shap

Topics in the source

• SHAP (SHapley Additive exPlanations)
• Overview
• When to Use This Skill
• Quick Start Guide
• Step 1: Select the Right Explainer
• Step 2: Compute SHAP Values
• Step 3: Visualize Results
• Core Workflows

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 SHAP works with all model types: tree-based models (XGBoost, LightGBM, CatBoost, Random Forest), deep learning models (TensorFlow, PyTorch, Keras), linear models, and black-box models. Trigger this skill when users ask about: "Explain which features are most important in my model" "Generate SHAP plots" (waterfall, beeswarm, bar, scatter, force, heatmap, etc.) "Why did my model make this prediction?" "Calculate SHAP values for my model" "Visualize feature importance using SHAP" "Debug my model's behavior" or "validate my model" "Check my model for bias" or "analyze fairness" "Compare feature importance across models" "Implement explainable AI" or "add explanations to my model" "Understand feature interactions" "Create model interpretation dashboard" Decision Tree: 1. Tree-based model? (XGBoost, LightGBM, CatBoost,

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