Package: e2tree 1.2.0.9000

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.

Authors:Massimo Aria [aut, cre, cph], Agostino Gnasso [aut, cph]

e2tree_1.2.0.9000.tar.gz
e2tree_1.2.0.9000.zip(r-4.7-x86_64)e2tree_1.2.0.9000.zip(r-4.7-arm64)e2tree_1.2.0.9000.zip(r-4.6-x86_64)e2tree_1.2.0.9000.zip(r-4.6-arm64)e2tree_1.2.0.9000.zip(r-4.5-x86_64)
e2tree_1.2.0.9000.tgz(r-4.6-x86_64)e2tree_1.2.0.9000.tgz(r-4.6-arm64)e2tree_1.2.0.9000.tgz(r-4.5-x86_64)e2tree_1.2.0.9000.tgz(r-4.5-arm64)
e2tree_1.2.0.9000.tar.gz(r-4.7-arm64)e2tree_1.2.0.9000.tar.gz(r-4.7-x86_64)e2tree_1.2.0.9000.tar.gz(r-4.6-arm64)e2tree_1.2.0.9000.tar.gz(r-4.6-x86_64)
e2tree_1.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
e2tree/json (API)

# Install 'e2tree' in R:
install.packages('e2tree', repos = c('https://massimoaria.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/massimoaria/e2tree/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • credit - Credit Scoring Dataset

On CRAN:

Conda:

explainable-machine-learningcppopenmp

6.28 score 8 stars 24 scripts 475 downloads 22 exports 49 dependencies

Last updated from:163220441f. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK181
linux-devel-x86_64OK751
source / vignettesOK215
linux-release-arm64OK185
linux-release-x86_64OK221
macos-release-arm64OK174
macos-release-x86_64OK301
macos-oldrel-arm64OK131
macos-oldrel-x86_64OK304
windows-devel-arm64OK410
windows-devel-x86_64OK207
windows-release-arm64OK373
windows-release-x86_64OK170
windows-oldrel-x86_64OK158
wasm-releaseOK129

Exports:as.rpartcreateDisMatrixe2splitse2treeePredTreeeValidationextract_terminal_nodesget_ensemble_predictionsget_ensemble_typeloiloi_permmeasuresnodesplot_e2treeplot_e2tree_clickplot_e2tree_visprint_e2tree_summaryproximityrocrpart2Treesave_e2tree_htmlvimp

Dependencies:apeclicodetoolscpp11digestdplyrfarverfuturefuture.applygenericsggplot2globalsgluegmpgtableisobandlabelinglatticelifecyclelistenvmagrittrMatrixnlmeparallellypartitionspillarpkgconfigpolynompurrrR6rbibutilsRColorBrewerRcppRdpackrlangrpartrpart.plotS7scalessetsstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Using e2tree with XGBoost, GBM, LightGBM, and CatBoost
Overview | XGBoost | Classification (iris) | Regression (mtcars) | GBM | Classification (iris, binary) | LightGBM | CatBoost | Adding a new backend
Rendered frommodels.Rmdusingknitr::rmarkdown

Last update: 2026-05-06
Started: 2026-05-06

Introduction to e2tree: Explainable Ensemble Trees
Overview | Installation | Workflow | Classification Example | Step 1: Prepare data and train ensemble | Step 2: Compute dissimilarity matrix | Step 3: Build the E2Tree | Step 4: Inspect the tree | Step 5: Visualization | Step 6: Prediction | Step 7: Variable importance | Regression Example | Validation of the E2Tree Structure | Association vs. Agreement | The Mantel Test | Divergence and Similarity Measures | Visualizing the Reconstruction | The Normalized Loss of Interpretability (nLoI) | LoI Decomposition: Diagnosing Reconstruction Quality | Permutation Test for LoI | Validation of the Regression Example | Comparison with Related Packages | Session Info
Rendered frome2tree-introduction.Rmdusingknitr::rmarkdown

Last update: 2026-03-22
Started: 2026-03-22