evaluating-machine-learning-models
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.
npx skills add majiayu000/claude-skill-registry --skill evaluating-machine-learning-models --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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-generator` for class-level error breakdowns - `scientific-reporting` when the evaluation must become a deliverable
- Overview
- When to Use This Skill
- Not For / Boundaries
- Typical Outputs
- Related Skills
What does the evaluating-machine-learning-models skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill evaluating-machine-learning-models --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.