The Explainable Ensemble Trees 'e2tree' approach has been proposed by Aria et al. (2024) <doi:10.1007/s00180-022-01312-6>. It aims to explain and interpret decision tree ensemble models using a single tree-like structure. 'e2tree' is a new way of explaining an ensemble tree trained through 'randomForest' or 'xgboost' packages.
Version: | 0.1.2 |
Imports: | dplyr, doParallel, parallel, foreach, future.apply, ggplot2, Matrix, partitions, purrr, tidyr, randomForest, rpart.plot, Rcpp, RSpectra, ape |
LinkingTo: | Rcpp |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2025-04-12 |
DOI: | 10.32614/CRAN.package.e2tree |
Author: | Massimo Aria |
Maintainer: | Massimo Aria <aria at unina.it> |
BugReports: | https://github.com/massimoaria/e2tree/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/massimoaria/e2tree |
NeedsCompilation: | yes |
Citation: | e2tree citation info |
Materials: | README NEWS |
CRAN checks: | e2tree results |
Reference manual: | e2tree.pdf |
Package source: | e2tree_0.1.2.tar.gz |
Windows binaries: | r-devel: e2tree_0.1.2.zip, r-release: e2tree_0.1.2.zip, r-oldrel: e2tree_0.1.2.zip |
macOS binaries: | r-release (arm64): e2tree_0.1.2.tgz, r-oldrel (arm64): e2tree_0.1.2.tgz, r-release (x86_64): e2tree_0.1.2.tgz, r-oldrel (x86_64): e2tree_0.1.2.tgz |
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