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, L
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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
[Description truncada. Veja o README completo no GitHub.]
