SHAP values are a model-explanation method that shows how each input feature moves a machine learning prediction from a baseline. They help people understand why a model produced one result and which features mattered across many results. Based on Shapley values from cooperative game theory, they distribute a prediction's change among features while accounting for combinations of inputs.
SHAP values
What are SHAP values?
SHAP values explain how each input feature moves a machine learning prediction from a baseline. SHAP means SHapley Additive exPlanations. Positive values raise a prediction, negative values lower it, and contributions add up to the model output scale.
How SHAP values explain a prediction
SHAP assigns credit for one prediction across its input features.
- Select a trained supervised learning model and record to explain.
- Choose representative background data for the baseline prediction.
- Estimate each feature's average contribution across feature combinations.
- Add contributions to the baseline to reconstruct the prediction.
- Confirm the explanation matches the output, which for binary classifiers may be log odds rather than probability.
How to interpret SHAP values and common plots
Local plots explain one result. Global plots summarize patterns across many results.
| View | Best for | Important limit |
|---|---|---|
| Waterfall plot | Showing why one prediction changed from its baseline | Does not describe the full dataset |
| Bar or beeswarm plot | Ranking overall influence and effect direction | Influence is not causation |
| Dependence or interaction plot | Exploring effects across values or features | Correlated inputs can distort patterns |
SHAP values vs. Shapley values and LIME
Shapley values are the game-theory concept. SHAP applies that concept to model explanations.
| Approach | Method | Typical tradeoff |
|---|---|---|
| SHAP | Uses additive Shapley-based attributions | Often more consistent, but can be computationally expensive |
| LIME | Fits a simple local approximation | Usually faster, but sampling can change results |
Benefits of SHAP values in machine learning
Used carefully, SHAP helps teams inspect supervised models rather than accept opaque outputs.
- Debug unexpected predictions and feature behavior.
- Spot possible data leakage when an implausible field dominates.
- Communicate model reasoning clearly.
- Monitor changes in feature influence after deployment.
Practical limitations of SHAP values
An explanation is only as trustworthy as its model, data, and assumptions.
- Correlated or duplicated features can split credit unexpectedly.
- Different background datasets can produce different explanations.
- Approximate methods trade runtime for precision.
- SHAP describes model behavior, not causation or proof of fairness.
A practical workflow for trustworthy SHAP analysis
Use SHAP as part of model validation, not as a substitute for it.
- Define the decision or failure mode to investigate.
- Validate predictive performance before interpreting explanations.
- Use representative background data and inspect local and global views.
- Check correlated inputs and compare relevant data slices.
- Document the model version, output scale, settings, and review results after updates.
Using SHAP values in AI applications built with Emergent
When an AI-enabled product presents recommendations, explainable outputs can simplify testing and review. Teams using an AI app builder can retain prediction inputs and explanations, then show trends through an AI dashboard builder. Emergent also supports an AI web app where users can inspect understandable model results alongside product workflows.
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