ODRF
0.0.5Oblique Decision Random Forest for Classification and Regression
Overview
The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) DOI:10.1201/9781315139470 and Random Forest of Breiman (2001) DOI:10.1023/A:1010933404324 respectively.
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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
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Show 1 earlier snapshots
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 84%
- Return-value docs
- 100%
- References docs
- 39%
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13 development-tooling and community-health practices detected across 7 families in the upstream repository
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Package metadata
- First published
- 2023-02-28
- Total releases
- 4 / 3 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 74 KB / 3 files
- Download size
- 247 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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citation("ODRF")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
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