shaplisted

# 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 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. ## When to Use This Skill **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" ## Quick Start Guide ### Step 1: Select the Right Explainer **Decision Tree**: 1. **Tree-based model?** (XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting) - Use `shap.TreeExplainer` (fast, exact) 2. **Deep neural network?** (