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Awesome GitHub RepositoriesFeature Importance Attribution
Methods and algorithms for quantifying the relative contribution of individual input variables to the predictions of machine learning models.
Distinguishing note: This category focuses specifically on interpretability and attribution techniques, distinct from general model training or data preprocessing.
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Awesome Feature Importance Attribution 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
Provides tools for calculating and visualizing the impact of specific input features on model predictions to improve interpretability.
Jupyter Notebookdeep-learningexplainabilitygradient-boosting
