If I was going to replicate a successful historical meal, it would be
#1078, ABFGKOP, which had a quality of 18 and models well. That’s a safe option for a good feast.
But I’d rather do something original, and
the best option according to the best model I’ve found is CDGHMOPS. Another plausible choice is AGOPRTV, which does very well in every decent model that I’ve tried.
Observations
With American Thanksgiving, I’d mostly expect the more dishes the better, because each person can choose which subset of the dishes they want to eat. The biggest costs are to lack of variety, because each person wants variety (e.g. some protein and some dessert) and different people want different things (e.g. spicy, not spicy, meat, meatless). Also, occasionally dishes are complementary (e.g. pie & ice cream, or cake & ice cream). The Feasts here are apparently not like that, because that is not how the data looks. Perhaps the dishes have strong odors, or the custom is to have some of everything, or the foods have unexpected magical interactions in your belly.
Taking another look after sleeping on it, I did find some of the sorts of meal-crafting patterns I was expecting, with effects from number of sweet dishes (AEGH) and number of spicy dishes (CFKSV). I actually crafted a ‘number of sweet things’ variable last night (AEGHP), but I just threw it in a linear model rather than looking at the data.
Updated choices
CDGHMOPS is still the best option according to my new best model. ABDMOPTV the new choice for doing well according to every decent model while still being close to the best according to my new best model. If I was picking now I’d go with the latter.
This is mainly based on a linear model which includes all pairwise interactions, with a few adjustments:
also accounts for number of sweet dishes (AEGH) - too many or too few is bad
also accounts for number of spicy dishes (CFKSV) - too many or too few is bad
also accounts for total number of dishes—too many or too few is bad
I made the linear interaction sparse, rounding small coefficients to zero
Displacer Dumplings seem to be irrelevant to meal quality, so I’m averaging in the same meal with or without D (but picking whichever one happened to do better)
I slightly averaged in a couple other models that I tried which aren’t as accurate on their own as the linear interaction model
I haven’t been able to reduce the error all the way down to where it seems to be among repeat meals, but it’s not that far off.
My opinion on other entries
I would attend James Camacho’s AGORTV feast if I wasn’t hosting my own
If I was going to replicate a successful historical meal, it would be
#1078, ABFGKOP, which had a quality of 18 and models well. That’s a safe option for a good feast.
But I’d rather do something original, and
the best option according to the best model I’ve found is CDGHMOPS. Another plausible choice is AGOPRTV, which does very well in every decent model that I’ve tried.
Observations
With American Thanksgiving, I’d mostly expect the more dishes the better, because each person can choose which subset of the dishes they want to eat. The biggest costs are to lack of variety, because each person wants variety (e.g. some protein and some dessert) and different people want different things (e.g. spicy, not spicy, meat, meatless). Also, occasionally dishes are complementary (e.g. pie & ice cream, or cake & ice cream).
The Feasts here are apparently not like that, because that is not how the data looks. Perhaps the dishes have strong odors, or the custom is to have some of everything, or the foods have unexpected magical interactions in your belly.
Updated observations
Taking another look after sleeping on it, I did find some of the sorts of meal-crafting patterns I was expecting, with effects from number of sweet dishes (AEGH) and number of spicy dishes (CFKSV). I actually crafted a ‘number of sweet things’ variable last night (AEGHP), but I just threw it in a linear model rather than looking at the data.
Updated choices
CDGHMOPS is still the best option according to my new best model. ABDMOPTV the new choice for doing well according to every decent model while still being close to the best according to my new best model. If I was picking now I’d go with the latter.
Updated choice
I’ll go with ABDMOPV.
My second choice would be FGHOPTV.
My process
This is mainly based on a linear model which includes all pairwise interactions, with a few adjustments:
also accounts for number of sweet dishes (AEGH) - too many or too few is bad
also accounts for number of spicy dishes (CFKSV) - too many or too few is bad
also accounts for total number of dishes—too many or too few is bad
I made the linear interaction sparse, rounding small coefficients to zero
Displacer Dumplings seem to be irrelevant to meal quality, so I’m averaging in the same meal with or without D (but picking whichever one happened to do better)
I slightly averaged in a couple other models that I tried which aren’t as accurate on their own as the linear interaction model
I haven’t been able to reduce the error all the way down to where it seems to be among repeat meals, but it’s not that far off.
My opinion on other entries
I would attend James Camacho’s AGORTV feast if I wasn’t hosting my own