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.">

e2tree: Explainable Ensemble Trees (original) (raw)

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 ORCID iD [aut, cre, cph], Agostino Gnasso ORCID iD [aut]
Maintainer: Massimo Aria
BugReports: https://github.com/massimoaria/e2tree/issues
License: MIT + file
URL: https://github.com/massimoaria/e2tree
NeedsCompilation: yes
Citation: e2tree citation info
Materials: README NEWS
CRAN checks: e2tree results

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