https://github.com/slundberg/shap
A game theoretic approach to explain the output of any machine learning model.
https://github.com/slundberg/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/slundberg/shap
- Owner: shap
- License: mit
- Created: 2016-11-22T19:17:08.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2025-06-25T20:02:53.000Z (11 days ago)
- Last Synced: 2025-06-25T21:19:30.119Z (11 days ago)
- Topics: deep-learning, explainability, gradient-boosting, interpretability, machine-learning, shap, shapley
- Language: Jupyter Notebook
- Homepage: https://shap.readthedocs.io
- Size: 279 MB
- Stars: 24,039
- Watchers: 245
- Forks: 3,385
- Open Issues: 701
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-python-machine-learning - SHAP - SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. (Uncategorized / Uncategorized)
- awesome-production-machine-learning - SHAP - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model. (Explaining Black Box Models and Datasets)
- awesome-ai-ethics - SHAP (SHapley Additive exPlanations) - A unified framework for interpreting machine learning model predictions. (Explainable AI (XAI))
- awesome-ai-testing-tools - https://github.com/slundberg/shap
- awesome-list - shap - A game theoretic approach to explain the output of any machine learning model. (Linear Algebra / Statistics Toolkit / Statistical Toolkit)
- awesome-mlops - SHAP - A game theoretic approach to explain the output of any machine learning model. (Model Interpretability)
- awesome-open-data-centric-ai - SHAP