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:
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')) |
- ConcreteCompressiveStrength - Concrete Compressive Strength Data Set
- autoMPG - Auto MPG Data Set
- breastCancer - Breast Cancer Wisconsin (Original) Data Set
- carEvaluation - Car Evaluation Data Set
- wineQualityRed - Wine Quality Data Set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:f0176efdd3. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 26 2024 |
| R-4.5-linux-x86_64 | OK | Oct 26 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 Oct 26 2024.Last update: 2014-09-18
Started: 2014-09-18
Variable Importance in Random Uniform Forests
Rendered fromVariableImportanceInRandomUniformForests.pdf.asisusingR.rsp::asison Oct 26 2024.Last update: 2015-12-05
Started: 2015-12-05
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Random Uniform Forests for Classification, Regression and Unsupervised Learning | randomUniformForest-package |
| Conversion of an unsupervised model into a supervised one | as.supervised |
| Auto MPG Data Set | autoMPG |
| Bootstrapped Prediction Intervals for Ensemble Models | bCI |
| Bias-Variance-Covariance Decomposition | biasVarCov |
| Breast Cancer Wisconsin (Original) Data Set | breastCancer |
| Car Evaluation Data Set | carEvaluation |
| Cluster (or classes) analysis of importance objects. | clusterAnalysis |
| Cluster observations of a (supervised) randomUniformForest object | clusteringObservations |
| Combine Unsupervised Learning objects | combineUnsupervised |
| Concrete Compressive Strength Data Set | ConcreteCompressiveStrength |
| Missing values imputation by randomUniformForest | fillNA2.randomUniformForest rufImpute |
| Generic k-fold cross-validation | generic.cv |
| Extract a tree from a forest | getTree getTree.randomUniformForest |
| Variable Importance for random Uniform Forests | importance importance.randomUniformForest plot.importance print.importance |
| Training and validation samples from data | init_values |
| All internal functions | A2Rplot 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, clusters | mergeClusters |
| Common statistics for a vector (or factor) of predictions and a vector (or factor) of responses | model.stats |
| Change number of clusters (and clusters shape) on the fly | modifyClusters |
| Partial Dependence between Predictors and effect over Response | partialDependenceBetweenPredictors |
| Partial Dependence Plots and Models | partialDependenceOverResponses |
| Partial Importance for random Uniform Forests | partialImportance |
| Plot a Random Uniform Decision Tree | plotTree |
| Post-processing for Regression | postProcessingVotes |
| Predict method for random Uniform Forests objects | predict predict.randomUniformForest |
| Random Uniform Forests for Classification, Regression and Unsupervised Learning | plot.randomUniformForest print.randomUniformForest randomUniformForest randomUniformForest.default randomUniformForest.formula summary.randomUniformForest |
| REplication of a Synthetic Minority Oversampling TEchnique for highly imbalanced datasets | reSMOTE |
| Remove trees from a random Uniform Forest | rm.trees |
| ROC and precision-recall curves for random Uniform Forests | roc.curve |
| Random Uniform Forests for Classification and Regression with large data sets | rUniformForest.big |
| Incremental learning for random Uniform Forests | rUniformForest.combine |
| Add trees to a random Uniform Forest | rUniformForest.grow |
| Simulation of Gaussian vector | simulationData |
| Split a cluster on the fly | splitClusters |
| Unsupervised Learning with Random Uniform Forests | plot.unsupervised print.unsupervised unsupervised unsupervised.randomUniformForest |
| Update Unsupervised Learning object | update update.unsupervised |
| Wine Quality Data Set | wineQualityRed |