The author is a very sharp individual but is there a reason he insists on labelling overfitting as a phenomenon from machine learning instead of from classical statistics?
It might simply be that he didn't trace the etymology back that far.
If it turned out that the term actually started in tailoring before statistics really got it's feet under it (which I absolutely cannot say that it did, just that trying to extrapolate backwards that sounds like a reasonable guess) then it wouldn't speak poorly of you if you hadn't also known that.
The author is an academic, it is important to give proper credit for ideas within reason. Same reason I call F = ma the law of Newton and now the law of my high school physics teacher, even though I learned it first from him.
The reason I have this quibble is because the author says things like
>you should consider building formal (mathematical) bridges between results on overfitting in machine learning, and problems in economics, political science, management science, operations research, and elsewhere
If we are appropriately modest and acknowledge the fact that overfitting is well-studied by statisticians (although, obviously not in the context of deep neural networks), it seems kind of ridiculous to make statements like, economists and political scientists should consider using statistics?
They don't say "classical statistics," but I don't see any implication that the phenomenon was born from machine learning, even if they say it's a common problem within machine learning. Maybe I missed it? They do mention modelling their conception of overfitting around Goodhart's Law, noting its origin in economics.
