The correct answer is A. training data.
Training data is used to build a data mining model. It is a set of data that is used to train the model to learn how to make predictions. The model is then tested on a set of test data to see how well it performs. If the model performs well on the test data, it can be used to make predictions on new data.
B. validation data is used to evaluate the model during training. It is a set of data that is separate from the training data and the test data. The model is trained on the training data and then evaluated on the validation data. This helps to ensure that the model is not overfitting the training data.
C. test data is used to evaluate the model after training. It is a set of data that is separate from the training data and the validation data. The model is trained on the training data and then evaluated on the test data. This helps to ensure that the model will generalize well to new data.
D. hidden data is data that is not used by the model. It is data that is not relevant to the task that the model is trying to learn.