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Awesome GitHub RepositoriesGame Theoretic Explainability
Tools for attributing model predictions to individual input features using cooperative game theory.
Distinguishing note: Focuses specifically on game-theoretic attribution methods like Shapley values, distinct from general model monitoring or visualization.
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Awesome Game Theoretic Explainability GitHub Repositories
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- shap/shap
shap/shap
25,049View on GitHubSHAP is an explainable AI toolkit that provides a game theoretic framework for interpreting machine learning model predictions. It functions as a feature attribution engine, decomposing model outputs into the sum of individual feature effects to clarify how specific input variables influence a final decision. By assigning importance values to these inputs, the library enables users to understand the logic behind complex predictive models. The project distinguishes itself through its versatility and specialized calculation methods. It operates as a model-agnostic diagnostic library, capable of
Applies cooperative game theory to quantify the contribution of individual features to machine learning model outputs.
Jupyter Notebookdeep-learningexplainabilitygradient-boosting
