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.
Abstract (translated)
URL
https://arxiv.org/abs/2610.01284
