cross validation error
Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation.
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Unlike splitting the data into one training and one holdout set, cross-validation computes its estimates over all the data by performing multiple splits and systematically swapping out samples for testing.
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