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: | 0.2.0 |
| Imports: | dplyr,doParallel, parallel,foreach,future.apply,ggplot2,Matrix,partitions,purrr,tidyr,ranger,randomForest,rpart.plot,Rcpp,RSpectra,ape |
| LinkingTo: | Rcpp |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2025-07-16 |
| 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 + fileLICENSE |
| URL: | https://github.com/massimoaria/e2tree |
| NeedsCompilation: | yes |
| Citation: | e2tree citation info |
| Materials: | README,NEWS |
| CRAN checks: | e2tree results |