I disagree only about B with one version of aphyer’s allocations. It is possible that that was out of date at the point when I said “we disagree only about B” but I’m not sure. Anyway, yes, now we do disagree with one another about P as well.
I took the 100 nearest (Euclidean distance in stat-space) students from each house and did linear regression to predict the value for the student in question, then arbitrarily changed my answers based on e.g. residuals of the nearest points or too low density near the point in question, and then did the same for the 20 nearest for certain of the incoming students (which I had noted to be questionable in some way or another, or which disagreed with aphyer or gjm), and in the end I may have decided some of the more ambiguous stuff based on too low local density of some houses, which may explain why my results are so similar to yours (I did not check your results until after arriving at mine).
edit: actually this did provide some insight, in terms of seeing how the regression coefficients change locally (e.g. often the lowest house-relevant stat is most relevant), and I did try a bit to come up with global formulas (like GuySrinivasan’s) but I didn’t get far with that.
My entry just before the deadline:
Dragonslayer: D,G,H,N,Q
Humblescrumble: E,I,L,M,R,T
Serpentyne: C,F,K,P
Thought-Talon: A,B,J,O,S
Compared with gjm, I disagree (unconfidently) on K and P
Compared with aphyer, I disagree (unconfidently) on B and K
Compared with Thomas Sepulchre, I disagree (unconfidently) on P only, agreeing with everything else.
(note that on my reading of aphyer and gjm’s entries, they disagree on B and P, despite them saying they only disagree on B)
I used ad-hoc local methods which ultimately does not provide much insight, unfortunately.
I disagree only about B with one version of aphyer’s allocations. It is possible that that was out of date at the point when I said “we disagree only about B” but I’m not sure. Anyway, yes, now we do disagree with one another about P as well.
Out of curiosity, can you, if you don’t mind, describe what methods you used?
methods:
I took the 100 nearest (Euclidean distance in stat-space) students from each house and did linear regression to predict the value for the student in question, then arbitrarily changed my answers based on e.g. residuals of the nearest points or too low density near the point in question, and then did the same for the 20 nearest for certain of the incoming students (which I had noted to be questionable in some way or another, or which disagreed with aphyer or gjm), and in the end I may have decided some of the more ambiguous stuff based on too low local density of some houses, which may explain why my results are so similar to yours (I did not check your results until after arriving at mine).
edit: actually this did provide some insight, in terms of seeing how the regression coefficients change locally (e.g. often the lowest house-relevant stat is most relevant), and I did try a bit to come up with global formulas (like GuySrinivasan’s) but I didn’t get far with that.