Hi all, I've been teaching myself how to do machine learning in Python lately, using the NumPy package and log data downloaded from Isotropic. My goal is to write a "kingdom analyzer" that looks at a ten-card kingdom and gives advice that would help a beginning or intermediate player understand what sort of deck the collective wisdom of skilled Isotropic players would build for that kingdom.
I've come up with a sort of "minimum viable implementation" and tested it on five days of game logs. Currently it does a good job picking two-and-three card combos and counters (e.g. buy Ironworks if Garden is available, but not if Bishop is also available), and it could probably find larger combos if I downloaded more data. But it's poor at designing a coherent deck, and it still makes "wtf" recommendations pretty regularly (I suspect the model I'm using overfits severely.) Of course, I'm just a level 18, so maybe the model is right and I'm wrong
Anyway, I'm curious to see if anyone else has done experiments along these lines, or would like to share thoughts and strategies. My current model puts the classifiers themselves into wrappers, so it would be pretty easy to swap out algorithms. I may put the code up on GitHub, although (1) I code like a scientist, so parts of my code are pretty bad and (2) I've never put anything up on GitHub before so I'm not really familiar with how it works.
