In training a pattern recognition system which data set measures the accuracy achieved by the model?

In training a pattern recognition system, the data set that measures the accuracy achieved by the model is the testing data set. The testing data set is a subset of the original data that is not used in the training process but is instead used to evaluate the performance of the trained model. The testing data set is labeled with known outputs, and the model’s predictions are compared against these known outputs to calculate metrics such as accuracy, precision, and recall. By evaluating the model’s performance on a separate testing data set, the model’s ability to generalize to new and unseen data can be assessed, and any overfitting or underfitting issues can be identified and addressed.