K-fold cross validation performs model selection by splitting the dataset into a set of non-overlapping randomly partitioned folds which are used as separate training and test datasets e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
CrossValidatorModel contains the model with the highest average cross-validation metric across folds and uses this model to transform input data.
Builder for a param grid used in grid search-based model selection.
Validation for hyper-parameter tuning.
Model from train validation split.