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A validation dataset is a sample of data held back from training your model that is used to give an estimate of model skill while tuning model’s hyperparameters.
The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of the final tuned model when comparing or selecting between final models.
– Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network.
A good (and older) example is the glossary of terms in Ripley’s book “Pattern Recognition and Neural Networks.” Specifically, training, validation, and test sets are defined as follows: – Training set: A set of examples used for learning, that is to fit the parameters of the classifier.
It involves randomly dividing the available set of observations into two parts, a training set and a validation set or hold-out set.