gabrielmoreira 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
shapfor SHAP-specific workflowsevaluating-machine-learning-modelswhen the question is whether the model is good enough