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randomUniformForest
View on CRAN: Click
here
Download and install randomUniformForest package within the R console
Install from CRAN:
install.packages("randomUniformForest")
Install from Github:
library("remotes")
install_github("cran/randomUniformForest")
Install by package version:
library("remotes")
install_version("randomUniformForest", "1.1.6")
Attach the package and use:
library("randomUniformForest")
Maintained by
Saip Ciss
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-05-29
Latest Update: 2022-06-21
Description:
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.
How to cite:
Saip Ciss (2014). randomUniformForest: Random Uniform Forests for Classification, Regression and Unsupervised Learning. R package version 1.1.6, https://cran.r-project.org/web/packages/randomUniformForest. Accessed 21 Dec. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for randomUniformForest
Functions, R codes and Examples using
the randomUniformForest R package
Some associated functions: ConcreteCompressiveStrength . as.supervised . autoMPG . bCI . biasVarCov . breastCancer . carEvaluation . clusterAnalysis . clusteringObservations . combineUnsupervised . fillNA2.randomUniformForest . generic.cv . getTree.randomUniformForest . importance.randomUniformForest . init_values . internalFunctions . mergeClusters . model.stats . modifyClusters . partialDependenceBetweenPredictors . partialDependenceOverResponses . partialImportance . plotTree . postProcessingVotes . predict.randomUniformForest . rUniformForest.big . rUniformForest.combine . rUniformForest.grow . randomUniformForest-package . randomUniformForest . reSMOTE . rm.trees . roc.curve . simulationData . splitClusters . unsupervised.randomUniformForest . update.unsupervised . wineQualityRed .
Some associated R codes: DecisionTreesCPP.R . OnliningRandomUniformForests.R . RandomUniformForestsCPP.R . genericFunctions.R . rUniformForestCppClass.R . Full randomUniformForest package functions and examples
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