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