SHAP Model Interpretability

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This skill provides comprehensive guidance for implementing SHAP (SHapley Additive exPlanations) to demystify machine learning outputs. It enables developers and data scientists to compute SHAP values for tree-based, deep learning, linear, and black-box models, generating intuitive visualizations like beeswarm and waterfall plots. Use this skill to debug model behavior, analyze feature interactions, ensure fairness and bias mitigation, and integrate explainable AI (XAI) into production-ready pipelines.

Características Principales

• Workflows for model debugging and identifying data leakage
• Fairness and bias analysis across protected attributes
• Global and local feature importance visualizations (Beeswarm, Waterfall, Bar)
• 8 GitHub stars
• Support for Tree, Deep, Linear, and Kernel explainers
• Performance optimization for large-scale production data

Casos de Uso

• Explaining individual model predictions to stakeholders
• Auditing models for fairness and demographic bias
• Debugging unexpected model behavior and misclassifications