Validating a model
The way to avoid this is to really hold the test set out—lock it away until you are completely done with learning and simply wish to obtain an independent evaluation of the final hypothesis. you have to obtain, and lock away, a completely new test set if you want to go back and find a better hypothesis.) — Stuart Russell and Peter Norvig, page 709, Artificial Intelligence: A Modern Approach, 2009 (3rd edition) Importantly, Russell and Norvig comment that the training dataset used to fit the model can be further split into a training set and a validation set, and that it is this subset of the training dataset, called the validation set, that can be used to get an early estimate of the skill of the model.
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.
Generally, the term “” and refers to a sample of the dataset held back from training the model.
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 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.Ideally, the model should be evaluated on samples that were not used to build or fine-tune the model, so that they provide an unbiased sense of model effectiveness.When a large amount of data is at hand, a set of samples can be set aside to evaluate the final model.After reading this post, you will know: I find it useful to see exactly how datasets are described by the practitioners and experts.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.