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

../_images/slundberg_shap-small.png

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

5 years 4 months

Backers

Lee Lab of AI for Biological and Medical Sciences (Creator)

Size score (1 to 10, higher is better)

4.25

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 March 31, 2022.

Statistic

Lifetime

Last 12 Months

Commits

2,168

241

Lines committed

6,330,395

723,619

Unique committers

197

39

Core committers

13

3

../_images/slundberg_shap-monthly-commits.png

Similar Projects

Project

Size Score

Trend Score

Byline

Seldon Alibi

3.25

7.5

Algorithms for monitoring and explaining machine learning models.

Shapash

2.75

8.25

Shapash makes Machine Learning models transparent and understandable by everyone