Individual skill
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.
bundled/skills/evaluating-machine-learning-models/SKILL.md
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- Apache-2.0
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SKILL.mdcore · 1,560 bytesassets/README.mddoc · 359 bytesassets/visualization_script.pyruns · 5,661 bytesreferences/README.mddoc · 397 bytesscripts/README.mddoc · 407 bytesscripts/data_loader.pyruns · 2,836 bytesscripts/evaluate_model.pyruns · 2,888 bytesscripts/metrics_calculator.pyruns · 2,918 bytes
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# 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