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evaluating-machine-learning-models

Evaluate trained machine learning models using metrics suited to the task. Compare candidates consistently, review decision thresholds and probability estimates, and identify weaknesses in validation before deployment or publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

foryourhealth111-pixel/Vibe-Skills
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이럴 때 유용합니다

  • Compare candidate models using consistent metrics
  • Review tradeoffs between decision thresholds
  • Check whether predicted probabilities match observed outcomes
  • Review validation methods before deployment or publication

포함된 파일(8)

  • SKILL.md1.5 KB
  • assets/README.md359 B
  • assets/visualization_script.py5.5 KB
  • references/README.md397 B
  • scripts/data_loader.py2.8 KB
  • scripts/evaluate_model.py2.8 KB
  • scripts/metrics_calculator.py2.8 KB
  • scripts/README.md407 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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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