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 ORCID iD [aut, cre, cph], Agostino Gnasso ORCID iD [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:

Reference manual: e2tree.pdf

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:

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