mlr v2.17.1


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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.
FeatSelControl Create control structures for feature selection.
FeatSelResult Result of feature selection.
Aggregation Aggregation object.
makeClassifTask Create a classification task.
ConfusionMatrix Confusion matrix
FailureModel Failure model.
makeCostSensTask Create a cost-sensitive classification task.
BenchmarkResult BenchmarkResult object.
makeClusterTask Create a cluster task.
TaskDesc Description object for task.
ResampleResult ResampleResult object.
Task Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
Prediction Prediction object.
MeasureProperties Query properties of measures.
TuneControl Control object for tuning
makeMultilabelTask Create a multilabel task.
asROCRPrediction Converts predictions to a format package ROCR can handle.
makeRegrTask Create a regression task.
batchmark Run machine learning benchmarks as distributed experiments.
calculateConfusionMatrix Confusion matrix.
ResamplePrediction Prediction from resampling.
agri.task European Union Agricultural Workforces clustering task.
analyzeFeatSelResult Show and visualize the steps of feature selection.
RLearner Internal construction / wrapping of learner object.
addRRMeasure Compute new measures for existing ResampleResult
benchmark Benchmark experiment for multiple learners and tasks.
calculateROCMeasures Calculate receiver operator measures.
TuneMultiCritControl Create control structures for multi-criteria tuning.
createDummyFeatures Generate dummy variables for factor features.
bc.task Wisconsin Breast Cancer classification task.
crossover Crossover.
convertMLBenchObjToTask Convert a machine learning benchmark / demo object from package mlbench to a task.
aggregations Aggregation methods.
cache_helpers Get or delete mlr cache directory
downsample Downsample (subsample) a task or a data.frame.
makeSurvTask Create a survival task.
bh.task Boston Housing regression task.
TuneResult Result of tuning.
capLargeValues Convert large/infinite numeric values in a data.frame or task.
TuneMultiCritResult Result of multi-criteria tuning.
costiris.task Iris cost-sensitive classification task.
createSpatialResamplingPlots Create (spatial) resampling plot objects.
extractFDADTWKernel DTW kernel features
dropFeatures Drop some features of task.
estimateRelativeOverfitting Estimate relative overfitting.
extractFDAFeatures Extract features from functional data.
checkLearner Exported for internal use only.
extractFDAFourier Fast Fourier transform features.
extractFDAWavelets Discrete Wavelet transform features.
changeData Change Task Data
generateFilterValuesData Calculates feature filter values.
filterFeatures Filter features by thresholding filter values.
generateCalibrationData Generate classifier calibration data.
extractFDAMultiResFeatures Multiresolution feature extraction.
checkPredictLearnerOutput Check output returned by predictLearner.
generateLearningCurveData Generates a learning curve.
extractFDATsfeatures Time-Series Feature Heuristics
fuelsubset.task FuelSubset functional data regression task.
extractFDAFPCA Extract functional principal component analysis features.
generatePartialDependenceData Generate partial dependence.
getBMRPredictions Extract the predictions from a benchmark result.
friedmanPostHocTestBMR Perform a posthoc Friedman-Nemenyi test.
generateHyperParsEffectData Generate hyperparameter effect data.
friedmanTestBMR Perform overall Friedman test for a BenchmarkResult.
generateThreshVsPerfData Generate threshold vs. performance(s) for 2-class classification.
configureMlr Configures the behavior of the package.
getBMRLearners Return learners used in benchmark.
getBMRMeasureIds Return measures IDs used in benchmark.
getBMRAggrPerformances Extract the aggregated performance values from a benchmark result.
convertBMRToRankMatrix Convert BenchmarkResult to a rank-matrix.
getCaretParamSet Get tuning parameters from a learner of the caret R-package.
getFeatureImportanceLearner.regr.randomForestSRC Calculates feature importance values for a given learner.
getBMRFeatSelResults Extract the feature selection results from a benchmark result.
getBMRFilteredFeatures Extract the feature selection results from a benchmark result.
getParamSet Get a description of all possible parameter settings for a learner.
getBMRPerformances Extract the test performance values from a benchmark result.
getBMRModels Extract all models from benchmark result.
getTaskTargetNames Get the name(s) of the target column(s).
getBMRTaskDescs Extract all task descriptions from benchmark result.
getLearnerParamSet Get the parameter set of the learner.
getFilteredFeatures Returns the filtered features.
