I am trying to build a prediction model using classification trees. While I tried the "rpart" package, the results were not entirely satisfactory. Hence, I thought of exploring conditional inference trees as well ("party" package in R)
Now, under the documentation for "ctree" function they have mentioned the following - "For example, when mincriterion = 0.95, the p-value must be smaller than 0.05 in order to split this node. This statistical approach ensures that the right sized tree is grown and no form of pruning or cross-validation or whatsoever is needed"
However, with the default mincriterion value of 0.95, I end up with just 1 split. Would it make sense, if I vary the mincriterion value (say from 0.95 to 0.90), cross validate the resulting models and pick the one with the lowest CV error?
If yes, is there a function within the party package which can help me do this? (roughly analogous to a "plotcp/printcp" function that we have in rpart)