document: Scalable look-ahead linear regression trees Most decision tree algorithms base their splitting decisions on a piecewise constant model . Often these splitting algorithms are extrapolated to trees with non-constant models at the leaf nodes . The motivation behind Look-ahead Linear Regression Trees ( LLRT ) is that out of all the methods proposed to date , there has been no scalable approach to exhaustively evaluate all possible models in the leaf nodes in order to obtain an optimal split . Using several optimizations , LLRT is able to generate and evaluate thousands of linear regression models per second . This allows for a near-exhaustive evaluation of all possible splits in a node , based on the quality of fit of linear regression models in the resulting branches . We decompose the calculation of the Residual Sum of Squares in such a way that a large part of it is pre-computed . The resulting method is highly scalable . We observe it to obtain high predictive accuracy for problems with strong mutual dependencies between attributes . We report on experiments with two simulated and seven real data sets .
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