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model-validation

separate

Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating model performance honestly, choosing a CV scheme, detecting leakage, or judging whether reported discrimination means the model is actually useful. For feature selection itself see machine-learning/biomarker-discovery; for confirmatory-trial inference see clinical-biostatistics/trial-reporting.

When to use

• estimating model performance honestly
• choosing a CV scheme
• detecting leakage
• judging whether reported discrimination means the model is actually useful
• nested cross-validation
• group/batch/temporal-aware splits
• probability calibration
• decision-curve net benefit

When not to use

• feature selection
• biomarker discovery
• confirmatory-trial inference
• clinical-biostatistics/trial-reporting

Skill page

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