Package: randomUniformForest 1.1.6

Saip Ciss

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.

Authors:Saip Ciss

randomUniformForest_1.1.6.tar.gz
randomUniformForest_1.1.6.tar.gz(r-4.5-noble)randomUniformForest_1.1.6.tar.gz(r-4.4-noble)
randomUniformForest_1.1.6.tgz(r-4.4-emscripten)randomUniformForest_1.1.6.tgz(r-4.3-emscripten)
randomUniformForest.pdf |randomUniformForest.html
randomUniformForest/json (API)
NEWS

# Install 'randomUniformForest' in R:
install.packages('randomUniformForest', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

3.77 score 3 stars 99 scripts 220 downloads 215 exports 36 dependencies

Last updated 2 years agofrom:f0176efdd3. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 25 2024
R-4.5-linux-x86_64OKNov 25 2024

Exports:as.supervisedas.true.matrixasymetricCrossEntropyCPPasymetricGiniCPPasymetricInformationGainCPPbCIbCICorebiasVarCovCheckSameValuesInAllAttributesCheckSameValuesInLabelscheckUniqueObsCPPclassifyCPPclassifyMatrixCPPclusterAnalysisclusteringObservationscombineRUFObjectscombineUnsupervisedconcatconcatCoreconditionalCrossEntropyCPPconditionalGiniCPPconfusion.matrixcopulaLikecount.factorcrossEntropyCPPdates2numericdefine_train_test_setsdifflogdummy.recodeentropyInformationGainCPPestimatePredictionAccuracyestimaterequiredSampleSizeexpectedSquaredBiasextractYFromDatafactor2matrixfactor2vectorfillNA2.randomUniformForestfillVariablesNamesfillWithfilter.forestfilter.objectfilterOutliersfind.first.idxfind.idxfind.rootfScorefullNodegap.statsgeneralization.errorgeneric.cvgeneric.loggeneric.smoothing.loggenericCbindgenericNodegenericOutputgetCorrgetOddEvengetTree.randomUniformForestgetVotesProbabilitygetVotesProbability2giniCPPgMeanhClustHuberDistIdimportanceimportance.randomUniformForestinDummiesinit_valuesinsert.in.vectorinsert.in.vector2interClassesVarianceintraClassesVarianceis.wholenumberkappaStatkBiggestProximitieskeep.indexkMeansL1AsymetricInformationGainCPPL1DistL1DistCPPL1InformationGainCPPL2.logDistL2AsymetricInformationGainCPPL2DistL2DistCPPL2InformationGainCPPlagFunctionleafNodeLInfCPPlocalTreeImportancelocalVariableImportancemajorityClassmatrix2factormatrix2factor2MDSscalemergeClustersmergeListsmergeOutliersmin_or_maxmodel.statsmodelingResidualsmodifyClustersmodXmonitorOOBErrormyAUCna.imputena.missingna.replaceNAfactor2matrixNAFeaturesNATreatmentobservationsImportanceonlineClassifyonlineCombineRUFOOBquantilesOOBVotesScaleoptimizeFalsePositivesoptions.filteroutputPerturbationSamplingoutsideConfIntLevelsoverSamplingparallelNA.replacepartialDependenceBetweenPredictorspartialDependenceOverResponsespartialImportancepermuteCatValuesperspWithcolplot.importanceplot.randomUniformForestplot.unsupervisedplotTreeplotTreeCoreplotTreeCore2postProcessingVotespredict.randomUniformForestpredictDecisionTreepredictionvsResponsesprint.importanceprint.randomUniformForestprint.unsupervisedproximitiesMatrixpseudoHuberDistpseudoNAReplacerandomCombinationrandomizerandomUniformForestrandomUniformForest.defaultrandomUniformForest.formularandomUniformForestCorerandomUniformForestCore.bigrandomUniformForestCore.mergerandomUniformForestCore.predictrandomWhichMaxrankingTrainDatareduce.treesresidualsRandomUniformForestreSMOTErewind.treesrm.coordinatesrm.correlationrm.InAListrm.stringrm.tempdirrm.treesrmInAListByNamesrmInfrmNArmNoiseroc.curverollApplyFunctionrufImputerunifMatrixCPPrUniformForest.bigrUniformForest.combinerUniformForest.growrUniformForest.mergerUniformForestPredictscale2AnyValuesscalingMDSsetManyDatasetssimulationDatasmoothing.logsomeErrorTypesortCPPsortDataframesortMatrixspecClustsplitClusterssplitVarCorestandardizestandardize_vectstrength_and_correlationsubsampleFilesummary.randomUniformForesttimertimeStampCoretwoColumnsImportanceuniformDecisionTreeunsupervisedunsupervised.randomUniformForestunsupervised2supervisedupdate.unsupervisedupdateCombined.unsupervisedvariancevector2factorvector2matrixweightedVoteweightedVoteModelwhich.is.duplicatewhich.is.factorwhich.is.nawhich.is.nearestCenterwhich.is.wholenumberXMinMaxCPP

Dependencies:cliclustercodetoolscolorspacedoParallelfansifarverforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrpROCR6RColorBrewerRcpprlangscalestibbleutf8vctrsviridisLitewithr

Random Uniform Forests in theory and practice

Rendered fromrandomUniformForestsOverview.pdf.asisusingR.rsp::asison Nov 25 2024.

Last update: 2014-09-18
Started: 2014-09-18

Variable Importance in Random Uniform Forests

Rendered fromVariableImportanceInRandomUniformForests.pdf.asisusingR.rsp::asison Nov 25 2024.

