SECRET: a scalable linear regression tree algorithm

Recently there has been an increasing interest in developing regression models for large datasets that are both accurate and easy to interpret. Regressors that have these properties are regression trees with linear models in the leaves, but so far, the algorithms proposed for constructing them are not scalable. In this paper we propose a novel regression tree construction algorithm that is both accurate and can truly scale to very large datasets. The main idea is, for every intermediate node, to use the EM algorithm for Gaussian mixtures to find two clusters in the data and to locally transform the regression problem into a classification problem based on closeness to these clusters. Goodness of split measures, like the gini gain, can then be used to determine the split variable and the split point much like in classification tree construction. Scalability of the algorithm can be enhanced by employing scalable versions of the EM and the classification tree construction algorithms. Tes...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Alin Dobra, Johannes Gehrke
• Keywords: Classification Tree Construction, Data Mining, KDD 2002, Regression Tree Algorithms, Regression Tree Construction