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.

Install the latest version of this package by entering the following in R:
install.packages("randomUniformForest")
AuthorSaip Ciss
Date of publication2015-02-16 21:29:00
MaintainerSaip Ciss <saip.ciss@wanadoo.fr>
LicenseBSD_3_clause + file LICENSE
Version1.1.5

View on CRAN

Man pages

as.supervised: Conversion of an unsupervised model into a supervised one

autoMPG: Auto MPG Data Set

bCI: Bootstrapped Prediction Intervals for Ensemble Models

biasVarCov: Bias-Variance-Covariance Decomposition

breastCancer: Breast Cancer Wisconsin (Original) Data Set

carEvaluation: Car Evaluation Data Set

clusterAnalysis: Cluster (or classes) analysis of importance objects.

clusteringObservations: Cluster observations of a (supervised) randomUniformForest...

combineUnsupervised: Combine Unsupervised Learning objects

ConcreteCompressiveStrength: Concrete Compressive Strength Data Set

fillNA2.randomUniformForest: Missing values imputation by randomUniformForest

generic.cv: Generic k-fold cross-validation

getTree.randomUniformForest: Extract a tree from a forest

importance.randomUniformForest: Variable Importance for random Uniform Forests

init_values: Training and validation samples from data

internalFunctions: All internal functions

mergeClusters: Merge two arbitrary, but adjacent, clusters

model.stats: Common statistics for a vector (or factor) of predictions and...

modifyClusters: Change number of clusters (and clusters shape) on the fly

partialDependenceBetweenPredictors: Partial Dependence between Predictors and effect over...

partialDependenceOverResponses: Partial Dependence Plots and Models

partialImportance: Partial Importance for random Uniform Forests

plotTree: Plot a Random Uniform Decision Tree

postProcessingVotes: Post-processing for Regression

predict.randomUniformForest: Predict method for random Uniform Forests objects

randomUniformForest: Random Uniform Forests for Classification, Regression and...

randomUniformForest-package: Random Uniform Forests for Classification, Regression and...

reSMOTE: REplication of a Synthetic Minority Oversampling TEchnique...

rm.trees: Remove trees from a random Uniform Forest

roc.curve: ROC and precision-recall curves for random Uniform Forests

rUniformForest.big: Random Uniform Forests for Classification and Regression with...

rUniformForest.combine: Incremental learning for random Uniform Forests

rUniformForest.grow: Add trees to a random Uniform Forest

simulationData: Simulation of Gaussian vector

splitClusters: Split a cluster on the fly

unsupervised.randomUniformForest: Unsupervised Learning with Random Uniform Forests

