Metadata-Version: 2.1
Name: fairkit-learn
Version: 1.2
Summary: A machine learning fairness toolkit
Home-page: https://github.com/brittjay0104/fairkit-learn
Author: Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, Sam Witty, Stephen Giguere, and Yuriy Brun
Author-email: bijohnsonphd@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown

# fairkit-learn fairness toolkit

Fairkit-learn is an open-source, publicly available Python toolkit designed
to help data scientists evaluate and explore machine learning models with
respect to quality and fairness metrics simultaneously.

Fairkit-learn builds on top of [scikit-learn](https://scikit-learn.org/stable/), the state-of-the-art tool suite
for data mining and data analysis, and [AI Fairness 360](https://aif360.mybluemix.net/), the state-of-the-art
Python toolkit for examining, reporting, and mitigating machine learning bias
in individual models. 

Fairkit-learn supports all metrics and learning algorithms available in scikit-learn and AI Fairness
360, and all of the bias mitigating pre- and post-processing algorithms available in AI Fairness 360, and provides extension points to add more metrics and algorithms.

# Installation

To install fairkit-learn, run the following command:

``` pip install fairkit-learn==1.0```

# Using fairkit-learn

Sample code for how to use fairkit-learn can be found in the examples
folder in the repo.


