Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models. A facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive". Bias detection and mitigation for datasets and models. The label is a column or feature which is the target for training a machine learning model. The label can have value(s) that designates that sample as having a "positive" outcome. A bias measure is a function that returns a bias metric. A bias metric is a numerical value indicating the level of bias detected as determined by a particular bias measure. A collection of bias metrics for a given dataset or a combination of a dataset and model.

Features

  • Install the package from PIP
  • Bias measure
  • Bias report
  • Bias metric
  • You can see examples on running the Bias metrics on the notebooks in the examples folder
  • Mitigation for datasets and models

Project Samples

Project Activity

See All Activity >

Categories

UML, Machine Learning

License

Apache License V2.0

Follow smclarify

smclarify Web Site

Other Useful Business Software
Network Performance Monitoring | Statseeker Icon
Network Performance Monitoring | Statseeker

Statseeker is a powerful network performance monitoring solution for businesses

Using just a single server or virtual machine, Statseeker can be up and running within minutes, and discovering your entire network in less than an hour, without any significant effect on your bandwidth availability.
Learn More
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of smclarify!

Additional Project Details

Programming Language

Python

Related Categories

Python UML Tool, Python Machine Learning Software

Registered

2022-06-29