Machine Learning in R

---

Documentation for package ‘mlr’ version 2.19.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

A B C D E F G H I J K L M N O P Q R S T U W Y

mlr-package

mlr: Machine Learning in R

-- A --

acc	Performance measures.
addRRMeasure	Compute new measures for existing ResampleResult
Aggregation	Aggregation object.
aggregations	Aggregation methods.
agri.task	European Union Agricultural Workforces clustering task.
analyzeFeatSelResult	Show and visualize the steps of feature selection.
asROCRPrediction	Converts predictions to a format package ROCR can handle.
auc	Performance measures.

-- B --

b632	Aggregation methods.
b632plus	Aggregation methods.
bac	Performance measures.
batchmark	Run machine learning benchmarks as distributed experiments.
bc.task	Wisconsin Breast Cancer classification task.
benchmark	Benchmark experiment for multiple learners and tasks.
BenchmarkResult	BenchmarkResult object.
ber	Performance measures.
bh.task	Boston Housing regression task.
bootstrapB632	Fit models according to a resampling strategy.
bootstrapB632plus	Fit models according to a resampling strategy.
bootstrapOOB	Fit models according to a resampling strategy.
brier	Performance measures.
brier.scaled	Performance measures.

-- C --

cache_helpers	Get or delete mlr cache directory
calculateConfusionMatrix	Confusion matrix.
calculateROCMeasures	Calculate receiver operator measures.
CalibrationData	Generate classifier calibration data.
capLargeValues	Convert large/infinite numeric values in a data.frame or task.
cindex	Performance measures.
cindex.uno	Performance measures.
ClassifTask	Create a classification task.
ClusterTask	Create a cluster task.
configureMlr	Configures the behavior of the package.
ConfusionMatrix	Confusion matrix
convertBMRToRankMatrix	Convert BenchmarkResult to a rank-matrix.
convertMLBenchObjToTask	Convert a machine learning benchmark / demo object from package mlbench to a task.
costiris.task	Iris cost-sensitive classification task.
CostSensClassifModel	Wraps a classification learner for use in cost-sensitive learning.
CostSensClassifWrapper	Wraps a classification learner for use in cost-sensitive learning.
CostSensRegrModel	Wraps a regression learner for use in cost-sensitive learning.
CostSensRegrWrapper	Wraps a regression learner for use in cost-sensitive learning.
CostSensTask	Create a cost-sensitive classification task.
CostSensWeightedPairsModel	Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
CostSensWeightedPairsWrapper	Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
createDummyFeatures	Generate dummy variables for factor features.
createSpatialResamplingPlots	Create (spatial) resampling plot objects.
crossover	Crossover.
crossval	Fit models according to a resampling strategy.
cv10	Create a description object for a resampling strategy.
cv2	Create a description object for a resampling strategy.
cv3	Create a description object for a resampling strategy.
cv5	Create a description object for a resampling strategy.

-- D --

db	Performance measures.
deleteCacheDir	Get or delete mlr cache directory
downsample	Downsample (subsample) a task or a data.frame.
dropFeatures	Drop some features of task.

-- E --

estimateRelativeOverfitting	Estimate relative overfitting.
estimateResidualVariance	Estimate the residual variance.
expvar	Performance measures.
extractFDABsignal	Bspline mlq features
extractFDADTWKernel	DTW kernel features
extractFDAFeatures	Extract features from functional data.
extractFDAFourier	Fast Fourier transform features.
extractFDAFPCA	Extract functional principal component analysis features.
extractFDAMultiResFeatures	Multiresolution feature extraction.
extractFDATsfeatures	Time-Series Feature Heuristics
extractFDAWavelets	Discrete Wavelet transform features.

