mlr v2.13

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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
crossover Crossover.
downsample Downsample (subsample) a task or a data.frame.
Aggregation Aggregation object.
BenchmarkResult BenchmarkResult object.
makeClassifTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
generateFeatureImportanceData Generate feature importance.
convertMLBenchObjToTask Convert a machine learning benchmark / demo object from package mlbench to a task.
costiris.task Iris cost-sensitive classification task.
generateFilterValuesData Calculates feature filter values.
TaskDesc Description object for task.
ConfusionMatrix Confusion matrix
estimateResidualVariance Estimate the residual variance.
extractFDAFPCA Extract functional principal component analysis features.
changeData Change Task Data
generateCalibrationData Generate classifier calibration data.
getBMRTuneResults Extract the tuning results from a benchmark result.
getCaretParamSet Get tuning parameters from a learner of the caret R-package.
getDefaultMeasure Get default measure.
generateCritDifferencesData Generate data for critical-differences plot.
FailureModel Failure model.
getFailureModelDump Return the error dump of FailureModel.
LearnerProperties Query properties of learners.
getMlrOptions Returns a list of mlr's options.
Prediction Prediction object.
checkLearner Exported for internal use only.
ResamplePrediction Prediction from resampling.
RLearner Internal construction / wrapping of learner object.
TuneMultiCritResult Result of multi-criteria tuning.
TuneResult Result of tuning.
bc.task Wisconsin Breast Cancer classification task.
getMultilabelBinaryPerformances Retrieve binary classification measures for multilabel classification predictions.
MeasureProperties Query properties of measures.
TuneControl Control object for tuning
filterFeatures Filter features by thresholding filter values.
getNestedTuneResultsOptPathDf Get the opt.paths from each tuning step from the outer resampling.
benchmark Benchmark experiment for multiple learners and tasks.
TuneMultiCritControl Create control structures for multi-criteria tuning.
calculateROCMeasures Calculate receiver operator measures.
capLargeValues Convert large/infinite numeric values in a data.frame or task.
ResampleResult ResampleResult object.
getBMRMeasureIds Return measures IDs used in benchmark.
convertBMRToRankMatrix Convert BenchmarkResult to a rank-matrix.
configureMlr Configures the behavior of the package.
getNestedTuneResultsX Get the tuned hyperparameter settings from a nested tuning.
getBMRMeasures Return measures used in benchmark.
dropFeatures Drop some features of task.
bh.task Boston Housing regression task.
createDummyFeatures Generate dummy variables for factor features.
getTaskCosts Extract costs in task.
calculateConfusionMatrix Confusion matrix.
getTaskData Extract data in task.
createSpatialResamplingPlots Create (spatial) resampling plot objects.
getBMRPredictions Extract the predictions from a benchmark result.
getBMRTaskDescriptions Extract all task descriptions from benchmark result (DEPRECATED).
hasProperties Deprecated, use hasLearnerProperties instead.
extractFDAMultiResFeatures Multiresolution feature extraction.
extractFDAWavelets Discrete Wavelet transform features.
classif.featureless Featureless classification learner.
checkPredictLearnerOutput Check output returned by predictLearner.
getFailureModelMsg Return error message of FailureModel.
friedmanPostHocTestBMR Perform a posthoc Friedman-Nemenyi test.
helpLearner Access help page of learner functions.
listFilterMethods List filter methods.
getBMRAggrPerformances Extract the aggregated performance values from a benchmark result.
estimateRelativeOverfitting Estimate relative overfitting.
listLearnerProperties List the supported learner properties
getBMRFeatSelResults Extract the feature selection results from a benchmark result.
extractFDAFeatures Extract features from functional data.
lung.task NCCTG Lung Cancer survival task.
extractFDAFourier Fast Fourier transform features.
getFeatSelResult Returns the selected feature set and optimization path after training.
generateHyperParsEffectData Generate hyperparameter effect data.
getBMRLearnerShortNames Return learner short.names used in benchmark.
generateLearningCurveData Generates a learning curve.
getPredictionTaskDesc Get summarizing task description from prediction.
