b/river/bandit/datasets/news.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +import pathlib + +from river import datasets + +from .base import BanditDataset + + +class NewsArticles(datasets.base.RemoteDataset, BanditDataset): + """News articles bandit dataset. + + This is a personalization dataset. It contains 10000 observations. There are 10 arms, and the + reward is binary. There are 100 features, which turns this into a contextual bandit problem. + + Examples + -------- + + >>> from river import bandit + + >>> dataset = bandit.datasets.NewsArticles() + >>> context, arm, reward = next(iter(dataset)) + + >>> len(context) + 100 + + >>> arm, reward + (2, False) + + References + ---------- + [^1]: [Machine Learning for Personalization homework](http://www.cs.columbia.edu/~jebara/6998/hw2.pdf) + [^2]: [Contextual Bandits Analysis of LinUCB Disjoint Algorithm with Dataset](https://kfoofw.github.io/contextual-bandits-linear-ucb-disjoint/) + + """ + + def __init__(self): + super().__init__( + url="http://www.cs.columbia.edu/~jebara/6998/dataset.txt", + size=2_149_159, + filename="dataset.txt", + unpack=False, + directory=pathlib.Path(__file__).parent, + n_features=100, + n_samples=10_000, + ) + + @property + def arms(self) -> list: + return list(range(1, 11, 1)) + + def _iter(self): + with open(self.path) as f: + for x in f: + arm, reward, *features = x.strip().split(" ") + arm = int(arm) + reward = reward == "1" + features = {i: float(x) for i, x in enumerate(features)} + yield features, arm, reward diff --git a/river/bandit/envs/__init__.py b/river/bandit/envs/__init__.py index 8f0f511aa6..673edc9277 100644 --- a/river/bandit/envs/__init__.py +++ b/river/bandit/envs/__init__.py @@ -1,7 +1,7 @@ from __future__ import annotations try: - import gym + import gymnasium as gym GYM_INSTALLED = True except ImportError: @@ -15,13 +15,13 @@ RIVER_NAMESPACE = "river_bandits" - if (env_id := f"{RIVER_NAMESPACE}/CandyCaneContest-v0") not in gym.envs.registry: + if (env_id := f"{RIVER_NAMESPACE}/CandyCaneContest-v0") not in gym.envs.registration.registry: gym.envs.registration.register( id=env_id, entry_point="river.bandit.envs:CandyCaneContest", max_episode_steps=CandyCaneContest.n_steps, ) - if (env_id := f"{RIVER_NAMESPACE}/KArmedTestbed-v0") not in gym.envs.registry: + if (env_id := f"{RIVER_NAMESPACE}/KArmedTestbed-v0") not in gym.envs.registration.registry: gym.envs.registration.register( id=env_id, entry_point="river.bandit.envs:KArmedTestbed", diff --git a/river/bandit/envs/candy_cane.py b/river/bandit/envs/candy_cane.py index 5bda95f241..fa95652dcd 100644 --- a/river/bandit/envs/candy_cane.py +++ b/river/bandit/envs/candy_cane.py @@ -2,7 +2,7 @@ import dataclasses -import gym +import gymnasium as gym @dataclasses.dataclass @@ -25,7 +25,7 @@ class CandyCaneContest(gym.Env): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import stats >>> env = gym.make('river_bandits/CandyCaneContest-v0') @@ -36,7 +36,7 @@ class CandyCaneContest(gym.Env): >>> while True: ... arm = env.action_space.sample() ... observation, reward, terminated, truncated, info = env.step(arm) - ... metric = metric.update(reward) + ... metric.update(reward) ... if terminated or truncated: ... break diff --git a/river/bandit/envs/testbed.py b/river/bandit/envs/testbed.py index c7f0bfb8da..7b8b725d3f 100644 --- a/river/bandit/envs/testbed.py +++ b/river/bandit/envs/testbed.py @@ -2,7 +2,7 @@ import math -import gym +import gymnasium as gym class KArmedTestbed(gym.Env): diff --git a/river/bandit/epsilon_greedy.py b/river/bandit/epsilon_greedy.py index 15568043b0..a422bd1daf 100644 --- a/river/bandit/epsilon_greedy.py +++ b/river/bandit/epsilon_greedy.py @@ -33,7 +33,7 @@ class EpsilonGreedy(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import stats @@ -49,8 +49,8 @@ class EpsilonGreedy(bandit.base.Policy): >>> while True: ... arm = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(arm) - ... policy = policy.update(arm, reward) - ... metric = metric.update(reward) + ... policy.update(arm, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -64,7 +64,12 @@ class EpsilonGreedy(bandit.base.Policy): """ def __init__( - self, epsilon: float, decay=0.0, reward_obj=None, burn_in=0, seed: int | None = None + self, + epsilon: float, + decay=0.0, + reward_obj=None, + burn_in=0, + seed: int | None = None, ): super().__init__(reward_obj=reward_obj, burn_in=burn_in) self.epsilon = epsilon diff --git a/river/bandit/evaluate.py b/river/bandit/evaluate.py index c026fe02c0..0079079c8e 100644 --- a/river/bandit/evaluate.py +++ b/river/bandit/evaluate.py @@ -5,7 +5,7 @@ import typing try: - import gym + import gymnasium as gym except ImportError: ... @@ -52,12 +52,12 @@ def evaluate( Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> trace = bandit.evaluate( ... policies=[ - ... bandit.UCB(delta=1), + ... bandit.UCB(delta=1, seed=42), ... bandit.EpsilonGreedy(epsilon=0.1, seed=42), ... ], ... env=gym.make( @@ -71,7 +71,7 @@ def evaluate( >>> for step in trace: ... print(step) ... break - {'episode': 0, 'step': 0, 'policy_idx': 0, 'arm': 0, 'reward': 0.0, 'reward_stat': 0.0} + {'episode': 0, 'step': 0, 'policy_idx': 0, 'arm': 81, 'reward': 0.0, 'reward_stat': 0.0} The return type of this function is a generator. Each step of the generator is a dictionary. You can pass the generator to a `pandas.DataFrame` to get a nice representation of the results. @@ -80,7 +80,7 @@ def evaluate( >>> trace = bandit.evaluate( ... policies=[ - ... bandit.UCB(delta=1), + ... bandit.UCB(delta=1, seed=42), ... bandit.EpsilonGreedy(epsilon=0.1, seed=42), ... ], ... env=gym.make( @@ -96,8 +96,8 @@ def evaluate( episode step policy_idx arm reward reward_stat 521 2 60 1 25 0.0 36.0 737 3 68 1 40 1.0 20.0 - 740 3 70 0 70 1.0 33.0 - 660 3 30 0 30 1.0 13.0 + 740 3 70 0 58 0.0 36.0 + 660 3 30 0 31 1.0 16.0 411 2 5 1 35 1.0 5.0 The length of the dataframe is the number of policies times the number of episodes times the @@ -157,15 +157,17 @@ def evaluate( History = typing.Iterator[ tuple[ list[ArmID], # arms available to pull from + dict, # context ArmID, # arm that was pulled - typing.Union[float, None], # noqa: UP007, probability of pulling the arm float, # reward ] ] def evaluate_offline( - policy: bandit.base.Policy, history: History, reward_stat: stats.base.Univariate = None + policy: bandit.base.Policy, + history: History | bandit.datasets.BanditDataset, + reward_stat: stats.base.Univariate | None = None, ) -> tuple[stats.base.Univariate, int]: """Evaluate a policy on historical logs using replay. @@ -183,9 +185,7 @@ def evaluate_offline( The policy to evaluate. history The history of the bandit problem. This is a generator that yields tuples of the form - `(context, arm, probability, reward)`. The probability is optional, and is the probability - the policy had of picking the arm. If provided, this probability is used to unbias the - final score via inverse propensity scoring. + `(arms, context, arm, reward)`. reward_stat The reward statistic to use. Defaults to `stats.Sum`. @@ -201,14 +201,16 @@ def evaluate_offline( >>> from river import bandit >>> rng = random.Random(42) - >>> arms = ['A', 'B', 'C'] >>> clicks = [ ... ( ... arms, + ... # no context + ... None, + ... # random arm ... rng.choice(arms), - ... (p := rng.random()), - ... p > 0.9 + ... # reward + ... rng.random() > 0.5 ... ) ... for _ in range(1000) ... ] @@ -219,10 +221,24 @@ def evaluate_offline( ... ) >>> total_reward - Sum: 33.626211 + Sum: 172. >>> n_samples_used - 323 + 321 + + This also works out of the box with datasets that inherit from `river.bandit.BanditDataset`. + + >>> news = bandit.datasets.NewsArticles() + >>> total_reward, n_samples_used = bandit.evaluate_offline( + ... policy=bandit.RandomPolicy(seed=42), + ... history=news, + ... ) + + >>> total_reward, n_samples_used + (Sum: 105., 1027) + + As expected, the policy succeeds in roughly 10% of cases. Indeed, there are 10 arms and 10000 + samples, so the expected number of successes is 1000. References ---------- @@ -232,17 +248,30 @@ def evaluate_offline( """ + if isinstance(history, bandit.datasets.BanditDataset): + arms = history.arms + history = ((arms, context, arm, reward) for context, arm, reward in history) + reward_stat = reward_stat or stats.Sum() - for arms_available, arm_pulled, probability, reward in history: - probability = 1 if probability is None else probability - arm = policy.pull(arms_available) + is_contextual = isinstance(policy, bandit.base.ContextualPolicy) + + for arms_available, context, chosen_arm, reward in history: + probability = 1 # TODO: use inverse propensity scoring + arm = ( + policy.pull(arms_available, context=context) # type: ignore[call-arg] + if is_contextual + else policy.pull(arms_available) + ) # Do nothing if the chosen arm differs from the arm in the historical data - if arm_pulled != arm: + if chosen_arm != arm: continue - policy.update(arm, reward) + if is_contextual: + policy.update(arm, context, reward) + else: + policy.update(arm, reward) reward_stat.update(reward / probability) # type: ignore # Normalize the reward statistic by the number of times the policy pulled the same arm as what diff --git a/river/bandit/exp3.py b/river/bandit/exp3.py index bb8ddd8a96..a675472dea 100644 --- a/river/bandit/exp3.py +++ b/river/bandit/exp3.py @@ -35,7 +35,7 @@ class Exp3(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import proba >>> from river import stats @@ -52,8 +52,8 @@ class Exp3(bandit.base.Policy): >>> while True: ... action = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(action) - ... policy = policy.update(action, reward) - ... metric = metric.update(reward) + ... policy.update(action, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -70,7 +70,12 @@ class Exp3(bandit.base.Policy): _REQUIRES_UNIVARIATE_REWARD = True def __init__( - self, gamma: float, reward_obj=None, reward_scaler=None, burn_in=0, seed: int | None = None + self, + gamma: float, + reward_obj=None, + reward_scaler=None, + burn_in=0, + seed: int | None = None, ): super().__init__(reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in) self.seed = seed @@ -94,10 +99,11 @@ def update(self, arm_id, *reward_args, **reward_kwargs): reward = reward_args[0] reward /= self._probabilities[arm_id] self._weights[arm_id] *= math.exp(self.gamma * reward / len(self._weights)) - return self @classmethod def _unit_test_params(cls): - yield {"gamma": 0.0} + yield {"gamma": 0} + yield {"gamma": 0.1} yield {"gamma": 0.5} - yield {"gamma": 1.0} + yield {"gamma": 0.9} + yield {"gamma": 1} diff --git a/river/bandit/lin_ucb.py b/river/bandit/lin_ucb.py new file mode 100644 index 0000000000..32c4bbde86 --- /dev/null +++ b/river/bandit/lin_ucb.py @@ -0,0 +1,93 @@ +from __future__ import annotations + +import collections +import functools +import random + +from river import bandit, linear_model + + +class LinUCBDisjoint(bandit.base.ContextualPolicy): + """LinUCB, disjoint variant. + + Although it works, as of yet it is too slow to realistically be used in practice. + + The way this works is that each arm is assigned a `linear_model.BayesianLinearRegression` + instance. This instance is updated every time the arm is pulled. The context is used as + features for the regression. The reward is used as the target. The posterior distribution + is used to compute the upper confidence bound. The arm with the highest upper confidence + bound is pulled. + + Parameters + ---------- + alpha + Parameter used in each Bayesian linear regression. + beta + Parameter used in each Bayesian linear regression. + smoothing + Parameter used in each Bayesian linear regression. + reward_obj + The reward object used to measure the performance of each arm. + burn_in + The number of time steps during which each arm is pulled once. + seed + Random number generator seed for reproducibility. + + References + ---------- + [^1]: [A Contextual-Bandit Approach to Personalized News Article Recommendation](https://arxiv.org/abs/1003.0146) + [^2:] [Contextual Bandits Analysis of LinUCB Disjoint Algorithm with Dataset](https://kfoofw.github.io/contextual-bandits-linear-ucb-disjoint/) + + """ + + _REQUIRES_UNIVARIATE_REWARD = True + + def __init__( + self, + alpha: float = 1.0, + beta: float = 1.0, + smoothing: float | None = None, + reward_obj=None, + burn_in=0, + seed: int | None = None, + ): + super().__init__(reward_obj, burn_in) + self.alpha = alpha + self.beta = beta + self.smoothing = smoothing + self._bayes_lin_regs: collections.defaultdict[ + bandit.base.ArmID, linear_model.BayesianLinearRegression + ] = collections.defaultdict( + functools.partial( + linear_model.BayesianLinearRegression, + alpha=self.alpha, + beta=self.beta, + smoothing=self.smoothing, + ) + ) + self.seed = seed + self._rng = random.Random(seed) + + def _pull(self, arm_ids, context): + def get_upper_bound(dist): + return dist.mu + dist.sigma + + upper_bounds = { + arm_id: get_upper_bound( + self._bayes_lin_regs[arm_id].predict_one(context, with_dist=True) + ) + for arm_id in arm_ids + } + biggest_upper_bound = max(upper_bounds.values()) + candidates = [ + arm_id + for arm_id, upper_bound in upper_bounds.items() + if upper_bound == biggest_upper_bound + ] + return self._rng.choice(candidates) if len(candidates) > 1 else candidates[0] + + def update(self, arm_id, context, *reward_args, **reward_kwargs): + """Rewrite update function""" + super().update(arm_id, None, *reward_args, **reward_kwargs) + reward = reward_args[0] + self._bayes_lin_regs[arm_id].learn_one(x=context, y=reward) diff --git a/river/bandit/random.py b/river/bandit/random.py new file mode 100644 index 0000000000..8b7ca85ac8 --- /dev/null +++ b/river/bandit/random.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import collections +import random + +from river import bandit, proba + + +class RandomPolicy(bandit.base.Policy): + """Random bandit policy. + + This policy simply pulls a random arm at each time step. It is useful as a baseline. + + Parameters + ---------- + reward_obj + The reward object that is used to update the posterior distribution. + burn_in + Number of initial observations per arm before using the posterior distribution. + seed + Random number generator seed for reproducibility. + + Examples + -------- + + >>> import gymnasium as gym + >>> from river import bandit + >>> from river import proba + >>> from river import stats + + >>> env = gym.make( + ... 'river_bandits/CandyCaneContest-v0' + ... ) + >>> _ = env.reset(seed=42) + >>> _ = env.action_space.seed(123) + + >>> policy = bandit.RandomPolicy(seed=123) + + >>> metric = stats.Sum() + >>> while True: + ... action = policy.pull(range(env.action_space.n)) + ... observation, reward, terminated, truncated, info = env.step(action) + ... policy.update(action, reward) + ... metric.update(reward) + ... if terminated or truncated: + ... break + + >>> metric + Sum: 755. + + """ + + def __init__(self, reward_obj=None, burn_in=0, seed: int | None = None): + super().__init__(reward_obj, burn_in) + self._posteriors: collections.defaultdict[bandit.base.ArmID, proba.Beta] = ( + collections.defaultdict(proba.Beta) + ) + self.seed = seed + self._rng = random.Random(seed) + + def _pull(self, arm_ids): + return self._rng.choice(arm_ids) diff --git a/river/bandit/test_envs.py b/river/bandit/test_envs.py index 0f79fa5759..9be9acf97b 100644 --- a/river/bandit/test_envs.py +++ b/river/bandit/test_envs.py @@ -1,6 +1,7 @@ from __future__ import annotations -import gym.utils.env_checker +import gymnasium as gym +import gymnasium.utils.env_checker import pytest from river import bandit diff --git a/river/bandit/test_policies.py b/river/bandit/test_policies.py index 9650f488e8..323439501e 100644 --- a/river/bandit/test_policies.py +++ b/river/bandit/test_policies.py @@ -5,7 +5,7 @@ import inspect import random -import gym +import gymnasium as gym import pytest from river import bandit, metrics @@ -25,21 +25,17 @@ def get(self): def bigger_is_better(self): return False - def revert(self): - ... + def revert(self): ... - def update(self): - ... + def update(self): ... - def works_with(self): - ... + def works_with(self): ... class DummyPolicy(bandit.base.Policy): def __init__(self): super().__init__(reward_obj=DummyMetric()) - def _pull(self, arms): - ... + def _pull(self, arms): ... policy = DummyPolicy() policy._rewards[0].value = 0 @@ -69,7 +65,7 @@ def _iter_policies(): yield policy(**params) -@pytest.mark.parametrize( +@pytest.mark.parametrize( # type: ignore[misc] "policy,env", [ pytest.param( @@ -80,7 +76,7 @@ def _iter_policies(): for env in _iter_envs() ], ) -@pytest.mark.skip(reason="flaky") +@pytest.mark.skip(reason="flaky") # type: ignore[misc] def test_better_than_random_policy(policy: bandit.base.Policy, env: gym.Env): """Test that the policy is better than random.""" @@ -111,7 +107,7 @@ def test_better_than_random_policy(policy: bandit.base.Policy, env: gym.Env): arm_id = policy.pull(arm_ids) # type: ignore observation, reward, terminated, truncated, info = env.step(arm_id) policy.update(arm_id, reward) - policy_reward += reward + policy_reward += float(reward) random_arm_id = random_policy.pull(arm_ids) # type: ignore ( @@ -122,7 +118,7 @@ def test_better_than_random_policy(policy: bandit.base.Policy, env: gym.Env): info, ) = random_env.step(random_arm_id) random_policy.update(random_arm_id, reward) - random_reward += reward + random_reward += float(reward) n_successes += policy_reward > random_reward diff --git a/river/bandit/thompson.py b/river/bandit/thompson.py index 2920b7a261..c53533448b 100644 --- a/river/bandit/thompson.py +++ b/river/bandit/thompson.py @@ -29,7 +29,7 @@ class ThompsonSampling(bandit.base.Policy): Parameters ---------- - dist + reward_obj A distribution to sample from. burn_in The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled @@ -40,7 +40,7 @@ class ThompsonSampling(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import proba >>> from river import stats @@ -51,14 +51,14 @@ class ThompsonSampling(bandit.base.Policy): >>> _ = env.reset(seed=42) >>> _ = env.action_space.seed(123) - >>> policy = bandit.ThompsonSampling(dist=proba.Beta(), seed=101) + >>> policy = bandit.ThompsonSampling(reward_obj=proba.Beta(), seed=101) >>> metric = stats.Sum() >>> while True: ... arm = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(arm) - ... policy = policy.update(arm, reward) - ... metric = metric.update(reward) + ... policy.update(arm, reward) + ... metric.update(reward) ... if terminated or truncated: ... break @@ -71,22 +71,23 @@ class ThompsonSampling(bandit.base.Policy): """ - def __init__(self, dist: proba.base.Distribution, burn_in=0, seed: int | None = None): - super().__init__(reward_obj=dist, burn_in=burn_in) + def __init__( + self, + reward_obj: proba.base.Distribution | None = None, + burn_in=0, + seed: int | None = None, + ): + super().__init__(reward_obj=reward_obj, burn_in=burn_in) self.seed = seed self._rng = random.Random(seed) - self._rewards.default_factory = self._clone_dist_with_seed + self._rewards.default_factory = self._clone_reward_obj_with_seed - def _clone_dist_with_seed(self): - return self.dist.clone({"seed": self._rng.randint(0, 2**32)}) - - @property - def dist(self): - return self.reward_obj + def _clone_reward_obj_with_seed(self): + return self.reward_obj.clone({"seed": self._rng.randint(0, 2**32)}) def _pull(self, arm_ids): return max(arm_ids, key=lambda arm_id: self._rewards[arm_id].sample()) @classmethod def _unit_test_params(cls): - yield {"dist": proba.Beta()} + yield {"reward_obj": proba.Beta()} diff --git a/river/bandit/ucb.py b/river/bandit/ucb.py index 15a6c64bdb..e37ee290d8 100644 --- a/river/bandit/ucb.py +++ b/river/bandit/ucb.py @@ -1,6 +1,7 @@ from __future__ import annotations import math +import random from river import bandit, proba @@ -31,7 +32,7 @@ class UCB(bandit.base.Policy): Examples -------- - >>> import gym + >>> import gymnasium as gym >>> from river import bandit >>> from river import preprocessing >>> from river import stats @@ -44,20 +45,21 @@ class UCB(bandit.base.Policy): >>> policy = bandit.UCB( ... delta=100, - ... reward_scaler=preprocessing.TargetStandardScaler(None) + ... reward_scaler=preprocessing.TargetStandardScaler(None), + ... seed=42 ... ) >>> metric = stats.Sum() >>> while True: ... arm = policy.pull(range(env.action_space.n)) ... observation, reward, terminated, truncated, info = env.step(arm) - ... policy = policy.update(arm, reward) - ... metric = metric.update(reward) + ... policy.update(arm, reward) + ... metric.update(reward) ... if terminated or truncated: ... break >>> metric - Sum: 737. + Sum: 744. References ---------- @@ -67,9 +69,18 @@ class UCB(bandit.base.Policy): """ - def __init__(self, delta: float, reward_obj=None, reward_scaler=None, burn_in=0): + def __init__( + self, + delta: float, + reward_obj=None, + reward_scaler=None, + burn_in=0, + seed: int | None = None, + ): super().__init__(reward_obj=reward_obj, reward_scaler=reward_scaler, burn_in=burn_in) self.delta = delta + self.seed = seed + self._rng = random.Random(seed) def _pull(self, arm_ids): upper_bounds = { @@ -83,7 +94,13 @@ def _pull(self, arm_ids): else math.inf for arm_id in arm_ids } - return max(arm_ids, key=lambda arm_id: upper_bounds[arm_id]) + biggest_upper_bound = max(upper_bounds.values()) + candidates = [ + arm_id + for arm_id, upper_bound in upper_bounds.items() + if upper_bound == biggest_upper_bound + ] + return self._rng.choice(candidates) if len(candidates) > 1 else candidates[0] @classmethod def _unit_test_params(cls): diff --git a/river/base/__init__.py b/river/base/__init__.py index b5442ef381..0aaa521934 100644 --- a/river/base/__init__.py +++ b/river/base/__init__.py @@ -11,6 +11,7 @@ This module also contains utilities for type hinting and tagging estimators. """ + from __future__ import annotations from . import tags, typing diff --git a/river/base/base.py b/river/base/base.py index e5607b725a..16e4f829b3 100644 --- a/river/base/base.py +++ b/river/base/base.py @@ -453,12 +453,12 @@ def log_method_calls( >>> with utils.log_method_calls(class_condition): ... for x, y in datasets.CreditCard().take(1): ... score = model.score_one(x) - ... model = model.learn_one(x) + ... model.learn_one(x) >>> print(logs.getvalue()) - MinMaxScaler.learn_one MinMaxScaler.transform_one HalfSpaceTrees.score_one + MinMaxScaler.learn_one MinMaxScaler.transform_one HalfSpaceTrees.learn_one diff --git a/river/base/classifier.py b/river/base/classifier.py index 95dd25dc70..876bef4e13 100644 --- a/river/base/classifier.py +++ b/river/base/classifier.py @@ -15,7 +15,7 @@ class Classifier(estimator.Estimator): """A classifier.""" @abc.abstractmethod - def learn_one(self, x: dict, y: base.typing.ClfTarget) -> Classifier: + def learn_one(self, x: dict, y: base.typing.ClfTarget) -> None: """Update the model with a set of features `x` and a label `y`. Parameters @@ -25,10 +25,6 @@ def learn_one(self, x: dict, y: base.typing.ClfTarget) -> Classifier: y A label. - Returns - ------- - self - """ def predict_proba_one(self, x: dict) -> dict[base.typing.ClfTarget, float]: @@ -85,7 +81,7 @@ class MiniBatchClassifier(Classifier): """A classifier that can operate on mini-batches.""" @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchClassifier: + def learn_many(self, X: pd.DataFrame, y: pd.Series) -> None: """Update the model with a mini-batch of features `X` and boolean targets `y`. Parameters @@ -95,10 +91,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchClassifier: y A series of boolean target values. - Returns - ------- - self - """ def predict_proba_many(self, X: pd.DataFrame) -> pd.DataFrame: diff --git a/river/base/clusterer.py b/river/base/clusterer.py index fd036920fa..35a73a71ad 100644 --- a/river/base/clusterer.py +++ b/river/base/clusterer.py @@ -13,7 +13,7 @@ def _supervised(self): return False @abc.abstractmethod - def learn_one(self, x: dict) -> Clusterer: + def learn_one(self, x: dict) -> None: """Update the model with a set of features `x`. Parameters @@ -21,10 +21,6 @@ def learn_one(self, x: dict) -> Clusterer: x A dictionary of features. - Returns - ------- - self - """ @abc.abstractmethod diff --git a/river/base/drift_detector.py b/river/base/drift_detector.py index 62a511d5bb..a350c40d4b 100644 --- a/river/base/drift_detector.py +++ b/river/base/drift_detector.py @@ -5,10 +5,10 @@ purposes. The properties are not meant to be modified by the user. """ + from __future__ import annotations import abc -import numbers from . import base @@ -23,12 +23,12 @@ class _BaseDriftDetector(base.Base): def __init__(self): self._drift_detected = False - def _reset(self): + def _reset(self) -> None: """Reset the detector's state.""" self._drift_detected = False @property - def drift_detected(self): + def drift_detected(self) -> bool: """Whether or not a drift is detected following the last update.""" return self._drift_detected @@ -58,7 +58,7 @@ class DriftDetector(_BaseDriftDetector): """A drift detector.""" @abc.abstractmethod - def update(self, x: numbers.Number) -> DriftDetector: + def update(self, x: int | float) -> None: """Update the detector with a single data point. Parameters @@ -66,10 +66,6 @@ def update(self, x: numbers.Number) -> DriftDetector: x Input value. - Returns - ------- - self - """ @@ -81,7 +77,7 @@ class BinaryDriftDetector(_BaseDriftDetector): """A drift detector for binary data.""" @abc.abstractmethod - def update(self, x: bool) -> BinaryDriftDetector: + def update(self, x: bool) -> None: """Update the detector with a single boolean input. Parameters @@ -89,10 +85,6 @@ def update(self, x: bool) -> BinaryDriftDetector: x Input boolean. - Returns - ------- - self - """ diff --git a/river/base/multi_output.py b/river/base/multi_output.py index d862d68ad9..078ed1a362 100644 --- a/river/base/multi_output.py +++ b/river/base/multi_output.py @@ -10,7 +10,7 @@ class MultiLabelClassifier(Estimator, abc.ABC): """Multi-label classifier.""" @abc.abstractmethod - def learn_one(self, x: dict, y: dict[FeatureName, bool]) -> MultiLabelClassifier: + def learn_one(self, x: dict, y: dict[FeatureName, bool]) -> None: """Update the model with a set of features `x` and the labels `y`. Parameters @@ -20,10 +20,6 @@ def learn_one(self, x: dict, y: dict[FeatureName, bool]) -> MultiLabelClassifier y A dictionary of labels. - Returns - ------- - self - """ def predict_proba_one(self, x: dict, **kwargs) -> dict[FeatureName, dict[bool, float]]: @@ -72,7 +68,7 @@ class MultiTargetRegressor(Estimator, abc.ABC): """Multi-target regressor.""" @abc.abstractmethod - def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs) -> MultiTargetRegressor: + def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs) -> None: """Fits to a set of features `x` and a real-valued target `y`. Parameters @@ -82,10 +78,6 @@ def learn_one(self, x: dict, y: dict[FeatureName, RegTarget], **kwargs) -> Multi y A dictionary of numeric targets. - Returns - ------- - self - """ @abc.abstractmethod diff --git a/river/base/regressor.py b/river/base/regressor.py index 48f353ea0f..09abacb2b6 100644 --- a/river/base/regressor.py +++ b/river/base/regressor.py @@ -15,7 +15,7 @@ class Regressor(estimator.Estimator): """A regressor.""" @abc.abstractmethod - def learn_one(self, x: dict, y: base.typing.RegTarget) -> Regressor: + def learn_one(self, x: dict, y: base.typing.RegTarget) -> None: """Fits to a set of features `x` and a real-valued target `y`. Parameters @@ -25,10 +25,6 @@ def learn_one(self, x: dict, y: base.typing.RegTarget) -> Regressor: y A numeric target. - Returns - ------- - self - """ @abc.abstractmethod @@ -51,7 +47,7 @@ class MiniBatchRegressor(Regressor): """A regressor that can operate on mini-batches.""" @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchRegressor: + def learn_many(self, X: pd.DataFrame, y: pd.Series) -> None: """Update the model with a mini-batch of features `X` and real-valued targets `y`. Parameters @@ -61,10 +57,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchRegressor: y A series of numbers. - Returns - ------- - self - """ @abc.abstractmethod diff --git a/river/base/test_base.py b/river/base/test_base.py index 2154f58a53..412303b93b 100644 --- a/river/base/test_base.py +++ b/river/base/test_base.py @@ -75,7 +75,7 @@ def test_mutate(): >>> for x, y in datasets.TrumpApproval(): ... _ = model.predict_one(x) - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> len(model[-1].weights) 6 diff --git a/river/base/transformer.py b/river/base/transformer.py index defc0c80f8..16f9aba276 100644 --- a/river/base/transformer.py +++ b/river/base/transformer.py @@ -57,7 +57,7 @@ class Transformer(base.Estimator, BaseTransformer): def _supervised(self): return False - def learn_one(self, x: dict) -> Transformer: + def learn_one(self, x: dict) -> None: """Update with a set of features `x`. A lot of transformers don't actually have to do anything during the `learn_one` step @@ -70,12 +70,8 @@ def learn_one(self, x: dict) -> Transformer: x A dictionary of features. - Returns - ------- - self - """ - return self + return class SupervisedTransformer(base.Estimator, BaseTransformer): @@ -85,7 +81,7 @@ class SupervisedTransformer(base.Estimator, BaseTransformer): def _supervised(self): return True - def learn_one(self, x: dict, y: base.typing.Target) -> SupervisedTransformer: + def learn_one(self, x: dict, y: base.typing.Target) -> None: """Update with a set of features `x` and a target `y`. Parameters @@ -95,12 +91,8 @@ def learn_one(self, x: dict, y: base.typing.Target) -> SupervisedTransformer: y A target. - Returns - ------- - self - """ - return self + return class MiniBatchTransformer(Transformer): @@ -121,7 +113,7 @@ def transform_many(self, X: pd.DataFrame) -> pd.DataFrame: """ - def learn_many(self, X: pd.DataFrame) -> Transformer: + def learn_many(self, X: pd.DataFrame) -> None: """Update with a mini-batch of features. A lot of transformers don't actually have to do anything during the `learn_many` step @@ -134,12 +126,8 @@ def learn_many(self, X: pd.DataFrame) -> Transformer: X A DataFrame of features. - Returns - ------- - self - """ - return self + return class MiniBatchSupervisedTransformer(Transformer): @@ -150,7 +138,7 @@ def _supervised(self): return True @abc.abstractmethod - def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchSupervisedTransformer: + def learn_many(self, X: pd.DataFrame, y: pd.Series) -> None: """Update the model with a mini-batch of features `X` and targets `y`. Parameters @@ -160,12 +148,8 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series) -> MiniBatchSupervisedTransf y A series of boolean target values. - Returns - ------- - self - """ - return self + return @abc.abstractmethod def transform_many(self, X: pd.DataFrame) -> pd.DataFrame: diff --git a/river/checks/__init__.py b/river/checks/__init__.py index 2f3176428a..e0f8234b3d 100644 --- a/river/checks/__init__.py +++ b/river/checks/__init__.py @@ -1,4 +1,5 @@ """Utilities for unit testing and sanity checking estimators.""" + from __future__ import annotations import functools diff --git a/river/checks/clf.py b/river/checks/clf.py index 3c22797bb2..ce9d754214 100644 --- a/river/checks/clf.py +++ b/river/checks/clf.py @@ -15,7 +15,7 @@ def check_predict_proba_one(classifier, dataset): for x, y in dataset: xx, yy = copy.deepcopy(x), copy.deepcopy(y) - classifier = classifier.learn_one(x, y) + classifier.learn_one(x, y) y_pred = classifier.predict_proba_one(x) if utils.inspect.isactivelearner(classifier): @@ -37,7 +37,7 @@ def check_predict_proba_one_binary(classifier, dataset): for x, y in dataset: y_pred = classifier.predict_proba_one(x) - classifier = classifier.learn_one(x, y) + classifier.learn_one(x, y) assert set(y_pred.keys()) == {False, True} diff --git a/river/cluster/__init__.py b/river/cluster/__init__.py index 7ab5d89ac7..21157d971b 100644 --- a/river/cluster/__init__.py +++ b/river/cluster/__init__.py @@ -1,11 +1,23 @@ """Unsupervised clustering.""" + from __future__ import annotations from .clustream import CluStream from .dbstream import DBSTREAM from .denstream import DenStream +from .hcluster import HierarchicalClustering from .k_means import KMeans +from .odac import ODAC from .streamkmeans import STREAMKMeans from .textclust import TextClust -__all__ = ["CluStream", "DBSTREAM", "DenStream", "KMeans", "STREAMKMeans", "TextClust"] +__all__ = [ + "CluStream", + "DBSTREAM", + "DenStream", + "HierarchicalClustering", + "KMeans", + "ODAC", + "STREAMKMeans", + "TextClust", +] diff --git a/river/cluster/clustream.py b/river/cluster/clustream.py index 1e6b9445ca..858e2f7302 100644 --- a/river/cluster/clustream.py +++ b/river/cluster/clustream.py @@ -103,7 +103,7 @@ class CluStream(base.Clusterer): ... ) >>> for x, _ in stream.iter_array(X): - ... clustream = clustream.learn_one(x) + ... clustream.learn_one(x) >>> clustream.predict_one({0: 1, 1: 1}) 1 @@ -216,7 +216,7 @@ def learn_one(self, x, w=1.0): if len(self.micro_clusters) == self.max_micro_clusters: self._initialized = True - return self + return # Determine the closest micro-cluster with respect to the new point instance closest_id, closest_dist = self._get_closest_mc(x) @@ -236,7 +236,7 @@ def learn_one(self, x, w=1.0): if closest_dist < radius: closest_mc.insert(x, w, self._timestamp) - return self + return # If the new point does not fit in the micro-cluster, micro-clusters # whose relevance stamps are less than the threshold are deleted. @@ -253,12 +253,10 @@ def learn_one(self, x, w=1.0): n_clusters=self.n_macro_clusters, seed=self.seed, **self.kwargs ) for center in self._mc_centers.values(): - self._kmeans_mc = self._kmeans_mc.learn_one(center) + self._kmeans_mc.learn_one(center) self.centers = self._kmeans_mc.centers - return self - def predict_one(self, x): index, _ = self._get_closest_mc(x) try: @@ -280,8 +278,13 @@ def __init__( self.x = x self.w = w self.timestamp = timestamp - self.var_x = {k: stats.Var().update(x[k], w) for k in x} - self.var_time = stats.Var().update(timestamp, w) + self.var_x = {} + for k in x: + v = stats.Var() + v.update(x[k], w) + self.var_x[k] = v + self.var_time = stats.Var() + self.var_time.update(timestamp, w) @property def center(self): diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py index 26560abb59..0b4408271f 100644 --- a/river/cluster/dbstream.py +++ b/river/cluster/dbstream.py @@ -75,7 +75,6 @@ class DBSTREAM(base.Clusterer): relative to the area cover by micro clusters. This parameter is used to determine whether a micro cluster or a shared density is weak. - Attributes ---------- n_clusters @@ -93,7 +92,7 @@ class DBSTREAM(base.Clusterer): References ---------- - [^1]: Michael Hahsler and Matthew Bolanos (2016, pp 1449-1461). Clsutering Data Streams Based on + [^1]: Michael Hahsler and Matthew Bolanos (2016, pp 1449-1461). Clustering Data Streams Based on Shared Density between Micro-Clusters, IEEE Transactions on Knowledge and Data Engineering 28(6) . In Proceedings of the Sixth SIAM International Conference on Data Mining, April 20–22, 2006, Bethesda, MD, USA. @@ -111,14 +110,16 @@ class DBSTREAM(base.Clusterer): ... [4, 1.5], [4, 2.25], [4, 2.5], [4, 3], [4, 3.25], [4, 3.5] ... ] - >>> dbstream = cluster.DBSTREAM(clustering_threshold = 1.5, - ... fading_factor = 0.05, - ... cleanup_interval = 4, - ... intersection_factor = 0.5, - ... minimum_weight = 1) + >>> dbstream = cluster.DBSTREAM( + ... clustering_threshold=1.5, + ... fading_factor=0.05, + ... cleanup_interval=4, + ... intersection_factor=0.5, + ... minimum_weight=1 + ... ) >>> for x, _ in stream.iter_array(X): - ... dbstream = dbstream.learn_one(x) + ... dbstream.learn_one(x) >>> dbstream.predict_one({0: 1, 1: 2}) 0 @@ -126,8 +127,9 @@ class DBSTREAM(base.Clusterer): >>> dbstream.predict_one({0: 5, 1: 2}) 1 - >>> dbstream.n_clusters + >>> dbstream._n_clusters 2 + """ def __init__( @@ -139,31 +141,33 @@ def __init__( minimum_weight: float = 1.0, ): super().__init__() - self.time_stamp = 0 + self._time_stamp = 0 self.clustering_threshold = clustering_threshold self.fading_factor = fading_factor self.cleanup_interval = cleanup_interval - self.minimum_weight = minimum_weight self.intersection_factor = intersection_factor + self.minimum_weight = minimum_weight - self.n_clusters = 0 - self.clusters: dict[int, DBSTREAMMicroCluster] = {} - self.centers: dict = {} - self.micro_clusters: dict[int, DBSTREAMMicroCluster] = {} + self._n_clusters: int = 0 + self._clusters: dict[int, DBSTREAMMicroCluster] = {} + self._centers: dict = {} + self._micro_clusters: dict[int, DBSTREAMMicroCluster] = {} self.s: dict[int, dict[int, float]] = {} self.s_t: dict[int, dict[int, float]] = {} + self.clustering_is_up_to_date = False + @staticmethod def _distance(point_a, point_b): - return math.sqrt(utils.math.minkowski_distance(point_a, point_b, 2)) + return utils.math.minkowski_distance(point_a, point_b, 2) def _find_fixed_radius_nn(self, x): fixed_radius_nn = {} - for i in self.micro_clusters.keys(): - if self._distance(self.micro_clusters[i].center, x) < self.clustering_threshold: - fixed_radius_nn[i] = self.micro_clusters[i] + for i in self._micro_clusters.keys(): + if self._distance(self._micro_clusters[i].center, x) < self.clustering_threshold: + fixed_radius_nn[i] = self._micro_clusters[i] return fixed_radius_nn def _gaussian_neighborhood(self, point_a, point_b): @@ -178,32 +182,38 @@ def _update(self, x): if len(neighbor_clusters) < 1: # create new micro cluster - self.micro_clusters[len(self.micro_clusters)] = DBSTREAMMicroCluster( - x=x, last_update=self.time_stamp, weight=1 - ) + if len(self._micro_clusters) > 0: + self._micro_clusters[max(self._micro_clusters.keys()) + 1] = DBSTREAMMicroCluster( + x=x, last_update=self._time_stamp, weight=1 + ) + else: + self._micro_clusters[0] = DBSTREAMMicroCluster( + x=x, last_update=self._time_stamp, weight=1 + ) else: # update existing micro clusters current_centers = {} for i in neighbor_clusters.keys(): - current_centers[i] = self.micro_clusters[i].center - self.micro_clusters[i].weight = ( - self.micro_clusters[i].weight + current_centers[i] = self._micro_clusters[i].center + self._micro_clusters[i].weight = ( + self._micro_clusters[i].weight * 2 ** ( - -self.fading_factor * (self.time_stamp - self.micro_clusters[i].last_update) + -self.fading_factor + * (self._time_stamp - self._micro_clusters[i].last_update) ) + 1 ) # Update the center (i) with overlapping keys (j) - self.micro_clusters[i].center = { - j: self.micro_clusters[i].center[j] - + self._gaussian_neighborhood(x, self.micro_clusters[i].center) - * (x[j] - self.micro_clusters[i].center[j]) - for j in self.micro_clusters[i].center.keys() + self._micro_clusters[i].center = { + j: self._micro_clusters[i].center[j] + + self._gaussian_neighborhood(x, self._micro_clusters[i].center) + * (x[j] - self._micro_clusters[i].center[j]) + for j in self._micro_clusters[i].center.keys() if j in x } - self.micro_clusters[i].last_update = self.time_stamp + self._micro_clusters[i].last_update = self._time_stamp # update shared density for j in neighbor_clusters.keys(): @@ -211,17 +221,17 @@ def _update(self, x): try: self.s[i][j] = ( self.s[i][j] - * 2 ** (-self.fading_factor * (self.time_stamp - self.s_t[i][j])) + * 2 ** (-self.fading_factor * (self._time_stamp - self.s_t[i][j])) + 1 ) - self.s_t[i][j] = self.time_stamp + self.s_t[i][j] = self._time_stamp except KeyError: try: - self.s[i][j] = 0 - self.s_t[i][j] = 0 + self.s[i][j] = 1 + self.s_t[i][j] = self._time_stamp except KeyError: - self.s[i] = {j: 0} - self.s_t[i] = {j: 0} + self.s[i] = {j: 1} + self.s_t[i] = {j: self._time_stamp} # prevent collapsing clusters for i in neighbor_clusters.keys(): @@ -229,39 +239,50 @@ def _update(self, x): if j > i: if ( self._distance( - self.micro_clusters[i].center, - self.micro_clusters[j].center, + self._micro_clusters[i].center, + self._micro_clusters[j].center, ) < self.clustering_threshold ): # revert centers of mc_i and mc_j to previous positions - self.micro_clusters[i].center = current_centers[i] - self.micro_clusters[j].center = current_centers[j] + self._micro_clusters[i].center = current_centers[i] + self._micro_clusters[j].center = current_centers[j] - self.time_stamp += 1 + self._time_stamp += 1 def _cleanup(self): # Algorithm 2 of Michael Hahsler and Matthew Bolanos: Cleanup process to remove # inactive clusters and shared density entries from memory weight_weak = 2 ** (-self.fading_factor * self.cleanup_interval) - micro_clusters = copy.deepcopy(self.micro_clusters) - for i, micro_cluster_i in self.micro_clusters.items(): + micro_clusters = copy.deepcopy(self._micro_clusters) + for i, micro_cluster_i in self._micro_clusters.items(): try: - value = 2 ** (self.fading_factor * (self.time_stamp - micro_cluster_i.last_update)) + value = 2 ** ( + -self.fading_factor * (self._time_stamp - micro_cluster_i.last_update) + ) except OverflowError: continue if micro_cluster_i.weight * value < weight_weak: micro_clusters.pop(i) + self.s.pop(i, None) + self.s_t.pop(i, None) + # Since self.s and self.s_t always have the same keys and are arranged in ascending orders + for j in self.s: + if j < i: + self.s[j].pop(i, None) + self.s_t[j].pop(i, None) + else: + break # Update microclusters - self.micro_clusters = micro_clusters + self._micro_clusters = micro_clusters for i in self.s.keys(): for j in self.s[i].keys(): try: - value = 2 ** (self.fading_factor * (self.time_stamp - self.s_t[i][j])) + value = 2 ** (-self.fading_factor * (self._time_stamp - self.s_t[i][j])) except OverflowError: continue @@ -275,18 +296,23 @@ def _generate_weighted_adjacency_matrix(self): weighted_adjacency_matrix = {} for i in list(self.s.keys()): for j in list(self.s[i].keys()): - if ( - self.micro_clusters[i].weight >= self.minimum_weight - and self.micro_clusters[j].weight >= self.minimum_weight - ): - value = self.s[i][j] / ( - (self.micro_clusters[i].weight + self.micro_clusters[j].weight) / 2 - ) - if value > self.intersection_factor: - try: - weighted_adjacency_matrix[i][j] = value - except KeyError: - weighted_adjacency_matrix[i] = {j: value} + try: + if ( + self._micro_clusters[i].weight <= self.minimum_weight + or self._micro_clusters[j].weight <= self.minimum_weight + ): + continue + except KeyError: + continue + + value = self.s[i][j] / ( + (self._micro_clusters[i].weight + self._micro_clusters[j].weight) / 2 + ) + if value > self.intersection_factor: + try: + weighted_adjacency_matrix[i][j] = value + except KeyError: + weighted_adjacency_matrix[i] = {j: value} return weighted_adjacency_matrix @@ -296,7 +322,7 @@ def _generate_labels(self, weighted_adjacency_list): # the DBSCAN algorithm proposed by Ester et al. for alpha-connected micro clusters # initiate labels of micro clusters to None - labels = {i: None for i in self.micro_clusters.keys()} + labels = {i: None for i in self._micro_clusters.keys()} # cluster counter; in this algorithm, cluster labels starts with 0 count = -1 @@ -325,7 +351,7 @@ def _generate_labels(self, weighted_adjacency_list): ) # add new neighbors to seed set for neighbor_neighbor in neighbor_neighbors: - if labels[neighbor_neighbor] is not None: + if labels[neighbor_neighbor] is None: seed_set.append(neighbor_neighbor) return labels @@ -340,12 +366,12 @@ def _generate_clusters_from_labels(self, cluster_labels): mcs_with_label_i = {} for index, label in cluster_labels.items(): if label == i: - mcs_with_label_i[j] = self.micro_clusters[index] + mcs_with_label_i[j] = self._micro_clusters[index] j += 1 # generate a final macro-cluster from clusters with the same label using the # merge function of DBStreamMicroCluster - macro_cluster = mcs_with_label_i[0] + macro_cluster = copy.deepcopy(mcs_with_label_i[0]) for m in range(1, len(mcs_with_label_i)): macro_cluster.merge(mcs_with_label_i[m]) @@ -358,24 +384,29 @@ def _generate_clusters_from_labels(self, cluster_labels): def _recluster(self): # Algorithm 3 of Michael Hahsler and Matthew Bolanos: Reclustering # using shared density graph + if self.clustering_is_up_to_date: + return + weighted_adjacency_list = self._generate_weighted_adjacency_matrix() labels = self._generate_labels(weighted_adjacency_list) # We can only update given we have labels (possibly not on first pass) if labels: - self.n_clusters, self.clusters = self._generate_clusters_from_labels(labels) - self.centers = {i: self.clusters[i].center for i in self.clusters.keys()} + self._n_clusters, self._clusters = self._generate_clusters_from_labels(labels) + self._centers = {i: self._clusters[i].center for i in self._clusters.keys()} + + self.clustering_is_up_to_date = True - def learn_one(self, x, sample_weight=None): + def learn_one(self, x, w=None): self._update(x) - if self.time_stamp % self.cleanup_interval == 0: + if self._time_stamp % self.cleanup_interval == 0: self._cleanup() - return self + self.clustering_is_up_to_date = False - def predict_one(self, x, sample_weight=None): + def predict_one(self, x, w=None): self._recluster() min_distance = math.inf @@ -384,13 +415,32 @@ def predict_one(self, x, sample_weight=None): # exists macro-clusters closest_cluster_index = 0 - for i, center_i in self.centers.items(): + for i, center_i in self._centers.items(): distance = self._distance(center_i, x) if distance < min_distance: min_distance = distance closest_cluster_index = i return closest_cluster_index + @property + def n_clusters(self) -> int: + self._recluster() + return self._n_clusters + + @property + def clusters(self) -> dict[int, DBSTREAMMicroCluster]: + self._recluster() + return self._clusters + + @property + def centers(self) -> dict: + self._recluster() + return self._centers + + @property + def micro_clusters(self) -> dict[int, DBSTREAMMicroCluster]: + return self._micro_clusters + class DBSTREAMMicroCluster(metaclass=ABCMeta): """DBStream Micro-cluster class""" diff --git a/river/cluster/denstream.py b/river/cluster/denstream.py index 0fabdf4797..5100b30e2b 100644 --- a/river/cluster/denstream.py +++ b/river/cluster/denstream.py @@ -117,15 +117,15 @@ class DenStream(base.Clusterer): ... n_samples_init=10) >>> for x, _ in stream.iter_array(X): - ... denstream = denstream.learn_one(x) + ... denstream.learn_one(x) >>> denstream.predict_one({0: -1, 1: -2}) 1 - >>> denstream.predict_one({0:5, 1:4}) + >>> denstream.predict_one({0: 5, 1: 4}) 2 - >>> denstream.predict_one({0:1, 1:1}) + >>> denstream.predict_one({0: 1, 1: 1}) 0 >>> denstream.n_clusters @@ -183,7 +183,7 @@ def centers(self): @staticmethod def _distance(point_a, point_b): - return math.sqrt(utils.math.minkowski_distance(point_a, point_b, 2)) + return utils.math.minkowski_distance(point_a, point_b, 2) def _get_closest_cluster_key(self, point, clusters): min_distance = math.inf @@ -313,7 +313,7 @@ def _initial_dbscan(self): else: item.covered = False - def learn_one(self, x, sample_weight=None): + def learn_one(self, x, w=None): self._n_samples_seen += 1 # control the stream speed if self._n_samples_seen % self.stream_speed == 0: @@ -326,7 +326,7 @@ def learn_one(self, x, sample_weight=None): self._initial_dbscan() self.initialized = True del self._init_buffer - return self + return # Merge self._merge(x) @@ -350,9 +350,8 @@ def learn_one(self, x, sample_weight=None): if o_micro_cluster_j.calc_weight(self.timestamp) < xi: # c_o might not grow into a p-micro-cluster, we can safely delete it self.o_micro_clusters.pop(j) - return self - def predict_one(self, x, sample_weight=None): + def predict_one(self, x, w=None): # This function handles the case when a clustering request arrives. # implementation of the DBSCAN algorithm proposed by Ester et al. if not self.initialized: diff --git a/river/cluster/hcluster.py b/river/cluster/hcluster.py new file mode 100644 index 0000000000..f2af5b9311 --- /dev/null +++ b/river/cluster/hcluster.py @@ -0,0 +1,402 @@ +from __future__ import annotations + +import functools + +from river import base, utils +from river.neighbors.base import DistanceFunc, FunctionWrapper + + +# Node of a binary tree for Hierarchical Clustering +class BinaryTreeNode: + def __init__(self, key: int, data: dict = None): + self.data = data + self.key = key + # Children and parent + self.left = None + self.right = None + self.parent = None + + +class HierarchicalClustering(base.Clusterer): + """Hierarchical Clustering. + + HierarchicalClustering is a stream hierarchical clustering algorithm. This algorithm [^1] inserts new nodes + near the nodes it is similar to without breaking clusters of very similar nodes. + + Beginning with the whole tree `T`, it will compare the new node to this respective tree: + * If `T` is just a leaf: merge + * Else, if the nodes of `T` are more similar between them than with the new node: merge + * Else, if the new node is more similar to the left subtree than to the right subtree: + redo from the first point with `T` equal to left subtree + * Else, if the new node is more similar to the right subtree than to the left subtree: + redo from the first point with `T` right subtree + + A window size can also be chosen to use only the most recent points to make sure that the tree is not overloaded. + + Parameters + ---------- + window_size + number of data points to use + dist_func + A distance function to use to compare the nodes. The Minkowski distance with `p=2` is used as default. + + Attributes + ---------- + n + number of nodes + x_clusters + data points used by the algorithm with the key of the node representing them + + References + ---------- + [^1]: Anand Rajagopalan, Aditya Krishna Menon, Qin Cao, Gui Citovsky, Baris Sumengen and Sanjiv Kumar (2019). Online + Hierarchical Clustering Approximations. arXiV:1909.09667. Available at: https://doi.org/10.48550/arXiv.1909.09667 + + Examples + -------- + + The first example is with leaving the window size to 100. In the second one we put it at 2 to see how it works. + + >>> from river import cluster + >>> from river import stream + + >>> X = [[1, 2, 1], [2, 1, 0], [3, 2, 1], [2, 2, 1], [4, 4, 4]] + + >>> hierarchical_clustering = cluster.HierarchicalClustering() + + >>> for x, _ in stream.iter_array(X): + ... hierarchical_clustering = hierarchical_clustering.learn_one(x) + + >>> hierarchical_clustering.x_clusters + {'[1, 2, 1]': 1, + '[2, 1, 0]': 2, + '[3, 2, 1]': 4, + '[2, 2, 1]': 6, + '[4, 4, 4]': 8} + + >>> hierarchical_clustering.n + 9 + + >>> print(hierarchical_clustering) + -> 8 + -> 9 + -> 6 + -> 7 + -> 4 + -> 5 + -> 2 + -> 3 + -> 1 + Printed Hierarchical Clustering Tree. + + >>> hierarchical_clustering.get_all_clusters() + [(1, ['[1, 2, 1]']), + (2, ['[2, 1, 0]']), + (4, ['[3, 2, 1]']), + (6, ['[2, 2, 1]']), + (8, ['[4, 4, 4]']), + (3, [1, 2]), + (5, [3, 7]), + (7, [4, 6]), + (9, [5, 8])] + + >>> hierarchical_clustering.get_clusters_by_point() + {'[1, 2, 1]': [1, 3, 5, 9], + '[2, 1, 0]': [2, 3, 5, 9], + '[3, 2, 1]': [4, 7, 5, 9], + '[2, 2, 1]': [6, 7, 5, 9], + '[4, 4, 4]': [8, 9]} + + >>> hierarchical_clustering.predict_one({0: 3, 1: 3, 2: 3}) + ([10, 11, 9], 8) + + >>> hierarchical_clustering = hierarchical_clustering.learn_one({0: 3, 1: 3, 2: 3}) + + >>> print(hierarchical_clustering) + -> 10 + -> 11 + -> 8 + -> 9 + -> 6 + -> 7 + -> 4 + -> 5 + -> 2 + -> 3 + -> 1 + Printed Hierarchical Clustering Tree. + + >>> hierarchical_clustering = cluster.HierarchicalClustering(window_size=2) + + >>> for x, _ in stream.iter_array(X): + ... hierarchical_clustering = hierarchical_clustering.learn_one(x) + + >>> hierarchical_clustering.x_clusters + {'[2, 2, 1]': 2, '[4, 4, 4]': 1} + + >>> hierarchical_clustering.n + 3 + + >>> print(hierarchical_clustering) + -> 2 + -> 3 + -> 1 + Printed Hierarchical Clustering Tree. + """ + + def __init__( + self, + window_size: int = 100, + dist_func: DistanceFunc | FunctionWrapper | None = None, + ): + # Number of nodes + self.n = 0 + # Max number of leaves + self.window_size = window_size + # Dict : x data (str(array of size m)) -> key of the node + self.x_clusters: dict[str, int] = {} + # Dict : key -> node + self.nodes: dict[int, BinaryTreeNode] = {} + # First node of the tree + self.root = None + # Distance function + if dist_func is None: + dist_func = functools.partial(utils.math.minkowski_distance, p=2) + self.dist_func = dist_func + + def otd_clustering(self, tree, x): + # Online top down clustering (OTD), the first algorithm for online hierarchical clustering. + # The algorithm performs highly efficient online updates and provably approximates Moseley-Wang revenue. + x_string = str(list(x.values())) + if self.n == 1: + # First node in the tree + self.root = self.nodes[1] + elif tree.data is not None: + # If T is a leaf, we merge the two nodes together + self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) + elif tree.left is None: + # If there is no node at the left of the intermediate node, we add it there + tree.left = self.nodes[self.x_clusters[x_string]] + self.nodes[self.x_clusters[x_string]].parent = tree + elif tree.right is None: + # If there is no node at the right of the intermediate node, we add it there + tree.right = self.nodes[self.x_clusters[x_string]] + self.nodes[self.x_clusters[x_string]].parent = tree + elif self.intra_subtree_similarity(tree) < self.inter_subtree_similarity( + tree, self.nodes[self.x_clusters[x_string]] + ): + # If the nodes in T are closer between them than with the new node, we merge T and the new node + self.merge_nodes(tree, self.nodes[self.x_clusters[x_string]]) + elif self.inter_subtree_similarity( + tree.left, self.nodes[self.x_clusters[x_string]] + ) > self.inter_subtree_similarity(tree.right, self.nodes[self.x_clusters[x_string]]): + # Continue to search where to merge the new node in the right part of T + self.otd_clustering(tree.right, x) + else: + # Continue to search where to merge the new node in the left part of T + self.otd_clustering(tree.left, x) + + def merge_nodes(self, tree, added_node): + # Merge a new node (added node) to the tree + # We create the node that will be the parent of the tree and the added node + self.n += 1 + new_node = BinaryTreeNode(self.n) + # We add the tree and the added node as its children + new_node.left = tree + new_node.right = added_node + # The parent of the new node is the parent of the tree + new_node.parent = tree.parent + # If the tree is not the root, we set the child of its parent as new node (instead of T) + if tree.parent is not None: + if tree.parent.left.key == tree.key: + tree.parent.left = new_node + else: + tree.parent.right = new_node + # We add the new node as the parent of the tree and the added node + tree.parent = new_node + added_node.parent = new_node + # We add the new node to the dict + self.nodes[self.n] = new_node + # If the tree was the root, the new node become the root + if self.root.key == tree.key: + self.root = self.nodes[self.n] + + def learn_one(self, x): + # We create the node for x and add it to the tree + if len(self.x_clusters.keys()) >= self.window_size: + # Delete the oldest data point and add a node with the same key as the one deleted + oldest_key = self.x_clusters[list(self.x_clusters.keys())[0]] + oldest = self.nodes[oldest_key] + if oldest.parent.left.key == oldest_key: + oldest.parent.left = None + else: + oldest.parent.right = None + del self.nodes[oldest_key] + del self.x_clusters[list(self.x_clusters.keys())[0]] + self.x_clusters[str(list(x.values()))] = oldest_key + self.nodes[oldest_key] = BinaryTreeNode(oldest_key, x) + else: + # Else, add a node + self.n += 1 + self.x_clusters[str(list(x.values()))] = self.n + self.nodes[self.n] = BinaryTreeNode(self.n, x) + # We add it to the tree + self.otd_clustering(self.root, x) + return self + + def predict_otd(self, x, node, clusters): + # get the list of predicted clusters for x + if node is None: + # If there is still no node in the tree + return [1], -1 + if node.data is not None: + # Add itself (n+1) and the key of the node that would merge x and node (n+2) + clusters.extend([self.n + 2, self.n + 1]) + return clusters, node.key + if self.intra_subtree_similarity(node) < self.inter_subtree_similarity( + node, BinaryTreeNode(self.n + 1, x) + ): + # Add itself (n+1) and the key of the node that would merge x and node (n+2) + clusters.extend([self.n + 2, self.n + 1]) + return clusters, node.key + else: + # Else, x would be added in the tree, we add the key of node + clusters.extend([node.key]) + if self.inter_subtree_similarity( + node.left, BinaryTreeNode(self.n + 1, x) + ) > self.inter_subtree_similarity(node.right, BinaryTreeNode(self.n + 1, x)): + # If the right part of the tree is closer to x than the left part, we continue in the right part + return self.predict_otd(x, node.right, clusters) + else: + # If the left part of the tree is closer to x than the right part, we continue in the left part + return self.predict_otd(x, node.left, clusters) + + def predict_one(self, x): + """Predicts the clusters for a set of features `x`. + + Parameters + ---------- + x + A dictionary of features. + Returns + ------- + (list, int) + A list of clusters (from node `x` to root) and the node to which it would have been merged. + + """ + # We predict to which cluster x would be if we added in the tree + r, merged = self.predict_otd(x, self.root, []) + r.reverse() + return r, merged + + @staticmethod + def find_path(root, path, k): + # find the path from root to k + # Adapted from https://www.geeksforgeeks.org/lowest-common-ancestor-binary-tree-set-1/ + + if root is None: + return False + + path.append(root) + + if root.key == k: + return True + + if (root.left is not None and HierarchicalClustering.find_path(root.left, path, k)) or ( + root.right is not None and HierarchicalClustering.find_path(root.right, path, k) + ): + return True + + path.pop() + return False + + def leaves(self, v): + # find all the leaves from node v + + if v is None: + return -1 + if v.data is not None: + return [v] + + leave_list = [] + leave_list.extend(self.leaves(v.left)) + leave_list.extend(self.leaves(v.right)) + return leave_list + + def inter_subtree_similarity(self, tree_a, tree_b): + # compute the mean distance (mean of distances) between two trees + leaves_a = self.leaves(tree_a) + leaves_b = self.leaves(tree_b) + r = 0 + nb = 0 + for i, w_i in enumerate(leaves_a): + for j, w_j in enumerate(leaves_b): + nb += 1 + r += self.dist_func(w_i.data, w_j.data) + return r / nb + + def intra_subtree_similarity(self, tree): + # compute mean of distances between the nodes from a certain tree + leaves = self.leaves(tree) + r = 0 + nb = 0 + if len(leaves) == 1: + return 0 + for i, w_i in enumerate(leaves): + for j, w_j in enumerate(leaves): + if i < j: + nb += 1 + r += self.dist_func(w_i.data, w_j.data) + return r / nb + + def __str__(self): + self.print_tree(self.root) + return "Printed Hierarchical Clustering Tree." + + @staticmethod + def print_tree(node, level=0): + # Print node and its children + # Adapted from https://stackoverflow.com/questions/34012886/print-binary-tree-level-by-level-in-python + if node is not None: + HierarchicalClustering.print_tree(node.right, level + 1) + print(" " * 4 * level + "-> " + str(node.key)) + HierarchicalClustering.print_tree(node.left, level + 1) + + def get_parents(self, node): + # Get all the parents of the node (the clusters it belongs to) + clusters = [node.key] + if node.parent is None: + return clusters + clusters.extend(self.get_parents(node.parent)) + return clusters + + def get_clusters_by_point(self): + """Returns the list of clusters (from the data point node to the root) for all data points. + + Returns + ------- + {x : list} + A dict of all the data points with their clusters. + """ + # Get all the clusters each data point belong to + clusters = {} + for x in self.x_clusters.keys(): + clusters[x] = self.get_parents(self.nodes[self.x_clusters[x]]) + return clusters + + def get_all_clusters(self): + """Returns all the clusters of the tree. + + Returns + ------- + {int : list} + A dict of all the clusters with their children (or the data point for the leaves). + """ + # Get the data of each cluster + clusters = {} + for i in range(1, self.n + 1): + if self.nodes[i].data is not None: + clusters[i] = [str(list(self.nodes[i].data.values()))] + else: + clusters[i] = [self.nodes[i].left.key, self.nodes[i].right.key] + return sorted(clusters.items(), key=lambda x: len(x[1])) diff --git a/river/cluster/k_means.py b/river/cluster/k_means.py index 9df9386ed3..149c791123 100644 --- a/river/cluster/k_means.py +++ b/river/cluster/k_means.py @@ -65,7 +65,7 @@ class KMeans(base.Clusterer): >>> k_means = cluster.KMeans(n_clusters=2, halflife=0.1, sigma=3, seed=42) >>> for i, (x, _) in enumerate(stream.iter_array(X)): - ... k_means = k_means.learn_one(x) + ... k_means.learn_one(x) ... print(f'{X[i]} is assigned to cluster {k_means.predict_one(x)}') [1, 2] is assigned to cluster 1 [1, 4] is assigned to cluster 1 @@ -118,7 +118,6 @@ def learn_predict_one(self, x): def learn_one(self, x): self.learn_predict_one(x) - return self def predict_one(self, x): def get_distance(c): diff --git a/river/cluster/odac.py b/river/cluster/odac.py new file mode 100644 index 0000000000..39cd912443 --- /dev/null +++ b/river/cluster/odac.py @@ -0,0 +1,613 @@ +from __future__ import annotations + +import collections +import itertools +import math +import typing + +from river import base, stats + + +class ODAC(base.Clusterer): + """The Online Divisive-Agglomerative Clustering (ODAC)[^1] aims at continuously maintaining + a hierarchical cluster structure from evolving time series data streams. + + The distance between time-series a and b is given by `rnomc(a, b) = sqrt((1 - corr(a, b)) / 2)`, + where `corr(a, b)` is the Pearson Correlation coefficient. If the cluster has only one time-series, + the diameter is given by the time-series variance. The cluster's diameter is given by the largest + distance between the cluster's time-series. + + ODAC continuously monitors the evolution of diameters, only of the leaves, and splits or merges them + by gathering more data or reacting to concept drift - a confidence level from the Hoeffding bound + supports such changes. + + So, the split operator, where the Hoeffding bound is applied, occurs when the difference between + the largest distance (diameter) and the second largest difference is greater than a constant. + Furthermore, the merge operator checks if one of the cluster's children has a diameter bigger + than their parent - applying the Hoeffding bound again. + + Parameters + ---------- + confidence_level + The confidence level that user wants to work. + n_min + Number of minimum observations to gather before checking whether or not + clusters must be split or merged. + tau + Threshold below which a split will be forced to break ties. + + Attributes + ---------- + structure_changed : bool + This variable is true when the structure changed, produced by splitting or aggregation. + + Examples + -------- + + >>> from river import cluster + >>> from river.datasets import synth + + >>> model = cluster.ODAC() + + >>> dataset = synth.FriedmanDrift(drift_type='gra', position=(150, 200), seed=42) + + >>> for i, (x, _) in enumerate(dataset.take(500)): + ... model.learn_one(x) + ... if model.structure_changed: + ... print(f"Structure changed at observation {i + 1}") + Structure changed at observation 1 + Structure changed at observation 100 + Structure changed at observation 200 + Structure changed at observation 300 + + >>> print(model.render_ascii()) + ROOT d1=0.79 d2=0.76 [NOT ACTIVE] + ├── CH1_LVL_1 d1=0.74 d2=0.72 [NOT ACTIVE] + │ ├── CH1_LVL_2 d1=0.08 [3] + │ └── CH2_LVL_2 d1=0.73 [2, 4] + └── CH2_LVL_1 d1=0.81 d2=0.78 [NOT ACTIVE] + ├── CH1_LVL_2 d1=0.73 d2=0.67 [NOT ACTIVE] + │ ├── CH1_LVL_3 d1=0.72 [0, 9] + │ └── CH2_LVL_3 d1=0.08 [1] + └── CH2_LVL_2 d1=0.74 d2=0.73 [NOT ACTIVE] + ├── CH1_LVL_3 d1=0.71 [5, 6] + └── CH2_LVL_3 d1=0.71 [7, 8] + + You can acess some properties of the clustering model directly: + + >>> model.n_clusters + 11 + + >>> model.n_active_clusters + 6 + + >>> model.height + 3 + + These properties are also available in a summarized form: + + >>> model.summary + {'n_clusters': 11, 'n_active_clusters': 6, 'height': 3} + + References + ---------- + [^1]: P. P. Rodrigues, J. Gama and J. Pedroso, "Hierarchical Clustering of Time-Series Data Streams" in IEEE Transactions + on Knowledge and Data Engineering, vol. 20, no. 5, pp. 615-627, May 2008, doi: 10.1109/TKDE.2007.190727. + + """ + + def __init__(self, confidence_level: float = 0.9, n_min: int = 100, tau: float = 0.1): + if not (confidence_level > 0.0 and confidence_level < 1.0): + raise ValueError("confidence_level must be between 0 and 1.") + if not n_min > 0: + raise ValueError("n_min must be greater than 1.") + if not tau > 0.0: + raise ValueError("tau must be greater than 0.") + + self._root_node = ODACCluster("ROOT") + self.confidence_level = confidence_level + self.n_min = n_min + self.tau = tau + + self._update_timer: int = n_min + self._n_observations: int = 0 + self._is_init = False + + self._structure_changed = False + + @property + def n_active_clusters(self): + return self._count_active_clusters(self._root_node) + + @property + def n_clusters(self): + return self._count_clusters(self._root_node) + + @property + def height(self) -> int: + return self._calculate_height(self._root_node) + + @property + def summary(self): + summary = { + "n_clusters": self.n_clusters, + "n_active_clusters": self.n_active_clusters, + "height": self.height, + } + return summary + + def _calculate_height(self, node: ODACCluster) -> int: + if node.children is not None: + child_heights = [ + self._calculate_height(child) + for child in [node.children.first, node.children.second] + ] + return max(child_heights) + 1 + else: + return 0 + + def _count_clusters(self, node: ODACCluster) -> int: + count = 1 + if node.children is not None: + for child in [node.children.first, node.children.second]: + count += self._count_clusters(child) + return count + + def _count_active_clusters(self, node: ODACCluster) -> int: + if node.active: + return 1 + elif node.children is not None: + return sum( + self._count_active_clusters(child) + for child in [node.children.first, node.children.second] + ) + else: + return 0 + + def _find_all_active_clusters(self, node: ODACCluster): + if node.active: + yield node + elif node.children is not None: + for child in [node.children.first, node.children.second]: + yield from self._find_all_active_clusters(child) + + def learn_one(self, x: dict): + # If x is empty, do nothing + if not x: + return + + if self._structure_changed: + self._structure_changed = False + + if not self._is_init: + # Initialize the first cluster which is the ROOT cluster + self._root_node(list(x.keys())) + self._structure_changed = True + self._is_init = True + + # Update the total observations received + self._n_observations += 1 + + # Split control + self._update_timer -= 1 + + # For each active cluster update the statistics and time to time verify if + # the cluster needs to merge or expand + for leaf in self._find_all_active_clusters(self._root_node): + # For safety + if not leaf.active: + continue + + # Update statistics + leaf.update_statistics(x) + + # Time to time approach + if self._update_timer == 0: + # Calculate all the crucial variables to the next procedure + leaf.calculate_coefficients(confidence_level=self.confidence_level) + + if leaf.test_aggregate() or leaf.test_split(tau=self.tau): + # Put the flag change_detected to true to indicate to the user that the structure changed + self._structure_changed = True + + # Reset the timer + if self._update_timer == 0: + self._update_timer = self.n_min + + # This algorithm does not predict anything. It builds a hierarchical cluster's structure + def predict_one(self, x: dict): + """This algorithm does not predict anything. It builds a hierarchical cluster's structure. + + Parameters + ---------- + x + A dictionary of features. + + """ + raise NotImplementedError() + + def render_ascii(self, n_decimal_places: int = 2) -> str: + """Method to render the hierarchical cluster's structure in text format. + + Parameters + ---------- + n_decimal_places + The number of decimal places that user wants to view in distances of each active cluster + in the hierarchical cluster's structure. + + """ + if not (n_decimal_places > 0 and n_decimal_places < 10): + raise ValueError("n_decimal_places must be between 1 and 9.") + + return self._root_node.design_structure(n_decimal_places).rstrip("\n") + + def draw( + self, + max_depth: int | None = None, + show_clusters_info: list[typing.Hashable] = ["timeseries_names", "d1", "d2", "e"], + n_decimal_places: int = 2, + ): + """Method to draw the hierarchical cluster's structure as a Graphviz graph. + + Parameters + ---------- + max_depth + The maximum depth of the tree to display. + show_clusters_info + List of cluster information to show. Valid options are: + - "timeseries_indexes": Shows the indexes of the timeseries in the cluster. + - "timeseries_names": Shows the names of the timeseries in the cluster. + - "name": Shows the cluster's name. + - "d1": Shows the d1 (the largest distance in the cluster). + - "d2": Shows the d2 (the second largest distance in the cluster). + - "e": Shows the error bound. + n_decimal_places + The number of decimal places to show for numerical values. + + """ + if not (n_decimal_places > 0 and n_decimal_places < 10): + raise ValueError("n_decimal_places must be between 1 and 9.") + + try: + import graphviz + except ImportError as e: + raise ValueError("You have to install graphviz to use the draw method.") from e + + counter = 0 + + dot = graphviz.Digraph( + graph_attr={"splines": "ortho", "forcelabels": "true", "overlap": "false"}, + node_attr={ + "shape": "box", + "penwidth": "1.2", + "fontname": "trebuchet", + "fontsize": "11", + "margin": "0.1,0.0", + }, + edge_attr={"penwidth": "0.6", "center": "true", "fontsize": "7 "}, + ) + + def iterate(node: ODACCluster, parent_node: str | None = None, depth: int = 0): + nonlocal counter, max_depth, show_clusters_info, n_decimal_places + + if max_depth is not None and depth > max_depth: + return + + node_n = str(counter) + counter += 1 + + label = "" + + # checks if user wants to see information about clusters + if len(show_clusters_info) > 0: + show_clusters_info_copy = show_clusters_info.copy() + + if "name" in show_clusters_info_copy: + label += f"{node.name}" + show_clusters_info_copy.remove("name") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "timeseries_indexes" in show_clusters_info_copy: + # Convert timeseries names to indexes + name_to_index = { + name: index for index, name in enumerate(self._root_node.timeseries_names) + } + timeseries_indexes = [ + name_to_index[_name] + for _name in node.timeseries_names + if _name in name_to_index + ] + + label += f"{timeseries_indexes}" + show_clusters_info_copy.remove("timeseries_indexes") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "timeseries_names" in show_clusters_info_copy: + label += f"{node.timeseries_names}" + show_clusters_info_copy.remove("timeseries_names") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "d1" in show_clusters_info_copy: + if node.d1 is not None: + label += f"d1={node.d1:.