What is Validation Set?
Validation Set — A separate dataset used during training to tune parameters and evaluate a model’s performance without overfitting.
A validation set is a portion of your data held back from training to evaluate model performance. It acts as a practice exam — letting you tune hyperparameters and detect overfitting before the final test. Typical splits are 80% training, 10% validation, 10% test.
Frequently Asked Questions
How is a validation set different from a test set?
The validation set is used during training to tune parameters and make decisions. The test set is used only once at the end for final evaluation. Using the test set during tuning invalidates its purpose.
How large should my validation set be?
Typically 10-20% of your total data. It must be large enough to give statistically meaningful performance estimates and representative of real-world data distribution.
What happens without a validation set?
You cannot reliably detect overfitting or compare model configurations. You risk deploying a model that performs well on training data but fails on real-world inputs.