e2tree: Explainable Ensemble Trees
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 |
Author: |
Massimo Aria
[aut, cre, cph],
Agostino Gnasso
[aut] |
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 |
Documentation:
Downloads:
Package source: |
e2tree_0.1.2.tar.gz |
Windows binaries: |
r-devel: not available, r-release: not available, r-oldrel: not available |
macOS binaries: |
r-devel (arm64): not available, r-release (arm64): not available, r-oldrel (arm64): not available, r-devel (x86_64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=e2tree
to link to this page.