Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research

arXiv:2610.01284v1 Announce Type: cross 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. E

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