Gallery

The best way to get started is to go through the notebook tutorials, which showcase the benefits and usage of each mitigation and metrics available in the Responsible AI Mitigations Library. Here you can find all notebooks grouped according to their goals.

Tutorials

DataProcessing

Encoding Examples

Data Scaling

BasicImputer Example

IterativeDataImputer Example

KNNDataImputer Example

SeqFeatSelection Example

CatBoostSelection Example

Checking Correlations Between Variables - A Comprehensive Guide

Rebalance Class (imblearn)

Synthesizer Class (SDV)

DataBalanceAnalysis

Data Balance Analysis using the Adult Census Income dataset

Python Data Balance Analysis

Cohort

Defining a Cohort

Managing cohorts

Cohort Manager - Scenarios and Examples

Decoupled Classifiers

Using scikit-learn’s Pipeline

Using Pipelines

Case Studies

Using the DataProcessing Module

Simple Example

Case Study 1

Case Study 2

Case Study 3

Case Study 1 - Multiple Runs

Case Study 2 - Multiple Runs

Case Study 3 - Multiple Runs

Using the DataBalanceAnalysis Module

End to End Data Balance and Error Mitigation

Using the Cohort Module

Cohort Case Study 1

Cohort Case Study 1 - Rebalancing

Cohort Case Study 1 - Using RAI ErrorAnalysis

Cohort Case Study 2

Cohort Case Study 3

Moving Cohorts from raimitigations to raiwidgets (and vice versa)

Decoupled Classifiers Case Study 1

Decoupled Classifiers Case Study 2

Decoupled Classifiers Case Study 3