I have a regression problem and compare different types of regressiont trees (simple regression tree, random forest, gradient boosted tree and the tree ensemble learner. The data I use has been joined, filtered and preprocessed in many ways. For this reason I use a domain calculator and drop possible values and min/max values of alle attributes.
I found differences between models with or without the use of the domain calculator (using cross fold validation). Most models score considerably lower with the domain calculator, especially the gradient boosted tree shows a serious decline of the scored statistics. One model seems not to be feasible for the use of the domain calculator: the simple regression tree. What is the reason for this? Do some models make use of domain information to learn the model and the simple regression learner not?