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

Documentation:

Reference manual:	randomUniformForest.pdf
Vignettes:	Variable Importance in Random Uniform Forests Random Uniform Forests in theory and practice

Downloads:

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

Linking:

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