-- F --

f1	Performance measures.
FailureModel	Failure model.
fdr	Performance measures.
featperc	Performance measures.
FeatSelControl	Create control structures for feature selection.
FeatSelControlExhaustive	Create control structures for feature selection.
FeatSelControlGA	Create control structures for feature selection.
FeatSelControlRandom	Create control structures for feature selection.
FeatSelControlSequential	Create control structures for feature selection.
FeatSelResult	Result of feature selection.
FeatureImportanceData	Generate feature importance.
filterFeatures	Filter features by thresholding filter values.
FilterValues	Calculates feature filter values.
fixedcv	Fit models according to a resampling strategy.
fn	Performance measures.
fnr	Performance measures.
fp	Performance measures.
fpr	Performance measures.
friedmanPostHocTestBMR	Perform a posthoc Friedman-Nemenyi test.
friedmanTestBMR	Perform overall Friedman test for a BenchmarkResult.
fuelsubset.task	FuelSubset functional data regression task.

-- G --

G1	Performance measures.
G2	Performance measures.
generateCalibrationData	Generate classifier calibration data.
generateCritDifferencesData	Generate data for critical-differences plot.
generateFeatureImportanceData	Generate feature importance.
generateFilterValuesData	Calculates feature filter values.
generateHyperParsEffectData	Generate hyperparameter effect data.
generateLearningCurveData	Generates a learning curve.
generatePartialDependenceData	Generate partial dependence.
generateThreshVsPerfData	Generate threshold vs. performance(s) for 2-class classification.
getBMRAggrPerformances	Extract the aggregated performance values from a benchmark result.
getBMRFeatSelResults	Extract the feature selection results from a benchmark result.
getBMRFilteredFeatures	Extract the feature selection results from a benchmark result.
getBMRLearnerIds	Return learner ids used in benchmark.
getBMRLearners	Return learners used in benchmark.
getBMRLearnerShortNames	Return learner short.names used in benchmark.
getBMRMeasureIds	Return measures IDs used in benchmark.
getBMRMeasures	Return measures used in benchmark.
getBMRModels	Extract all models from benchmark result.
getBMRPerformances	Extract the test performance values from a benchmark result.
getBMRPredictions	Extract the predictions from a benchmark result.
getBMRTaskDescriptions	Extract all task descriptions from benchmark result (DEPRECATED).
getBMRTaskDescs	Extract all task descriptions from benchmark result.
getBMRTaskIds	Return task ids used in benchmark.
getBMRTuneResults	Extract the tuning results from a benchmark result.
getCacheDir	Get or delete mlr cache directory
getCaretParamSet	Get tuning parameters from a learner of the caret R-package.
getClassWeightParam	Get the class weight parameter of a learner.
getConfMatrix	Confusion matrix.
getDefaultMeasure	Get default measure.
getFailureModelDump	Return the error dump of FailureModel.
getFailureModelMsg	Return error message of FailureModel.
getFeatSelResult	Returns the selected feature set and optimization path after training.
getFeatureImportance	Calculates feature importance values for trained models.
getFilteredFeatures	Returns the filtered features.
getFunctionalFeatures	Get only functional features from a task or a data.frame.
getFunctionalFeatures.data.frame	Get only functional features from a task or a data.frame.
getFunctionalFeatures.Task	Get only functional features from a task or a data.frame.
getHomogeneousEnsembleModels	Deprecated, use 'getLearnerModel' instead.
getHyperPars	Get current parameter settings for a learner.
getLearnerId	Get the ID of the learner.
getLearnerModel	Get underlying R model of learner integrated into mlr.
getLearnerNote	Get the note for the learner.
getLearnerPackages	Get the required R packages of the learner.
getLearnerParamSet	Get the parameter set of the learner.
getLearnerParVals	Get the parameter values of the learner.
getLearnerPredictType	Get the predict type of the learner.
getLearnerProperties	Query properties of learners.
getLearnerShortName	Get the short name of the learner.
getLearnerType	Get the type of the learner.
getMeasureProperties	Query properties of measures.
getMlrOptions	Returns a list of mlr's options.
getMultilabelBinaryPerformances	Retrieve binary classification measures for multilabel classification predictions.
getNestedTuneResultsOptPathDf	Get the 'opt.path's from each tuning step from the outer resampling.
getNestedTuneResultsX	Get the tuned hyperparameter settings from a nested tuning.
getOOBPreds	Extracts out-of-bag predictions from trained models.
getParamSet	Get a description of all possible parameter settings for a learner.
getPredictionDump	Return the error dump of a failed Prediction.
getPredictionProbabilities	Get probabilities for some classes.
getPredictionResponse	Get response / truth from prediction object.
getPredictionSE	Get response / truth from prediction object.
getPredictionTaskDesc	Get summarizing task description from prediction.
getPredictionTruth	Get response / truth from prediction object.
getProbabilities	Deprecated, use 'getPredictionProbabilities' instead.
getResamplingIndices	Get the resampling indices from a tuning or feature selection wrapper..
getRRDump	Return the error dump of ResampleResult.
getRRPredictionList	Get list of predictions for train and test set of each single resample iteration.
getRRPredictions	Get predictions from resample results.
getRRTaskDesc	Get task description from resample results (DEPRECATED).
getRRTaskDescription	Get task description from resample results (DEPRECATED).
getStackedBaseLearnerPredictions	Returns the predictions for each base learner.
getTaskClassLevels	Get the class levels for classification and multilabel tasks.
getTaskCosts	Extract costs in task.
getTaskData	Extract data in task.
getTaskDesc	Get a summarizing task description.
getTaskDescription	Deprecated, use getTaskDesc instead.
getTaskFeatureNames	Get feature names of task.
getTaskFormula	Get formula of a task.
getTaskId	Get the id of the task.
getTaskNFeats	Get number of features in task.
getTaskSize	Get number of observations in task.
getTaskTargetNames	Get the name(s) of the target column(s).
getTaskTargets	Get target data of task.
getTaskType	Get the type of the task.
getTuneResult	Returns the optimal hyperparameters and optimization path after training.
getTuneResultOptPath	Get the optimization path of a tuning result.
gmean	Performance measures.
gpr	Performance measures.
growingcv	Fit models according to a resampling strategy.
gunpoint.task	Gunpoint functional data classification task.

