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evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R

Author

Listed:
  • Thomas Grubinger

    ()

  • Achim Zeileis

    ()

  • Karl-Peter Pfeiffer

    ()

Abstract

Commonly 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".

Suggested Citation

  • Thomas Grubinger & Achim Zeileis & Karl-Peter Pfeiffer, 2011. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Working Papers 2011-20, Faculty of Economics and Statistics, University of Innsbruck.
  • Handle: RePEc:inn:wpaper:2011-20
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    References listed on IDEAS

    as
    1. Kurt Hornik & Christian Buchta & Achim Zeileis, 2009. "Open-source machine learning: R meets Weka," Computational Statistics, Springer, vol. 24(2), pages 225-232, May.
    2. Calcagno, Vincent & de Mazancourt, Claire, 2010. "glmulti: An R Package for Easy Automated Model Selection with (Generalized) Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i12).
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    Cited by:

    1. Susan Athey & Stefan Wager, 2017. "Efficient Policy Learning," Papers 1702.02896, arXiv.org, revised Oct 2017.
    2. Alvarez-Iglesias, Alberto & Hinde, John & Ferguson, John & Newell, John, 2017. "An alternative pruning based approach to unbiased recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 90-102.
    3. repec:gam:jeners:v:11:y:2018:i:4:p:949-:d:141385 is not listed on IDEAS
    4. repec:eee:energy:v:125:y:2017:i:c:p:771-787 is not listed on IDEAS

    More about this item

    Keywords

    machine learning; classification trees; regression trees; evolutionary algorithms; R;

    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

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