{n_decimal_places}f}" + else: + label += "d1=" + show_clusters_info_copy.remove("d1") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "d2" in show_clusters_info_copy and node.d2 is not None: + label += f"d2={node.d2:.{n_decimal_places}f}" + show_clusters_info_copy.remove("d2") + if len(show_clusters_info_copy) > 0: + label += "\n" + if "e" in show_clusters_info_copy: + label += f"e={node.e:.{n_decimal_places}f}" + + show_clusters_info_copy.clear() + + # Creates a node with different color to differentiate the active clusters from the non-active + if node.active: + dot.node(node_n, label, style="filled", fillcolor="#76b5c5") + else: + dot.node(node_n, label, style="filled", fillcolor="#f2f2f2") + + if parent_node is not None: + dot.edge(parent_node, node_n) + + if node.children is not None: + iterate(node=node.children.first, parent_node=node_n, depth=depth + 1) + iterate(node.children.second, parent_node=node_n, depth=depth + 1) + + iterate(node=self._root_node) + + return dot + + @property + def structure_changed(self) -> bool: + return self._structure_changed + + +class ODACCluster(base.Base): + """Cluster class for representing individual clusters.""" + + # Constructor method for Cluster class + def __init__(self, name: str, parent: ODACCluster | None = None): + self.name = name + self.parent: ODACCluster | None = parent + self.active = True + self.children: ODACChildren | None = None + + self.timeseries_names: list[typing.Hashable] = [] + self._statistics: ( + dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | stats.Var | None + ) + + self.d1: float | None = None + self.d2: float | None = None + self.e: float = 0 + self.d0: float | None = None + self.avg: float | None = None + + self.pivot_0: tuple[typing.Hashable, typing.Hashable] + self.pivot_1: tuple[typing.Hashable, typing.Hashable] + self.pivot_2: tuple[typing.Hashable, typing.Hashable] + + self.n = 0 + + # Method to design the structure of the cluster tree + def design_structure(self, decimal_places: int = 2) -> str: + pre_0 = " " + pre_1 = "│ " + pre_2 = "├── " + pre_3 = "└── " + node = self + prefix = ( + pre_2 + if node.parent is not None + and (node.parent.children is None or id(node) != id(node.parent.children.second)) # type: ignore + else pre_3 + ) + while node.parent is not None and node.parent.parent is not None: + if node.parent.parent.children is None or id(node.parent) != id( + node.parent.parent.children.second + ): # type: ignore + prefix = pre_1 + prefix + else: + prefix = pre_0 + prefix + node = node.parent + + if self.d1 is not None: + r_d1 = f"{self.d1:.{decimal_places}f}" + else: + r_d1 = "" + + if self.d2 is not None: + r_d2 = f" d2={self.d2:.{decimal_places}f}" + else: + r_d2 = "" + + if self.parent is None: + representation = f"{self.name} d1={r_d1}{r_d2}" + else: + representation = f"{prefix}{self.name} d1={r_d1}{r_d2}" + + if self.active is True: + return representation + f" {self.timeseries_names}\n" + else: + representation += " [NOT ACTIVE]\n" + # Do the structure recursively + if self.children is not None: + for child in [self.children.first, self.children.second]: + representation += child.design_structure(decimal_places) + return representation + + def __str__(self) -> str: + return self.design_structure() + + def __repr__(self) -> str: + return self.design_structure() + + def _init_stats( + self, + ) -> dict[tuple[typing.Hashable, typing.Hashable], stats.PearsonCorr] | stats.Var: + return ( + collections.defaultdict( + stats.PearsonCorr, + { + (k1, k2): stats.PearsonCorr() + for k1, k2 in itertools.combinations(self.timeseries_names, 2) + }, + ) + if len(self.timeseries_names) > 1 + else stats.Var() + ) + + # TODO: not sure if this is the best design + def __call__(self, ts_names: list[typing.Hashable]): + """Method that associates the time-series into the cluster and initiates the statistics.""" + self.timeseries_names = sorted(ts_names) # type: ignore + self._statistics = self._init_stats() + + def update_statistics(self, x: dict) -> None: + if len(self.timeseries_names) > 1: + # For each pair of time-series in the cluster update the correlation + # values with the data received + for (k1, k2), item in self._statistics.items(): # type: ignore + if x.get(k1, None) is None or x.get(k2, None) is None: + continue + item.update(float(x[k1]), float(x[k2])) + else: + self._statistics.update(float(x.get(self.timeseries_names[0]))) # type: ignore + + # Increment the number of observation in the cluster + self.n += 1 + + # Method to calculate the rnomc values of the cluster + def _calculate_rnomc_dict(self) -> dict[tuple[typing.Hashable, typing.Hashable], float]: + # Get the correlation values between time-series in the cluster + rnomc_dict = {} + + for k1, k2 in itertools.combinations(self.timeseries_names, 2): + value = abs((1 - self._statistics[(k1, k2)].get()) / 2.0) # type: ignore + rnomc_dict[(k1, k2)] = math.sqrt(value) + + return rnomc_dict + + # Method to calculate coefficients for splitting or aggregation + def calculate_coefficients(self, confidence_level: float) -> None: + if len(self.timeseries_names) > 1: + # Get the rnomc values + rnomc_dict = self._calculate_rnomc_dict() + + # Get the average distance in the cluster + self.avg = sum(rnomc_dict.values()) / self.n + + # Get the minimum distance and the pivot associated in the cluster + self.pivot_0, self.d0 = min(rnomc_dict.items(), key=lambda x: x[1]) + # Get the maximum distance and the pivot associated in the cluster + self.pivot_1, self.d1 = max(rnomc_dict.items(), key=lambda x: x[1]) + + # Get the second maximum distance and the pivot associated in the cluster + remaining = {k: v for k, v in rnomc_dict.items() if k != self.pivot_1} + + if len(remaining) > 0: + self.pivot_2, self.d2 = max(remaining.items(), key=lambda x: x[1]) + else: + self.pivot_2 = self.d2 = None # type: ignore + else: + self.d1 = self._statistics.get() # type: ignore + # Calculate the Hoeffding bound in the cluster + self.e = math.sqrt(math.log(1 / confidence_level) / (2 * self.n)) + + # Method that gives the closest cluster where the current time series is located + def _get_closest_cluster(self, pivot_1, pivot_2, current, rnormc_dict: dict) -> int: + dist_1 = rnormc_dict.get((min(pivot_1, current), max(pivot_1, current)), 0) + dist_2 = rnormc_dict.get((min(pivot_2, current), max(pivot_2, current)), 0) + return 2 if dist_1 >= dist_2 else 1 + + def _split_this_cluster( + self, + pivot_1: typing.Hashable, + pivot_2: typing.Hashable, + rnormc_dict: dict[tuple[typing.Hashable, typing.Hashable], float], + ): + """Expand into two clusters.""" + pivot_set = {pivot_1, pivot_2} + pivot_1_list = [pivot_1] + pivot_2_list = [pivot_2] + + # For each time-series in the cluster we need to find the closest pivot, to then associate with it + for ts_name in self.timeseries_names: + if ts_name not in pivot_set: + cluster = self._get_closest_cluster(pivot_1, pivot_2, ts_name, rnormc_dict) + if cluster == 1: + pivot_1_list.append(ts_name) + else: + pivot_2_list.append(ts_name) + + new_name = "1" if self.name == "ROOT" else str(int(self.name.split("_")[-1]) + 1) + + # Create the two new clusters. The children of this cluster + cluster_child_1 = ODACCluster("CH1_LVL_" + new_name, parent=self) + cluster_child_1(pivot_1_list) + + cluster_child_2 = ODACCluster("CH2_LVL_" + new_name, parent=self) + cluster_child_2(pivot_2_list) + + self.children = ODACChildren(cluster_child_1, cluster_child_2) + + # Set the active flag to false. Since this cluster is not an active cluster anymore. + self.active = False + + # Reset some attributes + self.avg = self.d0 = self.pivot_0 = self.pivot_1 = self.pivot_2 = self._statistics = None # type: ignore + + # Method that proceeds to merge on this cluster + def _aggregate_this_cluster(self): + # Reset statistics + self._statistics = self._init_stats() + + # Put the active flag to true. Since this cluster is an active cluster once again. + self.active = True + # Delete and disassociate the children. + if self.children is not None: + self.children.reset_parent() + self.children = None + # Reset the number of observations in this cluster + self.n = 0 + + # Method to test if the cluster should be split + def test_split(self, tau: float): + # Test if the cluster should be split based on specified tau + if self.d2 is not None: + if ((self.d1 - self.d2) > self.e) or (tau > self.e): # type: ignore + if ((self.d1 - self.d0) * abs(self.d1 + self.d0 - 2 * self.avg)) > self.e: # type: ignore + # Split this cluster + self._split_this_cluster( + pivot_1=self.pivot_1[0], + pivot_2=self.pivot_1[1], + rnormc_dict=self._calculate_rnomc_dict(), + ) + return True + return False + + # Method to test if the cluster should be aggregated + def test_aggregate(self): + # Test if the cluster should be aggregated + if self.parent is not None and self.d1 is not None: + if self.d1 - self.parent.d1 > max(self.parent.e, self.e): + self.parent._aggregate_this_cluster() + return True + return False + + +class ODACChildren(base.Base): + """Children class representing child clusters.""" + + def __init__(self, first: ODACCluster, second: ODACCluster): + self.first = first + self.second = second + + def reset_parent(self): + self.first.parent = None + self.second.parent = None diff --git a/river/cluster/streamkmeans.py b/river/cluster/streamkmeans.py index 083d24ae3e..df6faba1f7 100644 --- a/river/cluster/streamkmeans.py +++ b/river/cluster/streamkmeans.py @@ -63,7 +63,7 @@ class STREAMKMeans(base.Clusterer): >>> streamkmeans = cluster.STREAMKMeans(chunk_size=3, n_clusters=2, halflife=0.5, sigma=1.5, seed=0) >>> for x, _ in stream.iter_array(X): - ... streamkmeans = streamkmeans.learn_one(x) + ... streamkmeans.learn_one(x) >>> streamkmeans.predict_one({0: 1, 1: 0}) 0 @@ -84,7 +84,7 @@ def __init__(self, chunk_size=10, n_clusters=2, **kwargs): self._temp_chunk = {} self.centers = {} - def learn_one(self, x, sample_weight=None): + def learn_one(self, x, w=None): self.time_stamp += 1 index = self.time_stamp % self.chunk_size @@ -99,15 +99,13 @@ def learn_one(self, x, sample_weight=None): if index == 0: kmeans_i = cluster.KMeans(n_clusters=self.n_clusters, **self.kwargs) for point_j in self._temp_chunk.values(): - kmeans_i = kmeans_i.learn_one(point_j) + kmeans_i.learn_one(point_j) for center_j in kmeans_i.centers.values(): - self._kmeans = self._kmeans.learn_one(center_j) + self._kmeans.learn_one(center_j) self.centers = self._kmeans.centers - return self - - def predict_one(self, x, sample_weight=None): + def predict_one(self, x, w=None): def get_distance(c): return utils.math.minkowski_distance(self.centers[c], x, 2) diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py new file mode 100644 index 0000000000..2e2e3af965 --- /dev/null +++ b/river/cluster/test_dbstream.py @@ -0,0 +1,212 @@ +from __future__ import annotations + +import pytest +from sklearn.datasets import make_blobs + +from river import metrics, stream, utils +from river.cluster import DBSTREAM + + +def build_dbstream(fading_factor=0.01, intersection_factor=0.05): + return DBSTREAM( + fading_factor=fading_factor, + clustering_threshold=1, + cleanup_interval=1, + intersection_factor=intersection_factor, + ) + + +def add_cluster(dbstream, initial_point, move_towards, times=1): + dbstream.learn_one(initial_point) + for _ in range(times): + dbstream.learn_one(move_towards) + + +def assert_micro_cluster_properties(cluster, center, last_update=None): + assert cluster.center == pytest.approx(center) + if last_update is not None: + assert cluster.last_update == last_update + + +def test_cluster_formation_and_cleanup(): + dbstream = build_dbstream() + + X = [ + {1: 1}, + {1: 2}, + {1: 3}, + {1: 3}, + {1: 3}, + {1: 5}, + {1: 7}, + {1: 9}, + {1: 10}, + {1: 11}, + {1: 11}, + {1: 12}, + {1: 13}, + {1: 11}, + {1: 15}, + {1: 15}, + {1: 16}, + {1: 17}, + {1: 17}, + {1: 17}, + ] + + for x in X: + dbstream.learn_one(x) + + assert len(dbstream._micro_clusters) == 4 + assert_micro_cluster_properties(dbstream.micro_clusters[2], center={1: 3}, last_update=4) + assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 11}, last_update=13) + assert_micro_cluster_properties(dbstream.micro_clusters[8], center={1: 15}, last_update=15) + assert_micro_cluster_properties(dbstream.micro_clusters[10], center={1: 17}, last_update=19) + + assert dbstream.predict_one({1: 2.0}) == 0 + assert dbstream.predict_one({1: 13.0}) == 1 + assert dbstream.predict_one({1: 13 + 1e-10}) == 2 + assert dbstream.predict_one({1: 16 - 1e-10}) == 2 + assert dbstream.predict_one({1: 18}) == 3 + + assert len(dbstream._clusters) == 4 + assert dbstream.s == dbstream.s_t == {} + + +def test_with_two_micro_clusters(): + dbstream = build_dbstream() + + add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) + add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) + + assert len(dbstream.micro_clusters) == 2 + assert_micro_cluster_properties( + dbstream.micro_clusters[0], center={1: 2.137623, 2: 2.137623}, last_update=51 + ) + assert_micro_cluster_properties( + dbstream.micro_clusters[1], center={1: 2.914910, 2: 2.914910}, last_update=51 + ) + + assert dbstream.s == {0: {1: 23.033438964246173}} + assert dbstream.s_t == {0: {1: 51}} + + dbstream._recluster() + assert len(dbstream.clusters) == 1 + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.415239, 2: 2.415239}) + + +def test_density_graph_with_three_micro_clusters(): + dbstream = build_dbstream() + + add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) + add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) + # Points in the middle of first and second micro-clusters + for _ in range(5): + dbstream.learn_one({1: 2, 2: 2}) + + assert dbstream.s == {0: {1: 23.033438964246173}} + assert dbstream.s_t == {0: {1: 51}} + + add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25) + # Points in the middle of second and third micro-clusters + for _ in range(4): + dbstream.learn_one({1: 3, 2: 3}) + + assert len(dbstream._micro_clusters) == 3 + assert_micro_cluster_properties( + dbstream.micro_clusters[0], center={1: 2.0, 2: 2.0}, last_update=56 + ) + assert_micro_cluster_properties( + dbstream.micro_clusters[1], center={1: 3.0, 2: 3.0}, last_update=86 + ) + assert_micro_cluster_properties( + dbstream.micro_clusters[2], center={1: 3.982141, 2: 3.982141}, last_update=82 + ) + + assert dbstream.s[0] == pytest.approx({1: 23.033439}) + assert dbstream.s[1] == pytest.approx({2: 23.033439}) + assert dbstream.s_t == {0: {1: 51}, 1: {2: 82}} + + dbstream._recluster() + assert len(dbstream.clusters) == 1 + print(dbstream.clusters[0].center) + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.800788, 2: 2.800788}) + + +def test_density_graph_with_removed_microcluster(): + dbstream = build_dbstream(fading_factor=0.1, intersection_factor=0.3) + + add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25) + add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25) + # Points in the middle of first and second micro-clusters + for _ in range(5): + dbstream.learn_one({1: 2, 2: 2}) + + add_cluster(dbstream, initial_point={1: 3.5, 2: 3.5}, move_towards={1: 2.9, 2: 2.9}, times=25) + + # Points in the middle of second and third micro-clusters + for _ in range(4): + dbstream.learn_one({1: 2.6, 2: 2.6}) + + assert len(dbstream._micro_clusters) == 2 + assert_micro_cluster_properties( + dbstream.micro_clusters[0], center={1: 2.023498, 2: 2.023498}, last_update=86 + ) + assert_micro_cluster_properties( + dbstream.micro_clusters[1], center={1: 2.766543, 2: 2.766543}, last_update=86 + ) + + assert dbstream.s == {0: {1: 4.702391097045977}} + assert dbstream.s_t == {0: {1: 86}} + + dbstream._recluster() + assert len(dbstream.clusters) == 1 + assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.560647, 2: 2.560647}) + + +def test_dbstream_synthetic_sklearn(): + centers = [(-10, -10), (-5, -5), (0, 0), (5, 5), (10, 10)] + cluster_std = [0.6] * 5 + + # Create a dataset with 15000 data points with 5 centers and cluster SD of 0.6 each + X, y = make_blobs( + n_samples=15_000, cluster_std=cluster_std, centers=centers, n_features=2, random_state=42 + ) + + dbstream = DBSTREAM( + clustering_threshold=2, + fading_factor=0.05, + intersection_factor=0.1, + cleanup_interval=1.0, + minimum_weight=1.0, + ) + + # Use VBeta as the metric to investigate the performance of DBSTREAM + v_beta = metrics.VBeta(beta=1.0) + + for x, y_true in stream.iter_array(X, y): + dbstream.learn_one(x) + y_pred = dbstream.predict_one(x) + v_beta.update(y_true, y_pred) + + assert len(dbstream._micro_clusters) == 12 + assert round(v_beta.get(), 4) == 0.9816 + + assert dbstream.s.keys() == dbstream.s_t.keys() + + dbstream._recluster() + + # Check that the resulted cluster centers are close to the expected centers + dbstream_expected_centers = { + 0: {0: 10, 1: 10}, + 1: {0: -5, 1: -5}, + 2: {0: 0, 1: 0}, + 3: {0: 5, 1: 5}, + 4: {0: -10, 1: -10}, + } + + for i in dbstream.centers.keys(): + assert ( + utils.math.minkowski_distance(dbstream.centers[i], dbstream_expected_centers[i], 2) + < 0.2 + ) diff --git a/river/cluster/textclust.py b/river/cluster/textclust.py index aa42d81be1..2115fd1b03 100644 --- a/river/cluster/textclust.py +++ b/river/cluster/textclust.py @@ -96,13 +96,12 @@ class TextClust(base.Clusterer): >>> for x in corpus: ... y_pred = model.predict_one(x["text"]) ... y = x["cluster"] - ... metric = metric.update(y,y_pred) - ... model = model.learn_one(x["text"]) + ... metric.update(y,y_pred) + ... model.learn_one(x["text"]) >>> print(metric) AdjustedRand: -0.17647058823529413 - """ # constructor with default specification @@ -153,7 +152,7 @@ def __init__( self.micro_distance = self.distances(self.micro_distance) self.macro_distance = self.distances(self.macro_distance) - def learn_one(self, x, t=None, sample_weight=None): + def learn_one(self, x, t=None, w=None): localdict = {} for key in x.keys(): new_key = key @@ -192,7 +191,12 @@ def learn_one(self, x, t=None, sample_weight=None): self.micro_clusters[clusterId].n += 1 self.micro_clusters[clusterId].merge( - mc, self.t, self.omega, self.fading_factor, self.term_fading, self.realtime + mc, + self.t, + self.omega, + self.fading_factor, + self.term_fading, + self.realtime, ) self._dist_mean += min_dist @@ -209,11 +213,10 @@ def learn_one(self, x, t=None, sample_weight=None): ## increment observation counter self.n += 1 - return clusterId ## predicts the cluster number. The type specifies whether this should happen on micro-cluster ## or macro-cluster level - def predict_one(self, x, sample_weight=None, type="micro"): + def predict_one(self, x, w=None, type="micro"): localdict = {} for key in x.keys(): new_key = key @@ -329,7 +332,9 @@ def _mergemicroclusters(self): j = i + 1 while j < len(self.micro_clusters): m_dist = self.micro_distance.dist( - self.micro_clusters[micro_keys[i]], self.micro_clusters[micro_keys[j]], idf + self.micro_clusters[micro_keys[i]], + self.micro_clusters[micro_keys[j]], + idf, ) ## lets merge them diff --git a/river/compat/__init__.py b/river/compat/__init__.py index ed7ae9c634..c07a20db9a 100644 --- a/river/compat/__init__.py +++ b/river/compat/__init__.py @@ -6,6 +6,7 @@ `compat.convert_sklearn_to_river` function. """ + from __future__ import annotations __all__: list[str] = [] diff --git a/river/compat/river_to_sklearn.py b/river/compat/river_to_sklearn.py index 7329c207d1..b80781f5ed 100644 --- a/river/compat/river_to_sklearn.py +++ b/river/compat/river_to_sklearn.py @@ -530,7 +530,8 @@ def _partial_fit(self, X, y): # Call learn_one for each observation self.labels_ = np.empty(len(X), dtype=np.int32) for i, (x, _) in enumerate(STREAM_METHODS[type(X)](X)): - label = self.instance_.learn_one(x).predict_one(x) + self.instance_.learn_one(x) + label = self.instance_.predict_one(x) self.labels_[i] = label return self diff --git a/river/compat/sklearn_to_river.py b/river/compat/sklearn_to_river.py index 47c8845b81..93664bd342 100644 --- a/river/compat/sklearn_to_river.py +++ b/river/compat/sklearn_to_river.py @@ -5,7 +5,7 @@ import pandas as pd from sklearn import base as sklearn_base -from sklearn import exceptions +from sklearn import exceptions as sklearn_exceptions from river import base @@ -33,7 +33,7 @@ def convert_sklearn_to_river(estimator: sklearn_base.BaseEstimator, classes: lis (sklearn_base.RegressorMixin, SKL2RiverRegressor), ( sklearn_base.ClassifierMixin, - functools.partial(SKL2RiverClassifier, classes=classes), + functools.partial(SKL2RiverClassifier, classes=classes), # type:ignore[arg-type] ), ] @@ -47,6 +47,23 @@ def convert_sklearn_to_river(estimator: sklearn_base.BaseEstimator, classes: lis class SKL2RiverBase: def __init__(self, estimator: sklearn_base.BaseEstimator): self.estimator = estimator + self._feature_names: list | None = None + + def _align_dict(self, x: dict) -> list: + if self._feature_names is None: + self._feature_names = list(x.keys()) + return [x[k] for k in self._feature_names] + + def _align_df(self, X: pd.DataFrame) -> pd.DataFrame: + if self._feature_names is None: + self._feature_names = list(X.columns) + return X[self._feature_names] + + def _unit_test_skips(self): # noqa + return { + "check_emerging_features", + "check_disappearing_features", + } class SKL2RiverRegressor(SKL2RiverBase, base.Regressor): @@ -81,28 +98,26 @@ class SKL2RiverRegressor(SKL2RiverBase, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 84.519485 + MAE: 84.501421 """ def learn_one(self, x, y): - self.estimator.partial_fit(X=[list(x.values())], y=[y]) - return self + self.estimator.partial_fit(X=[self._align_dict(x)], y=[y]) def learn_many(self, X, y): - self.estimator.partial_fit(X=X, y=y) - return self + self.estimator.partial_fit(X=self._align_df(X), y=y) def predict_one(self, x): try: - return self.estimator.predict(X=[list(x.values())])[0] - except exceptions.NotFittedError: + return self.estimator.predict(X=[self._align_dict(x)])[0] + except sklearn_exceptions.NotFittedError: return 0 def predict_many(self, X): try: - return pd.Series(self.estimator.predict(X)) - except exceptions.NotFittedError: + return pd.Series(self.estimator.predict(self._align_df(X))) + except sklearn_exceptions.NotFittedError: return pd.Series([0] * len(X), index=X.index) @@ -145,7 +160,7 @@ class SKL2RiverClassifier(SKL2RiverBase, base.Classifier): >>> metric = metrics.LogLoss() >>> evaluate.progressive_val_score(dataset, model, metric) - LogLoss: 0.199554 + LogLoss: 0.198029 """ @@ -158,24 +173,22 @@ def _multiclass(self): return True def learn_one(self, x, y): - self.estimator.partial_fit(X=[list(x.values())], y=[y], classes=self.classes) - return self + self.estimator.partial_fit(X=[self._align_dict(x)], y=[y], classes=self.classes) def learn_many(self, X, y): - self.estimator.partial_fit(X=X, y=y, classes=self.classes) - return self + self.estimator.partial_fit(X=self._align_df(X), y=y, classes=self.classes) def predict_proba_one(self, x): try: - y_pred = self.estimator.predict_proba([list(x.values())])[0] + y_pred = self.estimator.predict_proba([self._align_dict(x)])[0] return {self.classes[i]: p for i, p in enumerate(y_pred)} - except exceptions.NotFittedError: + except sklearn_exceptions.NotFittedError: return {c: 1 / len(self.classes) for c in self.classes} def predict_proba_many(self, X): try: - return pd.Series(self.estimator.predict_proba(X), columns=self.classes) - except exceptions.NotFittedError: + return pd.Series(self.estimator.predict_proba(self._align_df(X)), columns=self.classes) + except sklearn_exceptions.NotFittedError: return pd.DataFrame( [[1 / len(self.classes)] * len(self.classes)] * len(X), columns=self.classes, @@ -184,13 +197,13 @@ def predict_proba_many(self, X): def predict_one(self, x): try: - y_pred = self.estimator.predict(X=[list(x.values())])[0] + y_pred = self.estimator.predict(X=[self._align_dict(x)])[0] return y_pred - except exceptions.NotFittedError: + except sklearn_exceptions.NotFittedError: return self.classes[0] def predict_many(self, X): try: - return pd.Series(self.estimator.predict(X)) - except exceptions.NotFittedError: + return pd.Series(self.estimator.predict(self._align_df(X))) + except sklearn_exceptions.NotFittedError: return pd.Series([self.classes[0]] * len(X), index=X.index) diff --git a/river/compat/test_sklearn.py b/river/compat/test_sklearn.py index 50f8de4c48..fed434302c 100644 --- a/river/compat/test_sklearn.py +++ b/river/compat/test_sklearn.py @@ -10,7 +10,7 @@ @pytest.mark.parametrize( - "estimator", + "estimator", # type: ignore[misc] [ pytest.param(estimator, id=str(estimator)) for estimator in [ @@ -21,7 +21,7 @@ ] ], ) -@pytest.mark.filterwarnings("ignore::sklearn.utils.estimator_checks.SkipTestWarning") +@pytest.mark.filterwarnings("ignore::sklearn.utils.estimator_checks.SkipTestWarning") # type: ignore[misc] def test_river_to_sklearn_check_estimator(estimator: base.Estimator): skl_estimator = compat.convert_river_to_sklearn(estimator) estimator_checks.check_estimator(skl_estimator) @@ -63,12 +63,12 @@ def test_not_fitted_still_works_regression(estimator): for n_classes in [2, 3] for estimator in [ compat.convert_sklearn_to_river( - sk_linear_model.SGDClassifier(loss="log"), classes=list(range(n_classes)) + sk_linear_model.SGDClassifier(loss="log_loss"), classes=list(range(n_classes)) ), ( preprocessing.StandardScaler() | compat.convert_sklearn_to_river( - sk_linear_model.SGDClassifier(loss="log"), classes=list(range(n_classes)) + sk_linear_model.SGDClassifier(loss="log_loss"), classes=list(range(n_classes)) ) ), ] diff --git a/river/compose/__init__.py b/river/compose/__init__.py index fc44117d0c..260b613970 100644 --- a/river/compose/__init__.py +++ b/river/compose/__init__.py @@ -4,11 +4,12 @@ pipelines are not the only way to process a stream of data, we highly encourage you to use them. """ + from __future__ import annotations from .func import FuncTransformer from .grouper import Grouper -from .pipeline import Pipeline, pure_inference_mode, warm_up_mode +from .pipeline import Pipeline, learn_during_predict from .product import TransformerProduct from .renamer import Prefixer, Renamer, Suffixer from .select import Discard, Select, SelectType @@ -29,5 +30,5 @@ "TargetTransformRegressor", "TransformerProduct", "TransformerUnion", - "warm_up_mode", + "learn_during_predict", ] diff --git a/river/compose/grouper.py b/river/compose/grouper.py index 9e6bb44074..7d19f50ebf 100644 --- a/river/compose/grouper.py +++ b/river/compose/grouper.py @@ -43,7 +43,6 @@ def _get_key(self, x): def learn_one(self, x): key = self._get_key(x) self.transformers[key].learn_one(x) - return self def transform_one(self, x): key = self._get_key(x) diff --git a/river/compose/pipeline.py b/river/compose/pipeline.py index 7260e2f3bf..2c894ede04 100644 --- a/river/compose/pipeline.py +++ b/river/compose/pipeline.py @@ -18,39 +18,36 @@ @contextlib.contextmanager -def warm_up_mode(): - """A context manager for training pipelines during a warm-up phase. +def learn_during_predict(): + """A context manager for fitting unsupervised steps during prediction. - You don't have to worry about anything when you call `predict_one` and `learn_one` with a - pipeline during in a training loop. The methods at each step of the pipeline will be called in - the correct order. + Usually, unsupervised parts of a pipeline are updated during `learn_one`. However, in the case + of online learning, it is possible to update them before, during the prediction step. This + context manager allows you to do so. - However, during a warm-up phase, you might just be calling `learn_one` because you don't need - the out-of-sample predictions. In this case the unsupervised estimators in the pipeline won't - be updated, because they are usually updated when `predict_one` is called. - - This context manager allows you to override that behavior and make it so that unsupervised - estimators are updated when `learn_one` is called. + This usually brings a slight performance improvement. But it is not done by default because it + is not intuitive and is more difficult to test. It also means that you have to call + `predict_one` before `learn_one` in order for the whole pipeline to be updated. Examples -------- - Let's first see what methods are called if we just call `learn_one`. + Let's first see what methods are called if we just call `predict_one`. >>> import io >>> import logging - >>> from river import anomaly >>> from river import compose >>> from river import datasets + >>> from river import linear_model >>> from river import preprocessing >>> from river import utils >>> model = compose.Pipeline( - ... preprocessing.MinMaxScaler(), - ... anomaly.HalfSpaceTrees() + ... preprocessing.StandardScaler(), + ... linear_model.LinearRegression() ... ) - >>> class_condition = lambda x: x.__class__.__name__ in ('MinMaxScaler', 'HalfSpaceTrees') + >>> class_condition = lambda x: x.__class__.__name__ in ('StandardScaler', 'LinearRegression') >>> logger = logging.getLogger() >>> logger.setLevel(logging.DEBUG) @@ -61,12 +58,12 @@ def warm_up_mode(): >>> logger.addHandler(sh) >>> with utils.log_method_calls(class_condition): - ... for x, y in datasets.CreditCard().take(1): - ... model = model.learn_one(x) + ... for x, y in datasets.TrumpApproval().take(1): + ... _ = model.predict_one(x) >>> print(logs.getvalue()) - MinMaxScaler.transform_one - HalfSpaceTrees.learn_one + StandardScaler.transform_one + LinearRegression.predict_one Now let's use the context manager and see what methods get called. @@ -75,58 +72,18 @@ def warm_up_mode(): >>> sh.setLevel(logging.DEBUG) >>> logger.addHandler(sh) - >>> with utils.log_method_calls(class_condition), compose.warm_up_mode(): - ... for x, y in datasets.CreditCard().take(1): - ... model = model.learn_one(x) + >>> with utils.log_method_calls(class_condition), compose.learn_during_predict(): + ... for x, y in datasets.TrumpApproval().take(1): + ... _ = model.predict_one(x) >>> print(logs.getvalue()) - MinMaxScaler.learn_one - MinMaxScaler.transform_one - HalfSpaceTrees.learn_one - - We can see that the scaler got updated before transforming the data. - - """ - Pipeline._WARM_UP = True - try: - yield - finally: - Pipeline._WARM_UP = False - - -@contextlib.contextmanager -def pure_inference_mode(): - """A context manager for making inferences with no side-effects. - - Calling `predict_one` with a pipeline will update the unsupervised steps of the pipeline. This - is the expected behavior for online machine learning. However, in some cases you might just - want to produce predictions without necessarily updating anything. - - This context manager allows you to override that behavior and make it so that unsupervised - estimators are not updated when `predict_one` is called. - - Examples - -------- - - Let's first see what methods are called if we just call `predict_one`. - - >>> import io - >>> import logging - >>> from river import compose - >>> from river import datasets - >>> from river import linear_model - >>> from river import preprocessing - >>> from river import utils - - >>> model = compose.Pipeline( - ... preprocessing.StandardScaler(), - ... linear_model.LinearRegression() - ... ) + StandardScaler.learn_one + StandardScaler.transform_one + LinearRegression.predict_one - >>> class_condition = lambda x: x.__class__.__name__ in ('StandardScaler', 'LinearRegression') + We can see that the scaler did not get updated before transforming the data. - >>> logger = logging.getLogger() - >>> logger.setLevel(logging.DEBUG) + This also works when working with mini-batches. >>> logs = io.StringIO() >>> sh = logging.StreamHandler(logs) @@ -135,43 +92,37 @@ def pure_inference_mode(): >>> with utils.log_method_calls(class_condition): ... for x, y in datasets.TrumpApproval().take(1): - ... _ = model.predict_one(x) - + ... _ = model.predict_many(pd.DataFrame([x])) >>> print(logs.getvalue()) - StandardScaler.learn_one - StandardScaler.transform_one - LinearRegression.predict_one - - Now let's use the context manager and see what methods get called. + StandardScaler.transform_many + LinearRegression.predict_many >>> logs = io.StringIO() >>> sh = logging.StreamHandler(logs) >>> sh.setLevel(logging.DEBUG) >>> logger.addHandler(sh) - >>> with utils.log_method_calls(class_condition), compose.pure_inference_mode(): + >>> with utils.log_method_calls(class_condition), compose.learn_during_predict(): ... for x, y in datasets.TrumpApproval().take(1): - ... _ = model.predict_one(x) - + ... _ = model.predict_many(pd.DataFrame([x])) >>> print(logs.getvalue()) - StandardScaler.transform_one - LinearRegression.predict_one - - We can see that the scaler did not get updated before transforming the data. + StandardScaler.learn_many + StandardScaler.transform_many + LinearRegression.predict_many """ - Pipeline._STATELESS = True + Pipeline._LEARN_UNSUPERVISED_DURING_PREDICT = True try: yield finally: - Pipeline._STATELESS = False + Pipeline._LEARN_UNSUPERVISED_DURING_PREDICT = False class Pipeline(base.Estimator): """A pipeline of estimators. Pipelines allow you to chain different steps into a sequence. Typically, when doing supervised - learning, a pipeline contains one ore more transformation steps, whilst it's is a regressor or + learning, a pipeline contains one or more transformation steps, whilst it's a regressor or a classifier. It is highly recommended to use pipelines with River. Indeed, in an online learning setting, it is very practical to have a model defined as a single object. Take a look at the [user guide](/recipes/pipelines) for further information and @@ -292,7 +243,7 @@ class Pipeline(base.Estimator): >>> model = (tfidf + counts) | mnb >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> x = dataset[0][0] >>> report = model.debug_one(dataset[0][0]) @@ -304,25 +255,24 @@ class Pipeline(base.Estimator): -------------------- 1.0 TFIDF | Prefixer -------------------- - tfidf_comment: 0.47606 (float) - tfidf_positive: 0.87942 (float) + tfidf_comment: 0.43017 (float) + tfidf_positive: 0.90275 (float) 1.1 BagOfWords | Prefixer ------------------------- count_comment: 1 (int) count_positive: 1 (int) count_comment: 1 (int) count_positive: 1 (int) - tfidf_comment: 0.50854 (float) - tfidf_positive: 0.86104 (float) + tfidf_comment: 0.43017 (float) + tfidf_positive: 0.90275 (float) 2. MultinomialNB ---------------- - False: 0.19313 - True: 0.80687 + False: 0.19221 + True: 0.80779 """ - _WARM_UP = False - _STATELESS = False + _LEARN_UNSUPERVISED_DURING_PREDICT = False def __init__(self, *steps): self.steps = collections.OrderedDict() @@ -477,55 +427,55 @@ def learn_one(self, x: dict, y=None, **params): """ - steps = iter(self.steps.values()) - is_anomaly = False - # Loop over the first n - 1 steps, which should all be transformers - for t in itertools.islice(steps, len(self) - 1): + for step in self.steps.values(): # There might be an anomaly filter in the pipeline. Its purpose is to prevent anomalous # data from being learned by the subsequent parts of the pipeline. - if utils.inspect.ischildobject(obj=t, class_name="AnomalyFilter"): - if t._supervised: - t.learn_one(x, y) - score = t.score_one(x, y) + if utils.inspect.ischildobject(obj=step, class_name="AnomalyFilter"): + if step._supervised: + step.learn_one(x, y) + score = step.score_one(x, y) else: - t.learn_one(x) - score = t.score_one(x) + step.learn_one(x) + score = step.score_one(x) # Skip the next parts of the pipeline if the score is classified as anomalous - if t.classify(score): - is_anomaly = True + if step.classify(score): break continue - if self._WARM_UP: - if isinstance(t, union.TransformerUnion): - for sub_t in t.transformers.values(): - if not sub_t._supervised: - sub_t.learn_one(x) - elif not t._supervised: - t.learn_one(x) - x_pre = x - x = t.transform_one(x) + if isinstance(step, base.Transformer): + # In case of _LEARN_UNSUPERVISED_DURING_PREDICT, then the unsupervised transformers + # are updated before transforming. + if not self._LEARN_UNSUPERVISED_DURING_PREDICT: + if isinstance(step, union.TransformerUnion): + for sub_step in step.transformers.values(): + if not sub_step._supervised: + sub_step.learn_one(x) + elif not step._supervised: + step.learn_one(x) + # Transform the data + x = step.transform_one(x) # The supervised transformers have to be updated. # Note that this is done after transforming in order to avoid target leakage. - if isinstance(t, union.TransformerUnion): - for sub_t in t.transformers.values(): - if sub_t._supervised: - sub_t.learn_one(x_pre, y) - - elif t._supervised: - t.learn_one(x_pre, y) - - if not is_anomaly: - last_step = next(steps) - if last_step._supervised: - last_step.learn_one(x=x, y=y, **params) + if isinstance(step, base.Transformer): + if isinstance(step, union.TransformerUnion): + for sub_step in step.transformers.values(): + if sub_step._supervised: + sub_step.learn_one(x=x_pre, y=y) + # Here the step is a supervised transformer, such as a TargetAgg. It's important + # to pass the original features to the transformer, not the transformed ones. + elif step._supervised: + step.learn_one(x=x_pre, y=y) # type: ignore + # Here the step is not a transformer, and it's supervised, such as a LinearRegression. + # This is usually the last step of the pipeline. + elif step._supervised: + step.learn_one(x=x, y=y) + # Here the step is not a transformer, and it's unsupervised, such as a KMeans. This + # is also usually the last step of the pipeline. else: - last_step.learn_one(x, **params) - - return self + step.learn_one(x=x) def _transform_one(self, x: dict): """This methods takes care of applying the first n - 1 steps of the pipeline, which are @@ -541,12 +491,13 @@ def _transform_one(self, x: dict): if utils.inspect.ischildobject(obj=t, class_name="AnomalyFilter"): continue - if not self._STATELESS: + # In case of _LEARN_UNSUPERVISED_DURING_PREDICT, then the unsupervised transformers + # are updated during the inference phase. + if self._LEARN_UNSUPERVISED_DURING_PREDICT: if isinstance(t, union.TransformerUnion): for sub_t in t.transformers.values(): if not sub_t._supervised: sub_t.learn_one(x) - elif not t._supervised: t.learn_one(x) @@ -565,7 +516,7 @@ def transform_one(self, x: dict, **params): """ x, last_step = self._transform_one(x) if isinstance(last_step, base.Transformer): - if not last_step._supervised: + if not last_step._supervised and self._LEARN_UNSUPERVISED_DURING_PREDICT: last_step.learn_one(x) return last_step.transform_one(x, **params) return x @@ -725,7 +676,7 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series | None = None, **params): # Loop over the first n - 1 steps, which should all be transformers for t in itertools.islice(steps, len(self) - 1): - if self._WARM_UP: + if not self._LEARN_UNSUPERVISED_DURING_PREDICT: if isinstance(t, union.TransformerUnion): for sub_t in t.transformers.values(): if not sub_t._supervised: @@ -752,8 +703,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series | None = None, **params): else: last_step.learn_many(X=X, **params) - return self - def _transform_many(self, X: pd.DataFrame): """This methods takes care of applying the first n - 1 steps of the pipeline, which are supposedly transformers. It also returns the final step so that other functions can do @@ -764,13 +713,14 @@ def _transform_many(self, X: pd.DataFrame): steps = iter(self.steps.values()) for t in itertools.islice(steps, len(self) - 1): - if isinstance(t, union.TransformerUnion): - for sub_t in t.transformers.values(): - if not sub_t._supervised: - sub_t.learn_many(X=X) + if self._LEARN_UNSUPERVISED_DURING_PREDICT: + if isinstance(t, union.TransformerUnion): + for sub_t in t.transformers.values(): + if not sub_t._supervised: + sub_t.learn_many(X=X) - elif not t._supervised: - t.learn_many(X=X) + elif not t._supervised: + t.learn_many(X=X) X = t.transform_many(X=X) @@ -787,7 +737,7 @@ def transform_many(self, X: pd.DataFrame): """ X, last_step = self._transform_many(X=X) if isinstance(last_step, base.MiniBatchTransformer): - if not last_step._supervised: + if not last_step._supervised and not self._LEARN_UNSUPERVISED_DURING_PREDICT: last_step.learn_many(X) return last_step.transform_many(X) return X @@ -801,3 +751,6 @@ def predict_proba_many(self, X: pd.DataFrame): """Call transform_many, and then predict_proba_many on the final step.""" X, last_step = self._transform_many(X=X) return last_step.predict_proba_many(X=X) + + def _unit_test_skips(self): + return self[-1]._unit_test_skips() diff --git a/river/compose/product.py b/river/compose/product.py index f451dd3008..4ffa82a3be 100644 --- a/river/compose/product.py +++ b/river/compose/product.py @@ -92,17 +92,36 @@ def multiply(a, b): # Fast-track for sparse[uint8] * sparse[uint8] if a.dtype == pd.SparseDtype("uint8") and b.dtype == pd.SparseDtype("uint8"): return a & b + # Fast-track for sparse[uint8] * numeric + if a.dtype == pd.SparseDtype("uint8"): + c = np.zeros_like(b) + true_mask = a.eq(1) + c[true_mask] = b[true_mask] + return pd.Series( + c, + index=b.index, + dtype=pd.SparseDtype(b.dtype, fill_value=0), + ) + # Fast-track for numeric * sparse[uint8] + if b.dtype == pd.SparseDtype("uint8"): + return multiply(b, a) # Fast-track for sparse * sparse - if pd.api.types.is_sparse(a) and pd.api.types.is_sparse(b): - return pd.arrays.SparseArray( - a * b, fill_value=a.sparse.fill_value * b.sparse.fill_value + if isinstance(a.dtype, pd.SparseDtype) and isinstance(b.dtype, pd.SparseDtype): + return pd.Series( + a * b, + index=a.index, + dtype=pd.SparseDtype( + b.dtype, fill_value=a.sparse.fill_value * b.sparse.fill_value + ), ) # Fast-track for sparse * numeric - if pd.api.types.is_sparse(a): - return pd.arrays.SparseArray(a * b, fill_value=a.sparse.fill_value) + if isinstance(a.dtype, pd.SparseDtype): + return pd.Series( + a * b, dtype=pd.SparseDtype(fill_value=a.sparse.fill_value, dtype=b.dtype) + ) # Fast-track for numeric * sparse - if pd.api.types.is_sparse(b): - return pd.arrays.SparseArray(a * b, fill_value=b.sparse.fill_value) + if isinstance(b.dtype, pd.SparseDtype): + return multiply(b, a) # Default return np.multiply(a, b) diff --git a/river/compose/target_transform.py b/river/compose/target_transform.py index 6e665701bc..a300b5bd05 100644 --- a/river/compose/target_transform.py +++ b/river/compose/target_transform.py @@ -42,7 +42,7 @@ class TargetTransformRegressor(base.Wrapper, base.Regressor): >>> metric = metrics.MSE() >>> evaluate.progressive_val_score(dataset, model, metric) - MSE: 8.759624 + MSE: 10.999752 """ @@ -60,13 +60,11 @@ def __init__( def _wrapped_model(self): return self.regressor - def _update(self, y): - ... + def _update(self, y): ... def learn_one(self, x, y): self._update(y) self.regressor.learn_one(x, self.func(y)) - return self def predict_one(self, x): y_pred = self.regressor.predict_one(x) diff --git a/river/compose/test_.py b/river/compose/test_.py index 694f7dcf9c..5453121186 100644 --- a/river/compose/test_.py +++ b/river/compose/test_.py @@ -55,7 +55,7 @@ def b(x): assert str(pipeline) == "a + b" -def test_learn_one_warm_up_mode(): +def test_learn_one_with_learn_during_predict(): pipeline = compose.Pipeline( ("scale", preprocessing.StandardScaler()), ("log_reg", linear_model.LogisticRegression()), @@ -65,17 +65,17 @@ def test_learn_one_warm_up_mode(): for x, y in dataset: counts_pre = dict(pipeline["scale"].counts) - with compose.warm_up_mode(): + with compose.learn_during_predict(): pipeline.learn_one(x, y) - counts_post = dict(pipeline["scale"].counts) - pipeline.learn_one(x, y) counts_no_learn = dict(pipeline["scale"].counts) + pipeline.learn_one(x, y) + counts_post = dict(pipeline["scale"].counts) assert counts_pre != counts_post - assert counts_post == counts_no_learn + assert counts_pre == counts_no_learn -def test_learn_many_warm_up_mode(): +def test_learn_many_with_learn_during_predict(): pipeline = compose.Pipeline( ("scale", preprocessing.StandardScaler()), ("log_reg", linear_model.LogisticRegression()), @@ -88,14 +88,14 @@ def test_learn_many_warm_up_mode(): y = pd.Series([bool(y % 2) for _, y in dataset][i : i + 5]) counts_pre = dict(pipeline["scale"].counts) - with compose.warm_up_mode(): + with compose.learn_during_predict(): pipeline.learn_many(X, y) - counts_post = dict(pipeline["scale"].counts) - pipeline.learn_many(X, y) counts_no_learn = dict(pipeline["scale"].counts) + pipeline.learn_many(X, y) + counts_post = dict(pipeline["scale"].counts) assert counts_pre != counts_post - assert counts_post == counts_no_learn + assert counts_pre == counts_no_learn def test_list_of_funcs(): diff --git a/river/compose/test_product.py b/river/compose/test_product.py index 300df6b308..746121b8b5 100644 --- a/river/compose/test_product.py +++ b/river/compose/test_product.py @@ -1,8 +1,11 @@ from __future__ import annotations +import numpy as np import pandas as pd +from sklearn import datasets as sk_datasets +from sklearn import linear_model as sk_linear_model -from river import compose, datasets, preprocessing, stream +from river import compat, compose, datasets, preprocessing, stream def test_issue_1238(): @@ -46,15 +49,15 @@ def test_issue_1243(): >>> group1 = compose.Select('z') >>> group2 = compose.Select('x') | preprocessing.StandardScaler() >>> model = group1 + group2 + group1 * group2 - >>> model = model.learn_many(X) + >>> model.learn_many(X) >>> for x in X.to_dict('records'): ... print(model.transform_one(x)) - {'z*x': 0.697074..., 'x': 0.697074..., 'z': 1} - {'z*x': -1.351716..., 'x': -1.351716..., 'z': 1} - {'z*x': -0.430886..., 'x': -0.430886..., 'z': 1} - {'z*x': -0.580868..., 'x': -0.580868..., 'z': 1} - {'z*x': 1.228463..., 'x': 1.228463..., 'z': 1} - {'z*x': 0.924721..., 'x': 0.924721..., 'z': 1} + {'z*x': 0.785..., 'x': 0.785..., 'z': 1} + {'z*x': -1.511..., 'x': -1.511..., 'z': 1} + {'z*x': -0.576..., 'x': -0.576..., 'z': 1} + {'z*x': -0.770..., 'x': -0.770..., 'z': 1} + {'z*x': 1.148..., 'x': 1.148..., 'z': 1} + {'z*x': 0.924..., 'x': 0.924..., 'z': 1} """ @@ -66,8 +69,8 @@ def test_issue_1253(): >>> import numpy as np >>> import pandas as pd - >>> from river import compose, preprocessing - >>> from sklearn import datasets + >>> from river import compat, compose, preprocessing + >>> from sklearn import datasets, linear_model >>> np.random.seed(1000) >>> X, y = datasets.make_regression(n_samples=5_000, n_features=2) @@ -86,6 +89,32 @@ def test_issue_1253(): >>> XT.sparse.to_dense().memory_usage().sum() // 1000 4455 + >>> X, y = datasets.make_regression(n_samples=6, n_features=2) + >>> X = pd.DataFrame(X) + >>> X.columns = ['feat_1','feat_2'] + >>> X['cat'] = np.random.randint(1, 3, X.shape[0]) + >>> y = pd.Series(y) + >>> group1 = compose.Select('cat') | preprocessing.OneHotEncoder() + >>> group2 = compose.Select('feat_2') | preprocessing.StandardScaler() + >>> sparsify = lambda X: X.astype({ + ... key: pd.SparseDtype(X.dtypes[key].type, fill_value=0) + ... for key in X.dtypes.keys() + ... }) + >>> model = ( + ... (group1 + group1 * group2) | + ... compose.FuncTransformer(sparsify) | + ... compat.convert_sklearn_to_river(linear_model.SGDRegressor(max_iter=3)) + ... ) + >>> _ = model.predict_many(X) + >>> model.transform_many(X) + cat_1*feat_2 cat_2*feat_2 cat_1 cat_2 + 0 -1.196841 0 1 0 + 1 1.304619 0 1 0 + 2 -1.294091 0 1 0 + 3 0.287426 0 1 0 + 4 -0.143960 0 1 0 + 5 0 1.042847 0 1 + """ @@ -174,3 +203,21 @@ def test_one_many_consistent(): # check_dtype=False to avoid int/float comparison pd.testing.assert_frame_equal(many_outputs[one_outputs.columns], one_outputs, check_dtype=False) + + +def test_issue_1310(): + X, y = sk_datasets.make_regression(n_samples=5000, n_features=2) + X = pd.DataFrame(X) + X.columns = ["feat_1", "feat_2"] + X["cat"] = np.random.randint(1, 100, X.shape[0]) + X["cat"] = X["cat"].astype("string") + y = pd.Series(y) + + group1 = compose.Select("cat") | preprocessing.OneHotEncoder() + group2 = compose.Select("feat_2") | preprocessing.StandardScaler() + model = group1 + group1 * group2 * group2 | compat.convert_sklearn_to_river( + sk_linear_model.SGDRegressor() + ) + + model.predict_many(X) + model.learn_many(X, y) diff --git a/river/compose/union.py b/river/compose/union.py index 6198a988aa..0210745a1e 100644 --- a/river/compose/union.py +++ b/river/compose/union.py @@ -66,7 +66,7 @@ class TransformerUnion(base.MiniBatchTransformer): >>> from pprint import pprint >>> for x in X: - ... agg = agg.learn_one(x) + ... agg.learn_one(x) ... pprint(agg.transform_one(x)) {'revenue_count_by_place': 1, 'revenue_mean_by_place': 42.0} {'revenue_count_by_place': 1, 'revenue_mean_by_place': 16.0} @@ -144,7 +144,7 @@ class TransformerUnion(base.MiniBatchTransformer): ... (compose.Select("revenue") | preprocessing.StandardScaler()) ... ) - >>> _ = agg.learn_many(X) + >>> agg.learn_many(X) >>> agg.transform_many(X) place revenue 0 2 0.441250 @@ -269,7 +269,6 @@ def learn_one(self, x, y=None): t.learn_one(x, y) else: t.learn_one(x) - return self def transform_one(self, x): """Passes the data through each transformer and packs the results together.""" @@ -294,7 +293,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series | None = None): t.learn_many(X, y) else: t.learn_many(X) - return self def transform_many(self, X): """Passes the data through each transformer and packs the results together.""" diff --git a/river/conf/__init__.py b/river/conf/__init__.py index 10dc5cad27..ef6df5a9b8 100644 --- a/river/conf/__init__.py +++ b/river/conf/__init__.py @@ -1,5 +1,6 @@ """Conformal predictions. This modules contains wrappers to enable conformal predictions on any regressor or classifier.""" + from __future__ import annotations from .interval import Interval diff --git a/river/conf/jackknife.py b/river/conf/jackknife.py index 2e2056ad34..543ba0b25b 100644 --- a/river/conf/jackknife.py +++ b/river/conf/jackknife.py @@ -56,20 +56,20 @@ class RegressionJackknife(base.Wrapper, base.Regressor): >>> for x, y in dataset: ... interval = model.predict_one(x, with_interval=True) - ... validity = validity.update(y in interval) - ... efficiency = efficiency.update(interval.width) - ... model = model.learn_one(x, y) + ... validity.update(y in interval) + ... efficiency.update(interval.width) + ... model.learn_one(x, y) The interval's validity is the proportion of times the true value is within the interval. We specified a confidence level of 90%, so we expect the validity to be around 90%. >>> validity - Mean: 0.903097 + Mean: 0.939061 The interval's efficiency is the average width of the intervals. >>> efficiency - Mean: 3.593173 + Mean: 4.078361 Lowering the confidence lowering will mechanically improve the efficiency. @@ -117,8 +117,6 @@ def learn_one(self, x, y, **kwargs): self.regressor.learn_one(x, y, **kwargs) - return self - def predict_one(self, x, with_interval=False, **kwargs): """Predict the output of features `x`. @@ -140,5 +138,6 @@ def predict_one(self, x, with_interval=False, **kwargs): return y_pred return interval.Interval( - lower=y_pred + (self._lower.get() or 0), upper=y_pred + (self._upper.get() or 0) + lower=y_pred + (self._lower.get() or 0), + upper=y_pred + (self._upper.get() or 0), ) diff --git a/river/conftest.py b/river/conftest.py index e8c824541e..8ea097838f 100644 --- a/river/conftest.py +++ b/river/conftest.py @@ -5,7 +5,6 @@ try: import sklearn # noqa: F401 except ImportError: - collect_ignore.append("compat/sklearn.py") collect_ignore.append("compat/test_sklearn.py") try: @@ -14,11 +13,6 @@ collect_ignore.append("stream/iter_sql.py") collect_ignore.append("stream/test_sql.py") -try: - import torch # noqa: F401 -except ImportError: - collect_ignore.append("compat/pytorch.py") - try: import vaex # noqa: F401 except ImportError: diff --git a/river/covariance/__init__.py b/river/covariance/__init__.py index 80c18a2e33..a026e17807 100644 --- a/river/covariance/__init__.py +++ b/river/covariance/__init__.py @@ -1,4 +1,5 @@ """Online estimation of covariance and precision matrices.""" + from __future__ import annotations from .emp import EmpiricalCovariance, EmpiricalPrecision diff --git a/river/covariance/emp.py b/river/covariance/emp.py index 090b127410..1b2f64a80e 100644 --- a/river/covariance/emp.py +++ b/river/covariance/emp.py @@ -14,8 +14,7 @@ class SymmetricMatrix(abc.ABC): @property @abc.abstractmethod - def matrix(self): - ... + def matrix(self) -> dict: ... def __getitem__(self, key): """ @@ -81,7 +80,7 @@ class EmpiricalCovariance(SymmetricMatrix): >>> cov = covariance.EmpiricalCovariance() >>> for x in X.to_dict(orient="records"): - ... cov = cov.update(x) + ... cov.update(x) >>> cov blue green red blue 0.076 0.020 -0.010 @@ -91,7 +90,7 @@ class EmpiricalCovariance(SymmetricMatrix): There is also an `update_many` method to process mini-batches. The results are identical. >>> cov = covariance.EmpiricalCovariance() - >>> cov = cov.update_many(X) + >>> cov.update_many(X) >>> cov blue green red blue 0.076 0.020 -0.010 @@ -133,7 +132,7 @@ def update(self, x: dict): cov = self[i, j] except KeyError: self._cov[i, j] = stats.Cov(self.ddof) - cov = self[i, j] + cov = self._cov[i, j] cov.update(x[i], x[j]) for i, xi in x.items(): @@ -141,11 +140,9 @@ def update(self, x: dict): var = self[i, i] except KeyError: self._cov[i, i] = stats.Var(self.ddof) - var = self[i, i] + var = self._cov[i, i] var.update(xi) - return self - def revert(self, x: dict): """Downdate with a single sample. @@ -162,8 +159,6 @@ def revert(self, x: dict): for i, xi in x.items(): self[i, i].revert(x[i]) - return self - def update_many(self, X: pd.DataFrame): """Update with a dataframe of samples. @@ -178,35 +173,83 @@ def update_many(self, X: pd.DataFrame): mean_arr = X_arr.mean(axis=0) cov_arr = np.cov(X_arr.T, ddof=self.ddof) + n = len(X) mean = dict(zip(X.columns, mean_arr)) cov = { (i, j): cov_arr[r, c] for (r, i), (c, j) in itertools.combinations_with_replacement(enumerate(X.columns), r=2) } - for i, j in itertools.combinations(sorted(X.columns), r=2): + self._update_from_state(n=n, mean=mean, cov=cov) + + def _update_from_state(self, n: int, mean: dict, cov: float | dict): + """Update from state information. + + Parameters + ---------- + n + The number of data points. + mean + A dictionary of variable means. + cov + A dictionary of covariance or variance values. + + Raises + ---------- + KeyError: If an element in `mean` or `cov` is missing. + """ + for i, j in itertools.combinations(sorted(mean.keys()), r=2): try: self[i, j] except KeyError: self._cov[i, j] = stats.Cov(self.ddof) + if isinstance(cov, dict): + cov_ = cov.get((i, j), cov.get((j, i))) + else: + cov_ = cov self._cov[i, j] += stats.Cov._from_state( - n=len(X), + n=n, mean_x=mean[i], mean_y=mean[j], - cov=cov.get((i, j), cov.get((j, i))), + cov=cov_, ddof=self.ddof, ) - for i in X.columns: + for i in mean.keys(): try: self[i, i] except KeyError: self._cov[i, i] = stats.Var(self.ddof) - self._cov[i, i] += stats.Var._from_state( - n=len(X), m=mean[i], sig=cov[i, i], ddof=self.ddof - ) + if isinstance(cov, dict): + cov_ = cov[i, i] + else: + cov_ = cov + self._cov[i, i] += stats.Var._from_state(n=n, m=mean[i], sig=cov_, ddof=self.ddof) - return self + @classmethod + def _from_state(cls, n: int, mean: dict, cov: float | dict, *, ddof=1): + """Create a new instance from state information. + + Parameters + ---------- + cls + The class type. + n + The number of data points. + mean + A dictionary of variable means. + cov + A dictionary of covariance or variance values. + ddof + Degrees of freedom for covariance calculation. Defaults to 1. + + Returns + ---------- + cls: A new instance of the class with updated covariance matrix. + """ + new = cls(ddof=ddof) + new._update_from_state(n=n, mean=mean, cov=cov) + return new class EmpiricalPrecision(SymmetricMatrix): @@ -237,7 +280,7 @@ class EmpiricalPrecision(SymmetricMatrix): >>> prec = covariance.EmpiricalPrecision() >>> for x in X.to_dict(orient="records"): - ... prec = prec.update(x) + ... prec.update(x) >>> prec 0 1 2 @@ -305,8 +348,6 @@ def update(self, x): for j, fj in enumerate(x): self._inv_cov[min((fi, fj), (fj, fi))] = row[j] - return self - def update_many(self, X: pd.DataFrame): """Update with a dataframe of samples. @@ -338,5 +379,3 @@ def update_many(self, X: pd.DataFrame): row = self._w[fi] * inv_cov[i] for j, fj in enumerate(X): self._inv_cov[min((fi, fj), (fj, fi))] = row[j] - - return self diff --git a/river/datasets/__init__.py b/river/datasets/__init__.py index 0d52732eb5..7f4cfbbee4 100644 --- a/river/datasets/__init__.py +++ b/river/datasets/__init__.py @@ -6,6 +6,7 @@ are interested in infinite synthetic data generators. """ + from __future__ import annotations from . import base, synth @@ -32,6 +33,7 @@ from .trec07 import TREC07 from .trump_approval import TrumpApproval from .water_flow import WaterFlow +from .web_traffic import WebTraffic __all__ = [ "AirlinePassengers", @@ -59,6 +61,7 @@ "TREC07", "TrumpApproval", "WaterFlow", + "WebTraffic", ] diff --git a/river/datasets/airline_passengers.py b/river/datasets/airline_passengers.py index 0f54115ac8..86082aa30c 100644 --- a/river/datasets/airline_passengers.py +++ b/river/datasets/airline_passengers.py @@ -14,7 +14,7 @@ class AirlinePassengers(base.FileDataset): References ---------- - [^1]: [International airline passengers: monthly totals in thousands. Jan 49 – Dec 60](https://datamarket.com/data/set/22u3/international-airline-passengers-monthly-totals-in-thousands-jan-49-dec-60#!ds=22u3&display=line) + [^1]: [International airline passengers: monthly totals in thousands. Jan 49 – Dec 60](https://rdrr.io/r/datasets/AirPassengers.html) """ diff --git a/river/datasets/base.py b/river/datasets/base.py index 63e234f0ba..53a5ba2f88 100644 --- a/river/datasets/base.py +++ b/river/datasets/base.py @@ -276,7 +276,7 @@ def download(self, force=False, verbose=True): try: n_bytes = int(meta["Content-Length"]) msg = f"Downloading {self.url} ({utils.pretty.humanize_bytes(n_bytes)})" - except KeyError: + except (KeyError, TypeError): msg = f"Downloading {self.url}" print(msg) diff --git a/river/datasets/synth/__init__.py b/river/datasets/synth/__init__.py index f14e6b6b67..a056f00029 100644 --- a/river/datasets/synth/__init__.py +++ b/river/datasets/synth/__init__.py @@ -5,6 +5,7 @@ the majority of these methods are infinite data generators. """ + from __future__ import annotations from .agrawal import Agrawal diff --git a/river/datasets/synth/sine.py b/river/datasets/synth/sine.py index 7d8e793206..1ad0fcc042 100644 --- a/river/datasets/synth/sine.py +++ b/river/datasets/synth/sine.py @@ -80,6 +80,7 @@ class Sine(datasets.base.SyntheticDataset): Springer Berlin Heidelberg, 2004. 286-295." """ + _N_BASE_FEATURES = 2 _N_FEATURES_INCLUDING_NOISE = 4 diff --git a/river/datasets/web_traffic.py b/river/datasets/web_traffic.py new file mode 100644 index 0000000000..6833ac7fb0 --- /dev/null +++ b/river/datasets/web_traffic.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import csv + +from river import stream + +from . import base + + +class PipeCSVDialect(csv.unix_dialect): + delimiter = "|" + + +class WebTraffic(base.RemoteDataset): + """Web sessions information from an events company based in South Africa. + + The goal is to predict the number of web sessions in 4 different regions in South Africa. + + The data consists of 15 minute interval traffic values between '2023-06-16 00:00:00' and + '2023-09-15 23:45:00' for each region. Two types of sessions are captured `sessionsA` and + `sessionsB`. The `isMissing` flag is equal to 1 if any of the servers failed to capture + sessions, otherwise if all servers functioned properly this flag is equal to 0. + + Things to consider: + + * region `R5` captures sessions in backup mode. Strictly speaking, `R5` is not necessary to predict. + * Can `sessionsA` and `sessionsB` events be predicted accurately for each region over the next day (next 96 intervals)? + * What is the best way to deal with the missing values? + * How can model selection be used (a multi-model approach)? + * Can dependence (correlation) between regions be utilised for more accurate predictions? + * Can both `sessionA` and `sessionB` be predicted simultaneously with one model? + + This dataset is well suited for time series forecasting models, as well as anomaly detection + methods. Ideally, the goal is to build a time series forecasting model that is robust to the + anomalous events and generalise well on normal operating conditions. + + """ + + def __init__(self): + super().__init__( + url="https://maxhalford.github.io/files/datasets/web-traffic.csv.zip", + filename="web-traffic.csv", + task=base.MO_REG, + n_features=3, + n_outputs=2, + n_samples=44_160, + size=2_769_905, + ) + + def _iter(self): + return stream.iter_csv( + self.path, + dialect=PipeCSVDialect, + target=["sessionsA", "sessionsB"], + converters={ + "region": str, + "isMissing": lambda x: x == "1.0", + "sessionsA": lambda x: float(x) if x and x != "0.0" else None, + "sessionsB": lambda x: float(x) if x and x != "0.0" else None, + }, + parse_dates={"dateTime": "%Y-%m-%d %H:%M:%S"}, + ) diff --git a/river/drift/__init__.py b/river/drift/__init__.py index 247dcc7dab..3c6e8aede7 100644 --- a/river/drift/__init__.py +++ b/river/drift/__init__.py @@ -6,21 +6,25 @@ the true positives while keeping the number of false positives to a minimum. """ + from __future__ import annotations -from . import binary +from . import binary, datasets from .adwin import ADWIN from .dummy import DummyDriftDetector from .kswin import KSWIN +from .no_drift import NoDrift from .page_hinkley import PageHinkley from .retrain import DriftRetrainingClassifier __all__ = [ "binary", + "datasets", "ADWIN", "DriftRetrainingClassifier", "DummyDriftDetector", "KSWIN", + "NoDrift", "PageHinkley", "PeriodicTrigger", ] diff --git a/river/drift/adwin.py b/river/drift/adwin.py index 2fd6809bd9..7ca6720e3b 100644 --- a/river/drift/adwin.py +++ b/river/drift/adwin.py @@ -6,7 +6,7 @@ class ADWIN(DriftDetector): - r"""Adaptive Windowing method for concept drift detection. + r"""Adaptive Windowing method for concept drift detection[^1]. ADWIN (ADaptive WINdowing) is a popular drift detection method with mathematical guarantees. ADWIN efficiently keeps a variable-length window of recent items; such that it holds that @@ -22,18 +22,21 @@ class ADWIN(DriftDetector): delta Significance value. clock - How often ADWIN should check for change. 1 means every new data point, default is 32. + How often ADWIN should check for changes. 1 means every new data point, default is 32. Higher values speed up processing, but may also lead to increased delay in change detection. max_buckets - The maximum number of buckets of each size that ADWIN should keep before merging buckets - (default is 5). + The maximum number of buckets of each size that ADWIN should keep before merging buckets. + The idea of data buckets comes from the compression algorithm introduced in the ADWIN2, + the second iteration of the ADWIN algorithm presented in the original research paper. + This is the ADWIN version available in River. min_window_length - The minimum length of each subwindow (default is 5). Lower values may decrease delay in - change detection but may also lead to more false positives. + The minimum length allowed for a subwindow when checking for concept drift. Subwindows whose size + is smaller than this value will be ignored during concept drift evaluation. Lower values may + decrease delay in change detection but may also lead to more false positives. grace_period ADWIN does not perform any change detection until at least this many data points have - arrived (default is 10). + arrived. Examples -------- @@ -48,7 +51,7 @@ class ADWIN(DriftDetector): >>> # Update drift detector and verify if change is detected >>> for i, val in enumerate(data_stream): - ... _ = adwin.update(val) + ... adwin.update(val) ... if adwin.drift_detected: ... print(f"Change detected at index {i}, input value: {val}") Change detected at index 1023, input value: 4 @@ -82,20 +85,23 @@ def _reset(self): ) @property - def width(self): + def width(self) -> int: """Window size""" return self._helper.get_width() @property - def n_detections(self): + def n_detections(self) -> int: + """The total number of detected changes.""" return self._helper.get_n_detections() @property - def variance(self): + def variance(self) -> float: + """The sample variance within the stored (adaptive) window.""" return self._helper.get_variance() @property - def total(self): + def total(self) -> float: + """The sum of the stored elements.""" return self._helper.get_total() @property @@ -127,5 +133,3 @@ def update(self, x): self._reset() self._drift_detected = self._helper.update(x) - - return self diff --git a/river/drift/adwin_c.pyi b/river/drift/adwin_c.pyi new file mode 100644 index 0000000000..75b5ad40f5 --- /dev/null +++ b/river/drift/adwin_c.pyi @@ -0,0 +1,27 @@ +class AdaptiveWindowing: + def __init__( + self, + delta: float = 0.002, + clock: int = 32, + max_buckets: int = 5, + min_window_length: int = 5, + grace_period: int = 10, + ) -> None: ... + def get_n_detections(self) -> int: ... + def get_width(self) -> float: ... + def get_total(self) -> float: ... + def get_variance(self) -> float: ... + @property + def variance_in_window(self) -> float: ... + def update(self, value: float) -> bool: ... + +class Bucket: + def __init__(self, max_size: int) -> None: ... + def clear_at(self, index: int) -> None: ... + def insert_data(self, value: float, variance: float) -> None: ... + def remove(self) -> None: ... + def compress(self, n_elements: int) -> None: ... + def get_total_at(self, index: int) -> float: ... + def get_variance_at(self, index: int) -> float: ... + def set_total_at(self, value: float, index: int) -> None: ... + def set_variance_at(self, value: float, index: int) -> None: ... diff --git a/river/drift/adwin_c.pyx b/river/drift/adwin_c.pyx index 322859c780..889145481b 100644 --- a/river/drift/adwin_c.pyx +++ b/river/drift/adwin_c.pyx @@ -241,7 +241,7 @@ cdef class AdaptiveWindowing: break bucket = self.bucket_deque[idx] - for k in range(bucket.current_idx - 1): + for k in range(bucket.current_idx): n2 = self._calculate_bucket_size(idx) # length of window 2 u2 = bucket.get_total_at(k) # total of window 2 # Warning: means are calculated inside the loop to get updated values. @@ -307,6 +307,7 @@ cdef class AdaptiveWindowing: + (1.0 / (n1 - self.min_window_length + 1))) epsilon = (sqrt(2 * m_recip * self.variance_in_window * delta_prime) + 2 / 3 * delta_prime * m_recip) + return fabs(delta_mean) > epsilon diff --git a/river/drift/binary/__init__.py b/river/drift/binary/__init__.py index 505b282cee..ccfb615948 100644 --- a/river/drift/binary/__init__.py +++ b/river/drift/binary/__init__.py @@ -1,9 +1,11 @@ """Drift detection for binary data.""" + from __future__ import annotations from .ddm import DDM from .eddm import EDDM +from .fhddm import FHDDM from .hddm_a import HDDM_A from .hddm_w import HDDM_W -__all__ = ["DDM", "EDDM", "HDDM_A", "HDDM_W"] +__all__ = ["DDM", "EDDM", "FHDDM", "HDDM_A", "HDDM_W"] diff --git a/river/drift/binary/ddm.py b/river/drift/binary/ddm.py index 24706d6137..1ef45f5643 100644 --- a/river/drift/binary/ddm.py +++ b/river/drift/binary/ddm.py @@ -79,7 +79,7 @@ class DDM(base.BinaryDriftAndWarningDetector): >>> print_warning = True >>> # Update drift detector and verify if change is detected >>> for i, x in enumerate(data_stream): - ... _ = ddm.update(x) + ... ddm.update(x) ... if ddm.warning_detected and print_warning: ... print(f"Warning detected at index {i}") ... print_warning = False @@ -145,5 +145,3 @@ def update(self, x): if p_i + s_i > self._p_min + self.drift_threshold * self._s_min: self._drift_detected = True self._warning_detected = False - - return self diff --git a/river/drift/binary/eddm.py b/river/drift/binary/eddm.py index ccb960b63b..4e65a0f0d3 100644 --- a/river/drift/binary/eddm.py +++ b/river/drift/binary/eddm.py @@ -69,7 +69,7 @@ class EDDM(base.BinaryDriftAndWarningDetector): >>> print_warning = True >>> # Update drift detector and verify if change is detected >>> for i, x in enumerate(data_stream): - ... _ = eddm.update(x) + ... eddm.update(x) ... if eddm.warning_detected and print_warning: ... print(f"Warning detected at index {i}") ... print_warning = False @@ -162,5 +162,3 @@ def update(self, x): # Update the index of the last error/failure detected self._last_error = self._n - - return self diff --git a/river/drift/binary/fhddm.py b/river/drift/binary/fhddm.py new file mode 100644 index 0000000000..20b442b9fd --- /dev/null +++ b/river/drift/binary/fhddm.py @@ -0,0 +1,130 @@ +from __future__ import annotations + +import collections +import itertools +import math + +from river import base + + +class FHDDM(base.BinaryDriftAndWarningDetector): + """Fast Hoeffding Drift Detection Method. + + FHDDM is a drift detection method based on the Hoeffding's inequality which uses + the input average as estimator. + + **Input:** `x` is an entry in a stream of bits, where 1 indicates error/failure and 0 + represents correct/normal values. + + For example, if a classifier's prediction $y'$ is right or wrong w.r.t. the + true target label $y$: + + - 0: Correct, $y=y'$ + + - 1: Error, $y \\neq y'$ + + *Implementation based on MOA.