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.

38 +0

2,738 +1

335 +0

xai by EthicalML

XAI - An eXplainability toolbox for machine learning

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

43 +0

1,125 +6

174 +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

918 +3

170 +0

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.

245 +1

22,880 +52

3,290 +0