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– 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.

– Test set: A set of examples used only to assess the performance of a fully-specified classifier.

If the test set is locked away, but you still want to measure performance on unseen data as a way of selecting a good hypothesis, then divide the available data (without the test set) into a training set and a validation set.In addition to reiterating Ripley’s glossary definitions, it goes on to discuss the common misuse of the terms “test set” and “validation set” in applied machine learning.The literature on machine learning often reverses the meaning of “validation” and “test” sets.There is much confusion in applied machine learning about what a validation dataset is exactly and how it differs from a test dataset.In this post, you will discover clear definitions for train, test, and validation datasets and how to use each in your own machine learning projects.

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