mlr v2.15.0

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Machine Learning in R

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.

Functions in mlr

Name Description
LearnerProperties Query properties of learners.
Aggregation Aggregation object.
ConfusionMatrix Confusion matrix
FeatSelResult Result of feature selection.
makeCostSensTask Create a cost-sensitive classification task.
makeClassifTask Create a classification task.
BenchmarkResult BenchmarkResult object.
FailureModel Failure model.
makeClusterTask Create a cluster task.
RLearner Internal construction / wrapping of learner object.
Prediction Prediction object.
Task Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
TaskDesc Description object for task.
makeSurvTask Create a survival task.
ResampleResult ResampleResult object.
addRRMeasure Compute new measures for existing ResampleResult
analyzeFeatSelResult Show and visualize the steps of feature selection.
agri.task European Union Agricultural Workforces clustering task.
makeRegrTask Create a regression task.
ResamplePrediction Prediction from resampling.
FeatSelControl Create control structures for feature selection.
aggregations Aggregation methods.
calculateConfusionMatrix Confusion matrix.
MeasureProperties Query properties of measures.
TuneMultiCritResult Result of multi-criteria tuning.
makeMultilabelTask Create a multilabel task.
bc.task Wisconsin Breast Cancer classification task.
batchmark Run machine learning benchmarks as distributed experiments.
asROCRPrediction Converts predictions to a format package ROCR can handle.
costiris.task Iris cost-sensitive classification task.
benchmark Benchmark experiment for multiple learners and tasks.
TuneResult Result of tuning.
TuneMultiCritControl Create control structures for multi-criteria tuning.
TuneControl Control object for tuning
checkLearner Exported for internal use only.
extractFDAWavelets Discrete Wavelet transform features.
generateThreshVsPerfData Generate threshold vs. performance(s) for 2-class classification.
classif.featureless Featureless classification learner.
filterFeatures Filter features by thresholding filter values.
checkPredictLearnerOutput Check output returned by predictLearner.
configureMlr Configures the behavior of the package.
bh.task Boston Housing regression task.
createDummyFeatures Generate dummy variables for factor features.
getBMRAggrPerformances Extract the aggregated performance values from a benchmark result.
createSpatialResamplingPlots Create (spatial) resampling plot objects.
cache_helpers Get or delete mlr cache directory
extractFDAFourier Fast Fourier transform features.
crossover Crossover.
friedmanPostHocTestBMR Perform a posthoc Friedman-Nemenyi test.
friedmanTestBMR Perform overall Friedman test for a BenchmarkResult.
getBMRMeasures Return measures used in benchmark.
generateFilterValuesData Calculates feature filter values.
getBMRLearnerShortNames Return learner short.names used in benchmark.
generateHyperParsEffectData Generate hyperparameter effect data.
getBMRLearnerIds Return learner ids used in benchmark.
extractFDAMultiResFeatures Multiresolution feature extraction.
getBMRModels Extract all models from benchmark result.
convertBMRToRankMatrix Convert BenchmarkResult to a rank-matrix.
getCaretParamSet Get tuning parameters from a learner of the caret R-package.
getFailureModelMsg Return error message of FailureModel.
getPredictionResponse Get response / truth from prediction object.
getFailureModelDump Return the error dump of FailureModel.
getPredictionTaskDesc Get summarizing task description from prediction.
estimateRelativeOverfitting Estimate relative overfitting.
calculateROCMeasures Calculate receiver operator measures.
convertMLBenchObjToTask Convert a machine learning benchmark / demo object from package mlbench to a task.
downsample Downsample (subsample) a task or a data.frame.
estimateResidualVariance Estimate the residual variance.
getBMRTuneResults Extract the tuning results from a benchmark result.
getHomogeneousEnsembleModels Deprecated, use getLearnerModel instead.
getBMRTaskIds Return task ids used in benchmark.
generateCritDifferencesData Generate data for critical-differences plot.
getHyperPars Get current parameter settings for a learner.
generateFeatureImportanceData Generate feature importance.
fuelsubset.task FuelSubset functional data regression task.
dropFeatures Drop some features of task.
getBMRFeatSelResults Extract the feature selection results from a benchmark result.
generateCalibrationData Generate classifier calibration data.
getBMRTaskDescs Extract all task descriptions 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).
getBMRFilteredFeatures Extract the feature selection results from a benchmark result.
helpLearnerParam Get specific help for a learner's parameters.
helpLearner Access help page of learner functions.
extractFDAFPCA Extract functional principal component analysis features.
generateLearningCurveData Generates a learning curve.
capLargeValues Convert large/infinite numeric values in a data.frame or task.