update.unsupervised: Update Unsupervised Learning object

wineQualityRed: Wine Quality Data Set

Functions

as.supervised Man page
autoMPG Man page
bCI Man page
biasVarCov Man page
breastCancer Man page
carEvaluation Man page
CheckSameValuesInAllAttributes} \alias{CheckSameValuesInLabels} Man page
classifyMatrixCPP} \alias{combineRUFObjects} \alias{concat} \ali Man page
clusterAnalysis Man page
clusteringObservations Man page
combineUnsupervised Man page
ConcreteCompressiveStrength Man page
dates2numeric Man page
fillNA2.randomUniformForest Man page
filterOutliers Man page
generic.cv Man page
getTree Man page
getTree.randomUniformForest Man page
importance Man page
importance.randomUniformForest Man page
init_values Man page
insert.in.vector} \alias{insert.in.vector2} \alias{is.wholenumbe Man page
L1InformationGainCPP} \alias{L2.logDist} \alias{L2Dist} \alias{L Man page
leafNode} \alias{localTreeImportance} \alias{localVariableImport Man page
MDSscale} \alias{mergeOutliers} \alias{unsupervised2supervised} Man page
mergeClusters Man page
model.stats Man page
modifyClusters Man page
NAfactor2matrix} \alias{OOBquantiles} \alias{XMinMaxCPP} \alias{ Man page
partialDependenceBetweenPredictors Man page
partialDependenceOverResponses Man page
partialImportance Man page
perspWithcol Man page
plot.importance Man page
plot.randomUniformForest Man page
plotTree Man page
plotTreeCore} \alias{plotTreeCore2} \alias{predictDecisionTree} Man page
plot.unsupervised Man page
postProcessingVotes Man page
predict Man page
predict.randomUniformForest Man page
print.importance Man page
print.randomUniformForest Man page
print.unsupervised Man page
randomUniformForest Man page
randomUniformForestCore} \alias{randomUniformForestCore.big} \al Man page
randomUniformForestCore.predict} \alias{randomWhichMax} \alias{r Man page
randomUniformForest.default Man page
randomUniformForest.formula Man page
randomUniformForest-package Man page
reSMOTE Man page
rmNA Man page
rmNoise} \alias{rollApplyFunction} \alias{runifMatrixCPP} \alias Man page
rm.trees Man page
roc.curve Man page
rufImpute Man page
rUniformForest.big Man page
rUniformForest.combine Man page
rUniformForest.grow Man page
simulationData Man page
specClust} \alias{rm.coordinates Man page
splitClusters Man page
standardize_vect} \alias{strength_and_correlation} \alias{subEst Man page
summary.randomUniformForest Man page
uniformDecisionTree} \alias{vector2factor} \alias{vector2matrix} Man page
unsupervised Man page
unsupervised.randomUniformForest Man page
update Man page
update.unsupervised Man page
which.is.duplicate} \alias{define_train_test_sets} \alias{difflo Man page
which.is.nearestCenter} \alias{variance} \alias{interClassesVari Man page
wineQualityRed Man page

Files

inst
inst/CITATION
inst/doc
inst/doc/randomUniformForestsOverview.pdf.asis
inst/doc/randomUniformForestsOverview.pdf
inst/doc/VariableImportanceInRandomUniformForests.pdf.asis
inst/doc/VariableImportanceInRandomUniformForests.pdf
src
src/cppFunctions.cpp
NAMESPACE
NEWS
data
data/wineQualityRed.RData
data/carEvaluation.RData
data/autoMPG.RData
data/breastCancer.RData
data/ConcreteCompressiveStrength.RData
R
R/genericFunctions.R R/OnliningRandomUniformForests.R R/rUniformForestCppClass.R R/DecisionTreesCPP.R R/RandomUniformForestsCPP.R
vignettes
vignettes/randomUniformForestsOverview.pdf.asis
vignettes/VariableImportanceInRandomUniformForests.pdf.asis
MD5
build
build/vignette.rds
DESCRIPTION
man
man/bCI.Rd man/rUniformForest.combine.Rd man/partialImportance.Rd man/randomUniformForest-package.Rd man/randomUniformForest.Rd man/unsupervised.randomUniformForest.Rd man/modifyClusters.Rd man/generic.cv.Rd man/partialDependenceBetweenPredictors.Rd man/wineQualityRed.Rd man/breastCancer.Rd man/rUniformForest.grow.Rd man/mergeClusters.Rd man/biasVarCov.Rd man/reSMOTE.Rd man/init_values.Rd man/fillNA2.randomUniformForest.Rd man/plotTree.Rd man/clusteringObservations.Rd man/update.unsupervised.Rd man/partialDependenceOverResponses.Rd man/combineUnsupervised.Rd man/clusterAnalysis.Rd man/rUniformForest.big.Rd man/getTree.randomUniformForest.Rd man/roc.curve.Rd man/model.stats.Rd man/splitClusters.Rd man/autoMPG.Rd man/postProcessingVotes.Rd man/ConcreteCompressiveStrength.Rd man/importance.randomUniformForest.Rd man/internalFunctions.Rd man/simulationData.Rd man/predict.randomUniformForest.Rd man/as.supervised.Rd man/rm.trees.Rd man/carEvaluation.Rd
LICENSE
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