Classification and regression trees are not particularly useful when it comes to deciphering linear relationships, having no choice but to produce a long line of splits on the same variable. If the modeler suspects strong linear relationships, small trees can first be fit to the data to partition it into a few more similar groups, and then standard parametric models can be run on these groups. Another alternative available in some software packages is creating linear combinations of the explanatory variables, then entering these as new explanatory variables for the tree.
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