CheckSameValuesInAllAttributes

CheckSameValuesInLabels

HuberDist

L1Dist

L1DistCPP

L1InformationGainCPP

L2.logDist

L2Dist

L2DistCPP

L2InformationGainCPP

NAFeatures

NAfactor2matrix

OOBquantiles

XMinMaxCPP

as.true.matrix

bCICore

binomialrequiredSampleSize

category2Proba

category2Quantile

categoryCombination

checkUniqueObsCPP

classifyCPP

classifyMatrixCPP

combineRUFObjects

concat

concatCore

conditionalCrossEntropyCPP

conditionalGiniCPP

confusion.matrix

count.factor

crossEntropyCPP

dates2numeric

which.is.duplicate

define_train_test_sets

difflog

dummy.recode

entropyInformationGainCPP

estimatePredictionAccuracy

estimaterequiredSampleSize

expectedSquaredBias

extractYFromData

fScore

factor2matrix

factor2vector

fillVariablesNames

fillWith

filter.forest

filter.object

find.first.idx

find.idx

find.root

fullNDCG

fullNode

gMean

generalization.error

generic.log

generic.smoothing.log

genericCbind

genericNode

genericOutput

getCorr

getOddEven

getVotesProbability

getVotesProbability2

giniCPP

imputeCategoryForTestData

inDummies

insert.in.vector

insert.in.vector2

is.wholenumber

keep.index

lagFunction

leafNode

localTreeImportance

localVariableImportance

majorityClass

matrix2factor

mergeLists

min_or_max

modX

monitorOOBError

na.impute

na.missing

na.replace

ndcg

onlineClassify

onlineCombineRUF

optimizeFalsePositives

options.filter

outputPerturbationSampling

outsideConfIntLevels

overSampling

parallelNA.replace

permuteCatValues

perspWithcol

plotTreeCore

plotTreeCore2

predictDecisionTree

predictionvsResponses

<p>Internal functions for random Uniform Forests</p>

internalFunctions

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.

internalFunctions: All internal functions

Description

Arguments