* + + Parameters + ---------- + sliding_window_size + The minimum required number of analyzed samples so change can be detected. + confidence_level + Confidence level used to determine the epsilon coefficient in Hoeffding’s inequality. The default value gives a 99\\% of confidence + level to the drift assessment. + short_window_size + The size of the short window size that it is used in a Stacking version of FHDDM [^2]. + + Examples + -------- + >>> import random + >>> from river import drift + + >>> rng = random.Random(42) + >>> # Traditional FHDDM [1] + >>> fhddm = drift.binary.FHDDM() + >>> # Stacking FHDDM [2] + >>> fhddm_s = drift.binary.FHDDM(short_window_size = 20) + >>> # Simulate a data stream where the first 250 instances come from a uniform distribution + >>> # of 1's and 0's + >>> data_stream = rng.choices([0, 1], k=250) + >>> # Increase the probability of 1's appearing in the next 250 instances + >>> data_stream = data_stream + rng.choices([0, 1], k=250, weights=[0.9, 0.1]) + >>> # Update drift detector and verify if change is detected + >>> for i, x in enumerate(data_stream): + ... fhddm.update(x) + ... fhddm_s.update(x) + ... if fhddm.drift_detected or fhddm_s.drift_detected: + ... print(f"Change detected at index {i}") + Change detected at index 279 + Change detected at index 315 + + References + ---------- + [^1]: A. Pesaranghader, H.L. Viktor, Fast Hoeffding Drift Detection Method for Evolving Data Streams. In the Proceedings of ECML-PKDD 2016. + [^2]: Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams. + + """ + + def __init__( + self, + sliding_window_size: int = 100, + confidence_level: float = 0.000001, + short_window_size: int | None = None, + ): + super().__init__() + self.sliding_window_size = sliding_window_size + self.confidence_level = confidence_level + self.short_window_size = short_window_size + self.n_one = 0 + self._reset() + + def _reset(self): + self._sliding_window = collections.deque(maxlen=self.sliding_window_size) + self._epsilon = math.sqrt( + (math.log(1 / self.confidence_level)) / (2 * self.sliding_window_size) + ) + self._u_max = 0 + self.n_one = 0 + if self.short_window_size is not None: + self._short_window = collections.deque(maxlen=self.short_window_size) + self._u_short_max = 0 + self._epsilon_s = math.sqrt( + (math.log(1 / self.confidence_level)) / (2 * self.short_window_size) + ) + + def update(self, x): + if self.drift_detected: + self._drift_detected = False + self._reset() + + self._sliding_window.append(x) + self.n_one += x + + if len(self._sliding_window) == self.sliding_window_size: + u = self.n_one / self.sliding_window_size + self._u_max = u if (self._u_max < u) else self._u_max + + short_win_drift_status = False + long_win_drift_status = False + + if self.short_window_size is not None: + u_s = ( + sum( + itertools.islice( + self._sliding_window, + self.sliding_window_size - self.short_window_size, + self.sliding_window_size, + ) + ) + / self.short_window_size + ) + self._u_short_max = u_s if self._u_short_max < u_s else self._u_short_max + short_win_drift_status = ( + True if self._u_short_max - u_s > self._epsilon_s else False + ) + + long_win_drift_status = True if (self._u_max - u > self._epsilon) else False + + self._drift_detected = long_win_drift_status or short_win_drift_status + self.n_one -= self._sliding_window.popleft() diff --git a/river/drift/binary/hddm_a.py b/river/drift/binary/hddm_a.py index c7e9e07f5b..7b8239970e 100644 --- a/river/drift/binary/hddm_a.py +++ b/river/drift/binary/hddm_a.py @@ -50,7 +50,7 @@ class HDDM_A(base.BinaryDriftAndWarningDetector): >>> print_warning = True >>> # Update drift detector and verify if change is detected >>> for i, x in enumerate(data_stream): - ... _ = hddm_a.update(x) + ... hddm_a.update(x) ... if hddm_a.warning_detected and print_warning: ... print(f"Warning detected at index {i}") ... print_warning = False @@ -80,9 +80,9 @@ def _reset(self): super()._reset() # To check if the global mean increased - self._x_mn = stats.Mean() + self._x_min = stats.Mean() # To check if the global mean decreased - self._x_mx = stats.Mean() + self._x_max = stats.Mean() # Global mean self._z = stats.Mean() @@ -108,29 +108,32 @@ def update(self, x): self._reset() self._z.update(x) - if self._x_mn.n == 0: - self._x_mn += self._z - if self._x_mx.n == 0: - self._x_mx += self._z + if self._x_min.n == 0: + self._x_min = self._z.clone(include_attributes=True) + if self._x_max.n == 0: + self._x_max = self._z.clone(include_attributes=True) # Bound the data eps_z = self._hoeffding_bound(self._z.n) - eps_x = self._hoeffding_bound(self._x_mn.n) + eps_x = self._hoeffding_bound(self._x_min.n) # Update the cut point for tracking mean increase - if self._x_mn.get() + eps_x >= self._z.get() + eps_z: - self._x_mn = stats.Mean._from_state(n=self._z.n, mean=self._z.get()) + if self._x_min.get() + eps_x >= self._z.get() + eps_z: + self._x_min = self._z.clone(include_attributes=True) - eps_x = self._hoeffding_bound(self._x_mx.n) + eps_x = self._hoeffding_bound(self._x_max.n) # Update the cut point for tracking mean decrease - if self._x_mx.get() - eps_x <= self._z.get() - eps_z: - self._x_mx = stats.Mean._from_state(n=self._z.n, mean=self._z.get()) + if self._x_max.get() - eps_x <= self._z.get() - eps_z: + self._x_max = self._z.clone(include_attributes=True) if self._mean_incr(self.drift_confidence): + self._warning_detected = False self._drift_detected = True elif self._mean_incr(self.warning_confidence): self._warning_detected = True + self._drift_detected = False else: self._warning_detected = False + self._drift_detected = False if self.two_sided_test: if self._mean_decr(self.drift_confidence): @@ -138,22 +141,20 @@ def update(self, x): elif self._mean_decr(self.warning_confidence): self._warning_detected = True - return self - # Check if the global mean increased def _mean_incr(self, confidence: float): - if self._x_mn.n == self._z.n: + if self._x_min.n == self._z.n: return False - m = (self._z.n - self._x_mn.n) / self._x_mn.n * (1.0 / self._z.n) + m = (self._z.n - self._x_min.n) / self._x_min.n * (1.0 / self._z.n) eps = math.sqrt(m / 2 * math.log(2.0 / confidence)) - return self._z.get() - self._x_mn.get() >= eps + return self._z.get() - self._x_min.get() >= eps # Check if the global mean decreased def _mean_decr(self, confidence: float): - if self._x_mx.n == self._z.n: + if self._x_max.n == self._z.n: return False - m = (self._z.n - self._x_mx.n) / self._x_mx.n * (1.0 / self._z.n) + m = (self._z.n - self._x_max.n) / self._x_max.n * (1.0 / self._z.n) eps = math.sqrt(m / 2 * math.log(2.0 / confidence)) - return self._x_mx.get() - self._z.get() >= eps + return self._x_max.get() - self._z.get() >= eps diff --git a/river/drift/binary/hddm_w.py b/river/drift/binary/hddm_w.py index 1eb8f6f60b..3d29742046 100644 --- a/river/drift/binary/hddm_w.py +++ b/river/drift/binary/hddm_w.py @@ -53,7 +53,7 @@ class HDDM_W(base.BinaryDriftAndWarningDetector): >>> print_warning = True >>> # Update drift detector and verify if change is detected >>> for i, x in enumerate(data_stream): - ... _ = hddm_w.update(x) + ... hddm_w.update(x) ... if hddm_w.warning_detected and print_warning: ... print(f"Warning detected at index {i}") ... print_warning = False @@ -123,11 +123,14 @@ def update(self, x): self._update_incr_stats(x, self.drift_confidence) if self._detect_mean_incr(self.drift_confidence): + self._warning_detected = False self._drift_detected = True elif self._detect_mean_incr(self.warning_confidence): self._warning_detected = True + self._drift_detected = False else: self._warning_detected = False + self._drift_detected = False self._update_decr_stats(x, self.drift_confidence) if self.two_sided_test: @@ -136,8 +139,6 @@ def update(self, x): elif self._detect_mean_decr(self.warning_confidence): self._warning_detected = True - return self - def _has_mean_changed( self, sample1: SampleInfo, sample2: SampleInfo, confidence: float ) -> bool: diff --git a/river/drift/datasets/__init__.py b/river/drift/datasets/__init__.py new file mode 100644 index 0000000000..b5766918f0 --- /dev/null +++ b/river/drift/datasets/__init__.py @@ -0,0 +1,19 @@ +from __future__ import annotations + +from .airline_passengers import AirlinePassengers +from .apple import Apple +from .bitcoin import Bitcoin +from .brent_crude_oil import BrentSpotPrice +from .occupancy import Occupancy +from .run_log import RunLog +from .uk_coal_employment import UKCoalEmploy + +__all__ = [ + "Bitcoin", + "BrentSpotPrice", + "UKCoalEmploy", + "AirlinePassengers", + "RunLog", + "Occupancy", + "Apple", +] diff --git a/river/drift/datasets/airline_passengers.csv b/river/drift/datasets/airline_passengers.csv new file mode 100644 index 0000000000..1270ba08db --- /dev/null +++ b/river/drift/datasets/airline_passengers.csv @@ -0,0 +1,469 @@ +date,Total Passengers +1977-Jan,1487588 +1977-Feb,1237719 +1977-Mar,1450395 +1977-Apr,1609159 +1977-May,1724392 +1977-Jun,1906396 +1977-Jul,2373334 +1977-Aug,2372987 +1977-Sep,2010979 +1977-Oct,1806198 +1977-Nov,1454126 +1977-Dec,1647415 +1978-Jan,1610669 +1978-Feb,1354608 +1978-Mar,1754176 +1978-Apr,1733544 +1978-May,1942570 +1978-Jun,2198429 +1978-Jul,2718260 +1978-Aug,2710884 +1978-Sep,2241586 +1978-Oct,1929316 +1978-Nov,1614560 +1978-Dec,1781091 +1979-Jan,1774076 +1979-Feb,1561256 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+2015-Nov,4342058 +2015-Dec,4697493 diff --git a/river/drift/datasets/airline_passengers.py b/river/drift/datasets/airline_passengers.py new file mode 100644 index 0000000000..bdc7e174f1 --- /dev/null +++ b/river/drift/datasets/airline_passengers.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from river import datasets, stream + +from .base import ChangePointFileDataset + + +class AirlinePassengers(ChangePointFileDataset): + """JFK Airline Passengers + + This dataset gives the number of passengers arriving and departing at JFK. + The data is obtained from New York State's official Kaggle page for this dataset. + + References + ---------- + [^1]: https://www.kaggle.com/new-york-state/nys-air-passenger-traffic,-port-authority-of-ny-nj#air-passenger-traffic-per-month-port-authority-of-ny-nj-beginning-1977.csv + + """ + + def __init__(self): + super().__init__( + annotations={"6": [299], "7": [], "8": [302], "9": [326, 382], "10": [296]}, + filename="airline_passengers.csv", + task=datasets.base.REG, + n_samples=468, + n_features=1, + ) + + def __iter__(self): + return stream.iter_csv( + self.path, + target="Total Passengers", + converters={ + "Total Passengers": int, + }, + parse_dates={"date": "%Y-%b"}, + ) diff --git a/river/drift/datasets/apple.csv b/river/drift/datasets/apple.csv new file mode 100644 index 0000000000..748e803eaa --- /dev/null +++ b/river/drift/datasets/apple.csv @@ -0,0 +1,1868 @@ +Date,Open,High,Low,Close,Adj Close,Volume +1996-12-12,0.861608,0.866072,0.852680,0.852680,0.181182,21750400 +1996-12-13,0.848216,0.852680,0.830356,0.830356,0.176439,22274000 +1996-12-16,0.839284,0.839284,0.803572,0.808036,0.171696,37310000 +1996-12-17,0.799108,0.803572,0.794644,0.803572,0.170748,39312000 +1996-12-18,0.812500,0.825892,0.808036,0.825892,0.175490,51268000 +1996-12-19,0.821428,0.830356,0.794644,0.794644,0.168851,34221600 +1996-12-20,0.803572,0.843752,0.763392,0.839284,0.178336,136609200 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Apple(ChangePointFileDataset): + """Apple Stock + + This dataset concerns the daily close price and volume of Apple stock around the year 2000. The dataset is sampled every 3 observations to reduce the length of the time series. + This dataset is retrieved from Yahoo Finance. + + References + ---------- + [^1]: https://finance.yahoo.com/quote/AAPL/history?period1=850348800&period2=1084579200&interval=1d&filter=history&frequency=1d + + """ + + def __init__(self): + super().__init__( + annotations={ + "6": [319], + "7": [319], + "8": [319], + "9": [53, 90, 197, 276, 319, 403, 463, 535], + "10": [319], + }, + filename="apple.csv", + task=datasets.base.REG, + n_samples=1867, + n_features=6, + ) + + def __iter__(self): + return stream.iter_csv( + self.path, + target=["Open", "High", "Low", "Close", "Adj Close", "Volume"], + converters={ + "Open": float, + "High": float, + "Low": float, + "Close": float, + "Adj Close": float, + "Volume": float, + }, + parse_dates={"Date": "%Y-%m-%d"}, + ) diff --git a/river/drift/datasets/base.py b/river/drift/datasets/base.py new file mode 100644 index 0000000000..6d30c5b2f1 --- /dev/null +++ b/river/drift/datasets/base.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import abc +import pathlib + +from river.datasets import base + + +class ChangePointFileDataset(base.FileDataset, abc.ABC): + """Base class for change point datasets that are stored in a local file. + + Datasets that are part of the Alan Turing Institute Change Point Detection project. + + Parameters + ---------- + annotations + The file's associated annotations. + desc + Extra dataset parameters to pass as keyword arguments. + + """ + + def __init__(self, annotations, **desc): + super().__init__(**desc, directory=pathlib.Path(__file__).parent) + self.annotations = annotations + + @property + def _repr_content(self): + repr_content = super()._repr_content + repr_content["Annotators"] = f"{len(self.annotations):,d}" + repr_content["Annotations"] = f"{sum(map(len, self.annotations.values())):,d}" + return repr_content + + def _annotations_aggregated(self, annotator_aggregation): + """The function `annotations_aggregated` takes an annotator aggregation method as input and returns + the aggregated annotations based on that method. + + Parameters + ---------- + annotator_aggregation + The parameter "annotator_aggregation" is a string that specifies the method of aggregating + annotations from different annotators. It can take one of the following values: + + Returns + ------- + the aggregated annotations based on the specified annotator aggregation method. + + """ + if annotator_aggregation == "union": + annotations = set() + for annotator in self._annotations: + annotations.update(self._annotations[annotator]) + elif annotator_aggregation == "intersection": + annotations = set(self._annotations[0]) + for annotator in self._annotations: + annotations.intersection_update(self._annotations[annotator]) + elif annotator_aggregation == "majority": + annotations = {} + for annotator in self._annotations: + for change_point in self._annotations[annotator]: + if change_point in annotations: + annotations[change_point] += 1 + else: + annotations[change_point] = 1 + annotations = { + change_point + for change_point, count in annotations.items() + if count > len(self._annotations) / 2 + } + else: + raise ValueError("Unknown annotator aggregation method.") + return annotations diff --git a/river/drift/datasets/bitcoin.csv b/river/drift/datasets/bitcoin.csv new file mode 100644 index 0000000000..5599c2c34e --- /dev/null +++ b/river/drift/datasets/bitcoin.csv @@ -0,0 +1,823 @@ +date,price +2013-02-11,24.1955 +2013-02-14,26.53092 +2013-02-17,25.6083 +2013-02-20,29.81231 +2013-02-23,29.27998 +2013-02-26,31.179 +2013-03-01,34.87799 +2013-03-04,36.35 +2013-03-07,42.49271 +2013-03-10,46.5 +2013-03-13,46.79999 +2013-03-16,47.11 +2013-03-19,57.76 +2013-03-22,71.5 +2013-03-25,73.88798 +2013-03-28,93.56701 +2013-03-31,92.50001 +2013-04-03,138.79 +2013-04-06,142.49765 +2013-04-09,237.99 +2013-04-12,76.488 +2013-04-15,91.0 +2013-04-18,96.5 +2013-04-21,125.0 +2013-04-24,153.20019 +2013-04-27,128.00001 +2013-04-30,139.109 +2013-05-03,93.15 +2013-05-06,120.94999 +2013-05-09,109.56154 +2013-05-12,114.32 +2013-05-15,114.98499 +2013-05-18,123.74995 +2013-05-21,122.5 +2013-05-24,131.5 +2013-05-27,132.60997 +2013-05-30,130.99589 +2013-06-02,120.60293 +2013-06-05,122.4051 +2013-06-08,107.8 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the goal is to predict the average USD market price across + major bitcoin exchanges. This data was collected from the official Blockchain website. There + is only one feature given, the day of exchange, which is in increments of three. The first + 500 lines have been removed because they are not interesting. + + References + ---------- + [^1]: https://www.blockchain.com/fr/explorer/charts/market-price?timespan=all + + """ + + def __init__(self): + super().__init__( + annotations={ + "6": [502, 580, 702, 747], + "8": [583], + "12": [597], + "13": [522, 579, 591, 629, 703, 747, 760], + "14": [93, 522, 540, 701, 747, 760, 772], + }, + filename="bitcoin.csv", + task=datasets.base.REG, + n_samples=822, + n_features=1, + ) + + def __iter__(self): + return stream.iter_csv( + self.path, + target="price", + converters={ + "price": float, + }, + parse_dates={"date": "%Y-%m-%d"}, + ) diff --git a/river/drift/datasets/brent_crude_oil.csv b/river/drift/datasets/brent_crude_oil.csv new file mode 100644 index 0000000000..ecc6b5301c --- /dev/null +++ b/river/drift/datasets/brent_crude_oil.csv @@ -0,0 +1,8196 @@ +day,DPB +05/20/1987,18.63 +05/21/1987,18.45 +05/22/1987,18.55 +05/25/1987,18.6 +05/26/1987,18.63 +05/27/1987,18.6 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datasets, stream + +from .base import ChangePointFileDataset + + +class BrentSpotPrice(ChangePointFileDataset): + """Brent Spot Price + + This is the USD price for Brent Crude oil, measured daily. We include the time series + from 2000 onwards. The data is sampled at every 10 original observations to reduce the length of the series. + + The data is obtained from the U.S. Energy Information Administration. Since the data is in the public domain, + we distribute it as part of this repository. + + Since the original data has observations only on trading days, there are arguably gaps in this time + series (on non-trading days). However we consider these to be consecutive, and thus also consider + the sampled time series to have consecutive observations. + + References + ---------- + [^1]: U.S. Energy Information Administration (Sep. 2019) + [^2]: https://www.eia.gov/opendata/v1/qb.php?sdid=PET.RBRTE.D + + """ + + def __init__(self): + super().__init__( + annotations={ + "6": [219, 230, 288], + "8": [227, 381], + "9": [86, 219, 230, 279, 375], + "12": [169, 172, 217, 228, 287, 368, 382, 389, 409], + "13": [170, 180, 219, 229, 246, 271, 286, 379, 409, 444, 483], + }, + filename="brent_crude_oil.csv", + task=datasets.base.REG, + n_samples=8_195, + n_features=1, + ) + + def __iter__(self): + return stream.iter_csv( + self.path, + target="DPB", + converters={ + "DPB": float, + }, + parse_dates={"day": "%m/%d/%Y"}, + ) diff --git a/river/drift/datasets/occupancy.csv b/river/drift/datasets/occupancy.csv new file mode 100644 index 0000000000..10330cbc22 --- /dev/null +++ b/river/drift/datasets/occupancy.csv @@ -0,0 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08:47:00,20.4266666666667,33.56,405.0,494.333333333333 +2015-02-10 09:03:00,20.65,34.145,419.0,532.0 +2015-02-10 09:19:00,20.9175,35.7175,433.0,706.25 diff --git a/river/drift/datasets/occupancy.py b/river/drift/datasets/occupancy.py new file mode 100644 index 0000000000..395ade5971 --- /dev/null +++ b/river/drift/datasets/occupancy.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from river import datasets, stream + +from .base import ChangePointFileDataset + + +class Occupancy(ChangePointFileDataset): + """Room occupancy data. + + Dataset on detecting room occupancy based on several variables. The dataset contains + temperature, humidity, light, and CO2 variables. + + The data is sampled at every 16 observations to reduce the length of the series. + + References + ---------- + Candanedo, Luis M., and Véronique Feldheim. "Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models." Energy and Buildings 112 (2016): 28-39. + + """ + + def __init__(self): + super().__init__( + annotations={ + "6": [238, 416], + "8": [53, 143, 238, 417], + "9": [53, 92, 142, 181, 236, 264, 341, 416, 436, 451, 506], + "10": [1, 52, 91, 142, 181, 234, 267, 324, 360, 416, 451, 506], + "12": [234, 415], + }, + filename="occupancy.csv", + task=datasets.base.REG, + n_samples=509, + n_features=4, + ) + + def __iter__(self): + return stream.iter_csv( + self.path, + target=["V1", "V2", "V3", "V4"], + converters={ + "V1": float, + "V2": float, + "V3": float, + "V4": float, + }, + parse_dates={"time": "%Y-%m-%d %H:%M:%S"}, + ) diff --git a/river/drift/datasets/run_log.csv b/river/drift/datasets/run_log.csv new file mode 100644 index 0000000000..348773b40e --- /dev/null +++ b/river/drift/datasets/run_log.csv @@ -0,0 +1,377 @@ +time,Pace,Distance +2018-07-31 18:22:28,30.88072,0.0 +2018-07-31 18:22:33,24.263573,1.359811 +2018-07-31 18:22:38,18.463263,9.4317665 +2018-07-31 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18:53:49,17.297058,4324.108 +2018-07-31 18:53:55,17.3851,4333.266 diff --git a/river/drift/datasets/run_log.py b/river/drift/datasets/run_log.py new file mode 100644 index 0000000000..ab2d170f6a --- /dev/null +++ b/river/drift/datasets/run_log.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from river import datasets, stream + +from .base import ChangePointFileDataset + + +class RunLog(ChangePointFileDataset): + """Interval Training Running Pace. + + This dataset shows the pace of a runner during an interval training session, where a mobile + application provides instructions on when to run and when to walk. + + """ + + def __init__(self): + super().__init__( + annotations={ + "6": [60, 96, 114, 174, 204, 240, 258, 317], + "7": [60, 96, 114, 177, 204, 240, 258, 317], + "8": [60, 96, 114, 174, 204, 240, 258, 317], + "10": [2, 60, 96, 114, 174, 204, 240, 258, 317], + "12": [], + }, + filename="run_log.csv", + task=datasets.base.REG, + n_samples=376, + n_features=2, + ) + + def __iter__(self): + return stream.iter_csv( + self.path, + target=["Pace", "Distance"], + converters={"Pace": float, "Distance": float}, + parse_dates={"time": "%Y-%m-%d %H:%M:%S"}, + ) diff --git a/river/drift/datasets/uk_coal_employment.csv b/river/drift/datasets/uk_coal_employment.csv new file mode 100644 index 0000000000..5cb91e3994 --- /dev/null +++ b/river/drift/datasets/uk_coal_employment.csv @@ -0,0 +1,106 @@ +Year,Employment +1913,1107000 +1914,1038000 +1915,935000 +1916,981000 +1917,1002000 +1918,990000 +1919,1136000 +1920,1191000 +1921,1191000 +1922,1085000 +1923,1151000 +1924,1163000 +1925,1078000 +1926,1078000 +1927,991000 +1928,915000 +1929,925000 +1930,910000 +1931,843000 +1932,796000 +1933,767000 +1934,768000 +1935,753000 +1936,750000 +1937,773000 +1938,776000 +1939,761000 +1940,744000 +1941,692000 +1942,704000 +1943,701000 +1944,704000 +1945,702000 +1946,693000 +1947,707000 +1948,720000 +1949,716000 +1950,693000 +1951,695000 +1952,712000 +1953,713000 +1954,707000 +1955,704000 +1956,703000 +1957,710000 +1958,699000 +1959,665000 +1960,607000 +1961,575000 +1962,556000 +1963,528000 +1964,502000 +1965,454700 +1966,422000 +1967,389500 +1968,330900 +1969,305700 +1970,290000 +1971,286100 +1972,273600 +1973,251800 +1974,252800 +1975,252000 +1976,249700 +1977,247900 +1978,240400 +1979,241600 +1980,236900 +1981,172000 +1982,164000 +1983,148000 +1984,139000 +1985,114000 +1986,91000 +1987,75000 +1988,69000 +1989,56000 +1990,49000 +1991,38000 +1992,28000 +1993,10000 +1994,7000 +1995,11657 +1996,10315 +1997,13768 +1998,11113 +1999,11973 +2000,10939 +2001,11439 +2002,9578 +2003,8250 +2004,7772 +2005,6054 +2006,5431 +2007,5538 +2008,6157 +2009,5912 +2010,6014 +2011,5972 +2012,5827 +2013,3715 +2014,3601 +2015,1975 +2016,831 +2017,620 diff --git a/river/drift/datasets/uk_coal_employment.py b/river/drift/datasets/uk_coal_employment.py new file mode 100644 index 0000000000..63cd060263 --- /dev/null +++ b/river/drift/datasets/uk_coal_employment.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +from river import datasets, stream + +from .base import ChangePointFileDataset + + +class UKCoalEmploy(ChangePointFileDataset): + """Historic Employment in UK Coal Mines + + This is historic data obtained from the UK government. + We use the employment column for the number of workers employed in the British coal mines + Missing values in the data are replaced with the value of the preceding year. + + References + ---------- + [^1]: https://www.gov.uk/government/statistical-data-sets/historical-coal-data-coal-production-availability-and-consumption + """ + + def __init__(self): + super().__init__( + annotations={ + "6": [15, 28, 45, 60, 68, 80], + "7": [18, 47, 81], + "8": [], + "9": [15, 27, 46, 68, 81], + "13": [19, 28, 45, 68, 80], + }, + filename="uk_coal_employment.csv", + task=datasets.base.REG, + n_samples=105, + n_features=1, + ) + + def __iter__(self): + return stream.iter_csv( + self.path, + target="Employment", + converters={ + "Employment": int, + }, + parse_dates={"Year": "%Y"}, + ) diff --git a/river/drift/dummy.py b/river/drift/dummy.py index 9919421b7d..a9cb428852 100644 --- a/river/drift/dummy.py +++ b/river/drift/dummy.py @@ -51,7 +51,7 @@ class DummyDriftDetector(base.DriftDetector): >>> ptrigger = DummyDriftDetector(t_0=500, seed=42) >>> for i, v in enumerate(data): - ... _ = ptrigger.update(v) + ... ptrigger.update(v) ... if ptrigger.drift_detected: ... print(f"Drift detected at instance {i}.") Drift detected at instance 499. @@ -67,7 +67,7 @@ class DummyDriftDetector(base.DriftDetector): ... seed=42 ... ) >>> for i, v in enumerate(data): - ... _ = rtrigger.update(v) + ... rtrigger.update(v) ... if rtrigger.drift_detected: ... print(f"Drift detected at instance {i}.") Drift detected at instance 368. @@ -86,7 +86,7 @@ class DummyDriftDetector(base.DriftDetector): >>> rtrigger = rtrigger.clone() >>> for i, v in enumerate(data): - ... _ = rtrigger.update(v) + ... rtrigger.update(v) ... if rtrigger.drift_detected: ... print(f"Drift detected at instance {i}.") Drift detected at instance 429. @@ -175,8 +175,6 @@ def update(self, x): self._drift_detected = False self._trigger() - return self - def clone(self): new = ( super().clone( diff --git a/river/drift/kswin.py b/river/drift/kswin.py index 6a959f9f4d..f2c799b67a 100644 --- a/river/drift/kswin.py +++ b/river/drift/kswin.py @@ -65,7 +65,7 @@ class KSWIN(DriftDetector): >>> # Update drift detector and verify if change is detected >>> for i, val in enumerate(data_stream): - ... _ = kswin.update(val) + ... kswin.update(val) ... if kswin.drift_detected: ... print(f"Change detected at index {i}, input value: {val}") Change detected at index 1016, input value: 6 @@ -109,7 +109,7 @@ def _reset(self): super()._reset() self.p_value = 0 self.n = 0 - self.window: collections.deque = collections.deque(maxlen=self.window_size) + self.window = collections.deque(maxlen=self.window_size) self._rng = random.Random(self.seed) def update(self, x): @@ -157,8 +157,6 @@ def update(self, x): else: # Not enough samples in the sliding window for a valid test self._drift_detected = False - return self - @classmethod def _unit_test_params(cls): yield {"seed": 1} diff --git a/river/drift/no_drift.py b/river/drift/no_drift.py new file mode 100644 index 0000000000..4d75e40210 --- /dev/null +++ b/river/drift/no_drift.py @@ -0,0 +1,74 @@ +from __future__ import annotations + +from river import base + + +class NoDrift(base.DriftDetector): + """Dummy class used to turn off concept drift detection capabilities of adaptive models. + It always signals that no concept drift was detected. + Examples + -------- + + >>> from river import drift + >>> from river import evaluate + >>> from river import forest + >>> from river import metrics + >>> from river.datasets import synth + + >>> dataset = synth.ConceptDriftStream( + ... seed=8, + ... position=500, + ... width=40, + ... ).take(700) + + We can turn off the warning detection capabilities of Adaptive Random Forest (ARF) or + other similar models. Thus, the base models will reset immediately after identifying a drift, + bypassing the background model building phase: + + >>> adaptive_model = forest.ARFClassifier( + ... leaf_prediction="mc", + ... warning_detector=drift.NoDrift(), + ... seed=8 + ... ) + + We can also turn off the concept drift handling capabilities completely: + + >>> stationary_model = forest.ARFClassifier( + ... leaf_prediction="mc", + ... warning_detector=drift.NoDrift(), + ... drift_detector=drift.NoDrift(), + ... seed=8 + ... ) + + Let's put that to test: + + >>> for x, y in dataset: + ... adaptive_model.learn_one(x, y) + ... stationary_model.learn_one(x, y) + + The adaptive model: + + >>> adaptive_model.n_drifts_detected() + 2 + + >>> adaptive_model.n_warnings_detected() + 0 + + The stationary one: + + >>> stationary_model.n_drifts_detected() + 0 + + >>> stationary_model.n_warnings_detected() + 0 + + """ + + def __init__(self): + super().__init__() + + def update(self, x: int | float): ... + + @property + def drift_detected(self): + return False diff --git a/river/drift/page_hinkley.py b/river/drift/page_hinkley.py index d332822eda..7202710020 100644 --- a/river/drift/page_hinkley.py +++ b/river/drift/page_hinkley.py @@ -40,7 +40,7 @@ class PageHinkley(DriftDetector): >>> # Update drift detector and verify if change is detected >>> for i, val in enumerate(data_stream): - ... _ = ph.update(val) + ... ph.update(val) ... if ph.drift_detected: ... print(f"Change detected at index {i}, input value: {val}") Change detected at index 1006, input value: 5 @@ -125,5 +125,3 @@ def update(self, x): test_increase = self._sum_increase - self._min_increase test_decrease = self._max_decrease - self._sum_decrease self._drift_detected = self._test_drift(test_increase, test_decrease) - - return self diff --git a/river/drift/retrain.py b/river/drift/retrain.py index 1aab223de8..3602203e37 100644 --- a/river/drift/retrain.py +++ b/river/drift/retrain.py @@ -39,7 +39,7 @@ class DriftRetrainingClassifier(base.Wrapper, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 86.40% + Accuracy: 86.46% """ @@ -65,11 +65,10 @@ def predict_proba_one(self, x, **kwargs): return self.model.predict_proba_one(x, **kwargs) def learn_one(self, x, y, **kwargs): - self._update_ddm(x, y) + self._update_detector(x, y) self.model.learn_one(x, y, **kwargs) - return self - def _update_ddm(self, x, y): + def _update_detector(self, x, y): y_pred = self.model.predict_one(x) if y_pred is None: return diff --git a/river/drift/test_drift_detectors.py b/river/drift/test_drift_detectors.py index 665a3f4eb2..cff6f37c2f 100644 --- a/river/drift/test_drift_detectors.py +++ b/river/drift/test_drift_detectors.py @@ -28,7 +28,7 @@ def test_adwin(): - expected_indices = [1055] + expected_indices = [1023] detected_indices = perform_test(drift.ADWIN(), data_stream_1) assert detected_indices == expected_indices diff --git a/river/dummy.py b/river/dummy.py index 967bcf8643..c42e3fb755 100644 --- a/river/dummy.py +++ b/river/dummy.py @@ -3,6 +3,7 @@ This module is here for testing purposes, as well as providing baseline performances. """ + from __future__ import annotations import collections @@ -44,7 +45,7 @@ class NoChangeClassifier(base.Classifier): >>> model = dummy.NoChangeClassifier() >>> for sentence, label in sentences: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> new_sentence = 'glad sad miserable pleasant glad' >>> model.predict_one(new_sentence) @@ -66,7 +67,6 @@ def _multiclass(self): def learn_one(self, x, y): self.last_class = y self.classes.add(y) - return self def predict_one(self, x): return self.last_class @@ -108,7 +108,7 @@ class PriorClassifier(base.Classifier): >>> model = dummy.PriorClassifier() >>> for sentence, label in sentences: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> new_sentence = 'glad sad miserable pleasant glad' >>> model.predict_one(new_sentence) @@ -133,7 +133,6 @@ def _multiclass(self): def learn_one(self, x, y): self.counts.update([y]) self.n += 1 - return self def predict_proba_one(self, x): return {label: count / self.n for label, count in self.counts.items()} @@ -163,7 +162,7 @@ class StatisticRegressor(base.Regressor): >>> model = dummy.StatisticRegressor(stats.Mean()) >>> for sentence, score in sentences: - ... model = model.learn_one(sentence, score) + ... model.learn_one(sentence, score) >>> new_sentence = 'glad sad miserable pleasant glad' >>> model.predict_one(new_sentence) @@ -180,7 +179,6 @@ def _unit_test_params(cls): def learn_one(self, x, y): self.statistic.update(y) - return self def predict_one(self, x): return self.statistic.get() diff --git a/river/ensemble/__init__.py b/river/ensemble/__init__.py index 26f524c7aa..1456fc84a9 100644 --- a/river/ensemble/__init__.py +++ b/river/ensemble/__init__.py @@ -6,6 +6,7 @@ aggregate predictions from different kinds of models. """ + from __future__ import annotations from .bagging import ( diff --git a/river/ensemble/bagging.py b/river/ensemble/bagging.py index 160171ad9c..19d35d4d82 100644 --- a/river/ensemble/bagging.py +++ b/river/ensemble/bagging.py @@ -19,8 +19,6 @@ def learn_one(self, x, y, **kwargs): for _ in range(utils.random.poisson(1, self._rng)): model.learn_one(x, y, **kwargs) - return self - class BaggingClassifier(BaseBagging, base.Classifier): """Online bootstrap aggregation for classification. @@ -68,7 +66,7 @@ class BaggingClassifier(BaseBagging, base.Classifier): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.83% + F1: 87.65% >>> print(model) BaggingClassifier(StandardScaler | LogisticRegression) @@ -143,7 +141,7 @@ class BaggingRegressor(BaseBagging, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.68886 + MAE: 0.677586 References ---------- @@ -204,7 +202,7 @@ class ADWINBaggingClassifier(BaggingClassifier): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.83% + F1: 87.65% References ---------- @@ -244,8 +242,6 @@ def learn_one(self, x, y, **kwargs): self.models[max_error_idx] = self.model.clone() self._drift_detectors[max_error_idx] = drift.ADWIN() - return self - class LeveragingBaggingClassifier(BaggingClassifier): """Leveraging Bagging ensemble classifier. @@ -310,7 +306,7 @@ class LeveragingBaggingClassifier(BaggingClassifier): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 88.73% + F1: 88.55% """ @@ -407,8 +403,6 @@ def learn_one(self, x, y, **kwargs): self[max_error_idx] = self.model.clone() self._drift_detectors[max_error_idx] = drift.ADWIN(delta=self.adwin_delta) - return self - @property def bagging_methods(self): """Valid bagging_method options.""" diff --git a/river/ensemble/boosting.py b/river/ensemble/boosting.py index 4712bd8284..23adcc8c44 100644 --- a/river/ensemble/boosting.py +++ b/river/ensemble/boosting.py @@ -91,7 +91,6 @@ def learn_one(self, x, y, **kwargs): lambda_poisson *= (self.correct_weight[i] + self.wrong_weight[i]) / ( 2 * self.wrong_weight[i] ) - return self def predict_proba_one(self, x, **kwargs): y_proba = collections.Counter() @@ -151,7 +150,7 @@ class ADWINBoostingClassifier(AdaBoostClassifier): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.41% + F1: 87.61% References ---------- @@ -204,7 +203,6 @@ def learn_one(self, x, y, **kwargs): self._drift_detectors[max_error_idx] = drift.ADWIN() self.correct_weight[max_error_idx] = 0 self.wrong_weight[max_error_idx] = 0 - return self class BOLEClassifier(AdaBoostClassifier): @@ -326,13 +324,10 @@ def learn_one(self, x, y, **kwargs): self.wrong_weight[pos] += lambda_poisson lambda_poisson *= (self.instances_seen) / (2 * self.wrong_weight[pos]) correct = False - return self def predict_proba_one(self, x, **kwargs): y_proba = collections.Counter() - y_proba_all = ( - collections.Counter() - ) # stores prediction of every model of the ensemble, if y_proba is null, returns y_proba_all + y_proba_all = collections.Counter() # stores prediction of every model of the ensemble, if y_proba is null, returns y_proba_all for i, model in enumerate(self): model_weight = 0.0 if self.correct_weight[i] > 0.0 and self.wrong_weight[i] > 0.0: diff --git a/river/ensemble/ewa.py b/river/ensemble/ewa.py index a4e3a282e5..4a3d77358b 100644 --- a/river/ensemble/ewa.py +++ b/river/ensemble/ewa.py @@ -52,9 +52,9 @@ class EWARegressor(base.Ensemble, base.Regressor): ... ) ... ... print(optimizer, evaluate.progressive_val_score(dataset, model, metric)) - SGD MAE: 0.555971 - RMSProp MAE: 0.528284 - AdaGrad MAE: 0.481461 + SGD MAE: 0.558735 + RMSProp MAE: 0.522449 + AdaGrad MAE: 0.477289 >>> dataset = datasets.TrumpApproval() >>> metric = metrics.MAE() @@ -70,7 +70,7 @@ class EWARegressor(base.Ensemble, base.Regressor): ... ) >>> evaluate.progressive_val_score(dataset, hedge, metric) - MAE: 0.494832 + MAE: 0.496298 References ---------- @@ -122,7 +122,6 @@ def learn_predict_one(self, x, y): def learn_one(self, x, y): self.learn_predict_one(x, y) - return self def predict_one(self, x): return sum(model.predict_one(x) * weight for model, weight in zip(self, self.weights)) diff --git a/river/ensemble/stacking.py b/river/ensemble/stacking.py index ff9dab74dd..d9edb0b388 100644 --- a/river/ensemble/stacking.py +++ b/river/ensemble/stacking.py @@ -81,8 +81,6 @@ def learn_one(self, x, y): # Update the meta-classifier using the predictions from the base classifiers self.meta_classifier.learn_one(oof, y) - return self - def predict_proba_one(self, x): oof = { f"oof_{i}_{k}": p diff --git a/river/ensemble/streaming_random_patches.py b/river/ensemble/streaming_random_patches.py index 6c2d023df0..415b30be10 100644 --- a/river/ensemble/streaming_random_patches.py +++ b/river/ensemble/streaming_random_patches.py @@ -109,9 +109,7 @@ def learn_one(self, x: dict, y: base.typing.Target, **kwargs): k = poisson(rate=self.lam, rng=self._rng) if k == 0: continue - model.learn_one(x=x, y=y, sample_weight=k, n_samples_seen=self._n_samples_seen) - - return self + model.learn_one(x=x, y=y, w=k, n_samples_seen=self._n_samples_seen) def _generate_subspaces(self, features: list): n_features = len(features) @@ -409,7 +407,7 @@ class SRPClassifier(BaseSRPEnsemble, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 72.77% + Accuracy: 72.17% Notes ----- @@ -532,7 +530,7 @@ def learn_one( x: dict, y: base.typing.ClfTarget, *, - sample_weight: int, + w: int, n_samples_seen: int, **kwargs, ): @@ -543,8 +541,8 @@ def learn_one( # Use all features x_subset = x - # TODO Find a way to verify if the model natively supports sample_weight - for _ in range(int(sample_weight)): + # TODO Find a way to verify if the model natively supports sample_weight (w) + for _ in range(int(w)): self.model.learn_one(x=x_subset, y=y, **kwargs) if self._background_learner: @@ -552,7 +550,10 @@ def learn_one( # Note: Pass the original instance x so features are correctly # selected based on the corresponding subspace self._background_learner.learn_one( - x=x, y=y, sample_weight=sample_weight, n_samples_seen=n_samples_seen # type: ignore + x=x, + y=y, # type: ignore[arg-type] + w=w, + n_samples_seen=n_samples_seen, # type: ignore ) if not self.disable_drift_detector and not self.is_background_learner: @@ -830,7 +831,7 @@ def learn_one( x: dict, y: base.typing.RegTarget, *, - sample_weight: int, + w: int, n_samples_seen: int, **kwargs, ): @@ -842,8 +843,8 @@ def learn_one( # Use all features x_subset = x - # TODO Find a way to verify if the model natively supports sample_weight - for _ in range(int(sample_weight)): + # TODO Find a way to verify if the model natively supports sample_weight (w) + for _ in range(int(w)): self.model.learn_one(x=x_subset, y=y, **kwargs) # Drift detection input @@ -860,7 +861,10 @@ def learn_one( # Note: Pass the original instance x so features are correctly # selected based on the corresponding subspace self._background_learner.learn_one( - x=x, y=y, sample_weight=sample_weight, n_samples_seen=n_samples_seen # type: ignore + x=x, + y=y, # type: ignore[arg-type] + w=w, + n_samples_seen=n_samples_seen, # type: ignore ) if not self.disable_drift_detector and not self.is_background_learner: diff --git a/river/ensemble/voting.py b/river/ensemble/voting.py index ffe3e5b108..332ec4a591 100644 --- a/river/ensemble/voting.py +++ b/river/ensemble/voting.py @@ -47,7 +47,7 @@ class VotingClassifier(base.Classifier, base.Ensemble): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.14% + F1: 86.94% """ @@ -62,7 +62,6 @@ def _multiclass(self): def learn_one(self, x, y): for model in self: model.learn_one(x, y) - return self def predict_one(self, x): if self.use_probabilities: diff --git a/river/evaluate/__init__.py b/river/evaluate/__init__.py index f97b863d45..55ed440aac 100644 --- a/river/evaluate/__init__.py +++ b/river/evaluate/__init__.py @@ -11,6 +11,7 @@ the River repository uses these tracks. """ + from __future__ import annotations from .progressive_validation import iter_progressive_val_score, progressive_val_score diff --git a/river/evaluate/progressive_validation.py b/river/evaluate/progressive_validation.py index bf70069349..00a3c290e2 100644 --- a/river/evaluate/progressive_validation.py +++ b/river/evaluate/progressive_validation.py @@ -19,6 +19,7 @@ def _progressive_validation( delay: str | int | dt.timedelta | typing.Callable | None = None, measure_time=False, measure_memory=False, + yield_predictions=False, ): # Check that the model and the metric are in accordance if not metric.works_with(model): @@ -45,7 +46,7 @@ def _progressive_validation( if measure_time: start = time.perf_counter() - def report(): + def report(y_pred): if isinstance(metric, metrics.base.Metrics): state = {m.__class__.__name__: m for m in metric} else: @@ -58,6 +59,9 @@ def report(): state["Time"] = dt.timedelta(seconds=now - start) if measure_memory: state["Memory"] = model._raw_memory_usage + if yield_predictions: + state["Prediction"] = y_pred + return state for i, x, y, *kwargs in stream.simulate_qa(dataset, moment, delay, copy=True): @@ -90,13 +94,13 @@ def report(): # Yield current results n_total_answers += 1 if n_total_answers == next_checkpoint: - yield report() + yield report(y_pred=y_pred) prev_checkpoint = next_checkpoint next_checkpoint = next(checkpoints, None) else: # If the dataset was exhausted, we need to make sure that we yield the final results if prev_checkpoint and n_total_answers != prev_checkpoint: - yield report() + yield report(y_pred=None) def iter_progressive_val_score( @@ -108,6 +112,7 @@ def iter_progressive_val_score( step=1, measure_time=False, measure_memory=False, + yield_predictions=False, ) -> typing.Generator: """Evaluates the performance of a model on a streaming dataset and yields results. @@ -143,6 +148,9 @@ def iter_progressive_val_score( Whether or not to measure the elapsed time. measure_memory Whether or not to measure the memory usage of the model. + yield_predictions + Whether or not to include predictions. If step is 1, then this is equivalent to yielding + the predictions at every iterations. Otherwise, not all predictions will be yielded. Examples -------- @@ -172,13 +180,33 @@ def iter_progressive_val_score( >>> for step in steps: ... print(step) - {'ROCAUC': ROCAUC: 89.80%, 'Step': 200} - {'ROCAUC': ROCAUC: 92.09%, 'Step': 400} - {'ROCAUC': ROCAUC: 93.13%, 'Step': 600} - {'ROCAUC': ROCAUC: 93.99%, 'Step': 800} - {'ROCAUC': ROCAUC: 94.74%, 'Step': 1000} - {'ROCAUC': ROCAUC: 95.03%, 'Step': 1200} - {'ROCAUC': ROCAUC: 95.04%, 'Step': 1250} + {'ROCAUC': ROCAUC: 90.20%, 'Step': 200} + {'ROCAUC': ROCAUC: 92.25%, 'Step': 400} + {'ROCAUC': ROCAUC: 93.23%, 'Step': 600} + {'ROCAUC': ROCAUC: 94.05%, 'Step': 800} + {'ROCAUC': ROCAUC: 94.79%, 'Step': 1000} + {'ROCAUC': ROCAUC: 95.07%, 'Step': 1200} + {'ROCAUC': ROCAUC: 95.07%, 'Step': 1250} + + The `yield_predictions` parameter can be used to include the predictions in the results: + + >>> import itertools + + >>> steps = evaluate.iter_progressive_val_score( + ... model=model, + ... dataset=datasets.Phishing(), + ... metric=metrics.ROCAUC(), + ... step=1, + ... yield_predictions=True + ... ) + + >>> for step in itertools.islice(steps, 100, 105): + ... print(step) + {'ROCAUC': ROCAUC: 94.68%, 'Step': 101, 'Prediction': {False: 0.966..., True: 0.033...}} + {'ROCAUC': ROCAUC: 94.75%, 'Step': 102, 'Prediction': {False: 0.035..., True: 0.964...}} + {'ROCAUC': ROCAUC: 94.82%, 'Step': 103, 'Prediction': {False: 0.043..., True: 0.956...}} + {'ROCAUC': ROCAUC: 94.89%, 'Step': 104, 'Prediction': {False: 0.816..., True: 0.183...}} + {'ROCAUC': ROCAUC: 94.96%, 'Step': 105, 'Prediction': {False: 0.041..., True: 0.958...}} References ---------- @@ -196,6 +224,7 @@ def iter_progressive_val_score( delay=delay, measure_time=measure_time, measure_memory=measure_memory, + yield_predictions=yield_predictions, ) @@ -287,14 +316,14 @@ def progressive_val_score( ... metric=metrics.ROCAUC(), ... print_every=200 ... ) - [200] ROCAUC: 89.80% - [400] ROCAUC: 92.09% - [600] ROCAUC: 93.13% - [800] ROCAUC: 93.99% - [1,000] ROCAUC: 94.74% - [1,200] ROCAUC: 95.03% - [1,250] ROCAUC: 95.04% - ROCAUC: 95.04% + [200] ROCAUC: 90.20% + [400] ROCAUC: 92.25% + [600] ROCAUC: 93.23% + [800] ROCAUC: 94.05% + [1,000] ROCAUC: 94.79% + [1,200] ROCAUC: 95.07% + [1,250] ROCAUC: 95.07% + ROCAUC: 95.07% We haven't specified a delay, therefore this is strictly equivalent to the following piece of code: @@ -308,11 +337,11 @@ def progressive_val_score( >>> for x, y in datasets.Phishing(): ... y_pred = model.predict_proba_one(x) - ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... metric.update(y, y_pred) + ... model.learn_one(x, y) >>> metric - ROCAUC: 95.04% + ROCAUC: 95.07% When `print_every` is specified, the current state is printed at regular intervals. Under the hood, Python's `print` method is being used. You can pass extra keyword arguments to diff --git a/river/facto/__init__.py b/river/facto/__init__.py index 2ace049514..6c517d5e56 100644 --- a/river/facto/__init__.py +++ b/river/facto/__init__.py @@ -1,4 +1,5 @@ """Factorization machines.""" + from __future__ import annotations from .ffm import FFMClassifier, FFMRegressor diff --git a/river/facto/base.py b/river/facto/base.py index 802e161d6f..2574c7d6ce 100644 --- a/river/facto/base.py +++ b/river/facto/base.py @@ -65,20 +65,20 @@ def __init__( def _init_latents(self) -> collections.defaultdict: """Initializes latent weights dict.""" - def learn_one(self, x, y, sample_weight=1.0): + def learn_one(self, x, y, w=1.0): x = self._ohe_cat_features(x) if self.sample_normalization: x_l2_norm = sum(xj**2 for xj in x.values()) ** 0.5 x = {j: xj / x_l2_norm for j, xj in x.items()} - return self._learn_one(x, y, sample_weight=sample_weight) + self._learn_one(x, y, w=w) def _ohe_cat_features(self, x): """One hot encodes string features considering them as categorical.""" return dict((f"{j}_{xj}", 1) if isinstance(xj, str) else (j, xj) for j, xj in x.items()) - def _learn_one(self, x, y, sample_weight=1.0): + def _learn_one(self, x, y, w=1.0): # Calculate the gradient of the loss with respect to the raw output g_loss = self.loss.gradient(y_true=y, y_pred=self._raw_dot(x)) @@ -86,7 +86,7 @@ def _learn_one(self, x, y, sample_weight=1.0): g_loss = utils.math.clamp(g_loss, minimum=-self.clip_gradient, maximum=self.clip_gradient) # Apply the sample weight - g_loss *= sample_weight + g_loss *= w # Update the intercept intercept_lr = self.intercept_lr.get(self.weight_optimizer.n_iterations) @@ -99,8 +99,6 @@ def _learn_one(self, x, y, sample_weight=1.0): # Update the latent weights self._update_latents(x, g_loss) - return self - def _raw_dot(self, x): # Start with the intercept y_pred = self.intercept diff --git a/river/facto/ffm.py b/river/facto/ffm.py index f7e9bde9df..01991887c2 100644 --- a/river/facto/ffm.py +++ b/river/facto/ffm.py @@ -194,7 +194,7 @@ class FFMRegressor(FFM, base.Regressor): ... ) >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14}) 5.319945 @@ -341,7 +341,7 @@ class FFMClassifier(FFM, base.Classifier): ... ) >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14}) True diff --git a/river/facto/fm.py b/river/facto/fm.py index 08a7499094..efe0c47ee6 100644 --- a/river/facto/fm.py +++ b/river/facto/fm.py @@ -179,7 +179,7 @@ class FMRegressor(FM, base.Regressor): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'Bob': 1, 'Harry Potter': 1}) 5.236504 @@ -320,7 +320,7 @@ class FMClassifier(FM, base.Classifier): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'Bob': 1, 'Harry Potter': 1}) True diff --git a/river/facto/fwfm.py b/river/facto/fwfm.py index 980fffd1fb..963522fc4d 100644 --- a/river/facto/fwfm.py +++ b/river/facto/fwfm.py @@ -215,7 +215,7 @@ class FwFMRegressor(FwFM, base.Regressor): ... ) >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'Bob': 1, 'Harry Potter': 1}) 5.236501 @@ -363,7 +363,7 @@ class FwFMClassifier(FwFM, base.Classifier): ... ) >>> for x, y in dataset: - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'Bob': 1, 'Harry Potter': 1}) True diff --git a/river/facto/hofm.py b/river/facto/hofm.py index db184f2ee8..330b5a55af 100644 --- a/river/facto/hofm.py +++ b/river/facto/hofm.py @@ -203,7 +203,7 @@ class HOFMRegressor(HOFM, base.Regressor): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14}) 5.311745 @@ -353,7 +353,7 @@ class HOFMClassifier(HOFM, base.Classifier): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'user': 'Bob', 'item': 'Harry Potter', 'time': .14}) True diff --git a/river/feature_extraction/__init__.py b/river/feature_extraction/__init__.py index 9e028e63f8..41d9327734 100644 --- a/river/feature_extraction/__init__.py +++ b/river/feature_extraction/__init__.py @@ -6,6 +6,7 @@ be processed by a particular machine learning algorithm. """ + from __future__ import annotations from .agg import Agg, TargetAgg diff --git a/river/feature_extraction/agg.py b/river/feature_extraction/agg.py index a059ba0afe..80f7426231 100644 --- a/river/feature_extraction/agg.py +++ b/river/feature_extraction/agg.py @@ -62,7 +62,7 @@ class Agg(base.Transformer): ... ) >>> for x in X: - ... agg = agg.learn_one(x) + ... agg.learn_one(x) ... print(agg.transform_one(x)) {'revenue_mean_by_place': 42.0} {'revenue_mean_by_place': 16.0} @@ -84,7 +84,7 @@ class Agg(base.Transformer): ... ) >>> for x in X: - ... agg = agg.learn_one(x) + ... agg.learn_one(x) ... print(agg.transform_one(x)) {'revenue_max_by_place_and_country': 42} {'revenue_max_by_place_and_country': 16} @@ -105,7 +105,7 @@ class Agg(base.Transformer): >>> import pprint >>> for x in X: - ... agg = agg.learn_one(x) + ... agg.learn_one(x) ... pprint.pprint(agg.transform_one(x)) {'revenue_max_by_place_and_country': 42, 'revenue_mean_by_place': 42.0} {'revenue_max_by_place_and_country': 16, 'revenue_mean_by_place': 16.0} @@ -155,7 +155,7 @@ class Agg(base.Transformer): ... "value": string.ascii_lowercase.index(g) + random.random(), ... } ... t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day) - ... agg = agg.learn_one(x, t=t) + ... agg.learn_one(x, t=t) >>> len(agg.state) 26 @@ -193,7 +193,6 @@ def learn_one(self, x, t=None): self._groups[key].update(x[self.on], t=t) else: self._groups[key].update(x[self.on]) - return self def transform_one(self, x): return {self._feature_name: self._groups[self._make_key(x)].get()} @@ -267,7 +266,7 @@ class TargetAgg(base.SupervisedTransformer, Agg): >>> for x, y in dataset: ... print(agg.transform_one(x)) - ... agg = agg.learn_one(x, y) + ... agg.learn_one(x, y) {'y_bayes_mean_by_place': 3.0} {'y_bayes_mean_by_place': 3.0} {'y_bayes_mean_by_place': 9.5} @@ -290,7 +289,7 @@ class TargetAgg(base.SupervisedTransformer, Agg): >>> for x, y in dataset: ... print(agg.transform_one(x)) - ... agg = agg.learn_one(x, y) + ... agg.learn_one(x, y) {'y_bayes_mean_by_place_and_country': 3.0} {'y_bayes_mean_by_place_and_country': 3.0} {'y_bayes_mean_by_place_and_country': 3.0} @@ -328,7 +327,7 @@ class TargetAgg(base.SupervisedTransformer, Agg): ... x = {"group": g} ... y = string.ascii_lowercase.index(g) + random.random() ... t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day) - ... agg = agg.learn_one(x, y, t=t) + ... agg.learn_one(x, y, t=t) References ---------- @@ -354,4 +353,3 @@ def learn_one(self, x, y, t=None): self._groups[key].update(y, t=t) else: self._groups[key].update(y) - return self diff --git a/river/feature_extraction/kernel_approx.py b/river/feature_extraction/kernel_approx.py index 697da3f4e0..02540ee99e 100644 --- a/river/feature_extraction/kernel_approx.py +++ b/river/feature_extraction/kernel_approx.py @@ -39,7 +39,7 @@ class RBFSampler(base.Transformer): >>> model = lm.LogisticRegression(optimizer=optim.SGD(.1)) >>> for x, y in stream.iter_array(X, Y): - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) ... y_pred = model.predict_one(x) ... print(y, int(y_pred)) 0 0 @@ -53,7 +53,7 @@ class RBFSampler(base.Transformer): ... ) >>> for x, y in stream.iter_array(X, Y): - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) ... y_pred = model.predict_one(x) ... print(y, int(y_pred)) 0 0 @@ -72,9 +72,9 @@ def __init__(self, gamma=1.0, n_components=100, seed: int | None = None): self.n_components = n_components self.seed = seed self.rng = random.Random(seed) - self.weights: collections.defaultdict[ - typing.Hashable, typing.Callable - ] = collections.defaultdict(self._random_weights) + self.weights: collections.defaultdict[typing.Hashable, typing.Callable] = ( + collections.defaultdict(self._random_weights) + ) self.offsets = [self.rng.uniform(0, 2 * math.pi) for _ in range(n_components)] def _random_weights(self): diff --git a/river/feature_extraction/test_agg.py b/river/feature_extraction/test_agg.py index d19e9e4d87..78b9fad198 100644 --- a/river/feature_extraction/test_agg.py +++ b/river/feature_extraction/test_agg.py @@ -29,7 +29,8 @@ def test_agg_lag(): ... Agg("revenue", None, Shift(2)) ... ) >>> for x in X: - ... print(agg.learn_one(x).transform_one(x)) + ... agg.learn_one(x) + ... print(agg.transform_one(x)) {'revenue_shift_2': None, 'customers_shift_2': None} {'revenue_shift_2': None, 'customers_shift_2': None} {'revenue_shift_2': 420, 'customers_shift_2': 10} @@ -50,7 +51,8 @@ def test_agg_lag(): ... for d in [1, 2, 3] ... ]) >>> for x in X: - ... print(agg.learn_one(x).transform_one(x)) + ... agg.learn_one(x) + ... print(agg.transform_one(x)) {'customers_shift_3': None, 'customers_shift_2': None, 'customers_shift_1': None} {'customers_shift_3': None, 'customers_shift_2': None, 'customers_shift_1': 10} {'customers_shift_3': None, 'customers_shift_2': 10, 'customers_shift_1': 10} @@ -67,7 +69,8 @@ def test_agg_lag(): >>> agg = Agg("customers", "shop", Shift(1)) >>> for x in X: - ... print(agg.learn_one(x).transform_one(x)) + ... agg.learn_one(x) + ... print(agg.transform_one(x)) {'customers_shift_1_by_shop': None} {'customers_shift_1_by_shop': None} {'customers_shift_1_by_shop': 10} @@ -104,7 +107,7 @@ def test_target_agg_lag(): >>> agg = TargetAgg(None, Shift(1)) + TargetAgg(None, Shift(2)) >>> for x, y in dataset: ... print(agg.transform_one(x)) - ... agg = agg.learn_one(x, y) + ... agg.learn_one(x, y) {'y_shift_2': None, 'y_shift_1': None} {'y_shift_2': None, 'y_shift_1': None} {'y_shift_2': None, 'y_shift_1': 42} @@ -119,7 +122,7 @@ def test_target_agg_lag(): >>> agg = TargetAgg("country", Shift(1)) + TargetAgg("country", Shift(2)) >>> for x, y in dataset: ... print(agg.transform_one(x)) - ... agg = agg.learn_one(x, y) + ... agg.learn_one(x, y) {'y_shift_2_by_country': None, 'y_shift_1_by_country': None} {'y_shift_2_by_country': None, 'y_shift_1_by_country': None} {'y_shift_2_by_country': None, 'y_shift_1_by_country': None} diff --git a/river/feature_extraction/vectorize.py b/river/feature_extraction/vectorize.py index a4970a3887..5f68450208 100644 --- a/river/feature_extraction/vectorize.py +++ b/river/feature_extraction/vectorize.py @@ -203,7 +203,7 @@ def __init__( # Stop word removal if self.stop_words: self.processing_steps.append( - functools.partial(remove_stop_words, stop_words=stop_words) + functools.partial(remove_stop_words, stop_words=self.stop_words) ) # n-grams @@ -359,7 +359,7 @@ def transform_many(self, X: pd.Series) -> pd.DataFrame: ) def learn_many(self, X): - return self + return class TFIDF(BagOfWords): @@ -421,7 +421,7 @@ class TFIDF(BagOfWords): ... ] >>> for sentence in corpus: - ... tfidf = tfidf.learn_one(sentence) + ... tfidf.learn_one(sentence) ... print(tfidf.transform_one(sentence)) {'this': 0.447, 'is': 0.447, 'the': 0.447, 'first': 0.447, 'document': 0.447} {'this': 0.333, 'document': 0.667, 'is': 0.333, 'the': 0.333, 'second': 0.469} @@ -435,7 +435,7 @@ class TFIDF(BagOfWords): >>> for sentence in corpus: ... x = {'sentence': sentence} - ... tfidf = tfidf.learn_one(x) + ... tfidf.learn_one(x) ... print(tfidf.transform_one(x)) {'this': 0.447, 'is': 0.447, 'the': 0.447, 'first': 0.447, 'document': 0.447} {'this': 0.333, 'document': 0.667, 'is': 0.333, 'the': 0.333, 'second': 0.469} @@ -474,8 +474,6 @@ def learn_one(self, x): # Increment the global document counter self.n += 1 - return self - def transform_one(self, x): term_counts = super().transform_one(x) n_terms = sum(term_counts.values()) diff --git a/river/feature_selection/__init__.py b/river/feature_selection/__init__.py index bf5ceef70f..59f0c635a9 100644 --- a/river/feature_selection/__init__.py +++ b/river/feature_selection/__init__.py @@ -1,4 +1,5 @@ """Feature selection.""" + from __future__ import annotations from .k_best import SelectKBest diff --git a/river/feature_selection/k_best.py b/river/feature_selection/k_best.py index bf029a8a11..887772541f 100644 --- a/river/feature_selection/k_best.py +++ b/river/feature_selection/k_best.py @@ -46,7 +46,7 @@ class SelectKBest(base.SupervisedTransformer): ... ) >>> for xi, yi, in stream.iter_array(X, y): - ... selector = selector.learn_one(xi, yi) + ... selector.learn_one(xi, yi) >>> pprint(selector.leaderboard) Counter({9: 0.7898, @@ -79,9 +79,8 @@ def _unit_test_params(cls): def learn_one(self, x, y): for i, xi in x.items(): - self.leaderboard[i] = self.similarities[i].update(xi, y).get() - - return self + self.similarities[i].update(xi, y) + self.leaderboard[i] = self.similarities[i].get() def transform_one(self, x): best_features = {pair[0] for pair in self.leaderboard.most_common(self.k)} diff --git a/river/feature_selection/variance.py b/river/feature_selection/variance.py index c2f20d2bd5..8d80e0fdad 100644 --- a/river/feature_selection/variance.py +++ b/river/feature_selection/variance.py @@ -35,7 +35,8 @@ class VarianceThreshold(base.Transformer): >>> selector = feature_selection.VarianceThreshold() >>> for x, _ in stream.iter_array(X): - ... print(selector.learn_one(x).transform_one(x)) + ... selector.learn_one(x) + ... print(selector.transform_one(x)) {0: 0, 1: 2, 2: 0, 3: 3} {1: 1, 2: 4} {1: 1, 2: 1} @@ -51,8 +52,6 @@ def learn_one(self, x): for i, xi in x.items(): self.variances[i].update(xi) - return self - def check_feature(self, feature): if feature not in self.variances: return True diff --git a/river/forest/__init__.py b/river/forest/__init__.py index c73ebd50ae..12a54da717 100644 --- a/river/forest/__init__.py +++ b/river/forest/__init__.py @@ -1,13 +1,15 @@ """This module implements forest-based classifiers and regressors.""" + from __future__ import annotations from .adaptive_random_forest import ARFClassifier, ARFRegressor -from .aggregated_mondrian_forest import AMFClassifier +from .aggregated_mondrian_forest import AMFClassifier, AMFRegressor from .online_extra_trees import OXTRegressor __all__ = [ "ARFClassifier", "ARFRegressor", "AMFClassifier", + "AMFRegressor", "OXTRegressor", ] diff --git a/river/forest/adaptive_random_forest.py b/river/forest/adaptive_random_forest.py index 44d4decaec..dd9a757f4d 100644 --- a/river/forest/adaptive_random_forest.py +++ b/river/forest/adaptive_random_forest.py @@ -10,7 +10,7 @@ import numpy as np from river import base, metrics, stats -from river.drift import ADWIN +from river.drift import ADWIN, NoDrift from river.tree.hoeffding_tree_classifier import HoeffdingTreeClassifier from river.tree.hoeffding_tree_regressor import HoeffdingTreeRegressor from river.tree.nodes.arf_htc_nodes import ( @@ -32,8 +32,8 @@ def __init__( n_models: int, max_features: bool | str | int, lambda_value: int, - drift_detector: base.DriftDetector | None, - warning_detector: base.DriftDetector | None, + drift_detector: base.DriftDetector, + warning_detector: base.DriftDetector, metric: metrics.base.MultiClassMetric | metrics.base.RegressionMetric, disable_weighted_vote, seed, @@ -47,11 +47,36 @@ def __init__( self.drift_detector = drift_detector self.warning_detector = warning_detector self.seed = seed + self._rng = random.Random(self.seed) - # Internal parameters - self._n_samples_seen = 0 - self._base_member_class: ForestMemberClassifier | ForestMemberRegressor | None = None + self._warning_detectors: list[base.DriftDetector] + self._warning_detection_disabled = True + if not isinstance(self.warning_detector, NoDrift): + self._warning_detectors = [self.warning_detector.clone() for _ in range(self.n_models)] + self._warning_detection_disabled = False + + self._drift_detectors: list[base.DriftDetector] + self._drift_detection_disabled = True + if not isinstance(self.drift_detector, NoDrift): + self._drift_detectors = [self.drift_detector.clone() for _ in range(self.n_models)] + self._drift_detection_disabled = False + + # The background models + self._background: list[BaseTreeClassifier | BaseTreeRegressor | None] = ( + None if self._warning_detection_disabled else [None] * self.n_models # type: ignore + ) + + # Performance metrics used for weighted voting/aggregation + self._metrics = [self.metric.clone() for _ in range(self.n_models)] + + # Drift and warning logging + self._warning_tracker: dict = ( + collections.defaultdict(int) if not self._warning_detection_disabled else None # type: ignore + ) + self._drift_tracker: dict = ( + collections.defaultdict(int) if not self._drift_detection_disabled else None # type: ignore + ) @property def _min_number_of_models(self): @@ -64,44 +89,129 @@ def _unit_test_params(cls): def _unit_test_skips(self): return {"check_shuffle_features_no_impact"} - def learn_one(self, x: dict, y: base.typing.Target, **kwargs): - self._n_samples_seen += 1 + @abc.abstractmethod + def _drift_detector_input( + self, + tree_id: int, + y_true, + y_pred, + ) -> int | float: + raise NotImplementedError + + @abc.abstractmethod + def _new_base_model(self) -> BaseTreeClassifier | BaseTreeRegressor: + raise NotImplementedError + + def n_warnings_detected(self, tree_id: int | None = None) -> int: + """Get the total number of concept drift warnings detected, or the number on an individual + tree basis (optionally). + + Parameters + ---------- + tree_id + The number of the base learner in the ensemble: `[0, self.n_models - 1]. If `None`, + the total number of warnings is returned instead. + + Returns + ------- + The number of concept drift warnings detected. + + """ + + if self._warning_detection_disabled: + return 0 + + if tree_id is None: + return sum(self._warning_tracker.values()) + + return self._warning_tracker[tree_id] + + def n_drifts_detected(self, tree_id: int | None = None) -> int: + """Get the total number of concept drifts detected, or such number on an individual + tree basis (optionally). + + Parameters + ---------- + tree_id + The number of the base learner in the ensemble: `[0, self.n_models - 1]. If `None`, + the total number of warnings is returned instead. + + Returns + ------- + The number of concept drifts detected. + + """ + + if self._drift_detection_disabled: + return 0 + + if tree_id is None: + return sum(self._drift_tracker.values()) + return self._drift_tracker[tree_id] + + def learn_one(self, x: dict, y: base.typing.Target, **kwargs): if len(self) == 0: self._init_ensemble(sorted(x.keys())) - for model in self: - # Get prediction for instance + for i, model in enumerate(self): y_pred = model.predict_one(x) # Update performance evaluator - model.metric.update(y_true=y, y_pred=y_pred) + self._metrics[i].update( + y_true=y, + y_pred=model.predict_proba_one(x) + if isinstance(self.metric, metrics.base.ClassificationMetric) + and not self.metric.requires_labels + else y_pred, + ) k = poisson(rate=self.lambda_value, rng=self._rng) if k > 0: - model.learn_one(x=x, y=y, sample_weight=k, n_samples_seen=self._n_samples_seen) + if not self._warning_detection_disabled and self._background[i] is not None: + self._background[i].learn_one(x=x, y=y, w=k) # type: ignore - return self + model.learn_one(x=x, y=y, w=k) - def _init_ensemble(self, features: list): - self._set_max_features(len(features)) + drift_input = None + if not self._warning_detection_disabled: + drift_input = self._drift_detector_input(i, y, y_pred) + self._warning_detectors[i].update(drift_input) - self.data = [ - self._base_member_class( # type: ignore - index_original=i, - model=self._new_base_model(), - created_on=self._n_samples_seen, - drift_detector=self.drift_detector, - warning_detector=self.warning_detector, - is_background_learner=False, - metric=self.metric, - ) - for i in range(self.n_models) - ] + if self._warning_detectors[i].drift_detected: + self._background[i] = self._new_base_model() # type: ignore + # Reset the warning detector for the current object + self._warning_detectors[i] = self.warning_detector.clone() - @abc.abstractmethod - def _new_base_model(self): - raise NotImplementedError + # Update warning tracker + self._warning_tracker[i] += 1 + + if not self._drift_detection_disabled: + drift_input = ( + drift_input + if drift_input is not None + else self._drift_detector_input(i, y, y_pred) + ) + self._drift_detectors[i].update(drift_input) + + if self._drift_detectors[i].drift_detected: + if not self._warning_detection_disabled and self._background[i] is not None: + self.data[i] = self._background[i] + self._background[i] = None + self._warning_detectors[i] = self.warning_detector.clone() + self._drift_detectors[i] = self.drift_detector.clone() + self._metrics[i] = self.metric.clone() + else: + self.data[i] = self._new_base_model() + self._drift_detectors[i] = self.drift_detector.clone() + self._metrics[i] = self.metric.clone() + + # Update warning tracker + self._drift_tracker[i] += 1 + + def _init_ensemble(self, features: list): + self._set_max_features(len(features)) + self.data = [self._new_base_model() for _ in range(self.n_models)] def _set_max_features(self, n_features): if self.max_features == "sqrt": @@ -159,6 +269,8 @@ def __init__( nominal_attributes: list | None = None, splitter: Splitter | None = None, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 1000000, stop_mem_management: bool = False, @@ -177,6 +289,8 @@ def __init__( nominal_attributes=nominal_attributes, splitter=splitter, binary_split=binary_split, + min_branch_fraction=min_branch_fraction, + max_share_to_split=max_share_to_split, max_size=max_size, memory_estimate_period=memory_estimate_period, stop_mem_management=stop_mem_management, @@ -221,12 +335,6 @@ def _new_leaf(self, initial_stats=None, parent=None): self.rng, ) - def new_instance(self): - new_instance = self.clone() - # Use existing rng to enforce a different model - new_instance.rng = self.rng - return new_instance - class BaseTreeRegressor(HoeffdingTreeRegressor): """ARF Hoeffding Tree regressor. @@ -334,12 +442,6 @@ def _new_leaf(self, initial_stats=None, parent=None): # noqa return new_adaptive - def new_instance(self): - new_instance = self.clone() - # Use existing rng to enforce a different model - new_instance.rng = self.rng - return new_instance - class ARFClassifier(BaseForest, base.Classifier): """Adaptive Random Forest classifier. @@ -388,7 +490,7 @@ class ARFClassifier(BaseForest, base.Classifier): split attempts. max_depth [*Tree parameter*] The maximum depth a tree can reach. If `None`, the - tree will grow indefinitely. + tree will grow until the system recursion limit. split_criterion [*Tree parameter*] Split criterion to use.
