My experience with
rpart model is the model seems to be always too simple / over pruning. In many experiments, I am seeing a over simplified tree with only 1 or 2 split, but I know from the domain knowledge the model should be more complicated.
Currently, I am manually experimenting with
minsplit parameter to get a more complicated tree, sometimes sacrifice cross validation performance to get a more "informative" tree.
Would other models like conditional inference trees be better in terms of over pruning? Or other suggestions to get a more complicated model?
PS, I aware of over-fitting risk, but think the model I got from CART is still too simple. And I remember I read from somewhere that the pruning method proposed in CART may have the problem of "over pruning".