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evaluating-machine-learning-models

separate

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.

When to use

• 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

When not to use

• feature engineering
• leakage auditing
• building the training pipeline
• checking train/test contamination

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