- 'gini' - Gini
@@ -421,8 +523,18 @@ class ARFClassifier(BaseForest, base.Classifier): if `splitter` is `None`. binary_split [*Tree parameter*] If True, only allow binary splits. + min_branch_fraction + [*Tree parameter*] The minimum percentage of observed data required for branches + resulting from split candidates. To validate a split candidate, at least two resulting + branches must have a percentage of samples greater than `min_branch_fraction`. This + criterion prevents unnecessary splits when the majority of instances are concentrated + in a single branch. + max_share_to_split + [*Tree parameter*] Only perform a split in a leaf if the proportion of elements + in the majority class is smaller than this parameter value. This parameter avoids + performing splits when most of the data belongs to a single class. max_size - [*Tree parameter*] Maximum memory (MB) consumed by the tree. + [*Tree parameter*] Maximum memory (MiB) consumed by the tree. memory_estimate_period [*Tree parameter*] Number of instances between memory consumption checks. stop_mem_management @@ -453,7 +565,19 @@ class ARFClassifier(BaseForest, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 71.07% + Accuracy: 67.97% + + The total number of warnings and drifts detected, respectively + >>> model.n_warnings_detected(), model.n_drifts_detected() + (2, 1) + + The number of warnings detected by tree number 2 + >>> model.n_warnings_detected(2) + 1 + + And the corresponding number of actual concept drift detected + >>> model.n_drifts_detected(2) + 1 References ---------- @@ -484,6 +608,8 @@ def __init__( nominal_attributes: list | None = None, splitter: Splitter | None = None, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 2_000_000, stop_mem_management: bool = False, @@ -502,9 +628,6 @@ def __init__( seed=seed, ) - self._n_samples_seen = 0 - self._base_member_class = ForestMemberClassifier # type: ignore - # Tree parameters self.grace_period = grace_period self.max_depth = max_depth @@ -516,6 +639,8 @@ def __init__( self.nominal_attributes = nominal_attributes self.splitter = splitter self.binary_split = binary_split + self.min_branch_fraction = min_branch_fraction + self.max_share_to_split = max_share_to_split self.max_size = max_size self.memory_estimate_period = memory_estimate_period self.stop_mem_management = stop_mem_management @@ -543,9 +668,9 @@ def predict_proba_one(self, x: dict) -> dict[base.typing.ClfTarget, float]: self._init_ensemble(sorted(x.keys())) return y_pred # type: ignore - for model in self: + for i, model in enumerate(self): y_proba_temp = model.predict_proba_one(x) - metric_value = model.metric.get() + metric_value = self._metrics[i].get() if not self.disable_weighted_vote and metric_value > 0.0: y_proba_temp = {k: val * metric_value for k, val in y_proba_temp.items()} y_pred.update(y_proba_temp) @@ -568,6 +693,8 @@ def _new_base_model(self): splitter=self.splitter, max_depth=self.max_depth, binary_split=self.binary_split, + min_branch_fraction=self.min_branch_fraction, + max_share_to_split=self.max_share_to_split, max_size=self.max_size, memory_estimate_period=self.memory_estimate_period, stop_mem_management=self.stop_mem_management, @@ -576,9 +703,14 @@ def _new_base_model(self): rng=self._rng, ) + def _drift_detector_input( + self, tree_id: int, y_true: base.typing.ClfTarget, y_pred: base.typing.ClfTarget + ) -> int | float: + return int(not y_true == y_pred) + class ARFRegressor(BaseForest, base.Regressor): - r"""Adaptive Random Forest regressor. + """Adaptive Random Forest regressor. The 3 most important aspects of Adaptive Random Forest [^1] are: @@ -596,7 +728,7 @@ class ARFRegressor(BaseForest, base.Regressor): predictions and check for concept drifts. The deviations of the predictions to the target are monitored and normalized in the [0, 1] range to fulfill ADWIN's requirements. We assume that the data subjected to the normalization follows - a normal distribution, and thus, lies within the interval of the mean $\pm3\sigma$. + a normal distribution, and thus, lies within the interval of the mean $\\pm3\\sigma$. Parameters ---------- @@ -635,7 +767,7 @@ class ARFRegressor(BaseForest, base.Regressor): split attempts. max_depth [*Tree parameter*] The maximum depth a tree can reach. If `None`, the - tree will grow indefinitely. + tree will grow until the system recursion limit. delta [*Tree parameter*] Allowed error in split decision, a value closer to 0 takes longer to decide. @@ -676,7 +808,7 @@ class ARFRegressor(BaseForest, base.Regressor): binary_split [*Tree parameter*] If True, only allow binary splits. max_size - [*Tree parameter*] Maximum memory (MB) consumed by the tree. + [*Tree parameter*] Maximum memory (MiB) consumed by the tree. memory_estimate_period [*Tree parameter*] Number of instances between memory consumption checks. stop_mem_management @@ -717,7 +849,7 @@ class ARFRegressor(BaseForest, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.800649 + MAE: 0.772113 """ @@ -766,9 +898,6 @@ def __init__( seed=seed, ) - self._n_samples_seen = 0 - self._base_member_class = ForestMemberRegressor # type: ignore - # Tree parameters self.grace_period = grace_period self.max_depth = max_depth @@ -795,6 +924,9 @@ def __init__( f"Valid values are: {self._VALID_AGGREGATION_METHOD}" ) + # Used to normalize the input for the drift trackers + self._drift_norm = [stats.Var() for _ in range(self.n_models)] + @property def _mutable_attributes(self): return { @@ -817,10 +949,10 @@ def predict_one(self, x: dict) -> base.typing.RegTarget: if not self.disable_weighted_vote and self.aggregation_method != self._MEDIAN: weights = np.zeros(self.n_models) sum_weights = 0.0 - for idx, model in enumerate(self): - y_pred[idx] = model.predict_one(x) - weights[idx] = model.metric.get() - sum_weights += weights[idx] + for i, model in enumerate(self): + y_pred[i] = model.predict_one(x) + weights[i] = self._metrics[i].get() + sum_weights += weights[i] if sum_weights != 0: # The higher the error, the worse is the tree @@ -829,8 +961,8 @@ def predict_one(self, x: dict) -> base.typing.RegTarget: weights /= weights.sum() y_pred *= weights else: - for idx, model in enumerate(self): - y_pred[idx] = model.predict_one(x) + for i, model in enumerate(self): + y_pred[i] = model.predict_one(x) if self.aggregation_method == self._MEAN: y_pred = y_pred.mean() @@ -860,214 +992,19 @@ def _new_base_model(self): rng=self._rng, ) - @property - def valid_aggregation_method(self): - """Valid aggregation_method values.""" - return self._VALID_AGGREGATION_METHOD - - -class BaseForestMember: - """Base forest member class. - - This class represents a tree member of the forest. It includes a - base tree model, the background learner, drift detectors and performance - tracking parameters. - - The main purpose of this class is to train the foreground model. - Optionally, it monitors drift detection. Depending on the configuration, - if drift is detected then the foreground model is reset or replaced by a - background model. - - Parameters - ---------- - index_original - Tree index within the ensemble. - model - Tree learner. - created_on - Number of instances seen by the tree. - drift_detector - Drift Detection method. - warning_detector - Warning Detection method. - is_background_learner - True if the tree is a background learner. - metric - Metric to track performance. - - """ - - def __init__( - self, - index_original: int, - model: BaseTreeClassifier | BaseTreeRegressor, - created_on: int, - drift_detector: base.DriftDetector, - warning_detector: base.DriftDetector, - is_background_learner, - metric: metrics.base.MultiClassMetric | metrics.base.RegressionMetric, - ): - self.index_original = index_original - self.model = model - self.created_on = created_on - self.is_background_learner = is_background_learner - self.metric = metric.clone() - self.background_learner = None - - # Drift and warning detection - self.last_drift_on = 0 - self.last_warning_on = 0 - self.n_drifts_detected = 0 - self.n_warnings_detected = 0 - - # Initialize drift and warning detectors - if drift_detector is not None: - self._use_drift_detector = True - self.drift_detector = drift_detector.clone() - else: - self._use_drift_detector = False - self.drift_detector = None - - if warning_detector is not None: - self._use_background_learner = True - self.warning_detector = warning_detector.clone() - else: - self._use_background_learner = False - self.warning_detector = None - - def reset(self, n_samples_seen): - if self._use_background_learner and self.background_learner is not None: - # Replace foreground model with background model - self.model = self.background_learner.model - self.warning_detector = self.background_learner.warning_detector - self.drift_detector = self.background_learner.drift_detector - self.metric = self.background_learner.metric - self.created_on = self.background_learner.created_on - self.background_learner = None - else: - # Reset model - self.model = self.model.new_instance() - self.metric = self.metric.clone() - self.created_on = n_samples_seen - self.drift_detector = self.drift_detector.clone() - - def learn_one(self, x: dict, y: base.typing.Target, *, sample_weight: int, n_samples_seen: int): - self.model.learn_one(x, y, sample_weight=sample_weight) - - if self.background_learner: - # Train the background learner - self.background_learner.model.learn_one(x=x, y=y, sample_weight=sample_weight) - - if self._use_drift_detector and not self.is_background_learner: - drift_detector_input = self._drift_detector_input( - y_true=y, y_pred=self.model.predict_one(x) # type: ignore - ) - - # Check for warning only if use_background_learner is set - if self._use_background_learner: - self.warning_detector.update(drift_detector_input) - # Check if there was a (warning) change - if self.warning_detector.drift_detected: - self.last_warning_on = n_samples_seen - self.n_warnings_detected += 1 - # Create a new background learner object - self.background_learner = self.__class__( # type: ignore - index_original=self.index_original, - model=self.model.new_instance(), - created_on=n_samples_seen, - drift_detector=self.drift_detector, - warning_detector=self.warning_detector, - is_background_learner=True, - metric=self.metric, - ) - # Reset the warning detector for the current object - self.warning_detector = self.warning_detector.clone() - - # Update the drift detector - self.drift_detector.update(drift_detector_input) - - # Check if there was a change - if self.drift_detector.drift_detected: - self.last_drift_on = n_samples_seen - self.n_drifts_detected += 1 - self.reset(n_samples_seen) - - @abc.abstractmethod def _drift_detector_input( self, - y_true: base.typing.ClfTarget | base.typing.RegTarget, - y_pred: base.typing.ClfTarget | base.typing.RegTarget, - ): - raise NotImplementedError - - -class ForestMemberClassifier(BaseForestMember, base.Classifier): # type: ignore - """Forest member class for classification""" - - def __init__( - self, - index_original: int, - model: BaseTreeClassifier, - created_on: int, - drift_detector: base.DriftDetector, - warning_detector: base.DriftDetector, - is_background_learner, - metric: metrics.base.MultiClassMetric, - ): - super().__init__( - index_original=index_original, - model=model, - created_on=created_on, - drift_detector=drift_detector, - warning_detector=warning_detector, - is_background_learner=is_background_learner, - metric=metric, - ) - - def _drift_detector_input( # type: ignore - self, y_true: base.typing.ClfTarget, y_pred: base.typing.ClfTarget - ): - return int(not y_true == y_pred) # Not correctly_classifies - - def predict_one(self, x): - return self.model.predict_one(x) - - def predict_proba_one(self, x): - return self.model.predict_proba_one(x) - - -class ForestMemberRegressor(BaseForestMember, base.Regressor): # type: ignore - """Forest member class for regression""" - - def __init__( - self, - index_original: int, - model: BaseTreeRegressor, - created_on: int, - drift_detector: base.DriftDetector, - warning_detector: base.DriftDetector, - is_background_learner, - metric: metrics.base.RegressionMetric, - ): - super().__init__( - index_original=index_original, - model=model, - created_on=created_on, - drift_detector=drift_detector, - warning_detector=warning_detector, - is_background_learner=is_background_learner, - metric=metric, - ) - self._var = stats.Var() # Used to track drift - - def _drift_detector_input(self, y_true: float, y_pred: float): # type: ignore + tree_id: int, + y_true: int | float, + y_pred: int | float, + ) -> int | float: drift_input = y_true - y_pred - self._var.update(drift_input) + self._drift_norm[tree_id].update(drift_input) - if self._var.mean.n == 1: + if self._drift_norm[tree_id].mean.n == 1: return 0.5 # The expected error is the normalized mean error - sd = math.sqrt(self._var.get()) + sd = math.sqrt(self._drift_norm[tree_id].get()) # We assume the error follows a normal distribution -> (empirical rule) # 99.73% of the values lie between [mean - 3*sd, mean + 3*sd]. We @@ -1075,10 +1012,7 @@ def _drift_detector_input(self, y_true: float, y_pred: float): # type: ignore # min-max norm to cope with ADWIN's requirements return (drift_input + 3 * sd) / (6 * sd) if sd > 0 else 0.5 - def reset(self, n_samples_seen): - super().reset(n_samples_seen) - # Reset the stats for the drift detector - self._var = stats.Var() - - def predict_one(self, x): - return self.model.predict_one(x) + @property + def valid_aggregation_method(self): + """Valid aggregation_method values.""" + return self._VALID_AGGREGATION_METHOD diff --git a/river/forest/aggregated_mondrian_forest.py b/river/forest/aggregated_mondrian_forest.py index 08b7f1f62b..12601d0cd5 100644 --- a/river/forest/aggregated_mondrian_forest.py +++ b/river/forest/aggregated_mondrian_forest.py @@ -4,7 +4,7 @@ import random from river import base -from river.tree.mondrian import MondrianTreeClassifier +from river.tree.mondrian import MondrianTreeClassifier, MondrianTreeRegressor class AMFLearner(base.Ensemble, abc.ABC): @@ -71,7 +71,7 @@ def _min_number_of_models(self): class AMFClassifier(AMFLearner, base.Classifier): """Aggregated Mondrian Forest classifier for online learning. - This implementation is truly online, in the sense that a single pass is performed, and that + This implementation is truly online[^1], in the sense that a single pass is performed, and that predictions can be produced anytime. Each node in a tree predicts according to the distribution of the labels @@ -139,11 +139,12 @@ class AMFClassifier(AMFLearner, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 84.97% + Accuracy: 85.37% References ---------- - J. Mourtada, S. Gaiffas and E. Scornet, *AMF: Aggregated Mondrian Forests for Online Learning*, arXiv:1906.10529, 2019. + [^1]: Mourtada, J., Gaïffas, S., & Scornet, E. (2021). AMF: Aggregated Mondrian forests for online + learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505-533. """ @@ -168,7 +169,7 @@ def __init__( # memory of the classes self._classes: set[base.typing.ClfTarget] = set() - def _initialize_trees(self): + def _initialize_trees(self) -> None: self.data: list[MondrianTreeClassifier] = [] for _ in range(self.n_estimators): tree = MondrianTreeClassifier( @@ -195,8 +196,6 @@ def learn_one(self, x, y): for tree in self: tree.learn_one(x, y) - return self - def predict_proba_one(self, x): # Checking that the model has been trained once at least # Otherwise return the default empty dict @@ -217,3 +216,114 @@ def predict_proba_one(self, x): @property def _multiclass(self): return True + + +class AMFRegressor(AMFLearner, base.Regressor): + """Aggregated Mondrian Forest regressor for online learning. + + This algorithm is truly online, in the sense that a single pass is performed, and that + predictions can be produced anytime. + + Each node in a tree predicts according to the average of the labels it contains. + The prediction for a sample is computed as the aggregated predictions of all the subtrees + along the path leading to the leaf node containing the sample. The aggregation weights are + exponential weights with learning rate `step` using a squared loss when `use_aggregation` + is `True`. + + This computation is performed exactly thanks to a context tree weighting algorithm. + More details can be found in the original paper[^1]. + + The final predictions are the average of the predictions of each of the + ``n_estimators`` trees in the forest. + + Parameters + ---------- + n_estimators + The number of trees in the forest. + step + Step-size for the aggregation weights. + use_aggregation + Controls if aggregation is used in the trees. It is highly recommended to + leave it as `True`. + seed + Random seed for reproducibility. + + Examples + -------- + + >>> from river import datasets + >>> from river import evaluate + >>> from river import forest + >>> from river import metrics + + >>> dataset = datasets.TrumpApproval() + >>> model = forest.AMFRegressor(seed=42) + >>> metric = metrics.MAE() + + >>> evaluate.progressive_val_score(dataset, model, metric) + MAE: 0.268533 + + References + ---------- + [^1]: Mourtada, J., Gaïffas, S., & Scornet, E. (2021). AMF: Aggregated Mondrian forests for online + learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505-533. + + """ + + def __init__( + self, + n_estimators: int = 10, + step: float = 1.0, + use_aggregation: bool = True, + seed: int | None = None, + ): + super().__init__( + n_estimators=n_estimators, + step=step, + loss="least-squares", + use_aggregation=use_aggregation, + seed=seed, + ) + + self.iteration = 0 + + def _initialize_trees(self) -> None: + """Initialize the forest.""" + + self.data: list[MondrianTreeRegressor] = [] + for _ in range(self.n_estimators): + # We don't want to have the same stochastic scheme for each tree, or it'll break the randomness + # Hence we introduce a new seed for each, that is derived of the given seed by a deterministic process + seed = self._rng.randint(0, 9999999) + + tree = MondrianTreeRegressor( + self.step, + self.use_aggregation, + self.iteration, + seed, + ) + self.data.append(tree) + + def learn_one(self, x, y): + # Checking if the forest has been created + if not self._is_initialized: + self._initialize_trees() + + # we fit all the trees using the new sample + for tree in self: + tree.learn_one(x, y) + + self.iteration += 1 + + def predict_one(self, x): + # Checking that the model has been trained once at least + if not self._is_initialized: + return None + + prediction = 0 + for tree in self: + tree.use_aggregation = self.use_aggregation + prediction += tree.predict_one(x) + prediction = prediction / self.n_estimators + + return prediction diff --git a/river/forest/online_extra_trees.py b/river/forest/online_extra_trees.py index cd7c6d7edc..ee361007eb 100644 --- a/river/forest/online_extra_trees.py +++ b/river/forest/online_extra_trees.py @@ -3,7 +3,6 @@ import abc import collections import math -import numbers import random import sys @@ -221,28 +220,28 @@ def __weight_sampler_factory(self) -> Sampler: def _detection_mode_all( drift_detector: base.DriftDetector, warning_detector: base.DriftDetector, - detector_input: numbers.Number, + detector_input: int | float, ) -> tuple[bool, bool]: - in_warning = warning_detector.update(detector_input).drift_detected - in_drift = drift_detector.update(detector_input).drift_detected + warning_detector.update(detector_input) + drift_detector.update(detector_input) - return in_drift, in_warning + return drift_detector.drift_detected, warning_detector.drift_detected @staticmethod def _detection_mode_drop( drift_detector: base.DriftDetector, warning_detector: base.DriftDetector, - detector_input: numbers.Number, + detector_input: int | float, ) -> tuple[bool, bool]: - in_drift = drift_detector.update(detector_input).drift_detected + drift_detector.update(detector_input) - return in_drift, False + return drift_detector.drift_detected, False @staticmethod def _detection_mode_off( drift_detector: base.DriftDetector, warning_detector: base.DriftDetector, - detector_input: numbers.Number, + detector_input: int | float, ) -> tuple[bool, bool]: return False, False @@ -315,18 +314,16 @@ def learn_one(self, x, y): if w == 0: # Skip model update if w is zero continue - model.learn_one(x, y, sample_weight=w) + model.learn_one(x, y, w=w) if i in self._background_trees: - self._background_trees[i].learn_one(x, y, sample_weight=w) + self._background_trees[i].learn_one(x, y, w=w) trained.append(i) # Increase by one the count of instances observed by each trained model self._sample_counter.update(trained) - return self - # Properties @property def n_warnings(self) -> collections.Counter: @@ -586,7 +583,7 @@ class OXTRegressor(ExtraTrees, base.Regressor): binary_split [*Tree parameter*] If True, only allow binary splits. max_size - [*Tree parameter*] Maximum memory (MB) consumed by the tree. + [*Tree parameter*] Maximum memory (MiB) consumed by the tree. memory_estimate_period [*Tree parameter*] Number of instances between memory consumption checks. stop_mem_management @@ -617,7 +614,7 @@ class OXTRegressor(ExtraTrees, base.Regressor): >>> metric = metrics.RMSE() >>> evaluate.progressive_val_score(dataset, model, metric) - RMSE: 3.127311 + RMSE: 3.16212 References ---------- diff --git a/river/forest/test_amf.py b/river/forest/test_amf.py new file mode 100644 index 0000000000..f424bbf896 --- /dev/null +++ b/river/forest/test_amf.py @@ -0,0 +1,18 @@ +from __future__ import annotations + + +def test_issue_1272(): + """ + + https://github.com/online-ml/river/issues/1272 + + >>> import river + >>> from river import forest, metrics + + >>> model = forest.ARFClassifier(metric=metrics.CrossEntropy()) + >>> model.learn_one({"x": 1}, True) + + >>> model = forest.ARFClassifier() + >>> model.learn_one({"x": 1}, True) + + """ diff --git a/river/imblearn/__init__.py b/river/imblearn/__init__.py index 4005b60e85..c551be9239 100644 --- a/river/imblearn/__init__.py +++ b/river/imblearn/__init__.py @@ -1,4 +1,5 @@ """Sampling methods.""" + from __future__ import annotations from .chebyshev import ChebyshevOverSampler, ChebyshevUnderSampler diff --git a/river/imblearn/chebyshev.py b/river/imblearn/chebyshev.py index cb7e370a9c..5c9c3baaf7 100644 --- a/river/imblearn/chebyshev.py +++ b/river/imblearn/chebyshev.py @@ -110,8 +110,6 @@ def learn_one(self, x, y, **kwargs): else: self.regressor.learn_one(x, y, **kwargs) - return self - @classmethod def _unit_test_params(cls): yield {"regressor": linear_model.LinearRegression(), "seed": 42} @@ -164,10 +162,10 @@ class ChebyshevOverSampler(base.Wrapper, base.Regressor): ... metrics.MAE(), ... print_every=500 ... ) - [500] MAE: 1.682627 - [1,000] MAE: 1.761306 - [1,001] MAE: 1.759576 - MAE: 1.759576 + [500] MAE: 1.629786 + [1,000] MAE: 1.663799 + [1,001] MAE: 1.66253 + MAE: 1.66253 References ---------- @@ -205,8 +203,6 @@ def learn_one(self, x, y, **kwargs): else: self.regressor.learn_one(x, y, **kwargs) - return self - @classmethod def _unit_test_params(cls): yield {"regressor": linear_model.LinearRegression()} diff --git a/river/imblearn/hard_sampling.py b/river/imblearn/hard_sampling.py index 994e9d014a..b4c1faf1ba 100644 --- a/river/imblearn/hard_sampling.py +++ b/river/imblearn/hard_sampling.py @@ -66,8 +66,6 @@ def learn_one(self, x, y, **kwargs): else: self.model.learn_one(x, y) - return self - class HardSamplingRegressor(HardSampling, base.Regressor): """Hard sampling regressor. @@ -80,7 +78,7 @@ class HardSamplingRegressor(HardSampling, base.Regressor): The hardness of an observation is evaluated with a loss function that compares the sample's ground truth with the wrapped model's prediction. If the buffer is not full, then the sample is added to the buffer. If the buffer is full and the new sample has a bigger loss than the - lowest loss in the buffer, then the sample takes it's place. + lowest loss in the buffer, then the sample takes its place. Parameters ---------- @@ -122,10 +120,10 @@ class HardSamplingRegressor(HardSampling, base.Regressor): ... metrics.MAE(), ... print_every=500 ... ) - [500] MAE: 2.292501 - [1,000] MAE: 1.395797 - [1,001] MAE: 1.394693 - MAE: 1.394693 + [500] MAE: 2.274021 + [1,000] MAE: 1.392399 + [1,001] MAE: 1.391246 + MAE: 1.391246 """ @@ -161,7 +159,7 @@ class HardSamplingClassifier(HardSampling, base.Classifier): The hardness of an observation is evaluated with a loss function that compares the sample's ground truth with the wrapped model's prediction. If the buffer is not full, then the sample is added to the buffer. If the buffer is full and the new sample has a bigger loss than the - lowest loss in the buffer, then the sample takes it's place. + lowest loss in the buffer, then the sample takes its place. Parameters ---------- @@ -203,10 +201,10 @@ class HardSamplingClassifier(HardSampling, base.Classifier): ... metric=metrics.ROCAUC(), ... print_every=500, ... ) - [500] ROCAUC: 92.71% - [1,000] ROCAUC: 94.75% - [1,250] ROCAUC: 95.05% - ROCAUC: 95.05% + [500] ROCAUC: 92.78% + [1,000] ROCAUC: 94.76% + [1,250] ROCAUC: 95.06% + ROCAUC: 95.06% """ diff --git a/river/imblearn/random.py b/river/imblearn/random.py index 469d3d1465..36e1861c04 100644 --- a/river/imblearn/random.py +++ b/river/imblearn/random.py @@ -66,7 +66,7 @@ class percentages. The values must sum up to 1. >>> metric = metrics.LogLoss() >>> evaluate.progressive_val_score(dataset, model, metric) - LogLoss: 0.0728 + LogLoss: 0.0336... References ---------- @@ -79,6 +79,7 @@ def __init__(self, classifier: base.Classifier, desired_dist: dict, seed: int | super().__init__(classifier=classifier, seed=seed) self.desired_dist = desired_dist self._actual_dist: typing.Counter = collections.Counter() + self._trained_on_dist: typing.Counter = collections.Counter() self._pivot = None def learn_one(self, x, y, **kwargs): @@ -90,18 +91,18 @@ def learn_one(self, x, y, **kwargs): if y != self._pivot: self._pivot = max(g.keys(), key=lambda y: f[y] / g[y]) else: + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) - return self + return # Determine the sampling ratio if the class is not the pivot M = f[self._pivot] / g[self._pivot] # Likelihood ratio ratio = f[y] / (M * g[y]) if ratio < 1 and self._rng.random() < ratio: + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) - return self - class RandomOverSampler(ClassificationSampler): """Random over-sampling. @@ -146,13 +147,14 @@ class percentages. The values must sum up to 1. >>> metric = metrics.LogLoss() >>> evaluate.progressive_val_score(dataset, model, metric) - LogLoss: 0.054338 + LogLoss: 0.0457... """ def __init__(self, classifier: base.Classifier, desired_dist: dict, seed: int | None = None): super().__init__(classifier=classifier, seed=seed) self.desired_dist = desired_dist + self._trained_on_dist: typing.Counter = collections.Counter() self._actual_dist: typing.Counter = collections.Counter() self._pivot = None @@ -165,17 +167,17 @@ def learn_one(self, x, y, **kwargs): if y != self._pivot: self._pivot = max(g.keys(), key=lambda y: g[y] / f[y]) else: + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) - return self + return M = g[self._pivot] / f[self._pivot] rate = M * f[y] / g[y] for _ in range(utils.random.poisson(rate, rng=self._rng)): + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) - return self - class RandomSampler(ClassificationSampler): """Random sampling by mixing under-sampling and over-sampling. @@ -224,7 +226,7 @@ class percentages. The values must sum up to 1. If set to `None`, then the obser >>> metric = metrics.LogLoss() >>> evaluate.progressive_val_score(dataset, model, metric) - LogLoss: 0.131988 + LogLoss: 0.09... """ @@ -237,6 +239,7 @@ def __init__( ): super().__init__(classifier=classifier, seed=seed) self.sampling_rate = sampling_rate + self._trained_on_dist: typing.Counter = collections.Counter() self._actual_dist: typing.Counter = collections.Counter() if desired_dist is None: desired_dist = self._actual_dist @@ -252,6 +255,5 @@ def learn_one(self, x, y, **kwargs): rate = self.sampling_rate * f[y] / (g[y] / self._n) for _ in range(utils.random.poisson(rate, rng=self._rng)): + self._trained_on_dist[y] += 1 self.classifier.learn_one(x, y, **kwargs) - - return self diff --git a/river/linear_model/__init__.py b/river/linear_model/__init__.py index ca472dc9c2..756720490a 100644 --- a/river/linear_model/__init__.py +++ b/river/linear_model/__init__.py @@ -1,4 +1,5 @@ """Linear models.""" + from __future__ import annotations from . import base diff --git a/river/linear_model/alma.py b/river/linear_model/alma.py index 260547cd9a..45a300a6c5 100644 --- a/river/linear_model/alma.py +++ b/river/linear_model/alma.py @@ -44,7 +44,7 @@ class ALMAClassifier(base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 82.64% + Accuracy: 82.56% References ---------- @@ -85,5 +85,3 @@ def learn_one(self, x, y): self.w[i] /= max(1, norm) self.k += 1 - - return self diff --git a/river/linear_model/base.py b/river/linear_model/base.py index b2e2bb6904..91d6ba7f70 100644 --- a/river/linear_model/base.py +++ b/river/linear_model/base.py @@ -122,8 +122,6 @@ def _fit(self, x, y, w, get_grad): self._update_weights(x) - return self - def _update_weights(self, x): # L1 cumulative penalty helper @@ -161,9 +159,9 @@ def _eval_gradient_one(self, x: dict, y: float, w: float) -> tuple[dict, float]: return (loss_gradient * utils.VectorDict(x), loss_gradient) - def learn_one(self, x, y, w=1.0): + def learn_one(self, x, y, w=1.0) -> None: with self._learn_mode(x): - return self._fit(x, y, w, get_grad=self._eval_gradient_one) + self._fit(x, y, w, get_grad=self._eval_gradient_one) # Mini-batch methods @@ -188,7 +186,7 @@ def _eval_gradient_many( return dict(zip(X.columns, gradient)), loss_gradient.mean() - def learn_many(self, X: pd.DataFrame, y: pd.Series, w: float | pd.Series = 1): + def learn_many(self, X: pd.DataFrame, y: pd.Series, w: float | pd.Series = 1) -> None: self._y_name = y.name with self._learn_mode(set(X)): - return self._fit(X, y, w, get_grad=self._eval_gradient_many) + self._fit(X, y, w, get_grad=self._eval_gradient_many) diff --git a/river/linear_model/bayesian_lin_reg.py b/river/linear_model/bayesian_lin_reg.py index 31b40f8cdc..f95ce53f0c 100644 --- a/river/linear_model/bayesian_lin_reg.py +++ b/river/linear_model/bayesian_lin_reg.py @@ -1,6 +1,7 @@ from __future__ import annotations import numpy as np +import pandas as pd from river import base, proba, utils @@ -22,6 +23,10 @@ class BayesianLinearRegression(base.Regressor): Prior parameter. beta Noise parameter. + smoothing + Smoothing allows the model to gradually "forget" the past, and focus on the more recent + data. It thus enables the model to deal with concept drift. Due to the current + implementation, activating smoothing may slow down the model. Examples -------- @@ -35,15 +40,66 @@ class BayesianLinearRegression(base.Regressor): >>> model = linear_model.BayesianLinearRegression() >>> metric = metrics.MAE() - >>> evaluate.progressive_val_score(dataset, model, metric).get() - 0.5818 + >>> evaluate.progressive_val_score(dataset, model, metric) + MAE: 0.586... >>> x, _ = next(iter(dataset)) >>> model.predict_one(x) - 43.61 + 43.852... >>> model.predict_one(x, with_dist=True) - 𝒩(μ=43.616, σ=1.003) + 𝒩(μ=43.85..., σ=1.00...) + + The `smoothing` parameter can be set to make the model robust to drift. The parameter is + expected to be between 0 and 1. To exemplify, let's generate some simulation data with an + abrupt concept drift right in the middle. + + >>> import itertools + >>> import random + + >>> def random_data(coefs, n, seed=42): + ... rng = random.Random(seed) + ... for _ in range(n): + ... x = {i: rng.random() for i, c in enumerate(coefs)} + ... y = sum(c * xi for c, xi in zip(coefs, x.values())) + ... yield x, y + + Here's how the model performs without any smoothing: + + >>> model = linear_model.BayesianLinearRegression() + >>> dataset = itertools.chain( + ... random_data([0.1, 3], 100), + ... random_data([10, -2], 100) + ... ) + >>> metric = metrics.MAE() + >>> evaluate.progressive_val_score(dataset, model, metric) + MAE: 1.284... + + And here's how it performs with some smoothing: + + >>> model = linear_model.BayesianLinearRegression(smoothing=0.8) + >>> dataset = itertools.chain( + ... random_data([0.1, 3], 100), + ... random_data([10, -2], 100) + ... ) + >>> metric = metrics.MAE() + >>> evaluate.progressive_val_score(dataset, model, metric) + MAE: 0.159... + + Smoothing allows the model to gradually "forget" the past, and focus on the more recent data. + + Note how this works better than standard linear regression, even when using an aggressive + learning rate. + + >>> from river import optim + >>> model = linear_model.LinearRegression(optimizer=optim.SGD(0.5)) + >>> dataset = itertools.chain( + ... random_data([0.1, 3], 100), + ... random_data([10, -2], 100) + ... ) + >>> metric = metrics.MAE() + >>> evaluate.progressive_val_score(dataset, model, metric) + MAE: 0.242... References ---------- @@ -53,12 +109,13 @@ class BayesianLinearRegression(base.Regressor): """ - def __init__(self, alpha=1, beta=1): + def __init__(self, alpha=1, beta=1, smoothing: float | None = None): self.alpha = alpha self.beta = beta - self._ss = {} - self._ss_inv = {} - self._m = {} + self.smoothing = smoothing + self._ss: dict[tuple[base.typing.FeatureName, base.typing.FeatureName], float] = {} + self._ss_inv: dict[tuple[base.typing.FeatureName, base.typing.FeatureName], float] = {} + self._m: dict[base.typing.FeatureName, float] = {} self._n = 1 def _unit_test_skips(self): @@ -67,7 +124,20 @@ def _unit_test_skips(self): def _get_arrays(self, features, m=True, ss=True, ss_inv=True): m_arr = np.array([self._m.get(i, 0.0) for i in features]) if m else None ss_arr = ( - np.array([[self._ss.get(min((i, j), (j, i)), 0.0) for j in features] for i in features]) + np.array( + [ + [ + self._ss.get( + # Get value if it exists + min((i, j), (j, i)), + # Initialize to eye matrix + 1.0 / self.alpha if i == j else 0.0, + ) + for j in features + ] + for i in features + ] + ) if ss else None ) @@ -75,7 +145,12 @@ def _get_arrays(self, features, m=True, ss=True, ss_inv=True): np.array( [ [ - self._ss_inv.get(min((i, j), (j, i)), 1.0 / self.alpha if i == j else 0.0) + self._ss_inv.get( + # Get value if it exists + min((i, j), (j, i)), + # Initialize to eye matrix + 1.0 / self.alpha if i == j else 0.0, + ) for j in features ] for i in features @@ -101,15 +176,23 @@ def learn_one(self, x, y): m_arr, ss_arr, ss_inv_arr = self._get_arrays(x.keys()) bx = self.beta * x_arr - utils.math.sherman_morrison(A=ss_inv_arr, u=bx, v=x_arr) - # Bishop equation 3.50 - m_arr = ss_inv_arr @ (ss_arr @ m_arr + bx * y) - # Bishop equation 3.51 - ss_arr += np.outer(bx, x_arr) - self._set_arrays(x.keys(), m_arr, ss_arr, ss_inv_arr) + if self.smoothing is None: + utils.math.sherman_morrison(A=ss_inv_arr, u=bx, v=x_arr) + # Bishop equation 3.50 + m_arr = ss_inv_arr @ (ss_arr @ m_arr + bx * y) + # Bishop equation 3.51 + ss_arr += np.outer(bx, x_arr) + else: + new_ss_arr = self.smoothing * ss_arr + (1 - self.smoothing) * np.outer(bx, x_arr) + # TODO: we use standard matrix inversion. This is not very efficient. However, we don't + # yet have a formula for the Sherman-Morrison approximation when a smoothing factor is + # involved. This is an interesting research direction! + ss_inv_arr = np.linalg.inv(new_ss_arr) + m_arr = ss_inv_arr @ (self.smoothing * ss_arr @ m_arr + (1 - self.smoothing) * bx * y) + ss_arr = new_ss_arr - return self + self._set_arrays(x.keys(), m_arr, ss_arr, ss_inv_arr) def predict_one(self, x, with_dist=False): """Predict the output of features `x`. @@ -133,8 +216,12 @@ def predict_one(self, x, with_dist=False): return y_pred_mean x_arr = np.array(list(x.values())) - _, _, ss_inv_arr = self._get_arrays(x.keys(), m=False, ss=False) + *_, ss_inv_arr = self._get_arrays(x.keys(), m=False, ss=False) # Bishop equation 3.59 y_pred_var = 1 / self.beta + x_arr @ ss_inv_arr @ x_arr.T return proba.Gaussian._from_state(n=1, m=y_pred_mean, sig=y_pred_var**0.5, ddof=0) + + def predict_many(self, X): + m, *_ = self._get_arrays(X.columns, m=True, ss=False, ss_inv=False) + return pd.Series(X.values @ m, index=X.index) diff --git a/river/linear_model/lin_reg.py b/river/linear_model/lin_reg.py index c152c7694c..8c33cb7079 100644 --- a/river/linear_model/lin_reg.py +++ b/river/linear_model/lin_reg.py @@ -62,7 +62,7 @@ class LinearRegression(linear_model.base.GLM, base.MiniBatchRegressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.555971 + MAE: 0.558735 >>> model['LinearRegression'].intercept 35.617670 diff --git a/river/linear_model/pa.py b/river/linear_model/pa.py index bf65f72163..697b6527b1 100644 --- a/river/linear_model/pa.py +++ b/river/linear_model/pa.py @@ -69,11 +69,12 @@ class PARegressor(BasePA, base.Regressor): >>> for xi, yi in stream.iter_array(X, y): ... y_pred = model.predict_one(xi) - ... model = model.learn_one(xi, yi) - ... metric = metric.update(yi, y_pred) + ... model.learn_one(xi, yi) + ... metric.update(yi, y_pred) >>> print(metric) - MAE: 9.809402, MSE: 472.393532 + MAE: 9.809402 + MSE: 472.393532 References ---------- @@ -96,8 +97,6 @@ def learn_one(self, x, y): if self.learn_intercept: self.intercept += step - return self - def predict_one(self, x): return utils.math.dot(x, self.weights) + self.intercept @@ -148,15 +147,16 @@ class PAClassifier(BasePA, base.Classifier): ... ) >>> for xi, yi in stream.iter_array(X_train, y_train): - ... y_pred = model.learn_one(xi, yi) + ... model.learn_one(xi, yi) >>> metric = metrics.Accuracy() + metrics.LogLoss() >>> for xi, yi in stream.iter_array(X_test, y_test): - ... metric = metric.update(yi, model.predict_proba_one(xi)) + ... metric.update(yi, model.predict_proba_one(xi)) >>> print(metric) - Accuracy: 88.46%, LogLoss: 0.325727 + Accuracy: 88.46% + LogLoss: 0.325727... References ---------- @@ -178,8 +178,6 @@ def learn_one(self, x, y): if self.learn_intercept: self.intercept += step - return self - def predict_proba_one(self, x): y_pred = utils.math.sigmoid(utils.math.dot(x, self.weights) + self.intercept) return {False: 1.0 - y_pred, True: y_pred} diff --git a/river/linear_model/softmax.py b/river/linear_model/softmax.py index fbacd49a62..4b4d0ef779 100644 --- a/river/linear_model/softmax.py +++ b/river/linear_model/softmax.py @@ -70,9 +70,7 @@ def __init__( self.optimizers = collections.defaultdict(new_optimizer) # type: ignore self.loss = optim.losses.CrossEntropy() if loss is None else loss self.l2 = l2 - self.weights = collections.defaultdict( - functools.partial(collections.defaultdict, float) - ) # type: ignore + self.weights = collections.defaultdict(functools.partial(collections.defaultdict, float)) # type: ignore @property def _multiclass(self): @@ -95,8 +93,6 @@ def learn_one(self, x, y): gradient = {i: xi * loss + self.l2 * weights.get(i, 0) for i, xi in x.items()} self.weights[label] = self.optimizers[label].step(w=weights, g=gradient) - return self - def predict_proba_one(self, x): return utils.math.softmax( {label: utils.math.dot(weights, x) for label, weights in self.weights.items()} diff --git a/river/linear_model/test_glm.py b/river/linear_model/test_glm.py index 2d95f2ce73..f274b39873 100644 --- a/river/linear_model/test_glm.py +++ b/river/linear_model/test_glm.py @@ -79,7 +79,8 @@ def test_finite_differences(lm, dataset): eps = 1e-6 for x, y in dataset: - x = scaler.learn_one(x).transform_one(x) + scaler.learn_one(x) + x = scaler.transform_one(x) # Store the current gradient and weights gradient, _ = lm._eval_gradient_one(x, y, 1) @@ -214,7 +215,8 @@ def test_lin_reg_sklearn_coherence(river_params, sklearn_params): sk = sklm.SGDRegressor(**sklearn_params) for x, y in datasets.TrumpApproval().take(100): - x = ss.learn_one(x).transform_one(x) + ss.learn_one(x) + x = ss.transform_one(x) rv.learn_one(x, y) sk.partial_fit([list(x.values())], [y]) @@ -238,7 +240,8 @@ def test_lin_reg_sklearn_learn_many_coherence(river_params, sklearn_params): sk = sklm.SGDRegressor(**sklearn_params) for x, y in datasets.TrumpApproval().take(100): - x = ss.learn_one(x).transform_one(x) + ss.learn_one(x) + x = ss.transform_one(x) rv.learn_many(pd.DataFrame([x]), pd.Series([y])) sk.partial_fit([list(x.values())], [y]) @@ -320,7 +323,8 @@ def test_log_reg_sklearn_coherence(river_params, sklearn_params): sk = sklm.SGDClassifier(**sklearn_params) for x, y in datasets.Bananas().take(100): - x = ss.learn_one(x).transform_one(x) + ss.learn_one(x) + x = ss.transform_one(x) rv.learn_one(x, y) sk.partial_fit([list(x.values())], [y], classes=[False, True]) @@ -360,7 +364,8 @@ def test_perceptron_sklearn_coherence(river_params, sklearn_params): sk = sklm.Perceptron(**sklearn_params) for x, y in datasets.Bananas().take(100): - x = ss.learn_one(x).transform_one(x) + ss.learn_one(x) + x = ss.transform_one(x) rv.learn_one(x, y) sk.partial_fit([list(x.values())], [y], classes=[False, True]) @@ -403,7 +408,8 @@ def test_lin_reg_sklearn_l1_non_regression(): ) for xi, yi in stream.iter_pandas(X, y): - xi_tr = ss.learn_one(xi).transform_one(xi) + ss.learn_one(xi) + xi_tr = ss.transform_one(xi) rv.learn_one(xi_tr, yi) sk.partial_fit([list(xi_tr.values())], [yi]) @@ -455,7 +461,8 @@ def test_log_reg_sklearn_l1_non_regression(): rv_pred = list() sk_pred = list() for xi, yi in stream.iter_pandas(X, y): - xi_tr = ss.learn_one(xi).transform_one(xi) + ss.learn_one(xi) + xi_tr = ss.transform_one(xi) rv.learn_one(xi_tr, yi) sk.partial_fit([list(xi_tr.values())], [yi], classes=[False, True]) diff --git a/river/metrics/__init__.py b/river/metrics/__init__.py index 5a1e102756..f798281f33 100644 --- a/river/metrics/__init__.py +++ b/river/metrics/__init__.py @@ -26,6 +26,7 @@ ``` """ + from __future__ import annotations from . import base, multioutput diff --git a/river/metrics/accuracy.py b/river/metrics/accuracy.py index 8e3cbd8bae..c7b6018823 100644 --- a/river/metrics/accuracy.py +++ b/river/metrics/accuracy.py @@ -25,7 +25,7 @@ class Accuracy(metrics.base.MultiClassMetric): >>> metric = metrics.Accuracy() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric Accuracy: 60.00% diff --git a/river/metrics/balanced_accuracy.py b/river/metrics/balanced_accuracy.py index dcfec44680..5d581ea2d0 100644 --- a/river/metrics/balanced_accuracy.py +++ b/river/metrics/balanced_accuracy.py @@ -27,7 +27,7 @@ class BalancedAccuracy(metrics.base.MultiClassMetric): >>> metric = metrics.BalancedAccuracy() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric BalancedAccuracy: 62.50% @@ -36,7 +36,7 @@ class BalancedAccuracy(metrics.base.MultiClassMetric): >>> y_pred = [0, 1, 0, 0, 0, 1] >>> metric = metrics.BalancedAccuracy() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric BalancedAccuracy: 62.50% diff --git a/river/metrics/base.py b/river/metrics/base.py index b34b571cac..788de42d3f 100644 --- a/river/metrics/base.py +++ b/river/metrics/base.py @@ -7,8 +7,6 @@ from river import base, stats, utils -from . import confusion - __all__ = [ "BinaryMetric", "ClassificationMetric", @@ -24,11 +22,11 @@ class Metric(base.Base, abc.ABC): """Mother class for all metrics.""" @abc.abstractmethod - def update(self, y_true, y_pred) -> Metric: + def update(self, y_true, y_pred) -> None: """Update the metric.""" @abc.abstractmethod - def revert(self, y_true, y_pred) -> Metric: + def revert(self, y_true, y_pred) -> None: """Revert the metric.""" @abc.abstractmethod @@ -79,26 +77,31 @@ class ClassificationMetric(Metric): _fmt = ".2%" # output a percentage, e.g. 0.427 becomes "42,7%" - def __init__(self, cm: confusion.ConfusionMatrix | None = None): + def __init__(self, cm=None): + # HACK: there is a circular dependency between ConfusionMatrix and ClassificationMetric. We + # use ConfusionMatrix here so as to express metrics in terms of false/true + # positives/negatives. But for UX reasons, we also want ConfusionMatrix to be a + # ClassificationMetric, so that it can used in places a ClassificationMetric can be used, + # such as evaluate.progressive_val_score. + from river import metrics + if cm is None: - cm = confusion.ConfusionMatrix() + cm = metrics.ConfusionMatrix() self.cm = cm - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0) -> None: self.cm.update( y_true, y_pred, - sample_weight=sample_weight, + w=w, ) - return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0) -> None: self.cm.revert( y_true, y_pred, - sample_weight=sample_weight, + w=w, ) - return self @property def bigger_is_better(self): @@ -148,21 +151,21 @@ def update( self, y_true: bool, y_pred: bool | float | dict[bool, float], - sample_weight=1.0, - ) -> BinaryMetric: + w=1.0, + ) -> None: if self.requires_labels: y_pred = y_pred == self.pos_val - return super().update(y_true == self.pos_val, y_pred, sample_weight) + return super().update(y_true == self.pos_val, y_pred, w) def revert( self, y_true: bool, y_pred: bool | float | dict[bool, float], - sample_weight=1.0, - ) -> BinaryMetric: + w=1.0, + ) -> None: if self.requires_labels: y_pred = y_pred == self.pos_val - return super().revert(y_true == self.pos_val, y_pred, sample_weight) + return super().revert(y_true == self.pos_val, y_pred, w) class MultiClassMetric(ClassificationMetric): @@ -187,11 +190,11 @@ class RegressionMetric(Metric): _fmt = ",.6f" # use commas to separate big numbers and show 6 decimals @abc.abstractmethod - def update(self, y_true: numbers.Number, y_pred: numbers.Number) -> RegressionMetric: + def update(self, y_true: numbers.Number, y_pred: numbers.Number) -> None: """Update the metric.""" @abc.abstractmethod - def revert(self, y_true: numbers.Number, y_pred: numbers.Number) -> RegressionMetric: + def revert(self, y_true: numbers.Number, y_pred: numbers.Number) -> None: """Revert the metric.""" @property @@ -220,11 +223,11 @@ class Metrics(Metric, collections.UserList): """ - def __init__(self, metrics, str_sep=", "): + def __init__(self, metrics, str_sep="\n"): super().__init__(metrics) self.str_sep = str_sep - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0) -> None: # If the metrics are classification metrics, then we have to handle the case where some # of the metrics require labels, whilst others need to be fed probabilities if hasattr(self, "requires_labels") and not self.requires_labels: @@ -233,26 +236,24 @@ def update(self, y_true, y_pred, sample_weight=1.0): m.update(y_true, max(y_pred, key=y_pred.get)) else: m.update(y_true, y_pred) - return self + return for m in self: m.update(y_true, y_pred) - return self - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0) -> None: # If the metrics are classification metrics, then we have to handle the case where some # of the metrics require labels, whilst others need to be fed probabilities if hasattr(self, "requires_labels") and not self.requires_labels: for m in self: if m.requires_labels: - m.revert(y_true, max(y_pred, key=y_pred.get), sample_weight) + m.revert(y_true, max(y_pred, key=y_pred.get), w) else: - m.revert(y_true, y_pred, sample_weight) - return self + m.revert(y_true, y_pred, w) + return for m in self: - m.revert(y_true, y_pred, sample_weight) - return self + m.revert(y_true, y_pred, w) def get(self): return [m.get() for m in self] @@ -333,13 +334,11 @@ def __init__(self): def _eval(self, y_true, y_pred): pass - def update(self, y_true, y_pred, sample_weight=1.0): - self._mean.update(x=self._eval(y_true, y_pred), w=sample_weight) - return self + def update(self, y_true, y_pred, w=1.0) -> None: + self._mean.update(x=self._eval(y_true, y_pred), w=w) - def revert(self, y_true, y_pred, sample_weight=1.0): - self._mean.revert(x=self._eval(y_true, y_pred), w=sample_weight) - return self + def revert(self, y_true, y_pred, w=1.0) -> None: + self._mean.revert(x=self._eval(y_true, y_pred), w=w) def get(self): return self._mean.get() @@ -354,11 +353,11 @@ class ClusteringMetric(base.Base, abc.ABC): _fmt = ",.6f" # Use commas to separate big numbers and show 6 decimals @abc.abstractmethod - def update(self, x, y_pred, centers, sample_weight=1.0) -> ClusteringMetric: + def update(self, x, y_pred, centers, w=1.0) -> None: """Update the metric.""" @abc.abstractmethod - def revert(self, x, y_pred, centers, sample_weight=1.0) -> ClusteringMetric: + def revert(self, x, y_pred, centers, w=1.0) -> None: """Revert the metric.""" @abc.abstractmethod diff --git a/river/metrics/confusion.py b/river/metrics/confusion.py index 8146aadefb..95737c6c84 100644 --- a/river/metrics/confusion.py +++ b/river/metrics/confusion.py @@ -3,10 +3,10 @@ import functools from collections import defaultdict -from river import utils +from river import metrics, utils -class ConfusionMatrix: +class ConfusionMatrix(metrics.base.MultiClassMetric): """Confusion Matrix for binary and multi-class classification. Parameters @@ -25,7 +25,7 @@ class ConfusionMatrix: >>> cm = metrics.ConfusionMatrix() >>> for yt, yp in zip(y_true, y_pred): - ... cm = cm.update(yt, yp) + ... cm.update(yt, yp) >>> cm ant bird cat @@ -62,22 +62,20 @@ def __getitem__(self, key): """Syntactic sugar for accessing the counts directly.""" return self.data[key] - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): self.n_samples += 1 - self._update(y_true, y_pred, sample_weight) - return self + self._update(y_true, y_pred, w) - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): self.n_samples -= 1 - # Revert is equal to subtracting so we pass the negative sample_weight - self._update(y_true, y_pred, -sample_weight) - return self + # Revert is equal to subtracting so we pass the negative sample_weight (w) + self._update(y_true, y_pred, -w) - def _update(self, y_true, y_pred, sample_weight): - self.data[y_true][y_pred] += sample_weight - self.total_weight += sample_weight - self.sum_row[y_true] += sample_weight - self.sum_col[y_pred] += sample_weight + def _update(self, y_true, y_pred, w): + self.data[y_true][y_pred] += w + self.total_weight += w + self.sum_row[y_true] += w + self.sum_col[y_pred] += w @property def classes(self): @@ -127,3 +125,10 @@ def total_false_positives(self): @property def total_false_negatives(self): return sum(self.false_negatives(label) for label in self.classes) + + @property + def bigger_is_better(self): + raise NotImplementedError + + def get(self): + raise NotImplementedError diff --git a/river/metrics/cross_entropy.py b/river/metrics/cross_entropy.py index fe1d66568a..6232331661 100644 --- a/river/metrics/cross_entropy.py +++ b/river/metrics/cross_entropy.py @@ -26,7 +26,7 @@ class CrossEntropy(metrics.base.MeanMetric, metrics.base.MultiClassMetric): >>> metric = metrics.CrossEntropy() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) ... print(metric.get()) 1.222454 1.169691 diff --git a/river/metrics/efficient_rollingrocauc/efficient_rollingrocauc.pyi b/river/metrics/efficient_rollingrocauc/efficient_rollingrocauc.pyi new file mode 100644 index 0000000000..943d1fa6e4 --- /dev/null +++ b/river/metrics/efficient_rollingrocauc/efficient_rollingrocauc.pyi @@ -0,0 +1,12 @@ +from collections.abc import Sequence +from typing import Any + +class EfficientRollingROCAUC: + def __cinit__(self, positiveLabel: int, windowSize: int) -> None: ... + def __dealloc__(self) -> None: ... + def update(self, label: bool, score: bool | float | dict[bool, float]) -> None: ... + def revert(self, label: bool, score: bool | float | dict[bool, float]) -> None: ... + def get(self) -> float: ... + def __getnewargs_ex__(self) -> tuple[tuple[int, int], dict[str, Any]]: ... + def __getstate__(self) -> tuple[Sequence[int], Sequence[float]]: ... + def __setstate__(self, state: tuple[Sequence[int], Sequence[float]]) -> None: ... diff --git a/river/metrics/expected_mutual_info.pyi b/river/metrics/expected_mutual_info.pyi new file mode 100644 index 0000000000..f3b584f2d5 --- /dev/null +++ b/river/metrics/expected_mutual_info.pyi @@ -0,0 +1,3 @@ +from river import metrics + +def expected_mutual_info(confusion_matrix: metrics.ConfusionMatrix) -> float: ... diff --git a/river/metrics/fbeta.py b/river/metrics/fbeta.py index cd399215a5..e594042726 100644 --- a/river/metrics/fbeta.py +++ b/river/metrics/fbeta.py @@ -52,7 +52,7 @@ class FBeta(metrics.base.BinaryMetric): >>> metric = metrics.FBeta(beta=2) >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric FBeta: 35.71% @@ -100,7 +100,8 @@ class MacroFBeta(metrics.base.MultiClassMetric): >>> metric = metrics.MacroFBeta(beta=.8) >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroFBeta: 100.00% MacroFBeta: 31.06% MacroFBeta: 54.04% @@ -164,7 +165,7 @@ class MicroFBeta(metrics.base.MultiClassMetric): >>> metric = metrics.MicroFBeta(beta=2) >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric MicroFBeta: 60.00% @@ -217,7 +218,8 @@ class WeightedFBeta(metrics.base.MultiClassMetric): >>> metric = metrics.WeightedFBeta(beta=0.8) >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedFBeta: 100.00% WeightedFBeta: 31.06% WeightedFBeta: 54.04% @@ -287,7 +289,8 @@ class MultiFBeta(metrics.base.MultiClassMetric): ... ) >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MultiFBeta: 100.00% MultiFBeta: 25.76% MultiFBeta: 62.88% @@ -353,7 +356,7 @@ class F1(FBeta): >>> metric = metrics.F1() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric F1: 40.00% @@ -387,7 +390,8 @@ class MacroF1(MacroFBeta): >>> metric = metrics.MacroF1() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroF1: 100.00% MacroF1: 33.33% MacroF1: 55.56% @@ -423,7 +427,7 @@ class MicroF1(MicroFBeta): >>> metric = metrics.MicroF1() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric MicroF1: 60.00% @@ -464,7 +468,8 @@ class WeightedF1(WeightedFBeta): >>> metric = metrics.WeightedF1() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedF1: 100.00% WeightedF1: 33.33% WeightedF1: 55.56% diff --git a/river/metrics/fowlkes_mallows.py b/river/metrics/fowlkes_mallows.py index 2b9b9b2d6d..b15fcf4c8e 100644 --- a/river/metrics/fowlkes_mallows.py +++ b/river/metrics/fowlkes_mallows.py @@ -48,7 +48,8 @@ class FowlkesMallows(metrics.base.MultiClassMetric): >>> metric = metrics.FowlkesMallows() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) FowlkesMallows: 0.00% FowlkesMallows: 100.00% FowlkesMallows: 57.74% diff --git a/river/metrics/geometric_mean.py b/river/metrics/geometric_mean.py index 3fbeb528b4..ce0bd87c66 100644 --- a/river/metrics/geometric_mean.py +++ b/river/metrics/geometric_mean.py @@ -39,7 +39,7 @@ class GeometricMean(metrics.base.MultiClassMetric): >>> metric = metrics.GeometricMean() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric GeometricMean: 69.34% diff --git a/river/metrics/jaccard.py b/river/metrics/jaccard.py index 87738acd11..04b2ef533e 100644 --- a/river/metrics/jaccard.py +++ b/river/metrics/jaccard.py @@ -27,7 +27,8 @@ class Jaccard(metrics.base.BinaryMetric): >>> metric = metrics.Jaccard() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) Jaccard: 0.00% Jaccard: 50.00% Jaccard: 66.67% @@ -69,7 +70,8 @@ class MacroJaccard(metrics.base.MultiClassMetric): >>> metric = metrics.MacroJaccard() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroJaccard: 100.00% MacroJaccard: 25.00% MacroJaccard: 50.00% @@ -115,7 +117,8 @@ class MicroJaccard(metrics.base.MultiClassMetric): >>> metric = metrics.MicroJaccard() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MicroJaccard: 100.00% MicroJaccard: 33.33% MicroJaccard: 50.00% @@ -156,7 +159,8 @@ class WeightedJaccard(metrics.base.MultiClassMetric): >>> metric = metrics.WeightedJaccard() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedJaccard: 100.00% WeightedJaccard: 25.00% WeightedJaccard: 50.00% diff --git a/river/metrics/kappa.py b/river/metrics/kappa.py index 9218b1de68..303f78f9c5 100644 --- a/river/metrics/kappa.py +++ b/river/metrics/kappa.py @@ -37,7 +37,7 @@ class CohenKappa(metrics.base.MultiClassMetric): >>> metric = metrics.CohenKappa() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric CohenKappa: 42.86% diff --git a/river/metrics/log_loss.py b/river/metrics/log_loss.py index 858c8f83fb..e998d1a372 100644 --- a/river/metrics/log_loss.py +++ b/river/metrics/log_loss.py @@ -20,7 +20,7 @@ class LogLoss(metrics.base.MeanMetric, metrics.base.BinaryMetric): >>> metric = metrics.LogLoss() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) ... print(metric.get()) 0.105360 0.105360 diff --git a/river/metrics/mae.py b/river/metrics/mae.py index fb4fb44d4c..78bd4ed629 100644 --- a/river/metrics/mae.py +++ b/river/metrics/mae.py @@ -19,7 +19,8 @@ class MAE(metrics.base.MeanMetric, metrics.base.RegressionMetric): >>> metric = metrics.MAE() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.5 0.5 0.333 diff --git a/river/metrics/mape.py b/river/metrics/mape.py index 3fdedf7bee..46df50084e 100644 --- a/river/metrics/mape.py +++ b/river/metrics/mape.py @@ -18,7 +18,7 @@ class MAPE(metrics.base.MeanMetric, metrics.base.RegressionMetric): >>> metric = metrics.MAPE() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric MAPE: 32.738095 diff --git a/river/metrics/mcc.py b/river/metrics/mcc.py index 9f5eb70a81..e4d5d1c4c8 100644 --- a/river/metrics/mcc.py +++ b/river/metrics/mcc.py @@ -29,7 +29,7 @@ class MCC(metrics.base.BinaryMetric): >>> mcc = metrics.MCC() >>> for yt, yp in zip(y_true, y_pred): - ... mcc = mcc.update(yt, yp) + ... mcc.update(yt, yp) >>> mcc MCC: -0.333333 diff --git a/river/metrics/mse.py b/river/metrics/mse.py index 6fcd750e8a..bda70174d8 100644 --- a/river/metrics/mse.py +++ b/river/metrics/mse.py @@ -21,7 +21,8 @@ class MSE(metrics.base.MeanMetric, metrics.base.RegressionMetric): >>> metric = metrics.MSE() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.25 0.25 0.1666 @@ -46,7 +47,8 @@ class RMSE(MSE): >>> metric = metrics.RMSE() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.5 0.5 0.408248 @@ -74,12 +76,12 @@ class RMSLE(RMSE): >>> metric = metrics.RMSLE() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric RMSLE: 0.357826 """ - def update(self, y_true, y_pred, sample_weight=1.0): - return super().update(math.log(y_true + 1), math.log(y_pred + 1), sample_weight) + def update(self, y_true, y_pred, w=1.0): + super().update(math.log(y_true + 1), math.log(y_pred + 1), w) diff --git a/river/metrics/multioutput/__init__.py b/river/metrics/multioutput/__init__.py index 2c368f1b26..ee0d1df1af 100644 --- a/river/metrics/multioutput/__init__.py +++ b/river/metrics/multioutput/__init__.py @@ -1,4 +1,5 @@ """Metrics for multi-output learning.""" + from __future__ import annotations from . import base @@ -7,6 +8,7 @@ from .macro import MacroAverage from .micro import MicroAverage from .per_output import PerOutput +from .sample_average import SampleAverage __all__ = [ "base", @@ -15,4 +17,5 @@ "ExactMatch", "MicroAverage", "PerOutput", + "SampleAverage", ] diff --git a/river/metrics/multioutput/base.py b/river/metrics/multioutput/base.py index 9eee7b21b9..eed2ab8283 100644 --- a/river/metrics/multioutput/base.py +++ b/river/metrics/multioutput/base.py @@ -37,22 +37,20 @@ def update( y_true: dict[str | int, base.typing.ClfTarget], y_pred: dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]], - sample_weight=1.0, - ) -> MultiOutputClassificationMetric: + w=1.0, + ) -> None: """Update the metric.""" - self.cm.update(y_true, y_pred, sample_weight) - return self + self.cm.update(y_true, y_pred, w) def revert( self, y_true: dict[str | int, base.typing.ClfTarget], y_pred: dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]], - sample_weight=1.0, - ) -> MultiOutputClassificationMetric: + w=1.0, + ) -> None: """Revert the metric.""" - self.cm.revert(y_true, y_pred, sample_weight) - return self + self.cm.revert(y_true, y_pred, w) def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) @@ -74,7 +72,7 @@ def update( self, y_true: dict[str | int, float | int], y_pred: dict[str | int, float | int], - ) -> MultiOutputRegressionMetric: + ) -> None: """Update the metric.""" @abc.abstractmethod @@ -82,7 +80,7 @@ def revert( self, y_true: dict[str | int, float | int], y_pred: dict[str | int, float | int], - ) -> MultiOutputRegressionMetric: + ) -> None: """Revert the metric.""" def works_with(self, model) -> bool: diff --git a/river/metrics/multioutput/confusion.py b/river/metrics/multioutput/confusion.py index 7d20b613ac..d0e9b3f6d8 100644 --- a/river/metrics/multioutput/confusion.py +++ b/river/metrics/multioutput/confusion.py @@ -28,7 +28,7 @@ class MultiLabelConfusionMatrix: ... ] >>> for yt, yp in zip(y_true, y_pred): - ... cm = cm.update(yt, yp) + ... cm.update(yt, yp) >>> cm 0 @@ -51,25 +51,23 @@ class MultiLabelConfusionMatrix: def __init__(self): self.data = dict() - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): for label, yt in y_true.items(): try: cm = self.data[label] except KeyError: cm = metrics.ConfusionMatrix() self.data[label] = cm - cm.update(yt, y_pred[label], sample_weight) - return self + cm.update(yt, y_pred[label], w) - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): for label, yt in y_true.items(): try: cm = self.data[label] except KeyError: cm = metrics.ConfusionMatrix() self.data[label] = cm - cm.update(yt, y_pred[label], sample_weight) - return self + cm.update(yt, y_pred[label], w) def __repr__(self): return "\n\n".join( diff --git a/river/metrics/multioutput/exact_match.py b/river/metrics/multioutput/exact_match.py index 288cbea168..3bfb14b91c 100644 --- a/river/metrics/multioutput/exact_match.py +++ b/river/metrics/multioutput/exact_match.py @@ -38,7 +38,7 @@ class ExactMatch(metrics.base.MeanMetric, MultiOutputClassificationMetric): >>> metric = metrics.multioutput.ExactMatch() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric ExactMatch: 33.33% diff --git a/river/metrics/multioutput/macro.py b/river/metrics/multioutput/macro.py index 45fbf0b1d7..873de0c0e5 100644 --- a/river/metrics/multioutput/macro.py +++ b/river/metrics/multioutput/macro.py @@ -37,15 +37,13 @@ def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) return utils.inspect.ismoregressor(model) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): for i in y_true: - self.metrics[i].update(y_true[i], y_pred[i], sample_weight) - return self + self.metrics[i].update(y_true[i], y_pred[i], w) - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): for i in y_true: - self.metrics[i].revert(y_true[i], y_pred[i], sample_weight) - return self + self.metrics[i].revert(y_true[i], y_pred[i], w) def get(self): return statistics.mean(metric.get() for metric in self.metrics.values()) diff --git a/river/metrics/multioutput/micro.py b/river/metrics/multioutput/micro.py index 3449563929..9f5b9479ef 100644 --- a/river/metrics/multioutput/micro.py +++ b/river/metrics/multioutput/micro.py @@ -30,15 +30,13 @@ def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) return utils.inspect.ismoregressor(model) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): for i in y_true: - self.metric.update(y_true[i], y_pred[i], sample_weight) - return self + self.metric.update(y_true[i], y_pred[i], w) - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): for i in y_true: - self.metric.revert(y_true[i], y_pred[i], sample_weight) - return self + self.metric.revert(y_true[i], y_pred[i], w) def get(self): return self.metric.get() diff --git a/river/metrics/multioutput/per_output.py b/river/metrics/multioutput/per_output.py index db75519add..9762b16618 100644 --- a/river/metrics/multioutput/per_output.py +++ b/river/metrics/multioutput/per_output.py @@ -35,15 +35,13 @@ def works_with(self, model) -> bool: return utils.inspect.ismoclassifier(model) return utils.inspect.ismoregressor(model) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): for i in y_true: - self.metrics[i].update(y_true[i], y_pred[i], sample_weight) - return self + self.metrics[i].update(y_true[i], y_pred[i], w) - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): for i in y_true: - self.metrics[i].revert(y_true[i], y_pred[i], sample_weight) - return self + self.metrics[i].revert(y_true[i], y_pred[i], w) def get(self): return dict(self.metrics) diff --git a/river/metrics/multioutput/sample_average.py b/river/metrics/multioutput/sample_average.py new file mode 100644 index 0000000000..246dd1553e --- /dev/null +++ b/river/metrics/multioutput/sample_average.