Cross-validation, sometimes called rotation estimation,[1][2][3] is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (testing dataset).[4] The goal of cross validation is to define a dataset to test the model in the training phase (i.e., the validation dataset), in order to limit problems like overfitting, give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance a real problem), etc.

cross validation\adjust_wold.m
................\ch_pls.m
................\ch_plspred.m
................\ch_plspredh.m
................\f_test.m
................\kfold.m
................\lmocv.m
................\loocv.m
................\mcfactor.m
cross validation

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