Artificial Intelligence Programming 2025 – 400 Free Practice Questions to Pass the Exam

The main purpose of using a validation set in model training is to evaluate the model's performance on unseen data. A validation set is a subset of the dataset that is not used during the training phase of the model. Instead, it acts as a proxy to assess how well the model is likely to perform on new, unseen data, which is crucial for ensuring the model's generalizability beyond the training data.

By evaluating the model on the validation set, one can obtain a measure of its performance and make informed decisions about adjustments, such as hyperparameter tuning, to improve model accuracy and effectiveness. This process helps prevent overfitting, where a model performs well on the training data but poorly on new data due to being too specialized to the training set specifics.

The other options relate less directly to the primary role of the validation set. While hyperparameter tuning can be a consideration when using a validation set, its main functional goal is still performance evaluation. Increasing the training set size or replacing cross-validation methods is not the primary function of a validation set and might not reflect best practices in model training.