shap by shap

A game theoretic approach to explain the output of any machine learning model.

created at Nov. 22, 2016, 7:17 p.m.

241 +1

21,966 +40

3,198 +4

xai by EthicalML

XAI - An eXplainability toolbox for machine learning

created at Jan. 11, 2019, 8 p.m.

44 +0

1,074 +3

161 +1

shapash by MAIF

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

created at April 29, 2020, 7:34 a.m.

36 +0

2,671 +1

326 +1

what-if-tool by PAIR-code

Source code/webpage/demos for the What-If Tool

created at Sept. 7, 2018, 8:26 p.m.

29 +0

891 +1

166 +2