This paper presents a new predictive modeling algorithm that draws inspiration from the Kolmogorov superposition theorem. An initial version of the algorithm is presented that combines gradient boosting with decision-tree methods to construct models that have the same overall mathematical structure as Kolmogorov’s superposition equation. Improvements to the algorithm are then presented that significantly increase its rate of convergence. The resulting algorithm, dubbed "transform regression," generates surprisingly good models compared to those produced by the underlying decision-tree method when the latter is applied outside the transform regression framework.
By: Edwin Pednault
Published in: RC23227 in 2004
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