Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
# 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"
Sign in to view the full prompt.
Sign In