randomUniformForest: Random Uniform Forests for Classification, Regression and Unsupervised Learning

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


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_1.1.6.zip
macOS binaries: r-release (arm64): randomUniformForest_1.1.6.tgz, r-oldrel (arm64): randomUniformForest_1.1.6.tgz, r-release (x86_64): randomUniformForest_1.1.6.tgz, r-oldrel (x86_64): randomUniformForest_1.1.6.tgz
Old sources: randomUniformForest archive


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