Note that the resulting partition is one of the main differences between classification trees and, e.g., linear regression models: While in linear regression the information from different predictor variables is combined linearly, here the range of possible combinations includes all rectangular partitions that can be derived by means of recursive splitting – including multiple splits in the same variable. In particular, this includes nonlinear and even nonmonotone association rules, that do not need to be specified in advance but are determined in a data driven way.
