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. Both can be used for classification and
regression 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.
| Version: |
0.0.4 |
| Depends: |
partykit, R (≥ 3.5.0) |
| Imports: |
doParallel, foreach, glue, graphics, grid, lifecycle, magrittr, nnet, parallel, Pursuit, Rcpp, rlang (≥ 0.4.11), stats |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Published: |
2023-05-28 |
| DOI: |
10.32614/CRAN.package.ODRF |
| Author: |
Yu Liu [aut, cre, cph],
Yingcun Xia [aut] |
| Maintainer: |
Yu Liu <liuyuchina123 at gmail.com> |
| BugReports: |
https://github.com/liuyu-star/ODRF/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://liuyu-star.github.io/ODRF/ |
| NeedsCompilation: |
yes |
| Language: |
en-US |
| Citation: |
ODRF citation info |
| Materials: |
README NEWS |
| CRAN checks: |
ODRF results |