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In this section, we will take a look at how the train, test, and validation datasets are defined and how they differ according to some of the top machine learning texts and references.Generally, the term “” and refers to a sample of the dataset held back from training the model.– Test set: A set of examples used only to assess the performance of a fully-specified classifier.— Brian Ripley, page 354, Pattern Recognition and Neural Networks, 1996 These are the recommended definitions and usages of the terms.The evaluation of a model skill on the training dataset would result in a biased score.Therefore the model is evaluated on the held-out sample to give an unbiased estimate of model skill.
The “training” data set is the general term for the samples used to create the model, while the “test” or “validation” data set is used to qualify performance.
The model is fit on the training set, and the fitted model is used to predict the responses for the observations in the validation set.
The resulting validation set error rate — typically assessed using MSE in the case of a quantitative response—provides an estimate of the test error rate.
This is typically called a train-test split approach to algorithm evaluation.
Suppose that we would like to estimate the test error associated with fitting a particular statistical learning method on a set of observations.
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.