shap and shapiq
While SHAP is a mature, general-purpose library for computing Shapley values and SHAP interactions across diverse model types, ShapIQ is a specialized library focused specifically on higher-order Shapley interactions (n-way feature interactions), making them **complements** that users might combine when investigating both individual feature importance and complex multi-feature interaction effects.
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 shapiq
mmschlk/shapiq
Shapley Interactions and Shapley Values for Machine Learning
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