SHAP

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"

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? (TensorFlow, PyTorch, Keras, CNNs, RNNs, Transformers)

   • Use shap.DeepExplainer or shap.GradientExplainer

3. Linear model? (Linear/Logistic Regression, GLMs)

   • Use shap.LinearExplainer (extremely fast)

4. Any other model? (SVMs, custom functions, black-box models)

   • Use shap.KernelExplainer (model-agnostic but slower)

5. Unsure?

   • Use shap.Explainer (automatically selects best algorithm)

See references/explainers.md for detailed information on all explainer types.

Create explainer

explainer = shap.TreeExplainer(model)