Model Explainability Tool
Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.
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# Model Explainability Tool ## Positioning Treat this skill as an explicit/manual helper for interpretability work. ## When to Use Use this skill when: - Understand why a machine learning model made a specific prediction. - Identify the most important features influencing a model's output. - Debug model performance issues by identifying unexpected feature interactions. - Communicate model insights to non-technical stakeholders. - Ensure fairness and transparency in model predictions. ## Not For / Boundaries - Model training and hyperparameter search: use `scikit-learn` - Benchmark comparison and threshold selection: use `evaluating-machine-learning-models` - Leakage or prediction-time audits: use `ml-data-leakage-guard` ## Typical Outputs - Feature importance or attribution summaries - Local explanation workflow for a concrete prediction - Notes on caveats, instability, or misleading explanations ## Related Skills - `shap` for SHAP-specific workflows - `evaluating-machine-learning-models` when the question is whether the model is good enough
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