Man pages for berndbischl/mlr
Machine Learning in R

addRRMeasureCompute new measures for existing ResampleResult
AggregationAggregation object.
aggregationsAggregation methods.
agri.taskEuropean Union Agricultural Workforces clustering task.
analyzeFeatSelResultShow and visualize the steps of feature selection.
asROCRPredictionConverts predictions to a format package ROCR can handle.
batchmarkRun machine learning benchmarks as distributed experiments.
bc.taskWisconsin Breast Cancer classification task.
benchmarkBenchmark experiment for multiple learners and tasks.
BenchmarkResultBenchmarkResult object.
bh.taskBoston Housing regression task.
calculateConfusionMatrixConfusion matrix.
calculateROCMeasuresCalculate receiver operator measures.
capLargeValuesConvert large/infinite numeric values in a data.frame or...
classif.featurelessFeatureless classification learner.
configureMlrConfigures the behavior of the package.
ConfusionMatrixConfusion matrix
convertBMRToRankMatrixConvert BenchmarkResult to a rank-matrix.
convertMLBenchObjToTaskConvert a machine learning benchmark / demo object from...
costiris.taskIris cost-sensitive classification task.
createDummyFeaturesGenerate dummy variables for factor features.
downsampleDownsample (subsample) a task or a data.frame.
dropFeaturesDrop some features of task.
estimateRelativeOverfittingEstimate relative overfitting.
estimateResidualVarianceEstimate the residual variance.
extractFDAFeaturesExtract features from functional data.
extractFDAFourierFast Fourier transform features.
extractFDAFPCAExtract functional principal component analysis features.
extractFDAMultiResFeaturesMultiresolution feature extraction.
extractFDAWaveletsDiscrete Wavelet transform features.
FailureModelFailure model.
FeatSelControlCreate control structures for feature selection.
FeatSelResultResult of feature selection.
filterFeaturesFilter features by thresholding filter values.
friedmanPostHocTestBMRPerform a posthoc Friedman-Nemenyi test.
friedmanTestBMRPerform overall Friedman test for a BenchmarkResult.
fuelsubset.taskFuelSubset functional data regression task.
generateCalibrationDataGenerate classifier calibration data.
generateCritDifferencesDataGenerate data for critical-differences plot.
generateFeatureImportanceDataGenerate feature importance.
generateFilterValuesDataCalculates feature filter values.
generateFunctionalANOVADataGenerate a functional ANOVA decomposition
generateHyperParsEffectDataGenerate hyperparameter effect data.
generateLearningCurveDataGenerates a learning curve.
generatePartialDependenceDataGenerate partial dependence.
generateThreshVsPerfDataGenerate threshold vs. performance(s) for 2-class...
getBMRAggrPerformancesExtract the aggregated performance values from a benchmark...
getBMRFeatSelResultsExtract the feature selection results from a benchmark...
getBMRFilteredFeaturesExtract the feature selection results from a benchmark...
getBMRLearnerIdsReturn learner ids used in benchmark.
getBMRLearnersReturn learners used in benchmark.
getBMRLearnerShortNamesReturn learner short.names used in benchmark.
getBMRMeasureIdsReturn measures IDs used in benchmark.
getBMRMeasuresReturn measures used in benchmark.
getBMRModelsExtract all models from benchmark result.
getBMRPerformancesExtract the test performance values from a benchmark result.
getBMRPredictionsExtract the predictions from a benchmark result.
getBMRTaskDescriptionsExtract all task descriptions from benchmark result...
getBMRTaskDescsExtract all task descriptions from benchmark result.
getBMRTaskIdsReturn task ids used in benchmark.
getBMRTuneResultsExtract the tuning results from a benchmark result.
getCaretParamSetGet tuning parameters from a learner of the caret R-package.
getClassWeightParamGet the class weight parameter of a learner.
getConfMatrixConfusion matrix.
getDefaultMeasureGet default measure.
getFailureModelDumpReturn the error dump of FailureModel.
