Aggregation             Aggregation object.
BenchmarkResult         BenchmarkResult object.
ConfusionMatrix         Confusion matrix
FailureModel            Failure model.
FeatSelControl          Create control structures for feature
                        selection.
FeatSelResult           Result of feature selection.
LearnerProperties       Query properties of learners.
MeasureProperties       Query properties of measures.
RLearner                Internal construction / wrapping of learner
                        object.
ResamplePrediction      Prediction from resampling.
ResampleResult          ResampleResult object.
Task                    Create a classification, regression, survival,
                        cluster, cost-sensitive classification or
                        multilabel task.
TaskDesc                Description object for task.
TuneControl             Control object for tuning
TuneMultiCritControl    Create control structures for multi-criteria
                        tuning.
TuneMultiCritResult     Result of multi-criteria tuning.
TuneResult              Result of tuning.
addRRMeasure            Compute new measures for existing
                        ResampleResult
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.
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.
cache_helpers           Get or delete mlr cache directory
calculateConfusionMatrix
                        Confusion matrix.
calculateROCMeasures    Calculate receiver operator measures.
capLargeValues          Convert large/infinite numeric values in a
                        data.frame or task.
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.
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.
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.
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   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 contains functional
                        features.
hasProperties           Deprecated, use 'hasLearnerProperties' instead.
helpLearner             Access help page of learner functions.
helpLearnerParam        Get specific help for a learner's parameters.
imputations             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.
listFilterEnsembleMethods
                        List ensemble filter methods.
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.
makeClassifTask         Create a classification task.
makeClassificationViaRegressionWrapper
                        Classification via regression wrapper.
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.
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.
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.
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.
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.
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.
measures                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              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
reimpute                Re-impute a data set
removeConstantFeatures
                        Remove constant features from a data set.
removeHyperPars         Remove hyperparameters settings of a learner.
resample                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.