-- H --

hasFunctionalFeatures	Check whether the object contains functional features.
hasLearnerProperties	Query properties of learners.
hasMeasureProperties	Query properties of measures.
hasProperties	Deprecated, use 'hasLearnerProperties' instead.
helpLearner	Access help page of learner functions.
helpLearnerParam	Get specific help for a learner's parameters.
holdout	Fit models according to a resampling strategy.
hout	Create a description object for a resampling strategy.

-- I --

iauc.uno	Performance measures.
ibrier	Performance measures.
imputations	Built-in imputation methods.
impute	Impute and re-impute data
imputeConstant	Built-in imputation methods.
imputeHist	Built-in imputation methods.
imputeLearner	Built-in imputation methods.
imputeMax	Built-in imputation methods.
imputeMean	Built-in imputation methods.
imputeMedian	Built-in imputation methods.
imputeMin	Built-in imputation methods.
imputeMode	Built-in imputation methods.
imputeNormal	Built-in imputation methods.
imputeUniform	Built-in imputation methods.
iris.task	Iris classification task.
isFailureModel	Is the model a FailureModel?

-- J --

joinClassLevels

Join some class existing levels to new, larger class levels for classification problems.

-- K --

kappa	Performance measures.
kendalltau	Performance measures.

-- L --

Learner	Create learner object.
learnerArgsToControl	Convert arguments to control structure.
LearnerProperties	Query properties of learners.
learners	List of supported learning algorithms.
LearningCurveData	Generates a learning curve.
listFilterEnsembleMethods	List ensemble filter methods.
listFilterMethods	List filter methods.
listLearnerProperties	List the supported learner properties
listLearners	Find matching learning algorithms.
listLearners.character	Find matching learning algorithms.
listLearners.default	Find matching learning algorithms.
listLearners.Task	Find matching learning algorithms.
listMeasureProperties	List the supported measure properties.
listMeasures	Find matching measures.
listMeasures.character	Find matching measures.
listMeasures.default	Find matching measures.
listMeasures.Task	Find matching measures.
listTaskTypes	List the supported task types in mlr
logloss	Performance measures.
lsr	Performance measures.
lung.task	NCCTG Lung Cancer survival task.

