This release is a pre-release and may not be stable for production use.
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, statistical significance testing with multiple-comparison corrections, 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,<1.9""scipy>=1.8.0, <=1.15.2""seaborn>=0.11.2, <=0.13.2""statsmodels>=0.13, <=0.14.4""tqdm>=4.66.4, <=4.67.1"
💾 Installation
You can install equiboots directly from PyPI:
pip install equiboots
📄 Official Documentation
https://uclamii.github.io/equiboots_docs
🌐 Author Website
⚖️ License
equiboots is distributed under the Apache License. See LICENSE for more information.
📚 Citing equiboots
If you use equiboots in your research or projects, please consider citing it.
@software{shpaner_2025_15086941,
author = {Shpaner, Leonid and
Funnell, Arthur and
Rahrooh, Al and
Beam, Colin and
Petousis, Panayiotis},
title = {EquiBoots},
month = mar,
year = 2025,
publisher = {Zenodo},
version = {0.0.1a14},
doi = {10.5281/zenodo.15086941},
url = {https://doi.org/10.5281/zenodo.15086941}
}
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 Alex Bui, PhD, for his invaluable guidance and support. Many thanks to David Elashoff, PhD, and Sitaram Vangala, M.S., for their statistical consultation. Thanks to Jayleen Mendoza for her contribution to model healing.
5 maintainers
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