ModelRefs / SHAP (SHapley Additive exPlanations) — AI Glossary

SHAP (SHapley Additive exPlanations) — AI Glossary

An explainability framework using game-theoretic Shapley values to assign each feature a fair attribution for a model's prediction.

Overview

SHAP (Lundberg & Lee 2017) computes the average marginal contribution of each feature across all possible feature subsets. TreeSHAP is exact and fast for tree models; KernelSHAP approximates for any model. For LLMs: SHAP values on input tokens explain which tokens drove a classification. The current standard for feature attribution.

Reference details

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Last reviewed	2026-06-24

Example: The attributions have to add up, and that is the point

The defining property is additivity: the feature contributions sum exactly to the prediction minus the base value. If the average prediction is 0.30 and this instance scores 0.82, the contributions must total 0.82 − 0.30 = 0.52 — say +0.30, +0.15, +0.09 and −0.02. That is a real constraint, and it is what makes the attributions comparable across features and across instances. Local surrogate methods offer no such guarantee, which is why their explanations can shift between runs. The cost is computation: exact values consider all feature subsets, so anything beyond tree models is an approximation with its own sampling error.

Commonly confused with

SHAP and LIME both attribute a prediction to features, on different foundations. SHAP computes Shapley values with consistency and additivity guarantees at higher cost; LIME fits a local surrogate — faster, and sensitive to how its neighbourhood was sampled. Where they disagree, the disagreement is information: it usually means the local behaviour is not well approximated by either simple account.

When to use it

Reach for it when:

Tabular models, especially trees, where the exact algorithm is fast
Regulated settings needing a defensible, reproducible attribution method
Global feature importance built by aggregating local attributions

Reach for something else when:

High-dimensional text or images without accepting a sampling approximation
As a causal claim — attribution describes the model's behaviour, not the world
Where correlated features make individual credit assignment inherently ambiguous

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Frequently asked questions

What is SHAP (SHapley Additive exPlanations)?