-- M --

mae	Performance measures.
makeAggregation	Specify your own aggregation of measures.
makeBaggingWrapper	Fuse learner with the bagging technique.
makeClassificationViaRegressionWrapper	Classification via regression wrapper.
makeClassifTask	Create a classification task.
makeClusterTask	Create a cluster task.
makeConstantClassWrapper	Wraps a classification learner to support problems where the class label is (almost) constant.
makeCostMeasure	Creates a measure for non-standard misclassification costs.
makeCostSensClassifWrapper	Wraps a classification learner for use in cost-sensitive learning.
makeCostSensRegrWrapper	Wraps a regression learner for use in cost-sensitive learning.
makeCostSensTask	Create a cost-sensitive classification task.
makeCostSensWeightedPairsWrapper	Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
makeCustomResampledMeasure	Construct your own resampled performance measure.
makeDownsampleWrapper	Fuse learner with simple downsampling (subsampling).
makeDummyFeaturesWrapper	Fuse learner with dummy feature creator.
makeExtractFDAFeatMethod	Constructor for FDA feature extraction methods.
makeExtractFDAFeatsWrapper	Fuse learner with an extractFDAFeatures method.
makeFeatSelControlExhaustive	Create control structures for feature selection.
makeFeatSelControlGA	Create control structures for feature selection.
makeFeatSelControlRandom	Create control structures for feature selection.
makeFeatSelControlSequential	Create control structures for feature selection.
makeFeatSelWrapper	Fuse learner with feature selection.
makeFilter	Create a feature filter.
makeFilterEnsemble	Create an ensemble feature filter.
makeFilterWrapper	Fuse learner with a feature filter method.
makeFixedHoldoutInstance	Generate a fixed holdout instance for resampling.
makeFunctionalData	Create a data.frame containing functional features from a normal data.frame.
makeImputeMethod	Create a custom imputation method.
makeImputeWrapper	Fuse learner with an imputation method.
makeLearner	Create learner object.
makeLearners	Create multiple learners at once.
makeMeasure	Construct performance measure.
makeModelMultiplexer	Create model multiplexer for model selection to tune over multiple possible models.
makeModelMultiplexerParamSet	Creates a parameter set for model multiplexer tuning.
makeMulticlassWrapper	Fuse learner with multiclass method.
makeMultilabelBinaryRelevanceWrapper	Use binary relevance method to create a multilabel learner.
makeMultilabelClassifierChainsWrapper	Use classifier chains method (CC) to create a multilabel learner.
makeMultilabelDBRWrapper	Use dependent binary relevance method (DBR) to create a multilabel learner.
makeMultilabelNestedStackingWrapper	Use nested stacking method to create a multilabel learner.
makeMultilabelStackingWrapper	Use stacking method (stacked generalization) to create a multilabel learner.
makeMultilabelTask	Create a multilabel task.
makeOverBaggingWrapper	Fuse learner with the bagging technique and oversampling for imbalancy correction.
makeOversampleWrapper	Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
makePreprocWrapper	Fuse learner with preprocessing.
makePreprocWrapperCaret	Fuse learner with preprocessing.
makeRegrTask	Create a regression task.
makeRemoveConstantFeaturesWrapper	Fuse learner with removal of constant features preprocessing.
makeResampleDesc	Create a description object for a resampling strategy.
makeResampleInstance	Instantiates a resampling strategy object.
makeRLearner	Internal construction / wrapping of learner object.
makeRLearner.classif.fdausc.glm	Classification of functional data by Generalized Linear Models.
makeRLearner.classif.fdausc.kernel	Learner for kernel classification for functional data.
makeRLearner.classif.fdausc.np	Learner for nonparametric classification for functional data.
makeRLearnerClassif	Internal construction / wrapping of learner object.
makeRLearnerCluster	Internal construction / wrapping of learner object.
makeRLearnerCostSens	Internal construction / wrapping of learner object.
makeRLearnerMultilabel	Internal construction / wrapping of learner object.
makeRLearnerRegr	Internal construction / wrapping of learner object.
makeRLearnerSurv	Internal construction / wrapping of learner object.
makeSMOTEWrapper	Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
makeStackedLearner	Create a stacked learner object.
