evtree: Evolutionary Learning of Globally Optimal Trees
Version 1.0-5

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.

Package details

AuthorThomas Grubinger [aut, cre], Achim Zeileis [aut], Karl-Peter Pfeiffer [aut]
Date of publication2017-04-05 18:18:58
MaintainerThomas Grubinger <Thomas.Grubinger@scch.at>
LicenseGPL-2 | GPL-3
Package repositoryView on R-Forge
Installation Install the latest version of this package by entering the following in R:
install.packages("evtree", repos="http://R-Forge.R-project.org")

Try the evtree package in your browser

Any scripts or data that you put into this service are public.

evtree documentation built on May 31, 2017, 4:58 a.m.