Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.
| Version: | 1.1.6 |
| Depends: | R (≥ 4.2.0) |
| Imports: | methods, Rcpp (≥ 0.11.1), parallel, doParallel, iterators, foreach (≥ 1.4.2), ggplot2, pROC, cluster, MASS |
| LinkingTo: | Rcpp |
| Suggests: | R.rsp |
| Published: | 2022-06-21 |
| Author: | Saip Ciss |
| Maintainer: | Saip Ciss <saip.ciss at wanadoo.fr> |
| License: | BSD_3_clause + file LICENSE |
| NeedsCompilation: | yes |
| Citation: | randomUniformForest citation info |
| Materials: | NEWS |
| CRAN checks: | randomUniformForest results |
| Reference manual: | randomUniformForest.pdf |
| Vignettes: |
Variable Importance in Random Uniform Forests Random Uniform Forests in theory and practice |
| Package source: | randomUniformForest_1.1.6.tar.gz |
| Windows binaries: | r-devel: randomUniformForest_1.1.6.zip, r-release: randomUniformForest_1.1.6.zip, r-oldrel: randomUniformForest_1.1.5.zip |
| macOS binaries: | r-release (arm64): randomUniformForest_1.1.6.tgz, r-oldrel (arm64): randomUniformForest_1.1.5.tgz, r-release (x86_64): randomUniformForest_1.1.6.tgz, r-oldrel (x86_64): randomUniformForest_1.1.5.tgz |
| Old sources: | randomUniformForest archive |
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