makeSurvTask	Create a survival task.
makeTuneControlCMAES	Create control object for hyperparameter tuning with CMAES.
makeTuneControlDesign	Create control object for hyperparameter tuning with predefined design.
makeTuneControlGenSA	Create control object for hyperparameter tuning with GenSA.
makeTuneControlGrid	Create control object for hyperparameter tuning with grid search.
makeTuneControlIrace	Create control object for hyperparameter tuning with Irace.
makeTuneControlMBO	Create control object for hyperparameter tuning with MBO.
makeTuneControlRandom	Create control object for hyperparameter tuning with random search.
makeTuneMultiCritControlGrid	Create control structures for multi-criteria tuning.
makeTuneMultiCritControlMBO	Create control structures for multi-criteria tuning.
makeTuneMultiCritControlNSGA2	Create control structures for multi-criteria tuning.
makeTuneMultiCritControlRandom	Create control structures for multi-criteria tuning.
makeTuneWrapper	Fuse learner with tuning.
makeUndersampleWrapper	Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
makeWeightedClassesWrapper	Wraps a classifier for weighted fitting where each class receives a weight.
makeWrappedModel	Induced model of learner.
mape	Performance measures.
mcc	Performance measures.
mcp	Performance measures.
meancosts	Performance measures.
Measure	Construct performance measure.
measureACC	Performance measures.
measureAU1P	Performance measures.
measureAU1U	Performance measures.
measureAUC	Performance measures.
measureAUNP	Performance measures.
measureAUNU	Performance measures.
measureBAC	Performance measures.
measureBER	Performance measures.
measureBrier	Performance measures.
measureBrierScaled	Performance measures.
measureEXPVAR	Performance measures.
measureF1	Performance measures.
measureFDR	Performance measures.
measureFN	Performance measures.
measureFNR	Performance measures.
measureFP	Performance measures.
measureFPR	Performance measures.
measureGMEAN	Performance measures.
measureGPR	Performance measures.
measureKAPPA	Performance measures.
measureKendallTau	Performance measures.
measureLogloss	Performance measures.
measureLSR	Performance measures.
measureMAE	Performance measures.
measureMAPE	Performance measures.
measureMCC	Performance measures.
measureMEDAE	Performance measures.
measureMEDSE	Performance measures.
measureMMCE	Performance measures.
measureMSE	Performance measures.
measureMSLE	Performance measures.
measureMulticlassBrier	Performance measures.
measureMultilabelACC	Performance measures.
measureMultilabelF1	Performance measures.
measureMultilabelHamloss	Performance measures.
measureMultilabelPPV	Performance measures.
measureMultilabelSubset01	Performance measures.
measureMultilabelTPR	Performance measures.
measureNPV	Performance measures.
measurePPV	Performance measures.
MeasureProperties	Query properties of measures.
measureQSR	Performance measures.
measureRAE	Performance measures.
measureRMSE	Performance measures.
measureRMSLE	Performance measures.
measureRRSE	Performance measures.
measureRSQ	Performance measures.
measures	Performance measures.
measureSAE	Performance measures.
measureSpearmanRho	Performance measures.
measureSSE	Performance measures.
measureSSR	Performance measures.
measureTN	Performance measures.
measureTNR	Performance measures.
measureTP	Performance measures.
measureTPR	Performance measures.
measureWKAPPA	Performance measures.
medae	Performance measures.
medse	Performance measures.
mergeBenchmarkResults	Merge different BenchmarkResult objects.
mergeSmallFactorLevels	Merges small levels of factors into new level.
mlr	mlr: Machine Learning in R
mlrFamilies	mlr documentation families
mmce	Performance measures.
ModelMultiplexer	Create model multiplexer for model selection to tune over multiple possible models.
mse	Performance measures.
msle	Performance measures.
mtcars.task	Motor Trend Car Road Tests clustering task.
multiclass.au1p	Performance measures.
multiclass.au1u	Performance measures.
multiclass.aunp	Performance measures.
multiclass.aunu	Performance measures.
multiclass.brier	Performance measures.
multilabel.acc	Performance measures.
multilabel.f1	Performance measures.
multilabel.hamloss	Performance measures.
multilabel.ppv	Performance measures.
multilabel.subset01	Performance measures.
multilabel.tpr	Performance measures.
MultilabelTask	Create a multilabel task.