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +from river import metrics, stats, utils +from river.metrics.multioutput.base import MultiOutputMetric + +__all__ = ["SampleAverage"] + + +class SampleAverage(MultiOutputMetric, metrics.base.WrapperMetric): + """Sample-average wrapper. + + The provided metric is evaluate on each sample. The arithmetic average over all the samples is + returned. This is equivalent to using `average='samples'` in scikit-learn. + + Parameters + ---------- + metric + A classification or a regression metric. + + Examples + -------- + + >>> from river import metrics + + >>> y_true = [ + ... {0: False, 1: True, 2: True}, + ... {0: True, 1: True, 2: False} + ... ] + >>> y_pred = [ + ... {0: True, 1: True, 2: True}, + ... {0: True, 1: False, 2: False} + ... ] + + >>> sample_jaccard = metrics.multioutput.SampleAverage(metrics.Jaccard()) + + >>> for yt, yp in zip(y_true, y_pred): + ... sample_jaccard.update(yt, yp) + + >>> sample_jaccard + SampleAverage(Jaccard): 58.33% + + """ + + def __init__(self, metric): + self._metric = metric + self._avg = stats.Mean() + + @property + def metric(self): + return self._metric + + def works_with(self, model) -> bool: + if isinstance(self.metric, metrics.base.ClassificationMetric): + return utils.inspect.ismoclassifier(model) + return utils.inspect.ismoregressor(model) + + def update(self, y_true, y_pred, w=1.0): + metric = self.metric.clone() + for i in y_true: + metric.update(y_true[i], y_pred[i]) + self._avg.update(metric.get(), w) + + def revert(self, y_true, y_pred, w=1.0): + metric = self.metric.clone() + for i in y_true: + metric.update(y_true[i], y_pred[i]) + self._avg.revert(metric.get(), w) + + def get(self): + return self._avg.get() diff --git a/river/metrics/multioutput/test_multioutput_metrics.py b/river/metrics/multioutput/test_multioutput_metrics.py index 592dd8da88..341bb4df3c 100644 --- a/river/metrics/multioutput/test_multioutput_metrics.py +++ b/river/metrics/multioutput/test_multioutput_metrics.py @@ -34,6 +34,10 @@ metrics.multioutput.MicroAverage(metrics.Precision()), functools.partial(sk_metrics.precision_score, average="micro", zero_division=0), ), + ( + metrics.multioutput.SampleAverage(metrics.Precision()), + functools.partial(sk_metrics.precision_score, average="samples", zero_division=0), + ), ( metrics.multioutput.MacroAverage(metrics.Recall()), functools.partial(sk_metrics.recall_score, average="macro", zero_division=0), @@ -42,6 +46,10 @@ metrics.multioutput.MicroAverage(metrics.Recall()), functools.partial(sk_metrics.recall_score, average="micro", zero_division=0), ), + ( + metrics.multioutput.SampleAverage(metrics.Recall()), + functools.partial(sk_metrics.recall_score, average="samples", zero_division=0), + ), ( metrics.multioutput.MacroAverage(metrics.F1()), functools.partial(sk_metrics.f1_score, average="macro", zero_division=0), @@ -50,6 +58,10 @@ metrics.multioutput.MicroAverage(metrics.F1()), functools.partial(sk_metrics.f1_score, average="micro", zero_division=0), ), + ( + metrics.multioutput.SampleAverage(metrics.F1()), + functools.partial(sk_metrics.f1_score, average="samples", zero_division=0), + ), ] ], ) diff --git a/river/metrics/mutual_info.py b/river/metrics/mutual_info.py index 7b09f13b54..1a9d22e398 100644 --- a/river/metrics/mutual_info.py +++ b/river/metrics/mutual_info.py @@ -60,7 +60,8 @@ class MutualInfo(metrics.base.MultiClassMetric): >>> metric = metrics.MutualInfo() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.0 0.0 0.0 @@ -144,7 +145,8 @@ class NormalizedMutualInfo(metrics.base.MultiClassMetric): >>> metric = metrics.NormalizedMutualInfo() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 @@ -161,6 +163,7 @@ class NormalizedMutualInfo(metrics.base.MultiClassMetric): In Wikipedia, The Free Encyclopedia, from https://en.wikipedia.org/w/index.php?title=Mutual_information&oldid=1012714929 """ + _AVERAGE_MIN = "min" _AVERAGE_MAX = "max" _AVERAGE_GEOMETRIC = "geometric" @@ -257,7 +260,8 @@ class AdjustedMutualInfo(metrics.base.MultiClassMetric): >>> metric = metrics.AdjustedMutualInfo() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 @@ -274,6 +278,7 @@ class AdjustedMutualInfo(metrics.base.MultiClassMetric): In Wikipedia, The Free Encyclopedia, from https://en.wikipedia.org/w/index.php?title=Mutual_information&oldid=1012714929 """ + _AVERAGE_MIN = "min" _AVERAGE_MAX = "max" _AVERAGE_GEOMETRIC = "geometric" diff --git a/river/metrics/precision.py b/river/metrics/precision.py index 676d03b657..7ae807f670 100644 --- a/river/metrics/precision.py +++ b/river/metrics/precision.py @@ -27,7 +27,8 @@ class Precision(metrics.base.BinaryMetric): >>> metric = metrics.Precision() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) Precision: 100.00% Precision: 50.00% Precision: 50.00% @@ -66,7 +67,8 @@ class MacroPrecision(metrics.base.MultiClassMetric): >>> metric = metrics.MacroPrecision() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroPrecision: 100.00% MacroPrecision: 25.00% MacroPrecision: 50.00% @@ -114,7 +116,8 @@ class MicroPrecision(metrics.base.MultiClassMetric): >>> metric = metrics.MicroPrecision() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MicroPrecision: 100.00% MicroPrecision: 50.00% MicroPrecision: 66.67% @@ -160,7 +163,8 @@ class WeightedPrecision(metrics.base.MultiClassMetric): >>> metric = metrics.WeightedPrecision() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedPrecision: 100.00% WeightedPrecision: 25.00% WeightedPrecision: 50.00% diff --git a/river/metrics/r2.py b/river/metrics/r2.py index 88685e4b28..e2785ead3c 100644 --- a/river/metrics/r2.py +++ b/river/metrics/r2.py @@ -30,7 +30,8 @@ class R2(metrics.base.RegressionMetric): >>> metric = metrics.R2() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 0.0 0.9183 0.9230 @@ -51,16 +52,14 @@ def __init__(self): def bigger_is_better(self): return True - def update(self, y_true, y_pred, sample_weight=1.0): - self._y_var.update(y_true, w=sample_weight) - squared_error = (y_true - y_pred) * (y_true - y_pred) * sample_weight + def update(self, y_true, y_pred, w=1.0): + self._y_var.update(y_true, w=w) + squared_error = (y_true - y_pred) * (y_true - y_pred) * w self._residual_sum_of_squares += squared_error - return self - def revert(self, y_true, y_pred, sample_weight=1.0): - self._y_var.update(y_true, w=-sample_weight) - self._residual_sum_of_squares -= (y_true - y_pred) * (y_true - y_pred) * sample_weight - return self + def revert(self, y_true, y_pred, w=1.0): + self._y_var.update(y_true, w=-w) + self._residual_sum_of_squares -= (y_true - y_pred) * (y_true - y_pred) * w def get(self): if self._y_var.mean.n > 1: diff --git a/river/metrics/rand.py b/river/metrics/rand.py index c3a037c72e..ddd6854400 100644 --- a/river/metrics/rand.py +++ b/river/metrics/rand.py @@ -78,7 +78,7 @@ class Rand(metrics.base.MultiClassMetric): >>> metric = metrics.Rand() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric Rand: 0.666667 @@ -147,7 +147,8 @@ class AdjustedRand(metrics.base.MultiClassMetric): >>> metric = metrics.AdjustedRand() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 diff --git a/river/metrics/recall.py b/river/metrics/recall.py index ada235e906..fe5eab9ec6 100644 --- a/river/metrics/recall.py +++ b/river/metrics/recall.py @@ -27,7 +27,8 @@ class Recall(metrics.base.BinaryMetric): >>> metric = metrics.Recall() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) Recall: 100.00% Recall: 100.00% Recall: 50.00% @@ -65,7 +66,8 @@ class MacroRecall(metrics.base.MultiClassMetric): >>> metric = metrics.MacroRecall() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MacroRecall: 100.00% MacroRecall: 50.00% MacroRecall: 66.67% @@ -110,7 +112,8 @@ class MicroRecall(metrics.MicroPrecision): >>> metric = metrics.MicroRecall() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) MicroRecall: 100.00% MicroRecall: 50.00% MicroRecall: 66.67% @@ -147,7 +150,8 @@ class WeightedRecall(metrics.base.MultiClassMetric): >>> metric = metrics.WeightedRecall() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp)) + ... metric.update(yt, yp) + ... print(metric) WeightedRecall: 100.00% WeightedRecall: 50.00% WeightedRecall: 66.67% diff --git a/river/metrics/report.py b/river/metrics/report.py index 1711323c57..0b47846885 100644 --- a/river/metrics/report.py +++ b/river/metrics/report.py @@ -34,7 +34,7 @@ class ClassificationReport(metrics.base.MultiClassMetric): >>> report = metrics.ClassificationReport() >>> for yt, yp in zip(y_true, y_pred): - ... report = report.update(yt, yp) + ... report.update(yt, yp) >>> print(report) Precision Recall F1 Support diff --git a/river/metrics/roc_auc.py b/river/metrics/roc_auc.py index 7f46432300..c3d6604f00 100644 --- a/river/metrics/roc_auc.py +++ b/river/metrics/roc_auc.py @@ -34,7 +34,7 @@ class ROCAUC(metrics.base.BinaryMetric): >>> metric = metrics.ROCAUC() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric ROCAUC: 87.50% @@ -45,7 +45,7 @@ class ROCAUC(metrics.base.BinaryMetric): >>> metric = metrics.ROCAUC(n_thresholds=20) >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric ROCAUC: 75.00% @@ -67,17 +67,15 @@ def works_with(self, model) -> bool: or utils.inspect.isanomalyfilter(model) ) - def update(self, y_true, y_pred, sample_weight=1.0): + def update(self, y_true, y_pred, w=1.0): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred for t, cm in zip(self.thresholds, self.cms): - cm.update(y_true == self.pos_val, p_true > t, sample_weight) - return self + cm.update(y_true == self.pos_val, p_true > t, w) - def revert(self, y_true, y_pred, sample_weight=1.0): + def revert(self, y_true, y_pred, w=1.0): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred for t, cm in zip(self.thresholds, self.cms): - cm.revert(y_true == self.pos_val, p_true > t, sample_weight) - return self + cm.revert(y_true == self.pos_val, p_true > t, w) @property def requires_labels(self): @@ -102,4 +100,4 @@ def safe_div(a, b): tprs[i] = safe_div(a=tp, b=tp + fn) fprs[i] = safe_div(a=fp, b=fp + tn) - return -integrate.trapz(x=fprs, y=tprs) + return -integrate.trapezoid(x=fprs, y=tprs) diff --git a/river/metrics/rolling_roc_auc.py b/river/metrics/rolling_roc_auc.py index 68421af9fe..7ac8aae4a9 100644 --- a/river/metrics/rolling_roc_auc.py +++ b/river/metrics/rolling_roc_auc.py @@ -43,7 +43,7 @@ class RollingROCAUC(metrics.base.BinaryMetric): >>> metric = metrics.RollingROCAUC(window_size=4) >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric RollingROCAUC: 75.00% @@ -53,7 +53,7 @@ class RollingROCAUC(metrics.base.BinaryMetric): def __init__(self, window_size=1000, pos_val=True): self.window_size = window_size self.pos_val = pos_val - self.__metric = EfficientRollingROCAUC(pos_val, window_size) + self._metric = EfficientRollingROCAUC(pos_val, window_size) def works_with(self, model) -> bool: return ( @@ -64,13 +64,11 @@ def works_with(self, model) -> bool: def update(self, y_true, y_pred): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred - self.__metric.update(y_true, p_true) - return self + self._metric.update(y_true, p_true) def revert(self, y_true, y_pred): p_true = y_pred.get(True, 0.0) if isinstance(y_pred, dict) else y_pred - self.__metric.revert(y_true, p_true) - return self + self._metric.revert(y_true, p_true) @property def requires_labels(self) -> bool: @@ -81,4 +79,4 @@ def works_with_weights(self) -> bool: return False def get(self): - return self.__metric.get() + return self._metric.get() diff --git a/river/metrics/silhouette.py b/river/metrics/silhouette.py index e3dd01cefa..3c0bb3d05c 100644 --- a/river/metrics/silhouette.py +++ b/river/metrics/silhouette.py @@ -42,9 +42,9 @@ class Silhouette(metrics.base.ClusteringMetric): >>> metric = metrics.Silhouette() >>> for x, _ in stream.iter_array(X): - ... k_means = k_means.learn_one(x) + ... k_means.learn_one(x) ... y_pred = k_means.predict_one(x) - ... metric = metric.update(x, y_pred, k_means.centers) + ... metric.update(x, y_pred, k_means.centers) >>> metric Silhouette: 0.32145 @@ -65,27 +65,23 @@ def __init__(self): @staticmethod def _find_distance_second_closest_center(centers, x): - distances = {i: math.sqrt(utils.math.minkowski_distance(centers[i], x, 2)) for i in centers} + distances = {i: utils.math.minkowski_distance(centers[i], x, 2) for i in centers} return sorted(distances.values())[-2] - def update(self, x, y_pred, centers, sample_weight=1.0): - distance_closest_centroid = math.sqrt(utils.math.minkowski_distance(centers[y_pred], x, 2)) + def update(self, x, y_pred, centers, w=1.0): + distance_closest_centroid = utils.math.minkowski_distance(centers[y_pred], x, 2) self._sum_distance_closest_centroid += distance_closest_centroid distance_second_closest_centroid = self._find_distance_second_closest_center(centers, x) self._sum_distance_second_closest_centroid += distance_second_closest_centroid - return self - - def revert(self, x, y_pred, centers, sample_weight=1.0): - distance_closest_centroid = math.sqrt(utils.math.minkowski_distance(centers[y_pred], x, 2)) + def revert(self, x, y_pred, centers, w=1.0): + distance_closest_centroid = utils.math.minkowski_distance(centers[y_pred], x, 2) self._sum_distance_closest_centroid -= distance_closest_centroid distance_second_closest_centroid = self._find_distance_second_closest_center(centers, x) self._sum_distance_second_closest_centroid -= distance_second_closest_centroid - return self - def get(self): try: return self._sum_distance_closest_centroid / self._sum_distance_second_closest_centroid diff --git a/river/metrics/smape.py b/river/metrics/smape.py index 802eba61d4..813bf3b1c0 100644 --- a/river/metrics/smape.py +++ b/river/metrics/smape.py @@ -18,7 +18,7 @@ class SMAPE(metrics.base.MeanMetric, metrics.base.RegressionMetric): >>> metric = metrics.SMAPE() >>> for yt, yp in zip(y_true, y_pred): - ... metric = metric.update(yt, yp) + ... metric.update(yt, yp) >>> metric SMAPE: 37.869392 diff --git a/river/metrics/test_confusion.py b/river/metrics/test_confusion.py new file mode 100644 index 0000000000..a30f4f2102 --- /dev/null +++ b/river/metrics/test_confusion.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +from river import datasets, evaluate, linear_model, metrics, optim, preprocessing + + +def test_issue_1443(): + dataset = datasets.Phishing() + + model = preprocessing.StandardScaler() | linear_model.LogisticRegression( + optimizer=optim.SGD(0.1) + ) + + metric = metrics.ConfusionMatrix() + + for _ in evaluate.iter_progressive_val_score(dataset, model, metric): + pass + + +def test_confusion_and_other_metrics(): + """ + + >>> dataset = datasets.Phishing() + + >>> model = preprocessing.StandardScaler() | linear_model.LogisticRegression( + ... optimizer=optim.SGD(0.1) + ... ) + + >>> metric = metrics.ConfusionMatrix() + metrics.F1() + metrics.Accuracy() + + >>> evaluate.progressive_val_score(dataset, model, metric) + False True + False 613 89 + True 49 499 + F1: 87.85% + Accuracy: 88.96% + + """ diff --git a/river/metrics/test_metrics.py b/river/metrics/test_metrics.py index df8c07e942..fa66a9fc6b 100644 --- a/river/metrics/test_metrics.py +++ b/river/metrics/test_metrics.py @@ -236,7 +236,7 @@ def test_metric(metric, sk_metric): m = copy.deepcopy(metric) for i, (yt, yp, w) in enumerate(zip(y_true, y_pred, sample_weights)): if metric.works_with_weights: - m.update(y_true=yt, y_pred=yp, sample_weight=w) + m.update(y_true=yt, y_pred=yp, w=w) else: m.update(y_true=yt, y_pred=yp) diff --git a/river/metrics/vbeta.py b/river/metrics/vbeta.py index 9e20caf1f1..78ee42bcc5 100644 --- a/river/metrics/vbeta.py +++ b/river/metrics/vbeta.py @@ -50,7 +50,8 @@ class Homogeneity(metrics.base.MultiClassMetric): >>> metric = metrics.Homogeneity() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 @@ -145,7 +146,8 @@ class Completeness(metrics.base.MultiClassMetric): >>> metric = metrics.Completeness() >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 1.0 @@ -242,7 +244,8 @@ class VBeta(metrics.base.MultiClassMetric): >>> metric = metrics.VBeta(beta=1.0) >>> for yt, yp in zip(y_true, y_pred): - ... print(metric.update(yt, yp).get()) + ... metric.update(yt, yp) + ... print(metric.get()) 1.0 1.0 0.0 diff --git a/river/misc/__init__.py b/river/misc/__init__.py index b5d013f9ec..d24ea0cc18 100644 --- a/river/misc/__init__.py +++ b/river/misc/__init__.py @@ -3,6 +3,7 @@ This module essentially regroups some implementations that have nowhere else to go. """ + from __future__ import annotations from .sdft import SDFT diff --git a/river/misc/sdft.py b/river/misc/sdft.py index 8c21b82eab..0f715f22e3 100644 --- a/river/misc/sdft.py +++ b/river/misc/sdft.py @@ -32,14 +32,13 @@ class SDFT(base.Base): >>> sdft = misc.SDFT(window_size) >>> for i, x in enumerate(X): - ... sdft = sdft.update(x) - ... + ... sdft.update(x) ... if i + 1 >= window_size: ... assert np.allclose(sdft.coefficients, np.fft.fft(X[i+1 - window_size:i+1])) References ---------- - [^1]: [Jacobsen, E. and Lyons, R., 2003. The sliding DFT. IEEE Signal Processing Magazine, 20(2), pp.74-80.](https://www.comm.utoronto.ca/~dimitris/ece431/slidingdft.pdf) + [^1]: [Jacobsen, E. asample_average.pynd Lyons, R., 2003. The sliding DFT. IEEE Signal Processing Magazine, 20(2), pp.74-80.](https://www.comm.utoronto.ca/~dimitris/ece431/slidingdft.pdf) [^2]: [Understanding and Implementing the Sliding DFT](https://www.dsprelated.com/showarticle/776.php) """ @@ -68,5 +67,3 @@ def update(self, x): for i, c in enumerate(self.coefficients): self.coefficients[i] = (c + diff) * np.exp(2j * np.pi * i / self.window_size) self.window.append(x) - - return self diff --git a/river/misc/skyline.py b/river/misc/skyline.py index 193762c301..8c0c543150 100644 --- a/river/misc/skyline.py +++ b/river/misc/skyline.py @@ -46,7 +46,7 @@ class Skyline(collections.UserList, base.Base): >>> for _ in range(100): ... house = random_house() - ... skyline = skyline.update(house) + ... skyline.update(house) >>> print(len(skyline)) 13 @@ -94,7 +94,7 @@ class Skyline(collections.UserList, base.Base): ... ) >>> for kart in karts: - ... skyline = skyline.update(kart._asdict()) + ... skyline.update(kart._asdict()) >>> best_cart_names = [kart['name'] for kart in skyline] >>> for name in best_cart_names: @@ -159,7 +159,7 @@ def update(self, x): # If the skyline is empty then the first element is part of the skyline if not self: self.append(x) - return self + return to_drop = [] is_dominated = False @@ -177,7 +177,7 @@ def update(self, x): to_drop.append(i) if is_dominated: - return self + return # Remove dominated elements if to_drop: @@ -187,4 +187,4 @@ def update(self, x): # Add x to the skyline self.append(x) - return self + return diff --git a/river/model_selection/__init__.py b/river/model_selection/__init__.py index f3a4ee482d..f010dcf5c6 100644 --- a/river/model_selection/__init__.py +++ b/river/model_selection/__init__.py @@ -12,6 +12,7 @@ The `utils.expand_param_grid` function can be used for this purpose. """ + from __future__ import annotations from . import base diff --git a/river/model_selection/bandit.py b/river/model_selection/bandit.py index 3910aa1c5a..1db7b7054d 100644 --- a/river/model_selection/bandit.py +++ b/river/model_selection/bandit.py @@ -88,7 +88,7 @@ class BanditRegressor(BanditModelSection, model_selection.base.ModelSelectionReg >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 3.13815 + MAE: 3.134089 Here's another example using the UCB policy. The latter is more sensitive to the target scale, and usually works better when the target is rescaled. @@ -114,7 +114,7 @@ class BanditRegressor(BanditModelSection, model_selection.base.ModelSelectionReg >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.875457 + MAE: 0.875333 """ @@ -131,11 +131,9 @@ def learn_one(self, x, y): for arm_id in self._pick_arms(): model = self[arm_id] y_pred = model.predict_one(x) - self.policy.update(arm_id, y_true=y, y_pred=y_pred) + self.policy.update(arm_id, y, y_pred) model.learn_one(x, y) - return self - class BanditClassifier(BanditModelSection, model_selection.base.ModelSelectionClassifier): """Bandit-based model selection for classification. @@ -206,7 +204,5 @@ def learn_one(self, x, y): y_pred = ( model.predict_one(x) if self.metric.requires_labels else model.predict_proba_one(x) ) - self.policy.update(arm_id, y_true=y, y_pred=y_pred) + self.policy.update(arm_id, y, y_pred) model.learn_one(x, y) - - return self diff --git a/river/model_selection/greedy.py b/river/model_selection/greedy.py index 8f2deb873e..fce0b87e70 100644 --- a/river/model_selection/greedy.py +++ b/river/model_selection/greedy.py @@ -45,7 +45,7 @@ class GreedyRegressor(ModelSelectionRegressor): ... ) >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 1.35 + MAE: 1.319678 """ @@ -69,8 +69,6 @@ def learn_one(self, x, y): self._best_model = model self._best_metric = metric - return self - @property def best_model(self): return self._best_model diff --git a/river/model_selection/sh.py b/river/model_selection/sh.py index c63a73f80e..03979b91b7 100644 --- a/river/model_selection/sh.py +++ b/river/model_selection/sh.py @@ -36,8 +36,7 @@ def __init__( self._best_model_idx = 0 @abc.abstractmethod - def _pred_func(self, model): - ... + def _pred_func(self, model): ... @property def best_model(self): @@ -92,8 +91,6 @@ def learn_one(self, x, y): self._s = cutoff self._r = math.floor(self.budget / (self._s * math.ceil(math.log(self._n, self.eta)))) - return self - class SuccessiveHalvingRegressor(SuccessiveHalving, ModelSelectionRegressor): r"""Successive halving algorithm for regression. @@ -198,11 +195,11 @@ class SuccessiveHalvingRegressor(SuccessiveHalving, ModelSelectionRegressor): ... model=sh, ... metric=metrics.MAE() ... ) - [1] 5 removed 5 left 50 iterations budget used: 500 budget left: 1500 best MAE: 4.540491 - [2] 2 removed 3 left 100 iterations budget used: 1000 budget left: 1000 best MAE: 2.458765 - [3] 1 removed 2 left 166 iterations budget used: 1498 budget left: 502 best MAE: 1.583751 - [4] 1 removed 1 left 250 iterations budget used: 1998 budget left: 2 best MAE: 1.147296 - MAE: 0.488387 + [1] 5 removed 5 left 50 iterations budget used: 500 budget left: 1500 best MAE: 4.419643 + [2] 2 removed 3 left 100 iterations budget used: 1000 budget left: 1000 best MAE: 2.392266 + [3] 1 removed 2 left 166 iterations budget used: 1498 budget left: 502 best MAE: 1.541383 + [4] 1 removed 1 left 250 iterations budget used: 1998 budget left: 2 best MAE: 1.112122 + MAE: 0.490688 We can now view the best model. @@ -359,7 +356,7 @@ class SuccessiveHalvingClassifier(SuccessiveHalving, ModelSelectionClassifier): [2] 2 removed 3 left 100 iterations budget used: 1000 budget left: 1000 best Accuracy: 84.00% [3] 1 removed 2 left 166 iterations budget used: 1498 budget left: 502 best Accuracy: 86.14% [4] 1 removed 1 left 250 iterations budget used: 1998 budget left: 2 best Accuracy: 84.80% - ROCAUC: 95.29% + ROCAUC: 95.22% We can now view the best model. diff --git a/river/model_selection/test_bandit.py b/river/model_selection/test_bandit.py new file mode 100644 index 0000000000..e66dcb5ace --- /dev/null +++ b/river/model_selection/test_bandit.py @@ -0,0 +1,122 @@ +from __future__ import annotations + +import importlib +import inspect + +import pytest + +from river import ( + bandit, + datasets, + evaluate, + linear_model, + metrics, + model_selection, + optim, + preprocessing, +) + + +def test_1259(): + """ + + https://github.com/online-ml/river/issues/1259 + + >>> from river import bandit + >>> from river import datasets + >>> from river import evaluate + >>> from river import linear_model + >>> from river import metrics + >>> from river import model_selection + >>> from river import optim + >>> from river import preprocessing + + >>> models = [ + ... linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr)) + ... for lr in [0.0001, 0.001, 1e-05, 0.01] + ... ] + + >>> dataset = datasets.Phishing() + >>> model = ( + ... preprocessing.StandardScaler() | + ... model_selection.BanditClassifier( + ... models, + ... metric=metrics.Accuracy(), + ... policy=bandit.Exp3( + ... gamma=0.5, + ... seed=42 + ... ) + ... ) + ... ) + >>> metric = metrics.Accuracy() + + >>> evaluate.progressive_val_score(dataset, model, metric) + Accuracy: 87.20% + + """ + + +@pytest.mark.parametrize( + "policy", + [ + pytest.param( + policy(**params), + id=f"{policy.__name__}", + ) + for _, policy in inspect.getmembers( + importlib.import_module("river.bandit"), + lambda obj: inspect.isclass(obj) + and issubclass(obj, bandit.base.Policy) + and not issubclass(obj, bandit.base.ContextualPolicy) + and obj.__name__ not in {"ThompsonSampling"}, + ) + for params in policy._unit_test_params() + ], +) +def test_bandit_classifier_with_each_policy(policy): + models = [ + linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr)) + for lr in [0.0001, 0.001, 1e-05, 0.01] + ] + + dataset = datasets.Phishing() + model = preprocessing.StandardScaler() | model_selection.BanditClassifier( + models, metric=metrics.Accuracy(), policy=policy + ) + metric = metrics.Accuracy() + + score = evaluate.progressive_val_score(dataset, model, metric) + assert score.get() > 0.5 + + +@pytest.mark.parametrize( + "policy", + [ + pytest.param( + policy(**params), + id=f"{policy.__name__}", + ) + for _, policy in inspect.getmembers( + importlib.import_module("river.bandit"), + lambda obj: inspect.isclass(obj) + and issubclass(obj, bandit.base.Policy) + and not issubclass(obj, bandit.base.ContextualPolicy) + and obj.__name__ not in {"ThompsonSampling", "Exp3"}, + ) + for params in policy._unit_test_params() + ], +) +def test_bandit_regressor_with_each_policy(policy): + models = [ + linear_model.LinearRegression(optimizer=optim.SGD(lr=lr)) + for lr in [0.0001, 0.001, 1e-05, 0.01] + ] + + dataset = datasets.TrumpApproval() + model = preprocessing.StandardScaler() | model_selection.BanditRegressor( + models, metric=metrics.MSE(), policy=policy + ) + metric = metrics.MSE() + + score = evaluate.progressive_val_score(dataset, model, metric) + assert score.get() < 300 diff --git a/river/multiclass/__init__.py b/river/multiclass/__init__.py index 55d1a2eb3d..902f3777f4 100644 --- a/river/multiclass/__init__.py +++ b/river/multiclass/__init__.py @@ -1,4 +1,5 @@ """Multi-class classification.""" + from __future__ import annotations from .occ import OutputCodeClassifier diff --git a/river/multiclass/occ.py b/river/multiclass/occ.py index f17bce7179..1a2b3af60c 100644 --- a/river/multiclass/occ.py +++ b/river/multiclass/occ.py @@ -74,7 +74,7 @@ class is converted to a code of 0s and 1s. The length of the code is called the >>> metric = metrics.MacroF1() >>> evaluate.progressive_val_score(dataset, model, metric) - MacroF1: 79.32% + MacroF1: 79.58% References ---------- @@ -145,8 +145,6 @@ def learn_one(self, x, y, **kwargs): for i, c in enumerate(code): self.classifiers[i].learn_one(x, c, **kwargs) - return self - def predict_one(self, x, **kwargs): if not self.code_book: # it's empty return None diff --git a/river/multiclass/ovo.py b/river/multiclass/ovo.py index b8a0ee0632..59c0a46dc0 100644 --- a/river/multiclass/ovo.py +++ b/river/multiclass/ovo.py @@ -80,8 +80,6 @@ def learn_one(self, x, y, **kwargs): pair = (c, y) if c < y else (y, c) self.classifiers[pair].learn_one(x, y=c < y, **kwargs) - return self - def predict_one(self, x, **kwargs): if not self.classifiers: # is empty return None diff --git a/river/multiclass/ovr.py b/river/multiclass/ovr.py index 9597e61014..b4506fac07 100644 --- a/river/multiclass/ovr.py +++ b/river/multiclass/ovr.py @@ -57,7 +57,7 @@ class OneVsRestClassifier(base.Wrapper, base.Classifier): >>> for X in pd.read_csv(dataset.path, chunksize=64): ... y = X.pop('category') ... y_pred = model.predict_many(X) - ... model = model.learn_many(X, y) + ... model.learn_many(X, y) """ @@ -87,8 +87,6 @@ def learn_one(self, x, y, **kwargs): for label, model in self.classifiers.items(): model.learn_one(x, y == label, **kwargs) - return self - def predict_proba_one(self, x, **kwargs): y_pred = {} total = 0.0 @@ -114,8 +112,6 @@ def learn_many(self, X, y, **kwargs): for label, model in self.classifiers.items(): model.learn_many(X, y == label, **kwargs) - return self - def predict_proba_many(self, X, **kwargs): y_pred = pd.DataFrame(columns=self.classifiers.keys(), index=X.index) diff --git a/river/multioutput/__init__.py b/river/multioutput/__init__.py index 43325dcedd..15a92e85f7 100644 --- a/river/multioutput/__init__.py +++ b/river/multioutput/__init__.py @@ -1,4 +1,5 @@ """Multi-output models.""" + from __future__ import annotations from .chain import ( diff --git a/river/multioutput/chain.py b/river/multioutput/chain.py index 2e011c3d3f..e4516fd708 100644 --- a/river/multioutput/chain.py +++ b/river/multioutput/chain.py @@ -2,10 +2,11 @@ import collections import copy +import functools import random from river import base, linear_model -from river.utils.math import prod +from river.utils.math import minkowski_distance, prod __all__ = [ "ClassifierChain", @@ -79,11 +80,11 @@ class ClassifierChain(BaseChain, base.MultiLabelClassifier): ... # Convert y values to booleans ... y = {i: yi == 'TRUE' for i, yi in y.items()} ... y_pred = model.predict_one(x) - ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... metric.update(y, y_pred) + ... model.learn_one(x, y) >>> metric - MicroAverage(Jaccard): 41.95% + MicroAverage(Jaccard): 41.81% References ---------- @@ -101,7 +102,12 @@ def _unit_test_params(cls): yield {"model": linear_model.LogisticRegression()} # binary classifier yield {"model": linear_model.SoftmaxRegression()} # multi-class classifier yield { - "model": neighbors.KNNClassifier(n_neighbors=1, window_size=5) + "model": neighbors.KNNClassifier( + n_neighbors=1, + engine=neighbors.LazySearch( + window_size=10, dist_func=functools.partial(minkowski_distance, p=2) + ), + ) } # multi-class classifier @property @@ -140,8 +146,6 @@ def learn_one(self, x, y, **kwargs): if o not in self.order: self.order.append(o) - return self - def predict_proba_one(self, x, **kwargs): x = copy.copy(x) y_pred = {} @@ -206,7 +210,7 @@ class RegressorChain(BaseChain, base.MultiTargetRegressor): >>> metric = metrics.multioutput.MicroAverage(metrics.MAE()) >>> evaluate.progressive_val_score(dataset, model, metric) - MicroAverage(MAE): 12.649592 + MicroAverage(MAE): 12.733525 """ @@ -245,8 +249,6 @@ def learn_one(self, x, y, **kwargs): if o not in self.order: self.order.append(o) - return self - def predict_one(self, x, **kwargs): x = copy.copy(x) y_pred = {} @@ -298,8 +300,8 @@ class ProbabilisticClassifierChain(ClassifierChain): >>> for x, y in dataset: ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} - ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... metric.update(y, y_pred) + ... model.learn_one(x, y) >>> metric MicroAverage(Jaccard): 51.84% @@ -393,8 +395,8 @@ class MonteCarloClassifierChain(ProbabilisticClassifierChain): >>> for x, y in dataset: ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} - ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... metric.update(y, y_pred) + ... model.learn_one(x, y) >>> metric MicroAverage(Jaccard): 51.79% diff --git a/river/multioutput/encoder.py b/river/multioutput/encoder.py index 86a49748eb..e7d6e94202 100644 --- a/river/multioutput/encoder.py +++ b/river/multioutput/encoder.py @@ -40,8 +40,8 @@ class MultiClassEncoder(base.MultiLabelClassifier): >>> for x, y in dataset: ... y_pred = model.predict_one(x) ... y_pred = {k: y_pred.get(k, 0) for k in y} - ... metric = metric.update(y, y_pred) - ... model = model.learn_one(x, y) + ... metric.update(y, y_pred) + ... model.learn_one(x, y) >>> metric MicroAverage(Jaccard): 95.10% @@ -71,8 +71,6 @@ def learn_one(self, x, y): # Update the classifier self.model.learn_one(x, y_encoded) - return self - def predict_proba_one(self, x, **kwargs): enc_probas = self.model.predict_proba_one(x, **kwargs) diff --git a/river/naive_bayes/__init__.py b/river/naive_bayes/__init__.py index dae4968b66..b1890dadc4 100644 --- a/river/naive_bayes/__init__.py +++ b/river/naive_bayes/__init__.py @@ -1,4 +1,5 @@ """Naive Bayes algorithms.""" + from __future__ import annotations from .bernoulli import BernoulliNB diff --git a/river/naive_bayes/bernoulli.py b/river/naive_bayes/bernoulli.py index d2cdc149ea..111dbabf89 100644 --- a/river/naive_bayes/bernoulli.py +++ b/river/naive_bayes/bernoulli.py @@ -54,7 +54,7 @@ class BernoulliNB(base.BaseNB): ... ) >>> for sentence, label in docs: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> model["nb"].p_class("yes") 0.75 @@ -62,7 +62,7 @@ class BernoulliNB(base.BaseNB): 0.25 >>> model.predict_proba_one("test") - {'yes': 0.8831539823829913, 'no': 0.11684601761700895} + {'yes': 0.883..., 'no': 0.116...} >>> model.predict_one("test") 'yes' @@ -86,7 +86,7 @@ class BernoulliNB(base.BaseNB): ... ("nb", naive_bayes.BernoulliNB(alpha=1)) ... ) - >>> model = model.learn_many(X, y) + >>> model.learn_many(X, y) >>> unseen = pd.Series(["Taiwanese Taipei", "Chinese Shanghai"]) @@ -122,18 +122,12 @@ def learn_one(self, x, y): y Target class. - Returns - ------- - self - """ self.class_counts.update((y,)) for i, xi in x.items(): self.feature_counts[i].update({y: xi > self.true_threshold}) - return self - def p_feature_given_class(self, f: str, c: str) -> float: num = self.feature_counts.get(f, {}).get(c, 0.0) + self.alpha den = self.class_counts[c] + self.alpha * 2 @@ -186,10 +180,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): y Target classes. - Returns - ------- - self - """ # One hot encode y and convert it into sparse matrix y = base.one_hot_encode(y) @@ -224,8 +214,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): for f, count in dict_count.items(): self.feature_counts[f].update(count) - return self - def _feature_log_prob(self, columns: list) -> pd.DataFrame: """Compute log probabilities of input features. @@ -288,4 +276,5 @@ def joint_log_likelihood_many(self, X: pd.DataFrame) -> pd.DataFrame: X @ (flp - neg_p).T + (np.log(self.p_class_many()) + neg_p.sum(axis=1).T).values, index=index, columns=self.class_counts.keys(), + dtype=float, ) diff --git a/river/naive_bayes/complement.py b/river/naive_bayes/complement.py index 71160339c8..80598b25eb 100644 --- a/river/naive_bayes/complement.py +++ b/river/naive_bayes/complement.py @@ -56,7 +56,7 @@ class ComplementNB(base.BaseNB): ... ) >>> for sentence, label in docs: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> model["nb"].p_class("yes") 0.5 @@ -92,7 +92,7 @@ class ComplementNB(base.BaseNB): ... ("nb", naive_bayes.ComplementNB(alpha=1)) ... ) - >>> model = model.learn_many(X, y) + >>> model.learn_many(X, y) >>> unseen = pd.Series(["Taiwanese Taipei", "Chinese Shanghai"]) @@ -133,10 +133,6 @@ def learn_one(self, x, y): y Target class. - Returns - ------- - self - """ self.class_counts.update((y,)) @@ -145,8 +141,6 @@ def learn_one(self, x, y): self.feature_totals.update({f: frequency}) self.class_totals.update({y: frequency}) - return self - def p_class(self, c): return self.class_counts[c] / sum(self.class_counts.values()) @@ -196,10 +190,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): y Target classes. - Returns - ------- - self - """ y = base.one_hot_encode(y) columns, classes = X.columns, y.columns @@ -232,8 +222,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): for f, count in dict_count.items(): self.feature_counts[f].update(count) - return self - def _feature_log_prob(self, unknown: list, columns: list) -> pd.DataFrame: """Compute log probabilities of input features. diff --git a/river/naive_bayes/gaussian.py b/river/naive_bayes/gaussian.py index 3f9c991fa8..b4ca430b52 100644 --- a/river/naive_bayes/gaussian.py +++ b/river/naive_bayes/gaussian.py @@ -33,7 +33,7 @@ class GaussianNB(base.Classifier): >>> model = naive_bayes.GaussianNB() >>> for x, y in stream.iter_array(X, Y): - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({0: -0.8, 1: -1}) 1 @@ -52,8 +52,6 @@ def learn_one(self, x, y): for i, xi in x.items(): self.gaussians[y][i].update(xi) - return self - def predict_proba_one(self, x): """Return probabilities using the log-likelihoods.""" jll = self.joint_log_likelihood(x) diff --git a/river/naive_bayes/multinomial.py b/river/naive_bayes/multinomial.py index b30f631342..97ce314d50 100644 --- a/river/naive_bayes/multinomial.py +++ b/river/naive_bayes/multinomial.py @@ -57,7 +57,7 @@ class MultinomialNB(base.BaseNB): ... ) >>> for sentence, label in docs: - ... model = model.learn_one(sentence, label) + ... model.learn_one(sentence, label) >>> model["nb"].p_class("yes") 0.5 @@ -93,7 +93,7 @@ class MultinomialNB(base.BaseNB): ... ("nb", naive_bayes.MultinomialNB(alpha=1)) ... ) - >>> model = model.learn_many(X, y) + >>> model.learn_many(X, y) >>> unseen = pd.Series(["Taiwanese Taipei", "Chinese Shanghai"]) @@ -132,10 +132,6 @@ def learn_one(self, x, y): y Target class. - Returns - ------- - self - """ self.class_counts.update((y,)) @@ -143,8 +139,6 @@ def learn_one(self, x, y): self.feature_counts[f].update({y: frequency}) self.class_totals.update({y: frequency}) - return self - @property def classes_(self): return list(self.class_counts.keys()) @@ -197,10 +191,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): y Target classes. - Returns - ------- - self - """ y = base.one_hot_encode(y) columns, classes = X.columns, y.columns @@ -229,8 +219,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.Series): for f, count in dict_count.items(): self.feature_counts[f].update(count) - return self - def _feature_log_prob(self, columns: list, known: list, unknown: list) -> pd.DataFrame: """Compute log probabilities of input features. diff --git a/river/naive_bayes/test_naive_bayes.py b/river/naive_bayes/test_naive_bayes.py index 384e0e0bc4..2dd15bb8a3 100644 --- a/river/naive_bayes/test_naive_bayes.py +++ b/river/naive_bayes/test_naive_bayes.py @@ -82,7 +82,7 @@ def test_learn_one_methods(model): assert model.predict_one("not fitted yet") is None for x, y in yield_dataset(): - model = model.learn_one(x, y) + model.learn_one(x, y) # Check class methods. if isinstance(model["model"], naive_bayes.ComplementNB) or isinstance( @@ -116,10 +116,10 @@ def test_learn_many_vs_learn_one(model, batch_model): ComplementNB with differents alpha parameters.. """ for x, y in yield_dataset(): - model = model.learn_one(x, y) + model.learn_one(x, y) for x, y in yield_batch_dataset(): - batch_model = batch_model.learn_many(x, y) + batch_model.learn_many(x, y) assert model["model"].p_class("yes") == batch_model["model"].p_class("yes") assert model["model"].p_class("no") == batch_model["model"].p_class("no") @@ -216,7 +216,7 @@ def test_river_vs_sklearn(model, sk_model, bag): ComplementNB with differents alpha parameters. """ for x, y in yield_batch_dataset(): - model = model.learn_many(x, y) + model.learn_many(x, y) X = pd.concat([x for x, _ in yield_batch_dataset()]) y = pd.concat([y for _, y in yield_batch_dataset()]) diff --git a/river/neighbors/__init__.py b/river/neighbors/__init__.py index d1f45ad01c..8d8211d9e9 100644 --- a/river/neighbors/__init__.py +++ b/river/neighbors/__init__.py @@ -4,14 +4,17 @@ until a query is received. """ + from __future__ import annotations -from .base import NearestNeighbors +from .ann import SWINN from .knn_classifier import KNNClassifier from .knn_regressor import KNNRegressor +from .lazy import LazySearch __all__ = [ - "NearestNeighbors", + "LazySearch", "KNNClassifier", "KNNRegressor", + "SWINN", ] diff --git a/river/neighbors/ann/__init__.py b/river/neighbors/ann/__init__.py new file mode 100644 index 0000000000..d73a4c76ac --- /dev/null +++ b/river/neighbors/ann/__init__.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from .swinn import SWINN + +__all__ = ["SWINN"] diff --git a/river/neighbors/ann/nn_vertex.py b/river/neighbors/ann/nn_vertex.py new file mode 100644 index 0000000000..d7edbca2df --- /dev/null +++ b/river/neighbors/ann/nn_vertex.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +import heapq +import math +import random + +from river import base + + +class Vertex(base.Base): + _isolated: set[int] = set() + + def __init__(self, item, uuid: int) -> None: + self.item = item + self.uuid = uuid + self.edges: dict[int, float] = {} + self.r_edges: dict[int, float] = {} + self.flags: set[int] = set() + self.worst_edge: int | None = None + + def __eq__(self, other) -> bool: + if not isinstance(other, Vertex): + raise NotImplementedError + + return self.uuid == other.uuid + + def __lt__(self, other) -> bool: + if not isinstance(other, Vertex): + raise NotImplementedError + + return self.uuid < other.uuid + + def farewell(self, vertex_pool: list[Vertex]): + for rn in list(self.r_edges): + vertex_pool[rn].rem_edge(self) + + for n in list(self.edges): + self.rem_edge(vertex_pool[n]) + + Vertex._isolated.discard(self.uuid) + + def fill(self, neighbors: list[Vertex], dists: list[float]): + for n, dist in zip(neighbors, dists): + self.edges[n.uuid] = dist + self.flags.add(n.uuid) + n.r_edges[self.uuid] = dist + + # Neighbors are ordered by distance, so the last neighbor + # is the farthest one + self.worst_edge = n.uuid + + def add_edge(self, vertex: Vertex, dist): + self.edges[vertex.uuid] = dist + self.flags.add(vertex.uuid) + vertex.r_edges[self.uuid] = dist + + if self.worst_edge is None or self.edges[self.worst_edge] < dist: + self.worst_edge = vertex.uuid + + def rem_edge(self, vertex: Vertex): + self.edges.pop(vertex.uuid) + vertex.r_edges.pop(self.uuid) + self.flags.discard(vertex.uuid) + + if self.has_neighbors(): + if vertex.uuid == self.worst_edge: + self.worst_edge = max(self.edges, key=self.edges.__getitem__) + else: + self.worst_edge = None + + if not self.has_rneighbors(): + Vertex._isolated.add(self.uuid) + + def push_edge( + self, node: Vertex, dist: float, max_edges: int, vertex_pool: list[Vertex] + ) -> int: + if self.is_neighbor(node) or node.uuid == self.uuid: + return 0 + + if len(self.edges) >= max_edges: + if self.worst_edge is None or self.edges.get(self.worst_edge, math.inf) <= dist: + return 0 + self.rem_edge(vertex_pool[self.worst_edge]) + + self.add_edge(node, dist) + + return 1 + + def is_neighbor(self, vertex: Vertex): + return vertex.uuid in self.edges or vertex.uuid in self.r_edges + + def get_edge(self, vertex: Vertex): + if vertex.uuid in self.edges: + return self, vertex, self.edges[vertex.uuid] + return vertex, self, self.r_edges[vertex.uuid] + + def has_neighbors(self) -> bool: + return len(self.edges) > 0 + + def has_rneighbors(self) -> bool: + return len(self.r_edges) > 0 + + @property + def sample_flags(self): + return list(map(lambda n: n in self.flags, self.edges.keys())) + + @sample_flags.setter + def sample_flags(self, sampled): + self.flags -= set(sampled) + + def neighbors(self) -> tuple[list[int], list[float]]: + res = tuple(map(list, zip(*((node, dist) for node, dist in self.edges.items())))) + return res if len(res) > 0 else ([], []) # type: ignore + + def r_neighbors(self) -> tuple[list[int], list[float]]: + res = tuple(map(list, zip(*((vertex, dist) for vertex, dist in self.r_edges.items())))) + return res if len(res) > 0 else ([], []) # type: ignore + + def all_neighbors(self) -> set[int]: + return set.union(set(self.edges.keys()), set(self.r_edges.keys())) + + def is_isolated(self): + return len(self.edges) == 0 and len(self.r_edges) == 0 + + def prune( + self, prune_prob: float, prune_trigger: int, vertex_pool: list[Vertex], rng: random.Random + ): + if prune_prob == 0: + return + + total_degree = len(self.edges) + len(self.r_edges) + if total_degree <= prune_trigger: + return + + edge_pool: list[tuple[float, int, bool]] = [] + for n, dist in self.edges.items(): + heapq.heappush(edge_pool, (dist, n, True)) + + for rn, dist in self.r_edges.items(): + heapq.heappush(edge_pool, (dist, rn, False)) + + # Start with the best undirected edge + selected: list[int] = [heapq.heappop(edge_pool)[1]] + while len(edge_pool) > 0: + c_dist, c, c_isdir = heapq.heappop(edge_pool) + discarded = False + for s in selected: + s_v = vertex_pool[s] + c_v = vertex_pool[c] + if s_v.is_neighbor(c_v) and rng.random() < prune_prob: + orig, dest, dist = s_v.get_edge(c_v) + if dist < c_dist: + if c_isdir: + self.rem_edge(c_v) + else: + c_v.rem_edge(self) + discarded = True + break + else: + orig.rem_edge(dest) + + if not discarded: + selected.append(c) diff --git a/river/neighbors/ann/swinn.py b/river/neighbors/ann/swinn.py new file mode 100644 index 0000000000..b9a2c3956d --- /dev/null +++ b/river/neighbors/ann/swinn.py @@ -0,0 +1,490 @@ +from __future__ import annotations + +import collections +import functools +import heapq +import itertools +import math +import operator +import random +import typing + +from river import utils +from river.neighbors.base import BaseNN, DistanceFunc, FunctionWrapper + +from .nn_vertex import Vertex + + +class SWINN(BaseNN): + """Sliding WIndow-based Nearest Neighbor (SWINN) search using Graphs. + + Extends the NNDescent algorithm[^1] to handle vertex addition and removal in a FIFO data + ingestion policy. SWINN builds and keeps a directed graph where edges connect the nearest + neighbors. Any distance metric can be used to build the graph. By using a directed graph, + the user must set the desired number of neighbors. More neighbors imply more accurate + search queries at the cost of increased running time and memory usage. Note that although + the number of directed neighbors is limited by the user, there is no direct control on the + number of reverse neighbors, i.e., the number of vertices that have an edge to a given vertex. + + The basic idea of SWINN and NNDescent is that "the neighbor of my neighbors might as well be + my neighbor". Hence, the connections are constantly revisited to improve the graph structure. + The algorithm for creating and maintaining the search graph can be described + in general lines as follows: + + * Start with a random neighborhood graph; + + * For each node in the search graph: refine the current neighborhood by checking if there + are better neighborhood options among the neighbors of the current neighbors; + + * If the total number of neighborhood changes is smaller than a given stopping criterion, then stop. + + SWINN adds strategies to remove vertices from the search graph and pruning redundant edges. SWINN is + more efficient when the selected `maxlen` is greater than 500. For small sized data windows, using + the lazy/exhaustive search, i.e., `neighbors.LazySearch` might be a better idea. + + Parameters + ---------- + graph_k + The maximum number of direct nearest neighbors each node has. + maxlen + The maximum size of the data buffer. + warm_up + How many data instances to observe before starting the search graph. + dist_func + The distance function used to compare two items. If not set, use the Minkowski distance + with `p=2`. + max_candidates + The maximum number of vertices to consider when performing local neighborhood joins. If not set + SWINN will use `min(50, max(50, self.graph_k))`. + delta + Early stop parameter for the neighborhood refinement procedure. NNDescent will stop running + if the maximum number of iterations is reached or the number of edge changes after an iteration + is smaller than or equal to `delta * graph_k * n_nodes`. In the last expression, `n_nodes` + refers to the number of graph nodes involved in the (local) neighborhood refinement. + prune_prob + The probability of removing redundant edges. Must be between `0` and `1`. If set to zero, + no edge will be pruned. When set to one, every potentially redundant edge will be dropped. + n_iters + The maximum number of NNDescent iterations to perform to refine the search index. + seed + Random seed for reproducibility. + + Notes + ----- + There is an accuracy/speed trade-off between `graph_k` and `sample_rate`. To ensure a single + connected component, and thus an effective search index, one can increase `graph_k`. The + `connectivity` method is a helper to determine whether the search index has a single connected component. + However, search accuracy might come at the cost of increased memory usage and slow processing. To alleviate + that, one can rely on decreasing the `sample_rate` to avoid exploring all the undirected edges of a node + during search queries and local graph refinements. Moreover, the edge pruning procedures also help + decreasing the computational costs. Note that, anything that limits the number of explored neighbors or + prunes edges might have a negative impact on search accuracy. + + References + ---------- + [^1]: Dong, W., Moses, C., & Li, K. (2011, March). Efficient k-nearest neighbor graph construction for + generic similarity measures. In Proceedings of the 20th international conference on World wide web (pp. 577-586). + + """ + + def __init__( + self, + graph_k: int = 20, + dist_func: DistanceFunc | FunctionWrapper | None = None, + maxlen: int = 1000, + warm_up: int = 500, + max_candidates: int | None = None, + delta: float = 0.0001, + prune_prob: float = 0.0, + n_iters: int = 10, + seed: int | None = None, + ): + self.graph_k = graph_k + if dist_func is None: + dist_func = functools.partial(utils.math.minkowski_distance, p=2) + self.dist_func = dist_func + + self.maxlen = maxlen + self.warm_up = warm_up + if max_candidates is None: + self.max_candidates = min(50, max(50, self.graph_k)) + else: + self.max_candidates = max_candidates + + self.delta = delta + self.prune_prob = prune_prob + + self.n_iters = n_iters + self.seed = seed + + self._data: collections.deque[Vertex | None] = collections.deque(maxlen=self.maxlen) + self._uuid = itertools.cycle(range(self.maxlen)) + self._rng = random.Random(self.seed) + self._index = False + + def __len__(self): + return len(self._data) + + def __getitem__(self, i): + return self._data[i] + + def __iter__(self): + yield from self._data + + def _init_graph(self): + """Create a random nearest neighbor graph.""" + n_nodes = len(self) + + nodes = set([i for i in range(n_nodes)]) + for nid in range(n_nodes): + nodes.remove(nid) + ns = self._rng.sample(tuple(nodes), self.graph_k) + dists = [math.inf for _ in range(self.graph_k)] + self[nid].fill([self[n] for n in ns], dists) + nodes.add(nid) + + def _fix_graph(self): + """Connect every isolated node in the graph to their nearest neighbors.""" + + for nid in list(Vertex._isolated): + if not self[nid].is_isolated(): + continue + neighbors, dists = self._search(self[nid].item, self.graph_k) + self[nid].fill(neighbors, dists) + + # Update class property + Vertex._isolated.clear() + + def _safe_node_removal(self, nid: int): + """Remove the oldest data point from the search graph. + + Make sure nodes are accessible from any given starting point after removing the oldest + node in the search graph. New traversal paths will be added in case the removed node was + the only bridge between its neighbors. + + """ + node = self[nid] + # Get previous neighborhood info + rns = node.r_neighbors()[0] + ns = node.neighbors()[0] + node.farewell(vertex_pool=self._data) + + # Nodes whose only direct neighbor was the removed node + rns = {rn for rn in rns if not self[rn].has_neighbors()} + # Nodes whose only reverse neighbor was the removed node + ns = {n for n in ns if not self[n].has_rneighbors()} + + affected = list(rns | ns) + isolated = rns.intersection(ns) + + # First we handle the unreachable nodes + for al in isolated: + neighbors, dists = self._search(self[al].item, self.graph_k) + self[al].fill(neighbors, dists) + + rns -= isolated + ns -= isolated + ns = tuple(ns) + + # Nodes with no direct neighbors + for rn in rns: + seed = None + # Check the group of nodes without reverse neighborhood for seeds + # Thus we can join two separate groups + if len(ns) > 0: + seed = self[self._rng.choice(ns)] + + # Use the search index to create new connections + neighbors, dists = self._search(self[rn].item, self.graph_k, seed=seed, exclude={rn}) + self[rn].fill(neighbors, dists) + + self._data[nid] = None + del node + + self._refine(affected) + + def _refine(self, nodes: list[int] | None = None): + """Update the nearest neighbor graph to improve the edge distances. + + Parameters + ---------- + nodes + The list of node ids for which the neighborhood refinement will be applied. + If `None`, all nodes will have their neighborhood enhanced. + """ + + if nodes is None: + nodes = [n.uuid for n in self] + + min_changes = self.delta * self.graph_k * len(nodes) + + tried = set() + for _ in range(self.n_iters): + total_changes = 0 + + new = collections.defaultdict(set) + old = collections.defaultdict(set) + + # Expand undirected neighborhood + for nid in nodes: + node = self[nid] + neighbors = node.neighbors()[0] + flags = node.sample_flags + + for neigh, flag in zip(neighbors, flags): + # To avoid evaluating previous neighbors again + tried.add((nid, neigh)) + if flag: + new[nid].add(neigh) + new[neigh].add(nid) + else: + old[nid].add(neigh) + old[neigh].add(nid) + + # Limits the maximum number of edges to explore and update sample flags + for nid in nodes: + if len(new[nid]) > self.max_candidates: + new[nid] = self._rng.sample(tuple(new[nid]), self.max_candidates) # type: ignore + + if len(old[nid]) > self.max_candidates: + old[nid] = self._rng.sample(tuple(old[nid]), self.max_candidates) # type: ignore + + self[nid].sample_flags = new[nid] + + # Perform local joins an attempt to improve the neighborhood + for nid in nodes: + # The origin of the join must have a boolean flag set to true + for n1 in new[nid]: + # Consider connections between vertices whose boolean flags are both true + for n2 in new[nid]: + if n1 == n2 or self[n1].is_neighbor(self[n2]): + continue + + if (n1, n2) in tried or (n2, n1) in tried: + continue + + dist = self.dist_func(self[n1].item, self[n2].item) + total_changes += self[n1].push_edge( + self[n2], dist, self.graph_k, self._data + ) + total_changes += self[n2].push_edge( + self[n1], dist, self.graph_k, self._data + ) + + tried.add((n1, n2)) + + # Or one of the connections has a boolean flag set to false + for n2 in old[nid]: + if n1 == n2 or self[n1].is_neighbor(self[n2]): + continue + + if (n1, n2) in tried or (n2, n1) in tried: + continue + + dist = self.dist_func(self[n1].item, self[n2].item) + total_changes += self[n1].push_edge( + self[n2], dist, self.graph_k, self._data + ) + total_changes += self[n2].push_edge( + self[n1], dist, self.graph_k, self._data + ) + + tried.add((n1, n2)) + + # Stopping criterion + if total_changes <= min_changes: + break + + # Reduce the number of edges, if needed + for n in nodes: + self[n].prune(self.prune_prob, self.max_candidates, self._data, self._rng) + + # Ensure that no node is isolated in the graph + self._fix_graph() + + def append(self, item: typing.Any, **kwargs): + """Add a new item to the search index. + + Data is stored using the FIFO strategy. Both the data buffer and the search graph are updated. The + addition of a new item will trigger the removal of the oldest item, if the maximum size was + reached. All edges of the removed node are also dropped and safety procedures are applied to ensure + its neighbors keep accessible. The addition of a new item also trigger local neighborhood refinement + procedures, to ensure the search index is effective and the node degree constraints are met. + + Parameters + ---------- + item + Item to be added. + kwargs + Not used in this implementation. + + """ + node = Vertex(item, next(self._uuid)) + if not self._index: + self._data.append(node) + if len(self) >= self.warm_up: + self._init_graph() + self._refine() + self._index = True + return + + # A slot will be replaced, so let's update the search graph first + if len(self) == self.maxlen: + self._safe_node_removal(node.uuid) + + # Assign the closest neighbors to the new item + neighbors, dists = self._search(node.item, self.graph_k) + + # Add the new element to the buffer + if len(self) == self.maxlen: + self._data[node.uuid] = node + else: + self._data.append(node) + node.fill(neighbors, dists) + + def _linear_scan(self, item, k): + # Lazy search while the warm-up period is not finished + points = [(p.item, self.dist_func(item, p.item)) for p in self] + + if points: + return tuple(map(list, zip(*sorted(points, key=operator.itemgetter(-1))[:k]))) + + return None + + def _search( + self, + item, + k, + epsilon: float = 0.1, + seed: Vertex | None = None, + exclude: set[int] | None = None, + ) -> tuple[list, list]: + # Limiter for the distance bound + distance_scale = 1 + epsilon + # Distance threshold for early stops + distance_bound = math.inf + + if exclude is None: + exclude = set() + + if seed is None: + # Make sure the starting point for the search is valid + while True: + # Random seed point to start the search + seed = self[self._rng.randint(0, len(self) - 1)] + if seed is not None and not seed.is_isolated() and seed.uuid not in exclude: + break + + dist = self.dist_func(item, seed.item) + + # To avoid computing distances more than once for a given node + visited = {seed.uuid} + visited |= exclude + + # Search pool is a minimum heap + pool = [(dist, seed)] + + # Results are stored in a maximum heap + result = [(-dist, seed)] + + c_dist, c_n = heapq.heappop(pool) + while c_dist < distance_bound: + tns = [self[n] for n in c_n.all_neighbors() if n not in visited] + + for n in tns: + dist = self.dist_func(item, n.item) + + if len(result) < k: + heapq.heappush(result, (-dist, n)) + heapq.heappush(pool, (dist, n)) + distance_bound = distance_scale * -result[0][0] + elif dist < -result[0][0]: + heapq.heapreplace(result, (-dist, n)) + heapq.heappush(pool, (dist, n)) + distance_bound = distance_scale * -result[0][0] + visited.add(n.uuid) + if len(pool) == 0: + break + c_dist, c_n = heapq.heappop(pool) + + result.sort(reverse=True) + neighbors, dists = map(list, zip(*((r[1], -r[0]) for r in result))) + + return neighbors, dists + + def search( + self, item: typing.Any, n_neighbors: int, epsilon: float = 0.1, **kwargs + ) -> tuple[list, list]: + """Search the underlying nearest neighbor graph given a query item. + + In case not enough samples were observed, i.e., the number of stored samples is smaller than + `warm_up`, then the search switches to a brute force strategy. + + Parameters + ---------- + item + The query item to search for nearest neighbors. + n_neighbors + The number of nearest neighbors to return. + epsilon + Distance bound to aid in avoiding local minima while traversing the search graph. Let $d_k$ + be the distance of the query item to current $k$-th nearest neighbor. At any given + moment, any point whose distance to the query item is smaller than or equal to + $(1 + \\epsilon) * d_k$ is kept as a potential path. After every addition to the heap of + candidate nodes, $d_k$ is updated. + kwargs + Not used in this implementation. + + Returns + ------- + neighbors, dists + A tuple containing the id of the neighbors in the buffer and the respective distances to them. + + """ + + if len(self) <= self.warm_up: + return self._linear_scan(item, n_neighbors) + + neighbors, dists = self._search(item, n_neighbors, epsilon) + return [n.item for n in neighbors], dists + + def connectivity(self) -> list[int]: + """Get a list with the size of each connected component in the search graph. + + This metric provides an overview of reachability in the search index by using Kruskal's + algorithm to build a forest of connected components. + + We want our search index to have a single connected component, i.e., the case where we get + a list containing a single number which is equal to `maxlen`. If that is not the case, not + every node in the search graph can be reached from any given starting point. You may want to try + increasing `graph_k` to improve connectivity. However, keep in mind the following aspects: + 1) computing this metric is a costly operation ($O(E\\log V)$), where $E$ and $V$ are, respectively, + the number of edges and vertices in the search graph; 2) often, connectivity comes at the price of + increased computational costs. Tweaking the `sample_rate` might help in such situations. The best + possible scenario is to decrease the value of `graph_k` while keeping a single connected + component. + + Returns + ------- + A list of the number of elements in each connected component of the graph. + + """ + forest = set() + trees = {n.uuid: {n.uuid} for n in self} + + edges = [((n1.uuid, n2), w) for n1 in self for n2, w in n1.edges.items()] + edges.sort(key=operator.itemgetter(1)) + + for (n1, n2), _ in edges: + if trees[n1].isdisjoint(trees[n2]): + forest.discard(frozenset(trees[n1])) + forest.discard(frozenset(trees[n2])) + + u = trees[n1] | trees[n2] + # Update the trees + for v in u: + trees[v] = u + + forest.add(frozenset(u)) + + return [len(tree) for tree in forest] diff --git a/river/neighbors/base.py b/river/neighbors/base.py index ad76a32929..496e69f3f7 100644 --- a/river/neighbors/base.py +++ b/river/neighbors/base.py @@ -1,13 +1,13 @@ from __future__ import annotations -import collections -import operator +import abc import typing +from river import base + class DistanceFunc(typing.Protocol): - def __call__(self, a: typing.Any, b: typing.Any, **kwargs) -> float: - ... + def __call__(self, a: typing.Any, b: typing.Any, **kwargs) -> float: ... class FunctionWrapper: @@ -22,7 +22,6 @@ class FunctionWrapper: ---------- distance_function The custom distance function to be wrapped. - """ def __init__(self, distance_function: DistanceFunc): @@ -33,112 +32,14 @@ def __call__(self, a, b): return self.distance_function(a[0], b[0]) -class NearestNeighbors: - """Exact nearest neighbors search data structure. - - Parameters - ---------- - window_size - Size of the sliding window use to search neighbors with. - min_distance_keep - The minimum distance (similarity) to consider adding a point to the window. - E.g., a value of 0.0 will add even exact duplicates. - distance_func - A distance function which accepts two input items to compare. - - Notes - ----- - Updates are by default stored by the FIFO (first in first out) method, - which means that when the size limit is reached, old samples are dumped to - give room for new samples. This is circular, meaning that older points - are dumped first. This also gives the implementation a temporal aspect, - because older samples are replaced with newer ones. - - The parameter `min_dinstance_keep` controls the addition of new items to the - window - items that are far enough away (> min_distance_keep) are added to - the window. Thus a value of 0 indicates that we add all points, and - increasing from 0 makes it less likely we will keep a new item. - - """ - - def __init__( - self, - window_size: int, - min_distance_keep: float, - distance_func: DistanceFunc | FunctionWrapper, - ): - self.window_size = window_size - - # A minimum distance (similarity) to determine adding to window - # The model will perform better with a more diverse window - # Since the distance function can be anything, it could be < 0 - self.min_distance_keep = min_distance_keep - - self.distance_func = distance_func - self.window: collections.deque = collections.deque(maxlen=self.window_size) - - def append(self, item: typing.Any, extra: typing.Any | None = None): - """Add a point to the window, optionally with extra metadata. - - Parameters - ---------- - item - The data intended to be provided to the distance function. It is always - the first item in the window, and typically this will be a tuple - (x,y) with features `x` and class or value `y`. - extra: - An extra set of metadata to add to the window that is not passed to - the distance function, and allows easy customization without needing - to always write a custom distance function. - - """ - self.window.append((item, *(extra or []))) - - def update( - self, - item: typing.Any, - n_neighbors: int = 1, - extra: typing.Any | None = None, - ): - """Update the window with a new point, only added if > min distance. - - If min distance is 0, we do not need to do the calculation. The item - (and extra metadata) will not be added to the window if it is too close - to an existing point. - - Parameters - ---------- - item - The data intended to be provided to the distance function. - extra - Metadata that is separate from the item that should also be added - to the window, but is not included to be passed to the distance - function. - - Returns - ------- - A boolean (true/false) to indicate if the point was added. - - """ - # If min distance is 0, we add all points - if self.min_distance_keep == 0: - self.append(item, extra=extra) - return True - - # Don't add VERY similar points to window - nearest = self.find_nearest(item, n_neighbors) - - # Distance always in the last index, (item distance) - if not nearest or nearest[0][-1] < self.min_distance_keep: - self.append(item, extra=extra) - return True - return False +class BaseNN(base.Estimator, abc.ABC): + def __init__(self, dist_func: DistanceFunc | FunctionWrapper): + self.dist_func = dist_func - def find_nearest(self, item: typing.Any, n_neighbors: int = 1): - """Find the `n_neighbors` closest points to `item`, along with their distances.""" - # Compute the distances to each point in the window - # The window is (item, , distance) - points = ((*p, self.distance_func(item, p[0])) for p in self.window) + @abc.abstractmethod + def append(self, item: typing.Any, **kwargs) -> None: + pass - # Return the k closest points (last index is distance) - return sorted(points, key=operator.itemgetter(-1))[:n_neighbors] + @abc.abstractmethod + def search(self, item: typing.Any, n_neighbors: int, **kwargs) -> tuple[list, list]: + pass diff --git a/river/neighbors/knn_classifier.py b/river/neighbors/knn_classifier.py index c13fbc37f8..45e0d3f89c 100644 --- a/river/neighbors/knn_classifier.py +++ b/river/neighbors/knn_classifier.py @@ -3,38 +3,32 @@ import functools from river import base, utils -from river.neighbors import NearestNeighbors -from river.neighbors.base import DistanceFunc, FunctionWrapper +from river.neighbors import SWINN + +from .base import BaseNN, FunctionWrapper class KNNClassifier(base.Classifier): """K-Nearest Neighbors (KNN) for classification. - This works by storing a buffer with the `window_size` most recent observations. - A brute-force search is used to find the `n_neighbors` nearest observations - in the buffer to make a prediction. See the NearestNeighbors parent class for more - details. + Samples are stored using a first-in, first-out strategy. The strategy to perform search + queries in the data buffer is defined by the `engine` parameter. Parameters ---------- n_neighbors The number of nearest neighbors to search for. - window_size - The maximum size of the window storing the last observed samples. - min_distance_keep - The minimum distance (similarity) to consider adding a point to the window. - E.g., a value of 0.0 will add even exact duplicates. Default is 0.05 to add - similar but not exactly the same points. + engine + The search engine used to store the instances and perform search queries. Depending + on the choose engine, search will be exact or approximate. Please, consult the + documentation of each available search engine for more details on its usage. + By default, use the `SWINN` search engine for approximate search queries. weighted - Weight the contribution of each neighbor by it's inverse distance. + Weight the contribution of each neighbor by its inverse distance. cleanup_every This determines at which rate old classes are cleaned up. Classes that have been seen in the past but that are not present in the current window are dropped. Classes are never dropped when this is set to 0. - distance_func - An optional distance function that should accept an a=, b=, and any - custom set of kwargs. If not defined, the Minkowski distance is used with - p=2 (Euclidean distance). See the example section for more details. softmax Whether or not to use softmax normalization to normalize the neighbors contributions. Votes are divided by the total number of votes if this is `False`. @@ -48,69 +42,61 @@ class KNNClassifier(base.Classifier): Examples -------- + >>> import functools >>> from river import datasets >>> from river import evaluate >>> from river import metrics >>> from river import neighbors >>> from river import preprocessing + >>> from river import utils >>> dataset = datasets.Phishing() - >>> model = ( - ... preprocessing.StandardScaler() | - ... neighbors.KNNClassifier(window_size=50) - ... ) - - >>> evaluate.progressive_val_score(dataset, model, metrics.Accuracy()) - Accuracy: 84.55% + To select a custom distance metric which takes one or several parameter, you can wrap your + chosen distance using `functools.partial`: - When defining a custom distance function you can rely on `functools.partial` to set default - parameter values. For instance, let's use the Manhattan function instead of the default Euclidean distance: + >>> l1_dist = functools.partial(utils.math.minkowski_distance, p=1) - >>> import functools - >>> from river import utils >>> model = ( ... preprocessing.StandardScaler() | ... neighbors.KNNClassifier( - ... window_size=50, - ... distance_func=functools.partial(utils.math.minkowski_distance, p=1) + ... engine=neighbors.SWINN( + ... dist_func=l1_dist, + ... seed=42 + ... ) ... ) ... ) + >>> evaluate.progressive_val_score(dataset, model, metrics.Accuracy()) - Accuracy: 86.87% + Accuracy: 89.59% """ def __init__( self, n_neighbors: int = 5, - window_size: int = 1000, - min_distance_keep: float = 0.0, + engine: BaseNN | None = None, weighted: bool = True, cleanup_every: int = 0, - distance_func: DistanceFunc | None = None, softmax: bool = False, ): self.n_neighbors = n_neighbors - self.window_size = window_size - self.min_distance_keep = min_distance_keep - self.distance_func = ( - distance_func - if distance_func is not None - else functools.partial(utils.math.minkowski_distance, p=2) - ) + if engine is None: + engine = SWINN(dist_func=functools.partial(utils.math.minkowski_distance, p=2)) + + if not isinstance(engine.dist_func, FunctionWrapper): + engine.dist_func = FunctionWrapper(engine.dist_func) + + self.engine = engine self.weighted = weighted self.cleanup_every = cleanup_every self.classes: set[base.typing.ClfTarget] = set() self.softmax = softmax self._cleanup_counter = cleanup_every - self._nn = NearestNeighbors( - window_size=self.window_size, - min_distance_keep=min_distance_keep, - distance_func=FunctionWrapper(self.distance_func), - ) + # Create a fresh copy of the supplied search engine + self._nn: BaseNN = self.engine.clone(include_attributes=True) @property def _multiclass(self): @@ -118,7 +104,14 @@ def _multiclass(self): @classmethod def _unit_test_params(cls): - yield {"n_neighbors": 3, "window_size": 30} + from river.neighbors import LazySearch + + yield { + "n_neighbors": 3, + "engine": LazySearch( + window_size=30, dist_func=functools.partial(utils.math.minkowski_distance, p=2) + ), + } def clean_up_classes(self): """Clean up classes added to the window. @@ -132,13 +125,14 @@ def clean_up_classes(self): self.classes = {x for x in self.window if x[0][1] is not None} def learn_one(self, x, y): - # Only add the class y to known classes if we actually add the point! - if self._nn.update((x, y), n_neighbors=self.n_neighbors): - self.classes.add(y) + # Update the data buffer + self._nn.append((x, y)) + + # Update the set of known classes + self.classes.add(y) # Ensure classes known to instance reflect window self._run_class_cleanup() - return self def _run_class_cleanup(self): """Helper function to run class cleanup, accounting for _cleanup_counter.""" @@ -149,10 +143,8 @@ def _run_class_cleanup(self): self.clean_up_classes() self._cleanup_counter = self.cleanup_every - return self - - def predict_proba_one(self, x): - nearest = self._nn.find_nearest((x, None), n_neighbors=self.n_neighbors) + def predict_proba_one(self, x, **kwargs): + nearest = self._nn.search((x, None), n_neighbors=self.n_neighbors, **kwargs) # Default prediction for every class we know is 0. # If class_cleanup is false this can include classes not in window @@ -164,14 +156,16 @@ def predict_proba_one(self, x): default_pred = 1 / len(self.classes) if self.classes else 0.0 return {c: default_pred for c in self.classes} + neighbors, distances = nearest + # If the closest is an exact match AND has a class, return it - if nearest[0][-1] == 0 and nearest[0][0][1] is not None: + if distances[0] == 0 and neighbors[0][1] is not None: # Update the class in our prediction from 0 to 1, 100% certain! - y_pred[nearest[0][0][1]] = 1.0 + y_pred[neighbors[0][1]] = 1.0 return y_pred - for neighbor in nearest: - (x, y), distance = neighbor + for neighbor, distance in zip(neighbors, distances): + (x, y) = neighbor # Weighted votes by inverse distance if self.weighted: diff --git a/river/neighbors/knn_regressor.py b/river/neighbors/knn_regressor.py index a14f3921ed..9fdefd3b5e 100644 --- a/river/neighbors/knn_regressor.py +++ b/river/neighbors/knn_regressor.py @@ -4,35 +4,32 @@ import statistics from river import base, utils -from river.neighbors import NearestNeighbors -from river.neighbors.base import DistanceFunc, FunctionWrapper +from river.neighbors import SWINN + +from .base import BaseNN, FunctionWrapper class KNNRegressor(base.Regressor): """K-Nearest Neighbors regressor. - This non-parametric regression method keeps track of the last `window_size` - training samples. Predictions are obtained by aggregating the values of the - closest n_neighbors stored samples with respect to a query sample. + Samples are stored using a first-in, first-out strategy. The strategy to perform search + queries in the data buffer is defined by the `engine` parameter. Predictions are obtained by + aggregating the values of the closest n_neighbors stored samples with respect to a query sample. Parameters ---------- n_neighbors The number of nearest neighbors to search for. - window_size - The maximum size of the window storing the last observed samples. + engine + The search engine used to store the instances and perform search queries. Depending + on the choose engine, search will be exact or approximate. Please, consult the + documentation of each available search engine for more details on its usage. + By default, use the `SWINN` search engine for approximate search queries. aggregation_method The method to aggregate the target values of neighbors. | 'mean' | 'median' | 'weighted_mean' - min_distance_keep - The minimum distance (similarity) to consider adding a point to the window. - E.g., a value of 0.0 will add even exact duplicates. - distance_func - An optional distance function that should accept an a=, b=, and any - custom set of kwargs. If not defined, the Minkowski distance is used with - p=2 (Euclidean distance). See the example section for more details. Examples -------- @@ -44,24 +41,9 @@ class KNNRegressor(base.Regressor): >>> dataset = datasets.TrumpApproval() - >>> model = neighbors.KNNRegressor(window_size=50) - >>> evaluate.progressive_val_score(dataset, model, metrics.RMSE()) - RMSE: 1.427746 - - When defining a custom distance function you can rely on `functools.partial` to set default - parameter values. For instance, let's use the Manhattan function instead of the default Euclidean distance: - - >>> import functools - >>> from river.utils.math import minkowski_distance - >>> model = ( - ... preprocessing.StandardScaler() | - ... neighbors.KNNRegressor( - ... window_size=50, - ... distance_func=functools.partial(minkowski_distance, p=1) - ... ) - ... ) + >>> model = neighbors.KNNRegressor() >>> evaluate.progressive_val_score(dataset, model, metrics.RMSE()) - RMSE: 1.460385 + RMSE: 1.427743 """ @@ -72,29 +54,36 @@ class KNNRegressor(base.Regressor): def __init__( self, n_neighbors: int = 5, - window_size: int = 1000, + engine: BaseNN | None = None, aggregation_method: str = "mean", - min_distance_keep: float = 0.0, - distance_func: DistanceFunc | None = None, ): self.n_neighbors = n_neighbors - self.window_size = window_size - self.min_distance_keep = min_distance_keep - self.distance_func = ( - distance_func - if distance_func is not None - else functools.partial(utils.math.minkowski_distance, p=2) - ) - - self._nn = NearestNeighbors( - window_size=self.window_size, - min_distance_keep=min_distance_keep, - distance_func=FunctionWrapper(self.distance_func), - ) + + if engine is None: + engine = SWINN(dist_func=functools.partial(utils.math.minkowski_distance, p=2)) + + if not isinstance(engine.dist_func, FunctionWrapper): + engine.dist_func = FunctionWrapper(engine.dist_func) + + self.engine = engine + + # Create a fresh copy of the supplied search engine + self._nn: BaseNN = self.engine.clone(include_attributes=True) self._check_aggregation_method(aggregation_method) self.aggregation_method = aggregation_method + @classmethod + def _unit_test_params(cls): + from river.neighbors import LazySearch + + yield { + "n_neighbors": 3, + "engine": LazySearch( + window_size=50, dist_func=functools.partial(utils.math.minkowski_distance, p=2) + ), + } + def _check_aggregation_method(self, method): """Ensure validation method is known to the model. @@ -107,43 +96,33 @@ def _check_aggregation_method(self, method): """ if method not in {self._MEAN, self._MEDIAN, self._WEIGHTED_MEAN}: raise ValueError( - "Invalid aggregation_method: {}.