This the first type of error that you don't have to care about minimizing. Getting a small value for this type of error doesn't mean that your model will work well over the unseen data (generalize). To better understand this type of error, we'll give a trivial example of a class scenario. The purpose of solving problems in the classroom is not to be able to solve the same problem again in the exam, but to be able to solve other problems that won’t necessarily be similar to the ones you practiced in the classroom. The exam problems could be from the same family of the classroom problems, but not necessarily identical.
Apparent error is the ability of the trained model to perform on the training set for which we already know the true outcome/output. If you manage to get 0 error over the training set, then it is a good indicator...
