>You link to index C twice, rather than linking to index D.
Whoops! Fixed now, thank you.
Reflections on my performance:
I failed to stick the landing for PVE; looking at gjm’s work, it seems like what I was most missing was feature-engineering while/before building ML models. I’ll know better next time.
For PVP, I did much better. My strategy was guessing (correctly, as it turned out) that everyone else would include a Professor, noticing that they’re weak to Javelineers, and making sure to include one as my backmidline.
Reflections on the challenge:
I really appreciated this challenge, largely because I got to use it as an excuse to teach myself ... (read more)
Just recording for posterity that yes, I have noticed that
Rangers are unusually good at handling Samurai, so it might make sense to have one on my PVE team.
However, I've also noticed that
Rangers are unusually BAD at handling Felons, to a similar or greater degree.
I think it makes more sense to keep Pyro Professor as my mid-range heavy-hitter in PVE.
(. . . to my surprise, this seems to be the only bit of hero-specific rock-paper-scissors that's relevant to the PVE challenge. I suspect I'm missing something here.)
Threw XGBoost at the problem and asked it about every possible matchup with FRS; it seems to think
my non-ML-based pick is either optimal or close-to-optimal for countering that lineup.
(I'm still wary of using ML on a problem instead of thinking things through, but if it confirms the answer I got by thinking things through, that's pretty reassuring.)
Therefore, I've decided
to keep HLP as my PVE team.
And I've DM'd aphyer my PVP selection.
My main finding thus far:
There's a single standard archetype which explains all the most successful teams. It goes like this: [someone powerful from the MPR cluster, ideally P], [a frontman, selected from GLS], [someone long-ranged, selected from CHJ]. In other words, this one is all about getting a good range of effective ranges in your team.
My tentative PVE submission is therefore:
Hurler, Legionary, Professor
Well, that's embarrassing. Fixed now; thank you.
Reflections x3 combo:
Just realized this could have been a perfect opportunity to show off that modelling library I built, except:
A) I didn't have access to the processing power I'd need to make it work well on a dataset of this size.
B) I was still thinking in terms of "what party archetype predicts success", when "what party archetype predicts failure" would have been more enlightening. Or in other words . . .
. . . I forgot to flip the problem turn-ways.
This stings my pride a little; I console myself with the fact that my "optimize conditional on Space and Life" allocation got a 64.7% success rate.
If I'd allocated more time, I would have tried a wider range of ML algorithms on this dataset, instead of just throwing XGBoost at it. I'm . . . not actually sure if that would have helped; in hindsight, trying the same algorithms on different subsets ("what if I built a model on only the 4-player games?") and/or doing more by-hand analysis ("is Princeliness like Voidliness, and if ... (read more)
The jankiness here is deliberate (which doesn't preclude it from being a mistake). My class on Bayesianism is intended to also be a class on the limitations thereof: that it fails when you haven't mapped out the entire sample space, that it doesn't apply 'cleanly' to any but the most idealised use cases, and that once you've calculated everything out you'll still be left with irreducible judgement calls.
(I have the "show P(sniper)" feature always enabled to "train" my neural network on this data, rather than trying to calculate this in my head)
That's among the intended use cases; I'm pleased to see someone thought of it independently.
If it helps, I for one am completely okay with you taking the weekend.
I used the python package Pandas.
(I also tried Excel, but the dataset was too large to load everything in. In retrospect, I realize I could have just loaded in the first million rows - 2/3 of the dataset, more than enough to get statistically significant results from - and analyzed that, possibly keeping the remaining ~400k rows as a testing set.)
My solution for winrate maximization:
Add a Page of Mind and a Seer of Void. (this should get us slightly better than 50% chance of success)
My solution conditional on the new universe having both Space and Life (I think Time, Space and Life are prerequisites for a universe I'd like):
Add a Prince of Space and a Sylph of Life; if the gender situation doesn't line up with that, replace the Prince with an Heir and/or replace the Sylph with a Page. (this should get us slightly worse than 50% chance of success)
My attempt at ranking the party members, based on cha... (read more)
I just checked and while the other answers are perfect, math.log(2)**math.exp(2) is 0.06665771193088375. ChatGPT is off by almost an order of magnitude when given a quantitative question it can't look up in its training data.
Thanks for putting in the time to make sense of my cryptic and didactic ranting.
