Model trees are an extension of regression trees that associate leaves with multiple regression models. In this paper a method for the top-down induction of model trees is presented, namely the Stepwise Model Tree Induction (SMOTI) method. Its main characteristic is the induction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and split nodes, which partition the sample space. The multiple linear model associated to each leaf is then obtained by combining straight-line regressions reported along the path from the root to the leaf. In this way, internal regression nodes contribute to the definition of multiple models and have a “global” effect, while straight-line regressions at leaves have only “local” effects. This peculiarity of SMOTI has been evaluated in an empirical study involving both real and artificial data.
Trading-off Local versus Global Effects of Regression Nodes in Model Trees
MALERBA D;APPICE, ANNALISA;CECI M;
2002-01-01
Abstract
Model trees are an extension of regression trees that associate leaves with multiple regression models. In this paper a method for the top-down induction of model trees is presented, namely the Stepwise Model Tree Induction (SMOTI) method. Its main characteristic is the induction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and split nodes, which partition the sample space. The multiple linear model associated to each leaf is then obtained by combining straight-line regressions reported along the path from the root to the leaf. In this way, internal regression nodes contribute to the definition of multiple models and have a “global” effect, while straight-line regressions at leaves have only “local” effects. This peculiarity of SMOTI has been evaluated in an empirical study involving both real and artificial data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