extractFDAFeatures Extract features from functional data.
changeData Change Task Data
generatePartialDependenceData Generate partial dependence.
getBMRLearners Return learners used in benchmark.
makeFunctionalData Create a data.frame containing functional features from a normal data.frame.
makeFixedHoldoutInstance Generate a fixed holdout instance for resampling.
getConfMatrix Confusion matrix.
getBMRMeasureIds Return measures IDs used in benchmark.
getLearnerParVals Get the parameter values of the learner.
makePreprocWrapperCaret Fuse learner with preprocessing.
makePreprocWrapper Fuse learner with preprocessing.
makeTaskDescInternal Exported for internal use.
makeClassifTaskDesc Exported for internal use.
getRRDump Return the error dump of ResampleResult.
getProbabilities Deprecated, use getPredictionProbabilities instead.
getLearnerParamSet Get the parameter set of the learner.
getResamplingIndices Get the resampling indices from a tuning or feature selection wrapper..
getDefaultMeasure Get default measure.
makeTuneControlIrace Create control object for hyperparameter tuning with Irace.
getLearnerModel Get underlying R model of learner integrated into mlr.
getNestedTuneResultsOptPathDf Get the opt.paths from each tuning step from the outer resampling.
getMultilabelBinaryPerformances Retrieve binary classification measures for multilabel classification predictions.
getPredictionDump Return the error dump of a failed Prediction.
getLearnerId Get the ID of the learner.
getLearnerNote Get the note for the learner.
makeTuneControlMBO Create control object for hyperparameter tuning with MBO.
getStackedBaseLearnerPredictions Returns the predictions for each base learner.
getTaskId Get the id of the task.
listMeasures Find matching measures.
getTaskFormula Get formula of a task.
impute Impute and re-impute data
imputations Built-in imputation methods.
getPredictionProbabilities Get probabilities for some classes.
mlr-package mlr: Machine Learning in R
getFeatureImportanceLearner.regr.randomForestSRC Calculates feature importance values for a given learner.
getTaskData Extract data in task.
getFilteredFeatures Returns the filtered features.
getLearnerType Get the type of the learner.
getTaskDescription Deprecated, use getTaskDesc instead.
getMlrOptions Returns a list of mlr's options.
getTaskFeatureNames Get feature names of task.
getLearnerPackages Get the required R packages of the learner.
getTuneResultOptPath Get the optimization path of a tuning result.
getNestedTuneResultsX Get the tuned hyperparameter settings from a nested tuning.
getClassWeightParam Get the class weight parameter of a learner.
getTaskDesc Get a summarizing task description.
gunpoint.task Gunpoint functional data classification task.
makeAggregation Specify your own aggregation of measures.
makeBaseWrapper Exported for internal use only.
lung.task NCCTG Lung Cancer survival task.
makeBaggingWrapper Fuse learner with the bagging technique.
getOOBPreds Extracts out-of-bag predictions from trained models.
getRRPredictionList Get list of predictions for train and test set of each single resample iteration.
getRRPredictions Get predictions from resample results.
getTaskNFeats Get number of features in task.
getTaskClassLevels Get the class levels for classification and multilabel tasks.
getFeatSelResult Returns the selected feature set and optimization path after training.
getTaskSize Get number of observations in task.
makeFilter Create a feature filter.
makeMulticlassWrapper Fuse learner with multiclass method.
makeRemoveConstantFeaturesWrapper Fuse learner with removal of constant features preprocessing.
makeFeatSelWrapper Fuse learner with feature selection.
makeSMOTEWrapper Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
makeModelMultiplexerParamSet Creates a parameter set for model multiplexer tuning.
makeRLearner.classif.fdausc.np Learner for nonparametric classification for functional data.
getTaskCosts Extract costs in task.
listTaskTypes List the supported task types in mlr
mlrFamilies mlr documentation families
predict.WrappedModel Predict new data.
resample Fit models according to a resampling strategy.
plotTuneMultiCritResult Plots multi-criteria results after tuning using ggplot2.
wpbc.task Wisonsin Prognostic Breast Cancer (WPBC) survival task.
selectFeatures Feature selection by wrapper approach.
yeast.task Yeast multilabel classification task.
makeStackedLearner Create a stacked learner object.
getFeatureImportance Calculates feature importance values for trained models.
learnerArgsToControl Convert arguments to control structure.
joinClassLevels Join some class existing levels to new, larger class levels for classification problems.
getTaskTargetNames Get the name(s) of the target column(s).
getLearnerShortName Get the short name of the learner.
getParamSet Get a description of all possible parameter settings for a learner.
getRRTaskDescription Get task description from resample results (DEPRECATED).
