Validation Set
Validation Set is a portion of a dataset used during machine learning development to evaluate a model's performance and tune its configuration before final evaluation on a separate test set.
What is Validation Set?
A validation set contains data that is kept separate from the training data. The model is evaluated on it during development to help select hyperparameters, compare model configurations, and monitor generalization. Unlike the training set, its examples are not directly used to update the model's parameters.
Why is Validation Set Important?
A validation set helps developers make informed decisions about model architecture, hyperparameters, and training duration without using the final test data. Keeping it separate from the test set helps preserve an unbiased estimate of performance on unseen data.
Common use cases
Validation Sets are commonly used in model selection, hyperparameter tuning, early stopping, cross-validation, deep learning, and machine learning development workflows.