> I'd imagine it would be hard to train a classifier to do something like this because you'd need to represent a good distribution of all the states

Yes, one general classifier would be very hard to train. However, you can create a sort of ensemble of classifiers, each trained in different tasks

I’m currently experimenting with this. So far I’ve combined classifiers for 13 different datasets, my target is 95 (the ones Laya used for training)