shap and shap-analysis-guide
The first is a core library implementing SHAP explainability methods, while the second is a non-technical interpretive guide for understanding SHAP outputs—making them complements where the guide helps users apply the library's results.
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 shap-analysis-guide
AidanCooper/shap-analysis-guide
How to Interpret SHAP Analyses: A Non-Technical Guide
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