getBMRMeasures Return measures used in benchmark.
getBMRTaskDescriptions Extract all task descriptions from benchmark result (DEPRECATED).
getBMRTuneResults Extract the tuning results from a benchmark result.
getBMRTaskIds Return task ids used in benchmark.
getClassWeightParam Get the class weight parameter of a learner.
getOOBPreds Extracts out-of-bag predictions from trained models.
getLearnerPredictType Get the predict type of the learner.
joinClassLevels Join some class existing levels to new, larger class levels for classification problems.
getPredictionDump Return the error dump of a failed Prediction.
getFunctionalFeatures Get only functional features from a task or a data.frame.
getHomogeneousEnsembleModels Deprecated, use getLearnerModel instead.
getLearnerModel Get underlying R model of learner integrated into mlr.
getFailureModelMsg Return error message of FailureModel.
getFailureModelDump Return the error dump of FailureModel.
getMlrOptions Returns a list of mlr's options.
getMultilabelBinaryPerformances Retrieve binary classification measures for multilabel classification predictions.
getProbabilities Deprecated, use getPredictionProbabilities instead.
getPredictionTaskDesc Get summarizing task description from prediction.
getFeatureImportance Calculates feature importance values for trained models.
getFeatSelResult Returns the selected feature set and optimization path after training.
getLearnerNote Get the note for the learner.
getNestedTuneResultsOptPathDf Get the opt.paths from each tuning step from the outer resampling.
getTaskCosts Extract costs in task.
getNestedTuneResultsX Get the tuned hyperparameter settings from a nested tuning.
getTaskData Extract data in task.
getTaskClassLevels Get the class levels for classification and multilabel tasks.
getStackedBaseLearnerPredictions Returns the predictions for each base learner.
extractFDABsignal Bspline mlq features
getPredictionResponse Get response / truth from prediction object.
getPredictionProbabilities Get probabilities for some classes.
getLearnerShortName Get the short name of the learner.
getLearnerType Get the type of the learner.
getTaskTargets Get target data of task.
getTaskNFeats Get number of features in task.
estimateResidualVariance Estimate the residual variance.
getTaskId Get the id of the task.
generateCritDifferencesData Generate data for critical-differences plot.
getRRDump Return the error dump of ResampleResult.
getTaskType Get the type of the task.
gunpoint.task Gunpoint functional data classification task.
getRRPredictions Get predictions from resample results.
generateFeatureImportanceData Generate feature importance.
helpLearnerParam Get specific help for a learner's parameters.
getRRPredictionList Get list of predictions for train and test set of each single resample iteration.
imputations Built-in imputation methods.
hasFunctionalFeatures Check whether the object contains functional features.
listLearnerProperties List the supported learner properties
listMeasureProperties List the supported measure properties.
hasProperties Deprecated, use hasLearnerProperties instead.
getTaskSize Get number of observations in task.
listMeasures Find matching measures.
listTaskTypes List the supported task types in mlr
getRRTaskDesc Get task description from resample results (DEPRECATED).
listLearners Find matching learning algorithms.
lung.task NCCTG Lung Cancer survival task.
getBMRLearnerIds Return learner ids used in benchmark.
getBMRLearnerShortNames Return learner short.names used in benchmark.
getDefaultMeasure Get default measure.
makeAggregation Specify your own aggregation of measures.
makeBaseWrapper Exported for internal use only.
getConfMatrix Confusion matrix.
makeChainModel Only exported for internal use.
makeCustomResampledMeasure Construct your own resampled performance measure.
makeDownsampleWrapper Fuse learner with simple downsampling (subsampling).
helpLearner Access help page of learner functions.
isFailureModel Is the model a FailureModel?
getLearnerPackages Get the required R packages of the learner.
makeFilterEnsemble Create an ensemble feature filter.
makeFunctionalData Create a data.frame containing functional features from a normal data.frame.
getHyperPars Get current parameter settings for a learner.
getRRTaskDescription Get task description from resample results (DEPRECATED).
getLearnerParVals Get the parameter values of the learner.
getLearnerId Get the ID of the learner.
