SHAP

As explained in Machine Learning System Design

A feature-attribution technique based on Shapley values that assigns contributions of input features to a model prediction.

SHAP (SHapley Additive exPlanations) is a model-agnostic feature-attribution technique. It estimates how much each feature contributes to a specific prediction by averaging its contribution across all possible feature combinations. This makes SHAP useful for explaining individual decisions rather than only describing overall model behavior. section 11.2.1

Use SHAP to:

• Explain why a particular prediction received its outcome by identifying the features that contributed to it. section 11.2.1
• Analyze deep-learning inputs at different levels, including pixels, tokens, characters, frequencies, and more abstract patterns. section 11.2.3
• Compare SHAP with permutation importance, LIME, and built-in measures such as linear coefficients or tree split counts. section 11.2.1 section 11.5.1

Retail feature selection

For a Supermegaretail demand model, SHAP can estimate how features such as promotion calendars, discounts, store traffic, weather, and prior sales contribute to the model’s outcome. The team uses those contributions alongside LIME, shuffle importance, and built-in measures; features contributing little may be dropped. section 11.5.1

Figure 11.3 A taxonomy of model explanation methods

Use the taxonomy to locate SHAP among model-agnostic explanation methods and distinguish local explanations from whole-model interpretation. section 11.2.1

How Machine Learning System Design covers it

How deep it goes

Defines contribution estimation by averaging across feature combinations, but does not derive the underlying Shapley mathematics.

Examples

Uses retail demand features, deep-learning pixels and tokens, and comparisons with LIME and shuffle importance.

What it assumes

Assumes a predictive model, input features, and basic feature-importance concepts; treats the model as a black box.

What you can do after

Evaluate/understand which input features contribute to predictions and inform feature-removal decisions.

How it connects

Connects model-agnostic explanation with permutation feature importance, LIME, anchor explanations, saliency maps, and built-in importance measures.

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Also explained in

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  Ensemble Methods for Machine LearningSHAP is a model-agnostic explainability technique that attributes an individual model prediction to its input features using Shapley values.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA SHAP value is a feature-attribution value based on Shapley values that quantifies a feature's contribution to an individual prediction.
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  Outlier Detection in PythonSHAP is an open-source Python library for explaining machine-learning model outputs using feature attribution methods based on Shapley values.
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  Financial AI in PracticeA feature-attribution method that explains a model prediction by assigning each input feature a contribution based on Shapley values.

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Related concepts

• interpretabilityused by · Interpretability is the degree to which people can understand a model’s behavior, outputs, predictions, or the factors that produce them.
• Shapley valueuses · A Shapley value is a cooperative-game-theoretic attribution that assigns a participant its average marginal contribution across all possible coalitions.
• model-agnostic methodkind of · A model-agnostic method analyzes predictions or estimates explanations without relying on a model’s internal structure, implementation, or architecture.
• local explanationused by · A local explanation describes how or why a model produced a particular prediction or decision for an individual input.
• feature attributionkind of · Feature attribution is a technique for estimating or assigning the contribution of individual input features to a model’s output.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.