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shap

combined

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. Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.

When to use

• "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"

When not to use

• Causal inference from observational data → SHAP shows association, not causation
• Feature selection for model training → use proper feature selection methods (LASSO, mutual information)
• Model training or hyperparameter tuning → use scikit-learn/optuna
• Explaining non-ML systems or rule-based logic → explanations are trivial in those cases

Skill page

Conflict

The K-Dense-AI copy is a rigorous, version-pinned procedure for SHAP 0.52.0 that includes operating rules, additivity validation, and security constraints. The other three copies (Vibe-Skills, medical-research-skills, OpenClaw-Medical-Skills) are nearly identical to each other and provide a broader, more general guide with specific workflows for debugging, feature engineering, model comparison, and fairness analysis that the K-Dense copy lacks. Because the K-Dense copy is a specialized, high-fidelity version of the core procedure while the others add distinct workflow steps, crowning one would hide the other's material steps.