-- N --

normalizeFeatures	Normalize features.
npv	Performance measures.

-- O --

oversample

Over- or undersample binary classification task to handle class imbalancy.

-- P --

parallelization	Supported parallelization methods
PartialDependenceData	Generate partial dependence.
performance	Measure performance of prediction.
phoneme.task	Phoneme functional data multilabel classification task.
pid.task	PimaIndiansDiabetes classification task.
plotBMRBoxplots	Create box or violin plots for a BenchmarkResult.
plotBMRRanksAsBarChart	Create a bar chart for ranks in a BenchmarkResult.
plotBMRSummary	Plot a benchmark summary.
plotCalibration	Plot calibration data using ggplot2.
plotCritDifferences	Plot critical differences for a selected measure.
plotFilterValues	Plot filter values using ggplot2.
plotHyperParsEffect	Plot the hyperparameter effects data
plotLearnerPrediction	Visualizes a learning algorithm on a 1D or 2D data set.
plotLearningCurve	Plot learning curve data using ggplot2.
plotPartialDependence	Plot a partial dependence with ggplot2.
plotResiduals	Create residual plots for prediction objects or benchmark results.
plotROCCurves	Plots a ROC curve using ggplot2.
plotThreshVsPerf	Plot threshold vs. performance(s) for 2-class classification using ggplot2.
plotTuneMultiCritResult	Plots multi-criteria results after tuning using ggplot2.
ppv	Performance measures.
predict.WrappedModel	Predict new data.
predictLearner	Predict new data with an R learner.
print.ConfusionMatrix	Confusion matrix.
print.ROCMeasures	Calculate receiver operator measures.

-- Q --

qsr	Performance measures.

-- R --

rae	Performance measures.
reduceBatchmarkResults	Reduce results of a batch-distributed benchmark.
reextractFDAFeatures	Re-extract features from a data set
RegrTask	Create a regression task.
reimpute	Re-impute a data set
removeConstantFeatures	Remove constant features from a data set.
removeHyperPars	Remove hyperparameters settings of a learner.
repcv	Fit models according to a resampling strategy.
resample	Fit models according to a resampling strategy.
ResampleDesc	Create a description object for a resampling strategy.
ResampleInstance	Instantiates a resampling strategy object.
ResamplePrediction	Prediction from resampling.
ResampleResult	ResampleResult object.
RLearner	Internal construction / wrapping of learner object.
RLearnerClassif	Internal construction / wrapping of learner object.
RLearnerCluster	Internal construction / wrapping of learner object.
RLearnerMultilabel	Internal construction / wrapping of learner object.
RLearnerRegr	Internal construction / wrapping of learner object.
RLearnerSurv	Internal construction / wrapping of learner object.
rmse	Performance measures.
rmsle	Performance measures.
rrse	Performance measures.
rsq	Performance measures.