friedmanTestBMR Perform overall Friedman test for a BenchmarkResult.
generatePartialDependenceData Generate partial dependence.
getBMRLearners Return learners used in benchmark.
fuelsubset.task FuelSubset functional data regression task.
getFilterValues Calculates feature filter values.
makeAggregation Specify your own aggregation of measures.
getBMRFilteredFeatures Extract the feature selection results from a benchmark result.
generateThreshVsPerfData Generate threshold vs. performance(s) for 2-class classification.
getLearnerPackages Get the required R packages of the learner.
getProbabilities Deprecated, use getPredictionProbabilities instead.
getFilteredFeatures Returns the filtered features.
getBMRTaskDescs Extract all task descriptions from benchmark result.
makeExtractFDAFeatMethod Constructor for FDA feature extraction methods.
getClassWeightParam Get the class weight parameter of a learner.
makeExtractFDAFeatsWrapper Fuse learner with an extractFDAFeatures method.
getBMRTaskIds Return task ids used in benchmark.
getOOBPreds Extracts out-of-bag predictions from trained models.
getLearnerParVals Get the parameter values of the learner.
makeModelMultiplexer Create model multiplexer for model selection to tune over multiple possible models.
makeMultilabelClassifierChainsWrapper Use classifier chains method (CC) to create a multilabel learner.
makeModelMultiplexerParamSet Creates a parameter set for model multiplexer tuning.
getHomogeneousEnsembleModels Deprecated, use getLearnerModel instead.
getHyperPars Get current parameter settings for a learner.
getRRDump Return the error dump of ResampleResult.
getBMRLearnerIds Return learner ids used in benchmark.
getRRPredictionList Get list of predictions for train and test set of each single resample iteration.
getLearnerType Get the type of the learner.
getConfMatrix Confusion matrix.
getLearnerShortName Get the short name of the learner.
getStackedBaseLearnerPredictions Returns the predictions for each base learner.
getTaskClassLevels Get the class levels for classification and multilabel tasks.
getFeatureImportance Calculates feature importance values for trained models.
getFeatureImportanceLearner.regr.randomForestSRC Calculates feature importance values for a given learner.
makeMultilabelDBRWrapper Use dependent binary relevance method (DBR) to create a multilabel learner.
getTaskSize Get number of observations in task.
getTaskTargetNames Get the name(s) of the target column(s).
impute Impute and re-impute data
iris.task Iris classification task.
getBMRModels Extract all models from benchmark result.
isFailureModel Is the model a FailureModel?
getLearnerId Get the ID of the learner.
getOOBPredsLearner Provides out-of-bag predictions for a given model and the corresponding learner.
getTaskId Get the id of the task.
getLearnerModel Get underlying R model of learner integrated into mlr.
joinClassLevels Join some class existing levels to new, larger class levels for classification problems.
makeRemoveConstantFeaturesWrapper Fuse learner with removal of constant features preprocessing.
getBMRPerformances Extract the test performance values from a benchmark result.
getLearnerParamSet Get the parameter set of the learner.
getTaskNFeats Get number of features in task.
makeConstantClassWrapper Wraps a classification learner to support problems where the class label is (almost) constant.
getLearnerPredictType Get the predict type of the learner.
getParamSet Get a description of all possible parameter settings for a learner.
makeResampleDesc Create a description object for a resampling strategy.
makeCostMeasure Creates a measure for non-standard misclassification costs.
getPredictionDump Return the error dump of a failed Prediction.
getPredictionProbabilities Get probabilities for some classes.
makeFeatSelWrapper Fuse learner with feature selection.
gunpoint.task Gunpoint functional data classification task.
makeMulticlassWrapper Fuse learner with multiclass method.
makeFilter Create a feature filter.
getTaskFeatureNames Get feature names of task.
getPredictionResponse Get response / truth from prediction object.
getRRTaskDescription Get task description from resample results (DEPRECATED).
getResamplingIndices Get the resampling indices from a tuning or feature selection wrapper..
hasFunctionalFeatures Check whether the object conatins functional features.
getRRPredictions Get predictions from resample results.