getFailureModelMsgReturn error message of FailureModel.
getFeatSelResultReturns the selected feature set and optimization path after...
getFeatureImportanceCalculates feature importance values for trained models.
getFeatureImportanceLearnerCalculates feature importance values for a given learner.
getFilteredFeaturesReturns the filtered features.
getFilterValuesCalculates feature filter values.
getHomogeneousEnsembleModelsDeprecated, use 'getLearnerModel' instead.
getHyperParsGet current parameter settings for a learner.
getLearnerIdGet the ID of the learner.
getLearnerModelGet underlying R model of learner integrated into mlr.
getLearnerPackagesGet the required R packages of the learner.
getLearnerParamSetGet the parameter set of the learner.
getLearnerParValsGet the parameter values of the learner.
getLearnerPredictTypeGet the predict type of the learner.
getLearnerShortNameGet the short name of the learner.
getLearnerTypeGet the type of the learner.
getMlrOptionsReturns a list of mlr's options.
getMultilabelBinaryPerformancesRetrieve binary classification measures for multilabel...
getNestedTuneResultsOptPathDfGet the 'opt.path's from each tuning step from the outer...
getNestedTuneResultsXGet the tuned hyperparameter settings from a nested tuning.
getOOBPredsExtracts out-of-bag predictions from trained models.
getOOBPredsLearnerProvides out-of-bag predictions for a given model and the...
getParamSetGet a description of all possible parameter settings for a...
getPredictionDumpReturn the error dump of a failed Prediction.
getPredictionProbabilitiesGet probabilities for some classes.
getPredictionResponseGet response / truth from prediction object.
getPredictionTaskDescGet summarizing task description from prediction.
getProbabilitiesDeprecated, use 'getPredictionProbabilities' instead.
getRRDumpReturn the error dump of ResampleResult.
getRRPredictionListGet list of predictions for train and test set of each single...
getRRPredictionsGet predictions from resample results.
getRRTaskDescGet task description from resample results (DEPRECATED).
getRRTaskDescriptionGet task description from resample results (DEPRECATED).
getStackedBaseLearnerPredictionsReturns the predictions for each base learner.
getTaskClassLevelsGet the class levels for classification and multilabel tasks.
getTaskCostsExtract costs in task.
getTaskDataExtract data in task.
getTaskDescGet a summarizing task description.
getTaskDescriptionDeprecated, use 'getTaskDesc' instead.
getTaskFeatureNamesGet feature names of task.
getTaskFormulaGet formula of a task.
getTaskIdGet the id of the task.
getTaskNFeatsGet number of features in task.
getTaskSizeGet number of observations in task.
getTaskTargetNamesGet the name(s) of the target column(s).
getTaskTargetsGet target data of task.
getTaskTypeGet the type of the task.
getTuneResultReturns the optimal hyperparameters and optimization path...
gunpoint.taskGunpoint functional data classification task.
hasFunctionalFeaturesCheck whether the object conatins functional features.
hasPropertiesDeprecated, use 'hasLearnerProperties' instead.
helpLearnerAccess help page of learner functions.
helpLearnerParamGet specific help for a learner's parameters.
imputationsBuilt-in imputation methods.
imputeImpute and re-impute data
iris.taskIris classification task.
isFailureModelIs the model a FailureModel?
joinClassLevelsJoin some class existing levels to new, larger class levels...
learnerArgsToControlConvert arguments to control structure.
