R-RerF (aka Randomer Forest (RerF) or Random Projection Forests) is an algorithm developed by Tomita (2016) <arXiv:1506.03410v2> which is similar to Random Forest - Random Combination (Forest-RC) developed by Breiman (2001) <doi:10.1023/A:1010933404324>. Random Forests create axis-parallel, or orthogonal trees. That is, the feature space is recursively split along directions parallel to the axes of the feature space. Thus, in cases in which the classes seem inseparable along any single dimension, Random Forests may be suboptimal. To address this, Breiman also proposed and characterized Forest-RC, which uses linear combinations of coordinates rather than individual coordinates, to split along. This package, 'rerf', implements RerF which is similar to Forest-RC. The difference between the two algorithms is where the random linear combinations occur: Forest-RC combines features at the per tree level whereas RerF takes linear combinations of coordinates at every node in the tree.
| Version: | 2.0.4 |
| Depends: | R (≥ 3.3.0), Rcpp (≥ 1.0.0) |
| Imports: | parallel, RcppZiggurat, utils, stats, dummies, mclust |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | roxygen2 (≥ 5.0.0), testthat |
| Published: | 2019-03-15 |
| Author: | Jesse Patsolic [ctb, cre], Benjamin Falk [ctb], Jaewon Chung [ctb], James Browne [aut], Tyler Tomita [aut], Joshua Vogelstein [ths] |
| Maintainer: | Jesse Patsolic <software at neurodata.io> |
| BugReports: | https://github.com/neurodata/R-RerF/issues |
| License: | Apache License 2.0 | file LICENSE |
| URL: | https://github.com/neurodata/R-RerF |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| Materials: | README NEWS |
| CRAN checks: | rerf results |
| Reference manual: | rerf.pdf |
| Package source: | rerf_2.0.4.tar.gz |
| Windows binaries: | r-devel: rerf_2.0.4.zip, r-release: rerf_2.0.4.zip, r-oldrel: rerf_2.0.4.zip |
| macOS binaries: | r-release: rerf_2.0.4.tgz, r-oldrel: rerf_2.0.4.tgz |
| Old sources: | rerf archive |
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