The probabilistic guarantee
		where N = size of training set            h = VC dimension of the model class            p = upper bound on probability that this bound fails     So if we train models with different complexity, we should pick the one that minimizes this bound     Actually, this is only sensible if we think the bound is fairly tight, which it usually isn’t. The theory provides insight, but in practice we still need some witchcraft.