What is the purpose of a validation set in model training?

A validation set is an essential component of the model training process, primarily used to assess the model's performance during training. By separating a portion of the dataset to serve as a validation set, you can monitor how well the model is learning without using the training data that it has seen already. This helps in tuning hyperparameters and making decisions about model adjustments throughout the training process.

Evaluating the model on the validation set allows you to gauge its performance in a more realistic scenario, as the model will be tested on data it hasn't encountered during training. This is crucial for understanding how well the model will generalize to unseen data, which is a critical factor in developing effective machine learning models.

Using a validation set also aids in preventing overfitting, where the model performs exceptionally well on training data but poorly on new, unseen data. It informs when the model begins to learn noise in the training data rather than relevant patterns.

In contrast, training the model exclusively refers to using the entire dataset for training without a separate validation set, which does not give insights into the model’s generalization abilities. Collecting additional data is outside the typical purpose of a validation set, as it focuses solely on testing model performance. Final tests before deployment are generally conducted using a