Earlier quoted context omitted.
> They're basically arguing that you're better off going to a school in the middle of nowhere because "hey, for being in such a crappy location, you did pretty well!". Well, no, not exactly. It's a subtle distinction, but what it's actually ranking is how well that school exceeds expectations , not best outcomes . This is not necessarily a list that will give a student the best school to go to, but rather (what it sa…
That's my point -- who decides what expectations are? Their results are incredibly dependent on the model specification. I imagine if they changed which indicators they used, the results would vary widely. Here's another way to see my concern. Suppose you had a classifier that achieves 1.0 R^2; then since it perfectly predicts each school's expected value, it'll assign each school a score of 0. I'm pretty suspicious…
If I'm understanding correctly, that result would indicate a world where the college you attend has no effect on your earning power. ie. choose any college you want, because you'll earn the same amount regardless of which one you choose.
This would only apply to colleges that people in your demographic group actually attended though. If the dataset doesn't contain any information about people like you who went to Harvard, then maybe Harvard would indeed increase your earning potential if there was a way for you to actually go there.