K-Fold Cross-Validation is a resampling procedure in which a dataset is partitioned into *k* equally sized folds, where each fold serves exactly once as a validation set while the remaining *k*−1 folds form the training set, enabling *k* iterations of model evaluation to produce a robust estimate of generalization performance.
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K-fold Cross Validation
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Machine learning algorithm validation with a limited sample size
Andrius Vabalas, Emma Gowen, Ellen Poliakoff et al. · PLoS ONE · 2019 · 1.7K citations · Full text
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