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. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions.
|Author||Thomas Grubinger [aut, cre], Achim Zeileis [aut], Karl-Peter Pfeiffer [aut]|
|Date of publication||2017-04-06 21:46:26 UTC|
|Maintainer||Thomas Grubinger <Thomas.Grubinger@scch.at>|
|License||GPL-2 | GPL-3|
|Package repository||View on CRAN|
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