You don't specify exactly how this second function can vary, whether it also has a few parameters or one parameter or many parameters?
Segmented linear regression usually does the trick. There's only one input, and I've never seen discontinuities be necessary when applying this method, so only a few segments (<10) are needed.
I didn't specify this because almost any regression algorithm would work and be interpretable, so readers can do whatever is most convenient to them.
There was a similar question a few months back; you may find the answers there helpful.
Nope. (Though since both that game and this one are weird administration-centric takes on Harry-Potter-style magical schools, I imagine there may have been some convergent evolution.)
Good catch; fixed now; thank you.
It was, though fortunately that was just the random Houses they would have been Allocated to, and as such provides no meaningful information. Still, I've updated the file to not have that column; thank you.
Buy battery packs for charging phones so you can stay connected during a local blackout.
Wait. As . . . a software developer? Not as a Data Scientist, even though you have experience with ML?
At least as far as I know, Data work is better paid, uses more LessWrong-ish skills, and (crucially) is more of a frontier situation: Software ate the world a while ago, but Data is still chewing, so there's been much less time for credentialism to seep in.
(I'm from the UK, and it's been a few years since I did a 'normal' jobhunt, so I could be wrong about this as it applies today and on your side of the Atlantic. But even taking that into account, I notice I'm still surprised.)
I'm curious as to what exactly you found there.
Briefly: I told my learner "assume there are two sources of income for Light Forest forts; assume they are log-linked functions of the data provided with no interactions between features; characterize these income sources."
The output graphs, properly interpreted, said back:
Reflections on my attempt:
It looks like I was basically right. I could have done slightly better by looking more closely at interactions between features, ore types especially; still, I (comfortably) survived and (barely) proved my point to the King, so I'm happy with the outcome I got.
(I'm also very pleased by the fact that I picked up on the ore-based-vs-wood-based distinction; or, rather, that the ML library I've been building automatically picked up on it. Looks like my homebaked interpretability tools work outside their usual contexts!)
Reflections on ... (read more)
4x Miner, 2x Woodcutter, 2x Warrior, 2x Crafter, 1x Brewer, 1x Farmer, 1x Smith
The handful of (dubious) insights that no-one seems to have had yet, which motivate the (slight) differences between this setup and everyone else's:
I liked this one a lot. In particular, I appreciate that it defied my expectations of a winning strategy: i.e., I couldn't get an optimal or leaderboard-topping solution with the "throw a GBT at the problem, then iterate over possible inputs" approach which won the last two games like this.
I think the Dark mana thing was a good sub-puzzle, and the fact that it was so soluble is a point in favor of that. It seemed a little unfair that it wasn't directly useful in getting a good answer, but on reflection I consider that unfairness to be a valuable lesson abo... (read more)
You make a valid point, but . . .
The 'basic encryption' you have in minds is a Computer Thing. To the journalists in question, it was a New Computer Thing. If you're a Computer Person, you're probably underestimating the reticence associated with attempting New Computer Things when you're not a Computer Person.
much easier to use
I think that's false, albeit on the merest technicalities. The OTP system I have in mind is awkward and time-consuming ( . . . and probably inferior to Tor for Wikileaks' use case), but in terms of easiness it's some... (read more)
I took an ML-based approach which gave me radically different answers; the machine seems to think that
Matching currently-strong mana types is much more important than countering your opponent's choices.
As such, my new best guess is
Fireball, Rays, Vambrace
give my master roughly 2:1 odds in favor.
I deduced the existence of Darkness Mana, determined that it almost certainly has a value in the 16-18 range, and then . . . couldn't figure out any clever way to use that information when strategizing. I suspect I'm missing something here.
My provisional answer is:
Fireball, Levee, Hammer
This is supported by the reasoning that:
Levee (Fire/Earth) does a passably mediocre job protecting against Missiles (Earth/Water) and Fireball (Air/Fire); Fireball (Air/Fire) and Hammer (Light/Air) can both sneak past Solar (Fire/Light) by sharing an element.
And more prosaically by the fact that:
When I filtered the dataset to have Wizard A with the opponent's spell list, the spells which most raised Wizard B's winrate were those three.
I've had a hard time figuring out how to weight "counter the oppone
If (like me) you're having a hard time reading the .bin format, here's a plaintext version of it in hexadecimal.
Confirmed and corrected; thank you again.
Yes, good catch, fixed now.
