This idea, whereas well justified and consistent with the principle of model trees, is hardly
applicable in practice due to the computational expense of creating linear models for each
outcome of each candidate split in each node. Practical model tree growing algorithms fall
back to simple and imperfect, but computationally efficient heuristics. Of those, the weighted
target function dispersion, as used for plain regression trees, remains the most popular choice