Editing Cross-validation (statistics) (section)

===k-fold cross-validation with validation and test set===

This is a type of k*l-fold cross-validation when ''l''&nbsp;=&nbsp;''k''&nbsp;-&nbsp;1. A single k-fold cross-validation is used with both a [[Training, validation, and test sets|validation and test set]]. The total data set is split into ''k'' sets. One by one, a set is selected as test set. Then, one by one, one of the remaining sets is used as a validation set and the other ''k''&nbsp;-&nbsp;2 sets are used as training sets until all possible combinations have been evaluated. Similar to the k*l-fold cross validation, the training set is used for model fitting and the validation set is used for model evaluation for each of the hyperparameter sets. Finally, for the selected parameter set, the test set is used to evaluate the model with the best parameter set. Here, two variants are possible: either evaluating the model that was trained on the training set or evaluating a new model that was fit on the combination of the training and the validation set.