Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb


Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_190


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, S1…Sk, 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 Si as the  training set and using Si as the  test set.

Cross References

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© Springer Science+Business Media, LLC 2011