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SHAP

Implementation of the most popular algorithm for black box explainability, from the original paper authors. This is actively maintained and also provides specialized support for different model types, including tree ensembles, transformers, and deep learning models.

Logo
Website	N/A
Repository	https://github.com/slundberg/shap
Byline	A game theoretic approach to explain the output of any machine learning model.
License	MIT
Project age	6 years
Backers	Lee Lab of AI for Biological and Medical Sciences (Creator)
Size score (1 to 10, higher is better)	4.75
Trend score (1 to 10, higher is better)	5.75

Education Resources

URL	Resource Type	Description
https://shap.readthedocs.io/en/latest/index.html	Documentation	Official project documentation.
https://papers.nips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html	Paper	Original paper at NeuroIPS 2017

Git Commit Statistics

Statistics computed using Git data through November 30, 2022.

Statistic	Lifetime	Last 12 Months
Commits	2,277	250
Lines committed	6,343,114	359,294
Unique committers	215	43
Core committers	12	3

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