\n" - "Valid options are: {}".format( - method, {self._MEAN, self._MEDIAN, self._WEIGHTED_MEAN} - ) + f"Invalid aggregation_method: {method}.\n" + f"Valid options are: {(self._MEAN, self._MEDIAN, self._WEIGHTED_MEAN)}" ) def learn_one(self, x, y): - self._nn.update((x, y), n_neighbors=self.n_neighbors) - return self + self._nn.append((x, y)) - def predict_one(self, x): + def predict_one(self, x, **kwargs): # Find the nearest neighbors! - nearest = self._nn.find_nearest((x, None), n_neighbors=self.n_neighbors) + nearest = self._nn.search((x, None), n_neighbors=self.n_neighbors, **kwargs) if not nearest: return 0.0 - # For each in nearest, call it 'item" - # item[0] is the original item (x, y) - # item[-1] is the distance - + neighbors, distances = nearest # If the closest distance is 0 (it's the same) return it's output (y) - # BUT only if the output (y) is not None. - if nearest[0][-1] == 0 and nearest[0][0][1] is not None: - return nearest[0][0][1] + if distances[0] == 0: + return neighbors[0][1] - # Only include neighbors in the sum that are not None - neighbor_vals = [n[0][1] for n in nearest if n[0][1] is not None] + neighbor_vals = [n[1] for n in neighbors] if self.aggregation_method == self._MEDIAN: return statistics.median(neighbor_vals) - dists = [n[-1] for n in nearest if n[0][1] is not None] - sum_ = sum(1 / d for d in dists) - + sum_ = sum(1 / d for d in distances) if self.aggregation_method == self._MEAN or sum_ == 0.0: return statistics.mean(neighbor_vals) # weighted mean based on distance - return sum(y / d for y, d in zip(neighbor_vals, dists)) / sum_ + return sum(y / d for y, d in zip(neighbor_vals, distances)) / sum_ diff --git a/river/neighbors/lazy.py b/river/neighbors/lazy.py new file mode 100644 index 0000000000..f6451d3d02 --- /dev/null +++ b/river/neighbors/lazy.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +import collections +import functools +import operator +import typing + +from river import utils + +from .base import BaseNN, DistanceFunc, FunctionWrapper + + +class LazySearch(BaseNN): + """Exact nearest neighbors using a lazy search estrategy. + + Parameters + ---------- + window_size + Size of the sliding window use to search neighbors with. + min_distance_keep + The minimum distance (similarity) to consider adding a point to the window. + E.g., a value of 0.0 will add even exact duplicates. + dist_func + A distance function which accepts two input items to compare. If not set, + use the Minkowski distance with `p=2`. + + Notes + ----- + Updates are by default stored by the FIFO (first in first out) method, + which means that when the size limit is reached, old samples are dumped to + give room for new samples. This is circular, meaning that older points + are dumped first. This also gives the implementation a temporal aspect, + because older samples are replaced with newer ones. + + The parameter `min_dinstance_keep` controls the addition of new items to the + window - items that are far enough away (> min_distance_keep) are added to + the window. Thus a value of 0 indicates that we add all points, and + increasing from 0 makes it less likely we will keep a new item. + + """ + + def __init__( + self, + window_size: int = 50, + min_distance_keep: float = 0.0, + dist_func: DistanceFunc | FunctionWrapper | None = None, + ): + self.window_size = window_size + + # A minimum distance (similarity) to determine adding to window + # The model will perform better with a more diverse window + # Since the distance function can be anything, it could be < 0 + self.min_distance_keep = min_distance_keep + + if dist_func is None: + dist_func = functools.partial(utils.math.minkowski_distance, p=2) + self.dist_func = dist_func + + self.window: collections.deque = collections.deque(maxlen=self.window_size) + + def append(self, item: typing.Any, extra: typing.Any | None = None, **kwargs): + """Add a point to the window, optionally with extra metadata. + + Parameters + ---------- + item + The data intended to be provided to the distance function. It is always + the first item in the window, and typically this will be a tuple + (x,y) with features `x` and class or value `y`. + extra: + An extra set of metadata to add to the window that is not passed to + the distance function, and allows easy customization without needing + to always write a custom distance function. + + """ + self.window.append((item, *(extra or []))) + + def update( + self, + item: typing.Any, + n_neighbors: int = 1, + extra: typing.Any | None = None, + ): + """Update the window with a new point, only added if > min distance. + + If min distance is 0, we do not need to do the calculation. The item + (and extra metadata) will not be added to the window if it is too close + to an existing point. + + Parameters + ---------- + item + The data intended to be provided to the distance function. + extra + Metadata that is separate from the item that should also be added + to the window, but is not included to be passed to the distance + function. + + Returns + ------- + A boolean (true/false) to indicate if the point was added. + + """ + # If min distance is 0, we add all points + if self.min_distance_keep == 0: + self.append(item, extra=extra) + return True + + # Don't add VERY similar points to window + nearest = self.search(item, n_neighbors) + + # Distance always in the last index, (item distance) + if not nearest or nearest[0][-1] < self.min_distance_keep: + self.append(item, extra=extra) + return True + return False + + def search(self, item: typing.Any, n_neighbors: int, **kwargs): + """Find the `n_neighbors` closest points to `item`, along with their distances.""" + # Compute the distances to each point in the window + # The window is (item, , distance) + points = ((*p, self.dist_func(item, p[0])) for p in self.window) + + # Return the k closest points + return tuple(map(list, zip(*sorted(points, key=operator.itemgetter(-1))[:n_neighbors]))) diff --git a/river/neural_net/__init__.py b/river/neural_net/__init__.py index 00ea313e35..9bc8928cb7 100644 --- a/river/neural_net/__init__.py +++ b/river/neural_net/__init__.py @@ -1,4 +1,5 @@ """Neural networks.""" + from __future__ import annotations from . import activations diff --git a/river/neural_net/mlp.py b/river/neural_net/mlp.py index 2a54b5f995..ecd9b849bd 100644 --- a/river/neural_net/mlp.py +++ b/river/neural_net/mlp.py @@ -162,8 +162,6 @@ def learn_many(self, X: pd.DataFrame, y: pd.DataFrame): z, a = self._forward(X) self._backward(z, a, y) - return self - def __call__(self, X: pd.DataFrame): """Make predictions. @@ -236,7 +234,7 @@ class MLPRegressor(base.Regressor, MLP): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 1.589827 + MAE: 1.580578 You can also use this to process mini-batches of data. @@ -261,19 +259,19 @@ class MLPRegressor(base.Regressor, MLP): ... for xb in pd.read_csv(dataset.path, chunksize=batch_size): ... yb = xb.pop('five_thirty_eight') ... y_pred = model.predict_many(xb) - ... model = model.learn_many(xb, yb) + ... model.learn_many(xb, yb) >>> model.predict_many(xb) five_thirty_eight - 992 39.361609 - 993 46.398536 - 994 42.094086 - 995 40.195802 - 996 40.782954 - 997 40.839678 - 998 40.896403 - 999 48.362659 - 1000 42.021849 + 992 39.405231 + 993 46.447481 + 994 42.121865 + 995 40.251148 + 996 40.836378 + 997 40.893153 + 998 40.949927 + 999 48.416504 + 1000 42.077830 """ @@ -310,10 +308,11 @@ def predict_many(self, X): def learn_one(self, x, y): # Multi-output if isinstance(y, dict): - return self.learn_many(X=pd.DataFrame([x]), y=pd.DataFrame([y])) + self.learn_many(X=pd.DataFrame([x]), y=pd.DataFrame([y])) + return # Single output - return self.learn_many(X=pd.DataFrame([x]), y=pd.Series([y])) + self.learn_many(X=pd.DataFrame([x]), y=pd.Series([y])) def predict_one(self, x): y_pred = self.predict_many(X=pd.DataFrame([x])) diff --git a/river/optim/__init__.py b/river/optim/__init__.py index 2a29bd5201..f1c35150d8 100644 --- a/river/optim/__init__.py +++ b/river/optim/__init__.py @@ -1,4 +1,5 @@ """Stochastic optimization.""" + from __future__ import annotations from . import base, initializers, losses, schedulers diff --git a/river/optim/ada_bound.py b/river/optim/ada_bound.py index 707c1c86a7..66ecec4b33 100644 --- a/river/optim/ada_bound.py +++ b/river/optim/ada_bound.py @@ -45,7 +45,7 @@ class AdaBound(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.90% + F1: 88.06% References ---------- diff --git a/river/optim/ada_max.py b/river/optim/ada_max.py index 921fe65ed1..95b03c6ebd 100644 --- a/river/optim/ada_max.py +++ b/river/optim/ada_max.py @@ -41,7 +41,7 @@ class AdaMax(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.53% + F1: 87.61% References ---------- diff --git a/river/optim/adam.py b/river/optim/adam.py index e581027aed..f78afe05ec 100644 --- a/river/optim/adam.py +++ b/river/optim/adam.py @@ -43,7 +43,7 @@ class Adam(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 86.50% + F1: 86.52% References ---------- diff --git a/river/optim/ams_grad.py b/river/optim/ams_grad.py index 9b1524290a..4d800c9cff 100644 --- a/river/optim/ams_grad.py +++ b/river/optim/ams_grad.py @@ -44,7 +44,7 @@ class AMSGrad(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 86.57% + F1: 86.60% References ---------- diff --git a/river/optim/average.py b/river/optim/average.py index c688cdb01f..90ca1957ce 100644 --- a/river/optim/average.py +++ b/river/optim/average.py @@ -6,7 +6,6 @@ class Averager(optim.base.Optimizer): - """Averaged stochastic gradient descent. This is a wrapper that can be applied to any stochastic gradient descent optimiser. Note that @@ -41,7 +40,7 @@ class Averager(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.89% + F1: 87.97% References ---------- diff --git a/river/optim/ftrl.py b/river/optim/ftrl.py index 5915c868cb..0583c02d12 100644 --- a/river/optim/ftrl.py +++ b/river/optim/ftrl.py @@ -43,7 +43,7 @@ class FTRLProximal(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 87.66% + F1: 87.56% References ---------- diff --git a/river/optim/initializers.py b/river/optim/initializers.py index ce6a21ea55..a5fc7d5a17 100644 --- a/river/optim/initializers.py +++ b/river/optim/initializers.py @@ -1,4 +1,5 @@ """Weight initializers.""" + from __future__ import annotations import numpy as np diff --git a/river/optim/losses.py b/river/optim/losses.py index 559bc9b894..a24ec76b2c 100644 --- a/river/optim/losses.py +++ b/river/optim/losses.py @@ -3,6 +3,7 @@ Each loss function is intended to work with both single values as well as numpy vectors. """ + from __future__ import annotations import math @@ -67,7 +68,7 @@ class Absolute(RegressionLoss): $$L = |p_i - y_i|$$ - It's gradient w.r.t. to $p_i$ is + Its gradient w.r.t. to $p_i$ is $$\\frac{\\partial L}{\\partial p_i} = sgn(p_i - y_i)$$ @@ -203,7 +204,7 @@ class Hinge(BinaryLoss): $$L = max(0, 1 - p_i * y_i)$$ - It's gradient w.r.t. to $p_i$ is + Its gradient w.r.t. to $p_i$ is $$ \\frac{\\partial L}{\\partial y_i} = \\left\{ @@ -404,7 +405,7 @@ class Squared(RegressionLoss): $$L = (p_i - y_i) ^ 2$$ - It's gradient w.r.t. to $p_i$ is + Its gradient w.r.t. to $p_i$ is $$\\frac{\\partial L}{\\partial p_i} = 2 (p_i - y_i)$$ @@ -539,7 +540,7 @@ class Poisson(RegressionLoss): $$L = exp(p_i) - y_i \\times p_i$$ - It's gradient w.r.t. to $p_i$ is + Its gradient w.r.t. to $p_i$ is $$\\frac{\\partial L}{\\partial p_i} = exp(p_i) - y_i$$ diff --git a/river/optim/momentum.py b/river/optim/momentum.py index b26f1c46a6..86f90a4e8d 100644 --- a/river/optim/momentum.py +++ b/river/optim/momentum.py @@ -34,7 +34,7 @@ class Momentum(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 84.16% + F1: 84.09% """ diff --git a/river/optim/nesterov.py b/river/optim/nesterov.py index ec4e39f0cd..0b930564b1 100644 --- a/river/optim/nesterov.py +++ b/river/optim/nesterov.py @@ -34,7 +34,7 @@ class NesterovMomentum(optim.base.Optimizer): >>> metric = metrics.F1() >>> evaluate.progressive_val_score(dataset, model, metric) - F1: 84.29% + F1: 84.22% """ diff --git a/river/optim/schedulers.py b/river/optim/schedulers.py index 624730c65d..d89c936977 100644 --- a/river/optim/schedulers.py +++ b/river/optim/schedulers.py @@ -1,4 +1,5 @@ """Learning rate schedulers.""" + from __future__ import annotations import math diff --git a/river/preprocessing/__init__.py b/river/preprocessing/__init__.py index 5a09a978b5..fb83db41ae 100644 --- a/river/preprocessing/__init__.py +++ b/river/preprocessing/__init__.py @@ -6,12 +6,14 @@ the latter extracts new information from the data """ + from __future__ import annotations from .feature_hasher import FeatureHasher from .impute import PreviousImputer, StatImputer from .lda import LDA from .one_hot import OneHotEncoder +from .ordinal import OrdinalEncoder from .pred_clipper import PredClipper from .random_projection import GaussianRandomProjector, SparseRandomProjector from .scale import ( @@ -35,6 +37,7 @@ "MinMaxScaler", "Normalizer", "OneHotEncoder", + "OrdinalEncoder", "PredClipper", "PreviousImputer", "RobustScaler", diff --git a/river/preprocessing/impute.py b/river/preprocessing/impute.py index baf14fdb41..2926a8878b 100644 --- a/river/preprocessing/impute.py +++ b/river/preprocessing/impute.py @@ -17,7 +17,7 @@ class PreviousImputer(base.Transformer): >>> imputer = preprocessing.PreviousImputer() - >>> imputer = imputer.learn_one({'x': 1, 'y': 2}) + >>> imputer.learn_one({'x': 1, 'y': 2}) >>> imputer.transform_one({'y': None}) {'y': 2} @@ -34,8 +34,6 @@ def learn_one(self, x): if v is not None: self._latest[i] = v - return self - def transform_one(self, x): for i, v in x.items(): if v is None: @@ -80,7 +78,7 @@ class StatImputer(base.Transformer): >>> imp = preprocessing.StatImputer(('temperature', stats.Mean())) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'temperature': 1} {'temperature': 8} @@ -104,7 +102,7 @@ class StatImputer(base.Transformer): >>> imp = preprocessing.StatImputer(('weather', stats.Mode())) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'weather': 'sunny'} {'weather': 'rainy'} @@ -119,7 +117,7 @@ class StatImputer(base.Transformer): >>> imp = preprocessing.StatImputer(('weather', 'missing')) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'weather': 'sunny'} {'weather': 'rainy'} @@ -149,7 +147,7 @@ class StatImputer(base.Transformer): ... ) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'weather': 'sunny', 'temperature': 8} {'weather': 'rainy', 'temperature': 3} @@ -187,7 +185,7 @@ class StatImputer(base.Transformer): ... ) >>> for x in X: - ... imp = imp.learn_one(x) + ... imp.learn_one(x) ... print(imp.transform_one(x)) {'weather': 'sunny', 'temperature': 8} {'weather': 'rainy', 'temperature': 3} @@ -216,8 +214,6 @@ def learn_one(self, x): if x[i] is not None: self.stats[i].update(x[i]) - return self - def transform_one(self, x): # Transformers are supposed to be pure, therefore we make a copy of the features x = x.copy() @@ -241,8 +237,7 @@ class Constant(stats.base.Univariate): def __init__(self, value: typing.Any): self.value = value - def update(self, x): - return self + def update(self, x): ... def get(self): return self.value diff --git a/river/preprocessing/lda.py b/river/preprocessing/lda.py index fdb5ac0ecd..4830e8bb73 100644 --- a/river/preprocessing/lda.py +++ b/river/preprocessing/lda.py @@ -95,14 +95,14 @@ class LDA(base.Transformer): ... ) >>> for x in X: - ... lda = lda.learn_one(x) + ... lda.learn_one(x) ... topics = lda.transform_one(x) ... print(topics) {0: 0.5, 1: 2.5} + {0: 2.499..., 1: 1.5} + {0: 0.5, 1: 3.5} + {0: 0.5, 1: 2.5} {0: 1.5, 1: 2.5} - {0: 3.5, 1: 0.5} - {0: 1.5, 1: 1.5} - {0: 2.5, 1: 1.5} References ---------- @@ -195,7 +195,6 @@ def learn_transform_one(self, x: dict) -> dict: def learn_one(self, x): self.learn_transform_one(x) - return self def transform_one(self, x): # Extracts words of the document as a list of words: diff --git a/river/preprocessing/one_hot.py b/river/preprocessing/one_hot.py index bb2d87d773..59d3337fae 100644 --- a/river/preprocessing/one_hot.py +++ b/river/preprocessing/one_hot.py @@ -21,6 +21,10 @@ class OneHotEncoder(base.MiniBatchTransformer): ---------- drop_zeros Whether or not 0s should be made explicit or not. + drop_first + Whether to get `k - 1` dummies out of `k` categorical levels by removing the first key. + This is useful in some statistical models where perfectly collinear features cause + problems. Examples -------- @@ -53,7 +57,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> oh = preprocessing.OneHotEncoder() >>> for x in X[:2]: - ... oh = oh.learn_one(x) + ... oh.learn_one(x) ... pprint(oh.transform_one(x)) {'c1_u': 1, 'c2_d': 1} {'c1_a': 1, 'c1_u': 0, 'c2_d': 0, 'c2_x': 1} @@ -63,13 +67,24 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> oh = preprocessing.OneHotEncoder(drop_zeros=True) >>> for x in X: - ... oh = oh.learn_one(x) + ... oh.learn_one(x) ... pprint(oh.transform_one(x)) {'c1_u': 1, 'c2_d': 1} {'c1_a': 1, 'c2_x': 1} {'c1_i': 1, 'c2_h': 1} {'c1_h': 1, 'c2_e': 1} + You can encode only `k - 1` features out of `k` by setting `drop_first` to `True`. + + >>> oh = preprocessing.OneHotEncoder(drop_first=True, drop_zeros=True) + >>> for x in X: + ... oh.learn_one(x) + ... pprint(oh.transform_one(x)) + {'c2_d': 1} + {'c2_x': 1} + {'c2_h': 1} + {'c2_e': 1} + A subset of the features can be one-hot encoded by piping a `compose.Select` into the `OneHotEncoder`. @@ -78,7 +93,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> pp = compose.Select('c1') | preprocessing.OneHotEncoder() >>> for x in X: - ... pp = pp.learn_one(x) + ... pp.learn_one(x) ... pprint(pp.transform_one(x)) {'c1_u': 1} {'c1_a': 1, 'c1_u': 0} @@ -91,7 +106,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> pp += compose.Select('c2') >>> for x in X: - ... pp = pp.learn_one(x) + ... pp.learn_one(x) ... pprint(pp.transform_one(x)) {'c1_u': 1, 'c2': 'd'} {'c1_a': 1, 'c1_u': 0, 'c2': 'x'} @@ -108,7 +123,7 @@ class OneHotEncoder(base.MiniBatchTransformer): >>> oh = preprocessing.OneHotEncoder(drop_zeros=True) >>> for x in X: - ... oh = oh.learn_one(x) + ... oh.learn_one(x) ... pprint(oh.transform_one(x)) {'c1_a': 1, 'c1_u': 1, 'c2_d': 1} {'c1_a': 1, 'c1_b': 1, 'c2_x': 1} @@ -137,8 +152,6 @@ class OneHotEncoder(base.MiniBatchTransformer): 2 i h >>> oh = preprocessing.OneHotEncoder(drop_zeros=True) - >>> oh = oh.learn_many(X) - >>> df = oh.transform_many(X) >>> df.sort_index(axis="columns") c1_a c1_i c1_u c2_d c2_h c2_x @@ -146,12 +159,20 @@ class OneHotEncoder(base.MiniBatchTransformer): 1 1 0 0 0 0 1 2 0 1 0 0 1 0 + >>> oh = preprocessing.OneHotEncoder(drop_zeros=True, drop_first=True) + >>> df = oh.transform_many(X) + >>> df.sort_index(axis="columns") + c1_i c1_u c2_d c2_h c2_x + 0 0 1 1 0 0 + 1 0 0 0 0 1 + 2 1 0 0 1 0 + Here's an example where the zeros are kept: >>> oh = preprocessing.OneHotEncoder(drop_zeros=False) >>> X_init = pd.DataFrame([{"c1": "Oranges", "c2": "Apples"}]) - >>> oh = oh.learn_many(X_init) - >>> oh = oh.learn_many(X) + >>> oh.learn_many(X_init) + >>> oh.learn_many(X) >>> df = oh.transform_many(X) >>> df.sort_index(axis="columns") @@ -173,13 +194,14 @@ class OneHotEncoder(base.MiniBatchTransformer): """ - def __init__(self, drop_zeros=False): + def __init__(self, drop_zeros=False, drop_first=False): self.drop_zeros = drop_zeros + self.drop_first = drop_first self.values = collections.defaultdict(set) def learn_one(self, x): if self.drop_zeros: - return self + return for i, xi in x.items(): if isinstance(xi, list) or isinstance(xi, set): @@ -188,8 +210,6 @@ def learn_one(self, x): else: self.values[i].add(xi) - return self - def transform_one(self, x, y=None): oh = {} @@ -205,19 +225,20 @@ def transform_one(self, x, y=None): else: oh[f"{i}_{xi}"] = 1 + if self.drop_first: + oh.pop(min(oh.keys())) + return oh def learn_many(self, X): if self.drop_zeros: - return self + return for col in X.columns: self.values[col].update(X[col].unique()) - return self - def transform_many(self, X): - oh = pd.get_dummies(X, sparse=True, dtype="uint8") + oh = pd.get_dummies(X, columns=X.columns, sparse=True, dtype="uint8") if not self.drop_zeros: seen_in_the_past = {f"{col}_{val}" for col, vals in self.values.items() for val in vals} @@ -225,4 +246,7 @@ def transform_many(self, X): for col in to_add: oh[col] = pd.arrays.SparseArray([0] * len(oh), dtype="uint8") + if self.drop_first: + oh = oh.drop(columns=min(oh.columns)) + return oh diff --git a/river/preprocessing/ordinal.py b/river/preprocessing/ordinal.py new file mode 100644 index 0000000000..01b981ac86 --- /dev/null +++ b/river/preprocessing/ordinal.py @@ -0,0 +1,134 @@ +from __future__ import annotations + +import collections +import functools +import itertools + +import numpy as np +import pandas as pd + +from river import base + + +def make_counter(skip): + return (i for i in itertools.count() if i not in skip) + + +class OrdinalEncoder(base.MiniBatchTransformer): + """Ordinal encoder. + + This transformer maps each feature to integers. It can useful when a feature has string values + (i.e. categorical variables). + + Parameters + ---------- + unknown_value + The value to use for unknown categories seen during `transform_one`. Unknown categories + will be mapped to an integer once they are seen during `learn_one`. This value can be set + to `None` in order to categories to `None` if they've never been seen before. + none_value + The value to encode `None` with. + + Attributes + ---------- + categories + A dict of dicts. The outer dict maps each feature to its inner dict. The inner dict maps + each category to its code. + + Examples + -------- + + >>> from river import preprocessing + + >>> X = [ + ... {"country": "France", "place": "Taco Bell"}, + ... {"country": None, "place": None}, + ... {"country": "Sweden", "place": "Burger King"}, + ... {"country": "France", "place": "Burger King"}, + ... {"country": "Russia", "place": "Starbucks"}, + ... {"country": "Russia", "place": "Starbucks"}, + ... {"country": "Sweden", "place": "Taco Bell"}, + ... {"country": None, "place": None}, + ... ] + + >>> encoder = preprocessing.OrdinalEncoder() + >>> for x in X: + ... print(encoder.transform_one(x)) + ... encoder.learn_one(x) + {'country': 0, 'place': 0} + {'country': -1, 'place': -1} + {'country': 0, 'place': 0} + {'country': 1, 'place': 2} + {'country': 0, 'place': 0} + {'country': 3, 'place': 3} + {'country': 2, 'place': 1} + {'country': -1, 'place': -1} + + >>> xb1 = pd.DataFrame(X[0:4], index=[0, 1, 2, 3]) + >>> xb2 = pd.DataFrame(X[4:8], index=[4, 5, 6, 7]) + + >>> encoder = preprocessing.OrdinalEncoder() + >>> encoder.transform_many(xb1) + country place + 0 0 0 + 1 -1 -1 + 2 0 0 + 3 0 0 + + >>> encoder.learn_many(xb1) + >>> encoder.transform_many(xb2) + country place + 4 0 0 + 5 0 0 + 6 2 1 + 7 -1 -1 + + """ + + def __init__( + self, + unknown_value: int | None = 0, + none_value: int = -1, + ): + self.unknown_value = unknown_value + self.none_value = none_value + + # We're going to have one auto-incrementing counter per feature. This counter will generate + # the category codes for each feature. + self._counters: collections.defaultdict = collections.defaultdict( + functools.partial(make_counter, {unknown_value, none_value}) + ) + + # We're going to store the categories in a dict of dicts. The outer dict will map each + # feature to its inner dict. The inner dict will map each category to its code. + self.categories: collections.defaultdict = collections.defaultdict(dict) + + def transform_one(self, x): + return { + i: self.none_value if xi is None else self.categories[i].get(xi, self.unknown_value) + for i, xi in x.items() + } + + def learn_one(self, x): + for i, xi in x.items(): + if xi is not None and xi not in self.categories[i]: + self.categories[i][xi] = next(self._counters[i]) + + def transform_many(self, X): + return pd.DataFrame( + { + i: pd.Series( + X[i] + .map({**self.categories[i], None: self.none_value}) + .fillna(self.unknown_value), + dtype=np.int64, + ) + for i in X.columns + } + ) + + def learn_many(self, X, y=None): + for i in X.columns: + for xi in X[i].dropna().unique(): + if xi not in self.categories[i]: + self.categories[i][xi] = next(self._counters[i]) diff --git a/river/preprocessing/pred_clipper.py b/river/preprocessing/pred_clipper.py index 6fa53ccfd0..24dfec8c65 100644 --- a/river/preprocessing/pred_clipper.py +++ b/river/preprocessing/pred_clipper.py @@ -35,7 +35,7 @@ class PredClipper(base.Wrapper, base.Regressor): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.predict_one({'a': -100, 'b': -200}) 0 @@ -56,7 +56,6 @@ def _wrapped_model(self): def learn_one(self, x, y, **kwargs): self.regressor.learn_one(x=x, y=y, **kwargs) - return self def predict_one(self, x, **kwargs): y_pred = self.regressor.predict_one(x=x, **kwargs) diff --git a/river/preprocessing/random_projection.py b/river/preprocessing/random_projection.py index 8db4647045..5faf56626d 100644 --- a/river/preprocessing/random_projection.py +++ b/river/preprocessing/random_projection.py @@ -39,7 +39,7 @@ class GaussianRandomProjector(base.Transformer): ... x = model.transform_one(x) ... print(x) ... break - {0: -61289.37139206629, 1: 141312.51039283074, 2: 279165.99370457436} + {0: -61289.371..., 1: 141312.510..., 2: 279165.993...} >>> model = ( ... preprocessing.GaussianRandomProjector( @@ -50,7 +50,7 @@ class GaussianRandomProjector(base.Transformer): ... linear_model.LinearRegression() ... ) >>> evaluate.progressive_val_score(dataset, model, metrics.MAE()) - MAE: 0.860464 + MAE: 0.933... References ---------- @@ -63,9 +63,9 @@ def __init__(self, n_components=10, seed: int | None = None): self.n_components = n_components self.seed = seed self._rng = random.Random(seed) - self._projection_matrix: collections.defaultdict[ - base.typing.FeatureName, float - ] = collections.defaultdict(self._rand_gauss) + self._projection_matrix: collections.defaultdict[base.typing.FeatureName, float] = ( + collections.defaultdict(self._rand_gauss) + ) def _rand_gauss(self): return self._rng.gauss(0, 1 / (self.n_components**0.5)) @@ -126,7 +126,7 @@ class SparseRandomProjector(base.Transformer): ... linear_model.LinearRegression() ... ) >>> evaluate.progressive_val_score(dataset, model, metrics.MAE()) - MAE: 1.296503 + MAE: 1.292572 References ---------- diff --git a/river/preprocessing/scale.py b/river/preprocessing/scale.py index ce60b35827..25ab2328dd 100644 --- a/river/preprocessing/scale.py +++ b/river/preprocessing/scale.py @@ -52,7 +52,8 @@ class Binarizer(base.Transformer): >>> binarizer = river.preprocessing.Binarizer() >>> for x in X: - ... print(binarizer.learn_one(x).transform_one(x)) + ... binarizer.learn_one(x) + ... print(binarizer.transform_one(x)) {'x1': False, 'x2': False} {'x1': True, 'x2': True} {'x1': True, 'x2': True} @@ -114,7 +115,8 @@ class StandardScaler(base.MiniBatchTransformer): >>> scaler = preprocessing.StandardScaler() >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 0.0, 'y': 0.0} {'x': -0.999, 'y': 0.999} {'x': 0.937, 'y': 1.350} @@ -129,8 +131,8 @@ class StandardScaler(base.MiniBatchTransformer): >>> X = pd.DataFrame.from_dict(X) >>> scaler = preprocessing.StandardScaler() - >>> scaler = scaler.learn_many(X[:3]) - >>> scaler = scaler.learn_many(X[3:]) + >>> scaler.learn_many(X[:3]) + >>> scaler.learn_many(X[3:]) You can then call `transform_many` to scale a mini-batch of features: @@ -166,8 +168,6 @@ def learn_one(self, x): (xi - old_mean) * (xi - self.means[i]) - self.vars[i] ) / self.counts[i] - return self - def transform_one(self, x): if self.with_std: return {i: safe_div(xi - self.means[i], self.vars[i] ** 0.5) for i, xi in x.items()} @@ -217,8 +217,6 @@ def learn_many(self, X: pd.DataFrame): self.vars[col] = a * old_var + b * new_var + a * b * (old_mean - new_mean) ** 2 self.counts[col] += new_count - return self - def transform_many(self, X: pd.DataFrame): """Scale a mini-batch of features. @@ -230,11 +228,20 @@ def transform_many(self, X: pd.DataFrame): """ - means = np.array([self.means[c] for c in X.columns]) + # Determine dtype of input + dtypes = X.dtypes.unique() + dtype = dtypes[0] if len(dtypes) == 1 else np.float64 + + # Check if the dtype is integer type and convert to corresponding float type + if np.issubdtype(dtype, np.integer): + bytes_size = dtype.itemsize + dtype = np.dtype(f"float{bytes_size * 8}") + + means = np.array([self.means[c] for c in X.columns], dtype=dtype) Xt = X.values - means if self.with_std: - stds = np.array([self.vars[c] ** 0.5 for c in X.columns]) + stds = np.array([self.vars[c] ** 0.5 for c in X.columns], dtype=dtype) np.divide(Xt, stds, where=stds > 0, out=Xt) return pd.DataFrame(Xt, index=X.index, columns=X.columns, copy=False) @@ -271,7 +278,8 @@ class MinMaxScaler(base.Transformer): >>> scaler = preprocessing.MinMaxScaler() >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 0.0} {'x': 0.0} {'x': 0.406920} @@ -289,8 +297,6 @@ def learn_one(self, x): self.min[i].update(xi) self.max[i].update(xi) - return self - def transform_one(self, x): return { i: safe_div(xi - self.min[i].get(), self.max[i].get() - self.min[i].get()) @@ -329,7 +335,8 @@ class MaxAbsScaler(base.Transformer): >>> scaler = preprocessing.MaxAbsScaler() >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 1.0} {'x': 0.767216} {'x': 0.861940} @@ -345,8 +352,6 @@ def learn_one(self, x): for i, xi in x.items(): self.abs_max[i].update(xi) - return self - def transform_one(self, x): return {i: safe_div(xi, self.abs_max[i].get()) for i, xi in x.items()} @@ -394,7 +399,8 @@ class RobustScaler(base.Transformer): >>> scaler = preprocessing.RobustScaler() >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 0.0} {'x': -1.0} {'x': 0.0} @@ -418,8 +424,6 @@ def learn_one(self, x): if self.with_scaling: self.iqr[i].update(xi) - return self - def transform_one(self, x): x_tf = {} @@ -516,7 +520,8 @@ class AdaptiveStandardScaler(base.Transformer): >>> scaler = preprocessing.AdaptiveStandardScaler(fading_factor=.6) >>> for x in X: - ... print(scaler.learn_one(x).transform_one(x)) + ... scaler.learn_one(x) + ... print(scaler.transform_one(x)) {'x': 0.0} {'x': -0.816} {'x': 0.812} @@ -537,7 +542,6 @@ def learn_one(self, x): for i, xi in x.items(): self.vars[i].update(xi) self.means[i].update(xi) - return self def transform_one(self, x): return { diff --git a/river/preprocessing/scale_target.py b/river/preprocessing/scale_target.py index 03da7452c4..a558d4d07e 100644 --- a/river/preprocessing/scale_target.py +++ b/river/preprocessing/scale_target.py @@ -47,9 +47,7 @@ class TargetStandardScaler(compose.TargetTransformRegressor): >>> metric = metrics.MSE() >>> evaluate.progressive_val_score(dataset, model, metric) - MSE: 2.003724 - - + MSE: 2.005999 """ @@ -94,7 +92,7 @@ class TargetMinMaxScaler(compose.TargetTransformRegressor): >>> metric = metrics.MSE() >>> evaluate.progressive_val_score(dataset, model, metric) - MSE: 2.01689 + MSE: 2.018905 """ diff --git a/river/preprocessing/test_lda.py b/river/preprocessing/test_lda.py index 8858fe6878..742531ba98 100644 --- a/river/preprocessing/test_lda.py +++ b/river/preprocessing/test_lda.py @@ -10,6 +10,7 @@ PyInfVov (https://github.com/kzhai/PyInfVoc) with a batch size of 1. """ + from __future__ import annotations import numpy as np @@ -264,7 +265,7 @@ def test_learn_transform(): for document in DOC_SET: tokens = {token: 1 for token in document.split(" ")} - lda = lda.learn_one(x=tokens) + lda.learn_one(x=tokens) components_list.append(lda.transform_one(x=tokens)) diff --git a/river/preprocessing/test_scale.py b/river/preprocessing/test_scale.py index dc43b94b2e..2d4fa2df02 100644 --- a/river/preprocessing/test_scale.py +++ b/river/preprocessing/test_scale.py @@ -58,3 +58,32 @@ def test_standard_scaler_add_remove_columns(): # Pick half of the columns at random cols = np.random.choice(X.columns, len(X.columns) // 2, replace=False) ss.learn_many(xb[cols]) + + +def test_issue_1313(): + """ + + >>> import numpy as np + >>> import pandas as pd + >>> from sklearn import datasets + >>> from river import preprocessing + >>> from river.compose import Select + + >>> X, y = datasets.make_regression(n_samples=6, n_features=2) + >>> X = pd.DataFrame(X) + >>> X.columns = ['feat_1','feat_2'] + + >>> model = Select('feat_1') | preprocessing.StandardScaler() + >>> X = X.astype('float32') + >>> X.dtypes + feat_1 float32 + feat_2 float32 + dtype: object + + >>> model.learn_many(X) + >>> X1 = model.transform_many(X) + >>> X1.dtypes + feat_1 float32 + dtype: object + + """ diff --git a/river/proba/__init__.py b/river/proba/__init__.py index a7a3263bce..23b60e8da4 100644 --- a/river/proba/__init__.py +++ b/river/proba/__init__.py @@ -1,9 +1,10 @@ """Probability distributions.""" + from __future__ import annotations from . import base from .beta import Beta -from .gaussian import Gaussian +from .gaussian import Gaussian, MultivariateGaussian from .multinomial import Multinomial -__all__ = ["base", "Beta", "Gaussian", "Multinomial"] +__all__ = ["base", "Beta", "Gaussian", "Multinomial", "MultivariateGaussian"] diff --git a/river/proba/base.py b/river/proba/base.py index 28bcb2d02f..f49366b5b9 100644 --- a/river/proba/base.py +++ b/river/proba/base.py @@ -56,11 +56,11 @@ class DiscreteDistribution(Distribution): """ @abc.abstractmethod - def update(self, x: typing.Hashable): + def update(self, x: typing.Hashable) -> None: """Updates the parameters of the distribution given a new observation.""" @abc.abstractmethod - def revert(self, x: typing.Hashable): + def revert(self, x: typing.Hashable) -> None: """Reverts the parameters of the distribution for a given observation.""" @@ -75,11 +75,11 @@ class BinaryDistribution(Distribution): """ @abc.abstractmethod - def update(self, x: bool): + def update(self, x: bool) -> None: """Updates the parameters of the distribution given a new observation.""" @abc.abstractmethod - def revert(self, x: bool): + def revert(self, x: bool) -> None: """Reverts the parameters of the distribution for a given observation.""" @@ -94,13 +94,36 @@ class ContinuousDistribution(Distribution): """ @abc.abstractmethod - def update(self, x: float): + def update(self, x: float) -> None: """Updates the parameters of the distribution given a new observation.""" @abc.abstractmethod - def revert(self, x: float): + def revert(self, x: float) -> None: """Reverts the parameters of the distribution for a given observation.""" @abc.abstractmethod def cdf(self, x: float): """Cumulative density function, i.e. P(X <= x).""" + + +class MultivariateContinuousDistribution(Distribution): + """A probability distribution for multivariate continuous values. + + Parameters + ---------- + seed + Random number generator seed for reproducibility. + + """ + + @abc.abstractmethod + def update(self, x: dict[str, float]) -> None: + """Updates the parameters of the distribution given a new observation.""" + + @abc.abstractmethod + def revert(self, x: dict[str, float]) -> None: + """Reverts the parameters of the distribution for a given observation.""" + + @abc.abstractmethod + def cdf(self, x: dict[str, float]) -> float: + """Cumulative density function, i.e. P(X <= x).""" diff --git a/river/proba/beta.py b/river/proba/beta.py index 0dbd02f927..f4581d4730 100644 --- a/river/proba/beta.py +++ b/river/proba/beta.py @@ -53,9 +53,9 @@ class Beta(base.ContinuousDistribution): (0.867..., 0.165...) >>> for success in range(100): - ... beta = beta.update(True) + ... beta.update(True) >>> for failure in range(200): - ... beta = beta.update(False) + ... beta.update(False) >>> beta(.21), beta(.35) (2.525...e-05, 0.841...) @@ -85,14 +85,12 @@ def update(self, x): self.alpha += 1 else: self.beta += 1 - return self def revert(self, x): if x: self.alpha -= 1 else: self.beta -= 1 - return self def __call__(self, p: float): return ( diff --git a/river/proba/gaussian.py b/river/proba/gaussian.py index 632d5e1888..cfe428f7a3 100644 --- a/river/proba/gaussian.py +++ b/river/proba/gaussian.py @@ -2,10 +2,14 @@ import math -from river import stats +import numpy as np +import pandas as pd +from scipy.stats import multivariate_normal + +from river import covariance, stats from river.proba import base -__all__ = ["Gaussian"] +__all__ = ["Gaussian", "MultivariateGaussian"] class Gaussian(base.ContinuousDistribution): @@ -21,7 +25,9 @@ class Gaussian(base.ContinuousDistribution): >>> from river import proba - >>> p = proba.Gaussian().update(6).update(7) + >>> p = proba.Gaussian() + >>> p.update(6) + >>> p.update(7) >>> p 𝒩(μ=6.500, σ=0.707) @@ -30,6 +36,7 @@ class Gaussian(base.ContinuousDistribution): 0.564189 >>> p.revert(7) + >>> p 𝒩(μ=6.000, σ=0.000) """ @@ -61,11 +68,9 @@ def __repr__(self): def update(self, x, w=1.0): self._var.update(x, w) - return self def revert(self, x, w=1.0): self._var.revert(x, w) - return self def __call__(self, x): var = self._var.get() @@ -90,3 +95,223 @@ def sample(self): @property def mode(self): return self.mu + + +class MultivariateGaussian(base.MultivariateContinuousDistribution): + """Multivariate normal distribution with parameters mu and var. + + Parameters + ---------- + seed + Random number generator seed for reproducibility. + + Examples + -------- + + >>> import numpy as np + >>> import pandas as pd + >>> from river import proba + + >>> np.random.seed(42) + >>> X = pd.DataFrame( + ... np.random.random((8, 3)), + ... columns=["red", "green", "blue"] + ... ) + >>> X + red green blue + 0 0.374540 0.950714 0.731994 + 1 0.598658 0.156019 0.155995 + 2 0.058084 0.866176 0.601115 + 3 0.708073 0.020584 0.969910 + 4 0.832443 0.212339 0.181825 + 5 0.183405 0.304242 0.524756 + 6 0.431945 0.291229 0.611853 + 7 0.139494 0.292145 0.366362 + + + >>> p = proba.MultivariateGaussian(seed=42) + >>> p.n_samples + 0.0 + + >>> for x in X.to_dict(orient="records"): + ... p.update(x) + >>> p.var + blue green red + blue 0.076119 0.020292 -0.010128 + green 0.020292 0.112931 -0.053268 + red -0.010128 -0.053268 0.078961 + + Retrieving current state in nice format is simple + >>> p + 𝒩( + μ=(0.518, 0.387, 0.416), + σ^2=( + [ 0.076 0.020 -0.010] + [ 0.020 0.113 -0.053] + [-0.010 -0.053 0.079] + ) + ) + + To retrieve number of samples and mode: + + >>> p.n_samples + 8.0 + >>> p.mode + {'blue': 0.5179..., 'green': 0.3866..., 'red': 0.4158...} + + To retrieve the PDF and CDF: + + >>> p(x) + 0.97967... + >>> p.cdf(x) + 0.00787... + + To sample data from distribution: + + >>> p.sample() + {'blue': -0.179..., 'green': -0.051..., 'red': 0.376...} + + MultivariateGaussian works with `utils.Rolling`: + + >>> from river import utils + + >>> p = utils.Rolling(MultivariateGaussian(), window_size=5) + >>> for x in X.to_dict(orient="records"): + ... p.update(x) + >>> p.var + blue green red + blue 0.087062 -0.022873 0.007765 + green -0.022873 0.014279 -0.025181 + red 0.007765 -0.025181 0.095066 + + MultivariateGaussian works with `utils.TimeRolling`: + + >>> from datetime import datetime as dt, timedelta as td + >>> X.index = [dt(2023, 3, 28, 0, 0, 0) + td(seconds=x) for x in range(8)] + >>> p = utils.TimeRolling(MultivariateGaussian(), period=td(seconds=5)) + >>> for t, x in X.iterrows(): + ... p.update(x.to_dict(), t=t) + >>> p.var + blue green red + blue 0.087062 -0.022873 0.007765 + green -0.022873 0.014279 -0.025181 + red 0.007765 -0.025181 0.095066 + + Variance on diagonal is consistent with `proba.Gaussian`. + + >>> multi = proba.MultivariateGaussian() + >>> single = proba.Gaussian() + >>> for x in X.to_dict(orient='records'): + ... multi.update(x) + ... single.update(x['blue']) + >>> multi.mu['blue'] == single.mu + True + >>> multi.sigma['blue']['blue'] == single.sigma + True + + """ + + def __init__(self, seed=None): + super().__init__(seed) + self._var = covariance.EmpiricalCovariance(ddof=1) + + @classmethod + def _from_state(cls, n, mean, cov, ddof, seed=None): + new = cls(seed) + new._var = covariance.EmpiricalCovariance._from_state(n, mean, cov, ddof=ddof) + return new + + @property + def n_samples(self) -> float: + if not self._var.matrix: + return 0.0 + else: + return list(self._var.matrix.values())[-1].mean.n + + @property + def mu(self) -> dict: + """The mean value of the distribution.""" + return { + key1: values.mean.get() + for (key1, key2), values in sorted(self._var.matrix.items()) + if key1 == key2 + } + + @property + def var(self) -> pd.DataFrame: + """The variance of the distribution.""" + variables = sorted(list({var for cov in self._var.matrix.keys() for var in cov})) + # Initialize the covariance matrix array + cov_array = np.zeros((len(variables), len(variables))) + + # Fill in the covariance matrix array + for i in range(len(variables)): + for j in range(i, len(variables)): + if i == j: + # Fill in the diagonal with variances + cov_array[i, j] = self._var[(variables[i], variables[j])].get() + else: + # Fill in the off-diagonal with covariances + cov_array[i, j] = self._var[(variables[i], variables[j])].get() + cov_array[j, i] = self._var[(variables[i], variables[j])].get() + + cov_array = pd.DataFrame(cov_array, index=variables, columns=variables) + return cov_array + + @property + def sigma(self) -> pd.DataFrame: + """The standard deviation of the distribution.""" + return self.var**0.5 + + def __repr__(self): + mu_str = ", ".join(f"{m:.3f}" for m in self.mu.values()) + var_str = self.var.to_string(float_format="{:0.3f}".format, header=False, index=False) + var_str = " [" + var_str.replace("\n", "]\n [") + "]" + return f"𝒩(\n μ=({mu_str}),\n σ^2=(\n{var_str}\n )\n)" + + def update(self, x): + # TODO: add support for weigthed samples + self._var.update(x) + + def revert(self, x): + # TODO: add support for weigthed samples + self._var.revert(x) + + def __call__(self, x: dict[str, float]): + """PDF(x) method.""" + x_ = [x[i] for i in self.mu] + var = self.var + if var is not None: + try: + pdf_ = multivariate_normal([*self.mu.values()], var).pdf(x_) + return float(pdf_) + # TODO: validate occurence of ValueError + # The input matrix must be symmetric positive semidefinite. + except ValueError: # pragma: no cover + return 0.0 + # TODO: validate occurence of OverflowError + except OverflowError: # pragma: no cover + return 0.0 + return 0.0 # pragma: no cover + + def cdf(self, x: dict[str, float]): + x_ = list(x.values()) + cdf_ = multivariate_normal( + [self.mu[i] for i in x], + self.var, + allow_singular=True, + ).cdf(x_) + return float(cdf_) + + def sample(self) -> dict[str, float]: + sample_ = map( + float, + multivariate_normal( + [*self.mu.values()], self.var, seed=self._rng.randint(0, 2**10) + ).rvs(), + ) + return dict(zip(self.mu.keys(), sample_)) + + @property + def mode(self) -> dict: + return self.mu diff --git a/river/proba/multinomial.py b/river/proba/multinomial.py index 9418e60cbc..2244d01bb6 100644 --- a/river/proba/multinomial.py +++ b/river/proba/multinomial.py @@ -24,16 +24,17 @@ class Multinomial(base.DiscreteDistribution): >>> from river import proba >>> p = proba.Multinomial(['green'] * 3) - >>> p = p.update('red') - + >>> p.update('red') >>> p('red') 0.25 - >>> p = p.update('red').update('red') + >>> p.update('red') + >>> p.update('red') >>> p('green') 0.5 - >>> p = p.revert('red').revert('red') + >>> p.revert('red') + >>> p.revert('red') >>> p('red') 0.25 @@ -49,7 +50,7 @@ class Multinomial(base.DiscreteDistribution): ... ) >>> for x in X: - ... dist = dist.update(x) + ... dist.update(x) ... print(dist) ... print() P(red) = 1.000 @@ -82,7 +83,7 @@ class Multinomial(base.DiscreteDistribution): ... ) >>> for x, day in zip(X, days): - ... dist = dist.update(x, t=dt.datetime(2019, 1, day)) + ... dist.update(x, t=dt.datetime(2019, 1, day)) ... print(dist) ... print() P(red) = 1.000 @@ -123,12 +124,10 @@ def mode(self): def update(self, x): self.counts.update([x]) self._n += 1 - return self def revert(self, x): self.counts.subtract([x]) self._n -= 1 - return self def sample(self): return self._rng.choices(list(self.counts.keys()), weights=list(self.counts.values()))[0] diff --git a/river/proba/test_gaussian.py b/river/proba/test_gaussian.py new file mode 100644 index 0000000000..92a1639fe7 --- /dev/null +++ b/river/proba/test_gaussian.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +import math + +import numpy as np +import pandas as pd +import pytest + +from river import proba + + +@pytest.mark.parametrize( + "p", + [ + pytest.param( + p, + id=f"{p=}", + ) + for p in [1, 3, 5] + ], +) +def test_univariate_multivariate_consistency(p): + X = pd.DataFrame(np.random.random((30, p)), columns=range(p)) + + multi = proba.MultivariateGaussian() + single = {c: proba.Gaussian() for c in X.columns} + + for x in X.to_dict(orient="records"): + multi.update(x) + for c, s in single.items(): + s.update(x[c]) + + for c in X.columns: + assert math.isclose(multi.mu[c], single[c].mu) + assert math.isclose(multi.sigma[c][c], single[c].sigma) diff --git a/river/reco/__init__.py b/river/reco/__init__.py index 05d8ba9b52..d849c8cf8b 100644 --- a/river/reco/__init__.py +++ b/river/reco/__init__.py @@ -15,6 +15,7 @@ value. It is up to the user to determine how to translate a user session into training data. """ + from __future__ import annotations from . import base diff --git a/river/reco/base.py b/river/reco/base.py index de746e5d1a..d2ecc29576 100644 --- a/river/reco/base.py +++ b/river/reco/base.py @@ -33,7 +33,7 @@ def is_contextual(self): return False @abc.abstractmethod - def learn_one(self, user: ID, item: ID, y: Reward, x: dict | None = None): + def learn_one(self, user: ID, item: ID, y: Reward, x: dict | None = None) -> None: """Fits a `user`-`item` pair and a real-valued target `y`. Parameters diff --git a/river/reco/baseline.py b/river/reco/baseline.py index fb8213fa59..75bbea0d27 100644 --- a/river/reco/baseline.py +++ b/river/reco/baseline.py @@ -69,7 +69,7 @@ class Baseline(reco.base.Ranker): >>> model = reco.Baseline(optimizer=optim.SGD(0.005)) >>> for x, y in dataset: - ... _ = model.learn_one(**x, y=y) + ... model.learn_one(**x, y=y) >>> model.predict_one(user='Bob', item='Harry Potter') 6.538120 @@ -103,12 +103,12 @@ def __init__( self.clip_gradient = clip_gradient self.global_mean = stats.Mean() - self.u_biases: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(initializer) - self.i_biases: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(initializer) + self.u_biases: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(initializer) + ) + self.i_biases: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(initializer) + ) def predict_one(self, user, item, x=None): return self.global_mean.get() + self.u_biases[user] + self.i_biases[item] @@ -130,5 +130,3 @@ def learn_one(self, user, item, y, x=None): # Update biases self.u_biases = self.u_optimizer.step(self.u_biases, u_grad_bias) self.i_biases = self.i_optimizer.step(self.i_biases, i_grad_bias) - - return self diff --git a/river/reco/biased_mf.py b/river/reco/biased_mf.py index 4032b31daa..8d65b4f07c 100644 --- a/river/reco/biased_mf.py +++ b/river/reco/biased_mf.py @@ -100,7 +100,7 @@ class BiasedMF(Ranker): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(**x, y=y) + ... model.learn_one(**x, y=y) >>> model.predict_one(user='Bob', item='Harry Potter') 6.489025 @@ -158,20 +158,20 @@ def __init__( self.clip_gradient = clip_gradient self.global_mean = stats.Mean() - self.u_biases: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(weight_initializer) - self.i_biases: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(weight_initializer) + self.u_biases: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(weight_initializer) + ) + self.i_biases: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(weight_initializer) + ) random_latents = functools.partial(self.latent_initializer, shape=self.n_factors) - self.u_latents: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(random_latents) - self.i_latents: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(random_latents) + self.u_latents: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(random_latents) + ) + self.i_latents: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(random_latents) + ) @property def _mutable_attributes(self): @@ -226,5 +226,3 @@ def learn_one(self, user, item, y, x=None): self.i_biases = self.i_bias_optimizer.step(self.i_biases, i_grad_bias) self.u_latents = self.u_latent_optimizer.step(self.u_latents, u_latent_grad) self.i_latents = self.i_latent_optimizer.step(self.i_latents, i_latent_grad) - - return self diff --git a/river/reco/funk_mf.py b/river/reco/funk_mf.py index 163f7b42be..1bf7285799 100644 --- a/river/reco/funk_mf.py +++ b/river/reco/funk_mf.py @@ -76,7 +76,7 @@ class FunkMF(reco.base.Ranker): ... ) >>> for x, y in dataset: - ... _ = model.learn_one(**x, y=y) + ... model.learn_one(**x, y=y) >>> model.predict_one(user='Bob', item='Harry Potter') 1.866272 @@ -115,12 +115,12 @@ def __init__( self.clip_gradient = clip_gradient random_latents = functools.partial(self.initializer, shape=self.n_factors) - self.u_latents: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(random_latents) - self.i_latents: collections.defaultdict[ - int, optim.initializers.Initializer - ] = collections.defaultdict(random_latents) + self.u_latents: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(random_latents) + ) + self.i_latents: collections.defaultdict[int, optim.initializers.Initializer] = ( + collections.defaultdict(random_latents) + ) @property def _mutable_attributes(self): @@ -143,5 +143,3 @@ def learn_one(self, user, item, y, x=None): # Update latent weights self.u_latents = self.u_optimizer.step(self.u_latents, u_latent_grad) self.i_latents = self.i_optimizer.step(self.i_latents, i_latent_grad) - - return self diff --git a/river/reco/normal.py b/river/reco/normal.py index b9acadc3ec..8bab210a16 100644 --- a/river/reco/normal.py +++ b/river/reco/normal.py @@ -44,7 +44,7 @@ class RandomNormal(reco.base.Ranker): >>> model = reco.RandomNormal(seed=42) >>> for x, y in dataset: - ... _ = model.learn_one(**x, y=y) + ... model.learn_one(**x, y=y) >>> model.predict_one(user='Bob', item='Harry Potter') 6.147299621751425 @@ -60,7 +60,6 @@ def __init__(self, seed=None): def learn_one(self, user, item, y, x=None): self.mean.update(y) self.variance.update(y) - return self def predict_one(self, user, item, x=None): μ = self.mean.get() or 0 diff --git a/river/rules/__init__.py b/river/rules/__init__.py index ade579452b..60bbd1e7af 100644 --- a/river/rules/__init__.py +++ b/river/rules/__init__.py @@ -1,4 +1,5 @@ """Decision rules-based algorithms.""" + from __future__ import annotations from .amrules import AMRules diff --git a/river/rules/amrules.py b/river/rules/amrules.py index fba7c1df01..245f71c1f4 100644 --- a/river/rules/amrules.py +++ b/river/rules/amrules.py @@ -19,7 +19,6 @@ def __init__(self): def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1): self.mean.update(y, w) - return self def predict_one(self, x: dict): return self.mean.get() @@ -51,8 +50,6 @@ def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1): for _ in range(int(w)): self.model_predictor.learn_one(x, y) - return self - def predict_one(self, x: dict): if self._mae_mean <= self._mae_model: return self.mean_predictor.predict_one(x) @@ -147,8 +144,6 @@ def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1): # type: ign self.update(x, y, w) self.pred_model.learn_one(x, y, w) - return self - def predict_one(self, x: dict): return self.pred_model.predict_one(x) @@ -259,7 +254,7 @@ class AMRules(base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 1.117705 + MAE: 1.119553 References ---------- @@ -356,7 +351,7 @@ def _new_rule(self) -> RegRule: drift_detector=self.drift_detector.clone(), ) - def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1) -> AMRules: + def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1): any_covered = False to_del = set() @@ -411,8 +406,6 @@ def learn_one(self, x: dict, y: base.typing.RegTarget, w: int = 1) -> AMRules: for rule_id in to_del: del self._rules[rule_id] - return self - def predict_one(self, x: dict) -> base.typing.RegTarget: y_pred = 0 hits = 0 @@ -466,7 +459,7 @@ def anomaly_score(self, x) -> tuple[float, float, float]: ... if i == 1000: ... # Skip the last example ... break - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> model.anomaly_score(x) (1.0168907243483933, 0.13045786430817402, 1.0) @@ -517,7 +510,7 @@ def debug_one(self, x) -> str: ... if i == 1000: ... # Skip the last example ... break - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> print(model.debug_one(x)) Rule 0: 3 > 0.5060 and 0 > 0.2538 diff --git a/river/sketch/__init__.py b/river/sketch/__init__.py index 150c802544..5eaf41c62d 100644 --- a/river/sketch/__init__.py +++ b/river/sketch/__init__.py @@ -4,6 +4,7 @@ of memory and processing time. """ + from __future__ import annotations from .counter import Counter diff --git a/river/sketch/counter.py b/river/sketch/counter.py index 3c82744b88..1e54b7b8e5 100644 --- a/river/sketch/counter.py +++ b/river/sketch/counter.py @@ -72,7 +72,7 @@ class Counter(base.Base): >>> vals = [] >>> for _ in range(10000): ... v = rng.randint(-1000, 1000) - ... cms = cms.update(v) + ... cms.update(v) ... counter[v] += 1 ... vals.append(v) @@ -107,7 +107,7 @@ class Counter(base.Base): >>> cms_a = sketch.Counter(epsilon=0.001, delta=0.01, seed=0) >>> for v in vals: - ... cms_a = cms_a.update(v) + ... cms_a.update(v) >>> cms_a[7] 5 @@ -123,7 +123,7 @@ class Counter(base.Base): >>> for _ in range(10000): ... v = rng.randint(-1000, 1000) - ... cms_b = cms_b.update(v) + ... cms_b.update(v) Now, we can define a cosine distance function: @@ -181,8 +181,6 @@ def __len__(self): def update(self, x: typing.Hashable, w: int = 1): self._cms[self._hash(x)] += w - return self - def total(self) -> int: """Return the total count.""" return sum(self._cms[0, :]) diff --git a/river/sketch/heavy_hitters.py b/river/sketch/heavy_hitters.py index 75e29d32d7..0d1b124a95 100644 --- a/river/sketch/heavy_hitters.py +++ b/river/sketch/heavy_hitters.py @@ -54,7 +54,7 @@ class HeavyHitters(base.Base): We will feed the counter with printable ASCII characters: >>> for _ in range(10_000): - ... hh = hh.update(rng.choice(string.printable)) + ... hh.update(rng.choice(string.printable)) We can retrieve estimates of the `n` top elements and their frequencies. Let's try `n=3` >>> hh.most_common(3) @@ -119,8 +119,6 @@ def update(self, x: typing.Hashable): self._delta = self._bucket_width + self._delta * self.fading_factor - return self - def most_common(self, n: int | None = None) -> list[tuple[typing.Hashable, float]]: res = [] for key in self._entries: diff --git a/river/sketch/histogram.py b/river/sketch/histogram.py index 6a3428172f..1f1b497827 100644 --- a/river/sketch/histogram.py +++ b/river/sketch/histogram.py @@ -101,7 +101,7 @@ class Histogram(collections.UserList, base.Base): >>> hist = sketch.Histogram(max_bins=15) >>> for x in values: - ... hist = hist.update(x) + ... hist.update(x) >>> for bin in hist: ... print(bin) @@ -140,7 +140,7 @@ def update(self, x): # Insert the bin if the histogram is empty if not self: self.append(b) - return self + return # Use bisection to find where to insert # We don't use the bisect module in order to save some CPU cycles @@ -169,8 +169,6 @@ def update(self, x): if len(self) > self.max_bins: self._shrink(1) - return self - def _shrink(self, k): """Shrinks the histogram by merging the two closest bins.""" @@ -208,7 +206,7 @@ def iter_cdf(self, X, verbose=False): >>> hist = sketch.Histogram() >>> for x in range(4): - ... hist = hist.update(x) + ... hist.update(x) >>> print(hist) [0.00000, 0.00000]: 1 @@ -255,7 +253,7 @@ def cdf(self, x): >>> hist = sketch.Histogram() >>> for x in range(4): - ... hist = hist.update(x) + ... hist.update(x) >>> print(hist) [0.00000, 0.00000]: 1 diff --git a/river/sketch/set.py b/river/sketch/set.py index 48e614de36..344b1f6548 100644 --- a/river/sketch/set.py +++ b/river/sketch/set.py @@ -172,8 +172,6 @@ def update(self, values: typing.Iterable): for x in values: self.add(x) - return self - def __contains__(self, x: typing.Hashable): proj = [] pos = self._hash(x) diff --git a/river/stats/__init__.py b/river/stats/__init__.py index 50c0625ef5..e62e9db344 100644 --- a/river/stats/__init__.py +++ b/river/stats/__init__.py @@ -1,4 +1,5 @@ """Running statistics""" + from __future__ import annotations from . import base @@ -9,6 +10,7 @@ from .ewmean import EWMean from .ewvar import EWVar from .iqr import IQR, RollingIQR +from .kolmogorov_smirnov import KolmogorovSmirnov from .kurtosis import Kurtosis from .link import Link from .mad import MAD @@ -37,6 +39,7 @@ "EWMean", "EWVar", "IQR", + "KolmogorovSmirnov", "Kurtosis", "Link", "MAD", diff --git a/river/stats/_rust_stats.pyi b/river/stats/_rust_stats.pyi index b342a08715..c163809bd0 100644 --- a/river/stats/_rust_stats.pyi +++ b/river/stats/_rust_stats.pyi @@ -1,3 +1,5 @@ +from __future__ import annotations + class RsQuantile: def __init__(self, q: float): ... def update(self, x: float): ... diff --git a/river/stats/auto_corr.py b/river/stats/auto_corr.py index dd65882473..9764d823ef 100644 --- a/river/stats/auto_corr.py +++ b/river/stats/auto_corr.py @@ -25,7 +25,8 @@ class AutoCorr(stats.base.Univariate): >>> auto_corr = stats.AutoCorr(lag=1) >>> for x in [0.25, 0.5, 0.2, -0.05]: - ... print(auto_corr.update(x).get()) + ... auto_corr.update(x) + ... print(auto_corr.get()) 0 0 -1.0 @@ -33,7 +34,8 @@ class AutoCorr(stats.base.Univariate): >>> auto_corr = stats.AutoCorr(lag=2) >>> for x in [0.25, 0.5, 0.2, -0.05]: - ... print(auto_corr.update(x).get()) + ... auto_corr.update(x) + ... print(auto_corr.get()) 0 0 0 @@ -41,7 +43,8 @@ class AutoCorr(stats.base.Univariate): >>> auto_corr = stats.AutoCorr(lag=1) >>> for x in [1, 0, 0, 0]: - ... print(auto_corr.update(x).get()) + ... auto_corr.update(x) + ... print(auto_corr.get()) 0 0 0 @@ -66,7 +69,5 @@ def update(self, x): # Add x to the window self.window.append(x) - return self - def get(self): return self.pearson.get() diff --git a/river/stats/base.py b/river/stats/base.py index ddb5214197..9d3302e5c2 100644 --- a/river/stats/base.py +++ b/river/stats/base.py @@ -37,8 +37,8 @@ class Univariate(Statistic): """A univariate statistic measures a property of a variable.""" @abc.abstractmethod - def update(self, x: numbers.Number): - """Update and return the called instance.""" + def update(self, x: numbers.Number) -> None: + """Update the called instance.""" raise NotImplementedError @property @@ -68,5 +68,5 @@ class Bivariate(Statistic): """A bivariate statistic measures a relationship between two variables.""" @abc.abstractmethod - def update(self, x, y): - """Update and return the called instance.""" + def update(self, x, y) -> None: + """Update the called instance.""" diff --git a/river/stats/count.py b/river/stats/count.py index 0b89b920b4..94abbab9ec 100644 --- a/river/stats/count.py +++ b/river/stats/count.py @@ -18,7 +18,6 @@ def __init__(self): def update(self, x=None): self.n += 1 - return self def get(self): return self.n diff --git a/river/stats/cov.py b/river/stats/cov.py index 0310a45573..9d8d114f44 100644 --- a/river/stats/cov.py +++ b/river/stats/cov.py @@ -26,7 +26,8 @@ class Cov(stats.base.Bivariate): >>> cov = stats.Cov() >>> for xi, yi in zip(x, y): - ... print(cov.update(xi, yi).get()) + ... cov.update(xi, yi) + ... print(cov.get()) 0.0 -1.044999 -4.286 @@ -41,7 +42,8 @@ class Cov(stats.base.Bivariate): >>> rcov = utils.Rolling(stats.Cov(), window_size=3) >>> for xi, yi in zip(x, y): - ... print(rcov.update(xi, yi).get()) + ... rcov.update(xi, yi) + ... print(rcov.get()) 0.0 -1.045 -4.286 @@ -79,21 +81,18 @@ def update(self, x, y, w=1.0): self.mean_x.update(x, w) self.mean_y.update(y, w) self.cov += w * (dx * (y - self.mean_y.get()) - self.cov) / max(self.n - self.ddof, 1) - return self def revert(self, x, y, w=1.0): dx = x - self.mean_x.get() self.mean_x.revert(x, w) self.mean_y.revert(y, w) self.cov -= w * (dx * (y - self.mean_y.get()) - self.cov) / max(self.n - self.ddof, 1) - return self def update_many(self, X: np.ndarray, Y: np.ndarray): dx = X - self.mean_x.get() self.mean_x.update_many(X) self.mean_y.update_many(Y) self.cov += (dx * (Y - self.mean_y.get()) - self.cov).sum() / max(self.n - self.ddof, 1) - return self def get(self): return self.cov diff --git a/river/stats/entropy.py b/river/stats/entropy.py index 1eef016dc9..59c9cba062 100644 --- a/river/stats/entropy.py +++ b/river/stats/entropy.py @@ -35,8 +35,8 @@ class Entropy(stats.base.Univariate): >>> from river import stats >>> def entropy_list(labels, base=None): - ... value,counts = np.unique(labels, return_counts=True) - ... return entropy(counts, base=base) + ... value,counts = np.unique(labels, return_counts=True) + ... return entropy(counts, base=base) >>> SEED = 42 * 1337 >>> random.seed(SEED) @@ -49,7 +49,7 @@ class Entropy(stats.base.Univariate): >>> random.shuffle(list_animal) >>> for animal in list_animal: - ... _ = entro.update(animal) + ... entro.update(animal) >>> print(f'{entro.get():.6f}') 1.058093 @@ -91,7 +91,5 @@ def update(self, x): self.n += 1 self.counter.update([x]) - return self - def get(self): return self.entropy diff --git a/river/stats/ewmean.py b/river/stats/ewmean.py index a788d69be4..df3a7db14a 100644 --- a/river/stats/ewmean.py +++ b/river/stats/ewmean.py @@ -25,7 +25,8 @@ class EWMean(stats.base.Univariate): >>> X = [1, 3, 5, 4, 6, 8, 7, 9, 11] >>> ewm = stats.EWMean(fading_factor=0.5) >>> for x in X: - ... print(ewm.update(x).get()) + ... ewm.update(x) + ... print(ewm.get()) 1.0 2.0 3.5 @@ -58,7 +59,6 @@ def name(self): def update(self, x): self._ewmean.update(x) - return self def get(self): return self._ewmean.get() diff --git a/river/stats/ewvar.py b/river/stats/ewvar.py index 8b4c48e974..6bd06c7b29 100644 --- a/river/stats/ewvar.py +++ b/river/stats/ewvar.py @@ -28,7 +28,8 @@ class EWVar(stats.base.Univariate): >>> X = [1, 3, 5, 4, 6, 8, 7, 9, 11] >>> ewv = stats.EWVar(fading_factor=0.5) >>> for x in X: - ... print(ewv.update(x).get()) + ... ewv.update(x) + ... print(ewv.get()) 0.0 1.0 2.75 @@ -61,7 +62,6 @@ def name(self): def update(self, x): self._ewvar.update(x) - return self def get(self): return self._ewvar.get() diff --git a/river/stats/iqr.py b/river/stats/iqr.py index 34e566819d..583a1260a3 100644 --- a/river/stats/iqr.py +++ b/river/stats/iqr.py @@ -22,7 +22,7 @@ class IQR(stats.base.Univariate): >>> iqr = stats.IQR(q_inf=0.25, q_sup=0.75) >>> for i in range(0, 1001): - ... iqr = iqr.update(i) + ... iqr.update(i) ... if i % 100 == 0: ... print(iqr.get()) 0.0 @@ -59,9 +59,12 @@ def update(self, x): self._iqr.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): + # HACK: Avoid crash if get is called before update + # panicked at 'index out of bounds: the len is 0 but the index is 0' + if not self._is_updated: + return None return self._iqr.get() def __repr__(self): @@ -98,7 +101,7 @@ class RollingIQR(stats.base.RollingUnivariate): ... ) >>> for i in range(0, 1001): - ... rolling_iqr = rolling_iqr.update(i) + ... rolling_iqr.update(i) ... if i % 100 == 0: ... print(rolling_iqr.get()) 0.0 @@ -130,7 +133,6 @@ def update(self, x): self._rolling_iqr.