Last update: 2015-12-05
Started: 2015-12-05

Readme and manuals

Help Manual

Help pageTopics
Random Uniform Forests for Classification, Regression and Unsupervised LearningrandomUniformForest-package
Conversion of an unsupervised model into a supervised oneas.supervised
Auto MPG Data SetautoMPG
Bootstrapped Prediction Intervals for Ensemble ModelsbCI
Bias-Variance-Covariance DecompositionbiasVarCov
Breast Cancer Wisconsin (Original) Data SetbreastCancer
Car Evaluation Data SetcarEvaluation
Cluster (or classes) analysis of importance objects.clusterAnalysis
Cluster observations of a (supervised) randomUniformForest objectclusteringObservations
Combine Unsupervised Learning objectscombineUnsupervised
Concrete Compressive Strength Data SetConcreteCompressiveStrength
Missing values imputation by randomUniformForestfillNA2.randomUniformForest rufImpute
Generic k-fold cross-validationgeneric.cv
Extract a tree from a forestgetTree getTree.randomUniformForest
Variable Importance for random Uniform Forestsimportance importance.randomUniformForest plot.importance print.importance
Training and validation samples from datainit_values
All internal functionsA2Rplot A2Rplot.default A2Rplot.hclust as.true.matrix asymetricCrossEntropyCPP asymetricGiniCPP asymetricInformationGainCPP bCICore category2Proba category2Quantile categoryCombination CheckSameValuesInAllAttributes CheckSameValuesInLabels checkUniqueObsCPP classifyCPP classifyMatrixCPP combineRUFObjects concat concatCore conditionalCrossEntropyCPP conditionalGiniCPP confusion.matrix copulaLike count.factor crossEntropyCPP cutree.order dates2numeric define_train_test_sets difflog dummy.recode entropyInformationGainCPP estimatePredictionAccuracy estimaterequiredSampleSize expectedSquaredBias extractYFromData factor2matrix factor2vector fillVariablesNames fillWith filter.forest filter.object filterOutliers find.first.idx find.idx find.root fScore fullNDCG fullNode gap.stats generalization.error generic.log generic.smoothing.log genericCbind genericNode genericOutput getCorr getOddEven getVotesProbability getVotesProbability2 giniCPP gMean hClust HuberDist Id imputeCategoryForTestData inDummies insert.in.vector insert.in.vector2 interClassesVariance intraClassesVariance is.wholenumber kappaStat kBiggestProximities keep.index kMeans L1AsymetricInformationGainCPP L1Dist L1DistCPP L1InformationGainCPP L2.logDist L2AsymetricInformationGainCPP L2Dist L2DistCPP L2InformationGainCPP lagFunction leafNode LInfCPP localTreeImportance localVariableImportance majorityClass matrix2factor matrix2factor2 MDSscale mergeLists mergeOutliers min_or_max modelingResiduals modX monitorOOBError myAUC na.impute na.missing na.replace NAfactor2matrix NAFeatures NATreatment ndcg observationsImportance onlineClassify onlineCombineRUF OOBquantiles OOBVotesScale optimizeFalsePositives options.filter outputPerturbationSampling outsideConfIntLevels overSampling parallelNA.replace permuteCatValues perspWithcol plotTreeCore plotTreeCore2 predictDecisionTree predictionvsResponses proximitiesMatrix pseudoHuberDist pseudoNAReplace randomCombination randomize randomUniformForestCore randomUniformForestCore.big randomUniformForestCore.merge randomUniformForestCore.predict randomWhichMax rankingTrainData reduce.trees residualsRandomUniformForest rewind.trees rm.coordinates rm.correlation rm.InAList rm.string rm.tempdir rmInAListByNames rmInf rmNA rmNoise rollApplyFunction runifMatrixCPP rUniformForest.merge rUniformForestPredict sampleDirichlet scale2AnyValues scalingMDS setManyDatasets smoothing.log someErrorType sortCPP sortDataframe sortMatrix specClust splitVarCore standardize standardize_vect strength_and_correlation subsampleFile timer timeStampCore twoColumnsImportance uniformDecisionTree unsupervised2supervised updateCombined.unsupervised variance vector2factor vector2matrix weightedVote weightedVoteModel which.is.duplicate which.is.factor which.is.na which.is.nearestCenter which.is.wholenumber XMinMaxCPP
Merge two arbitrary, but adjacent, clustersmergeClusters
Common statistics for a vector (or factor) of predictions and a vector (or factor) of responsesmodel.stats
Change number of clusters (and clusters shape) on the flymodifyClusters
Partial Dependence between Predictors and effect over ResponsepartialDependenceBetweenPredictors
Partial Dependence Plots and ModelspartialDependenceOverResponses
Partial Importance for random Uniform ForestspartialImportance
Plot a Random Uniform Decision TreeplotTree
Post-processing for RegressionpostProcessingVotes
Predict method for random Uniform Forests objectspredict predict.randomUniformForest
Random Uniform Forests for Classification, Regression and Unsupervised Learningplot.randomUniformForest print.randomUniformForest randomUniformForest randomUniformForest.default randomUniformForest.formula summary.randomUniformForest
REplication of a Synthetic Minority Oversampling TEchnique for highly imbalanced datasetsreSMOTE
Remove trees from a random Uniform Forestrm.trees
ROC and precision-recall curves for random Uniform Forestsroc.curve
Random Uniform Forests for Classification and Regression with large data setsrUniformForest.big
Incremental learning for random Uniform ForestsrUniformForest.combine
Add trees to a random Uniform ForestrUniformForest.grow
Simulation of Gaussian vectorsimulationData
Split a cluster on the flysplitClusters
Unsupervised Learning with Random Uniform Forestsplot.unsupervised print.unsupervised unsupervised unsupervised.randomUniformForest
Update Unsupervised Learning objectupdate update.unsupervised
Wine Quality Data SetwineQualityRed