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

By Thomas Grubinger, Achim Zeileis, K. Pfeiffer. 2014

208 citations recorded by the source corpus.

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 package is leveraged for representing the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. evtree is compared to the open-source CART implementation rpart, conditional inference trees (ctree), and the open-source C4.5 implementation J48. A benchmark study of predictive accuracy and complexity is carried out in which evtree achieved at least similar and most of the time better results compared to rpart, ctree, and J48. Furthermore, the usefulness of evtree in practice is illustrated in a textbook customer classification task.

Subfields: Artificial Intelligence

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Citations in the corpus

Mathematical optimization in classification and regression trees (2021, methodology): “To enhance their performance, the greedy procedures were extended in different directions, such as the use of global optimization approaches (Barros et al. 2011; Fu et al. 2003; Grubinger et al. 2014).”
Efficient Policy Learning (2017, methodology): “Finally, we got π̂ by optimizing (18) over depth-2 trees with the R-package evtree, which learns an optimal classification tree using an evolutionary algorithm (Grubinger et al., 2014).”
Profit Driven Decision Trees for Churn Prediction (2017, methodology): “ProfTree is also implemented in R and it exploits the functionalitities available in the evtree package (Grubinger et al., 2011) and the EMP package (Bravo et al., 2015).”
Optimal trees for prediction and prescription (2018, background): “Nevertheless, there have been many efforts previously to develop effective ways of constructing optimal univariate decision trees using a variety of heuristic methods for growing the tree in one step, including linear optimization [7], continuous optimization [8], dynamic programming [37, 99], genet”
An extensive experimental survey of regression methods (2019, methodology): “evtree [47] Table 6: List of regressors and references grouped by families (see Appendix A for a brief description of each regressor).”
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials (2018, methodology): “Two papers which propose response adaptive methods in a similar framework to ours are Hahn et al. (2011) and Chambaz et al.”