Validation Set
Definition
A set of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters.
Deep Dive
In machine learning, the validation set is a distinct subset of the data used for hyperparameter tuning and model selection during the iterative training process. Its crucial role is to provide an unbiased evaluation of a model's performance on unseen data *during development*, allowing practitioners to compare different model configurations (e.g., learning rates, number of hidden layers, regularization strength) and choose the best-performing one without "peeking" at the final, untouched test set. By iterating and optimizing hyperparameters based on performance on the validation set, developers aim to build a model that generalizes well to new, real-world data.
Examples & Use Cases
- 1Comparing the accuracy of a neural network with 3 hidden layers versus 5 hidden layers on the validation set to decide which architecture is better.
- 2Experimenting with different learning rates for a gradient descent optimizer and selecting the one that yields the lowest error on the validation set.
- 3Using the validation set to determine when to stop training a model to prevent overfitting (early stopping).