Welcome to the EquiBoots Documentation!

Note

This documentation is for equiboots version 0.0.0a13.

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. Classification: Google Colab Example Notebook

4. Regression: Google Colab Example Notebook

Note

The examples in this documentation use the adult income dataset with xgboost version 2.1.4. The Colab example notebook, however, runs the up-to-date version of xgboost (version 3.2.0 as of June 26, 2026), so you may see minor differences in the results between the documentation and the Colab notebook due to changes in the underlying library.

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.