Which subset of data is used to evaluate the performance of a trained model?

The subset of data used to evaluate the performance of a trained model is known as testing data. This data is distinct from the training data, which is utilized to train the model, and the validation data, which is often used during the model training process to tune hyperparameters and prevent overfitting.

Testing data is specifically reserved for the final evaluation of the model's performance metrics. It provides an unbiased assessment of how the model is likely to perform on unseen data. By using this separate dataset, practitioners can ensure that the evaluation is a true reflection of the model's capabilities rather than its ability to memorize the training data.

In summary, using testing data allows for a rigorous measure of the model’s generalization ability and overall effectiveness in real-world scenarios.