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mlr

mlrFamilies

mlr documentation families

Description

List of all mlr documentation families with members.

Arguments

`benchmark`	batchmark, reduceBatchmarkResults, benchmark, benchmarkParallel, getBMRTaskIds, getBMRLearners, getBMRLearnerIds, getBMRLearnerShortNames, getBMRMeasures, getBMRMeasureIds, getBMRPredictions, getBMRPerformances, getBMRAggrPerformances, getBMRTuneResults, getBMRFeatSelResults, getBMRFilteredFeatures, getBMRModels, getBMRTaskDescs, convertBMRToRankMatrix, friedmanPostHocTestBMR, friedmanTestBMR, plotBMRBoxplots, plotBMRRanksAsBarChart, generateCritDifferencesData, plotCritDifferences
`calibration`	generateCalibrationData, plotCalibration
`configure`	configureMlr, getMlrOptions
`costsens`	makeCostSensTask, makeCostSensWeightedPairsWrapper
`debug`	predictFailureModel, getPredictionDump, getRRDump, print.ResampleResult
`downsample`	downsample
`eda_and_preprocess`	capLargeValues, createDummyFeatures, dropFeatures, mergeSmallFactorLevels, normalizeFeatures, removeConstantFeatures, summarizeColumns, summarizeLevels
`extractFDAFeatures`	reextractFDAFeatures
`fda_featextractor`	extractFDAFourier, extractFDAWavelets, extractFDAFPCA, extractFDAMultiResFeatures
`fda`	makeExtractFDAFeatMethod, extractFDAFeatures
`featsel`	analyzeFeatSelResult, makeFeatSelControl, getFeatSelResult, selectFeatures
`filter`	filterFeatures, makeFilter, listFilterMethods, getFilteredFeatures, generateFilterValuesData, getFilterValues
`generate_plot_data`	generateFeatureImportanceData, plotFilterValues, generatePartialDependenceData
`help`	helpLearner, helpLearnerParam
`imbalancy`	oversample, smote
`impute`	makeImputeMethod, imputeConstant, impute, reimpute
`learner`	getClassWeightParam, getHyperPars, getParamSet.Learner, getLearnerType, getLearnerId, getLearnerPredictType, getLearnerPackages, getLearnerParamSet, getLearnerParVals, setLearnerId, getLearnerShortName, getLearnerProperties, makeLearner, makeLearners, removeHyperPars, setHyperPars, setId, setPredictThreshold, setPredictType
`learning_curve`	generateLearningCurveData
`multilabel`	getMultilabelBinaryPerformances, makeMultilabelBinaryRelevanceWrapper, makeMultilabelClassifierChainsWrapper, makeMultilabelDBRWrapper, makeMultilabelNestedStackingWrapper, makeMultilabelStackingWrapper
`performance`	calculateConfusionMatrix, calculateROCMeasures, makeCustomResampledMeasure, makeCostMeasure, setMeasurePars, setAggregation, makeMeasure, featperc, performance, estimateRelativeOverfitting
`plot`	createSpatialResamplingPlots, plotLearningCurve, plotPartialDependence, plotBMRSummary, plotResiduals
`predict`	asROCRPrediction, getPredictionProbabilities, getPredictionTaskDesc, getPredictionResponse, predict.WrappedModel
`resample`	makeResampleDesc, makeResampleInstance, makeResamplePrediction, resample, getRRPredictions, getRRTaskDescription, getRRTaskDesc, getRRPredictionList, addRRMeasure
`task`	getTaskDesc, getTaskType, getTaskId, getTaskTargetNames, getTaskClassLevels, getTaskFeatureNames, getTaskNFeats, getTaskSize, getTaskFormula, getTaskTargets, getTaskData, getTaskCosts, subsetTask
`thresh_vs_perf`	generateThreshVsPerfData, plotThreshVsPerf, plotROCCurves
`tune`	getNestedTuneResultsX, getNestedTuneResultsOptPathDf, getResamplingIndices, getTuneResult, makeModelMultiplexerParamSet, makeModelMultiplexer, makeTuneControlCMAES, makeTuneControlDesign, makeTuneControlGenSA, makeTuneControlGrid, makeTuneControlIrace, makeTuneControlMBO, makeTuneControl, makeTuneControlRandom, tuneParams, tuneThreshold
`tune_multicrit`	plotTuneMultiCritResult, makeTuneMultiCritControl, tuneParamsMultiCrit
`wrapper`	makeBaggingWrapper, makeClassificationViaRegressionWrapper, makeConstantClassWrapper, makeCostSensClassifWrapper, makeCostSensRegrWrapper, makeDownsampleWrapper, makeDummyFeaturesWrapper, makeExtractFDAFeatsWrapper, makeFeatSelWrapper, makeFilterWrapper, makeImputeWrapper, makeMulticlassWrapper, makeOverBaggingWrapper, makeUndersampleWrapper, makePreprocWrapperCaret, makePreprocWrapper, makeRemoveConstantFeaturesWrapper, makeSMOTEWrapper, makeTuneWrapper, makeWeightedClassesWrapper

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release

mlrFamilies

Description

Arguments

mlr

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