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 test.mean test.sd test.median test.min test.max test.sum test.range test.rmse train.mean train.sd train.median train.min train.max train.sum train.range train.rmse b632 b632plus testgroup.mean testgroup.sd test.join: 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 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.paths 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.