yes, thanks v much. edited.
The precision-recall tradeoff definitely varies from one task to another. I split tasks into "precision-maxxing" (where false-positives are costlier than false-negatives) and "recall-maxxing" (where false-negatives are costlier than false-positives).I disagree with your estimate of the relative costs in history and in medical research. The truth is that academia does surprisingly well at filtering out the good from the bad.Suppose I select two medical papers at random — one from the set of good medical papers, and one from the set of crap medical papers. If a wizard offered to permanently delete both papers from reality, that would rarely be a good deal because the benefit of deleting the crap paper is negligible compared to the cost of losing the good paper. But what if the wizard offered to delete M crap papers and one good paper? How large must M be before this is a good deal? The minimal acceptable M is CFN/CFP, so τ⋆=CFP/(CFP+CFN)=1/(1+M). I'd guess that M is at least 30, so τ⋆ is at most 3.5%.
Moreover, even if the post shouldn't have been published with hindsight, that does not entail the post shouldn't have been published without hindsight.
You are correct that precision is (in general) higher than the threshold. So if Alice publishes anything with at least 10% likelihood of being good, then more than 10% of her poems will be good. Whereas, if Alice aims for a precision of 10% then her promising threshold will be less than 10%.Unless I've made a typo somewhere (and please let me know if I have), I don't claim the optimal promising threshold τ⋆ = 10%. You can see in Graph 5 that I propose a promising threshold of 3.5%, which gives a precision of 10%.I'll edit the article to dispel any confusion. I was wary of giving exact values for the promising threshold, because τ⋆=3.5 yields 10% precision only for these graphs, which are of course invented for illustrative purposes.