What is the Difference Between Test and Validation Datasets?

Last Updated on August 14, 2020

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.

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.

After reading this post, you will know:

• How experts in the field of machine learning define train, test, and validation datasets.
• The difference between validation and test datasets in practice.
• Procedures that you can use to make the best use of validation and test datasets when evaluating your models.

Let’s get started.

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