What role does a validation set play during model training?

The validation set plays a crucial role in the model training process by helping to tune hyperparameters. Hyperparameters are configuration settings used to control the training process, such as the learning rate, batch size, and the number of hidden layers in a neural network. A validation set is a subset of the data that is not used during the model training but rather serves to evaluate the model’s performance at various stages of training.

By using the validation set, practitioners can assess how different hyperparameter settings affect the model’s ability to generalize to unseen data. This process involves training the model multiple times with different hyperparameter configurations and measuring performance on the validation set. The hyperparameters are then adjusted based on these performance metrics until the optimal set of parameters is found that provides the best balance between training accuracy and validation accuracy.

In contrast, the other options do not accurately describe the function of a validation set. It does not serve to solely test finished models; rather, it is used iteratively during the training phase. While it can inform adjustments to the training algorithm, this is not its primary purpose compared to hyperparameter tuning. Finally, the validation set is not additional training data, as including it in training would defeat its purpose of providing an independent measure of model