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: |
1.0.0 |
| Depends: |
R (≥ 3.5) |
| Imports: |
ape, dplyr, parallel, future.apply, ggplot2, Matrix, partitions, purrr, tidyr, Rcpp |
| LinkingTo: |
Rcpp |
| Suggests: |
doParallel, foreach, htmlwidgets, jsonlite, randomForest, ranger, rpart.plot, RSpectra, testthat (≥ 3.0.0), visNetwork |
| Published: |
2026-03-13 |
| DOI: |
10.32614/CRAN.package.e2tree |
| 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 |