What is the purpose of using a validation set during model training?

Using a validation set during model training serves a critical purpose in the overall machine learning workflow, particularly in the context of model performance and generalization. The key function of a validation set is to provide an unbiased evaluation of a model’s predictive capability during the training phase. By holding out a portion of the data as a validation set, you allow for assessing how the model is likely to perform on unseen data.

The primary goal of using a validation set is to prevent overfitting, which is when a model learns not only the underlying patterns in the training data but also the noise and specific details that do not generalize to new data. Overfitting can significantly impair a model's ability to perform well in real-world applications. A validation set is utilized to tune hyperparameters effectively and to select the best model among multiple candidates based on their performance on this subset of data. By monitoring metrics such as accuracy or loss on the validation set, adjustments can be made to improve model performance, ensuring that it captures the essence of the data being learned rather than memorizing it.

In contrast, other options do not align with the intended use of a validation set. For instance, increasing the number of training samples is not the function of a validation set; instead,