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SHAP

Scott Lundberg / SHAP Community — The industry-standard game-theoretic machine learning explainability and feature attribution framework.

privacy-security-optimizationv0.46.0MITqualified
Official DocumentationSource Repository

Model Training

Not Supported

This library is a dedicated runtime engine for inference serving and does not train models.

Model Inference

Supported
Inference Accelerators:
CPUCUDA
Deployment Targets:server

What It Does

  • +Game-theoretic Shapley value computation satisfying efficiency, symmetry, dummy, and additivity axioms
  • +TreeSHAP: exact polynomial-time explanation for tree ensembles (XGBoost, LightGBM, CatBoost, scikit-learn)
  • +Model-agnostic KernelSHAP for black-box models
  • +Rich interactive JavaScript visualizations (summary beeswarm, waterfall, force, and dependence plots)

What It Does Not Do

  • -Train predictive models directly
  • -Scale KernelSHAP to large datasets without extreme subsampling (exponential complexity on black-box models)
  • -Run on edge microcontrollers

>Suitable Work Types

  • Explaining individual credit and loan rejection decisions for regulatory compliance (FCRA/ECOA)
  • Healthcare clinical diagnostic explanations justifying AI risk scores to doctors
  • Debugging feature leakage and spurious correlations in machine learning models

>Unsuitable Work Types

  • Real-time millisecond ad bidding auctions where explanation compute exceeds latency budgets
  • High-frequency algorithmic trading execution
Data Residency Implications

Runs strictly locally in server memory. Zero data leaves the local process.

Security Considerations

Permissive MIT license. Safe for enterprise auditing and compliance pipelines.

Operational Profile & Known Limitations

Maturity:mature
Learning Curve:moderate
Ops Complexity:low
Cost Tier:free-oss
> Known Limitations:
  • KernelSHAP on deep neural networks or complex black-box pipelines requires thousands of function evaluations, making it extremely slow without TreeSHAP optimizations.

Associated Incident Patterns (Incidentpedia)

Enforce safeguards and monitoring to guard against these documented real-world failure modes:

thread-pool-exhaustion

> Primary Evidence & Benchmark Citations

SHAP Documentationofficial-docs • >=0.45.0, <=0.46.x
2026-09-25HIGH