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Awesome GitHub RepositoriesModel Agnostic Diagnostics
Frameworks for interpreting and debugging machine learning models without requiring access to internal architecture.
Distinguishing note: Focuses on architecture-independent diagnostic utilities, distinct from model-specific debugging or training frameworks.
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Awesome Model Agnostic Diagnostics 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 diagnostic functions to interpret the behavior of any machine learning model regardless of its internal architecture.
Jupyter Notebookdeep-learningexplainabilitygradient-boosting
