Computational vs intuitionistic or expert-based information gain in decision trees?
This confuses me.
Plenty of literature on how information gain can be used when it's calculated computationally. But what if there's a competing sense of "intuitionistic (or expert-based) information importance"? That is, the researcher has an intution about relative importances and this may not actually be conveyed in the training set. Or some of it may be lost by the model.
If one'd use computational methods to infer good split points, then it's possible that these match the training set, but not necessarily the intuition.
It's also possible that the intuitional approach would later prove inaccurate in some sense, if new observations would come that display computational information gains that suggest readjusting intuitionistic bounds.
So is there some middle-ground to combine these two views?