A parallel, n-fold cross validation technique.
K-Folds strategy for splitting data that makes sure groups in each fold are non-overlapping, i.e no group is present in both training and testing splits.
K-Folds strategy for splitting data that makes sure groups in each fold are non-overlapping, i.e no group is present in both training and testing splits. Folds try to be as balanced as possible, i.e. the number of test examples in each fold is approximately the same.
A parallel hyperparameter search using n-fold cross validation.
K-Folds strategy for splitting data.