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From what I understood, these models differ from CARTs for regression, mostly because they fit a linear model at the leaves of the tree instead of simply taking an average. They also "smooth" the tree by generating linear models in the intermediate steps of the tree growth process.

I have been using the R implementation of them a bit for regression getting very good results. But I wonder about the assumptions of a usual linear model? Multicolinearity, Autocorrelation, Non-Normality being violated doesnt worry people in this case?

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I'm not sure what exactly is the question you're asking? Can you be more specific? – Momo Feb 20 '13 at 22:59
If you are using linear regressions at different steps in your primary model, shouldnt you worry about the theoretical assumptions of a linear regression? For example, does everything work fine even if you have highly correlated variabes, or autocorrelated observations? – JEquihua Feb 20 '13 at 23:33
I asked for specifics because it depends on algorithm, purpose and usage. Inference most certainly needs assumptions, simple prediction may not. Also there are many ways to estimate a linear model. What do you mean by "does everything work"? Furthermore, why do you have the impression that "people are not worried"? – Momo Feb 20 '13 at 23:55

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