DataTerminology.comNEW
Game-theoretic feature attributions based on Shapley values, explaining individual predictions consistently across models.
DESCRIPTION
SHAP values attribute prediction changes to features using Shapley values from cooperative game theory. They provide local explanations with desirable properties like consistency and additivity. SHAP supports model-agnostic and model-specific estimators, enabling global summaries and instance-level insights. Visualizations include summary plots, dependence plots, and force plots. SHAP is widely adopted for debugging, governance, and stakeholder communication.
ALTERNATIVE DEFINITIONS
SHAP Documentation
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.
Emphasizes model-agnostic game-theoretic foundation.
arXiv
We present SHAP (SHapley Additive exPlanations), a unified approach to interpreting model predictions.
Introduces SHAP with unified feature attribution grounded in Shapley values.
RELATED TERMS
Shapley Value Explainability Explainable AI Feature Importance Game Theory
COMPETING TERMS
LIME Permutation Importance Integrated Gradients
KNOWLEDGE AREAS
AI Data Science AI Governance Data Visualization Mathematics
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer BI Specialist Chief Data Officer
EXTERNAL SOURCES
- Wikipedia - Shapley value
- SHAP - Official SHAP library
- Microsoft - Explain models using SHAP
- AWS - SageMaker Clarify SHAP
TOP QUESTIONS
What makes SHAP Values distinct from other methods?
They satisfy consistency and additivity, grounded in Shapley values, offering theoretically principled local explanations.
Category: Opinion
Are SHAP Values model-agnostic?
Yes, with kernel SHAP; there are also model-specific implementations for trees and deep models that improve efficiency.
Category: Requirements
What are common SHAP visualizations?
Summary plots, force plots, and dependence plots reveal global patterns and local contributions.
Category: Example
What are limitations of SHAP Values?
Computational cost can be high, and correlated features complicate attribution interpretation.
Category: Risks
How do SHAP Values support governance?
They provide transparent, instance-level explanations aiding audits, compliance, and stakeholder communication.
Category: Relevancy
Do SHAP Values guarantee causal explanations?
No. They quantify contributions under a predictive model, not causal effects, unless coupled with causal assumptions.
Category: Misunderstanding
How to compute SHAP Values efficiently?
Use model-specific algorithms (e.g., TreeSHAP), sampling approximations, and parallel computation.
Category: Best practices
Where should I start with SHAP?
Install the SHAP library and follow documentation examples, then integrate with model pipelines and visualization.
Category: Getting started
What are alternatives to SHAP Values?
LIME, permutation importance, and Integrated Gradients offer different trade-offs in speed, fidelity, and assumptions.
Category: Alternatives