fastshap: Fast Approximate Shapley Values for Model Explainability

fastshap computes approximate Shapley values for any supervised learning model, offering a faster alternative to other implementations. The method explains predictions from black box models using game-theoretic principles established by Strumbelj and Kononenko (2014). The tool was authored by Brandon Greenwell and is hosted on the paperswithcode platform.

Use Cases

• Explain individual model predictions based on Shapley values.
• Perform feature importance analysis for any supervised model using game theory.
• Audit and debug black-box model behavior by attributing output to input features.
• Generate model-agnostic explanations for stakeholder communication.

Strengths

• Provides a fast implementation for Shapley value approximation relative to other methods.
• Works with any supervised learning model, offering broad compatibility.
• Based on a peer-reviewed game-theoretic framework for model explainability.

Limitations

• Row count, column definitions, and sample data are unavailable, limiting suitability assessment.
• License, file formats, and last update date are unknown, which may affect usage.
• The description lacks specifics on computational accuracy or approximation bounds.

Provenance

Source

Brandon Greenwell, via paperswithcode

Collection Method

Likely a software implementation for computing Shapley values.

Time Range

Temporal coverage is unknown.

Freshness

Last update date is unknown; freshness unverified.

Geography

Spatial coverage is unknown.