Function reference • mlr

Task
`agri.task`	European Union Agricultural Workforces clustering task.
`bc.task`	Wisconsin Breast Cancer classification task.
`bh.task`	Boston Housing regression task.
`costiris.task`	Iris cost-sensitive classification task.
`fuelsubset.task`	FuelSubset functional data regression task.
`gunpoint.task`	Gunpoint functional data classification task.
`iris.task`	Iris classification task.
`lung.task`	NCCTG Lung Cancer survival task.
`mtcars.task`	Motor Trend Car Road Tests clustering task.
`phoneme.task`	Phoneme functional data multilabel classification task.
`pid.task`	PimaIndiansDiabetes classification task.
`sonar.task`	Sonar classification task.
`spam.task`	Spam classification task.
`spatial.task`	J. Muenchow's Ecuador landslide data set
`wpbc.task`	Wisonsin Prognostic Breast Cancer (WPBC) survival task.
`yeast.task`	Yeast multilabel classification task.
`Task`	Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
`makeClassifTask()`	Create a classification task.
`makeClusterTask()`	Create a cluster task.
`makeCostSensTask()`	Create a cost-sensitive classification task.
`makeMultilabelTask()`	Create a multilabel task.
`makeRegrTask()`	Create a regression task.
`makeSurvTask()`	Create a survival task.
`convertMLBenchObjToTask()`	Convert a machine learning benchmark / demo object from package mlbench to a task.
`getFunctionalFeatures()`	Get only functional features from a task or a data.frame.
`listLearners()`	Find matching learning algorithms.
`listMeasures()`	Find matching measures.
`subsetTask()`	Subset data in task.
`TaskDesc`	Description object for task.
`listTaskTypes()`	List the supported task types in mlr
Training
`train()`	Train a learning algorithm.
`trainLearner()`	Train an R learner.
Tuning
`TuneControl`	Control object for tuning
`makeTuneMultiCritControlGrid()` `makeTuneMultiCritControlMBO()` `makeTuneMultiCritControlNSGA2()` `makeTuneMultiCritControlRandom()`	Create control structures for multi-criteria tuning.
`TuneMultiCritResult`	Result of multi-criteria tuning.
`TuneResult`	Result of tuning.
`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.
`tuneParams()`	Hyperparameter tuning.
`tuneParamsMultiCrit()`	Hyperparameter tuning for multiple measures at once.
`tuneThreshold()`	Tune prediction threshold.
`removeHyperPars()`	Remove hyperparameters settings of a learner.
Prediction
`predict(<WrappedModel>)`	Predict new data.
`asROCRPrediction()`	Converts predictions to a format package ROCR can handle.
`predictLearner()`	Predict new data with an R learner.
Learner
`makeRLearner()` `makeRLearnerClassif()` `makeRLearnerMultilabel()` `makeRLearnerRegr()` `makeRLearnerSurv()` `makeRLearnerCluster()` `makeRLearnerCostSens()`	Internal construction / wrapping of learner object.
`listLearners()`	Find matching learning algorithms.
`learners`	List of supported learning algorithms.
`makeLearner()`	Create learner object.
`listLearnerProperties()`	List the supported learner properties
`makeLearners()`	Create multiple learners at once.
`learnerArgsToControl()`	Convert arguments to control structure.
`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.
`makeStackedLearner()`	Create a stacked learner object.
Resampling
`resample()` `crossval()` `repcv()` `holdout()` `subsample()` `bootstrapOOB()` `bootstrapB632()` `bootstrapB632plus()` `growingcv()` `fixedcv()`	Fit models according to a resampling strategy.
`ResamplePrediction`	Prediction from resampling.
`ResampleResult`	ResampleResult object.
`makeCustomResampledMeasure()`	Construct your own resampled performance measure.
`makeResampleDesc()`	Create a description object for a resampling strategy.
`makeResampleInstance()`	Instantiates a resampling strategy object.
`makeFixedHoldoutInstance()`	Generate a fixed holdout instance for resampling.
Parallelization
`batchmark()`	Run machine learning benchmarks as distributed experiments.
`parallelization`	Supported parallelization methods
Preprocessing
