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 [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: Linking:Please use the canonical form https://CRAN.R-project.org/package=e2tree to link to this page.
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