Adapting peepholing to regression trees

Abstract

This paper presents an adaptation of the peepholing method to regression trees. Peepholing was described as a means to overcome the major computational bottleneck of growing classification trees by Catlett [3]. This method involves two major steps: shortlisting and bunkering. The former has the goal of eliminating some continuous variables from consideration when growing the tree, while the second tries to restrict the range of values of the remaining continuous variables that should be considered when searching for the best cut point split. Both are effective means of overcoming the most costly step of growing tree-based models: sorting the values of the continuous variables before selecting their best split. In this work we describe the adaptations that are necessary to use this method within regression trees. The major adaptations involve developing means to obtain biased estimates of the criterion used to select the best split of these models. We present some preliminary experiments that show the effectiveness of our proposal. © Springer-Verlag Berlin Heidelberg 2005.