evaluating-machine-learning-models
This Claude Code skill provides guidance on selecting appropriate evaluation metrics and comparing machine learning models to determine if they meet performance requirements. Use it when an existing model needs assessment through consistent metrics like precision, recall, F1 score, or AUC, or when validating multiple candidate models before deployment.
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/evaluating-machine-learning-models && cp -r /tmp/evaluating-machine-learning-models/bundled/skills/evaluating-machine-learning-models ~/.claude/skills/evaluating-machine-learning-modelsSKILL.md
# Model Evaluation Suite Use this skill when the model exists and the question is whether it is good enough. ## Overview This skill focuses on choosing and interpreting the right evaluation metrics for the problem, then comparing candidate models or thresholds. ## When to Use This Skill - Comparing candidate models with consistent metrics - Reviewing precision/recall/F1/AUC, regression error, calibration, or ranking quality - Stress-testing validation strategy before deployment or publication ## Not For / Boundaries - Building the training pipeline itself: use `scikit-learn` for classical modeling or `ml-pipeline-workflow` for end-to-end workflow ownership - Engineering features: use `preprocessing-data-with-automated-pipelines` - Checking train/test contamination: use `ml-data-leakage-guard` ## Typical Outputs - Metric suite recommendations - Model comparison tables - Notes on threshold tradeoffs, calibration, and validation weaknesses ## Related Skills - `scikit-learn` for class-level error breakdowns and confusion matrices - `scientific-reporting` when the evaluation must become a deliverable
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