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Awesome GitHub RepositoriesModel Interpretability Tools
Tools and techniques for explaining and interpreting the predictions of complex machine learning models.
Distinguishing note: Focuses on local surrogate models for interpretability, distinct from general model training or deployment frameworks.
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Awesome Model Interpretability Tools 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
Approximates complex model behavior using local linear regression to provide insights into feature importance and decision logic.
Jupyter Notebookdeep-learningexplainabilitygradient-boosting