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): # HACK: Avoid crash if get is called before update diff --git a/river/stats/kolmogorov_smirnov.py b/river/stats/kolmogorov_smirnov.py new file mode 100644 index 0000000000..e3689a9613 --- /dev/null +++ b/river/stats/kolmogorov_smirnov.py @@ -0,0 +1,287 @@ +from __future__ import annotations + +import math +import random + +from river import base, stats + +__all__ = ["KolmogorovSmirnov"] + + +class Treap(base.Base): + """Class representing Treap (Cartesian Tree) used to calculate the Incremental KS statistics.""" + + def __init__(self, key, value=0): + self.key = key + self.value = value + self.priority = random.random() + self.size = 1 + self.height = 1 + self.lazy = 0 + self.max_value = value + self.min_value = value + self.left = None + self.right = None + + @staticmethod + def sum_all(node, value): + if node is None: + return + node.value += value + node.max_value += value + node.min_value += value + node.lazy += value + + @classmethod + def unlazy(cls, node): + cls.sum_all(node.left, node.lazy) + cls.sum_all(node.right, node.lazy) + node.lazy = 0 + + @classmethod + def update(cls, node): + if node is None: + return + cls.unlazy(node) + node.size = 1 + node.height = 0 + node.max_value = node.value + node.min_value = node.value + + if node.left is not None: + node.size += node.left.size + node.height += node.left.height + node.max_value = max(node.max_value, node.left.max_value) + node.min_value = min(node.min_value, node.left.min_value) + + if node.right is not None: + node.size += node.right.size + node.height = max(node.height, node.right.height) + node.max_value = max(node.max_value, node.right.max_value) + node.min_value = min(node.min_value, node.right.min_value) + + node.height += 1 + + @classmethod + def split_keep_right(cls, node, key): + if node is None: + return None, None + + left, right = None, None + + cls.unlazy(node) + + if key <= node.key: + left, node.left = cls.split_keep_right(node.left, key) + right = node + else: + node.right, right = cls.split_keep_right(node.right, key) + left = node + + cls.update(left) + cls.update(right) + + return left, right + + @classmethod + def merge(cls, left, right): + if left is None: + return right + if right is None: + return left + + node = None + + if left.priority > right.priority: + cls.unlazy(left) + left.right = cls.merge(left.right, right) + node = left + else: + cls.unlazy(right) + right.left = cls.merge(left, right.left) + node = right + + cls.update(node) + + return node + + @classmethod + def split_smallest(cls, node): + if node is None: + return None, None + + left, right = None, None + + cls.unlazy(node) + + if node.left is not None: + left, node.left = cls.split_smallest(node.left) + right = node + else: + right = node.right + node.right = None + left = node + + cls.update(left) + cls.update(right) + + return left, right + + @classmethod + def split_greatest(cls, node): + if node is None: + return None, None + + cls.unlazy(node) + + if node.right is not None: + node.right, right = cls.split_greatest(node.right) + left = node + else: + left = node.left + node.left = None + right = node + + cls.update(left) + cls.update(right) + + return left, right + + @staticmethod + def get_size(node): + return 0 if node is None else node.size + + @staticmethod + def get_height(node): + return 0 if node is None else node.height + + +class KolmogorovSmirnov(stats.base.Bivariate): + r"""Incremental Kolmogorov-Smirnov statistics. + + The two-sample Kolmogorov-Smirnov test quantifies the distance between the empirical functions of two samples, + with the null distribution of this statistic is calculated under the null hypothesis that the samples are drawn from + the same distribution. The formula can be described as + + $$ + D_{n, m} = \sup_x \| F_{1, n}(x) - F_{2, m}(x) \|. + $$ + + This implementation is the incremental version of the previously mentioned statistics, with the change being in + the ability to insert and remove an observation thorugh time. This can be done using a randomized tree called + Treap (or Cartesian Tree) [^2] with bulk operation and lazy propagation. + + The implemented algorithm is able to perform the insertion and removal operations + in O(logN) with high probability and calculate the Kolmogorov-Smirnov test in O(1), + where N is the number of sample observations. This is a significant improvement compared + to the O(N logN) cost of non-incremental implementation. + + This implementation also supports the calculation of the Kuiper statistics. Different from the orginial + Kolmogorov-Smirnov statistics, Kuiper's test [^3] calculates the sum of the absolute sizes of the most positive and + most negative differences between the two cumulative distribution functions taken into account. As such, + Kuiper's test is very sensitive in the tails as at the median. + + Last but not least, this implementation is also based on the original implementation within the supplementary + material of the authors of paper [^1], at + [the following Github repository](https://github.com/denismr/incremental-ks/tree/master). + + Parameters + ---------- + statistic + The method used to calculate the statistic, can be either "ks" or "kuiper". + The default value is set as "ks". + + Examples + -------- + + >>> import numpy as np + >>> from river import stats + + >>> stream_a = [1, 1, 2, 2, 3, 3, 4, 4] + >>> stream_b = [1, 1, 1, 1, 2, 2, 2, 2] + + >>> incremental_ks = stats.KolmogorovSmirnov(statistic="ks") + >>> for a, b in zip(stream_a, stream_b): + ... incremental_ks.update(a, b) + + >>> incremental_ks + KolmogorovSmirnov: 0.5 + + >>> incremental_ks.n_samples + 8 + + References + ---------- + [^1]: dos Reis, D.M. et al. (2016) ‘Fast unsupervised online drift detection using incremental Kolmogorov-Smirnov + test’, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. + doi:10.1145/2939672.2939836. + [^2]: C. R. Aragon and R. G. Seidel. Randomized search trees. In FOCS, pages 540–545. IEEE, 1989. + [^3]: Kuiper, N. H. (1960). "Tests concerning random points on a circle". + Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, Series A. 63: 38–47. + + """ + + def __init__(self, statistic="ks"): + self.treap = None + self.n_samples = 0 + self.statistic = statistic + + def update(self, x, y): + keys = ((x, 0), (y, 1)) + + self.n_samples += 1 + + for key in keys: + left, left_g, right, val = None, None, None, None + + left, right = Treap.split_keep_right(self.treap, key) + left, left_g = Treap.split_greatest(left) + val = 0 if left_g is None else left_g.value + + left = Treap.merge(left, left_g) + right = Treap.merge(Treap(key, val), right) + Treap.sum_all(right, 1 if key[1] == 0 else -1) + + self.treap = Treap.merge(left, right) + + def revert(self, x, y): + keys = ((x, 0), (y, 1)) + + self.n_samples -= 1 + + for key in keys: + left, right, right_l = None, None, None + + left, right = Treap.split_keep_right(self.treap, key) + right_l, right = Treap.split_smallest(right) + + if right_l is not None and right_l.key == key: + Treap.sum_all(right, -1 if key[1] == 0 else 1) + else: + right = Treap.merge(right_l, right) + + self.treap = Treap.merge(left, right) + + def get(self): + assert self.statistic in ["ks", "kuiper"] + if self.n_samples == 0: + return 0 + + if self.statistic == "ks": + return max(self.treap.max_value, -self.treap.min_value) / self.n_samples + elif self.statistic == "kuiper": + return max(self.treap.max_value - self.treap.min_value) / self.n_samples + else: + raise ValueError(f"Unknown statistic {self.statistic}, expected one of: ks, kuiper") + + @staticmethod + def _ca(p_value): + return (-0.5 * math.log(p_value)) ** 0.5 + + def _test_ks_threshold(self, ca): + """ + Test whether the reference and sliding window follows the same or different probability distribution. + This test will return `True` if we **reject** the null hypothesis that + the two windows follow the same distribution. + """ + return self.get() > ca * (2 * self.n_samples / self.n_samples**2) ** 0.5 diff --git a/river/stats/kurtosis.py b/river/stats/kurtosis.py index 63524ae353..2813fa5ec2 100644 --- a/river/stats/kurtosis.py +++ b/river/stats/kurtosis.py @@ -24,7 +24,8 @@ class Kurtosis(stats.base.Univariate): >>> kurtosis = stats.Kurtosis(bias=False) >>> for x in X: - ... print(kurtosis.update(x).get()) + ... kurtosis.update(x) + ... print(kurtosis.get()) -3.0 -2.0 -1.5 @@ -50,7 +51,8 @@ class Kurtosis(stats.base.Univariate): >>> kurtosis = stats.Kurtosis(bias=True) >>> for x in X: - ... print(kurtosis.update(x).get()) + ... kurtosis.update(x) + ... print(kurtosis.get()) -3.0 -2.0 -1.5 @@ -87,7 +89,6 @@ def __init__(self, bias=False): def update(self, x): self._kurtosis.update(x) - return self def get(self): return self._kurtosis.get() diff --git a/river/stats/link.py b/river/stats/link.py index cb0c43e8bf..459dd4038a 100644 --- a/river/stats/link.py +++ b/river/stats/link.py @@ -34,10 +34,10 @@ class Link(stats.base.Univariate): Let us now call `update`. - >>> stat = stat.update(1) + >>> stat.update(1) The output from `get` will still be 0. The reason is that `stats.Shift` has not enough - values, and therefore outputs it's default value, which is `None`. The `stats.Mean` + values, and therefore outputs its default value, which is `None`. The `stats.Mean` instance is therefore not updated. >>> stat.get() @@ -47,17 +47,17 @@ class Link(stats.base.Univariate): therefore the mean can be updated. The mean is therefore equal to 1, because that's the only value from the past. - >>> stat = stat.update(3) + >>> stat.update(3) >>> stat.get() 1.0 On the subsequent call to update, the mean will be updated with the value 3. - >>> stat = stat.update(4) + >>> stat.update(4) >>> stat.get() 2.0 - Note that composing statistics returns a new statistic with it's own name. + Note that composing statistics returns a new statistic with its own name. >>> stat.name 'mean_of_shift_1' @@ -73,7 +73,6 @@ def update(self, x): y = self.left.get() if y is not None: self.right.update(y) - return self def get(self): return self.right.get() diff --git a/river/stats/mad.py b/river/stats/mad.py index 7e34efa0b0..0f1bdd9cb0 100644 --- a/river/stats/mad.py +++ b/river/stats/mad.py @@ -22,7 +22,8 @@ class MAD(quantile.Quantile): >>> mad = stats.MAD() >>> for x in X: - ... print(mad.update(x).get()) + ... mad.update(x) + ... print(mad.get()) 0.0 2.0 1.0 @@ -49,4 +50,3 @@ def __init__(self): def update(self, x): self.median.update(x) super().update(abs(x - self.median.get())) - return self diff --git a/river/stats/maximum.py b/river/stats/maximum.py index 692f2312c7..6581b7a970 100644 --- a/river/stats/maximum.py +++ b/river/stats/maximum.py @@ -19,9 +19,10 @@ class Max(stats.base.Univariate): >>> from river import stats >>> X = [1, -4, 3, -2, 5, -6] - >>> _max = stats.Max() + >>> maximum = stats.Max() >>> for x in X: - ... print(_max.update(x).get()) + ... maximum.update(x) + ... print(maximum.get()) 1 1 3 @@ -37,7 +38,6 @@ def __init__(self): def update(self, x): if x > self.max: self.max = x - return self def get(self): return self.max @@ -59,7 +59,8 @@ class RollingMax(stats.base.RollingUnivariate): >>> X = [1, -4, 3, -2, 2, 1] >>> rolling_max = stats.RollingMax(window_size=2) >>> for x in X: - ... print(rolling_max.update(x).get()) + ... rolling_max.update(x) + ... print(rolling_max.get()) 1 1 3 @@ -78,7 +79,6 @@ def window_size(self): def update(self, x): self.window.append(x) - return self def get(self): try: @@ -103,7 +103,8 @@ class AbsMax(stats.base.Univariate): >>> X = [1, -4, 3, -2, 5, -6] >>> abs_max = stats.AbsMax() >>> for x in X: - ... print(abs_max.update(x).get()) + ... abs_max.update(x) + ... print(abs_max.get()) 1 4 4 @@ -119,7 +120,6 @@ def __init__(self): def update(self, x): if abs(x) > self.abs_max: self.abs_max = abs(x) - return self def get(self): return self.abs_max @@ -141,7 +141,8 @@ class RollingAbsMax(stats.base.RollingUnivariate): >>> X = [1, -4, 3, -2, 2, 1] >>> rolling_absmax = stats.RollingAbsMax(window_size=2) >>> for x in X: - ... print(rolling_absmax.update(x).get()) + ... rolling_absmax.update(x) + ... print(rolling_absmax.get()) 1 4 4 @@ -160,7 +161,6 @@ def window_size(self): def update(self, x): self.window.append(abs(x)) - return self def get(self): try: diff --git a/river/stats/mean.py b/river/stats/mean.py index 81f6ae02a9..7fe2e7e140 100644 --- a/river/stats/mean.py +++ b/river/stats/mean.py @@ -24,7 +24,8 @@ class Mean(stats.base.Univariate): >>> X = [-5, -3, -1, 1, 3, 5] >>> mean = stats.Mean() >>> for x in X: - ... print(mean.update(x).get()) + ... mean.update(x) + ... print(mean.get()) -5.0 -4.0 -3.0 @@ -40,7 +41,8 @@ class Mean(stats.base.Univariate): >>> rmean = utils.Rolling(stats.Mean(), window_size=2) >>> for x in X: - ... print(rmean.update(x).get()) + ... rmean.update(x) + ... print(rmean.get()) 1.0 1.5 2.5 @@ -63,14 +65,12 @@ def __init__(self): def update(self, x, w=1.0): self.n += w self._mean += (w / self.n) * (x - self._mean) - return self def update_many(self, X: np.ndarray): a = self.n / (self.n + len(X)) b = len(X) / (self.n + len(X)) self._mean = a * self._mean + b * np.mean(X) self.n += len(X) - return self def revert(self, x, w=1.0): self.n -= w @@ -80,7 +80,6 @@ def revert(self, x, w=1.0): self._mean = 0.0 else: self._mean -= (w / self.n) * (x - self._mean) - return self def get(self): return self._mean @@ -148,11 +147,9 @@ def name(self): def update(self, x): self._mean.update(x) - return self def revert(self, x): self._mean.revert(x) - return self def get(self): # Uses the notation from https://www.wikiwand.com/en/Bayes_estimator#/Practical_example_of_Bayes_estimators diff --git a/river/stats/minimum.py b/river/stats/minimum.py index 58728058fa..bf07d1374b 100644 --- a/river/stats/minimum.py +++ b/river/stats/minimum.py @@ -21,7 +21,6 @@ def __init__(self): def update(self, x): if x < self.min: self.min = x - return self def get(self): return self.min @@ -43,7 +42,8 @@ class RollingMin(stats.base.RollingUnivariate): >>> X = [1, -4, 3, -2, 2, 1] >>> rolling_min = stats.RollingMin(2) >>> for x in X: - ... print(rolling_min.update(x).get()) + ... rolling_min.update(x) + ... print(rolling_min.get()) 1 -4 -4 @@ -62,7 +62,6 @@ def window_size(self): def update(self, x): self.window.append(x) - return self def get(self): try: diff --git a/river/stats/mode.py b/river/stats/mode.py index b35afa44ac..dc1553b10c 100644 --- a/river/stats/mode.py +++ b/river/stats/mode.py @@ -29,7 +29,8 @@ class Mode(stats.base.Univariate): >>> X = ['sunny', 'cloudy', 'cloudy', 'rainy', 'rainy', 'rainy'] >>> mode = stats.Mode(k=2) >>> for x in X: - ... print(mode.update(x).get()) + ... mode.update(x) + ... print(mode.get()) sunny sunny cloudy @@ -39,7 +40,8 @@ class Mode(stats.base.Univariate): >>> mode = stats.Mode(k=-1) >>> for x in X: - ... print(mode.update(x).get()) + ... mode.update(x) + ... print(mode.get()) sunny sunny cloudy @@ -60,7 +62,6 @@ def name(self): def update(self, x): if self.k == -1 or x in self.counts or len(self.counts) < self.k: self.counts[x] += 1 - return self def get(self): return max(self.counts, key=self.counts.get, default=None) @@ -89,7 +90,8 @@ class RollingMode(stats.base.RollingUnivariate): >>> X = ['sunny', 'sunny', 'sunny', 'rainy', 'rainy', 'rainy', 'rainy'] >>> rolling_mode = stats.RollingMode(window_size=2) >>> for x in X: - ... print(rolling_mode.update(x).get()) + ... rolling_mode.update(x) + ... print(rolling_mode.get()) sunny sunny sunny @@ -100,7 +102,8 @@ class RollingMode(stats.base.RollingUnivariate): >>> rolling_mode = stats.RollingMode(window_size=5) >>> for x in X: - ... print(rolling_mode.update(x).get()) + ... rolling_mode.update(x) + ... print(rolling_mode.get()) sunny sunny sunny @@ -131,7 +134,6 @@ def update(self, x): self.counts[x] += 1 self.window.append(x) - return self def get(self): return max(self.counts, key=self.counts.get, default=None) diff --git a/river/stats/moments.py b/river/stats/moments.py deleted file mode 100644 index 2d1212158d..0000000000 --- a/river/stats/moments.py +++ /dev/null @@ -1,68 +0,0 @@ -from __future__ import annotations - -from river import stats - - -class CentralMoments(stats.base.Univariate): - """Computes central moments using Welford's algorithm. - - Attributes - ---------- - count : stats.Count - delta: float - sum_delta : float - Mean of sum of differences. - M1 : float - Sums of powers of differences from the mean order 1. - M2 : float) - Sums of powers of differences from the mean order 2. - M3 : float - Sums of powers of differences from the mean order 3. - M4 : float - Sums of powers of differences from the mean order 4. - - References - ---------- - [^1]: [Wikipedia article on algorithms for calculating variance](https://www.wikiwand.com/en/Algorithms_for_calculating_variance#/Covariance) - - """ - - def __init__(self): - self.count = stats.Count() - - self.delta = 0 - self.sum_delta = 0 - - self.M1 = 0 - self.M2 = 0 - self.M3 = 0 - self.M4 = 0 - - def _update_delta(self, x): - self.delta = (x - self.sum_delta) / self.count.get() - return self - - def _update_sum_delta(self): - self.sum_delta += self.delta - return self - - def _update_m1(self, x): - self.M1 = (x - self.sum_delta) * self.delta * (self.count.get() - 1) - return self - - def _update_m2(self): - self.M2 += self.M1 - return self - - def _update_m3(self): - self.M3 += self.M1 * self.delta * (self.count.get() - 2) - 3 * self.delta * self.M2 - return self - - def _update_m4(self): - delta_square = self.delta**2 - self.M4 += ( - self.M1 * delta_square * (self.count.get() ** 2 - 3 * self.count.get() + 3) - + 6 * delta_square * self.M2 - - 4 * self.delta * self.M3 - ) - return self diff --git a/river/stats/n_unique.py b/river/stats/n_unique.py index 608433fcf0..527398be06 100644 --- a/river/stats/n_unique.py +++ b/river/stats/n_unique.py @@ -37,14 +37,16 @@ class NUnique(stats.base.Univariate): >>> alphabet = string.ascii_lowercase >>> n_unique = stats.NUnique(error_rate=0.2, seed=42) - >>> n_unique.update('a').get() + >>> n_unique.update('a') + >>> n_unique.get() 1 - >>> n_unique.update('b').get() + >>> n_unique.update('b') + >>> n_unique.get() 2 >>> for letter in alphabet: - ... n_unique = n_unique.update(letter) + ... n_unique.update(letter) >>> n_unique.get() 31 @@ -52,7 +54,7 @@ class NUnique(stats.base.Univariate): >>> n_unique = stats.NUnique(error_rate=0.01, seed=42) >>> for letter in alphabet: - ... n_unique = n_unique.update(letter) + ... n_unique.update(letter) >>> n_unique.get() 26 @@ -87,7 +89,6 @@ def update(self, x): i = x & NUnique.P32 - 1 >> 32 - self.n_bits z = 35 - len(bin(NUnique.P32 - 1 & x << self.n_bits | 1 << self.n_bits - 1)) self.buckets[i] = max(self.buckets[i], z) - return self def get(self): a = ( diff --git a/river/stats/pearson.py b/river/stats/pearson.py index 2679553d9c..1080293bc2 100644 --- a/river/stats/pearson.py +++ b/river/stats/pearson.py @@ -31,7 +31,8 @@ class PearsonCorr(stats.base.Bivariate): >>> pearson = stats.PearsonCorr() >>> for xi, yi in zip(x, y): - ... print(pearson.update(xi, yi).get()) + ... pearson.update(xi, yi) + ... print(pearson.get()) 0 0 0 @@ -50,7 +51,8 @@ class PearsonCorr(stats.base.Bivariate): >>> pearson = utils.Rolling(stats.PearsonCorr(), window_size=4) >>> for xi, yi in zip(x, y): - ... print(pearson.update(xi, yi).get()) + ... pearson.update(xi, yi) + ... print(pearson.get()) 0 0 0 @@ -74,13 +76,11 @@ def update(self, x, y): self.var_x.update(x) self.var_y.update(y) self.cov_xy.update(x, y) - return self def revert(self, x, y): self.var_x.revert(x) self.var_y.revert(y) self.cov_xy.revert(x, y) - return self def get(self): var_x = self.var_x.get() diff --git a/river/stats/ptp.py b/river/stats/ptp.py index 30aaecfaae..802812b2ca 100644 --- a/river/stats/ptp.py +++ b/river/stats/ptp.py @@ -15,7 +15,8 @@ class PeakToPeak(stats.base.Univariate): >>> X = [1, -4, 3, -2, 2, 4] >>> ptp = stats.PeakToPeak() >>> for x in X: - ... print(ptp.update(x).get()) + ... ptp.update(x) + ... print(ptp.get()) 0. 5. 7. @@ -37,7 +38,6 @@ def update(self, x): self._ptp.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): if not self._is_updated: @@ -68,7 +68,8 @@ class RollingPeakToPeak(stats.base.RollingUnivariate): >>> X = [1, -4, 3, -2, 2, 1] >>> ptp = stats.RollingPeakToPeak(window_size=2) >>> for x in X: - ... print(ptp.update(x).get()) + ... ptp.update(x) + ... print(ptp.get()) 0 5 7 @@ -89,7 +90,6 @@ def window_size(self): def update(self, x): self.max.update(x) self.min.update(x) - return self def get(self): maximum = self.max.get() diff --git a/river/stats/quantile.py b/river/stats/quantile.py index d4ab469e77..5292c3331b 100644 --- a/river/stats/quantile.py +++ b/river/stats/quantile.py @@ -65,7 +65,6 @@ def update(self, x): self._quantile.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): # HACK: Avoid this following error in `QuantileFilter` @@ -106,7 +105,7 @@ class RollingQuantile(stats.base.RollingUnivariate): ... ) >>> for i in range(1001): - ... rolling_quantile = rolling_quantile.update(i) + ... rolling_quantile.update(i) ... if i % 100 == 0: ... print(rolling_quantile.get()) 0.0 @@ -141,7 +140,6 @@ def update(self, x): self._rolling_quantile.update(x) if not self._is_updated: self._is_updated = True - return self def get(self): if not self._is_updated: diff --git a/river/stats/sem.py b/river/stats/sem.py index 6459c04a1e..38ebac7ac1 100644 --- a/river/stats/sem.py +++ b/river/stats/sem.py @@ -26,7 +26,8 @@ class SEM(var.Var): >>> sem = stats.SEM() >>> for x in X: - ... print(sem.update(x).get()) + ... sem.update(x) + ... print(sem.get()) 0.0 1.0 0.577350 @@ -40,7 +41,8 @@ class SEM(var.Var): >>> rolling_sem = utils.Rolling(stats.SEM(ddof=1), window_size=3) >>> for x in X: - ... print(rolling_sem.update(x).get()) + ... rolling_sem.update(x) + ... print(rolling_sem.get()) 0.0 1.5 0.881917 diff --git a/river/stats/shift.py b/river/stats/shift.py index b4b6d09737..a84940356a 100644 --- a/river/stats/shift.py +++ b/river/stats/shift.py @@ -35,7 +35,7 @@ class Shift(stats.base.Univariate): >>> stat = stats.Shift(1) | stats.Mean() >>> for i in range(5): - ... stat = stat.update(i) + ... stat.update(i) ... print(stat.get()) 0.0 0.0 @@ -70,7 +70,7 @@ class Shift(stats.base.Univariate): Now let's call the `learn_one` method to update our feature extractor. >>> x = next(X) - >>> agg = agg.learn_one(x) + >>> agg.learn_one(x) At this point, the average defaults to the initial value of `stats.Mean`, which is 0. @@ -79,11 +79,11 @@ class Shift(stats.base.Univariate): We can now update our feature extractor with the next data point and check the output. - >>> agg = agg.learn_one(next(X)) + >>> agg.learn_one(next(X)) >>> agg.transform_one(x) {'sales_mean_of_shift_1_by_shop': 10.0} - >>> agg = agg.learn_one(next(X)) + >>> agg.learn_one(next(X)) >>> agg.transform_one(x) {'sales_mean_of_shift_1_by_shop': 12.5} @@ -96,7 +96,6 @@ def __init__(self, amount=1, fill_value=None): def update(self, x): self.buffer.append(x) - return self def get(self): try: diff --git a/river/stats/skew.py b/river/stats/skew.py index 66ff7daf9b..0ed1196af5 100644 --- a/river/stats/skew.py +++ b/river/stats/skew.py @@ -23,7 +23,8 @@ class Skew(stats.base.Univariate): >>> skew = stats.Skew(bias=False) >>> for x in X: - ... print(skew.update(x).get()) + ... skew.update(x) + ... print(skew.get()) 0.0 0.0 -1.4802398132849872 @@ -37,7 +38,8 @@ class Skew(stats.base.Univariate): >>> skew = stats.Skew(bias=True) >>> for x in X: - ... print(skew.update(x).get()) + ... skew.update(x) + ... print(skew.get()) 0.0 0.0 -0.6043053732501439 @@ -66,7 +68,6 @@ def name(self): def update(self, x): self._skew.update(x) - return self def get(self): return self._skew.get() diff --git a/river/stats/summing.py b/river/stats/summing.py index 93f1a2de27..eb8c99b0ee 100644 --- a/river/stats/summing.py +++ b/river/stats/summing.py @@ -19,7 +19,8 @@ class Sum(stats.base.Univariate): >>> X = [-5, -3, -1, 1, 3, 5] >>> mean = stats.Sum() >>> for x in X: - ... print(mean.update(x).get()) + ... mean.update(x) + ... print(mean.get()) -5.0 -8.0 -9.0 @@ -32,7 +33,8 @@ class Sum(stats.base.Univariate): >>> X = [1, -4, 3, -2, 2, 1] >>> rolling_sum = utils.Rolling(stats.Sum(), window_size=2) >>> for x in X: - ... print(rolling_sum.update(x).get()) + ... rolling_sum.update(x) + ... print(rolling_sum.get()) 1.0 -3.0 -1.0 @@ -47,11 +49,9 @@ def __init__(self): def update(self, x): self.sum += x - return self def revert(self, x): self.sum -= x - return self def get(self): return self.sum diff --git a/river/stats/test_kolmogorov_smirnov.py b/river/stats/test_kolmogorov_smirnov.py new file mode 100644 index 0000000000..24b2fd4f97 --- /dev/null +++ b/river/stats/test_kolmogorov_smirnov.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +from collections import deque + +import numpy as np +from scipy.stats import ks_2samp + +from river import stats + + +def test_incremental_ks_statistics(): + initial_a = np.random.normal(loc=0, scale=1, size=500) + initial_b = np.random.normal(loc=1, scale=1, size=500) + + stream_a = np.random.normal(loc=0, scale=1, size=5000) + stream_b = np.random.normal(loc=1, scale=1, size=5000) + + incremental_ks_statistics = [] + incremental_ks = stats.KolmogorovSmirnov(statistic="ks") + sliding_a = deque(initial_a) + sliding_b = deque(initial_b) + + for a, b in zip(initial_a, initial_b): + incremental_ks.update(a, b) + for a, b in zip(stream_a, stream_b): + incremental_ks.revert(sliding_a.popleft(), sliding_b.popleft()) + sliding_a.append(a) + sliding_b.append(b) + incremental_ks.update(a, b) + incremental_ks_statistics.append(incremental_ks.get()) + + ks_2samp_statistics = [] + sliding_a = deque(initial_a) + sliding_b = deque(initial_b) + + for a, b in zip(stream_a, stream_b): + sliding_a.popleft() + sliding_b.popleft() + sliding_a.append(a) + sliding_b.append(b) + ks_2samp_statistics.append(ks_2samp(sliding_a, sliding_b).statistic) + + assert np.allclose(np.array(incremental_ks_statistics), np.array(ks_2samp_statistics)) + + assert incremental_ks._test_ks_threshold(ca=incremental_ks._ca(p_value=0.05)) is True diff --git a/river/stats/test_parallel.py b/river/stats/test_parallel.py index a29017729c..4032094c23 100644 --- a/river/stats/test_parallel.py +++ b/river/stats/test_parallel.py @@ -22,7 +22,8 @@ def test_add_mean(): for i, (x, y, w) in enumerate(zip(X, Y, W)): A.update(x, w) B.update(y, w) - C.update(x, w).update(y, w) + C.update(x, w) + C.update(y, w) D = A + B assert math.isclose(C.get(), D.get()) @@ -55,7 +56,8 @@ def test_add_var(ddof): for i, (x, y, w) in enumerate(zip(X, Y, W)): A.update(x, w) B.update(y, w) - C.update(x, w).update(y, w) + C.update(x, w) + C.update(y, w) D = A + B assert math.isclose(C.get(), D.get()) @@ -89,7 +91,8 @@ def test_sub(stat): for x, y, w in zip(X, Y, W): A.update(x, w) B.update(y, w) - C.update(x, w).update(y, w) + C.update(x, w) + C.update(y, w) D = C - B assert math.isclose(D.get(), A.get()) diff --git a/river/stats/test_quantile.py b/river/stats/test_quantile.py index 6e245f8f17..51a32f9482 100644 --- a/river/stats/test_quantile.py +++ b/river/stats/test_quantile.py @@ -14,7 +14,7 @@ def test_issue_1178(): >>> q = stats.Quantile(0.01) >>> for x in [5, 0, 0, 0, 0, 0, 0, 0]: - ... q = q.update(x) + ... q.update(x) ... print(q) Quantile: 5. Quantile: 0. @@ -27,7 +27,7 @@ def test_issue_1178(): >>> q = stats.Quantile(0.99) >>> for x in [5, 0, 0, 0, 0, 0, 0, 0]: - ... q = q.update(x) + ... q.update(x) ... print(q) Quantile: 5. Quantile: 5. diff --git a/river/stats/var.py b/river/stats/var.py index 67a06910b5..6532e22bec 100644 --- a/river/stats/var.py +++ b/river/stats/var.py @@ -35,7 +35,8 @@ class Var(stats.base.Univariate): >>> var = stats.Var() >>> for x in X: - ... print(var.update(x).get()) + ... var.update(x) + ... print(var.get()) 0.0 2.0 1.0 @@ -50,7 +51,8 @@ class Var(stats.base.Univariate): >>> X = [1, 4, 2, -4, -8, 0] >>> rvar = utils.Rolling(stats.Var(ddof=1), window_size=3) >>> for x in X: - ... print(rvar.update(x).get()) + ... rvar.update(x) + ... print(rvar.get()) 0.0 4.5 2.333333 @@ -82,21 +84,18 @@ def update(self, x, w=1.0): self.mean.update(x, w) mean_new = self.mean.get() self._S += w * (x - mean_old) * (x - mean_new) - return self def revert(self, x, w=1.0): mean_old = self.mean.get() self.mean.revert(x, w) mean_new = self.mean.get() self._S -= w * (x - mean_old) * (x - mean_new) - return self def update_many(self, X: np.ndarray): mean_old = self.mean.get() self.mean.update_many(X) mean_new = self.mean.get() self._S += np.sum(np.multiply(np.subtract(X, mean_old), np.subtract(X, mean_new))) - return self def get(self): if self.n > self.ddof: diff --git a/river/stream/__init__.py b/river/stream/__init__.py index 0433a06105..16ddfc7049 100644 --- a/river/stream/__init__.py +++ b/river/stream/__init__.py @@ -3,6 +3,7 @@ The module includes tools to iterate over data streams. """ + from __future__ import annotations from .cache import Cache @@ -27,6 +28,13 @@ "TwitchChatStream", ] +try: + from .iter_polars import iter_polars + + __all__ += ["iter_polars"] +except ImportError: + pass + try: from .iter_pandas import iter_pandas diff --git a/river/stream/iter_arff.py b/river/stream/iter_arff.py index 1903445ecc..f9eec46ded 100644 --- a/river/stream/iter_arff.py +++ b/river/stream/iter_arff.py @@ -1,6 +1,7 @@ from __future__ import annotations -from scipy.io.arff import arffread +import scipy.io.arff +from scipy.io.arff._arffread import read_header from river import base @@ -8,7 +9,7 @@ def iter_arff( - filepath_or_buffer, target: str | None = None, compression="infer" + filepath_or_buffer, target: str | list[str] | None = None, compression="infer", sparse=False ) -> base.typing.Stream: """Iterates over rows from an ARFF file. @@ -18,11 +19,131 @@ def iter_arff( Either a string indicating the location of a file, or a buffer object that has a `read` method. target - Name of the target field. + Name(s) of the target field. If `None`, then the target field is ignored. If a list of + names is passed, then a dictionary is returned instead of a single value. compression For on-the-fly decompression of on-disk data. If this is set to 'infer' and `filepath_or_buffer` is a path, then the decompression method is inferred for the following extensions: '.gz', '.zip'. + sparse + Whether the data is sparse or not. + + Examples + -------- + + >>> cars = ''' + ... @relation CarData + ... @attribute make {Toyota, Honda, Ford, Chevrolet} + ... @attribute model string + ... @attribute year numeric + ... @attribute price numeric + ... @attribute mpg numeric + ... @data + ... Toyota, Corolla, 2018, 15000, 30.5 + ... Honda, Civic, 2019, 16000, 32.2 + ... Ford, Mustang, 2020, 25000, 25.0 + ... Chevrolet, Malibu, 2017, 18000, 28.9 + ... Toyota, Camry, 2019, 22000, 29.8 + ... ''' + >>> with open('cars.arff', mode='w') as f: + ... _ = f.write(cars) + + >>> from river import stream + + >>> for x, y in stream.iter_arff('cars.arff', target='price'): + ... print(x, y) + {'make': 'Toyota', 'model': ' Corolla', 'year': 2018.0, 'mpg': 30.5} 15000.0 + {'make': 'Honda', 'model': ' Civic', 'year': 2019.0, 'mpg': 32.2} 16000.0 + {'make': 'Ford', 'model': ' Mustang', 'year': 2020.0, 'mpg': 25.0} 25000.0 + {'make': 'Chevrolet', 'model': ' Malibu', 'year': 2017.0, 'mpg': 28.9} 18000.0 + {'make': 'Toyota', 'model': ' Camry', 'year': 2019.0, 'mpg': 29.8} 22000.0 + + Finally, let's delete the example file. + + >>> import os; os.remove('cars.arff') + + ARFF files support sparse data. Let's create a sparse ARFF file. + + >>> sparse = ''' + ... % traindata + ... @RELATION "traindata: -C 6" + ... @ATTRIBUTE y0 {0, 1} + ... @ATTRIBUTE y1 {0, 1} + ... @ATTRIBUTE y2 {0, 1} + ... @ATTRIBUTE y3 {0, 1} + ... @ATTRIBUTE y4 {0, 1} + ... @ATTRIBUTE y5 {0, 1} + ... @ATTRIBUTE X0 NUMERIC + ... @ATTRIBUTE X1 NUMERIC + ... @ATTRIBUTE X2 NUMERIC + ... @DATA + ... { 3 1,6 0.863382,8 0.820094 } + ... { 2 1,6 0.659761 } + ... { 0 1,3 1,6 0.437881,8 0.818882 } + ... { 2 1,6 0.676477,7 0.724635,8 0.755123 } + ... ''' + + >>> with open('sparse.arff', mode='w') as f: + ... _ = f.write(sparse) + + In addition, we'll specify that there are several target fields. + + >>> arff_stream = stream.iter_arff( + ... 'sparse.arff', + ... target=['y0', 'y1', 'y2', 'y3', 'y4', 'y5'], + ... sparse=True + ... ) + + >>> for x, y in arff_stream: + ... print(x) + ... print(y) + {'X0': '0.863382', 'X2': '0.820094'} + {'y0': 0, 'y1': 0, 'y2': 0, 'y3': '1', 'y4': 0, 'y5': 0} + {'X0': '0.659761'} + {'y0': 0, 'y1': 0, 'y2': '1', 'y3': 0, 'y4': 0, 'y5': 0} + {'X0': '0.437881', 'X2': '0.818882'} + {'y0': '1', 'y1': 0, 'y2': 0, 'y3': '1', 'y4': 0, 'y5': 0} + {'X0': '0.676477', 'X1': '0.724635', 'X2': '0.755123'} + {'y0': 0, 'y1': 0, 'y2': '1', 'y3': 0, 'y4': 0, 'y5': 0} + + This function can also deal with missing features in non-sparse data. These are indicated with + a question mark. + + >>> data = ''' + ... @relation giveMeLoan-weka.filters.unsupervised.attribute.Remove-R1 + ... @attribute RevolvingUtilizationOfUnsecuredLines numeric + ... @attribute age numeric + ... @attribute NumberOfTime30-59DaysPastDueNotWorse numeric + ... @attribute DebtRatio numeric + ... @attribute MonthlyIncome numeric + ... @attribute NumberOfOpenCreditLinesAndLoans numeric + ... @attribute NumberOfTimes90DaysLate numeric + ... @attribute NumberRealEstateLoansOrLines numeric + ... @attribute NumberOfTime60-89DaysPastDueNotWorse numeric + ... @attribute NumberOfDependents numeric + ... @attribute isFraud {0,1} + ... @data + ... 0.213179,74,0,0.375607,3500,3,0,1,0,1,0 + ... 0.305682,57,0,5710,?,8,0,3,0,0,0 + ... 0.754464,39,0,0.20994,3500,8,0,0,0,0,0 + ... 0.116951,27,0,46,?,2,0,0,0,0,0 + ... 0.189169,57,0,0.606291,23684,9,0,4,0,2,0 + ... ''' + + >>> with open('data.arff', mode='w') as f: + ... _ = f.write(data) + + >>> for x, y in stream.iter_arff('data.arff', target='isFraud'): + ... print(len(x)) + 10 + 9 + 10 + 9 + 10 + + References + ---------- + [^1]: [ARFF format description from Weka](https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/) """ @@ -32,26 +153,42 @@ def iter_arff( buffer = utils.open_filepath(buffer, compression) try: - rel, attrs = arffread.read_header(buffer) + rel, attrs = read_header(buffer) except ValueError as e: msg = f"Error while parsing header, error was: {e}" - raise arffread.ParseArffError(msg) + raise scipy.io.arff.ParseArffError(msg) names = [attr.name for attr in attrs] - types = [float if isinstance(attr, arffread.NumericAttribute) else None for attr in attrs] + # HACK: it's a bit hacky to rely on class name to determine what casting to apply + casts = [float if attr.__class__.__name__ == "NumericAttribute" else None for attr in attrs] for r in buffer: if len(r) == 0: continue - x = { - name: typ(val) if typ else val - for name, typ, val in zip(names, types, r.rstrip().split(",")) - } - try: - y = x.pop(target) if target else None - except KeyError as e: - print(r) - raise e + + # Read row + if sparse: + x = {} + for s in r.rstrip()[1:-1].strip().split(","): + name_index, val = s.split(" ", 1) + x[names[int(name_index)]] = val + else: + x = { + name: cast(val) if cast else val + for name, cast, val in zip(names, casts, r.rstrip().split(",")) + if val != "?" and val != "" + } + + # Handle target + y = None + if target is not None: + if isinstance(target, list): + y = {name: x.pop(name, 0) for name in target} + else: + try: + y = x.pop(target) if target else None + except KeyError: + y = None yield x, y diff --git a/river/stream/iter_polars.py b/river/stream/iter_polars.py new file mode 100644 index 0000000000..d36e628abb --- /dev/null +++ b/river/stream/iter_polars.py @@ -0,0 +1,49 @@ +from __future__ import annotations + +import polars as pl + +from river import base, stream + + +def iter_polars( + X: pl.DataFrame, y: pl.Series | pl.DataFrame | None = None, **kwargs +) -> base.typing.Stream: + """Iterates over the rows of a `polars.DataFrame`. + + Parameters + ---------- + X + A dataframe of features. + y + A series or a dataframe with one column per target. + kwargs + Extra keyword arguments are passed to the underlying call to `stream.iter_array`. + + Examples + -------- + + >>> import polars as pl + >>> from river import stream + + >>> X = pl.DataFrame({ + ... 'x1': [1, 2, 3, 4], + ... 'x2': ['blue', 'yellow', 'yellow', 'blue'], + ... 'y': [True, False, False, True] + ... }) + >>> y = X.get_column('y') + >>> X=X.drop("y") + + >>> for xi, yi in stream.iter_polars(X, y): + ... print(xi, yi) + {'x1': 1, 'x2': 'blue'} True + {'x1': 2, 'x2': 'yellow'} False + {'x1': 3, 'x2': 'yellow'} False + {'x1': 4, 'x2': 'blue'} True + + """ + + kwargs["feature_names"] = X.columns + if isinstance(y, pl.DataFrame): + kwargs["target_names"] = y.columns + + yield from stream.iter_array(X=X.to_numpy(), y=y if y is None else y.to_numpy(), **kwargs) diff --git a/river/stream/qa.py b/river/stream/qa.py index b7a60d0fe5..54f2ca639d 100644 --- a/river/stream/qa.py +++ b/river/stream/qa.py @@ -157,10 +157,14 @@ def simulate_qa( break # Reveal the ground truth and pop the observation from the queue - yield (i_old, x_old, y_old, kwargs_old) if kwargs_old else ( - i_old, - x_old, - y_old, + yield ( + (i_old, x_old, y_old, kwargs_old) + if kwargs_old + else ( + i_old, + x_old, + y_old, + ) ) del mementos[0] @@ -170,8 +174,12 @@ def simulate_qa( yield (i, x, None, kwargs) if kwargs else (i, x, None) for memento in mementos: - yield (memento.i, memento.x, memento.y, memento.kwargs) if memento.kwargs else ( - memento.i, - memento.x, - memento.y, + yield ( + (memento.i, memento.x, memento.y, memento.kwargs) + if memento.kwargs + else ( + memento.i, + memento.x, + memento.y, + ) ) diff --git a/river/stream/twitch_chat_stream.py b/river/stream/twitch_chat_stream.py index ddb6397981..3547737ebd 100644 --- a/river/stream/twitch_chat_stream.py +++ b/river/stream/twitch_chat_stream.py @@ -46,7 +46,7 @@ class TwitchChatStream: channels A list of channel names like `["asmongold", "shroud"]` you want to collect messages from. buffer_size - Size of buffer in bytes used for receiving responses from Twitch with IRC (default 2 kB). + Size of buffer in bytes used for receiving responses from Twitch with IRC (default 2 KiB). timeout A timeout value in seconds for waiting response from Twitch (default 60s). It can be useful if all requested channels are offline or chat is not active enough. @@ -145,7 +145,7 @@ def _gen_items(self, sock: socket.socket) -> Iterator[ChatMessageItem]: continue resp = data.decode(ENCODING) now = dt.datetime.now() - except socket.timeout as e: + except TimeoutError as e: raise TimeoutError(f"Twitch did not respond in {self.timeout:,d} seconds") from e except UnicodeDecodeError: continue diff --git a/river/test_estimators.py b/river/test_estimators.py index 87a4de6fef..f332981ece 100644 --- a/river/test_estimators.py +++ b/river/test_estimators.py @@ -1,15 +1,18 @@ """General tests that all estimators need to pass.""" + from __future__ import annotations import importlib import inspect import pytest +from sklearn import linear_model as sk_linear_model from river import ( anomaly, base, checks, + compat, compose, facto, feature_extraction, @@ -23,13 +26,6 @@ preprocessing, time_series, ) - -try: - from river.compat.pytorch import PyTorch2RiverBase - - PYTORCH_INSTALLED = True -except ImportError: - PYTORCH_INSTALLED = False from river.compat.river_to_sklearn import River2SKLBase from river.compat.sklearn_to_river import SKL2RiverBase @@ -50,6 +46,7 @@ def iter_estimators_which_can_be_tested(): ignored = ( River2SKLBase, SKL2RiverBase, + anomaly.LocalOutlierFactor, # needs warm-start to work correctly compose.FuncTransformer, compose.Grouper, compose.Pipeline, @@ -72,7 +69,7 @@ def iter_estimators_which_can_be_tested(): imblearn.RandomUnderSampler, imblearn.RandomSampler, model_selection.SuccessiveHalvingClassifier, - neighbors.NearestNeighbors, + neighbors.LazySearch, neural_net.MLPRegressor, preprocessing.PreviousImputer, preprocessing.OneHotEncoder, @@ -80,9 +77,6 @@ def iter_estimators_which_can_be_tested(): time_series.base.Forecaster, ) - if PYTORCH_INSTALLED: - ignored = (*ignored, PyTorch2RiverBase) - def can_be_tested(estimator): return not inspect.isabstract(estimator) and not issubclass(estimator, ignored) @@ -124,6 +118,10 @@ def can_be_tested(estimator): | linear_model.LinearRegression() ), preprocessing.MinMaxScaler() | anomaly.HalfSpaceTrees(), + ( + preprocessing.StandardScaler() + | compat.convert_sklearn_to_river(sk_linear_model.SGDRegressor(tol=1e-10)) + ), ] for check in checks.yield_checks(estimator) if check.__name__ not in estimator._unit_test_skips() diff --git a/river/time_series/__init__.py b/river/time_series/__init__.py index 393a45dbe9..7fb887f5e2 100644 --- a/river/time_series/__init__.py +++ b/river/time_series/__init__.py @@ -1,4 +1,5 @@ """Time series forecasting.""" + from __future__ import annotations from . import base diff --git a/river/time_series/base.py b/river/time_series/base.py index 1486f9815b..bcfc3ed492 100644 --- a/river/time_series/base.py +++ b/river/time_series/base.py @@ -13,7 +13,7 @@ def _supervised(self): return True @abc.abstractmethod - def learn_one(self, y: float, x: dict | None = None) -> Forecaster: + def learn_one(self, y: float, x: dict | None = None) -> None: """Updates the model. Parameters diff --git a/river/time_series/holt_winters.py b/river/time_series/holt_winters.py index 52241be66a..1e53c187e5 100644 --- a/river/time_series/holt_winters.py +++ b/river/time_series/holt_winters.py @@ -9,8 +9,7 @@ __all__ = ["HoltWinters"] -class Component(deque): - ... +class Component(deque): ... class AdditiveLevel(Component): @@ -192,11 +191,11 @@ def learn_one(self, y, x=None): self.trend.update(y, self.level) if self.season is not None: self.season.update(y, self.level, self.trend) - return self + return self._first_values.append(y) if len(self._first_values) < max(2, self.seasonality): - return self + return # The components can be initialized now that enough values have been observed self.level.append(statistics.mean(self._first_values)) @@ -208,8 +207,6 @@ def learn_one(self, y, x=None): self._initialized = True - return self - def forecast(self, horizon, xs=None): op = operator.mul if self.multiplicative else operator.add return [ diff --git a/river/time_series/metrics.py b/river/time_series/metrics.py index 8dfa50ca4f..405ef4a6bc 100644 --- a/river/time_series/metrics.py +++ b/river/time_series/metrics.py @@ -9,7 +9,7 @@ class ForecastingMetric(base.Base, abc.ABC): @abc.abstractmethod - def update(self, y_true: list[Number], y_pred: list[Number]) -> ForecastingMetric: + def update(self, y_true: list[Number], y_pred: list[Number]) -> None: """Update the metric at each step along the horizon. Parameters @@ -19,10 +19,6 @@ def update(self, y_true: list[Number], y_pred: list[Number]) -> ForecastingMetri y_pred Predicted values at each time step of the horizon. - Returns - ------- - self - """ @abc.abstractmethod @@ -87,8 +83,6 @@ def update(self, y_true, y_pred): metric.update(yt, yp) - return self - def get(self): return [metric.get() for metric in self.metrics] diff --git a/river/time_series/snarimax.py b/river/time_series/snarimax.py index e32a28f9c9..150f15c2bb 100644 --- a/river/time_series/snarimax.py +++ b/river/time_series/snarimax.py @@ -176,7 +176,7 @@ class SNARIMAX(time_series.base.Forecaster): ... ) >>> for t, (x, y) in enumerate(datasets.AirlinePassengers()): - ... model = model.learn_one(y) + ... model.learn_one(y) >>> horizon = 12 >>> future = [ @@ -186,18 +186,18 @@ class SNARIMAX(time_series.base.Forecaster): >>> forecast = model.forecast(horizon=horizon) >>> for x, y_pred in zip(future, forecast): ... print(x['month'], f'{y_pred:.3f}') - 1961-01-01 491.988 - 1961-02-01 447.593 - 1961-03-01 481.405 - 1961-04-01 566.278 - 1961-05-01 551.561 - 1961-06-01 605.414 - 1961-07-01 711.140 - 1961-08-01 668.204 - 1961-09-01 570.517 - 1961-10-01 549.589 - 1961-11-01 466.344 - 1961-12-01 506.945 + 1961-01-01 494.542 + 1961-02-01 450.825 + 1961-03-01 484.972 + 1961-04-01 576.401 + 1961-05-01 559.489 + 1961-06-01 612.251 + 1961-07-01 722.410 + 1961-08-01 674.604 + 1961-09-01 575.716 + 1961-10-01 562.808 + 1961-11-01 477.049 + 1961-12-01 515.191 Classic ARIMA models learn solely on the time series values. You can also include features built at each step. @@ -244,23 +244,23 @@ class SNARIMAX(time_series.base.Forecaster): ... ) >>> for x, y in datasets.AirlinePassengers(): - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> forecast = model.forecast(horizon=horizon) >>> for x, y_pred in zip(future, forecast): ... print(x['month'], f'{y_pred:.3f}') - 1961-01-01 446.874 - 1961-02-01 423.998 - 1961-03-01 439.957 - 1961-04-01 457.958 - 1961-05-01 457.303 - 1961-06-01 496.554 - 1961-07-01 553.798 - 1961-08-01 551.388 - 1961-09-01 479.620 - 1961-10-01 440.613 - 1961-11-01 409.914 - 1961-12-01 433.774 + 1961-01-01 444.821 + 1961-02-01 432.612 + 1961-03-01 457.739 + 1961-04-01 465.544 + 1961-05-01 476.575 + 1961-06-01 516.255 + 1961-07-01 565.405 + 1961-08-01 572.470 + 1961-09-01 512.645 + 1961-10-01 475.919 + 1961-11-01 438.033 + 1961-12-01 456.892 References ---------- @@ -349,8 +349,6 @@ def learn_one(self, y, x=None): self.y_hist.appendleft(y) - return self - def forecast(self, horizon, xs=None): if xs is None: xs = [{}] * horizon diff --git a/river/time_series/test_evaluate.py b/river/time_series/test_evaluate.py index 369b8ae95c..7644598b92 100644 --- a/river/time_series/test_evaluate.py +++ b/river/time_series/test_evaluate.py @@ -9,7 +9,6 @@ def __init__(self): def learn_one(self, y, x=None): self.mean.update(y) - return self def forecast(self, horizon, xs=None): return [self.mean.get()] * horizon diff --git a/river/tree/__init__.py b/river/tree/__init__.py index 5166c553fb..ad53a1ecf8 100755 --- a/river/tree/__init__.py +++ b/river/tree/__init__.py @@ -49,6 +49,7 @@ dynamic and static feature quantizers to deal with numerical inputs. """ + from __future__ import annotations from . import base, splitter @@ -58,6 +59,7 @@ from .hoeffding_tree_classifier import HoeffdingTreeClassifier from .hoeffding_tree_regressor import HoeffdingTreeRegressor from .isoup_tree_regressor import iSOUPTreeRegressor +from .last_classifier import LASTClassifier from .stochastic_gradient_tree import SGTClassifier, SGTRegressor __all__ = [ @@ -69,6 +71,7 @@ "HoeffdingTreeRegressor", "HoeffdingAdaptiveTreeRegressor", "iSOUPTreeRegressor", + "LASTClassifier", "SGTClassifier", "SGTRegressor", ] diff --git a/river/tree/base.py b/river/tree/base.py index bb074741a8..2d337b20e0 100644 --- a/river/tree/base.py +++ b/river/tree/base.py @@ -8,6 +8,7 @@ intention is to provide utilities for walking over a tree and visualizing it. """ + from __future__ import annotations import abc diff --git a/river/tree/extremely_fast_decision_tree.py b/river/tree/extremely_fast_decision_tree.py index 567cc79e65..5bd3233e26 100755 --- a/river/tree/extremely_fast_decision_tree.py +++ b/river/tree/extremely_fast_decision_tree.py @@ -18,16 +18,28 @@ class ExtremelyFastDecisionTreeClassifier(HoeffdingTreeClassifier): - """Extremely Fast Decision Tree classifier. + """Extremely Fast Decision Tree (EFDT) classifier. + + Also referred to as the Hoeffding AnyTime Tree (HATT) classifier. In practice, + despite the name, EFDTs are typically slower than a vanilla Hoeffding Tree + to process data. The speed differences come from the mechanism of split + re-evaluation present in EFDT. Nonetheless, EFDT has theoretical properties + that ensure it converges faster than the vanilla Hoeffding Tree to the structure + that would be created by a batch decision tree model (such as Classification and + Regression Trees - CART). Keep in mind that such propositions hold when processing + a stationary data stream. When dealing with non-stationary data, EFDT is somewhat + robust to concept drifts as it continually revisits and updates its internal + decision tree structure. Still, in such cases, the Hoeffind Adaptive Tree might + be a better option, as it was specifically designed to handle non-stationarity. - Also referred to as Hoeffding AnyTime Tree (HATT) classifier. Parameters ---------- grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. min_samples_reevaluate Number of instances a node should observe before reevaluating the best split. split_criterion @@ -59,8 +71,17 @@ class ExtremelyFastDecisionTreeClassifier(HoeffdingTreeClassifier): By default, `tree.splitter.GaussianSplitter` is used if `splitter` is `None`. binary_split If True, only allow binary splits. + min_branch_fraction + The minimum percentage of observed data required for branches resulting from split + candidates. To validate a split candidate, at least two resulting branches must have + a percentage of samples greater than `min_branch_fraction`. This criterion prevents + unnecessary splits when the majority of instances are concentrated in a single branch. + max_share_to_split + Only perform a split in a leaf if the proportion of elements in the majority class is + smaller than this parameter value. This parameter avoids performing splits when most + of the data belongs to a single class. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management @@ -122,6 +143,8 @@ def __init__( nominal_attributes: list | None = None, splitter: Splitter | None = None, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 1000000, stop_mem_management: bool = False, @@ -139,6 +162,8 @@ def __init__( nominal_attributes=nominal_attributes, splitter=splitter, binary_split=binary_split, + min_branch_fraction=min_branch_fraction, + max_share_to_split=max_share_to_split, max_size=max_size, memory_estimate_period=memory_estimate_period, stop_mem_management=stop_mem_management, @@ -180,7 +205,7 @@ def _branch_selector(self, numerical_feature=True, multiway_split=False) -> type else: return EFDTNominalMultiwayBranch - def learn_one(self, x, y, *, sample_weight=1.0): + def learn_one(self, x, y, *, w=1.0): """Incrementally train the model Parameters @@ -189,7 +214,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): Instance attributes. y The label of the instance. - sample_weight + w The weight of the sample. Notes @@ -202,28 +227,22 @@ def learn_one(self, x, y, *, sample_weight=1.0): * Reevaluate the best split for each internal node. * Attempt to split the leaf. - Returns - ------- - self - """ # Updates the set of observed classes self.classes.add(y) - self._train_weight_seen_by_model += sample_weight + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() self._n_active_leaves = 1 # Sort instance X into a leaf - self._sort_to_leaf(x, y, sample_weight) + self._sort_to_leaf(x, y, w) # Process all nodes, starting from root to the leaf where the instance x belongs. - self._process_nodes(x, y, sample_weight, self._root, None, None) - - return self + self._process_nodes(x, y, w, self._root, None, None) - def _sort_to_leaf(self, x, y, sample_weight): + def _sort_to_leaf(self, x, y, w): """For a given instance, find the corresponding leaf and update it. Private function where leaf learn from instance. @@ -238,7 +257,7 @@ def _sort_to_leaf(self, x, y, sample_weight): Instance attributes. y The instance label. - sample_weight + w The weight of the sample. """ @@ -263,12 +282,12 @@ def _sort_to_leaf(self, x, y, sample_weight): node = node.traverse(x, until_leaf=False) if isinstance(node, HTLeaf): break - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - def _process_nodes(self, x, y, sample_weight, node, parent, branch_index): + def _process_nodes(self, x, y, w, node, parent, branch_index): """Process nodes from the root to the leaf where the instance belongs. 1. If the node is internal: @@ -282,7 +301,7 @@ def _process_nodes(self, x, y, sample_weight, node, parent, branch_index): Instance attributes. y The label of the instance. - sample_weight + w The weight of the sample. node The node to process. @@ -293,7 +312,7 @@ def _process_nodes(self, x, y, sample_weight, node, parent, branch_index): """ if isinstance(node, BaseEFDTBranch): # Update split nodes as the tree is traversed - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) old_weight = node.last_split_reevaluation_at new_weight = node.total_weight @@ -318,7 +337,7 @@ def _process_nodes(self, x, y, sample_weight, node, parent, branch_index): child = node.children[child_index] except KeyError: child_index, child = node.most_common_path() - self._process_nodes(x, y, sample_weight, child, node, child_index) + self._process_nodes(x, y, w, child, node, child_index) elif self._growth_allowed and node.is_active(): if node.depth >= self.max_depth: # Max depth reached node.deactivate() @@ -408,7 +427,7 @@ def _reevaluate_best_split(self, node, parent, branch_index, **kwargs): # update EFDT if parent is None: - # Root case : replace the root node by a new split node + # Root case : replace the root node by the new node self._root = best_split else: parent.children[branch_index] = best_split @@ -427,59 +446,67 @@ def _reevaluate_best_split(self, node, parent, branch_index, **kwargs): # Manage memory self._enforce_size_limit() - - elif ( - x_best.merit - x_current.merit > hoeffding_bound or hoeffding_bound < self.tau - ) and (id_current != id_best): - # Create a new branch - branch = self._branch_selector(x_best.numerical_feature, x_best.multiway_split) - leaves = tuple( - self._new_leaf(initial_stats, parent=node) - for initial_stats in x_best.children_stats - ) - - new_split = x_best.assemble(branch, node.stats, node.depth, *leaves, **kwargs) - # Update weights in new_split - new_split.last_split_reevaluation_at = node.total_weight - - n_active = n_inactive = 0 - for leaf in node.iter_leaves(): - if leaf.is_active(): - n_active += 1 + elif x_current is not None: + if ( + x_best.merit - x_current.merit > hoeffding_bound + or hoeffding_bound < self.tau + ) and (id_current != id_best): + # Create a new branch + branch = self._branch_selector( + x_best.numerical_feature, x_best.multiway_split + ) + leaves = tuple( + self._new_leaf(initial_stats, parent=node) + for initial_stats in x_best.children_stats + ) + + new_split = x_best.assemble( + branch, node.stats, node.depth, *leaves, **kwargs + ) + # Update weights in new_split + new_split.last_split_reevaluation_at = node.total_weight + + n_active = n_inactive = 0 + for leaf in node.iter_leaves(): + if leaf.is_active(): + n_active += 1 + else: + n_inactive += 1 + + self._n_active_leaves -= n_active + self._n_inactive_leaves -= n_inactive + self._n_active_leaves += len(leaves) + + if parent is None: + # Root case : replace the root node by a new split node + self._root = new_split else: - n_inactive += 1 - - self._n_active_leaves -= n_active - self._n_inactive_leaves -= n_inactive - self._n_active_leaves += len(leaves) - - if parent is None: - # Root case : replace the root node by a new split node - self._root = new_split - else: - parent.children[branch_index] = new_split - - stop_flag = True - - # Manage memory - self._enforce_size_limit() - - elif ( - x_best.merit - x_current.merit > hoeffding_bound or hoeffding_bound < self.tau - ) and (id_current == id_best): - branch = self._branch_selector(x_best.numerical_feature, x_best.multiway_split) - # Change the branch but keep the existing children nodes - new_split = x_best.assemble( - branch, node.stats, node.depth, *tuple(node.children), **kwargs - ) - # Update weights in new_split - new_split.last_split_reevaluation_at = node.total_weight - - if parent is None: - # Root case : replace the root node by a new split node - self._root = new_split - else: - parent.children[branch_index] = new_split + parent.children[branch_index] = new_split + + stop_flag = True + + # Manage memory + self._enforce_size_limit() + + elif ( + x_best.merit - x_current.merit > hoeffding_bound + or hoeffding_bound < self.tau + ) and (id_current == id_best): + branch = self._branch_selector( + x_best.numerical_feature, x_best.multiway_split + ) + # Change the branch but keep the existing children nodes + new_split = x_best.assemble( + branch, node.stats, node.depth, *tuple(node.children), **kwargs + ) + # Update weights in new_split + new_split.last_split_reevaluation_at = node.total_weight + + if parent is None: + # Root case : replace the root node by a new split node + self._root = new_split + else: + parent.children[branch_index] = new_split return stop_flag @@ -527,7 +554,12 @@ def _attempt_to_split(self, node, parent, branch_index, **kwargs): node.total_weight, ) - if x_best.merit - x_null.merit > hoeffding_bound or hoeffding_bound < self.tau: + if x_best.feature is None: + # Pre-pruning - null wins + node.deactivate() + self._n_inactive_leaves += 1 + self._n_active_leaves -= 1 + elif x_best.merit - x_null.merit > hoeffding_bound or hoeffding_bound < self.tau: # Create a new branch branch = self._branch_selector(x_best.numerical_feature, x_best.multiway_split) leaves = tuple( diff --git a/river/tree/hoeffding_adaptive_tree_classifier.py b/river/tree/hoeffding_adaptive_tree_classifier.py index 208616de95..c825fa5f38 100644 --- a/river/tree/hoeffding_adaptive_tree_classifier.py +++ b/river/tree/hoeffding_adaptive_tree_classifier.py @@ -27,7 +27,8 @@ class HoeffdingAdaptiveTreeClassifier(HoeffdingTreeClassifier): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. split_criterion Split criterion to use.
- 'gini' - Gini
@@ -67,8 +68,17 @@ class HoeffdingAdaptiveTreeClassifier(HoeffdingTreeClassifier): than their main subtree counterparts. binary_split If True, only allow binary splits. + min_branch_fraction + The minimum percentage of observed data required for branches resulting from split + candidates. To validate a split candidate, at least two resulting branches must have + a percentage of samples greater than `min_branch_fraction`. This criterion prevents + unnecessary splits when the majority of instances are concentrated in a single branch. + max_share_to_split + Only perform a split in a leaf if the proportion of elements in the majority class is + smaller than this parameter value. This parameter avoids performing splits when most + of the data belongs to a single class. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management @@ -139,6 +149,8 @@ def __init__( drift_detector: base.DriftDetector | None = None, switch_significance: float = 0.05, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 1000000, stop_mem_management: bool = False, @@ -157,6 +169,8 @@ def __init__( nominal_attributes=nominal_attributes, splitter=splitter, binary_split=binary_split, + min_branch_fraction=min_branch_fraction, + max_share_to_split=max_share_to_split, max_size=max_size, memory_estimate_period=memory_estimate_period, stop_mem_management=stop_mem_management, @@ -204,23 +218,21 @@ def summary(self): ) return summ - def learn_one(self, x, y, *, sample_weight=1.0): + def learn_one(self, x, y, *, w=1.0): # Updates the set of observed classes self.classes.add(y) - self._train_weight_seen_by_model += sample_weight + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() self._n_active_leaves = 1 - self._root.learn_one(x, y, sample_weight=sample_weight, tree=self) + self._root.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - return self - # Override HoeffdingTreeClassifier def predict_proba_one(self, x): proba = {c: 0.0 for c in self.classes} diff --git a/river/tree/hoeffding_adaptive_tree_regressor.py b/river/tree/hoeffding_adaptive_tree_regressor.py index a885efcc0d..6c609cc8f4 100644 --- a/river/tree/hoeffding_adaptive_tree_regressor.py +++ b/river/tree/hoeffding_adaptive_tree_regressor.py @@ -36,7 +36,8 @@ class HoeffdingAdaptiveTreeRegressor(HoeffdingTreeRegressor): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. delta Significance level to calculate the Hoeffding bound. The significance level is given by `1 - delta`. Values closer to zero imply longer split decision delays. @@ -84,7 +85,7 @@ class HoeffdingAdaptiveTreeRegressor(HoeffdingTreeRegressor): binary_split If True, only allow binary splits. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management @@ -139,7 +140,8 @@ class HoeffdingAdaptiveTreeRegressor(HoeffdingTreeRegressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.809874 + MAE: 0.917576 + """ def __init__( @@ -226,19 +228,17 @@ def summary(self): ) return summ - def learn_one(self, x, y, *, sample_weight=1.0): - self._train_weight_seen_by_model += sample_weight + def learn_one(self, x, y, *, w=1.0): + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() self._n_active_leaves = 1 - self._root.learn_one(x, y, sample_weight=sample_weight, tree=self) + self._root.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - return self - def predict_one(self, x): pred = 0.0 if self._root is not None: diff --git a/river/tree/hoeffding_tree.py b/river/tree/hoeffding_tree.py index f1e1a0e168..80005f3754 100644 --- a/river/tree/hoeffding_tree.py +++ b/river/tree/hoeffding_tree.py @@ -4,6 +4,7 @@ import functools import io import math +import sys from abc import ABC, abstractmethod from river import base @@ -30,11 +31,12 @@ class HoeffdingTree(ABC): Parameters ---------- max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. binary_split If True, only allow binary splits. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management @@ -60,7 +62,7 @@ def __init__( self._split_criterion: str = "" self._leaf_prediction: str = "" - self.max_depth: float = max_depth if max_depth is not None else math.inf + self.max_depth: int = max_depth if max_depth is not None else (sys.getrecursionlimit() - 20) self.binary_split: bool = binary_split self._max_size: float = max_size self._max_byte_size: float = self._max_size * (2**20) # convert to byte @@ -111,7 +113,7 @@ def _hoeffding_bound(range_val, confidence, n): @property def max_size(self): - """Max allowed size tree can reach (in MB).""" + """Max allowed size tree can reach (in MiB).""" return self._max_size @max_size.setter @@ -394,7 +396,7 @@ def draw(self, max_depth: int | None = None): ... tau=0.05, ... ) >>> for x, y in datasets.Phishing(): - ... model = model.learn_one(x, y) + ... model.learn_one(x, y) >>> dot = model.draw() .. image:: ../../docs/img/dtree_draw.svg diff --git a/river/tree/hoeffding_tree_classifier.py b/river/tree/hoeffding_tree_classifier.py index ab4f179ba6..b3dc172461 100755 --- a/river/tree/hoeffding_tree_classifier.py +++ b/river/tree/hoeffding_tree_classifier.py @@ -18,7 +18,8 @@ class HoeffdingTreeClassifier(HoeffdingTree, base.Classifier): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. split_criterion Split criterion to use.
- 'gini' - Gini
@@ -48,8 +49,17 @@ class HoeffdingTreeClassifier(HoeffdingTree, base.Classifier): By default, `tree.splitter.GaussianSplitter` is used if `splitter` is `None`. binary_split If True, only allow binary splits. + min_branch_fraction + The minimum percentage of observed data required for branches resulting from split + candidates. To validate a split candidate, at least two resulting branches must have + a percentage of samples greater than `min_branch_fraction`. This criterion prevents + unnecessary splits when the majority of instances are concentrated in a single branch. + max_share_to_split + Only perform a split in a leaf if the proportion of elements in the majority class is + smaller than this parameter value. This parameter avoids performing splits when most + of the data belongs to a single class. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management @@ -129,6 +139,8 @@ def __init__( nominal_attributes: list | None = None, splitter: Splitter | None = None, binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, max_size: float = 100.0, memory_estimate_period: int = 1000000, stop_mem_management: bool = False, @@ -159,6 +171,9 @@ def __init__( raise ValueError("The chosen splitter cannot be used in classification tasks.") self.splitter = splitter # type: ignore + self.min_branch_fraction = min_branch_fraction + self.max_share_to_split = max_share_to_split + # To keep track of the observed classes self.classes: set = set() @@ -170,9 +185,7 @@ def _mutable_attributes(self): def split_criterion(self, split_criterion): if split_criterion not in self._VALID_SPLIT_CRITERIA: print( - "Invalid split_criterion option {}', will use default '{}'".format( - split_criterion, self._INFO_GAIN_SPLIT - ) + f"Invalid split_criterion option {split_criterion}', will use default '{self._INFO_GAIN_SPLIT}'" ) self._split_criterion = self._INFO_GAIN_SPLIT else: @@ -182,9 +195,7 @@ def split_criterion(self, split_criterion): def leaf_prediction(self, leaf_prediction): if leaf_prediction not in self._VALID_LEAF_PREDICTION: print( - "Invalid leaf_prediction option {}', will use default '{}'".format( - leaf_prediction, self._NAIVE_BAYES_ADAPTIVE - ) + f"Invalid leaf_prediction option {leaf_prediction}', will use default '{self._NAIVE_BAYES_ADAPTIVE}'" ) self._leaf_prediction = self._NAIVE_BAYES_ADAPTIVE else: @@ -207,13 +218,13 @@ def _new_leaf(self, initial_stats=None, parent=None): def _new_split_criterion(self): if self._split_criterion == self._GINI_SPLIT: - split_criterion = GiniSplitCriterion() + split_criterion = GiniSplitCriterion(self.min_branch_fraction) elif self._split_criterion == self._INFO_GAIN_SPLIT: - split_criterion = InfoGainSplitCriterion() + split_criterion = InfoGainSplitCriterion(self.min_branch_fraction) elif self._split_criterion == self._HELLINGER_SPLIT: - split_criterion = HellingerDistanceCriterion() + split_criterion = HellingerDistanceCriterion(self.min_branch_fraction) else: - split_criterion = InfoGainSplitCriterion() + split_criterion = InfoGainSplitCriterion(self.min_branch_fraction) return split_criterion @@ -304,7 +315,7 @@ def _attempt_to_split(self, leaf: HTLeaf, parent: DTBranch, parent_branch: int, # Manage memory self._enforce_size_limit() - def learn_one(self, x, y, *, sample_weight=1.0): + def learn_one(self, x, y, *, w=1.0): """Train the model on instance x and corresponding target y. Parameters @@ -313,13 +324,9 @@ def learn_one(self, x, y, *, sample_weight=1.0): Instance attributes. y Class label for sample x. - sample_weight + w Sample weight. - Returns - ------- - self - Notes ----- Training tasks: @@ -335,7 +342,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): # Updates the set of observed classes self.classes.add(y) - self._train_weight_seen_by_model += sample_weight + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() @@ -355,7 +362,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): node = self._root if isinstance(node, HTLeaf): - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._growth_allowed and node.is_active(): if node.depth >= self.max_depth: # Max depth reached node.deactivate() @@ -389,13 +396,11 @@ def learn_one(self, x, y, *, sample_weight=1.0): if isinstance(node, HTLeaf): break # Learn from the sample - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - return self - def predict_proba_one(self, x): proba = {c: 0.0 for c in sorted(self.classes)} if self._root is not None: diff --git a/river/tree/hoeffding_tree_regressor.py b/river/tree/hoeffding_tree_regressor.py index 903e191aa1..d6d5deba11 100644 --- a/river/tree/hoeffding_tree_regressor.py +++ b/river/tree/hoeffding_tree_regressor.py @@ -20,7 +20,8 @@ class HoeffdingTreeRegressor(HoeffdingTree, base.Regressor): grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. delta Significance level to calculate the Hoeffding bound. The significance level is given by `1 - delta`. Values closer to zero imply longer split decision delays. @@ -57,7 +58,7 @@ class HoeffdingTreeRegressor(HoeffdingTree, base.Regressor): binary_split If True, only allow binary splits. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management @@ -98,7 +99,8 @@ class HoeffdingTreeRegressor(HoeffdingTree, base.Regressor): >>> metric = metrics.MAE() >>> evaluate.progressive_val_score(dataset, model, metric) - MAE: 0.781781 + MAE: 0.793345 + """ _TARGET_MEAN = "mean" @@ -161,9 +163,7 @@ def _mutable_attributes(self): def leaf_prediction(self, leaf_prediction): if leaf_prediction not in self._VALID_LEAF_PREDICTION: print( - 'Invalid leaf_prediction option "{}", will use default "{}"'.format( - leaf_prediction, self._MODEL - ) + f'Invalid leaf_prediction option "{leaf_prediction}", will use default "{self._MODEL}"' ) self._leaf_prediction = self._MODEL else: @@ -216,7 +216,7 @@ def _new_leaf(self, initial_stats=None, parent=None): return new_adaptive - def learn_one(self, x, y, *, sample_weight=1.0): + def learn_one(self, x, y, *, w=1.0): """Train the tree model on sample x and corresponding target y. Parameters @@ -225,15 +225,12 @@ def learn_one(self, x, y, *, sample_weight=1.0): Instance attributes. y Target value for sample x. - sample_weight + w The weight of the sample. - Returns - ------- - self """ - self._train_weight_seen_by_model += sample_weight + self._train_weight_seen_by_model += w if self._root is None: self._root = self._new_leaf() @@ -253,7 +250,7 @@ def learn_one(self, x, y, *, sample_weight=1.0): node = self._root if isinstance(node, HTLeaf): - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._growth_allowed and node.is_active(): if node.depth >= self.max_depth: # Max depth reached node.deactivate() @@ -287,13 +284,11 @@ def learn_one(self, x, y, *, sample_weight=1.0): if isinstance(node, HTLeaf): break # Learn from the sample - node.learn_one(x, y, sample_weight=sample_weight, tree=self) + node.learn_one(x, y, w=w, tree=self) if self._train_weight_seen_by_model % self.memory_estimate_period == 0: self._estimate_model_size() - return self - def predict_one(self, x): """Predict the target value using one of the leaf prediction strategies. diff --git a/river/tree/isoup_tree_regressor.py b/river/tree/isoup_tree_regressor.py index c61a9c9f3a..abe519154f 100644 --- a/river/tree/isoup_tree_regressor.py +++ b/river/tree/isoup_tree_regressor.py @@ -21,7 +21,8 @@ class iSOUPTreeRegressor(tree.HoeffdingTreeRegressor, base.MultiTargetRegressor) grace_period Number of instances a leaf should observe between split attempts. max_depth - The maximum depth a tree can reach. If `None`, the tree will grow indefinitely. + The maximum depth a tree can reach. If `None`, the tree will grow until + the system recursion limit. delta Allowed error in split decision, a value closer to 0 takes longer to decide. @@ -62,7 +63,7 @@ class iSOUPTreeRegressor(tree.HoeffdingTreeRegressor, base.MultiTargetRegressor) binary_split If True, only allow binary splits. max_size - The max size of the tree, in Megabytes (MB). + The max size of the tree, in mebibytes (MiB). memory_estimate_period Interval (number of processed instances) between memory consumption checks. stop_mem_management @@ -207,7 +208,7 @@ def _new_leaf(self, initial_stats=None, parent=None): return new_adaptive - def learn_one(self, x, y, *, sample_weight: float = 1.0) -> iSOUPTreeRegressor: # type: ignore + def learn_one(self, x, y, *, w: float = 1.0, **kwargs) -> None: """Incrementally train the model with one sample. Training tasks: @@ -225,15 +226,13 @@ def learn_one(self, x, y, *, sample_weight: float = 1.0) -> iSOUPTreeRegressor: Instance attributes. y Target values. - sample_weight + w The weight of the passed sample. """ # Update target set self.targets.update(y.keys()) - super().learn_one(x, y, sample_weight=sample_weight) # type: ignore - - return self + super().learn_one(x, y, w=w) def predict_one(self, x): pred = {} diff --git a/river/tree/last_classifier.py b/river/tree/last_classifier.py new file mode 100644 index 0000000000..391ba83668 --- /dev/null +++ b/river/tree/last_classifier.py @@ -0,0 +1,363 @@ +from __future__ import annotations + +from river import base, drift + +from .hoeffding_tree_classifier import HoeffdingTreeClassifier +from .nodes.branch import DTBranch +from .nodes.last_nodes import ( + LeafMajorityClassWithDetector, + LeafNaiveBayesAdaptiveWithDetector, + LeafNaiveBayesWithDetector, +) +from .nodes.leaf import HTLeaf +from .split_criterion import GiniSplitCriterion, HellingerDistanceCriterion, InfoGainSplitCriterion +from .splitter import Splitter + + +class LASTClassifier(HoeffdingTreeClassifier, base.Classifier): + """Local Adaptive Streaming Tree Classifier. + + Local Adaptive Streaming Tree [^1] (LAST) is an incremental decision tree with + adaptive splitting mechanisms. LAST maintains a change detector at each leaf and splits + this node if a change is detected in the error or the leaf`s data distribution. + + LAST is still not suitable for use as a base classifier in ensembles due to the change detectors. + The authors in [^1] are working on a version of LAST that overcomes this limitation. + + Parameters + ---------- + max_depth + The maximum depth a tree can reach. If `None`, the tree will grow until the system recursion limit. + split_criterion + Split criterion to use.
+ - 'gini' - Gini
+ - 'info_gain' - Information Gain
+ - 'hellinger' - Helinger Distance
+ leaf_prediction + Prediction mechanism used at leafs.
