I am looking for different modelling approaches which are able to build regression trees (i.e. with continuous input and output variables) with multiple input and output levels.
The most common approach (e.g. with CART) is to recursively do binary splits. I am looking for methods that could split the target variable into multiple branches (according to some sensible metric) within every step.
Any hints, i.e. papers, links etc. are very welcome.
(The reason for me asking this question is that I want to find a good method to extend my OneR package for solving regression problems.)
To clarify: I am looking for a method which could split the respective input variable into $n$ intervals where each interval leads to an interval in the target variable. I guess you would need some constraints (e.g. the max number of intervals) to get useful results.
To clarify further just an illustrative example for one input and the target variable:
If x = (-5.01,-3.45] then y = (-1.06,1.06] If x = (-3.45,-1.85] then y = (1.06,3.88] If x = (-1.85,-0.25] then y = (-1.06,1.06] If x = (-0.25,1.38] then y = (-3.88,-1.06] If x = (1.38,3.01] then y = (-1.06,1.06]
An algorithm for just one input variable would be sufficient for my purposes (so I only need a one-step regression tree).