explaining-machine-learning-models
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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tessl review fix ./bundled/skills/explaining-machine-learning-models/SKILL.mdModel 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
shapfor SHAP-specific workflowsevaluating-machine-learning-modelswhen the question is whether the model is good enough
- Repository
- foryourhealth111-pixel/Vibe-Skills
- Commit
f627ab5
- Last updated
- First committed
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