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: 2 months ago
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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 7 years ago)
• Default Branch: master
• Last Pushed: 2024-04-05T12:28:39.000Z (2 months ago)
• Last Synced: 2024-04-06T11:24:55.794Z (2 months ago)
• Topics: deep-learning, explainability, gradient-boosting, interpretability, machine-learning, shap, shapley
• Language: Jupyter Notebook
• Homepage: https://shap.readthedocs.io
• Size: 265 MB
• Stars: 21,478
• Watchers: 241
• Forks: 3,146
• Open Issues: 1,247
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Lists

• 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)
• ai-links - GitHub - slundberg/shap: A game theoretic approach to explain the output of any machine learning model.
• Awesome-AIML-Data-Ops - SHAP - SHapley Additive exPlanations is a unified approach to explain the output of any machine learning model. (Explaining Black Box Models and Datasets)
• AwesomeResponsibleAI - Shap
• awesome-stars - shap - A unified approach to explain the output of any machine learning model. (Jupyter Notebook)
• awesome-stars - slundberg/shap - A game theoretic approach to explain the output of any machine learning model. (Jupyter Notebook)
• 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-Interpretable-ML - Shap
• awesome-machine-learning-interpretability - shap
• awesome-meteo - shap
• awesome-mlops - SHAP - A game theoretic approach to explain the output of any machine learning model. (Model Interpretability)
• Awesome-explainable-AI - https://github.com/slundberg/shap
• awesome-decision-tree-papers - [Code
• awesome-python-machine-learning-resources - GitHub - 69% open · ⏱️ 16.06.2022): (模型的可解释性)
• awesome_quantmetry - SHAP
• awesome-python-data-science - shap - A unified approach to explain the output of any machine learning model. <img height="20" src="img/sklearn_big.png" alt="sklearn"> (Model Explanation / Others)
• awesome-xai - slundberg/shap - A Python module for using Shapley Additive Explanations. (Repositories / Critiques)
• awesome-list - shap - A game theoretic approach to explain the output of any machine learning model. (Linear Algebra / Statistics Toolkit / Statistical Toolkit)
• awesome-deep-reinforcement-learning - slundberg/shap
• my-awesome-stars - slundberg/shap - A game theoretic approach to explain the output of any machine learning model. (Jupyter Notebook)
• awesome-python-data-science - shap - A unified approach to explain the output of any machine learning model. (Exploration)
• awesome-list - shap - A unified approach to explain the output of any machine learning model (Reporting)
• awesome-decision-tree-papers - [Code
• awesome-stars - slundberg/shap - A game theoretic approach to explain the output of any machine learning model. (Jupyter Notebook)
• awesome-biomedical-machine-learning - shap: A game theoretic approach to explain the output of any machine learning model
• awesome-open-data-centric-ai - SHAP
• awesome-xai - slundberg/shap
• awesome-machine-learning-engineer - SHAP: SHapley Additive exPlanations - How to explain a model's output with Shapley values (30 min) (Machine Learning / Explainability)
• awesome-python-data-science - shap - A unified approach to explain the output of any machine learning model. <img height="20" src="img/sklearn_big.png" alt="sklearn"> (Model Explanation / NLP)
• 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-stars - shap - A game theoretic approach to explain the output of any machine learning model. (Jupyter Notebook)
• 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-stars - shap
• awesome-efficient-xai - SHAP: A a Game Theoretic Package to Explain Machine Learning Models
• awesome-explainable-reinforcement-learning - SHAP
• StarryDivineSky - slundberg/shap - learn和pyspark tree模型支持快速C++实现。 (其他_机器学习与深度学习)