  The following note proves that conditional entropy of a sequence is almost
time-reversal invariant, specifically they only differ by a small constant
factor dependent only upon the forward and backward models that the entropies
are being calculated with respect to. This gives rise to a numerical value that
quantifies learnability, as well as a methodology to control for distributional
shift between datasets. Rough guidelines are given for practitioners.
