evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R
AbstractCommonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. This paper describes the "evtree" package, which implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. Computationally intensive tasks are fully computed in C++ while the "partykit" (Hothorn and Zeileis 2011) package is leveraged for representing the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. "evtree" is compared to "rpart" (Therneau and Atkinson 1997), the open-source CART implementation, and conditional inference trees ("ctree", Hothorn, Hornik, and Zeileis 2006). The usefulness of "evtree" is illustrated in a textbook customer classification task and a benchmark study of predictive accuracy in which "evtree" achieved at least similar and most of the time better results compared to the recursive algorithms "rpart" and "ctree".
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Bibliographic InfoPaper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2011-20.
Date of creation: Sep 2011
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machine learning; classification trees; regression trees; evolutionary algorithms; R;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-10-09 (All new papers)
- NEP-CMP-2011-10-09 (Computational Economics)
- NEP-ORE-2011-10-09 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kurt Hornik & Christian Buchta & Achim Zeileis, 2009. "Open-source machine learning: R meets Weka," Computational Statistics, Springer, Springer, vol. 24(2), pages 225-232, May.
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