All functions

Aggregation

Aggregation object.

BenchmarkResult

BenchmarkResult object.

ConfusionMatrix

Confusion matrix

FailureModel

Failure model.

makeFeatSelControlExhaustive() makeFeatSelControlGA() makeFeatSelControlRandom() makeFeatSelControlSequential()

Create control structures for feature selection.

FeatSelResult

Result of feature selection.

getLearnerProperties() hasLearnerProperties()

Query properties of learners.

getMeasureProperties() hasMeasureProperties()

Query properties of measures.

makeRLearner() makeRLearnerClassif() makeRLearnerMultilabel() makeRLearnerRegr() makeRLearnerSurv() makeRLearnerCluster() makeRLearnerCostSens()

Internal construction / wrapping of learner object.

ResamplePrediction

Prediction from resampling.

ResampleResult

ResampleResult object.

makeClassifTask() makeClusterTask() makeCostSensTask() makeMultilabelTask() makeRegrTask() makeSurvTask()

Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.

TaskDesc

Description object for task.

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.

addRRMeasure()

Compute new measures for existing ResampleResult

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.

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.

batchmark()

Run machine learning benchmarks as distributed experiments.

bc.task

Wisconsin Breast Cancer classification task.

benchmark()

Benchmark experiment for multiple learners and tasks.

bh.task

Boston Housing regression task.

calculateConfusionMatrix() print(<ConfusionMatrix>)

Confusion matrix.

calculateROCMeasures() print(<ROCMeasures>)

Calculate receiver operator measures.

capLargeValues()

Convert large/infinite numeric values in a data.frame or task.

classif.featureless

Featureless classification learner.

configureMlr()

Configures the behavior of the package.

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.

createDummyFeatures()

Generate dummy variables for factor features.

createSpatialResamplingPlots()

Create (spatial) resampling plot objects.

crossover

Crossover.

downsample()

Downsample (subsample) a task or a data.frame.

dropFeatures()

Drop some features of task.

estimateRelativeOverfitting()

Estimate relative overfitting.

estimateResidualVariance()

Estimate the residual variance.

extractFDAFPCA()

Extract functional principal component analysis features.

extractFDAFeatures()

Extract features from functional data.

extractFDAFourier()

Fast Fourier transform features.

extractFDAMultiResFeatures()

Multiresolution feature extraction.

extractFDAWavelets()

Discrete Wavelet transform features.

filterFeatures()

Filter features by thresholding filter values.

friedmanPostHocTestBMR()

Perform a posthoc Friedman-Nemenyi test.

friedmanTestBMR()

Perform overall Friedman test for a BenchmarkResult.

fuelsubset.task

FuelSubset functional data regression task.

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.

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.

getFilterValues()

Calculates feature filter values.

getFilteredFeatures()

Returns the filtered features.

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.

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.

gunpoint.task

Gunpoint functional data classification task.

hasFunctionalFeatures()

Check whether the object conatins functional features.

hasProperties()

Deprecated, use hasLearnerProperties instead.

helpLearner()

Access help page of learner functions.

helpLearnerParam()

Get specific help for a learner's parameters.

imputeConstant() imputeMedian() imputeMean() imputeMode() imputeMin() imputeMax() imputeUniform() imputeNormal() imputeHist() imputeLearner()

Built-in imputation methods.

impute()

Impute and re-impute data

iris.task

Iris classification task.

isFailureModel()

Is the model a FailureModel?

joinClassLevels()

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

learnerArgsToControl()

Convert arguments to control structure.

learners

List of supported learning algorithms.

listFilterMethods()

List filter methods.

listLearnerProperties()

List the supported learner properties

listLearners()

Find matching learning algorithms.

listMeasureProperties()

List the supported measure properties.

listMeasures()

Find matching measures.

listTaskTypes()

List the supported task types in mlr

lung.task

NCCTG Lung Cancer survival task.

makeAggregation()

Specify your own aggregation of measures.

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.

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.

