alterlab-shap

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"

Does NOT Trigger

Scenario	Use Instead
Profiling a raw dataset (distributions, missingness, correlations) before any model exists	alterlab-eda
Building, cross-validating, or tuning the model itself	alterlab-scikit-learn
Estimating the causal effect of an intervention (SHAP describes model associations, not causes)	alterlab-causal-inference

Hand-off rule. When the request matches a row above, open your answer by naming that skill by its exact id (for example alterlab-eda) and saying in one line why it fits better, then stop applying this skill's procedure. Name it even when you also need to ask for missing input. If only part of the request is out of scope, name the skill for that part and do only the in-scope part. A brief general pointer is fine; the named skill does the work.