ODRF: Oblique Decision Random Forest for Classification and Regression (original) (raw)
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 |
| 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 |
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