Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
Top-Down Induction of Model Trees with Regression and Splitting Nodes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
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In statistics, logistic regression is a regression model to predict a binomially distributed response variable. Recent research has investigated the opportunity of combining logistic regression with decision tree learners. Following this idea, we propose a novel Logistic Model Tree induction system, SILoRT, which induces trees with two types of nodes: regression nodes, which perform only univariate logistic regression, and splitting nodes, which partition the feature space. The multiple regression model associated with a leaf is then built stepwise by combining univariate logistic regressions along the path from the root to the leaf. Internal regression nodes contribute to the definition of multiple models and have a global effect, while univariate regressions at leaves have only local effects. Experimental results are reported.