explaining-machine-learning-models

Files (10)

• SKILL.md
• README.md · assets/
• example_explanation.json · assets/
• explanation_template.html · assets/
• visualization_styles.css · assets/
• README.md · references/
• README.md · scripts/
• data_preprocessing.py · scripts/
• explain_model.py · scripts/
• feature_importance.py · scripts/

SKILL.md (first lines)

# 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

From foryourhealth111-pixel/Vibe-Skills, MIT.

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