`createDummyFeatures()`	Generate dummy variables for factor features.
`downsample()`	Downsample (subsample) a task or a data.frame.
`dropFeatures()`	Drop some features of task.
`generateCalibrationData()`	Generate classifier calibration data.
`capLargeValues()`	Convert large/infinite numeric values in a data.frame or task.
`crossover`	Crossover.
`joinClassLevels()`	Join some class existing levels to new, larger class levels for classification problems.
`mergeSmallFactorLevels()`	Merges small levels of factors into new level.
`normalizeFeatures()`	Normalize features.
`oversample()` `undersample()`	Over- or undersample binary classification task to handle class imbalancy.
`removeConstantFeatures()`	Remove constant features from a data set.
`summarizeColumns()`	Summarize columns of data.frame or task.
`summarizeLevels()`	Summarizes factors of a data.frame by tabling them.
`smote()`	Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
Benchmark
`benchmark()`	Benchmark experiment for multiple learners and tasks.
`BenchmarkResult`	BenchmarkResult object.
`makeModelMultiplexer()`	Create model multiplexer for model selection to tune over multiple possible models.
`makeModelMultiplexerParamSet()`	Creates a parameter set for model multiplexer tuning.
`mergeBenchmarkResults()`	Merge different BenchmarkResult objects.
`reduceBatchmarkResults()`	Reduce results of a batch-distributed benchmark.
`convertBMRToRankMatrix()`	Convert BenchmarkResult to a rank-matrix.
`friedmanPostHocTestBMR()`	Perform a posthoc Friedman-Nemenyi test.
`friedmanTestBMR()`	Perform overall Friedman test for a BenchmarkResult.
`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.
`getBMRLearnerShortNames()`	Return learner short.names used in benchmark.
`getBMRLearners()`	Return learners 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.
`plotBMRBoxplots()`	Create box or violin plots for a BenchmarkResult.
`plotBMRRanksAsBarChart()`	Create a bar chart for ranks in a BenchmarkResult.
`plotBMRSummary()`	Plot a benchmark summary.
Feature selection
`makeFeatSelControlExhaustive()` `makeFeatSelControlGA()` `makeFeatSelControlRandom()` `makeFeatSelControlSequential()`	Create control structures for feature selection.
`FeatSelResult`	Result of feature selection.
`filterFeatures()`	Filter features by thresholding filter values.
`generateFilterValuesData()`	Calculates feature filter values.
`getBMRFilteredFeatures()`	Extract the feature selection results from a benchmark result.
`getFilteredFeatures()`	Returns the filtered features.
`listFilterEnsembleMethods()`	List ensemble filter methods.
`listFilterMethods()`	List filter methods.
`makeFilter()`	Create a feature filter.
`makeFilterEnsemble()`	Create an ensemble feature filter.
`makeFilterWrapper()`	Fuse learner with a feature filter method.
`plotFilterValues()`	Plot filter values using ggplot2.
`generateFeatureImportanceData()`	Generate feature importance.
`analyzeFeatSelResult()`	Show and visualize the steps of feature selection.
`selectFeatures()`	Feature selection by wrapper approach.
Model evaluation
`calculateConfusionMatrix()` `print(<ConfusionMatrix>)`	Confusion matrix.
`estimateRelativeOverfitting()`	Estimate relative overfitting.
`estimateResidualVariance()`	Estimate the residual variance.
`generateHyperParsEffectData()`	Generate hyperparameter effect data.
`generateLearningCurveData()`	Generates a learning curve.
`generatePartialDependenceData()`	Generate partial dependence.
`generateThreshVsPerfData()`	Generate threshold vs. performance(s) for 2-class classification.
`generateCritDifferencesData()`	Generate data for critical-differences plot.
`performance()`	Measure performance of prediction.
Measures
