In cross-validation, what two subsets does the process typically involve?

In cross-validation, the process typically involves creating two main subsets: a training set and a testing set. The purpose of these subsets is to evaluate the performance of a model while ensuring it generalizes well to unseen data.

The training set is used to train the model, allowing it to learn the underlying patterns and relationships present in the data. Once the model has been trained, the testing set, which consists of data that the model has not previously encountered, is then used to evaluate its performance. This helps in assessing how well the model will predict outcomes for new, unseen data, and is crucial for understanding its accuracy and robustness.

Other options mentioned, such as the validation set, typically represent a different methodology; they are used in different contexts, often in situations where hyperparameter tuning is required. In the case of cross-validation specifically, the distinction between training and testing sets is fundamental for achieving effective model evaluation.