shap

Why we included this project

SHAP answers a question every ML team eventually hits: why did the model make this prediction? It applies Shapley values from game theory to split a single prediction into per-feature contributions, so the explanation is local to that one output and the attributions add up to the prediction itself. The library has optimized implementations for tree ensembles, deep networks, and linear models, which makes it useful both for debugging a gradient-boosted model in production and for auditing a neural network before it ships. Data scientists and ML engineers reach for it when they need to explain a model to stakeholders, check for data leakage or bias, or document behavior in regulated settings. It also unifies several earlier attribution methods into one framework, which is a practical reason to start here rather than wiring together multiple tools.