Metadata-Version: 2.4
Name: fair-mango
Version: 0.2.2
Summary: Explore your AI model's fairness
Maintainer-email: Marc Bresson <marc.bresson@datategy.net>, Nacer Kroudir <nacer.kroudir@datategy.net>
Project-URL: Repository, https://github.com/datategy/Fair-Mango.git
Project-URL: Issues, https://github.com/datategy/Fair-Mango/issues
Project-URL: Changelog, https://github.com/datategy/Fair-Mango/blob/main/CHANGELOG.md
Keywords: fairness,AI,ML,machine learning,XAI,explanaible AI
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn>=1.1.3
Provides-Extra: dev
Requires-Dist: twine; extra == "dev"
Provides-Extra: old-pandas
Requires-Dist: pandas<2.2.0; extra == "old-pandas"
Requires-Dist: numpy<2.0; extra == "old-pandas"
Provides-Extra: new-pandas
Requires-Dist: pandas>=2.2.0; extra == "new-pandas"
Requires-Dist: numpy>=2.0; extra == "new-pandas"
Dynamic: license-file

# Fair Mango

Fair Mango is a Python package that helps developers evaluate their model's performance and fairness across different groups.

## Supported Fairness Metrics

- Demographic Parity / Statistical Parity
- Disparate Impact
- Equalised Odds
- Equality of Opportunity
- Predictive Rate Parity
- Group Benefit Disparity
- False Positive Rate
- False Negative Rate
- True Positive Rate / Sensitivity
- True Negative Rate / specificity
