Welcome to the EquiBoots Documentation!

Note

This documentation is for equiboots version 0.0.0a8.

EquiBoots is a fairness-aware model evaluation toolkit for auditing performance disparities across demographic groups in machine learning models. It provides robust, bootstrapped evaluation metrics for binary, multi-class, and multi-label classification tasks, as well as regression models.

The library supports:

• Group-wise performance slicing

• Fairness diagnostics and disparity metrics

• Confidence intervals via bootstrapping

• Customizable and publication-ready visualizations

• Statistical tests to assess performance differences

EquiBoots is suited for applications in clinical, social, and policy contexts: domains where transparency, bias mitigation, and equitable outcomes are essential for responsible AI/ML deployment.

Project Links

1. PyPI Page

2. GitHub Repository

3. Google Colab Example Notebook

Prerequisites

Before you install equiboots, ensure your system meets the following requirements:

• Python (version 3.7.4 or higher)

Additionally, equiboots depends on the following packages, which will be automatically installed when you install equiboots:

• matplotlib: version 3.5.3 or higher, but capped at 3.10.1

• numpy: version 1.21.6 or higher, but capped at 2.2.4

• pandas: version 1.3.5 or higher, but capped at 2.2.3

• scikit-learn: version 1.0.2 or higher, but capped at 1.5.2

• scipy: version 1.8.0 or higher, but capped at 1.15.2

• seaborn: version 0.11.2 or higher, but capped at 0.13.2

• statsmodels: version 0.13 or higher, but capped at 0.14.4

• tqdm`: version 4.66.4 or higher, but capped below 4.67.1

Installation

You can install equiboots directly from PyPI:

pip install equiboots

Description

This guide provides detailed instructions and examples for using the functions provided in the equiboots library and how to use them effectively in your projects.