| Class | Description |
|---|---|
| CrossValidator |
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 |
CrossValidatorModel contains the model with the highest average cross-validation
metric across folds and uses this model to transform input data.
|
| CrossValidatorModel.CrossValidatorModelWriter |
Writer for CrossValidatorModel.
|
| ParamGridBuilder |
Builder for a param grid used in grid search-based model selection.
|
| TrainValidationSplit |
Validation for hyper-parameter tuning.
|
| TrainValidationSplitModel |
Model from train validation split.
|
| TrainValidationSplitModel.TrainValidationSplitModelWriter |
Writer for TrainValidationSplitModel.
|