K Fold#
K Fold is a cross validation technique that splits the training set into k individual folds and for each training round uses 1 of the folds to test the model and the rest as training data. The final score is the average validation score over all of the k rounds. K Fold has the advantage of both training and testing on each sample in the dataset at least once.
Interfaces: Validator, Parallel
Parameters#
| # | Name | Default | Type | Description |
|---|---|---|---|---|
| 1 | k | 5 | int | The number of folds to split the dataset into. |
Example#
use Rubix\ML\CrossValidation\KFold;
$validator = new KFold(5, true);