This page lists all of the estimators and top-level functions in dask_ml.
Unless otherwise noted, the estimators implemented in dask-ml are
appropriate for parallel and distributed training.
:mod:`dask_ml.model_selection`: Model Selection
.. automodule:: dask_ml.model_selection :no-members: :no-inherited-members:
.. currentmodule:: dask_ml
Dask-ML has a few cross validation utilities.
.. autosummary:: :toctree: generated/ model_selection.train_test_split
:func:`model_selection.train_test_split` is a simple helper that uses :class:`model_selection.ShuffleSplit` internally.
.. autosummary:: :toctree: generated :template: class.rst model_selection.ShuffleSplit model_selection.KFold
Dask-ML provides drop-in replacements for grid and randomized search. These are appropriate for datasets where the CV splits fit in memory.
.. autosummary:: :toctree: generated/ :template: class.rst model_selection.GridSearchCV model_selection.RandomizedSearchCV
For hyperparameter optimization on larger-than-memory datasets, Dask-ML provides the following:
.. autosummary:: :toctree: generated/ :template: class.rst model_selection.IncrementalSearchCV model_selection.HyperbandSearchCV model_selection.SuccessiveHalvingSearchCV model_selection.InverseDecaySearchCV
:mod:`dask_ml.ensemble`: Ensemble Methods
.. automodule:: dask_ml.ensemble :no-members: :no-inherited-members:
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst ensemble.BlockwiseVotingClassifier ensemble.BlockwiseVotingRegressor
:mod:`dask_ml.linear_model`: Generalized Linear Models
.. automodule:: dask_ml.linear_model :no-members: :no-inherited-members:
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst linear_model.LinearRegression linear_model.LogisticRegression linear_model.PoissonRegression
:mod:`dask_ml.naive_bayes`: Naive Bayes
.. automodule:: dask_ml.naive_bayes :no-members: :no-inherited-members:
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst naive_bayes.GaussianNB
:mod:`dask_ml.wrappers`: Meta-Estimators
dask-ml provides some meta-estimators that help use regular estimators that follow the scikit-learn API. These meta-estimators make the underlying estimator work well with Dask Arrays or DataFrames.
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst wrappers.ParallelPostFit wrappers.Incremental
:mod:`dask_ml.cluster`: Clustering
.. automodule:: dask_ml.cluster :no-members: :no-inherited-members:
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst cluster.KMeans cluster.SpectralClustering
:mod:`dask_ml.decomposition`: Matrix Decomposition
.. automodule:: dask_ml.decomposition :no-members: :no-inherited-members:
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst decomposition.IncrementalPCA decomposition.PCA decomposition.TruncatedSVD
:mod:`dask_ml.preprocessing`: Preprocessing Data
.. automodule:: dask_ml.preprocessing
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst preprocessing.StandardScaler preprocessing.RobustScaler preprocessing.MinMaxScaler preprocessing.QuantileTransformer preprocessing.StandardScaler preprocessing.Categorizer preprocessing.DummyEncoder preprocessing.OrdinalEncoder preprocessing.LabelEncoder preprocessing.PolynomialFeatures preprocessing.BlockTransformer
:mod:`dask_ml.feature_extraction.text`: Feature extraction
.. automodule:: dask_ml.preprocessing.text:
.. autosummary:: :toctree: generated/ :template: class.rst feature_extraction.text.CountVectorizer feature_extraction.text.HashingVectorizer feature_extraction.text.FeatureHasher
:mod:`dask_ml.compose`: Composite Estimators
Meta-estimators for building composite models with transformers.
.. automodule:: dask_ml.compose
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst compose.ColumnTransformer
.. autosummary:: :toctree: generated/ compose.make_column_transformer
:mod:`dask_ml.impute`: Imputing Missing Data
.. automodule:: dask_ml.impute
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ :template: class.rst impute.SimpleImputer
:mod:`dask_ml.metrics`: Metrics
Score functions, performance metrics, and pairwise distance computations.
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ metrics.mean_absolute_error metrics.mean_absolute_percentage_error metrics.mean_squared_error metrics.mean_squared_log_error metrics.r2_score
.. currentmodule:: dask_ml
.. autosummary:: :toctree: generated/ metrics.accuracy_score metrics.log_loss
:mod:`dask_ml.datasets`: Datasets
dask-ml provides some utilities for generating toy datasets.
.. automodule:: dask_ml.datasets
.. currentmodule:: dask_ml.datasets
.. autosummary:: :toctree: generated/ make_counts make_blobs make_regression make_classification make_classification_df