Aggregation             Aggregation object.
BenchmarkResult         Result of a benchmark run.
FailureModel            Failure model.
FeatSelControl          Create control structures for feature
                        selection.
FeatSelResult           Result of feature selection.
FilterValues            Result of 'getFilterValues'.
LearnerProperties       Set, add, remove or query properties of
                        learners
Prediction              Prediction object.
RLearner                Internal construction / wrapping of learner
                        object.
ResamplePrediction      Prediction from resampling.
ResampleResult          ResampleResult object.
TaskDesc                Description object for task.
TuneControl             Create control structures for tuning.
TuneMultiCritControl    Create control structures for multi-criteria
                        tuning.
TuneMultiCritResult     Result of multi-criteria tuning.
TuneResult              Result of tuning.
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.
bc.task                 Wisconsin Breast Cancer classification task
benchmark               Benchmark experiment for multiple learners and
                        tasks.
bh.task                 Boston Housing regression task
capLargeValues          Convert large/infinite numeric values in a
                        data.frame or task.
configureMlr            Configures the behavior of the package.
costiris.task           Iris cost-sensitive classification task
createDummyFeatures     Generate dummy variables for factor features.
crossover               crossover
downsample              Downsample (subsample) a task or a data.frame.
dropFeatures            Drop some features of task.
estimateResidualVariance
                        Estimate the residual variance
filterFeatures          Filter features by thresholding filter values.
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.
getBMRPerformances      Extract the test performance values from a
                        benchmark result.
getBMRPredictions       Extract the predictions from a benchmark
                        result.
getBMRTaskIds           Return task ids used in benchmark.
getBMRTuneResults       Extract the tuning results from a benchmark
                        result.
getConfMatrix           Confusion matrix.
getFailureModelMsg      Return error message of FailureModel.
getFeatSelResult        Returns the selected feature set and
                        optimization path after training.
getFilterValues         Calculates feature filter values.
getFilteredFeatures     Returns the filtered features.
getHomogeneousEnsembleModels
                        Returns the list of fitted models.
getHyperPars            Get current parameter settings for a learner.
getLearnerModel         Get underlying R model of learner integrated
                        into mlr.
getMlrOptions           Returns a list of mlr's options
getParamSet             Get a description of all possible parameter
                        settings for a learner.
getProbabilities        Get probabilities for some classes.
getStackedBaseLearnerPredictions
                        Returns the predictions for each base learner.
getTaskCosts            Extract costs in task.
getTaskData             Extract data in task.
getTaskDescription      Get a summarizing task description.
getTaskFeatureNames     Get feature names of task.
getTaskFormulaAsString
                        Get formula of a task.
getTaskId               Get the id of the task.
getTaskNFeats           Get number of features in task.
getTaskTargetNames      Get the name(s) of the target column(s).
getTaskTargets          Get target column of task.
getTaskType             Get the type of the task.
getTuneResult           Returns the optimal hyperparameters and
                        optimization path after training.
imputations             Built in imputation methods The built-ins are:
                        • 'imputeConstant(const)' for imputation using
                        a constant value, • 'imputeMedian()' for
                        imputation using the median, • 'imputeMode()'
                        for imputation using the mode, •
                        'imputeMin(multiplier)' for imputing constant
                        values shifted below the minimum using 'min(x)
                        - multiplier * diff(range(x))', •
                        'imputeMin(multiplier)' for imputing constant
                        values shifted above the maximum using 'max(x)
                        + multiplier * diff(range(x))', •
                        'imputeNormal(mean, sd)' for imputation using
                        normally distributed random values. Mean and
                        standard deviation will be calculated from the
                        data if not provided.  • 'imputeHist(breaks,
                        use.mids)' for imputation using random values
                        with probabilities calculated using 'table' or
                        'hist'.  • 'imputeLearner(learner, preimpute)'
                        for imputations using the response of a
                        classification or regression learner.
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
listLearners            Find matching learning algorithms.
listMeasures            Find matching measures.
lung.task               NCCTG Lung Cancer survival task
makeAggregation         Specifiy your own aggregation of measures
makeBaggingWrapper      Fuse learner with the bagging technique.
makeClassifTask         Create a classification, regression, survival,
                        cluster, or cost-sensitive classification task.
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).
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.
makeImputeMethod        Create a custom imputation method.
makeImputeWrapper       Fuse learner with an imputation method.
makeLearner             Create learner object.
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.
makeOverBaggingWrapper
                        Fuse learner with the bagging technique and
                        oversampling for imbalancy correction.
makePreprocWrapper      Fuse learner with preprocessing.
makePreprocWrapperCaret
                        Fuse learner with 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.
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.
mergeSmallFactorLevels
                        Merges small levels of factors into new level.
mtcars.task             Motor Trend Car Road Tests clustering task
normalizeFeatures       Normalize features
oversample              Over- or undersample binary classification task
                        to handle class imbalancy.
performance             Measure performance of prediction.
pid.task                PimaIndiansDiabetes classification task
plotFilterValues        Plot filter values.
plotLearnerPrediction   Visualizes a learning algorithm on a 1D or 2D
                        data set.
plotROCRCurves          Visualize binary classification predictions via
                        ROCR ROC curves.
plotThreshVsPerf        Plot threshold vs. performance(s) for 2-class
                        classification.
plotTuneMultiCritResult
                        Plots multi-criteria results after tuning.
plotViperCharts         Visualize binary classification predictions via
                        ViperCharts system.
predict.WrappedModel    Predict new data.
predictLearner          Predict new data with an R learner.
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.
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.
showHyperPars           Display all possible hyperparameter settings
                        for a learner that mlr knows.
smote                   Synthetic Minority Oversampling Technique to
                        handle class imbalancy in binary
                        classification.
sonar.task              Sonar classification task
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
