Explaining machine learning models

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