shap and Shapley_regressions
SHAP is a widely-adopted production library for model-agnostic feature attribution via Shapley values, while Shapley_regressions is a specialized academic tool for conducting statistical inference on Shapley-based explanations—making them complements that address different stages of the explainability workflow.
About shap
shap/shap
A game theoretic approach to explain the output of any machine learning model.
Based on the README, here's a technical summary: Implements fast exact algorithms for tree ensemble models (XGBoost, LightGBM, CatBoost, scikit-learn, PySpark) via optimized C++ backends, alongside approximation methods for deep learning (DeepExplainer leveraging DeepLIFT) and NLP transformers using coalitional game rules. Provides multiple visualization outputs—waterfall plots, force plots, dependence scatter plots, and beeswarm distributions—to show feature contributions at instance and global levels. Integrates directly with popular ML frameworks and Hugging Face transformers, supporting both tabular and text-based model explanations.
About Shapley_regressions
bank-of-england/Shapley_regressions
Statistical inference on machine learning or general non-parametric models
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