shap/shap

Health v2 · maintenance only

Adoption not part of the score

25704 stars · 3747 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

SHAP (SHapley Additive exPlanations) is a Python library that explains the output of any machine learning model using Shapley values from game theory. It provides fast exact algorithms for tree ensembles and model-agnostic explainers with rich visualization tools.

Use cases

• explain predictions of an xgboost model
• compute feature importance with shapley values
• interpret a neural network's output
• visualize which features drove a model's prediction
• explain text classification model decisions
• audit model fairness and bias
• debug why a model made a specific prediction

When to choose

• you need theoretically grounded, consistent feature attributions for any model
• you use tree ensembles like XGBoost, LightGBM, or CatBoost and want fast exact SHAP values
• you need plots and explainers for tabular, text, and image models

When to avoid

• you only need simple built-in feature importances and not per-prediction explanations
• your dataset is so large that Shapley value computation is prohibitively expensive
• you need a non-Python environment

Facets

library · maturity active

machine-learning data-visualization nlp computer-vision machine-learning data-science deep-learning artificial-intelligence python cross-platform explainability shapley-values interpretability feature-attribution xgboost model-agnostic gpu

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markdown · JSON · MCP: product_card(name="shap/shap")