makeMultilabelBinaryRelevanceWrapper Use binary relevance method to create a multilabel learner.
listMeasures Find matching measures.
makePreprocWrapperCaret Fuse learner with preprocessing.
listTaskTypes List the supported task types in mlr
makeRLearner.classif.fdausc.glm Classification of functional data by Generalized Linear Models.
makeResampleInstance Instantiates a resampling strategy object.
makeCostSensClassifWrapper Wraps a classification learner for use in cost-sensitive learning.
getRRTaskDesc Get task description from resample results (DEPRECATED).
getTaskTargets Get target data of task.
getTaskFormula Get formula of a 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.
makeCostSensRegrWrapper Wraps a regression learner for use in cost-sensitive learning.
makeFilterWrapper Fuse learner with a feature filter method.
makeDownsampleWrapper Fuse learner with simple downsampling (subsampling).
makeTuneControlMBO Create control object for hyperparameter tuning with MBO.
makeSMOTEWrapper Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
makeDummyFeaturesWrapper Fuse learner with dummy feature creator.
getTaskDesc Get a summarizing task description.
makeTuneControlGrid Create control object for hyperparameter tuning with grid search.
makeTuneControlIrace Create control object for hyperparameter tuning with Irace.
makeLearners Create multiple learners at once.
getTaskDescription Deprecated, use getTaskDesc instead.
makeTuneControlRandom Create control object for hyperparameter tuning with random search.
makeMeasure Construct performance measure.
makeFixedHoldoutInstance Generate a fixed holdout instance for resampling.
makeRLearner.classif.fdausc.kernel Learner for kernel classification for functional data.
listLearners Find matching learning algorithms.
helpLearnerParam Get specific help for a learner's parameters.
makeFunctionalData Create a data.frame containing functional features from a normal data.frame.
imputations Built-in imputation methods.
parallelization Supported parallelization methods
makeImputeMethod Create a custom imputation method.
performance Measure performance of prediction.
makeMultilabelNestedStackingWrapper Use nested stacking method to create a multilabel learner.
learnerArgsToControl Convert arguments to control structure.
listMeasureProperties List the supported measure properties.
makeRLearner.classif.fdausc.np Learner for nonparametric classification for functional data.
phoneme.task Phoneme functional data multilabel classification task.
makeMultilabelStackingWrapper Use stacking method (stacked generalization) to create a multilabel learner.
makeTuneWrapper Fuse learner with tuning.
pid.task PimaIndiansDiabetes classification task.
plotBMRBoxplots Create box or violin plots for a BenchmarkResult.
plotBMRRanksAsBarChart Create a bar chart for ranks in a BenchmarkResult.
makeTuneControlDesign Create control object for hyperparameter tuning with predefined design.
plotLearningCurve Plot learning curve data using ggplot2.
learners List of supported learning algorithms.
plotPartialDependence Plot a partial dependence with ggplot2.
makeTuneControlGenSA Create control object for hyperparameter tuning with GenSA.
predict.WrappedModel Predict new data.
makeWrappedModel Induced model of learner.
makeWeightedClassesWrapper Wraps a classifier for weighted fitting where each class receives a weight.
makeBaggingWrapper Fuse learner with the bagging technique.
setId Set the id of a learner object.
setLearnerId Set the ID of a learner object.
makeChainModel Only exported for internal use.
makeBaseWrapper Exported for internal use only.
predictLearner Predict new data with an R learner.
plotLearnerPrediction Visualizes a learning algorithm on a 1D or 2D data set.
plotHyperParsEffect Plot the hyperparameter effects data
makeClassificationViaRegressionWrapper Classification via regression wrapper.
makeCostSensWeightedPairsWrapper Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
reduceBatchmarkResults Reduce results of a batch-distributed benchmark.
reextractFDAFeatures Re-extract features from a data set
reimpute Re-impute a data set
setHyperPars2 Only exported for internal use.
removeConstantFeatures Remove constant features from a data set.
setHyperPars Set the hyperparameters of a learner object.
makeCustomResampledMeasure Construct your own resampled performance measure.
setPredictType Set the type of predictions the learner should return.