LearnerPropertiesQuery properties of learners.
learnersList of supported learning algorithms.
listFilterMethodsList filter methods.
listLearnerPropertiesList the supported learner properties
listLearnersFind matching learning algorithms.
listMeasurePropertiesList the supported measure properties.
listMeasuresFind matching measures.
listTaskTypesList the supported task types in mlr
lung.taskNCCTG Lung Cancer survival task.
makeAggregationSpecify your own aggregation of measures.
makeBaggingWrapperFuse learner with the bagging technique.
makeClassificationViaRegressionWrapperClassification via regression wrapper.
makeConstantClassWrapperWraps a classification learner to support problems where the...
makeCostMeasureCreates a measure for non-standard misclassification costs.
makeCostSensClassifWrapperWraps a classification learner for use in cost-sensitive...
makeCostSensRegrWrapperWraps a regression learner for use in cost-sensitive...
makeCostSensWeightedPairsWrapperWraps a classifier for cost-sensitive learning to produce a...
makeCustomResampledMeasureConstruct your own resampled performance measure.
makeDownsampleWrapperFuse learner with simple downsampling (subsampling).
makeDummyFeaturesWrapperFuse learner with dummy feature creator.
makeExtractFDAFeatMethodConstructor for FDA feature extraction methods.
makeExtractFDAFeatsWrapperFuse learner with an extractFDAFeatures method.
makeFeatSelWrapperFuse learner with feature selection.
makeFilterCreate a feature filter.
makeFilterWrapperFuse learner with a feature filter method.
makeFixedHoldoutInstanceGenerate a fixed holdout instance for resampling.
makeFunctionalDataCreate a data.frame containing functional features from a...
makeImputeMethodCreate a custom imputation method.
makeImputeWrapperFuse learner with an imputation method.
makeLearnerCreate learner object.
makeLearnersCreate multiple learners at once.
makeMeasureConstruct performance measure.
makeModelMultiplexerCreate model multiplexer for model selection to tune over...
makeModelMultiplexerParamSetCreates a parameter set for model multiplexer tuning.
makeMulticlassWrapperFuse learner with multiclass method.
makeMultilabelBinaryRelevanceWrapperUse binary relevance method to create a multilabel learner.
makeMultilabelClassifierChainsWrapperUse classifier chains method (CC) to create a multilabel...
makeMultilabelDBRWrapperUse dependent binary relevance method (DBR) to create a...
makeMultilabelNestedStackingWrapperUse nested stacking method to create a multilabel learner.
makeMultilabelStackingWrapperUse stacking method (stacked generalization) to create a...
makeOverBaggingWrapperFuse learner with the bagging technique and oversampling for...
makePreprocWrapperFuse learner with preprocessing.
makePreprocWrapperCaretFuse learner with preprocessing.
makeRemoveConstantFeaturesWrapperFuse learner with removal of constant features preprocessing.
makeResampleDescCreate a description object for a resampling strategy.
makeResampleInstanceInstantiates a resampling strategy object.
makeRLearner.classif.fdausc.glmClassification of functional data by Generalized Linear...
makeRLearner.classif.fdausc.kernelLearner for kernel classification for functional data.
makeRLearner.classif.fdausc.npLearner for nonparametric classification for functional data.
makeSMOTEWrapperFuse learner with SMOTE oversampling for imbalancy correction...
makeStackedLearnerCreate a stacked learner object.
makeTuneControlCMAESCreate control object for hyperparameter tuning with CMAES.
makeTuneControlDesignCreate control object for hyperparameter tuning with...
makeTuneControlGenSACreate control object for hyperparameter tuning with GenSA.
makeTuneControlGridCreate control object for hyperparameter tuning with grid...
makeTuneControlIraceCreate control object for hyperparameter tuning with Irace.
makeTuneControlMBOCreate control object for hyperparameter tuning with MBO.
makeTuneControlRandomCreate control object for hyperparameter tuning with random...
makeTuneWrapperFuse learner with tuning.
makeUndersampleWrapperFuse learner with simple ove/underrsampling for imbalancy...
makeWeightedClassesWrapperWraps a classifier for weighted fitting where each class...
makeWrappedModelInduced model of learner.
