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shap

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Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Feature importance, dependence plots, interaction effects, and fairness analysis for any black-box model.

scientific-skills/Data Analysis/shap/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)


# SHAP (SHapley Additive exPlanations)

## Overview

SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for:

- Computing SHAP values for any model type
- Creating visualizations to understand feature importance
- Debugging and validating model behavior
- Analyzing fairness and bias
- Implementing explainable AI in production
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