ValidationSet

is an option for Predict, Classify, NetTrain, and related functions that specifies the validation set to be used during the training phase.

Details

With ValidationSetdata, model and hyperparameter selections are done by testing performance on data. data can be given in any format allowed for the training set.
With ValidationSetAutomatic, cross-validation methods on the original data supplied to Predict, Classify, etc. will be used instead.
ValidationSetdata is typically used when the data in the training set and the data that one wishes to predict or classify come from different sources.

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Train a classifier:

Obtain the L2 regularization coefficient of the classifier:

Specify the validation set explicitly:

A different L2 regularization coefficient has been selected:

Train a predictor with a specified validation set:

Obtain the L2 regularization coefficient of the trained predictor:

Train a predictor without a specified validation set:

A different L2 regularization coefficient has been selected:

Load handwritten digits from the MNIST dataset:

Construct a set of digits from your own writing:

Train a classifier that uses the MNIST digits for training and your own digits for validating models and hyperparameters:

Introduced in 2014

(10.0)

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