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.
| Version: |
1.0-8 |
| Depends: |
R (≥ 3.3.0), partykit |
| Suggests: |
Formula, kernlab, lattice, mlbench, multcomp, party, rpart, xtable |
| Published: |
2019-05-26 |
| DOI: |
10.32614/CRAN.package.evtree |
| Author: |
Thomas Grubinger [aut, cre],
Achim Zeileis 
[aut],
Karl-Peter Pfeiffer [aut] |
| Maintainer: |
Thomas Grubinger <ThomasGrubinger at gmail.com> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
yes |
| Citation: |
evtree citation info |
| Materials: |
NEWS |
| In views: |
MachineLearning |
| CRAN checks: |
evtree results |
Documentation:
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