Functional Models for Regression Tree Leaves
Functional Models for Regression Tree Leaves
Luís Torgo
LIACC - University of Porto
R. Campo Alegre, 823 - 4150 Porto - PORTUGAL
email : ltorgo@liacc.up.pt
WWW : http://www.liacc.up.pt/~ltorgo
Abstract
This paper presents a study about functional models for regression tree leaves. We evaluate experimentally several alternatives to the averages commonly used in regression trees. We have implemented a regression tree learner (HTL) that is able to use several alternative models in the tree leaves. We study the effect on accuracy and the computational cost of these alternatives. The experiments carried out on 11 data sets revealed that it is possible to significantly outperform the "naive" averages of regression trees. Among the four alternative models that we evaluated, kernel regressors were usually the best in terms of accuracy. Our study also indicates that by integrating regression trees with other regression approaches we are able to overcome the limitations of individual methods both in terms of accuracy as well as in computational efficiency.
1 INTRODUCTION
2 REGRESSION PROBLEMS
3 HYBRID REGRESSION TREES
4 EXPERIMENTS
5 FUTURE IMPROVEMENTS
6 CONCLUSIONS
Contents