getLearnerPredictType Get the predict type of the learner.
getRRTaskDesc Get task description from resample results (DEPRECATED).
getOOBPredsLearner Provides out-of-bag predictions for a given model and the corresponding learner.
makeWrappedModel Induced model of learner.
measures Performance measures.
makeCostSensClassifWrapper Wraps a classification learner for use in cost-sensitive learning.
hasFunctionalFeatures Check whether the object conatins functional features.
listLearners Find matching learning algorithms.
hasProperties Deprecated, use hasLearnerProperties instead.
getTaskTargets Get target data of task.
getTaskType Get the type of the task.
getTuneResult Returns the optimal hyperparameters and optimization path after training.
plotLearnerPrediction Visualizes a learning algorithm on a 1D or 2D data set.
listMeasureProperties List the supported measure properties.
reextractFDAFeatures Re-extract features from a data set
regr.featureless Featureless regression learner.
iris.task Iris classification task.
plotLearningCurve Plot learning curve data using ggplot2.
isFailureModel Is the model a FailureModel?
makeCostSensWeightedPairsWrapper Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
makeCostSensRegrWrapper Wraps a regression learner for use in cost-sensitive learning.
listLearnerProperties List the supported learner properties
listFilterMethods List filter methods.
makeConstantClassWrapper Wraps a classification learner to support problems where the class label is (almost) constant.
makeCustomResampledMeasure Construct your own resampled performance measure.
makeImputeMethod Create a custom imputation method.
removeConstantFeatures Remove constant features from a data set.
removeHyperPars Remove hyperparameters settings of a learner.
makeImputeWrapper Fuse learner with an imputation method.
makeMultilabelNestedStackingWrapper Use nested stacking method to create a multilabel learner.
makeResampleDesc Create a description object for a resampling strategy.
makeMultilabelDBRWrapper Use dependent binary relevance method (DBR) to create a multilabel learner.
makeCostMeasure Creates a measure for non-standard misclassification costs.
performance Measure performance of prediction.
phoneme.task Phoneme functional data multilabel classification task.
makeResampleInstance Instantiates a resampling strategy object.
makeFilterEnsemble Create an ensemble feature filter.
sonar.task Sonar classification task.
smote Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
summarizeLevels Summarizes factors of a data.frame by tabling them.
makeDownsampleWrapper Fuse learner with simple downsampling (subsampling).
train Train a learning algorithm.
makeExtractFDAFeatsWrapper Fuse learner with an extractFDAFeatures method.
makeClassificationViaRegressionWrapper Classification via regression wrapper.
makeExtractFDAFeatMethod Constructor for FDA feature extraction methods.
makeChainModel Only exported for internal use.
learners List of supported learning algorithms.
listFilterEnsembleMethods List ensemble filter methods.
plotCalibration Plot calibration data using ggplot2.
makeDummyFeaturesWrapper Fuse learner with dummy feature creator.
plotCritDifferences Plot critical differences for a selected measure.
predictLearner Predict new data with an R learner.
makeOverBaggingWrapper Fuse learner with the bagging technique and oversampling for imbalancy correction.
makeModelMultiplexer Create model multiplexer for model selection to tune over multiple possible models.
makeMultilabelStackingWrapper Use stacking method (stacked generalization) to create a multilabel learner.
makeMeasure Construct performance measure.
makeTuneControlCMAES Create control object for hyperparameter tuning with CMAES.
makeLearner Create learner object.
makeTuneControlGenSA Create control object for hyperparameter tuning with GenSA.
makeMultilabelBinaryRelevanceWrapper Use binary relevance method to create a multilabel learner.
makeTuneControlGrid Create control object for hyperparameter tuning with grid search.
makeMultilabelClassifierChainsWrapper Use classifier chains method (CC) to create a multilabel learner.
makeRLearner.classif.fdausc.glm Classification of functional data by Generalized Linear Models.
makeFilterWrapper Fuse learner with a feature filter method.
makeRLearner.classif.fdausc.kernel Learner for kernel classification for functional data.
makeTuneControlDesign Create control object for hyperparameter tuning with predefined design.
makeWeightedClassesWrapper Wraps a classifier for weighted fitting where each class receives a weight.
makeUndersampleWrapper Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
pid.task PimaIndiansDiabetes classification task.
mergeSmallFactorLevels Merges small levels of factors into new level.
normalizeFeatures Normalize features.
mergeBenchmarkResults Merge different BenchmarkResult objects.
mtcars.task Motor Trend Car Road Tests clustering task.
plotBMRBoxplots Create box or violin plots for a BenchmarkResult.
setThreshold Set threshold of prediction object.
setAggregation Set aggregation function of measure.