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.
makeImputeMethod Create a custom imputation method.
getTaskDesc Get a summarizing task description.
impute Impute and re-impute data
getResamplingIndices Get the resampling indices from a tuning or feature selection wrapper..
getOOBPredsLearner Provides out-of-bag predictions for a given model and the corresponding learner.
makeFilter Create a feature filter.
makeMulticlassWrapper Fuse learner with multiclass method.
makeModelMultiplexer Create model multiplexer for model selection to tune over multiple possible models.
makeModelMultiplexerParamSet Creates a parameter set for model multiplexer tuning.
makeBaggingWrapper Fuse learner with the bagging technique.
makeMultilabelBinaryRelevanceWrapper Use binary relevance method to create a multilabel learner.
makeLearners Create multiple learners at once.
getTaskDescription Deprecated, use getTaskDesc instead.
getTuneResultOptPath Get the optimization path of a tuning result.
getTuneResult Returns the optimal hyperparameters and optimization path after training.
makeMeasure Construct performance measure.
makePreprocWrapperCaret Fuse learner with preprocessing.
makeMultilabelNestedStackingWrapper Use nested stacking method to create a multilabel learner.
listFilterEnsembleMethods List ensemble filter methods.
makeMultilabelStackingWrapper Use stacking method (stacked generalization) to create a multilabel learner.
listFilterMethods List filter methods.
makeClassificationViaRegressionWrapper Classification via regression wrapper.
makeConstantClassWrapper Wraps a classification learner to support problems where the class label is (almost) constant.
makeImputeWrapper Fuse learner with an imputation method.
makeExtractFDAFeatsWrapper Fuse learner with an extractFDAFeatures method.
getTaskFeatureNames Get feature names of task.
makeFeatSelWrapper Fuse learner with feature selection.
makeRLearner.classif.fdausc.kernel Learner for kernel classification for functional data.
iris.task Iris classification task.
learnerArgsToControl Convert arguments to control structure.
getTaskFormula Get formula of a task.
makeCostMeasure Creates a measure for non-standard misclassification costs.
makeTaskDescInternal Exported for internal use.
predictLearner Predict new data with an R learner.
makeTuneControlCMAES Create control object for hyperparameter tuning with CMAES.
makeRLearner.classif.fdausc.glm Classification of functional data by Generalized Linear Models.
learners List of supported learning algorithms.
makeCostSensClassifWrapper Wraps a classification learner for use in cost-sensitive learning.
predict.WrappedModel Predict new data.
makeFilterWrapper Fuse learner with a feature filter method.
plotPartialDependence Plot a partial dependence with ggplot2.
makeFixedHoldoutInstance Generate a fixed holdout instance for resampling.
makeExtractFDAFeatMethod Constructor for FDA feature extraction methods.
makeDummyFeaturesWrapper Fuse learner with dummy feature creator. Learner for nonparametric classification for functional data.
makeLearner Create learner object.
makePreprocWrapper Fuse learner with preprocessing.
makeTuneWrapper Fuse learner with tuning.
makeOverBaggingWrapper Fuse learner with the bagging technique and oversampling for imbalancy correction.
makeMultilabelClassifierChainsWrapper Use classifier chains method (CC) to create a multilabel learner.
makeMultilabelDBRWrapper Use dependent binary relevance method (DBR) to create a multilabel learner.
makeTuneControlRandom Create control object for hyperparameter tuning with random search.
makeTuneControlGenSA Create control object for hyperparameter tuning with GenSA.
makeRemoveConstantFeaturesWrapper Fuse learner with removal of constant features preprocessing.
makeTuneControlGrid Create control object for hyperparameter tuning with grid search.
makeTuneControlIrace Create control object for hyperparameter tuning with Irace.
makeSMOTEWrapper Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
makeTuneControlMBO Create control object for hyperparameter tuning with MBO.
performance Measure performance of prediction.
parallelization Supported parallelization methods
makeStackedLearner Create a stacked learner object.
makeResampleInstance Instantiates a resampling strategy object.
mergeBenchmarkResults Merge different BenchmarkResult objects.
measures Performance measures.
makeWeightedClassesWrapper Wraps a classifier for weighted fitting where each class receives a weight.
makeResampleDesc Create a description object for a resampling strategy.
makeClassifTaskDesc Exported for internal use.
makeWrappedModel Induced model of learner.
makeUndersampleWrapper Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
reimpute Re-impute a data set
mergeSmallFactorLevels Merges small levels of factors into new level.
plotBMRSummary Plot a benchmark summary.
summarizeLevels Summarizes factors of a data.frame by tabling them.
