Documentation API of the 'org.encog.neural.networks.training.cross.CrossValidationKFold' Java class
CrossValidationKFold
org.encog.neural.networks.training.cross

Class CrossValidationKFold

  • All Implemented Interfaces:
    MLTrain


    public class CrossValidationKFoldextends CrossTraining
    Train using K-Fold cross validation. Each iteration will train a number of times equal to the number of folds - 1. Each of these sub iterations will train all of the data minus the fold. The fold is used to validate. Therefore, you are seeing an error that reflects data that was not always used as part of training. This should give you a better error result based on how the network will perform on non-trained data.(validation). The cross validation trainer must be provided with some other sort of trainer, perhaps RPROP, to actually perform the training. The training data must be the FoldedDataSet. The folded dataset can wrap most other training sets.

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