SHAP Model Explainability

关于

This skill enables Claude to provide deep insights into machine learning models by implementing SHAP (SHapley Additive exPlanations). It guides users through selecting the right explainer for various model types—including tree-based, deep learning, and linear models—while generating essential visualizations like beeswarm, waterfall, and scatter plots. It is particularly useful for debugging model behavior, analyzing feature interactions, ensuring fairness, and implementing explainable AI (XAI) in production environments to make complex models transparent and trustworthy.

主要功能

• Automated selection of optimal SHAP explainers for Tree, Deep, Linear, and Black-box models
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• Comprehensive guidance for calculating feature contributions and complex feature interactions
• Advanced workflows for model debugging, bias detection, and demographic fairness analysis
• Generation of global and local visualizations including beeswarm, waterfall, bar, and force plots
• Performance optimization strategies for large datasets and production-ready XAI deployments

使用场景

• Detecting and mitigating data leakage or bias in machine learning pipelines
• Identifying which specific features drive individual predictions for model transparency
• Comparing feature importance across different model architectures to select the most interpretable version