e2tree
1.2.0Explainable Ensemble Trees
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1813 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 0%
- Documented parameters
- 96%
- Return-value docs
- 91%
- References docs
- 0%
Downloads
Repository
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Repository practices
2 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/massimoaria/e2tree on 2026-08-16.
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
4 releases. Pick two to compare their code metrics. R releases are shown for context.
Package metadata
- First published
- 2025-04-12
- Total releases
- 4 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 2.1 KB / 1 file
- Download size
- 3.4 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("e2tree")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.