What is the correct term to describe the difference between the predicted values and testing values in machine learning? We generally use the term residuals to refer to the difference between the training data values and the model's predicted values, but I wonder if there is a different/better term to refer to the difference between the testing data values and the model's predicted values, particularly within the field of machine learning?
Would that just be variance? (Variance sounds too general and could cause some confusion, no?)

For better or for worse, machine learning by seems to call these “errors”. Yes, statistics might prefer to reserve that term for the theoretical error distribution, but it kind of makes sense: your model make a prediction that missed the truth by some amount, so that amount is the error.
