Functional Models for Regression Tree Leaves
Luís Torgo
1997
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
an inductive learning system (HTL) that learns
regression trees and is able to use several alternative
models in the tree leaves. We study the effect on
accuracy of using the different models and evaluate
their computational cost. The experiments carried out
on 11 datasets 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.