Explain Model Predictions with SHAP

Why explainability

For trust, debugging, and compliance, you often need to know why a model made a prediction. SHAP assigns each feature a contribution to a given prediction.

import shap
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)

What you learn

Which features drive predictions overall, and which pushed a specific case toward its outcome. This catches data leakage, surfaces bias, and helps you explain decisions to stakeholders and users.