I would give you more time, but
you've already reached an optimal answer.
(Also, you can always just refuse to read the ruleset until you're done with the data.)
As DM, I can confirm that skills provided with the help of the Chaos Deity or Eldritch Abomination are identical to those provided by the goddess alone.
Nope; cheats are commutative.
Nope. But (I'll edit the op to clarify this) the only effect a collaborator has is on which cheat skills are provided, so you could get the same effect as the Eldritch Abomination by choosing MR+AA, and get the same effect as the Chaos Deity by choosing randomly.
AVAILABLE FOR PROJECTS
Currently in the UK, near London. Remote work is both a possibility and a preference.
I do a variety of Data Science/Analysis work, but my niche is producing unusually human-legible predictive models. Further details are on my website; let me know if you have comments or questions.
My client is running low on things I can usefully do for them, so this post is relevant again. In the ~year since I posted it, I've tested my interpretability-first modelling approach in real-world contexts, confirmed it works, and found that in a few - admittedly niche - cases, it can not only match but actually outperform industry-standard black-box models.
I have a website here which elaborates on what I'm offering. If you have any comments or questions, don't hesitate to message me.
That...is in fact a join?
What I was (haphazardly, inarticulately) getting at is that I never used any built-in functions with 'join' in the name, or for that matter thought anything along the lines of "I will Do a Join now". In other words, I don't think needing to know about joins was a barrier to entry, because I never explicitly used that information when working on this problem.
I found this challenge difficult and awkward due to the high number of possible response-predictor pairs (disaster A in province B is predicted by disaster/omen X in province Y with a Z-year delay), low number of rows (if you look at each province seperately there are only 1080 records to play with), and probablistic linkages (if events had predicted each other more reliably, the shortage of data would have been less of an issue).
This isn't necessarily a criticism - sometimes reality is difficult and awkward, and it's good to prepare for that - and I get t... (read more)
Insane, unendorsed bonus plan:
Spend most of the money on earthquakeproofing all nine provinces (including the six we don't own), to greatly decrease the probability of black dove sightings (doves and quakes correlate super hard for some reason), so they can't predict plagues, so no plagues happen.
In addition to this being inherently ridiculous, it's rendered extra-implausible by the fact that:
Doves seem to have been getting slightly more common over time, but plagues (and for that matter every other omen and disaster) haven't, suggesting that causality doesn't work that way.
I hope to do more digging and build off other people's comments later in the week, but my preliminary/solo answer would be:
Grainhoard and plagueproof in all three provinces.
On the basis that:
Doves in the previous year strongly predict global plague and weakly predict local famine; also, a crude "ignore every predictor, just look at average output of response variables lol" approach suggests that stockpiling grain is the highest-EV intervention.
However . . .
I haven't been able to figure out how pillaging works at all, and I really doubt it's as random/irrel
The title says 'nine black doves', but the dataset says Germania (and only Germania) had no black dove sightings in 1080. Was this intentional?
Strong-upvoted for reminding me how much I miss teaching/tutoring.
It's definitely a feature as well; the exact tradeoff comes down to personal taste.
My PvE approach, as I mentioned, was to copy the plan that worked best in a comparable game: train a model to predict deck success, feed it the target deck, then optimize the opposing deck for maximum success chance. I feel pretty good about how well this worked. If I'd allocated more time, I would have tried to figure out analytically why the local maxima I found worked (my model noticed Lotus Ramp as well as Sword Aggro but couldn't optimize it as competently for some reason), and/or try multiple model types to see what they agr... (read more)
I did things this way because my applied stats knowledge is almost entirely self-taught, with all the random gaps in knowledge that implies. Thank you for letting me know about Stan and related techs: while it's hard to tell whether they would have been a better match for my modelling context (which had some relevant weirdnesses I can't share because confidentiality), they definitely would have made a better backup plan than "halt a key part of your organization for a few days every time you need a new model". I'll be sure to look into MCMC next time I deal with a comparable problem.
Fixed, thank you again.
2x Angel, 3x Minotaur Hooligan, 3x Pirate, 4x Sword.
Absolutely no reasoning was applied in reaching this conclusion; all my attempts to solve this one analytically met dead ends. Instead, I copied the ML-based approach gjm won Defenders of the Storm with - except using gradient descent to search deckspace instead of trying all possible options - and got an answer I have no way to explain or evaluate. I'm very curious to see if this works!