Abstract
Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stable result. This tutorial reviews hold-out validation, train/validation/test designs, repeated random subsampling, k-fold and repeated stratified cross-validation, leave-one-out and leave-p-out schemes, group-aware validation, and nested group cross-validation. General machine-learning principles are linked to EEG epochs, paired-eye OCT images, repeated clinical measurements, and multicenter data. Eight controlled scenarios compare flawed and leakage-safe designs: seven use locked confusion matrices with auditable metrics, and one uses a reproducible repeated-study simulation. The scenarios cover global feature selection, normalization leakage, dependent records, center mixing, repeated test-set use, and estimator instability. Bias, variance, metric aggregation, uncertainty, and computational cost are also examined. A data-size matrix, a decision tree, and reporting checklists are provided. Reproducible MATLAB templates and scikit-learn counterparts are included. The results show that no validation method is universally best. The independent unit must match the intended deployment target. Every data-dependent operation must also exclude the observations used for performance estimation.
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Publication details
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- Green open access
Cite this article
APA 7
Baygin, M., Dogan, S., & Tuncer, T. (2026). Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research. https://omanscience.com/en/articles/model-validation-in-machine-learning-a-scenario-based-guide-from-hold-out-splits-to-nested-group-cross-validation-in-biomedical-and-applied-research
MLA 9
Baygin, Mehmet, et al. "Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research." https://omanscience.com/en/articles/model-validation-in-machine-learning-a-scenario-based-guide-from-hold-out-splits-to-nested-group-cross-validation-in-biomedical-and-applied-research.
Chicago (author–date)
Baygin, Mehmet, Sengul Dogan, and Turker Tuncer. 2026. "Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research." https://omanscience.com/en/articles/model-validation-in-machine-learning-a-scenario-based-guide-from-hold-out-splits-to-nested-group-cross-validation-in-biomedical-and-applied-research.
Harvard
Baygin, M., Dogan, S. and Tuncer, T. (2026) 'Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research', Available at: https://omanscience.com/en/articles/model-validation-in-machine-learning-a-scenario-based-guide-from-hold-out-splits-to-nested-group-cross-validation-in-biomedical-and-applied-research.
Vancouver
Baygin M, Dogan S, Tuncer T. Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research. https://omanscience.com/en/articles/model-validation-in-machine-learning-a-scenario-based-guide-from-hold-out-splits-to-nested-group-cross-validation-in-biomedical-and-applied-research
IEEE
M. Baygin, S. Dogan, and T. Tuncer, "Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research," https://omanscience.com/en/articles/model-validation-in-machine-learning-a-scenario-based-guide-from-hold-out-splits-to-nested-group-cross-validation-in-biomedical-and-applied-research.