R: Machine Learning in R

# Machine Learning in R

## Documentation for package ‘mlr’ version 2.19.3

- 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.