Definition
Cross-validation is a process for creating a distribution of pairs of training and test sets out of a single data set. In cross validation the data are partitioned into k subsets, S 1 …S k , each called a fold. The folds are usually of approximately the same size. The learning algorithm is then applied k times, for i = 1 to k, each time using the union of all subsets other than S i as the training set and using S i as the test set.
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(2011). Cross-Validation. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_190
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DOI: https://doi.org/10.1007/978-0-387-30164-8_190
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
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