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.

makeFeatSelWrapper()

Fuse learner with feature selection.

makeFilter()

Create a 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.

makeOverBaggingWrapper()

Fuse learner with the bagging technique and oversampling for imbalancy correction.

makePreprocWrapper()

Fuse learner with preprocessing.

makePreprocWrapperCaret()

Fuse learner with preprocessing.

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.

makeRemoveConstantFeaturesWrapper()

Fuse learner with removal of constant features preprocessing.

makeResampleDesc()

Create a description object for a resampling strategy.

makeResampleInstance()

Instantiates a resampling strategy object.

makeSMOTEWrapper()

Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.

makeStackedLearner()

Create a stacked learner object.

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.

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.

featperc timetrain timepredict timeboth sse measureSSE() mse measureMSE() rmse measureRMSE() medse measureMEDSE() sae measureSAE() mae measureMAE() medae measureMEDAE() rsq measureRSQ() expvar measureEXPVAR() arsq rrse measureRRSE() rae measureRAE() mape measureMAPE() msle measureMSLE() rmsle measureRMSLE() kendalltau measureKendallTau() spearmanrho measureSpearmanRho() mmce measureMMCE() acc measureACC() ber measureBER() multiclass.aunu measureAUNU() multiclass.aunp measureAUNP() multiclass.au1u measureAU1U() multiclass.au1p measureAU1P() multiclass.brier measureMulticlassBrier() logloss measureLogloss() ssr measureSSR() qsr measureQSR() lsr measureLSR() kappa measureKAPPA() wkappa measureWKAPPA() auc measureAUC() brier measureBrier() brier.scaled measureBrierScaled() bac measureBAC() tp measureTP() tn measureTN() fp measureFP() fn measureFN() tpr measureTPR() tnr measureTNR() fpr measureFPR() fnr measureFNR() ppv measurePPV() npv measureNPV() fdr measureFDR() mcc measureMCC() f1 measureF1() gmean measureGMEAN() gpr measureGPR() multilabel.hamloss measureMultilabelHamloss() multilabel.subset01 measureMultilabelSubset01() multilabel.f1 measureMultilabelF1() multilabel.acc measureMultilabelACC() multilabel.ppv measureMultilabelPPV() multilabel.tpr measureMultilabelTPR() cindex cindex.uno iauc.uno ibrier meancosts mcp db dunn G1 G2 silhouette

Performance measures.

mergeBenchmarkResults()

Merge different BenchmarkResult objects.

mergeSmallFactorLevels()

Merges small levels of factors into new level.

mlr-package

mlr: Machine Learning in R

mlrFamilies

mlr documentation families

mtcars.task

Motor Trend Car Road Tests clustering task.

normalizeFeatures()

Normalize features.

oversample() undersample()

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

parallelization

Supported parallelization methods

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.

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.

predict(<WrappedModel>)

Predict new data.

predictLearner()

Predict new data with an R learner.

reduceBatchmarkResults()

Reduce results of a batch-distributed benchmark.

reextractFDAFeatures()

Re-extract features from a data set

regr.featureless

Featureless regression learner.

regr.randomForest

RandomForest regression learner.

reimpute()

Re-impute a data set

removeConstantFeatures()

Remove constant features from a data set.

removeHyperPars()

Remove hyperparameters settings of a learner.

resample() crossval() repcv() holdout() subsample() bootstrapOOB() bootstrapB632() bootstrapB632plus() growingcv() fixedcv()

Fit models according to a resampling strategy.

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.

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

subsetTask()

Subset data in task.

summarizeColumns()

Summarize columns of data.frame or task.

summarizeLevels()

Summarizes factors of a data.frame by tabling them.

train()

Train a learning algorithm.

trainLearner()

Train an R learner.

tuneParams()

Hyperparameter tuning.

tuneParamsMultiCrit()

Hyperparameter tuning for multiple measures at once.

tuneThreshold()

Tune prediction threshold.

wpbc.task

Wisonsin Prognostic Breast Cancer (WPBC) survival task.

yeast.task

Yeast multilabel classification task.