foryourhealth111-pixel/evaluating-machine-learning-models
bundled/skills/evaluating-machine-learning-models/SKILL.md
Evaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.
$ install --global
skillsauthnpx skillsauth add foryourhealth111-pixel/vco-skills-codex evaluating-machine-learning-modelsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 26, 2026, 3:45 AM68.1s8 files scanned
SKILL.md
- name:
- evaluating-machine-learning-models
- description:
- |
- allowed-tools:
- Read, Write, Edit, Grep, Glob, Bash(cmd:*)
- version:
- 1.0.0
- author:
- Jeremy Longshore <[email protected]>
- license:
- MIT
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
training-machine-learning-models - Engineering features: use
engineering-features-for-machine-learning - 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
confusion-matrix-generatorfor class-level error breakdownsscientific-reportingwhen the evaluation must become a deliverable
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