-- S --

sae	Performance measures.
selectFeatures	Feature selection by wrapper approach.
setAggregation	Set aggregation function of measure.
setHyperPars	Set the hyperparameters of a learner object.
setHyperPars2	Only exported for internal use.
setId	Set the id of a learner object.
setLearnerId	Set the ID of a learner object.
setMeasurePars	Set parameters of performance measures
setPredictThreshold	Set the probability threshold the learner should use.
setPredictType	Set the type of predictions the learner should return.
setThreshold	Set threshold of prediction object.
silhouette	Performance measures.
simplifyMeasureNames	Simplify measure names.
smote	Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
sonar.task	Sonar classification task.
spam.task	Spam classification task.
spatial.task	J. Muenchow's Ecuador landslide data set
spearmanrho	Performance measures.
sse	Performance measures.
ssr	Performance measures.
subsample	Fit models according to a resampling strategy.
subsetTask	Subset data in task.
summarizeColumns	Summarize columns of data.frame or task.
summarizeLevels	Summarizes factors of a data.frame by tabling them.
SurvTask	Create a survival task.

-- T --

Task	Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
TaskDesc	Description object for task.
test.join	Aggregation methods.
test.max	Aggregation methods.
test.mean	Aggregation methods.
test.median	Aggregation methods.
test.min	Aggregation methods.
test.range	Aggregation methods.
test.rmse	Aggregation methods.
test.sd	Aggregation methods.
test.sum	Aggregation methods.
testgroup.mean	Aggregation methods.
testgroup.sd	Aggregation methods.
ThreshVsPerfData	Generate threshold vs. performance(s) for 2-class classification.
timeboth	Performance measures.
timepredict	Performance measures.
timetrain	Performance measures.
tn	Performance measures.
tnr	Performance measures.
tp	Performance measures.
tpr	Performance measures.
train	Train a learning algorithm.
train.max	Aggregation methods.
train.mean	Aggregation methods.
train.median	Aggregation methods.
train.min	Aggregation methods.
train.range	Aggregation methods.
train.rmse	Aggregation methods.
train.sd	Aggregation methods.
train.sum	Aggregation methods.
trainLearner	Train an R learner.
TuneControl	Control object for tuning
TuneControlCMAES	Create control object for hyperparameter tuning with CMAES.
TuneControlDesign	Create control object for hyperparameter tuning with predefined design.
TuneControlGenSA	Create control object for hyperparameter tuning with GenSA.
TuneControlGrid	Create control object for hyperparameter tuning with grid search.
TuneControlIrace	Create control object for hyperparameter tuning with Irace.
TuneControlMBO	Create control object for hyperparameter tuning with MBO.
TuneControlRandom	Create control object for hyperparameter tuning with random search.
TuneMultiCritControl	Create control structures for multi-criteria tuning.
TuneMultiCritControlGrid	Create control structures for multi-criteria tuning.
TuneMultiCritControlMBO	Create control structures for multi-criteria tuning.
TuneMultiCritControlNSGA2	Create control structures for multi-criteria tuning.
TuneMultiCritControlRandom	Create control structures for multi-criteria tuning.
TuneMultiCritResult	Result of multi-criteria tuning.
tuneParams	Hyperparameter tuning.
tuneParamsMultiCrit	Hyperparameter tuning for multiple measures at once.
TuneResult	Result of tuning.
tuneThreshold	Tune prediction threshold.

-- U --

undersample

Over- or undersample binary classification task to handle class imbalancy.

-- W --

wkappa	Performance measures.
wpbc.task	Wisonsin Prognostic Breast Cancer (WPBC) survival task.
WrappedModel	Induced model of learner.

-- Y --

yeast.task

Yeast multilabel classification task.