setHyperPars Set the hyperparameters of a learner object.
simplifyMeasureNames Simplify measure names.
reduceBatchmarkResults Reduce results of a batch-distributed benchmark.
plotROCCurves Plots a ROC curve using ggplot2.
plotPartialDependence Plot a partial dependence with ggplot2.
regr.randomForest RandomForest regression learner.
reimpute Re-impute a data set
makeLearners Create multiple learners at once.
plotBMRSummary Plot a benchmark summary.
plotBMRRanksAsBarChart Create a bar chart for ranks in a BenchmarkResult.
setHyperPars2 Only exported for internal use.
setMeasurePars Set parameters of performance measures
subsetTask Subset data in task.
setLearnerId Set the ID of a learner object.
setId Set the id of a learner object.
summarizeColumns Summarize columns of data.frame or task.
spam.task Spam classification task.
spatial.task J. Muenchow's Ecuador landslide data set
parallelization Supported parallelization methods
makeTuneWrapper Fuse learner with tuning.
makeTuneControlRandom Create control object for hyperparameter tuning with random search.
plotFilterValues Plot filter values using ggplot2.
oversample Over- or undersample binary classification task to handle class imbalancy.
tuneParamsMultiCrit Hyperparameter tuning for multiple measures at once.
plotHyperParsEffect Plot the hyperparameter effects data
plotResiduals Create residual plots for prediction objects or benchmark results.
tuneThreshold Tune prediction threshold.
plotThreshVsPerf Plot threshold vs. performance(s) for 2-class classification using ggplot2.
setPredictType Set the type of predictions the learner should return.
tuneParams Hyperparameter tuning.
trainLearner Train an R learner.
setPredictThreshold Set the probability threshold the learner should use.
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License BSD_2_clause + file LICENSE
URL https://github.com/mlr-org/mlr
BugReports https://github.com/mlr-org/mlr/issues
VignetteBuilder knitr
ByteCompile yes
Encoding UTF-8
LazyData yes
RoxygenNote 6.1.1
NeedsCompilation yes
Packaged 2019-08-06 09:10:20 UTC; pjs
Repository CRAN
Date/Publication 2019-08-06 15:10:02 UTC
suggests ada , adabag , bartMachine , batchtools , brnn , bst , C50 , care , caret (>= 6.0-57) , class , clue , cluster , ClusterR , clusterSim (>= 0.44-5) , clValid , cmaes , cowplot , CoxBoost , crs , Cubist , deepnet , DiceKriging , DiscriMiner , e1071 , earth , elasticnet , emoa , evtree , extraTrees , fda.usc , FDboost , flare , FNN , fpc , frbs , FSelector , FSelectorRcpp (>= 0.2.1) , gbm , GenSA , ggpubr , glmnet , GPfit , h2o (>= 3.6.0.8) , Hmisc , hrbrthemes , irace (>= 2.0) , kernlab , kknn , klaR , knitr , laGP , LiblineaR , lintr (>= 1.0.0.9001) , MASS , mboost , mco , mda , memoise , mlbench , mldr , mlrMBO , mmpf , modeltools , mRMRe , neuralnet , nnet , nodeHarvest (>= 0.7-3) , numDeriv , pamr , pander , party , penalized (>= 0.9-47) , pls , PMCMR (>= 4.1) , praznik (>= 5.0.0) , randomForest , randomForestSRC (>= 2.7.0) , ranger (>= 0.8.0) , rappdirs , refund , rex , rFerns , rgenoud , rknn , rmarkdown , ROCR , rotationForest , rpart , RRF , rrlda , rsm , RSNNS , RWeka , sda , sf , smoof , sparseLDA , stepPlr , survAUC , svglite , SwarmSVM , testthat , tgp , TH.data , wavelets , xgboost (>= 0.6-2)
imports backports (>= 1.1.0) , BBmisc (>= 1.11) , checkmate (>= 1.8.2) , data.table , ggplot2 , methods , parallelMap (>= 1.3) , stats , stringi , survival , utils , XML
depends ParamHelpers (>= 1.10) , R (>= 3.0.2)
Contributors Pascal Kerschke, Giuseppe Casalicchio, Michel Lang, Jakob Bossek, Tobias Kuehn, Erich Studerus, Zachary Jones, Schiffner Julia, Bernd Bischl, Jakob Richter, Leonard Judt, Florian Fendt, Janek Thomas, Mason Gallo, Philipp Probst, Xudong Sun, Bruno Vieira, Lars Kotthoff, Laura Beggel, Quay Au, Martin Binder, Florian Pfisterer, Stefan Coors, Steve Bronder, Alexander Engelhardt, Christoph Molnar

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