+ - 'mc' - Majority Class
+ - 'nb' - Naive Bayes
+ - 'nba' - Naive Bayes Adaptive
+ change_detector + Change detector that will be created at each leaf of the tree. + track_error + If True, the change detector will have binary inputs for error predictions, + otherwise the input will be the split criteria. + nb_threshold + Number of instances a leaf should observe before allowing Naive Bayes. + nominal_attributes + List of Nominal attributes identifiers. If empty, then assume that all numeric + attributes should be treated as continuous. + splitter + The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric + features and perform splits. Splitters are available in the `tree.splitter` module. + Different splitters are available for classification and regression tasks. Classification + and regression splitters can be distinguished by their property `is_target_class`. + This is an advanced option. Special care must be taken when choosing different splitters. + By default, `tree.splitter.GaussianSplitter` is used if `splitter` is `None`. + binary_split + If True, only allow binary splits. + min_branch_fraction + The minimum percentage of observed data required for branches resulting from split + candidates. To validate a split candidate, at least two resulting branches must have + a percentage of samples greater than `min_branch_fraction`. This criterion prevents + unnecessary splits when the majority of instances are concentrated in a single branch. + max_share_to_split + Only perform a split in a leaf if the proportion of elements in the majority class is + smaller than this parameter value. This parameter avoids performing splits when most + of the data belongs to a single class. + max_size + The max size of the tree, in Megabytes (MB). + memory_estimate_period + Interval (number of processed instances) between memory consumption checks. + stop_mem_management + If True, stop growing as soon as memory limit is hit. + remove_poor_attrs + If True, disable poor attributes to reduce memory usage. + merit_preprune + If True, enable merit-based tree pre-pruning. + + References + ---------- + + [^1]: Daniel Nowak Assis, Jean Paul Barddal, and Fabrício Enembreck. + Just Change on Change: Adaptive Splitting Time for Decision Trees in + Data Stream Classification . In Proceedings of ACM SAC Conference (SAC’24). + + Examples + -------- + + >>> from river.datasets import synth + >>> from river import evaluate + >>> from river import metrics + >>> from river import tree + + >>> gen = synth.ConceptDriftStream(stream=synth.SEA(seed=42, variant=0), + ... drift_stream=synth.SEA(seed=42, variant=1), + ... seed=1, position=1500, width=50) + >>> dataset = iter(gen.take(3000)) + + >>> model = tree.LASTClassifier() + + >>> metric = metrics.Accuracy() + + >>> evaluate.progressive_val_score(dataset, model, metric) + Accuracy: 91.10% + + """ + + def __init__( + self, + max_depth: int | None = None, + split_criterion: str = "info_gain", + leaf_prediction: str = "nba", + change_detector: base.DriftDetector | None = None, + track_error: bool = True, + nb_threshold: int = 0, + nominal_attributes: list | None = None, + splitter: Splitter | None = None, + binary_split: bool = False, + min_branch_fraction: float = 0.01, + max_share_to_split: float = 0.99, + max_size: float = 100.0, + memory_estimate_period: int = 1000000, + stop_mem_management: bool = False, + remove_poor_attrs: bool = False, + merit_preprune: bool = True, + ): + super().__init__( + grace_period=1, # no usage + max_depth=max_depth, + split_criterion=split_criterion, + delta=1.0, # no usage + tau=1, # no usage + leaf_prediction=leaf_prediction, + nb_threshold=nb_threshold, + binary_split=binary_split, + max_size=max_size, + memory_estimate_period=memory_estimate_period, + stop_mem_management=stop_mem_management, + remove_poor_attrs=remove_poor_attrs, + merit_preprune=merit_preprune, + nominal_attributes=nominal_attributes, + splitter=splitter, + min_branch_fraction=min_branch_fraction, + max_share_to_split=max_share_to_split, + ) + self.change_detector = change_detector if change_detector is not None else drift.ADWIN() + self.track_error = track_error + + # To keep track of the observed classes + self.classes: set = set() + + @property + def _mutable_attributes(self): + return {} + + def _new_leaf(self, initial_stats=None, parent=None): + if initial_stats is None: + initial_stats = {} + if parent is None: + depth = 0 + else: + depth = parent.depth + 1 + + if self._leaf_prediction == self._MAJORITY_CLASS: + return LeafMajorityClassWithDetector( + initial_stats, + depth, + self.splitter, + self.change_detector.clone(), + split_criterion=self._new_split_criterion() if not self.track_error else None, + ) + elif self._leaf_prediction == self._NAIVE_BAYES: + return LeafNaiveBayesWithDetector( + initial_stats, + depth, + self.splitter, + self.change_detector.clone(), + split_criterion=self._new_split_criterion() if not self.track_error else None, + ) + else: # Naives Bayes Adaptive (default) + return LeafNaiveBayesAdaptiveWithDetector( + initial_stats, + depth, + self.splitter, + self.change_detector.clone(), + split_criterion=self._new_split_criterion() if not self.track_error else None, + ) + + def _new_split_criterion(self): + if self._split_criterion == self._GINI_SPLIT: + split_criterion = GiniSplitCriterion(self.min_branch_fraction) + elif self._split_criterion == self._INFO_GAIN_SPLIT: + split_criterion = InfoGainSplitCriterion(self.min_branch_fraction) + elif self._split_criterion == self._HELLINGER_SPLIT: + if not self.track_error: + raise ValueError( + "The Heillinger distance cannot estimate the purity of a single distribution.\ + Use another split criterion or set track_error to True" + ) + split_criterion = HellingerDistanceCriterion(self.min_branch_fraction) + else: + split_criterion = InfoGainSplitCriterion(self.min_branch_fraction) + + return split_criterion + + def _attempt_to_split(self, leaf: HTLeaf, parent: DTBranch, parent_branch: int, **kwargs): + """Attempt to split a leaf. + + If the samples seen so far are not from the same class then: + + 1. Find split candidates and select the top 1. + 2. If the top1 is greater than zero: + 3.1 Replace the leaf node by a split node (branch node). + 3.2 Add a new leaf node on each branch of the new split node. + 3.3 Update tree's metrics + + Optional: Disable poor attributes. Depends on the tree's configuration. + + Parameters + ---------- + leaf + The leaf to evaluate. + parent + The leaf's parent. + parent_branch + Parent leaf's branch index. + kwargs + Other parameters passed to the new branch. + """ + if not leaf.observed_class_distribution_is_pure(): # type: ignore + split_criterion = self._new_split_criterion() + + best_split_suggestions = leaf.best_split_suggestions(split_criterion, self) + should_split = False + if len(best_split_suggestions) < 2: + should_split = len(best_split_suggestions) > 0 + else: + best_suggestion = max(best_split_suggestions) + should_split = best_suggestion.merit > 0.0 + if self.remove_poor_attrs: + poor_atts = set() + # Add any poor attribute to set + for suggestion in best_split_suggestions: + poor_atts.add(suggestion.feature) + for poor_att in poor_atts: + leaf.disable_attribute(poor_att) + if should_split: + split_decision = max(best_split_suggestions) + if split_decision.feature is None: + # Pre-pruning - null wins + leaf.deactivate() + self._n_inactive_leaves += 1 + self._n_active_leaves -= 1 + else: + branch = self._branch_selector( + split_decision.numerical_feature, split_decision.multiway_split + ) + leaves = tuple( + self._new_leaf(initial_stats, parent=leaf) + for initial_stats in split_decision.children_stats # type: ignore + ) + + new_split = split_decision.assemble( + branch, leaf.stats, leaf.depth, *leaves, **kwargs + ) + + self._n_active_leaves -= 1 + self._n_active_leaves += len(leaves) + if parent is None: + self._root = new_split + else: + parent.children[parent_branch] = new_split + + # Manage memory + self._enforce_size_limit() + + def learn_one(self, x, y, *, w=1.0): + """Train the model on instance x and corresponding target y. + + Parameters + ---------- + x + Instance attributes. + y + Class label for sample x. + w + Sample weight. + + Notes + ----- + Training tasks: + + * If the tree is empty, create a leaf node as the root. + * If the tree is already initialized, find the corresponding leaf for + the instance and update the leaf node statistics. + * Update the leaf change detector with (1 if the tree misclassified the instance, + or 0 if it correctly classified) or the data distribution purity + * If growth is allowed then attempt + to split. + """ + + # Updates the set of observed classes + self.classes.add(y) + + self._train_weight_seen_by_model += w + + if self._root is None: + self._root = self._new_leaf() + self._n_active_leaves = 1 + + p_node = None + node = None + if isinstance(self._root, DTBranch): + path = iter(self._root.walk(x, until_leaf=False)) + while True: + aux = next(path, None) + if aux is None: + break + p_node = node + node = aux + else: + node = self._root + + if isinstance(node, HTLeaf): + node.learn_one(x, y, w=w, tree=self) + if self._growth_allowed and node.is_active(): + if node.depth >= self.max_depth: # Max depth reached + node.deactivate() + self._n_active_leaves -= 1 + self._n_inactive_leaves += 1 + else: + weight_seen = node.total_weight + # check if the change detector triggered a change + if node.change_detector.drift_detected: + p_branch = p_node.branch_no(x) if isinstance(p_node, DTBranch) else None + self._attempt_to_split(node, p_node, p_branch) + node.last_split_attempt_at = weight_seen + else: + while True: + # Split node encountered a previously unseen categorical value (in a multi-way + # test), so there is no branch to sort the instance to + if node.max_branches() == -1 and node.feature in x: + # Create a new branch to the new categorical value + leaf = self._new_leaf(parent=node) + node.add_child(x[node.feature], leaf) + self._n_active_leaves += 1 + node = leaf + # The split feature is missing in the instance. Hence, we pass the new example + # to the most traversed path in the current subtree + else: + _, node = node.most_common_path() + # And we keep trying to reach a leaf + if isinstance(node, DTBranch): + node = node.traverse(x, until_leaf=False) + # Once a leaf is reached, the traversal can stop + if isinstance(node, HTLeaf): + break + # Learn from the sample + node.learn_one(x, y, w=w, tree=self) + + if self._train_weight_seen_by_model % self.memory_estimate_period == 0: + self._estimate_model_size() diff --git a/river/tree/mondrian/__init__.py b/river/tree/mondrian/__init__.py index c009792d3b..b26365acd0 100644 --- a/river/tree/mondrian/__init__.py +++ b/river/tree/mondrian/__init__.py @@ -3,12 +3,14 @@ implementations for the Mondrian trees. Note that this module is not exposed in the tree module, and is instead used by the -AMFClassifier class in the ensemble module. +AMFClassifier and AMFRegressor classes in the ensemble module. """ + from __future__ import annotations from .mondrian_tree import MondrianTree from .mondrian_tree_classifier import MondrianTreeClassifier +from .mondrian_tree_regressor import MondrianTreeRegressor -__all__ = ["MondrianTree", "MondrianTreeClassifier"] +__all__ = ["MondrianTree", "MondrianTreeClassifier", "MondrianTreeRegressor"] diff --git a/river/tree/mondrian/mondrian_tree.py b/river/tree/mondrian/mondrian_tree.py index 6894bd5b95..f9b664ba8e 100644 --- a/river/tree/mondrian/mondrian_tree.py +++ b/river/tree/mondrian/mondrian_tree.py @@ -16,16 +16,15 @@ class MondrianTree(abc.ABC): step Step parameter of the tree. loss - Loss to minimize for each node of the tree - Pick between: "log", ... + Loss to minimize for each node of the tree. At the moment it is a placeholder. + In the future, different optimization metrics might become available. use_aggregation Whether or not the tree should it use aggregation. - split_pure - Whether or not the tree should split pure leaves when training. iteration Number of iterations to run when training. seed Random seed for reproducibility. + """ def __init__( @@ -33,7 +32,6 @@ def __init__( step: float = 0.1, loss: str = "log", use_aggregation: bool = True, - split_pure: bool = False, iteration: int = 0, seed: int | None = None, ): @@ -41,7 +39,6 @@ def __init__( self.step = step self.loss = loss self.use_aggregation = use_aggregation - self.split_pure = split_pure self.iteration = iteration # Controls the randomness in the tree diff --git a/river/tree/mondrian/mondrian_tree_classifier.py b/river/tree/mondrian/mondrian_tree_classifier.py index d2302733be..c2c786ee65 100644 --- a/river/tree/mondrian/mondrian_tree_classifier.py +++ b/river/tree/mondrian/mondrian_tree_classifier.py @@ -1,7 +1,6 @@ from __future__ import annotations import math -import sys from river import base, utils from river.tree.mondrian.mondrian_tree import MondrianTree @@ -54,7 +53,7 @@ class MondrianTreeClassifier(MondrianTree, base.Classifier): >>> metric = metrics.Accuracy() >>> evaluate.progressive_val_score(dataset, model, metric) - Accuracy: 57.52% + Accuracy: 58.52% References ---------- @@ -76,11 +75,12 @@ def __init__( step=step, loss="log", use_aggregation=use_aggregation, - split_pure=split_pure, iteration=iteration, seed=seed, ) + self.dirichlet = dirichlet + self.split_pure = split_pure # Training attributes # The previously observed classes set @@ -107,6 +107,7 @@ def _score(self, node: MondrianNodeClassifier) -> float: ---------- node Node to evaluate the score. + """ return node.score(self._y, self.dirichlet, len(self._classes)) @@ -118,6 +119,7 @@ def _predict(self, node: MondrianNodeClassifier) -> dict[base.typing.ClfTarget, ---------- node Node to make predictions. + """ return node.predict(self.dirichlet, self._classes, len(self._classes)) @@ -129,6 +131,7 @@ def _loss(self, node: MondrianNodeClassifier) -> float: ---------- node Node to evaluate the loss. + """ return node.loss(self._y, self.dirichlet, len(self._classes)) @@ -140,6 +143,7 @@ def _update_weight(self, node: MondrianNodeClassifier) -> float: ---------- node Node to update the weight. + """ return node.update_weight( @@ -154,6 +158,7 @@ def _update_count(self, node: MondrianNodeClassifier): ---------- node Target node. + """ node.update_count(self._y) @@ -169,6 +174,7 @@ def _update_downwards( Target node. do_weight_update Whether we should update the weights or not. + """ return node.update_downwards( @@ -193,6 +199,7 @@ def _compute_split_time( ---------- node Target node. + """ # Don't split if the node is pure: all labels are equal to the one of y_t @@ -202,11 +209,7 @@ def _compute_split_time( # If x_t extends the current range of the node if extensions_sum > 0: # Sample an exponential with intensity = extensions_sum - # try catch to handle the Overflow situation in the exponential - try: - T = math.exp(1 / extensions_sum) - except OverflowError: - T = sys.float_info.max # we get the largest possible output instead + T = utils.random.exponential(1 / extensions_sum, rng=self._rng) time = node.time # Splitting time of the node (if splitting occurs) @@ -246,6 +249,7 @@ def _split( Feature of the node. is_right_extension Should we extend the tree in the right or left direction. + """ new_depth = node.depth + 1 @@ -420,6 +424,7 @@ def _go_upwards(self, leaf: MondrianLeafClassifier): ---------- leaf Leaf to start from when going upward. + """ current_node = leaf @@ -451,7 +456,6 @@ def learn_one(self, x, y): # Incrementing iteration self.iteration += 1 - return self def predict_proba_one(self, x): """Predict the probability of the samples. @@ -460,10 +464,12 @@ def predict_proba_one(self, x): ---------- x Feature vector. + """ # If the tree hasn't seen any sample, then it should return # the default empty dict + if not self._is_initialized: return {} diff --git a/river/tree/mondrian/mondrian_tree_nodes.py b/river/tree/mondrian/mondrian_tree_nodes.py index f262515175..d1c3a5cd37 100644 --- a/river/tree/mondrian/mondrian_tree_nodes.py +++ b/river/tree/mondrian/mondrian_tree_nodes.py @@ -3,7 +3,7 @@ import collections import math -from river import base +from river import base, stats from river.tree.base import Branch, Leaf from river.utils.math import log_sum_2_exp @@ -19,6 +19,7 @@ class MondrianLeaf(Leaf): Split time of the node for Mondrian process. depth Depth of the leaf. + """ def __init__(self, parent, time, depth): @@ -81,6 +82,7 @@ def update_depth(self, depth): ---------- depth Depth of the node. + """ self.depth = depth @@ -112,6 +114,7 @@ def range(self, feature) -> tuple[float, float]: ---------- feature Feature for which you want to know the range. + """ return ( @@ -126,6 +129,7 @@ def range_extension(self, x) -> tuple[float, dict[base.typing.ClfTarget, float]] ---------- x Sample to deal with. + """ extensions: dict[base.typing.ClfTarget, float] = {} @@ -176,6 +180,7 @@ def score(self, y: base.typing.ClfTarget, dirichlet: float, n_classes: int) -> f Notes ----- This uses Jeffreys prior with Dirichlet parameter for smoothing. + """ count = self.counts[y] @@ -197,6 +202,7 @@ def predict( The set of classes seen so far n_classes The total number of classes of the problem. + """ scores = {} @@ -215,6 +221,7 @@ def loss(self, y: base.typing.ClfTarget, dirichlet: float, n_classes: int) -> fl Dirichlet parameter of the problem. n_classes The total number of classes of the problem. + """ sc = self.score(y, dirichlet, n_classes) @@ -242,6 +249,7 @@ def update_weight( Step parameter of the tree. n_classes The total number of classes of the problem. + """ loss_t = self.loss(y, dirichlet, n_classes) @@ -257,6 +265,7 @@ def update_count(self, y): ---------- y Class of a given sample. + """ self.counts[y] += 1 @@ -269,6 +278,7 @@ def is_dirac(self, y: base.typing.ClfTarget) -> bool: ---------- y Class of a given sample. + """ return self.n_samples == self.counts[y] @@ -288,9 +298,9 @@ def update_downwards( Parameters ---------- x - Sample to proceed (as a list). + Sample to proceed. y - Class of the sample x_t. + Class of the sample x. dirichlet Dirichlet parameter of the tree. use_aggregation @@ -301,6 +311,7 @@ def update_downwards( Should we update the weights of the node as well. n_classes The total number of classes of the problem. + """ # Updating the range of the feature values known by the node @@ -339,6 +350,7 @@ class MondrianLeafClassifier(MondrianNodeClassifier, MondrianLeaf): Split time of the node. depth The depth of the leaf. + """ def __init__(self, parent, time, depth): @@ -362,6 +374,159 @@ class MondrianBranchClassifier(MondrianNodeClassifier, MondrianBranch): Acceptation threshold of the branch. *children Children nodes of the branch. + + """ + + def __init__(self, parent, time, depth, feature, threshold, *children): + super().__init__(parent, time, depth, feature, threshold, *children) + + +class MondrianNodeRegressor(MondrianNode): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + self.n_samples = 0 + self.mean = stats.Mean() + + def replant(self, leaf: MondrianNodeRegressor, copy_all: bool = False): + """Transfer information from a leaf to a new branch.""" + self.weight = leaf.weight # type: ignore + self.log_weight_tree = leaf.log_weight_tree # type: ignore + self.mean = leaf.mean + + if copy_all: + self.memory_range_min = leaf.memory_range_min + self.memory_range_max = leaf.memory_range_max + self.n_samples = leaf.n_samples + + def predict(self) -> base.typing.RegTarget: + """Return the prediction of the node.""" + return self.mean.get() + + def loss(self, sample_value: base.typing.RegTarget) -> float: + """Compute the loss of the node. + + Parameters + ---------- + sample_value + A given value. + + """ + + r = self.predict() - sample_value # type: ignore + return r * r / 2 + + def update_weight( + self, + sample_value: base.typing.RegTarget, + use_aggregation: bool, + step: float, + ) -> float: + """Update the weight of the node given a label and the method used. + + Parameters + ---------- + sample_value + Label of a given sample. + use_aggregation + Whether to use aggregation or not during computation (given by the tree). + step + Step parameter of the tree. + + """ + + loss_t = self.loss(sample_value) + if use_aggregation: + self.weight -= step * loss_t + return loss_t + + def update_downwards( + self, + x, + sample_value: base.typing.RegTarget, + use_aggregation: bool, + step: float, + do_update_weight: bool, + ): + """Update the node when running a downward procedure updating the tree. + + Parameters + ---------- + x + Sample to proceed (as a list). + sample_value + Label of the sample x. + use_aggregation + Should it use the aggregation or not + step + Step of the tree. + do_update_weight + Should we update the weights of the node as well. + + """ + + # Updating the range of the feature values known by the node + # If it is the first sample, we copy the features vector into the min and max range + if self.n_samples == 0: + for feature in x: + x_f = x[feature] + self.memory_range_min[feature] = x_f + self.memory_range_max[feature] = x_f + # Otherwise, we update the range + else: + for feature in x: + x_f = x[feature] + if x_f < self.memory_range_min[feature]: + self.memory_range_min[feature] = x_f + if x_f > self.memory_range_max[feature]: + self.memory_range_max[feature] = x_f + + # One more sample in the node + self.n_samples += 1 + + if do_update_weight: + self.update_weight(sample_value, use_aggregation, step) + + # Update the mean of the labels in the node online + self.mean.update(sample_value) + + +class MondrianLeafRegressor(MondrianNodeRegressor, MondrianLeaf): + """Mondrian Tree Regressor leaf node. + + Parameters + ---------- + parent + Parent node. + time + Split time of the node. + depth + The depth of the leaf. + + """ + + def __init__(self, parent, time, depth): + super().__init__(parent, time, depth) + + +class MondrianBranchRegressor(MondrianNodeRegressor, MondrianBranch): + """Mondrian Tree Regressor branch node. + + Parameters + ---------- + parent + Parent node of the branch. + time + Split time characterizing the branch. + depth + Depth of the branch in the tree. + feature + Feature of the branch. + threshold + Acceptation threshold of the branch. + *children + Children nodes of the branch. + """ def __init__(self, parent, time, depth, feature, threshold, *children): diff --git a/river/tree/mondrian/mondrian_tree_regressor.py b/river/tree/mondrian/mondrian_tree_regressor.py new file mode 100644 index 0000000000..13b302de1e --- /dev/null +++ b/river/tree/mondrian/mondrian_tree_regressor.py @@ -0,0 +1,427 @@ +from __future__ import annotations + +import math + +from river import base, utils +from river.tree.mondrian.mondrian_tree import MondrianTree +from river.tree.mondrian.mondrian_tree_nodes import ( + MondrianBranchRegressor, + MondrianLeafRegressor, + MondrianNodeRegressor, +) + + +class MondrianTreeRegressor(MondrianTree, base.Regressor): + """Mondrian Tree Regressor. + + Parameters + ---------- + step + Step of the tree. + use_aggregation + Whether to use aggregation weighting techniques or not. + iteration + Number iterations to do during training. + seed + Random seed for reproducibility. + + Notes + ----- + The Mondrian Tree Regressor is a type of decision tree that bases splitting decisions over a + Mondrian process. + + References + ---------- + [^1]: Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh. Mondrian Forests: Efficient Online Random Forests. + arXiv:1406.2673, pages 2-4. + + """ + + def __init__( + self, + step: float = 0.1, + use_aggregation: bool = True, + iteration: int = 0, + seed: int | None = None, + ): + super().__init__( + step=step, + loss="least-squares", + use_aggregation=use_aggregation, + iteration=iteration, + seed=seed, + ) + # Controls the randomness in the tree + self.seed = seed + + # The current sample being proceeded + self._x: dict[base.typing.FeatureName, int | float] + # The current label index being proceeded + self._y: base.typing.RegTarget + + # Initialization of the root of the tree + # It's the root so it doesn't have any parent (hence None) + self._root = MondrianLeafRegressor(None, 0.0, 0) + + def _is_initialized(self): + """Check if the tree has learnt at least one sample""" + return self.iteration != 0 + + def _predict(self, node: MondrianNodeRegressor) -> base.typing.RegTarget: + """Compute the prediction. + + Parameters + ---------- + node + Node to make predictions. + + """ + + return node.predict() # type: ignore + + def _loss(self, node: MondrianNodeRegressor) -> float: + """Compute the loss for the given node regarding the current label. + + Parameters + ---------- + node + Node to evaluate the loss. + + """ + + return node.loss(self._y) + + def _update_weight(self, node: MondrianNodeRegressor) -> float: + """Update the weight of the node regarding the current label with the tree parameters. + + Parameters + ---------- + node + Node to update the weight. + + """ + + return node.update_weight(self._y, self.use_aggregation, self.step) + + def _update_downwards(self, node: MondrianNodeRegressor, do_update_weight): + """Update the node when running a downward procedure updating the tree. + + Parameters + ---------- + node + Target node. + do_update_weight + Whether we should update the weights or not. + + """ + + return node.update_downwards( + self._x, self._y, self.use_aggregation, self.step, do_update_weight + ) + + def _compute_split_time( + self, + node: MondrianLeafRegressor | MondrianBranchRegressor, + extensions_sum: float, + ) -> float: + """Computes the split time of the given node. + + Parameters + ---------- + node + Target node. + + """ + + if extensions_sum > 0: + # Sample an exponential with intensity = extensions_sum + T = utils.random.exponential(1 / extensions_sum, rng=self._rng) + + time = node.time + # Splitting time of the node (if splitting occurs) + split_time = time + T + # If the node is a leaf we must split it + if isinstance(node, MondrianLeafRegressor): + return split_time + # Otherwise we apply Mondrian process dark magic :) + # 1. We get the creation time of the childs (left and right is the same) + left, _ = node.children + child_time = left.time + # 2. We check if splitting time occurs before child creation time + if split_time < child_time: + return split_time + + return 0.0 + + def _split( + self, + node: MondrianLeafRegressor | MondrianBranchRegressor, + split_time: float, + threshold: float, + feature: base.typing.FeatureName, + is_right_extension: bool, + ) -> MondrianBranchRegressor: + """Split the given node and attributes the split time, threshold, etc... to the node. + + Parameters + ---------- + node + Target node. + split_time + Split time of the node in the Mondrian process. + threshold + Threshold of acceptance of the node. + feature + Feature index of the node. + is_right_extension + Should we extend the tree in the right or left direction. + + """ + + new_depth = node.depth + 1 + + # To calm down mypy + left: MondrianLeafRegressor | MondrianBranchRegressor + right: MondrianLeafRegressor | MondrianBranchRegressor + + # The node is already a branch: we create a new branch above it and move the existing + # node one level down the tree + if isinstance(node, MondrianBranchRegressor): + old_left, old_right = node.children + if is_right_extension: + left = MondrianBranchRegressor( + node, split_time, new_depth, node.feature, node.threshold + ) + right = MondrianLeafRegressor(node, split_time, new_depth) + left.replant(node) + + old_left.parent = left + old_right.parent = left + + left.children = (old_left, old_right) + else: + right = MondrianBranchRegressor( + node, split_time, new_depth, node.feature, node.threshold + ) + left = MondrianLeafRegressor(node, split_time, new_depth) + right.replant(node) + + old_left.parent = right + old_right.parent = right + + right.children = (old_left, old_right) + + # Update the level of the modified nodes + new_depth += 1 + old_left.update_depth(new_depth) + old_right.update_depth(new_depth) + + # Update split info + node.feature = feature + node.threshold = threshold + node.children = (left, right) + + return node + + # We promote the leaf to a branch + branch = MondrianBranchRegressor(node.parent, node.time, node.depth, feature, threshold) + left = MondrianLeafRegressor(branch, split_time, new_depth) + right = MondrianLeafRegressor(branch, split_time, new_depth) + branch.children = (left, right) + + # Copy properties from the previous leaf + branch.replant(node, True) + + if is_right_extension: + left.replant(node) + else: + right.replant(node) + + # To avoid leaving garbage behind + del node + + return branch + + def _go_downwards(self): + """Update the tree (downward procedure).""" + + # We update the nodes along the path which leads to the leaf containing the current + # sample. For each node on the path, we consider the possibility of splitting it, + # following the Mondrian process definition. + + # We start at the root + current_node = self._root + + if self.iteration == 0: + # If it's the first iteration, we just put the current sample in the range of root + self._update_downwards(current_node, False) + return current_node + else: + # Path from the parent to the current node + branch_no = None + while True: + # Computing the extensions to get the intensities + extensions_sum, extensions = current_node.range_extension(self._x) + + # If it's not the first iteration (otherwise the current node + # is root with no range), we consider the possibility of a split + split_time = self._compute_split_time(current_node, extensions_sum) + + if split_time > 0: + # We split the current node: because the current node is a + # leaf, or because we add a new node along the path + + # We normalize the range extensions to get probabilities + intensities = utils.norm.normalize_values_in_dict(extensions, inplace=False) + + # Sample the feature at random with a probability + # proportional to the range extensions + + candidates = sorted(list(self._x.keys())) + feature = self._rng.choices( + candidates, [intensities[c] for c in candidates], k=1 + )[0] + + x_f = self._x[feature] + + # Is it a right extension of the node ? + range_min, range_max = current_node.range(feature) + is_right_extension = x_f > range_max + if is_right_extension: + threshold = self._rng.uniform(range_max, x_f) + else: + threshold = self._rng.uniform(x_f, range_min) + + was_leaf = isinstance(current_node, MondrianLeafRegressor) + + # We split the current node + current_node = self._split( + current_node, + split_time, + threshold, + feature, + is_right_extension, + ) + + # The root node has become a branch + if current_node.parent is None: + self._root = current_node + # Update path from the previous parent to the recently updated node + elif was_leaf: + parent = current_node.parent + if branch_no == 0: + parent.children = (current_node, parent.children[1]) + else: + parent.children = (parent.children[0], current_node) + + # Update the current node + self._update_downwards(current_node, True) + + left, right = current_node.children + + # Now, get the next node + if is_right_extension: + current_node = right + else: + current_node = left + + # This is the leaf containing the sample point (we've just + # splitted the current node with the data point) + leaf = current_node + self._update_downwards(leaf, False) + return leaf + else: + # There is no split, so we just update the node and go to the next one + self._update_downwards(current_node, True) + if isinstance(current_node, MondrianLeafRegressor): + return current_node + else: + # Save the path direction to keep the tree consistent + try: + branch_no = current_node.branch_no(self._x) + current_node = current_node.children[branch_no] + except KeyError: # Missing split feature + branch_no, current_node = current_node.most_common_path() + + def _go_upwards(self, leaf: MondrianLeafRegressor): + """Update the tree (upwards procedure). + + Parameters + ---------- + leaf + Leaf to start from when going upward. + + """ + + current_node = leaf + + if self.iteration >= 1: + while True: + current_node.update_weight_tree() + if current_node.parent is None: + # We arrived at the root + break + # Note that the root node is updated as well + # We go up to the root in the tree + current_node = current_node.parent + + def learn_one(self, x, y): + # Setting current sample + self._x = x + self._y = y + + # Learning step + leaf = self._go_downwards() + if self.use_aggregation: + self._go_upwards(leaf) + + # Incrementing iteration + self.iteration += 1 + + def predict_one(self, x): + """Predict the label of the samples. + + Parameters + ---------- + x + Feature vector. + + """ + + # If the tree hasn't seen any sample, then it should return + # the default empty dict + if not self._is_initialized: + return + + leaf = ( + self._root.traverse(x, until_leaf=True) + if isinstance(self._root, MondrianBranchRegressor) + else self._root + ) + + if not self.use_aggregation: + return self._predict(leaf) + + current = leaf + prediction = 0.0 + + while True: + # This test is useless ? + if isinstance(current, MondrianLeafRegressor): + prediction = self._predict(current) + else: + weight = current.weight + log_weight_tree = current.log_weight_tree + w = math.exp(weight - log_weight_tree) + # Get the predictions of the current node + pred_new = self._predict(current) + prediction = 0.5 * w * pred_new + (1 - 0.5 * w) * prediction + + # Root must be updated as well + if current.parent is None: + break + + # And now we go up + current = current.parent + + return prediction diff --git a/river/tree/nodes/arf_htc_nodes.py b/river/tree/nodes/arf_htc_nodes.py index f40cc965c9..f25eb50475 100644 --- a/river/tree/nodes/arf_htc_nodes.py +++ b/river/tree/nodes/arf_htc_nodes.py @@ -44,7 +44,9 @@ def _iter_features(self, x) -> typing.Iterable: yield att_id, x[att_id] def _sample_features(self, x, max_features): - return self.rng.sample(sorted(x.keys()), k=max_features) + if len(x) >= max_features: + return self.rng.sample(sorted(x.keys()), k=max_features) + return sorted(x.keys()) class RandomLeafMajorityClass(BaseRandomLeaf, LeafMajorityClass): diff --git a/river/tree/nodes/efdtc_nodes.py b/river/tree/nodes/efdtc_nodes.py index 46acc12d5c..5fb611f034 100644 --- a/river/tree/nodes/efdtc_nodes.py +++ b/river/tree/nodes/efdtc_nodes.py @@ -3,6 +3,7 @@ import copy import numbers +from river.tree.utils import BranchFactory from river.utils.norm import normalize_values_in_dict from ..splitter.nominal_splitter_classif import NominalSplitterClassif @@ -56,6 +57,12 @@ def best_split_suggestions(self, criterion, tree): ------- The list of split candidates. """ + maj_class = max(self.stats.values()) + # Only perform split attempts when the majority class does not dominate + # the amount of observed instances + if maj_class and maj_class / self.total_weight > tree.max_share_to_split: + return [BranchFactory()] + best_suggestions = [] pre_split_dist = self.stats @@ -104,13 +111,13 @@ def total_weight(self) -> float: def new_nominal_splitter(): return NominalSplitterClassif() - def update_stats(self, y, sample_weight): + def update_stats(self, y, w): try: - self.stats[y] += sample_weight + self.stats[y] += w except KeyError: - self.stats[y] = sample_weight + self.stats[y] = w - def update_splitters(self, x, y, sample_weight, nominal_attributes): + def update_splitters(self, x, y, w, nominal_attributes): for att_id, att_val in x.items(): if att_id in self._disabled_attrs: continue @@ -126,9 +133,9 @@ def update_splitters(self, x, y, sample_weight, nominal_attributes): splitter = copy.deepcopy(self.splitter) self.splitters[att_id] = splitter - splitter.update(att_val, y, sample_weight) + splitter.update(att_val, y, w) - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): """Update branch with the provided sample. Parameters @@ -137,13 +144,13 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): Sample attributes for updating the node. y Target value. - sample_weight + w Sample weight. tree Tree to update. """ - self.update_stats(y, sample_weight) - self.update_splitters(x, y, sample_weight, tree.nominal_attributes) + self.update_stats(y, w) + self.update_splitters(x, y, w, tree.nominal_attributes) def prediction(self, x, *, tree=None): return normalize_values_in_dict(self.stats, inplace=False) diff --git a/river/tree/nodes/hatc_nodes.py b/river/tree/nodes/hatc_nodes.py index 0f9fa241e5..e85cbc4334 100644 --- a/river/tree/nodes/hatc_nodes.py +++ b/river/tree/nodes/hatc_nodes.py @@ -2,6 +2,7 @@ import math +from river import base from river import stats as st from river.utils.norm import normalize_values_in_dict from river.utils.random import poisson @@ -47,12 +48,12 @@ def __init__(self, stats, depth, splitter, drift_detector, rng, **kwargs): def kill_tree_children(self, hat): pass - def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_branch=None): + def learn_one(self, x, y, *, w=1.0, tree=None, parent=None, parent_branch=None): if tree.bootstrap_sampling: # Perform bootstrap-sampling k = poisson(rate=1, rng=self.rng) if k > 0: - sample_weight *= k + w *= k aux = self.prediction(x, tree=tree) y_pred = max(aux, key=aux.get) if aux else None @@ -71,7 +72,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b self._mean_error = self._mean_error.clone() # Update statistics - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) weight_seen = self.total_weight @@ -133,7 +134,7 @@ class AdaBranchClassifier(DTBranch): Other parameters passed to the split node. """ - def __init__(self, stats, *children, drift_detector, **attributes): + def __init__(self, stats: dict, *children, drift_detector: base.DriftDetector, **attributes): super().__init__(stats, *children, **attributes) self.drift_detector = drift_detector self._alternate_tree = None @@ -176,7 +177,7 @@ def iter_leaves(self): if isinstance(child, AdaBranchClassifier) and child._alternate_tree: yield from child._alternate_tree.iter_leaves() - def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_branch=None): + def learn_one(self, x, y, *, w=1.0, tree=None, parent=None, parent_branch=None): leaf = super().traverse(x, until_leaf=True) aux = leaf.prediction(x, tree=tree) y_pred = max(aux, key=aux.get) if aux else None @@ -185,9 +186,9 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b # Update stats as traverse the tree to improve predictions (in case split nodes are used # to provide responses) try: - self.stats[y] += sample_weight + self.stats[y] += w except KeyError: - self.stats[y] = sample_weight + self.stats[y] = w old_error = self._mean_error.get() @@ -250,7 +251,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b self._alternate_tree.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=parent, parent_branch=parent_branch, @@ -265,7 +266,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b child.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=self.branch_no(x), @@ -280,7 +281,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b leaf.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=self.branch_no(x), @@ -292,7 +293,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b child.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=child_id, diff --git a/river/tree/nodes/hatr_nodes.py b/river/tree/nodes/hatr_nodes.py index a8e134ddd4..aa496c4f4d 100644 --- a/river/tree/nodes/hatr_nodes.py +++ b/river/tree/nodes/hatr_nodes.py @@ -47,14 +47,14 @@ def __init__(self, stats, depth, splitter, drift_detector, rng, **kwargs): def kill_tree_children(self, hatr): pass - def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_branch=None): + def learn_one(self, x, y, *, w=1.0, tree=None, parent=None, parent_branch=None): y_pred = self.prediction(x, tree=tree) if tree.bootstrap_sampling: # Perform bootstrap-sampling k = poisson(rate=1, rng=self.rng) if k > 0: - sample_weight *= k + w *= k drift_input = abs(y - y_pred) old_error = self._error_tracker.mean.get() @@ -69,7 +69,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b self._error_tracker = self._error_tracker.clone() # Update learning model - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) weight_seen = self.total_weight @@ -149,13 +149,13 @@ def iter_leaves(self): if isinstance(child, AdaBranchRegressor) and child._alternate_tree: yield from child._alternate_tree.iter_leaves() - def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_branch=None): + def learn_one(self, x, y, *, w=1.0, tree=None, parent=None, parent_branch=None): leaf = super().traverse(x, until_leaf=True) y_pred = leaf.prediction(x, tree=tree) # Update stats as traverse the tree to improve predictions (in case split nodes are used # to provide responses) - self.stats.update(y, sample_weight) + self.stats.update(y, w) drift_input = abs(y - y_pred) old_error = self._error_tracker.mean.get() @@ -224,7 +224,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b self._alternate_tree.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=parent, parent_branch=parent_branch, @@ -238,7 +238,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b child.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=self.branch_no(x), @@ -253,7 +253,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b leaf.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=self.branch_no(x), @@ -265,7 +265,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None, parent=None, parent_b child.learn_one( x, y, - sample_weight=sample_weight, + w=w, tree=tree, parent=self, parent_branch=child_id, diff --git a/river/tree/nodes/htc_nodes.py b/river/tree/nodes/htc_nodes.py index ceb7d03eee..d35549ee79 100644 --- a/river/tree/nodes/htc_nodes.py +++ b/river/tree/nodes/htc_nodes.py @@ -1,5 +1,6 @@ from __future__ import annotations +from river.tree.utils import BranchFactory from river.utils.norm import normalize_values_in_dict from ..splitter.nominal_splitter_classif import NominalSplitterClassif @@ -30,11 +31,11 @@ def __init__(self, stats, depth, splitter, **kwargs): def new_nominal_splitter(): return NominalSplitterClassif() - def update_stats(self, y, sample_weight): + def update_stats(self, y, w): try: - self.stats[y] += sample_weight + self.stats[y] += w except KeyError: - self.stats[y] = sample_weight + self.stats[y] = w def prediction(self, x, *, tree=None): return normalize_values_in_dict(self.stats, inplace=False) @@ -50,6 +51,15 @@ def total_weight(self): """ return sum(self.stats.values()) if self.stats else 0 + def best_split_suggestions(self, criterion, tree) -> list[BranchFactory]: + maj_class = max(self.stats.values()) + # Only perform split attempts when the majority class does not dominate + # the amount of observed instances + if maj_class and maj_class / self.total_weight > tree.max_share_to_split: + return [BranchFactory()] + + return super().best_split_suggestions(criterion, tree) + def calculate_promise(self): """Calculate how likely a node is going to be split. @@ -154,7 +164,7 @@ def __init__(self, stats, depth, splitter, **kwargs): self._mc_correct_weight = 0.0 self._nb_correct_weight = 0.0 - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): """Update the node with the provided instance. Parameters @@ -163,7 +173,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): Instance attributes for updating the node. y Instance class. - sample_weight + w The instance's weight. tree The Hoeffding Tree to update. @@ -174,13 +184,13 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): # Empty node (assume the majority class will be the best option) or majority # class prediction is correct if len(self.stats) == 0 or max(mc_pred, key=mc_pred.get) == y: - self._mc_correct_weight += sample_weight + self._mc_correct_weight += w nb_pred = do_naive_bayes_prediction(x, self.stats, self.splitters) if len(nb_pred) > 0 and max(nb_pred, key=nb_pred.get) == y: - self._nb_correct_weight += sample_weight + self._nb_correct_weight += w - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) def prediction(self, x, *, tree=None): """Get the probabilities per class for a given instance. diff --git a/river/tree/nodes/htr_nodes.py b/river/tree/nodes/htr_nodes.py index 13a04d484a..248164399a 100644 --- a/river/tree/nodes/htr_nodes.py +++ b/river/tree/nodes/htr_nodes.py @@ -64,8 +64,8 @@ def manage_memory(self, criterion, last_check_ratio, last_check_vr, last_check_e pre_split_dist=self.stats, ) - def update_stats(self, y, sample_weight): - self.stats.update(y, sample_weight) + def update_stats(self, y, w): + self.stats.update(y, w) def prediction(self, x, *, tree=None): return self.stats.mean.get() @@ -133,13 +133,13 @@ def __init__(self, stats, depth, splitter, leaf_model, **kwargs): sign = inspect.signature(leaf_model.learn_one).parameters self._model_supports_weights = "sample_weight" in sign or "w" in sign - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + def learn_one(self, x, y, *, w=1.0, tree=None): + super().learn_one(x, y, w=w, tree=tree) if self._model_supports_weights: - self._leaf_model.learn_one(x, y, sample_weight) + self._leaf_model.learn_one(x, y, w) else: - for _ in range(int(sample_weight)): + for _ in range(int(w)): self._leaf_model.learn_one(x, y) def prediction(self, x, *, tree=None): @@ -173,14 +173,14 @@ def __init__(self, stats, depth, splitter, leaf_model, **kwargs): self._fmse_mean = 0.0 self._fmse_model = 0.0 - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): pred_mean = self.stats.mean.get() pred_model = self._leaf_model.predict_one(x) self._fmse_mean = tree.model_selector_decay * self._fmse_mean + (y - pred_mean) ** 2 self._fmse_model = tree.model_selector_decay * self._fmse_model + (y - pred_model) ** 2 - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) def prediction(self, x, *, tree=None): if self._fmse_mean < self._fmse_model: # Act as a regression tree diff --git a/river/tree/nodes/isouptr_nodes.py b/river/tree/nodes/isouptr_nodes.py index bfdb632094..4a20f6328f 100644 --- a/river/tree/nodes/isouptr_nodes.py +++ b/river/tree/nodes/isouptr_nodes.py @@ -33,9 +33,9 @@ def __init__(self, stats, depth, splitter, **kwargs): stats = stats if stats else VectorDict(default_factory=functools.partial(Var)) super().__init__(stats, depth, splitter, **kwargs) - def update_stats(self, y, sample_weight): + def update_stats(self, y, w): for t in y: - self.stats[t].update(y[t], sample_weight) + self.stats[t].update(y[t], w) def prediction(self, x, *, tree=None): return {t: self.stats[t].mean.get() if t in self.stats else 0.0 for t in tree.targets} @@ -82,8 +82,8 @@ def __init__(self, stats, depth, splitter, leaf_models, **kwargs): sign = inspect.signature(self._leaf_models[t].learn_one).parameters self._model_supports_weights[t] = "sample_weight" in sign or "w" in sign - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + def learn_one(self, x, y, *, w=1.0, tree=None): + super().learn_one(x, y, w=w, tree=tree) for target_id, y_ in y.items(): try: @@ -107,9 +107,9 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): # Now the proper training if self._model_supports_weights[target_id]: - model.learn_one(x, y_, sample_weight) + model.learn_one(x, y_, w) else: - for _ in range(int(sample_weight)): + for _ in range(int(w)): model.learn_one(x, y_) def prediction(self, x, *, tree=None): @@ -145,7 +145,7 @@ def __init__(self, stats, depth, splitter, leaf_models, **kwargs): self._fmse_mean = collections.defaultdict(float) self._fmse_model = collections.defaultdict(float) - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): pred_mean = {t: self.stats[t].mean.get() if t in self.stats else 0.0 for t in tree.targets} pred_model = super().prediction(x, tree=tree) @@ -157,7 +157,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): tree.model_selector_decay * self._fmse_model[t] + (y[t] - pred_model[t]) ** 2 ) - super().learn_one(x, y, sample_weight=sample_weight, tree=tree) + super().learn_one(x, y, w=w, tree=tree) def prediction(self, x, *, tree=None): pred = {} diff --git a/river/tree/nodes/last_nodes.py b/river/tree/nodes/last_nodes.py new file mode 100644 index 0000000000..f130fe17ac --- /dev/null +++ b/river/tree/nodes/last_nodes.py @@ -0,0 +1,206 @@ +from __future__ import annotations + +from ..utils import do_naive_bayes_prediction +from .htc_nodes import LeafMajorityClass + + +class LeafMajorityClassWithDetector(LeafMajorityClass): + """Leaf that always predicts the majority class. + + Parameters + ---------- + stats + Initial class observations. + depth + The depth of the node. + splitter + The numeric attribute observer algorithm used to monitor target statistics + and perform split attempts. + change_detector + Change detector that monitors the leaf error rate or class distribution and + determines when the leaf will split. + split_criterion + Split criterion used in the tree for updating the change detector if it + monitors the class distribution. + kwargs + Other parameters passed to the learning node. + """ + + def __init__(self, stats, depth, splitter, change_detector, split_criterion=None, **kwargs): + super().__init__(stats, depth, splitter, **kwargs) + self.change_detector = change_detector + # change this in future PR's by acessing the tree parameter in the leaf + self.split_criterion = ( + split_criterion # if None, the change detector will have binary inputs + ) + + def learn_one(self, x, y, *, w=1, tree=None): + self.update_stats(y, w) + if self.is_active(): + if self.split_criterion is None: + mc_pred = self.prediction(x) + detector_input = max(mc_pred, key=mc_pred.get) != y + self.change_detector.update(detector_input) + else: + detector_input = self.split_criterion.current_merit(self.stats) + self.change_detector.update(detector_input) + self.update_splitters(x, y, w, tree.nominal_attributes) + + +class LeafNaiveBayesWithDetector(LeafMajorityClassWithDetector): + """Leaf that uses Naive Bayes models. + + Parameters + ---------- + stats + Initial class observations. + depth + The depth of the node. + splitter + The numeric attribute observer algorithm used to monitor target statistics + and perform split attempts. + change_detector + Change detector that monitors the leaf error rate or class distribution and + determines when the leaf will split. + split_criterion + Split criterion used in the tree for updating the change detector if it + monitors the class distribution. + kwargs + Other parameters passed to the learning node. + """ + + def __init__(self, stats, depth, splitter, change_detector, split_criterion=None, **kwargs): + super().__init__(stats, depth, splitter, change_detector, split_criterion, **kwargs) + + def learn_one(self, x, y, *, w=1, tree=None): + self.update_stats(y, w) + if self.is_active(): + if self.split_criterion is None: + nb_pred = self.prediction(x) + detector_input = max(nb_pred, key=nb_pred.get) == y + self.change_detector.update(detector_input) + else: + detector_input = self.split_criterion.current_merit(self.stats) + self.change_detector.update(detector_input) + self.update_splitters(x, y, w, tree.nominal_attributes) + + def prediction(self, x, *, tree=None): + if self.is_active() and self.total_weight >= tree.nb_threshold: + return do_naive_bayes_prediction(x, self.stats, self.splitters) + else: + return super().prediction(x) + + def disable_attribute(self, att_index): + """Disable an attribute observer. + + Disabled in Nodes using Naive Bayes, since poor attributes are used in + Naive Bayes calculation. + + Parameters + ---------- + att_index + Attribute index. + """ + pass + + +class LeafNaiveBayesAdaptiveWithDetector(LeafMajorityClassWithDetector): + """Learning node that uses Adaptive Naive Bayes models. + + Parameters + ---------- + stats + Initial class observations. + depth + The depth of the node. + splitter + The numeric attribute observer algorithm used to monitor target statistics + and perform split attempts. + change_detector + Change detector that monitors the leaf error rate or class distribution and + determines when the leaf will split. + split_criterion + Split criterion used in the tree for updating the change detector if it + monitors the class distribution. + kwargs + Other parameters passed to the learning node. + """ + + def __init__(self, stats, depth, splitter, change_detector, split_criterion=None, **kwargs): + super().__init__(stats, depth, splitter, change_detector, split_criterion, **kwargs) + self._mc_correct_weight = 0.0 + self._nb_correct_weight = 0.0 + + def learn_one(self, x, y, *, w=1.0, tree=None): + """Update the node with the provided instance. + + Parameters + ---------- + x + Instance attributes for updating the node. + y + Instance class. + w + The instance's weight. + tree + The tree to update. + + """ + detector_input_mc = 1 + detector_input_nb = 1 + if self.is_active(): + mc_pred = super().prediction(x) + # Empty node (assume the majority class will be the best option) or majority + # class prediction is correct + if len(self.stats) == 0 or max(mc_pred, key=mc_pred.get) == y: + self._mc_correct_weight += w + detector_input_mc = 0 + nb_pred = do_naive_bayes_prediction(x, self.stats, self.splitters) + if len(nb_pred) > 0 and max(nb_pred, key=nb_pred.get) == y: + self._nb_correct_weight += w + detector_input_nb = 0 + + self.update_stats(y, w) + if self.is_active(): + if self.split_criterion is None: + if self._nb_correct_weight >= self._mc_correct_weight: + self.change_detector.update(detector_input_nb) + else: + self.change_detector.update(detector_input_mc) + else: + detector_input = self.split_criterion.current_merit(self.stats) + self.change_detector.update(detector_input) + self.update_splitters(x, y, w, tree.nominal_attributes) + + def prediction(self, x, *, tree=None): + """Get the probabilities per class for a given instance. + + Parameters + ---------- + x + Instance attributes. + tree + LAST Tree. + + Returns + ------- + Class votes for the given instance. + + """ + if self.is_active() and self._nb_correct_weight >= self._mc_correct_weight: + return do_naive_bayes_prediction(x, self.stats, self.splitters) + else: + return super().prediction(x) + + def disable_attribute(self, att_index): + """Disable an attribute observer. + + Disabled in Nodes using Naive Bayes, since poor attributes are used in + Naive Bayes calculation. + + Parameters + ---------- + att_index + Attribute index. + """ + pass diff --git a/river/tree/nodes/leaf.py b/river/tree/nodes/leaf.py index 0333815970..dfab307504 100644 --- a/river/tree/nodes/leaf.py +++ b/river/tree/nodes/leaf.py @@ -77,7 +77,7 @@ def new_nominal_splitter(): pass @abc.abstractmethod - def update_stats(self, y, sample_weight): + def update_stats(self, y, w): pass def _iter_features(self, x) -> typing.Iterable: @@ -90,7 +90,7 @@ def _iter_features(self, x) -> typing.Iterable: """ yield from x.items() - def update_splitters(self, x, y, sample_weight, nominal_attributes): + def update_splitters(self, x, y, w, nominal_attributes): for att_id, att_val in self._iter_features(x): if att_id in self._disabled_attrs: continue @@ -106,7 +106,7 @@ def update_splitters(self, x, y, sample_weight, nominal_attributes): splitter = self.splitter.clone() self.splitters[att_id] = splitter - splitter.update(att_val, y, sample_weight) + splitter.update(att_val, y, w) def best_split_suggestions(self, criterion, tree) -> list[BranchFactory]: """Find possible split candidates. @@ -149,7 +149,7 @@ def disable_attribute(self, att_id): del self.splitters[att_id] self._disabled_attrs.add(att_id) - def learn_one(self, x, y, *, sample_weight=1.0, tree=None): + def learn_one(self, x, y, *, w=1.0, tree=None): """Update the node with the provided sample. Parameters @@ -158,7 +158,7 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): Sample attributes for updating the node. y Target value. - sample_weight + w Sample weight. tree Tree to update. @@ -169,9 +169,9 @@ def learn_one(self, x, y, *, sample_weight=1.0, tree=None): All classes overriding this method should include a call to `super().learn_one` to guarantee the learning process happens consistently. """ - self.update_stats(y, sample_weight) + self.update_stats(y, w) if self.is_active(): - self.update_splitters(x, y, sample_weight, tree.nominal_attributes) + self.update_splitters(x, y, w, tree.nominal_attributes) @abc.abstractmethod def prediction(self, x, *, tree=None) -> dict: diff --git a/river/tree/nodes/sgt_nodes.py b/river/tree/nodes/sgt_nodes.py index e5a0542999..f53da21355 100644 --- a/river/tree/nodes/sgt_nodes.py +++ b/river/tree/nodes/sgt_nodes.py @@ -31,7 +31,7 @@ class SGTLeaf(Leaf): Parameters passed to the feature quantizers. """ - def __init__(self, prediction=0.0, depth=0, split_params=None): + def __init__(self, prediction: float = 0.0, depth: int = 0, split_params: dict | None = None): super().__init__() self._prediction = prediction self.depth = depth @@ -42,9 +42,10 @@ def __init__(self, prediction=0.0, depth=0, split_params=None): ) self.last_split_attempt_at = 0 - self._split_stats: dict[ - FeatureName, dict[Hashable, GradHessStats] | DynamicQuantizer | StaticQuantizer - ] | None = {} + self._split_stats: ( + dict[FeatureName, dict[Hashable, GradHessStats] | DynamicQuantizer | StaticQuantizer] + | None + ) = {} self._update_stats = GradHessStats() def reset(self): @@ -52,7 +53,7 @@ def reset(self): self._update_stats = GradHessStats() @staticmethod - def is_categorical(idx, x_val, nominal_attributes): + def is_categorical(idx: str, x_val, nominal_attributes: list[str]) -> bool: return not isinstance(x_val, numbers.Number) or idx in nominal_attributes def update(self, x: dict, gh: GradHess, sgt, w: float = 1.0): diff --git a/river/tree/split_criterion/base.py b/river/tree/split_criterion/base.py index d329bf6d58..84da388b4b 100644 --- a/river/tree/split_criterion/base.py +++ b/river/tree/split_criterion/base.py @@ -32,6 +32,20 @@ def merit_of_split(self, pre_split_dist, post_split_dist): Value of the merit of splitting """ + @abc.abstractmethod + def current_merit(self, dist): + """Compute the merit of the distribution. + + Parameters + ---------- + dist + The data distribution. + + Returns + ------- + Value of merit of the distribution according to the splitting criterion + """ + @staticmethod @abc.abstractmethod def range_of_merit(pre_split_dist): diff --git a/river/tree/split_criterion/gini_split_criterion.py b/river/tree/split_criterion/gini_split_criterion.py index fc6ef58a85..1a71c1b651 100644 --- a/river/tree/split_criterion/gini_split_criterion.py +++ b/river/tree/split_criterion/gini_split_criterion.py @@ -1,12 +1,21 @@ from __future__ import annotations +import math + from .base import SplitCriterion class GiniSplitCriterion(SplitCriterion): """Gini Impurity split criterion.""" + def __init__(self, min_branch_fraction): + super().__init__() + self.min_branch_fraction = min_branch_fraction + def merit_of_split(self, pre_split_dist, post_split_dist): + if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_fraction) < 2: + return -math.inf + total_weight = 0.0 dist_weights = [0.0] * len(post_split_dist) for i in range(len(post_split_dist)): @@ -19,6 +28,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): ) return 1.0 - gini + def current_merit(self, dist): + return self.compute_gini(dist, sum(dist.values())) + @staticmethod def compute_gini(dist, dist_sum_of_weights): gini = 1.0 @@ -31,3 +43,18 @@ def compute_gini(dist, dist_sum_of_weights): @staticmethod def range_of_merit(pre_split_dist): return 1.0 + + @staticmethod + def num_subsets_greater_than_frac(distributions, min_frac): + total_weight = 0.0 + dist_sums = [0.0] * len(distributions) + for i in range(len(dist_sums)): + dist_sums[i] = sum(distributions[i].values()) + total_weight += dist_sums[i] + num_greater = 0 + + if total_weight > 0: + for d in dist_sums: + if (d / total_weight) > min_frac: + num_greater += 1 + return num_greater diff --git a/river/tree/split_criterion/hellinger_distance_criterion.py b/river/tree/split_criterion/hellinger_distance_criterion.py index fb27edbc8d..236564f9bd 100644 --- a/river/tree/split_criterion/hellinger_distance_criterion.py +++ b/river/tree/split_criterion/hellinger_distance_criterion.py @@ -19,17 +19,18 @@ class HellingerDistanceCriterion(SplitCriterion): and Knowledge Discovery 24, no. 1 (2012): 136-158. """ - def __init__(self, min_branch_frac_option=0.01): + def __init__(self, min_branch_fraction): super().__init__() - self.min_branch_frac_option = min_branch_frac_option - self.lowest_entropy = None - self.best_idx = 0 + self.min_branch_fraction = min_branch_fraction def merit_of_split(self, pre_split_dist, post_split_dist): - if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_frac_option) < 2: + if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_fraction) < 2: return -math.inf return self.compute_hellinger(post_split_dist) + def current_merit(self, dist): + raise ValueError("The Heillinger distance is for 2 or more sets of data.") + @staticmethod def compute_hellinger(dist): try: diff --git a/river/tree/split_criterion/info_gain_split_criterion.py b/river/tree/split_criterion/info_gain_split_criterion.py index 1123b1b06a..b112ff829d 100644 --- a/river/tree/split_criterion/info_gain_split_criterion.py +++ b/river/tree/split_criterion/info_gain_split_criterion.py @@ -18,13 +18,12 @@ class InfoGainSplitCriterion(SplitCriterion): """ - def __init__(self, min_branch_frac_option=0.01): + def __init__(self, min_branch_fraction): super().__init__() - # Minimum fraction of weight required down at least two branches. - self.min_branch_frac_option = min_branch_frac_option + self.min_branch_fraction = min_branch_fraction def merit_of_split(self, pre_split_dist, post_split_dist): - if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_frac_option) < 2: + if self.num_subsets_greater_than_frac(post_split_dist, self.min_branch_fraction) < 2: return -math.inf return self.compute_entropy(pre_split_dist) - self.compute_entropy(post_split_dist) @@ -40,6 +39,9 @@ def compute_entropy(self, dist): elif isinstance(dist, list): return self._compute_entropy_list(dist) + def current_merit(self, dist): + return self.compute_entropy(dist) + @staticmethod def _compute_entropy_dict(dist): entropy = 0.0 diff --git a/river/tree/split_criterion/intra_cluster_variance_reduction_split_criterion.py b/river/tree/split_criterion/intra_cluster_variance_reduction_split_criterion.py index 1436e817e2..0a1af74773 100644 --- a/river/tree/split_criterion/intra_cluster_variance_reduction_split_criterion.py +++ b/river/tree/split_criterion/intra_cluster_variance_reduction_split_criterion.py @@ -27,6 +27,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): icvr -= n_i / n * self.compute_var(dist) return icvr + def current_merit(self, dist): + return self.compute_var(dist) + @staticmethod def compute_var(dist): icvr = [vr.get() for vr in dist.values()] diff --git a/river/tree/split_criterion/variance_ratio_split_criterion.py b/river/tree/split_criterion/variance_ratio_split_criterion.py index c51df25a14..dfdff8ea55 100644 --- a/river/tree/split_criterion/variance_ratio_split_criterion.py +++ b/river/tree/split_criterion/variance_ratio_split_criterion.py @@ -34,6 +34,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): vr -= (n_i / n) * (self.compute_var(post_split_dist[i]) / var) return vr + def current_merit(self, dist): + return self.compute_var(dist) + @staticmethod def compute_var(dist): return dist.get() diff --git a/river/tree/split_criterion/variance_reduction_split_criterion.py b/river/tree/split_criterion/variance_reduction_split_criterion.py index f52cfa7bd3..147ead573b 100644 --- a/river/tree/split_criterion/variance_reduction_split_criterion.py +++ b/river/tree/split_criterion/variance_reduction_split_criterion.py @@ -35,6 +35,9 @@ def merit_of_split(self, pre_split_dist, post_split_dist): vr -= n_i / n * self.compute_var(post_split_dist[i]) return vr + def current_merit(self, dist): + return self.compute_var(dist) + @staticmethod def compute_var(dist): return dist.get() diff --git a/river/tree/splitter/__init__.py b/river/tree/splitter/__init__.py index 67df2ca337..15d966599a 100644 --- a/river/tree/splitter/__init__.py +++ b/river/tree/splitter/__init__.py @@ -13,6 +13,7 @@ when choosing the correct feature splitter. """ + from __future__ import annotations from .base import Quantizer, Splitter diff --git a/river/tree/splitter/base.py b/river/tree/splitter/base.py index 98056dbffb..f963fbafba 100644 --- a/river/tree/splitter/base.py +++ b/river/tree/splitter/base.py @@ -21,7 +21,7 @@ class Splitter(base.Estimator, abc.ABC): """ @abc.abstractmethod - def update(self, att_val, target_val: base.typing.Target, sample_weight: float): + def update(self, att_val, target_val: base.typing.Target, w: float) -> None: """Update statistics of this observer given an attribute value, its target value and the weight of the instance observed. @@ -31,8 +31,9 @@ def update(self, att_val, target_val: base.typing.Target, sample_weight: float): The value of the monitored attribute. target_val The target value. - sample_weight + w The weight of the instance. + """ @abc.abstractmethod @@ -49,6 +50,7 @@ def cond_proba(self, att_val, target_val: base.typing.ClfTarget) -> float: Returns ------- Probability for an attribute value given a class. + """ @abc.abstractmethod @@ -107,7 +109,7 @@ def __len__(self): pass @abc.abstractmethod - def update(self, x_val, gh: GradHess, w: float): + def update(self, x_val, gh: GradHess, w: float) -> None: pass @abc.abstractmethod diff --git a/river/tree/splitter/ebst_splitter.py b/river/tree/splitter/ebst_splitter.py index 17bffc6269..ae5ac39aa8 100644 --- a/river/tree/splitter/ebst_splitter.py +++ b/river/tree/splitter/ebst_splitter.py @@ -33,6 +33,7 @@ class EBSTSplitter(Splitter): data streams. Data mining and knowledge discovery, 23(1), 128-168. [^2]: [Osojnik, Aljaž. 2017. Structured output prediction on Data Streams (Doctoral Dissertation)](http://kt.ijs.si/theses/phd_aljaz_osojnik.pdf) + """ def __init__(self): @@ -43,14 +44,14 @@ def __init__(self): def is_target_class(self) -> bool: return False - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: return else: if self._root is None: - self._root = EBSTNode(att_val, target_val, sample_weight) + self._root = EBSTNode(att_val, target_val, w) else: - self._root.insert_value(att_val, target_val, sample_weight) + self._root.insert_value(att_val, target_val, w) def cond_proba(self, att_val, target_val): """Not implemented in regression splitters.""" @@ -237,7 +238,7 @@ def _remove_bad_split_nodes(self, current_node, parent=None, is_left_child=True) class EBSTNode: - def __init__(self, att_val, target_val, sample_weight): + def __init__(self, att_val, target_val, w): self.att_val = att_val if isinstance(target_val, dict): @@ -250,23 +251,23 @@ def __init__(self, att_val, target_val, sample_weight): self.estimator = Var() self._update_estimator = self._update_estimator_univariate - self._update_estimator(self, target_val, sample_weight) + self._update_estimator(self, target_val, w) self._left = None self._right = None @staticmethod - def _update_estimator_univariate(node, target, sample_weight): - node.estimator.update(target, sample_weight) + def _update_estimator_univariate(node, target, w): + node.estimator.update(target, w) @staticmethod - def _update_estimator_multivariate(node, target, sample_weight): + def _update_estimator_multivariate(node, target, w): for t in target: - node.estimator[t].update(target[t], sample_weight) + node.estimator[t].update(target[t], w) # Incremental implementation of the insert method. Avoiding unnecessary # stack tracing must decrease memory costs - def insert_value(self, att_val, target_val, sample_weight): + def insert_value(self, att_val, target_val, w): current = self antecedent = None is_right = False @@ -274,10 +275,10 @@ def insert_value(self, att_val, target_val, sample_weight): while current is not None: antecedent = current if att_val == current.att_val: - self._update_estimator(current, target_val, sample_weight) + self._update_estimator(current, target_val, w) return elif att_val < current.att_val: - self._update_estimator(current, target_val, sample_weight) + self._update_estimator(current, target_val, w) current = current._left is_right = False @@ -287,6 +288,6 @@ def insert_value(self, att_val, target_val, sample_weight): # Value was not yet added to the tree if is_right: - antecedent._right = EBSTNode(att_val, target_val, sample_weight) + antecedent._right = EBSTNode(att_val, target_val, w) else: - antecedent._left = EBSTNode(att_val, target_val, sample_weight) + antecedent._left = EBSTNode(att_val, target_val, w) diff --git a/river/tree/splitter/exhaustive_splitter.py b/river/tree/splitter/exhaustive_splitter.py index 7ba3b8ad83..945f00daea 100644 --- a/river/tree/splitter/exhaustive_splitter.py +++ b/river/tree/splitter/exhaustive_splitter.py @@ -30,14 +30,14 @@ def __init__(self): super().__init__() self._root = None - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: return else: if self._root is None: - self._root = ExhaustiveNode(att_val, target_val, sample_weight) + self._root = ExhaustiveNode(att_val, target_val, w) else: - self._root.insert_value(att_val, target_val, sample_weight) + self._root.insert_value(att_val, target_val, w) def cond_proba(self, att_val, target_val): """The underlying data structure used to monitor the input does not allow probability @@ -148,27 +148,27 @@ def _search_for_best_split_option( class ExhaustiveNode: - def __init__(self, att_val, target_val, sample_weight): + def __init__(self, att_val, target_val, w): self.class_count_left = defaultdict(float) self.class_count_right = defaultdict(float) self._left = None self._right = None self.cut_point = att_val - self.class_count_left[target_val] += sample_weight + self.class_count_left[target_val] += w - def insert_value(self, val, label, sample_weight): + def insert_value(self, val, label, w): if val == self.cut_point: - self.class_count_left[label] += sample_weight + self.class_count_left[label] += w elif val < self.cut_point: - self.class_count_left[label] += sample_weight + self.class_count_left[label] += w if self._left is None: - self._left = ExhaustiveNode(val, label, sample_weight) + self._left = ExhaustiveNode(val, label, w) else: - self._left.insert_value(val, label, sample_weight) + self._left.insert_value(val, label, w) else: - self.class_count_right[label] += sample_weight + self.class_count_right[label] += w if self._right is None: - self._right = ExhaustiveNode(val, label, sample_weight) + self._right = ExhaustiveNode(val, label, w) else: - self._right.insert_value(val, label, sample_weight) + self._right.insert_value(val, label, w) diff --git a/river/tree/splitter/gaussian_splitter.py b/river/tree/splitter/gaussian_splitter.py index b7edb9d4ea..cdf50f5cc5 100644 --- a/river/tree/splitter/gaussian_splitter.py +++ b/river/tree/splitter/gaussian_splitter.py @@ -30,7 +30,7 @@ def __init__(self, n_splits: int = 10): self._att_dist_per_class: dict[ClfTarget, Gaussian] = {} self.n_splits = n_splits - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: return else: @@ -46,7 +46,7 @@ def update(self, att_val, target_val, sample_weight): self._min_per_class[target_val] = att_val self._max_per_class[target_val] = att_val - val_dist.update(att_val, sample_weight) + val_dist.update(att_val, w) def cond_proba(self, att_val, target_val): if target_val in self._att_dist_per_class: diff --git a/river/tree/splitter/histogram_splitter.py b/river/tree/splitter/histogram_splitter.py index 55a5f6bdf7..cde9d490c3 100644 --- a/river/tree/splitter/histogram_splitter.py +++ b/river/tree/splitter/histogram_splitter.py @@ -33,8 +33,8 @@ def __init__(self, n_bins: int = 256, n_splits: int = 32): functools.partial(sketch.Histogram, max_bins=self.n_bins) ) - def update(self, att_val, target_val, sample_weight): - for _ in range(int(sample_weight)): + def update(self, att_val, target_val, w): + for _ in range(int(w)): self.hists[target_val].update(att_val) def cond_proba(self, att_val, target_val): diff --git a/river/tree/splitter/nominal_splitter_classif.py b/river/tree/splitter/nominal_splitter_classif.py index 988078d64d..189d04b3d5 100644 --- a/river/tree/splitter/nominal_splitter_classif.py +++ b/river/tree/splitter/nominal_splitter_classif.py @@ -1,7 +1,6 @@ from __future__ import annotations import collections -import functools from ..utils import BranchFactory from .base import Splitter @@ -18,26 +17,32 @@ def __init__(self): super().__init__() self._total_weight_observed = 0.0 self._missing_weight_observed = 0.0 - self._att_dist_per_class = collections.defaultdict( - functools.partial(collections.defaultdict, float) - ) + self._att_dist_per_class = collections.defaultdict(dict) self._att_values = set() @property def is_numeric(self): return False - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: - self._missing_weight_observed += sample_weight + self._missing_weight_observed += w else: self._att_values.add(att_val) - self._att_dist_per_class[target_val][att_val] += sample_weight - self._total_weight_observed += sample_weight + try: + self._att_dist_per_class[target_val][att_val] += w + except KeyError: + self._att_dist_per_class[target_val][att_val] = w + + self._total_weight_observed += w def cond_proba(self, att_val, target_val): class_dist = self._att_dist_per_class[target_val] + + if att_val not in class_dist: + return 0.0 + value = class_dist[att_val] try: return value / sum(class_dist.values()) diff --git a/river/tree/splitter/nominal_splitter_reg.py b/river/tree/splitter/nominal_splitter_reg.py index ebe7bf7b5e..a2ebff001c 100644 --- a/river/tree/splitter/nominal_splitter_reg.py +++ b/river/tree/splitter/nominal_splitter_reg.py @@ -28,16 +28,16 @@ def is_numeric(self): return False @staticmethod - def _update_estimator_univariate(estimator, target, sample_weight): - estimator.update(target, sample_weight) + def _update_estimator_univariate(estimator, target, w): + estimator.update(target, w) @staticmethod - def _update_estimator_multivariate(estimator, target, sample_weight): + def _update_estimator_multivariate(estimator, target, w): for t in target: - estimator[t].update(target[t], sample_weight) + estimator[t].update(target[t], w) - def update(self, att_val, target_val, sample_weight): - if att_val is None or sample_weight is None: + def update(self, att_val, target_val, w): + if att_val is None or w is None: return else: try: @@ -49,7 +49,7 @@ def update(self, att_val, target_val, sample_weight): else: self._statistics[att_val] = Var() estimator = self._statistics[att_val] - self._update_estimator(estimator, target_val, sample_weight) + self._update_estimator(estimator, target_val, w) def cond_proba(self, att_val, target_val): """Not implemented in regression splitters.""" diff --git a/river/tree/splitter/qo_splitter.py b/river/tree/splitter/qo_splitter.py index 8e246e17a4..269249a19b 100644 --- a/river/tree/splitter/qo_splitter.py +++ b/river/tree/splitter/qo_splitter.py @@ -66,11 +66,11 @@ def __init__(self, radius: float = 0.25, allow_multiway_splits=False): self.allow_multiway_splits = allow_multiway_splits - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): if att_val is None: return else: - self._quantizer.update(att_val, target_val, sample_weight) + self._quantizer.update(att_val, target_val, w) def cond_proba(self, att_val, target_val): raise NotImplementedError @@ -162,12 +162,12 @@ def _init_estimator(self, y): self.y_stats = stats.Var() self._update_estimator = self._update_estimator_univariate - def _update_estimator_univariate(self, target, sample_weight): - self.y_stats.update(target, sample_weight) + def _update_estimator_univariate(self, target, w): + self.y_stats.update(target, w) - def _update_estimator_multivariate(self, target, sample_weight): + def _update_estimator_multivariate(self, target, w): for t in target: - self.y_stats[t].update(target[t], sample_weight) + self.y_stats[t].update(target[t], w) def __iadd__(self, o): self.x_stats += o.x_stats @@ -175,9 +175,9 @@ def __iadd__(self, o): return self - def update(self, x, y, sample_weight): - self.x_stats.update(x, sample_weight) - self._update_estimator(y, sample_weight) + def update(self, x, y, w): + self.x_stats.update(x, w) + self._update_estimator(y, w) class FeatureQuantizer: diff --git a/river/tree/splitter/random_splitter.py b/river/tree/splitter/random_splitter.py index f198083414..aa6562f975 100644 --- a/river/tree/splitter/random_splitter.py +++ b/river/tree/splitter/random_splitter.py @@ -29,18 +29,18 @@ def clone(self, new_params: dict | None = None, include_attributes=False): return super().clone(new_params, include_attributes) @abc.abstractmethod - def _update_stats(self, branch, target_val, sample_weight): + def _update_stats(self, branch, target_val, w): pass def cond_proba(self, att_val, class_val) -> float: """This attribute observer does not support probability density estimation.""" raise NotImplementedError - def update(self, att_val, target_val, sample_weight) -> Splitter: + def update(self, att_val, target_val, w) -> None: if self.threshold is None: if len(self._buffer) < self.buffer_size: - self._buffer.append((att_val, target_val, sample_weight)) - return self + self._buffer.append((att_val, target_val, w)) + return mn = min(self._buffer, key=lambda t: t[0])[0] mx = max(self._buffer, key=lambda t: t[0])[0] @@ -51,11 +51,9 @@ def update(self, att_val, target_val, sample_weight) -> Splitter: self._update_stats(0 if a <= self.threshold else 1, t, w) self._buffer = None - return self + return - self._update_stats(0 if att_val <= self.threshold else 1, target_val, sample_weight) - - return self + self._update_stats(0 if att_val <= self.threshold else 1, target_val, w) def best_evaluated_split_suggestion(self, criterion, pre_split_dist, att_idx, binary_only): post_split_dist = [self.stats[0], self.stats[1]] @@ -76,8 +74,8 @@ def __init__(self, seed, buffer_size): super().__init__(seed, buffer_size) self.stats = {0: stats.Var(), 1: stats.Var()} - def _update_stats(self, branch, target_val, sample_weight): - self.stats[branch].update(target_val, sample_weight) + def _update_stats(self, branch, target_val, w): + self.stats[branch].update(target_val, w) @property def is_target_class(self) -> bool: diff --git a/river/tree/splitter/tebst_splitter.py b/river/tree/splitter/tebst_splitter.py index f5b623060e..68e2212b82 100644 --- a/river/tree/splitter/tebst_splitter.py +++ b/river/tree/splitter/tebst_splitter.py @@ -22,10 +22,10 @@ def __init__(self, digits: int = 1): super().__init__() self.digits = digits - def update(self, att_val, target_val, sample_weight): + def update(self, att_val, target_val, w): try: att_val = round(att_val, self.digits) - super().update(att_val, target_val, sample_weight) + super().update(att_val, target_val, w) except TypeError: # feature value is None pass diff --git a/river/tree/stochastic_gradient_tree.py b/river/tree/stochastic_gradient_tree.py index 0312e67868..8ea1d76371 100644 --- a/river/tree/stochastic_gradient_tree.py +++ b/river/tree/stochastic_gradient_tree.py @@ -1,13 +1,13 @@ from __future__ import annotations import abc -import math +import sys from scipy.stats import f as f_dist from river import base, tree -from .losses import BinaryCrossEntropyLoss, SquaredErrorLoss +from .losses import BinaryCrossEntropyLoss, Loss, SquaredErrorLoss from .nodes.branch import DTBranch, NominalMultiwayBranch, NumericBinaryBranch from .nodes.sgt_nodes import SGTLeaf from .utils import BranchFactory, GradHessMerit @@ -23,22 +23,22 @@ class StochasticGradientTree(base.Estimator, abc.ABC): def __init__( self, - loss_func, - delta, - grace_period, - init_pred, - max_depth, - lambda_value, - gamma, - nominal_attributes, - feature_quantizer, + loss_func: Loss, + delta: float, + grace_period: int, + init_pred: float, + max_depth: int | None, + lambda_value: float, + gamma: float, + nominal_attributes: list[str] | None, + feature_quantizer: tree.splitter.Quantizer | None, ): # What really defines how a SGT works is its loss function self.loss_func = loss_func self.delta = delta self.grace_period = grace_period self.init_pred = init_pred - self.max_depth = max_depth if max_depth else math.inf + self.max_depth = max_depth if max_depth else (sys.getrecursionlimit() - 20) if lambda_value < 0.0: raise ValueError('Invalid value: "lambda_value" must be positive.') @@ -56,7 +56,7 @@ def __init__( self._root: SGTLeaf | DTBranch = SGTLeaf(prediction=self.init_pred) # set used to check whether categorical feature has been already split - self._split_features = set() + self._split_features: set[str] = set() self._n_splits = 0 self._n_node_updates = 0 self._n_observations = 0 @@ -124,7 +124,7 @@ def learn_one(self, x, y, *, w=1.0): node.update(x, grad_hess, self, w) if node.total_weight - node.last_split_attempt_at < self.grace_period: - return self + return # Update split attempt data node.last_split_attempt_at = node.total_weight @@ -150,8 +150,6 @@ def learn_one(self, x, y, *, w=1.0): p_branch = p_node.branch_no(x) if isinstance(p_node, DTBranch) else None node.apply_split(best_split, p_node, p_branch, self) - return self - @staticmethod def _compute_p_value(merit, n_observations): # Null hypothesis: expected loss is zero diff --git a/river/tree/utils.py b/river/tree/utils.py index 3826d02af4..541e9a31ed 100644 --- a/river/tree/utils.py +++ b/river/tree/utils.py @@ -78,9 +78,12 @@ class BranchFactory: merit: float = -math.inf feature: FeatureName | None = None - split_info: typing.Hashable | list[typing.Hashable] | tuple[ - typing.Hashable, list[typing.Hashable] - ] | None = None + split_info: ( + typing.Hashable + | list[typing.Hashable] + | tuple[typing.Hashable, list[typing.Hashable]] + | None + ) = None children_stats: list | None = None numerical_feature: bool = True multiway_split: bool = False @@ -251,7 +254,7 @@ def calculate_object_size(obj: typing.Any, unit: str = "byte") -> int: Object to evaluate. unit The unit in which the accounted value is going to be returned. - Values: 'byte', 'kB', 'MB' (Default: 'byte'). + Values: 'byte', 'KiB', 'MiB' (Default: 'byte'). Returns ------- @@ -295,9 +298,9 @@ def calculate_object_size(obj: typing.Any, unit: str = "byte") -> int: for i in obj: to_visit.append(i) - if unit == "kB": + if unit == "KiB": final_size = byte_size / 1024 - elif unit == "MB": + elif unit == "MiB": final_size = byte_size / (2**20) else: final_size = byte_size diff --git a/river/utils/__init__.py b/river/utils/__init__.py index 6474b84c5e..860427bbb6 100644 --- a/river/utils/__init__.py +++ b/river/utils/__init__.py @@ -1,22 +1,20 @@ """Shared utility classes and functions""" + from __future__ import annotations from . import inspect, math, norm, pretty, random from .context_managers import log_method_calls -from .data_conversion import dict2numpy, numpy2dict from .param_grid import expand_param_grid from .rolling import Rolling, TimeRolling from .sorted_window import SortedWindow from .vectordict import VectorDict __all__ = [ - "dict2numpy", "expand_param_grid", "inspect", "log_method_calls", "math", "pretty", - "numpy2dict", "random", "norm", "Rolling", diff --git a/river/utils/data_conversion.py b/river/utils/data_conversion.py deleted file mode 100644 index 660da2ed03..0000000000 --- a/river/utils/data_conversion.py +++ /dev/null @@ -1,56 +0,0 @@ -from __future__ import annotations - -import numpy as np - - -def dict2numpy(data) -> np.ndarray: - """Convert a dictionary containing data to a numpy array. - - There is not restriction to the type of keys in `data`, but values must - be strictly numeric. To make sure random permutations of the features - do not impact on the learning algorithms, keys are first converted to - strings and then sorted prior to the conversion. - - Parameters - ---------- - data - A dictionary whose keys represent input attributes and the values - represent their observed contents. - - Returns - ------- - An array representation of the values in `data`. - - Examples - -------- - >>> from river.utils import dict2numpy - >>> dict2numpy({'a': 1, 'b': 2, 3: 3}) - array([3, 1, 2]) - - """ - data_ = {str(k): v for k, v in data.items()} - return np.asarray(list(x for _, x in sorted(data_.items()))) - - -def numpy2dict(data: np.ndarray) -> dict: - """Convert a numpy array to a dictionary. - - Parameters - ---------- - data - An one-dimensional numpy.array. - - Returns - ------- - A dictionary where keys are integers $k \\in \\left{0, 1, ..., |\\text{data}| - 1\\right}$, - and the values are each one of the $k$ entries in `data`. - - Examples - -------- - >>> import numpy as np - >>> from river.utils import numpy2dict - >>> numpy2dict(np.array([1.0, 2.0, 3.0])) - {0: 1.0, 1: 2.0, 2: 3.0} - - """ - return {k: v for k, v in enumerate(data)} diff --git a/river/utils/inspect.py b/river/utils/inspect.py index 68aee6e6b0..e7f1434dac 100644 --- a/river/utils/inspect.py +++ b/river/utils/inspect.py @@ -6,6 +6,7 @@ thus provides utilities for determining an arbitrary model's type. """ + from __future__ import annotations import inspect diff --git a/river/utils/math.py b/river/utils/math.py index cf4ab394e5..5d018b4b43 100644 --- a/river/utils/math.py +++ b/river/utils/math.py @@ -3,6 +3,7 @@ A lot of this is experimental and has a high probability of changing in the future. """ + from __future__ import annotations import functools @@ -162,7 +163,7 @@ def minkowski_distance(a: dict, b: dict, p: int): Manhattan distance. When `p=2`, this is equivalent to using the Euclidean distance. """ - return sum((abs(a.get(k, 0.0) - b.get(k, 0.0))) ** p for k in {*a.keys(), *b.keys()}) + return sum((abs(a.get(k, 0.0) - b.get(k, 0.0))) ** p for k in {*a.keys(), *b.keys()}) ** (1 / p) def softmax(y_pred: dict): diff --git a/river/utils/pretty.py b/river/utils/pretty.py index a3b1c71e61..df9d7c926a 100644 --- a/river/utils/pretty.py +++ b/river/utils/pretty.py @@ -34,7 +34,7 @@ def print_table( raise ValueError("all the columns must be of the same length") # Determine the width of each column based on the maximum length of it's elements - col_widths = [max(*map(len, col), len(header)) for header, col in zip(headers, columns)] + col_widths = [max(max(map(len, col)), len(header)) for header, col in zip(headers, columns)] # Make a template to print out rows one by one row_format = " ".join(["{:" + str(width + 2) + "s}" for width in col_widths]) @@ -64,12 +64,12 @@ def humanize_bytes(n_bytes: int): n_bytes """ - suffixes = ["B", "KB", "MB", "GB", "TB", "PB"] + suffixes = ["B", "KiB", "MiB", "GiB", "TiB", "PiB"] human = float(n_bytes) rank = 0 if n_bytes != 0: rank = int((math.log10(n_bytes)) / 3) rank = min(rank, len(suffixes) - 1) human = n_bytes / (1024.0**rank) - f = ("%.2f" % human).rstrip("0").rstrip(".") + f = f"{human:.2f}".rstrip("0").rstrip(".") return f"{f} {suffixes[rank]}" diff --git a/river/utils/random.py b/river/utils/random.py index ce99b8f5c6..346eaa9591 100644 --- a/river/utils/random.py +++ b/river/utils/random.py @@ -3,7 +3,7 @@ import math import random -__all__ = ["poisson"] +__all__ = ["poisson", "exponential"] def poisson(rate: float, rng=random) -> int: @@ -29,3 +29,24 @@ def poisson(rate: float, rng=random) -> int: p *= rng.random() return k - 1 + + +def exponential(rate: float = 1.0, rng=random) -> float: + """Sample a random value from a Poisson distribution. + + Parameters + ---------- + rate + rng + + References + ---------- + [^1]: [Wikipedia article](https://www.wikiwand.com/en/Exponential_distribution#Random_variate_generation) + + """ + + u = rng.random() + + # Retrive the λ value from the rate (β): β = 1 / λ + lmbda = 1.0 / rate + return -math.log(1 - u) / lmbda diff --git a/river/utils/rolling.py b/river/utils/rolling.py index 849f88ba0b..f662b941b4 100644 --- a/river/utils/rolling.py +++ b/river/utils/rolling.py @@ -8,11 +8,9 @@ @typing.runtime_checkable class Rollable(typing.Protocol): - def update(self, *args, **kwargs): - ... + def update(self, *args, **kwargs) -> None: ... - def revert(self, *args, **kwargs): - ... + def revert(self, *args, **kwargs) -> None: ... class BaseRolling: @@ -60,7 +58,8 @@ class Rolling(BaseRolling): >>> rmean = utils.Rolling(stats.Mean(), window_size=3) >>> for x in X: - ... print(rmean.update(x).get()) + ... rmean.update(x) + ... print(rmean.get()) 1.0 2.0 3.0 @@ -81,7 +80,6 @@ def update(self, *args, **kwargs): self.obj.revert(*self.window[0][0], **self.window[0][1]) self.obj.update(*args, **kwargs) self.window.append((args, kwargs)) - return self class TimeRolling(BaseRolling): @@ -114,7 +112,8 @@ class TimeRolling(BaseRolling): >>> rmean = utils.TimeRolling(stats.Mean(), period=dt.timedelta(days=3)) >>> for t, x in X.items(): - ... print(rmean.update(x, t=t).get()) + ... rmean.update(x, t=t) + ... print(rmean.get()) 1.0 3.0 5.0 @@ -125,12 +124,15 @@ class TimeRolling(BaseRolling): def __init__(self, obj: Rollable, period: dt.timedelta): super().__init__(obj) self.period = period - self._events: list[tuple[dt.datetime, typing.Any]] = [] + self._timestamps: list[dt.datetime] = [] + self._datum: list[typing.Any] = [] self._latest = dt.datetime(1, 1, 1) def update(self, *args, t: dt.datetime, **kwargs): self.obj.update(*args, **kwargs) - bisect.insort_left(self._events, (t, (args, kwargs))) + i = bisect.bisect_left(self._timestamps, t) + self._timestamps.insert(i, t) + self._datum.insert(i, (args, kwargs)) # There will only be events to revert if the new event if younger than the previously seen # youngest event @@ -138,7 +140,7 @@ def update(self, *args, t: dt.datetime, **kwargs): self._latest = t i = 0 - for ti, (argsi, kwargsi) in self._events: + for ti, (argsi, kwargsi) in zip(self._timestamps, self._datum): if ti > t - self.period: break self.obj.revert(*argsi, **kwargsi) @@ -146,6 +148,5 @@ def update(self, *args, t: dt.datetime, **kwargs): # Remove expired events if i > 0: - self._events = self._events[i:] - - return self + self._timestamps = self._timestamps[i:] + self._datum = self._datum[i:] diff --git a/river/utils/sorted_window.py b/river/utils/sorted_window.py index d718158c6a..1dcdd6eeec 100644 --- a/river/utils/sorted_window.py +++ b/river/utils/sorted_window.py @@ -20,7 +20,8 @@ class SortedWindow(collections.UserList): >>> window = utils.SortedWindow(size=3) >>> for i in reversed(range(9)): - ... print(window.append(i)) + ... window.append(i) + ... print(window) [8] [7, 8] [6, 7, 8] @@ -45,7 +46,7 @@ def __init__(self, size: int): def size(self): return self.unsorted_window.maxlen - def append(self, x): + def append(self, x) -> None: if len(self) >= self.size: # The window is sorted, and a binary search is more optimized than linear search start_deque = bisect.bisect_left(self, self.unsorted_window[0]) @@ -53,5 +54,3 @@ def append(self, x): bisect.insort_left(self, x) self.unsorted_window.append(x) - - return self diff --git a/river/utils/test_rolling.py b/river/utils/test_rolling.py index 6d1cedb4de..5d484ac7de 100644 --- a/river/utils/test_rolling.py +++ b/river/utils/test_rolling.py @@ -4,7 +4,7 @@ import pytest -from river import stats, utils +from river import proba, stats, utils def test_with_counter(): @@ -16,7 +16,7 @@ def test_with_counter(): >>> counter = utils.Rolling(collections.Counter(), window_size=3) >>> for i in range(5): - ... counter = counter.update([i]) + ... counter.update([i]) >>> counter Counter({2: 1, 3: 1, 4: 1, 0: 0, 1: 0}) @@ -38,3 +38,15 @@ def test_rolling_with_not_rollable(): def test_time_rolling_with_not_rollable(): with pytest.raises(ValueError): utils.TimeRolling(stats.Quantile(), period=dt.timedelta(seconds=10)) + + +def test_issue_1343(): + """ + + https://github.com/online-ml/river/issues/1343 + + """ + rmean = utils.TimeRolling(proba.MultivariateGaussian(), period=dt.timedelta(microseconds=1)) + t = dt.datetime.utcnow() + rmean.update({"a": 0}, t=t) + rmean.update({"a": 1}, t=t) diff --git a/river/utils/vectordict.pyi b/river/utils/vectordict.pyi new file mode 100644 index 0000000000..faa8086966 --- /dev/null +++ b/river/utils/vectordict.pyi @@ -0,0 +1,56 @@ +from collections.abc import Callable + +import numpy + +def get_union_keys(left: VectorDict, right: VectorDict): ... +def get_intersection_keys(left: VectorDict, right: VectorDict): ... + +class VectorDict: + def __init__( + self, + data: VectorDict | dict | None = None, + default_factory: Callable | None = None, + mask: VectorDict | set | None = None, + copy: bool = False, + ) -> None: ... + def with_mask(self, mask, copy=False): ... + def to_dict(self): ... + def to_numpy(self, fields) -> numpy.ndarray: ... + def __contains__(self, key): ... + def __delitem__(self, key): ... + def __format__(self, format_spec): ... + def __getitem__(self, key): ... + def __iter__(self): ... + def __len__(self): ... + def __repr__(self): ... + def __setitem__(self, key, value): ... + def __str__(self): ... + def clear(self): ... + def get(self, key, *args, **kwargs): ... + def items(self): ... + def keys(self): ... + def pop(self, *args, **kwargs): ... + def popitem(self): ... + def setdefault(self, key, *args, **kwargs): ... + def update(self, *args, **kwargs): ... + def values(self): ... + def __eq__(left, right): ... + def __add__(left, right): ... + def __iadd__(self, other): ... + def __sub__(left, right): ... + def __isub__(self, other): ... + def __mul__(left, right): ... + def __imul__(self, other): ... + def __truediv__(left, right): ... + def __itruediv__(self, other): ... + def __pow__(left, right): ... + def __ipow__(self, other): ... + def __matmul__(left, right): ... + def __neg__(self): ... + def __pos__(self): ... + def __abs__(self): ... + def abs(self): ... + def min(self): ... + def max(self): ... + def minimum(self, other): ... + def maximum(self, other): ... diff --git a/river/utils/vectordict.pyx b/river/utils/vectordict.pyx index 5c7ffdecef..00d8c1911d 100644 --- a/river/utils/vectordict.pyx +++ b/river/utils/vectordict.pyx @@ -1,3 +1,5 @@ +# cython: c_api_binop_methods=True + cimport cython import itertools @@ -425,8 +427,8 @@ cdef class VectorDict: return NotImplemented return self - def __pow__(left, right, modulo): - if not isinstance(left, VectorDict) or modulo is not None: + def __pow__(left, right): + if not isinstance(left, VectorDict): return NotImplemented left_ = left res = left_._to_dict(force_copy=True) diff --git a/setup.py b/setup.py deleted file mode 100644 index dbf4ecd524..0000000000 --- a/setup.py +++ /dev/null @@ -1,143 +0,0 @@ -from __future__ import annotations - -import io -import os -import platform -import subprocess -import sys - -import setuptools # type: ignore -from setuptools_rust import Binding, RustExtension # type: ignore - -try: - from numpy import __version__ as numpy_version - from numpy import get_include -except ImportError: - subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"]) - from numpy import __version__ as numpy_version - from numpy import get_include - -try: - from Cython.Build import cythonize -except ImportError: - subprocess.check_call([sys.executable, "-m", "pip", "install", "Cython"]) - from Cython.Build import cythonize # type: ignore - -NAME = "river" -DESCRIPTION = "Online machine learning in Python" -LONG_DESCRIPTION_CONTENT_TYPE = "text/markdown" -URL = "https://github.com/online-ml/river" -EMAIL = "maxhalford25@gmail.com" -AUTHOR = "Max Halford" -REQUIRES_PYTHON = ">=3.8.0" - -here = os.path.abspath(os.path.dirname(__file__)) -with open(os.path.join(here, "README.md"), encoding="utf-8") as f: - long_description = "\n" + f.read() - -about: dict = {} -with open(os.path.join(here, NAME, "__version__.py")) as f: - exec(f.read(), about) - -setuptools.setup( - name=NAME, - version=about["__version__"], - description=DESCRIPTION, - long_description=long_description, - long_description_content_type=LONG_DESCRIPTION_CONTENT_TYPE, - author=AUTHOR, - author_email=EMAIL, - python_requires=REQUIRES_PYTHON, - url=URL, - packages=setuptools.find_packages(exclude=("tests",)), - install_requires=(base_packages := [f"numpy>={numpy_version}", "scipy>=1.5", "pandas>=1.3"]), - extras_require={ - "dev": base_packages - + [ - "black>=22.1.0", - "graphviz>=0.10.1", - "gym>=0.26.1", - "matplotlib>=3.0.2", - "mypy>=0.980", - "pre-commit>=2.9.2", - "pytest>=4.5.0", - "ruff>=0.0.213", - "scikit-learn>=1.0.1", - "sqlalchemy>=2.0.0", - "sympy>=1.10.1", - ], - "compat": base_packages - + [ - "scikit-learn", - "sqlalchemy>=2.0.0", - "vaex", - ], - "docs": base_packages - + [ - "dominate", - "flask", - "ipykernel", - "jupyter-client", - "mike", - "mkdocs", - "mkdocs-awesome-pages-plugin", - "mkdocs-charts-plugin", - "mkdocs-material", - "nbconvert", - "numpydoc", - "python-slugify", - "spacy", - "tabulate", - "watermark", - ], - "extra": ["river_extra"], - "deep": ["deep-river"], - "torch": ["river_torch"], - "benchmark": base_packages - + [ - "scikit-learn==1.1.2", - "scipy==1.9.3", - "torch==1.13.0", - "vowpalwabbit==9.6.0", - "torch==1.13.0", - "dominate==2.7.0", - "slugify==0.0.1", - "watermark==2.3.1", - "river-torch==0.1.2", - ], - ":python_version == '3.6'": ["dataclasses"], - }, - include_package_data=True, - license="BSD-3", - classifiers=[ - # Trove classifiers - # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers - "License :: OSI Approved :: BSD License", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: Implementation :: CPython", - "Programming Language :: Python :: Implementation :: PyPy", - ], - ext_modules=cythonize( - module_list=[ - setuptools.Extension( - "*", - sources=["**/*.pyx"], - include_dirs=[get_include()], - libraries=[] if platform.system() == "Windows" else ["m"], - define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")], - ) - ], - compiler_directives={ - "language_level": 3, - "binding": True, - "embedsignature": True, - }, - ), - rust_extensions=[RustExtension("river.stats._rust_stats", binding=Binding.PyO3)], - # rust extensions are not zip safe, just like C-extensions. - zip_safe=False, -)