randomUniformForest: Random Uniform Forests for Classification, Regression and Unsupervised Learning
- ️Tue Jun 21 2022
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 |
| DOI: | 10.32614/CRAN.package.randomUniformForest |
| 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 |
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