ml_binary_classification_gridsearch_hyperOpt

This repository contains Python code for binary classification using grid search and hyperparameter optimization techniques.

Overview

Binary classification is a common machine learning task where the goal is to categorize data into one of two classes. This repository provides a framework for performing binary classification using various machine learning algorithms and optimizing their hyperparameters through grid search and hyperparameter optimization techniques.

Getting Started

Prerequisites

Before you can run the code in this repository, make sure you have the following prerequisites installed:

• Python (>=3.6) -requirements.txt built for python3.10.12
• NumPy
• Pandas
• Scikit-Learn
• HyperOpt (for hyperparameter optimization)
• Pytorch

You can install these dependencies using pip:

pip install numpy pandas scikit-learn hyperopt

Installation

Windows:

1. Clone the repository:

   git clone https://github.com/SamoraHunter/ml_binary_classification_gridsearch_hyperOpt.git
   cd ml_binary_classification_gridsearch_hyperOpt

2. Run the installation script:

   install.bat

Unix/Linux:

1. Clone the repository:

   git clone https://github.com/SamoraHunter/ml_binary_classification_gridsearch_hyperOpt.git
   cd ml_binary_classification_gridsearch_hyperOpt

2. Run the installation script:

   chmod +x install.sh
   ./install.sh

import sys
sys.path.append('/path/to/ml_grid')
import ml_grid

Usage

See Appendix

Examples

See [ml_grid/tests/unit_test_synthetic.ipynb]

Contributing If you would like to contribute to this project, please follow these steps:

Fork the repository on GitHub. Create a new branch for your feature or bug fix. Make your changes and commit them with descriptive commit messages. Push your changes to your fork. Create a pull request to the main repository's master branch. License This project is licensed under the MIT License - see the LICENSE file for details.

Appendix

Acknowledgments scikit-learn hyperopt