`measureSSE()` `measureMSE()` `measureRMSE()` `measureMEDSE()` `measureSAE()` `measureMAE()` `measureMEDAE()` `measureRSQ()` `measureEXPVAR()` `measureRRSE()` `measureRAE()` `measureMAPE()` `measureMSLE()` `measureRMSLE()` `measureKendallTau()` `measureSpearmanRho()` `measureMMCE()` `measureACC()` `measureBER()` `measureAUNU()` `measureAUNP()` `measureAU1U()` `measureAU1P()` `measureMulticlassBrier()` `measureLogloss()` `measureSSR()` `measureQSR()` `measureLSR()` `measureKAPPA()` `measureWKAPPA()` `measureAUC()` `measureBrier()` `measureBrierScaled()` `measureBAC()` `measureTP()` `measureTN()` `measureFP()` `measureFN()` `measureTPR()` `measureTNR()` `measureFPR()` `measureFNR()` `measurePPV()` `measureNPV()` `measureFDR()` `measureMCC()` `measureF1()` `measureGMEAN()` `measureGPR()` `measureMultilabelHamloss()` `measureMultilabelSubset01()` `measureMultilabelF1()` `measureMultilabelACC()` `measureMultilabelPPV()` `measureMultilabelTPR()`	Performance measures.
`Aggregation`	Aggregation object.
`ConfusionMatrix`	Confusion matrix
`addRRMeasure()`	Compute new measures for existing ResampleResult
`aggregations`	Aggregation methods.
`calculateROCMeasures()` `print(<ROCMeasures>)`	Calculate receiver operator measures.
`makeAggregation()`	Specify your own aggregation of measures.
`makeMeasure()`	Construct performance measure.
`makeCostMeasure()`	Creates a measure for non-standard misclassification costs.
`listMeasureProperties()`	List the supported measure properties.
`setAggregation()`	Set aggregation function of measure.
`simplifyMeasureNames()`	Simplify measure names.
Imputation
`imputeConstant()` `imputeMedian()` `imputeMean()` `imputeMode()` `imputeMin()` `imputeMax()` `imputeUniform()` `imputeNormal()` `imputeHist()` `imputeLearner()`	Built-in imputation methods.
`impute()`	Impute and re-impute data
`makeImputeMethod()`	Create a custom imputation method.
`reimpute()`	Re-impute a data set
Spatial
`createSpatialResamplingPlots()`	Create (spatial) resampling plot objects.
Functional data analysis
`extractFDABsignal()`	Bspline mlq features
`extractFDADTWKernel()`	DTW kernel features
`extractFDAFPCA()`	Extract functional principal component analysis features.
`extractFDAFeatures()`	Extract features from functional data.
`extractFDAFourier()`	Fast Fourier transform features.
`extractFDAMultiResFeatures()`	Multiresolution feature extraction.
`extractFDATsfeatures()`	Time-Series Feature Heuristics
`extractFDAWavelets()`	Discrete Wavelet transform features.
`makeExtractFDAFeatMethod()`	Constructor for FDA feature extraction methods.
`makeExtractFDAFeatsWrapper()`	Fuse learner with an extractFDAFeatures method.
`reextractFDAFeatures()`	Re-extract features from a data set
`getFunctionalFeatures()`	Get only functional features from a task or a data.frame.
`hasFunctionalFeatures()`	Check whether the object contains functional features.
`makeFunctionalData()`	Create a data.frame containing functional features from a normal data.frame.
mlr configuration
`configureMlr()`	Configures the behavior of the package.
`mlr-package`	mlr: Machine Learning in R
`mlrFamilies`	mlr documentation families
`getMlrOptions()`	Returns a list of mlr's options.
Visualization
`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.
`plotROCCurves()`	Plots a ROC curve using ggplot2.
`plotResiduals()`	Create residual plots for prediction objects or benchmark results.
`plotThreshVsPerf()`	Plot threshold vs. performance(s) for 2-class classification using ggplot2.
`plotTuneMultiCritResult()`	Plots multi-criteria results after tuning using ggplot2.
Cost-sensitive classification
`makeCostSensTask()`	Create a cost-sensitive classification task.
`makeCostSensClassifWrapper()`	Wraps a classification learner for use in cost-sensitive learning.
`makeCostSensRegrWrapper()`	Wraps a regression learner for use in cost-sensitive learning.
`makeCostSensWeightedPairsWrapper()`	Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
Extract functions
`extractFDABsignal()`	Bspline mlq features
`extractFDADTWKernel()`	DTW kernel features
`extractFDAFPCA()`	Extract functional principal component analysis features.
`extractFDAFeatures()`	Extract features from functional data.
