shap and streamlit-shap
The streamlit-shap wrapper is a complementary frontend integration tool that enables visualization of SHAP's game-theoretic explanations within Streamlit applications, making them ecosystem complements rather than competitors.
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 streamlit-shap
snehankekre/streamlit-shap
streamlit-shap provides a wrapper to display SHAP plots in Streamlit.
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