Hello all,

This is more a statistical question then an R question, but I am sure it
will have an R interpretation to it.

If I wish to predict an outcome based on some potential features, I could
(in some cases) use either regression or regression-tree.
However, if my observations are divided to groups (for example by
"subject"), I might then want to model that using a random effect for the
"subject" and a fixed effect for the other features I wish to model for the
prediction.
My question is what (if exist) is the parallel of this in regression trees ?
Is it simply like adding the "subject" classifier to the tree? or is this
leading to a different model scheme all together? (and if so - what is it)


Thanks,
Tal




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