Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/shap/shap
A game theoretic approach to explain the output of any machine learning model.
https://github.com/shap/shap
deep-learning explainability gradient-boosting interpretability machine-learning shap shapley
Last synced: 5 days ago
JSON representation
A game theoretic approach to explain the output of any machine learning model.
- Host: GitHub
- URL: https://github.com/shap/shap
- Owner: shap
- License: mit
- Created: 2016-11-22T19:17:08.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-12-30T23:22:36.000Z (12 days ago)
- Last Synced: 2025-01-01T10:57:02.258Z (10 days ago)
- Topics: deep-learning, explainability, gradient-boosting, interpretability, machine-learning, shap, shapley
- Language: Jupyter Notebook
- Homepage: https://shap.readthedocs.io
- Size: 300 MB
- Stars: 23,160
- Watchers: 245
- Forks: 3,304
- Open Issues: 748
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- my-awesome-starred - shap/shap - A game theoretic approach to explain the output of any machine learning model. (Jupyter Notebook)
- awesome-learning - SHAP - A game theoretic approach to explain the output of any machine learning model. (Learning)
- awesome-production-machine-learning - SHAP - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model. (Explainability and Fairness)
- my-awesome - shap/shap - learning,explainability,gradient-boosting,interpretability,machine-learning,shap,shapley pushed_at:2025-01 star:23.2k fork:3.3k A game theoretic approach to explain the output of any machine learning model. (Jupyter Notebook)