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shap

Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.

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# SHAP

Use SHAP to describe how a fitted predictive model maps inputs to outputs. Work from the modern `shap.Explanation` API, make the explained output and background distribution explicit, and validate every explanation before interpreting it.

This skill is aligned with **SHAP 0.52.0** (released 2026-05-28). That release requires Python 3.12 or newer.

## Operating Rules

1. Explain a fixed, evaluated model; do not use SHAP as a substitute for predictive validation.
2. Use held-out or clearly labeled analysis rows for explanations. Choose background rows only from an appropriate training or reference population.
3. State the explained output: regression value, raw margin, probability, log loss, logit, or another model method.
4. Keep explanations as `shap.Explanation` objects. Call `explainer(X)`; use `.shap_values(X)` only when maintaining legacy code.
5. For multi-output models, select one output before using tabular plots: `explanation[..., output_index]`.
6. Check `base_values + values.sum(...)` against the exact model output being explained.