CrossK-fold cross validation is primarily used in applied machine learning to estimate the performance of a machine learning model on unseen data. It is a re-sampling procedure to evaluate machine learning models in limited data. In summary, the k-fold step splits the data set into k different subsets and iterates over them using one of them as the test set and the remaining k-1 elements as the training set. In the figure blow there is a example for k equals 10 shown.
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