I'm creating a binary classification model to develop relevant segments for a business problem. The ctree-function does a great job, especially in combination with the
minbucket-argument, which avoids having too small leafs.
Because my dataset is quite large (several 100.000 cases) the algorithm often produces leafs, that only differ slightly in the proportion of positive cases.
Ideally, I'd like to prune trees, so that they are only split, if the proportion differs by more than say 5%pp. I've been trying to use mincriterion, to use the underlying test statistic for this, but this hasn't had an impact yet (probably because the dataset is so large).