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
- OK2026-04-2214 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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Releases over time
Issues over time
PRs over time
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 2026-07-19.
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 2.0 median / 101 max
- Test cases
- 71 / 0.33 per code line
Test coverage
Line coverage
23%
Expression
26.1%
Tests / Examples
23.0% / 0% ex
Functions
135 22 exported
Complexity
5.2 avg / 101 max
Call network
135 nodes / 110 edges
Call graph
Open call graph →Lowest coverage
135 functions| Function | Cyclo | Coverage |
|---|---|---|
| as.rpart exp | 1 | 0% |
| e2splits exp | 1 | 0% |
| eValidation exp | 14 | 0% |
| loi exp | 9 | 0% |
| loi_perm exp | 8 | 0% |
| measures exp | 1 | 0% |
Datasets
| Name | Class | Rows × Cols | Also ships in |
|---|---|---|---|
| credit | tbl_df/tbl/data.frame | 468 × 12 | – |
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
- 1.8 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet