AWS Certified Machine Learning Specialty (MLS-C01) Practice Test

The validation set plays a crucial role in model training, primarily serving to evaluate the performance of a model during the training process. It acts as an intermediary dataset that is set aside to assess how well the model generalizes to new, unseen data. By monitoring the performance of the model on the validation set at various stages of training, you can identify whether the model is learning effectively or beginning to overfit to the training data.

Overfitting occurs when a model captures noise and fluctuations in the training data, leading to poor generalization on new data. The validation set helps in catching this issue early by providing feedback on the model's performance. If the model shows high accuracy on training data but lower accuracy on the validation set, it indicates overfitting. As a result, techniques such as early stopping, adjusting hyperparameters, and modifying the model architecture can be employed to improve performance.

In contrast, other options focus on aspects that do not directly relate to the purpose of a validation set. Providing final results refers to the testing phase, where a separate test set is used to report the model's performance after training is completed. Storing raw data pertains to data management rather than model evaluation. Automating the training process does not reflect the validation set's