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 foryourhealth111-pixel/Vibe-Skills --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, cali
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 foryourhealth111-pixel/Vibe-Skills --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 foryourhealth111-pixel/Vibe-Skills, a repository with 2,593 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.