To me, under and overfitting are the two of the most vague concepts in machine learning.
From Google's first link when you look up these definitions.
A model is said to be underfitted if it "performs badly" on the training as well as test set.
A model is said to be overfitted if it "performs well" on the training set but "performs poorly" on the test set.
And it is usually follow by either a graph of the training/validation error plot or some curve associated a particular model (model is never specified, hence curve not reproducible).
I don't need to go into the details why "performs badly, well, good", etc. is subjective and leaves a lot of room for guessing. I also don't want to go into detail why deep network tend not to overfit even when you train for a very high number of epochs. Why is this concept so central to machine learning when it is so vague at the same time?
Is there a better metric or descriptor of generalization of a model as of 2020 than "over/underfitting"?
A more radical idea: should we completely abandon this notion because it is vague?