Which dataset is used to verify a model's performance on unseen data?

The test data set is specifically designed to evaluate a model's performance on data that it has not encountered during training or validation. This separation between the test data set and other datasets is crucial because it helps in assessing how well the model generalizes to new, unseen data. By utilizing this distinct set, practitioners can gain a realistic estimate of the model’s predictive capability and overall effectiveness in practical applications.

In contrast, the training data set is the portion of the data used to teach the model—this is where the model learns the patterns and features. The validation data set serves as a means to tune the model's parameters and make decisions about model architecture, but it is not intended for final performance evaluation. The complete data set contains all available data, which would defeat the purpose of having a test set, as the model would have been trained on all available examples, leading to biased performance assessments. Thus, the test data set is indispensable for obtaining an unbiased evaluation of model performance in real-world scenarios.