`extractFDAFourier()`	Fast Fourier transform features.
`extractFDAMultiResFeatures()`	Multiresolution feature extraction.
`extractFDATsfeatures()`	Time-Series Feature Heuristics
`extractFDAWavelets()`	Discrete Wavelet transform features.
Helpers
`getCacheDir()` `deleteCacheDir()`	Get or delete mlr cache directory
`helpLearner()`	Access help page of learner functions.
`helpLearnerParam()`	Get specific help for a learner's parameters.
`FailureModel`	Failure model.
`hasProperties()`	Deprecated, use `hasLearnerProperties` instead.
`isFailureModel()`	Is the model a FailureModel?
Wrappers
`makeBaggingWrapper()`	Fuse learner with the bagging technique.
`makeClassificationViaRegressionWrapper()`	Classification via regression wrapper.
`makeConstantClassWrapper()`	Wraps a classification learner to support problems where the class label is (almost) constant.
`makeCostSensClassifWrapper()`	Wraps a classification learner for use in cost-sensitive learning.
`makeCostSensRegrWrapper()`	Wraps a regression learner for use in cost-sensitive learning.
`makeCostSensWeightedPairsWrapper()`	Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
`makeDownsampleWrapper()`	Fuse learner with simple downsampling (subsampling).
`makeDummyFeaturesWrapper()`	Fuse learner with dummy feature creator.
`makeExtractFDAFeatsWrapper()`	Fuse learner with an extractFDAFeatures method.
`makeFeatSelWrapper()`	Fuse learner with feature selection.
`makeFilterWrapper()`	Fuse learner with a feature filter method.
`makeImputeWrapper()`	Fuse learner with an imputation method.
`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.
`makeOverBaggingWrapper()`	Fuse learner with the bagging technique and oversampling for imbalancy correction.
`makePreprocWrapper()`	Fuse learner with preprocessing.
`makePreprocWrapperCaret()`	Fuse learner with preprocessing.
`makeRemoveConstantFeaturesWrapper()`	Fuse learner with removal of constant features preprocessing.
`makeSMOTEWrapper()`	Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
`makeTuneWrapper()`	Fuse learner with tuning.
`makeUndersampleWrapper()` `makeOversampleWrapper()`	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.
Getter functions
`getLearnerProperties()` `hasLearnerProperties()`	Query properties of learners.
`getMeasureProperties()` `hasMeasureProperties()`	Query properties of measures.
`getCacheDir()` `deleteCacheDir()`	Get or delete mlr cache directory
`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.
`getBMRLearnerShortNames()`	Return learner short.names used in benchmark.
`getBMRLearners()`	Return learners 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.
`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.
`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.
`getLearnerParVals()`	Get the parameter values of the learner.
`getLearnerParamSet()`	Get the parameter set of the learner.
`getLearnerPredictType()`	Get the predict type of the learner.
`getLearnerShortName()`	Get the short name of the learner.
`getLearnerType()`	Get the type of the learner.
`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()` `getPredictionSE()` `getPredictionTruth()`	Get response / truth from prediction object.
`getPredictionTaskDesc()`	Get summarizing task description from prediction.
`getProbabilities()`	Deprecated, use `getPredictionProbabilities` instead.
`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).
`getResamplingIndices()`	Get the resampling indices from a tuning or feature selection wrapper..
`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.
Setter functions
`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.

Reference

Task

Training

Tuning

Prediction

Learner

Resampling

Parallelization

Preprocessing

Benchmark

Feature selection

Model evaluation

Measures

Imputation

Spatial

Functional data analysis

mlr configuration

Visualization

Cost-sensitive classification

Extract functions

Helpers

Wrappers

Getter functions

Setter functions