Oblivious Piecewise-Linear Decision Trees

Gradient boosting ensembles of decision trees are a very popular machine learning model with several popular implementations. Some of those implementations utilize oblivious decision trees - decision trees of specific structures that allow for improved regularization and prediction speed. Over the last years, several research works have been published related to piecewise-linear decision trees and their utilization in gradient boosting ensembles. In this paper, oblivious piecewise-linear decision trees were introduced, and it was shown that it is possible to train such trees in gradient boosting ensembles efficiently. Furthermore, it was shown that such oblivious piecewise-linear decision trees have significantly faster prediction time than regular decision trees and piecewise-linear decision trees of similar depths. Ensembles of oblivious piecewise-linear decision trees achieve competitive quality on open datasets.