setThreshold Set threshold of prediction object.
spatial.task J. Muenchow's Ecuador landslide data set
makeImputeWrapper Fuse learner with an imputation method.
yeast.task Yeast multilabel classification task.
subsetTask Subset data in task.
simplifyMeasureNames Simplify measure names.
summarizeColumns Summarize columns of data.frame or task.
makeLearner Create learner object.
makeUndersampleWrapper Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
makeStackedLearner Create a stacked learner object.
makeClassifTaskDesc Exported for internal use.
smote Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
makeOverBaggingWrapper Fuse learner with the bagging technique and oversampling for imbalancy correction.
makePreprocWrapper Fuse learner with preprocessing.
mergeSmallFactorLevels Merges small levels of factors into new level.
normalizeFeatures Normalize features.
oversample Over- or undersample binary classification task to handle class imbalancy.
plotThreshVsPerf Plot threshold vs. performance(s) for 2-class classification using ggplot2.
summarizeLevels Summarizes factors of a data.frame by tabling them.
mlr-package mlr: Machine Learning in R
plotBMRSummary Plot a benchmark summary.
makeTaskDescInternal Exported for internal use.
plotCalibration Plot calibration data using ggplot2.
plotTuneMultiCritResult Plots multi-criteria results after tuning using ggplot2.
selectFeatures Feature selection by wrapper approach.
makeTuneControlCMAES Create control object for hyperparameter tuning with CMAES.
setAggregation Set aggregation function of measure.
setMeasurePars Set parameters of performance measures
plotROCCurves Plots a ROC curve using ggplot2.
setPredictThreshold Set the probability threshold the learner should use.
tuneParams Hyperparameter tuning.
sonar.task Sonar classification task.
plotResiduals Create residual plots for prediction objects or benchmark results.
mergeBenchmarkResults Merge different BenchmarkResult objects.
measures Performance measures.
spam.task Spam classification task.
train Train a learning algorithm.
trainLearner Train an R learner.
tuneParamsMultiCrit Hyperparameter tuning for multiple measures at once.
mlrFamilies mlr documentation families
plotCritDifferences Plot critical differences for a selected measure.
mtcars.task Motor Trend Car Road Tests clustering task.
plotFilterValues Plot filter values using ggplot2.
regr.featureless Featureless regression learner.
regr.randomForest RandomForest regression learner.
removeHyperPars Remove hyperparameters settings of a learner.
resample Fit models according to a resampling strategy.
tuneThreshold Tune prediction threshold.
wpbc.task Wisonsin Prognostic Breast Cancer (WPBC) survival task.
FeatSelResult Result of feature selection.
FeatSelControl Create control structures for feature selection.
addRRMeasure Compute new measures for existing ResampleResult
aggregations Aggregation methods.
agri.task European Union Agricultural Workforces clustering task.
asROCRPrediction Converts predictions to a format package ROCR can handle.
batchmark Run machine learning benchmarks as distributed experiments.
analyzeFeatSelResult Show and visualize the steps of feature selection.
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URL https://github.com/mlr-org/mlr
BugReports https://github.com/mlr-org/mlr/issues
License BSD_2_clause + file LICENSE
Encoding UTF-8
LazyData yes
ByteCompile yes
VignetteBuilder knitr
RoxygenNote 6.1.0
NeedsCompilation yes
Packaged 2018-08-28 11:14:36 UTC; bischl
Repository CRAN
Date/Publication 2018-08-28 12:30:03 UTC
suggests ada , adabag , bartMachine , batchtools , brnn , bst , C50 , care , caret (>= 6.0-57) , class , clue , cluster , 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 , gbm , GenSA , 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 , mlbench , mldr , mlrMBO , mmpf , modeltools , mRMRe , neuralnet , nnet , nodeHarvest (>= 0.7-3) , numDeriv , pamr , party , penalized (>= 0.9-47) , pls , PMCMR (>= 4.1) , purrr , randomForest , randomForestSRC (>= 2.7.0) , ranger (>= 0.8.0) , 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, Jakob Richter, Leonard Judt, Florian Fendt, Patrick Schratz, 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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