explaining-machine-learning-models
This skill provides interpretability analysis for machine learning models, enabling users to understand prediction logic, identify influential features, and debug performance issues. Use it when you need to explain specific model outputs, investigate feature interactions, or communicate model behavior to stakeholders.
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/explaining-machine-learning-models && cp -r /tmp/explaining-machine-learning-models/bundled/skills/explaining-machine-learning-models ~/.claude/skills/explaining-machine-learning-modelsSKILL.md
# 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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