Glossary term
Glossary term
Foundations
The subset of the dataset that performs initial evaluation against a trained model. Typically, you evaluate the trained model against the validation set several times before evaluating the model against the test set.
Traditionally, you divide the examples in the dataset into the following three distinct subsets:
a validation set
a test set
Ideally, each example in the dataset should belong to only one of the preceding subsets. For example, a single example shouldn't belong to both the training set and the validation set.
See Datasets: Dividing the original dataset in Machine Learning Crash Course for more information.
Created for this library
An ML team uses a separate validation set to tune hyperparameters so the test set is reserved for the final, unbiased evaluation.
A research team uses k-fold cross-validation to use the available data as both training and validation across folds when the dataset is small.
An ML platform team requires a frozen validation set per release so hyperparameter choices stay comparable across versions.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License