Regression trees are tree-based models used to solve those prediction problems in which the response variable is numeric. They differ from the better-known classification or decision trees only in that they have a numeric value rather than a class label associated with the leaves. Model trees are an extension of regression trees in the sense that they associate leaves with multivariate linear m...

The purpose of this methodological study was to compare methods of developing predictive rules that are parsimonious and clinically interpretable from electronic health record (EHR) home visit data, contrasting logistic regression with three data mining classification models. We address three problems commonly encountered in EHRs: the value of including clinically important variables with littl...

The random forest method is a commonly used tool for classification with high-dimensional data that is able to rank candidate predictors through its inbuilt variable importance measures (VIMs). It can be applied to various kinds of regression problems including nominal, metric and survival response variables. While classification and regression problems using random forest methodology have been...

The party package (Hothorn, Hornik, and Zeileis 2006) aims at providing a recursive part(y)itioning laboratory assembling various highand low-level tools for building tree-based regression and classification models. This includes conditional inference trees (ctree), conditional inference forests (cforest) and parametric model trees (mob). At the core of the package is ctree, an implementation o...

in the present study, classification trees (cts-j48 algorithm) were used to study the occurrence of roach in rivers in flanders (belgium). the presence/absence of roach was modelled based on a set of river characteristics. the predictive performance of the cts models was assessed based on the percentage of correctly classified instances (cci) and cohen's kappa statistics. to find the best model...

We address the problem of computing various types of expressive tests for decision trees and regression trees. Using expressive tests is promising, because it may improve the prediction accuracy of trees, and it may also provide us some hints on scientific discovery. The drawback is that computing an optimal test could be costly. We present a unified framework to approach this problem, and we r...

In many data mining applications we are given a set of training examples and asked to construct a regression machine or a classifier that has low prediction error or low error rate on new examples. An important issue is speed especially when there are large amounts of data. We show how both classification accuracy and prediction error can be reduced by using boosting techniques to implement com...

In many data mining applications we are given a set of training examples and asked to construct a regression machine or a classifier that has low prediction error or low error rate on new examples, respectively. An important issue is speed especially when there are large amounts of data. We show how both classification and prediction error can be reduced by using boosting techniques to implemen...