Explaining Machine Learning Models

---
name: explaining-machine-learning-models
description: |
  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.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT
---
# 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