Package: evtree 1.0-8

Thomas Grubinger

evtree: Evolutionary Learning of Globally Optimal Trees

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.

Authors:Thomas Grubinger [aut, cre], Achim Zeileis [aut], Karl-Peter Pfeiffer [aut]

evtree_1.0-8.tar.gz
evtree_1.0-8.tar.gz(r-4.5-noble)evtree_1.0-8.tar.gz(r-4.4-noble)
evtree_1.0-8.tgz(r-4.4-emscripten)evtree_1.0-8.tgz(r-4.3-emscripten)
evtree.pdf |evtree.html
evtree/json (API)
NEWS

# Install 'evtree' in R:
install.packages('evtree', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.69 score 7 stars 2 packages 126 scripts 1.6k downloads 6 mentions 2 exports 9 dependencies

Last updated 6 years agofrom:e0d856a2a2. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-linux-x86_64OKOct 31 2024

Exports:evtreeevtree.control

Dependencies:FormulainumlatticelibcoinMatrixmvtnormpartykitrpartsurvival

Evolutionary Learning of Globally Optimal Classification and Regression Trees in R

Rendered fromevtree.Rnwusingutils::Sweaveon Oct 31 2024.

Last update: 2019-05-26
Started: 2012-04-11