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Pre-release

This release is a pre-release and may not be stable for production use.

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The equiboots library is a fairness-aware model evaluation toolkit designed to audit performance disparities across demographic groups. It provides robust, bootstrapped metrics for binary, multi-class, and multi-label classification, as well as regression models. The library supports group-wise performance slicing, fairness diagnostics, and customizable visualizations to support equitable AI/ML development.

equiboots is particularly useful in clinical, social, and policy domains where transparency, bias mitigation, and outcome fairness are critical for responsible deployment.

Prerequisites

Before installing equiboots, ensure your system meets the following requirements:

Python Version

equiboots requires Python 3.7.4 or higher. Specific dependency versions vary depending on your Python version.

Dependencies

The following dependencies will be automatically installed with equiboots:

matplotlib>=3.5.3, <=3.10.1 numpy>=1.21.6, <=2.2.4 pandas>=1.3.5, <=2.2.3 scikit-learn>=1.0.2, <=1.5.2 scipy>=1.8.0, <=1.15.2 seaborn>=0.11.2, <=0.13.2 tqdm>=4.66.4, <=4.67.1 `

💾 Installation

You can install equiboots directly from PyPI:

pip install equiboots

📄 Official Documentation

Coming soon.

🌐 Author Website

https://www.mii.ucla.edu/

⚖️ License

equiboots is distributed under the Apache License. See LICENSE for more information.

Support

If you have any questions or issues with equiboots, please open an issue on this GitHub repository.

Acknowledgements

This work was supported by the UCLA Medical Informatics Institute (MII) and the Clinical and Translational Science Institute (CTSI). Special thanks to Dr. Alex Bui for his invaluable guidance and support, and to Panayiotis Petousis, PhD, for his contributions to this codebase.

Metadata

Release files for equiboots 0.0.0a1

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equiboots-0.0.0a1-py3-none-any.whl Python 3 none any Details

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