What is the benefit of using a validation set during training?

Using a validation set during training provides crucial insights into a model's performance on unseen data. This is an essential aspect of machine learning, as the ultimate goal is not just to fit the model to the training data, but to ensure that it generalizes well to data it has not encountered before. By evaluating the model on the validation set, you can monitor its performance metrics—such as accuracy, precision, recall, and loss—during training. This helps in assessing whether the model is learning effectively or if it may be overfitting, which means performing well on the training data but poorly on new, unseen data.

In this context, the validation set serves as a proxy for the real-world data distribution that the model will face when deployed. Insights gained from the validation set can guide decisions on hyperparameter tuning, model selection, and early stopping to prevent overfitting. It allows data scientists to iteratively improve the model's performance through careful observation of the validation metrics, ultimately leading to better generalization capabilities once the model is put into production.