MeasurePropertiesQuery properties of measures.
measuresPerformance measures.
mergeBenchmarkResultsMerge different BenchmarkResult objects.
mergeSmallFactorLevelsMerges small levels of factors into new level.
mlrFamiliesmlr documentation families
mtcars.taskMotor Trend Car Road Tests clustering task.
normalizeFeaturesNormalize features.
oversampleOver- or undersample binary classification task to handle...
parallelizationSupported parallelization methods
performanceMeasure performance of prediction.
phoneme.taskPhoneme functional data multilabel classification task.
pid.taskPimaIndiansDiabetes classification task.
plotBMRBoxplotsCreate box or violin plots for a BenchmarkResult.
plotBMRRanksAsBarChartCreate a bar chart for ranks in a BenchmarkResult.
plotBMRSummaryPlot a benchmark summary.
plotCalibrationPlot calibration data using ggplot2.
plotCritDifferencesPlot critical differences for a selected measure.
plotFilterValuesPlot filter values using ggplot2.
plotFilterValuesGGVISPlot filter values using ggvis.
plotHyperParsEffectPlot the hyperparameter effects data
plotLearnerPredictionVisualizes a learning algorithm on a 1D or 2D data set.
plotLearningCurvePlot learning curve data using ggplot2.
plotLearningCurveGGVISPlot learning curve data using ggvis.
plotPartialDependencePlot a partial dependence with ggplot2.
plotPartialDependenceGGVISPlot a partial dependence using ggvis.
plotResidualsCreate residual plots for prediction objects or benchmark...
plotROCCurvesPlots a ROC curve using ggplot2.
plotThreshVsPerfPlot threshold vs. performance(s) for 2-class classification...
plotThreshVsPerfGGVISPlot threshold vs. performance(s) for 2-class classification...
plotTuneMultiCritResultPlots multi-criteria results after tuning using ggplot2.
plotTuneMultiCritResultGGVISPlots multi-criteria results after tuning using ggvis.
plotViperChartsVisualize binary classification predictions via ViperCharts...
PredictionPrediction object.
predictLearnerPredict new data with an R learner.
predict.WrappedModelPredict new data.
reduceBatchmarkResultsReduce results of a batch-distributed benchmark.
reextractFDAFeaturesRe-extract features from a data set
regr.featurelessFeatureless regression learner.
regr.randomForestRandomForest regression learner.
reimputeRe-impute a data set
removeConstantFeaturesRemove constant features from a data set.
removeHyperParsRemove hyperparameters settings of a learner.
resampleFit models according to a resampling strategy.
ResamplePredictionPrediction from resampling.
ResampleResultResampleResult object.
RLearnerInternal construction / wrapping of learner object.
selectFeaturesFeature selection by wrapper approach.
setAggregationSet aggregation function of measure.
setHyperParsSet the hyperparameters of a learner object.
setHyperPars2Only exported for internal use.
setIdSet the id of a learner object.
setLearnerIdSet the ID of a learner object.
setMeasureParsSet parameters of performance measures
setPredictThresholdSet the probability threshold the learner should use.
setPredictTypeSet the type of predictions the learner should return.
setThresholdSet threshold of prediction object.
simplifyMeasureNamesSimplify measure names.
smoteSynthetic Minority Oversampling Technique to handle class...
sonar.taskSonar classification task.
subsetTaskSubset data in task.
summarizeColumnsSummarize columns of data.frame or task.
summarizeLevelsSummarizes factors of a data.frame by tabling them.
TaskCreate a classification, regression, survival, cluster,...
TaskDescDescription object for task.
trainTrain a learning algorithm.
trainLearnerTrain an R learner.
TuneControlControl object for tuning
TuneMultiCritControlCreate control structures for multi-criteria tuning.
TuneMultiCritResultResult of multi-criteria tuning.
tuneParamsHyperparameter tuning.
tuneParamsMultiCritHyperparameter tuning for multiple measures at once.
TuneResultResult of tuning.
tuneThresholdTune prediction threshold.
wpbc.taskWisonsin Prognostic Breast Cancer (WPBC) survival task.
yeast.taskYeast multilabel classification task.
berndbischl/mlr documentation built on Oct. 13, 2017, 9:25 a.m.