summarizeColumns Summarize columns of data.frame or task.
setAggregation Set aggregation function of measure.
plotCalibration Plot calibration data using ggplot2.
selectFeatures Feature selection by wrapper approach.
setPredictType Set the type of predictions the learner should return.
setThreshold Set threshold of prediction object.
train Train a learning algorithm.
removeConstantFeatures Remove constant features from a data set.
phoneme.task Phoneme functional data multilabel classification task.
mlrFamilies mlr documentation families
setMeasurePars Set parameters of performance measures
mlr-package mlr: Machine Learning in R
resample Fit models according to a resampling strategy.
tuneThreshold Tune prediction threshold.
plotCritDifferences Plot critical differences for a selected measure.
plotROCCurves Plots a ROC curve using ggplot2.
plotFilterValues Plot filter values using ggplot2.
setPredictThreshold Set the probability threshold the learner should use.
setId Set the id of a learner object.
pid.task PimaIndiansDiabetes classification task.
plotResiduals Create residual plots for prediction objects or benchmark results.
wpbc.task Wisonsin Prognostic Breast Cancer (WPBC) survival task.
mtcars.task Motor Trend Car Road Tests clustering task.
plotLearningCurve Plot learning curve data using ggplot2.
makeTuneControlDesign Create control object for hyperparameter tuning with predefined design.
plotHyperParsEffect Plot the hyperparameter effects data
setLearnerId Set the ID of a learner object.
normalizeFeatures Normalize features.
oversample Over- or undersample binary classification task to handle class imbalancy.
smote Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
simplifyMeasureNames Simplify measure names.
setHyperPars Set the hyperparameters of a learner object.
plotBMRBoxplots Create box or violin plots for a BenchmarkResult.
yeast.task Yeast multilabel classification task.
plotBMRRanksAsBarChart Create a bar chart for ranks in a BenchmarkResult.
plotLearnerPrediction Visualizes a learning algorithm on a 1D or 2D data set.
tuneParamsMultiCrit Hyperparameter tuning for multiple measures at once.
plotThreshVsPerf Plot threshold vs. performance(s) for 2-class classification using ggplot2.
spatial.task J. Muenchow's Ecuador landslide data set
reextractFDAFeatures Re-extract features from a data set
reduceBatchmarkResults Reduce results of a batch-distributed benchmark.
sonar.task Sonar classification task.
setHyperPars2 Only exported for internal use.
removeHyperPars Remove hyperparameters settings of a learner.
plotTuneMultiCritResult Plots multi-criteria results after tuning using ggplot2.
spam.task Spam classification task.
tuneParams Hyperparameter tuning.
subsetTask Subset data in task.
trainLearner Train an R learner.
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License BSD_2_clause + file LICENSE
VignetteBuilder knitr
ByteCompile yes
Encoding UTF-8
LazyData yes
RoxygenNote 7.1.0
SystemRequirements gdal (optional), geos (optional), proj (optional), udunits (optional), gsl (optional), gmp (optional), glu (optional), jags (optional)
NeedsCompilation yes
Packaged 2020-03-24 09:57:32 UTC; pjs
Repository CRAN
Date/Publication 2020-03-24 10:40:02 UTC
suggests ada , adabag , bartMachine , batchtools , brnn , bst , C50 , care , caret (>= 6.0-57) , class , clue , cluster , ClusterR , clusterSim (>= 0.44-5) , cmaes , cowplot , CoxBoost , crs , Cubist , deepnet , DiceKriging , DiscriMiner , e1071 , earth , elasticnet , emoa , evtree , extraTrees , fda.usc , FDboost , FNN , forecast (>= 8.3) , fpc , frbs , FSelector , FSelectorRcpp (>= 0.2.1) , gbm , GenSA , ggpubr , glmnet , GPfit , h2o (>= , Hmisc , hrbrthemes , irace (>= 2.0) , kernlab , kknn , klaR , knitr , laGP , LiblineaR , lintr (>= , 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 , rucrdtw , RWeka , sda , sf , smoof , sparseLDA , stepPlr , survAUC , svglite , SwarmSVM , testthat , tgp , , tidyr , tsfeatures , vdiffr , wavelets , xgboost (>= 0.7)
imports backports (>= 1.1.0) , BBmisc (>= 1.11) , checkmate (>= 1.8.2) , data.table (>= 1.12.4) , 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, Annette Spooner

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