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Also known as: SHapley Additive exPlanations
An explainability method that uses Shapley values to attribute each feature’s contribution to individual model predictions consistently.
DESCRIPTION
SHAP unifies feature attribution approaches by applying Shapley values from cooperative game theory to model explanations. It provides local and global explanations, ensuring consistency and additivity under defined assumptions. Implementations support tree models, deep learning, and kernel-based methods. SHAP aids debugging, fairness assessment, and regulatory reporting. Careful background data selection and computational constraints influence results.
ALTERNATIVE DEFINITIONS
arXiv
SHAP assigns each feature an importance value for a particular prediction, unifying several existing methods through Shapley values.
Highlights unification and per-prediction feature importance.
SHAP Documentation
SHAP (SHapley Additive exPlanations) explains the output of any machine learning model by computing Shapley values of its features.
Positions SHAP as model-agnostic using Shapley values.
AWS
SHAP values provide a theoretically sound way to attribute the contribution of each feature to a model’s prediction.
Emphasizes theoretical grounding and practical use in SageMaker Clarify.
RELATED TERMS
Explainable AI Shapley Value Feature Attribution Model Interpretation Partial Dependence Individual Conditional Expectation Fairness
COMPETING TERMS
LIME Integrated Gradients Permutation Importance
KNOWLEDGE AREAS
AI Data Science AI Governance Testing & Validation Data Quality
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Data Governance Manager Chief Data Officer
EXTERNAL SOURCES
- Wikipedia - Shapley value
- arXiv - Original SHAP paper
- SHAP Documentation - API and usage guide
- Google Cloud - Explainable AI concepts including SHAP
TOP QUESTIONS
Why is SHAP popular for model explainability?
It provides consistent, theoretically grounded feature attributions and works across many model types, supporting local and global insights.
Category: Relevancy
How do SHAP values differ from feature importance?
SHAP values explain individual predictions, while feature importance usually summarizes global impact across the dataset.
Category: Misunderstanding
What are common mistakes when using SHAP?
Poor background distribution selection, ignoring correlated features, and interpreting local values as global truths are common issues.
Category: Mistakes
How can SHAP support AI governance?
It helps document decision logic, fairness assessments, and regulatory compliance by explaining model predictions transparently.
Category: Governance
Is SHAP computationally expensive?
Exact computation can be costly; approximations and model-specific implementations mitigate runtime.
Category: Risk
What is an alternative to SHAP?
LIME and Integrated Gradients are alternatives. Choice depends on model type, interpretability needs, and computational budget.
Category: Alternatives
How to start with SHAP in practice?
Use the SHAP library, begin with tree models for efficient computation, and validate explanations with domain experts.
Category: Getting Started
Does SHAP handle feature interactions?
Yes, interaction values can quantify pairwise contributions, revealing combined effects.
Category: Capabilities
What is a hot topic around SHAP?
Scaling SHAP to deep models, handling correlations, and integrating explanations into continuous monitoring are active areas.
Category: Topic