mlr v2.12.1

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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
ConfusionMatrix Confusion matrix
FeatSelControl Create control structures for feature selection.
RLearner Internal construction / wrapping of learner object.
Prediction Prediction object.
LearnerProperties Query properties of learners.
FailureModel Failure model.
MeasureProperties Query properties of measures.
Aggregation Aggregation object.
BenchmarkResult BenchmarkResult object.
FeatSelResult Result of feature selection.
ResampleResult ResampleResult object.
TaskDesc Description object for task.
ResamplePrediction Prediction from resampling.
TuneControl Control object for tuning
addRRMeasure Compute new measures for existing ResampleResult
aggregations Aggregation methods.
TuneMultiCritResult Result of multi-criteria tuning.
makeClassifTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
TuneResult Result of tuning.
calculateROCMeasures Calculate receiver operator measures.
calculateConfusionMatrix Confusion matrix.
asROCRPrediction Converts predictions to a format package ROCR can handle.
TuneMultiCritControl Create control structures for multi-criteria tuning.
bh.task Boston Housing regression task.
capLargeValues Convert large/infinite numeric values in a data.frame or task.
batchmark Run machine learning benchmarks as distributed experiments.
agri.task European Union Agricultural Workforces clustering task.
analyzeFeatSelResult Show and visualize the steps of feature selection.
changeData Change Task Data
crossover Crossover.
checkLearner Exported for internal use only.
bc.task Wisconsin Breast Cancer classification task.
configureMlr Configures the behavior of the package.
extractFDAFPCA Extract functional principal component analysis features.
costiris.task Iris cost-sensitive classification task.
benchmark Benchmark experiment for multiple learners and tasks.
extractFDAFeatures Extract features from functional data.
createDummyFeatures Generate dummy variables for factor features.
extractFDAWavelets Discrete Wavelet transform features.
convertBMRToRankMatrix Convert BenchmarkResult to a rank-matrix.
convertMLBenchObjToTask Convert a machine learning benchmark / demo object from package mlbench to a task.
filterFeatures Filter features by thresholding filter values.
checkPredictLearnerOutput Check output returned by predictLearner.
downsample Downsample (subsample) a task or a data.frame.
classif.featureless Featureless classification learner.
friedmanTestBMR Perform overall Friedman test for a BenchmarkResult.
dropFeatures Drop some features of task.
generateCritDifferencesData Generate data for critical-differences plot.
generateCalibrationData Generate classifier calibration data.
generateLearningCurveData Generates a learning curve.
estimateRelativeOverfitting Estimate relative overfitting.
friedmanPostHocTestBMR Perform a posthoc Friedman-Nemenyi test.
generatePartialDependenceData Generate partial dependence.
estimateResidualVariance Estimate the residual variance.
generateFilterValuesData Calculates feature filter values.
fuelsubset.task FuelSubset functional data regression task.
generateThreshVsPerfData Generate threshold vs. performance(s) for 2-class classification.
extractFDAFourier Fast Fourier transform features.
extractFDAMultiResFeatures Multiresolution feature extraction.
getBMRAggrPerformances Extract the aggregated performance values from a benchmark result.
getBMRFeatSelResults Extract the feature selection results from a benchmark result.
getBMRLearners Return learners used in benchmark.
generateHyperParsEffectData Generate hyperparameter effect data.
getBMRFilteredFeatures Extract the feature selection results from a benchmark result.
generateFeatureImportanceData Generate feature importance.
getBMRMeasureIds Return measures IDs used in benchmark.
getBMRLearnerIds Return learner ids used in benchmark.
getCaretParamSet Get tuning parameters from a learner of the caret R-package.
getBMRLearnerShortNames Return learner short.names used in benchmark.
getClassWeightParam Get the class weight parameter of a learner.
getBMRTaskDescriptions Extract all task descriptions from benchmark result (DEPRECATED).
getBMRMeasures Return measures used in benchmark.
getBMRModels Extract all models from benchmark result.
getBMRTaskDescs Extract all task descriptions from benchmark result.
getLearnerParVals Get the parameter values of the learner.
getLearnerParamSet Get the parameter set of the learner.
getBMRTaskIds Return task ids used in benchmark.
getFeatSelResult Returns the selected feature set and optimization path after training.
getNestedTuneResultsX Get the tuned hyperparameter settings from a nested tuning.
getBMRTuneResults Extract the tuning results from a benchmark result.
getOOBPreds Extracts out-of-bag predictions from trained models.
getFeatureImportance Calculates feature importance values for trained models.
getFeatureImportanceLearner.regr.randomForestSRC Calculates feature importance values for a given learner.
getRRTaskDesc Get task description from resample results (DEPRECATED).
getFilterValues Calculates feature filter values.
getRRTaskDescription Get task description from resample results (DEPRECATED).
getPredictionResponse Get response / truth from prediction object.
getConfMatrix Confusion matrix.
getHomogeneousEnsembleModels Deprecated, use getLearnerModel instead.
getPredictionTaskDesc Get summarizing task description from prediction.
getNestedTuneResultsOptPathDf Get the opt.paths from each tuning step from the outer resampling.
getDefaultMeasure Get default measure.
getLearnerModel Get underlying R model of learner integrated into mlr.
getTaskCosts Extract costs in task.
getLearnerPackages Get the required R packages of the learner.
getTaskSize Get number of observations in task.
getTaskData Extract data in task.
getTaskTargetNames Get the name(s) of the target column(s).
isFailureModel Is the model a FailureModel?
getMultilabelBinaryPerformances Retrieve binary classification measures for multilabel classification predictions.
joinClassLevels Join some class existing levels to new, larger class levels for classification problems.
getLearnerPredictType Get the predict type of the learner.
learnerArgsToControl Convert arguments to control structure.
getBMRPerformances Extract the test performance values from a benchmark result.
learners List of supported learning algorithms.
getTaskTargets Get target data of task.
getLearnerShortName Get the short name of the learner.
getFilteredFeatures Returns the filtered features.
getTaskType Get the type of the task.
helpLearnerParam Get specific help for a learner's parameters.
getTaskId Get the id of the task.
getStackedBaseLearnerPredictions Returns the predictions for each base learner.
imputations Built-in imputation methods.
getTaskClassLevels Get the class levels for classification and multilabel tasks.
getTaskNFeats Get number of features in task.
getTaskFeatureNames Get feature names of task.
listLearners Find matching learning algorithms.
getPredictionDump Return the error dump of a failed Prediction.
hasProperties Deprecated, use hasLearnerProperties instead.
getBMRPredictions Extract the predictions from a benchmark result.
listMeasureProperties List the supported measure properties.
getPredictionProbabilities Get probabilities for some classes.
getFailureModelDump Return the error dump of FailureModel.
makeConstantClassWrapper Wraps a classification learner to support problems where the class label is (almost) constant.
helpLearner Access help page of learner functions.
getFailureModelMsg Return error message of FailureModel.
lung.task NCCTG Lung Cancer survival task.
makeBaseWrapper Exported for internal use only.
getTaskFormula Get formula of a task.
makeAggregation Specify your own aggregation of measures.
makeFilterWrapper Fuse learner with a feature filter method.
listFilterMethods List filter methods.
getLearnerType Get the type of the learner.
makeFixedHoldoutInstance Generate a fixed holdout instance for resampling.
makeBaggingWrapper Fuse learner with the bagging technique.
getMlrOptions Returns a list of mlr's options.
listLearnerProperties List the supported learner properties
makeCostMeasure Creates a measure for non-standard misclassification costs.
makeCostSensRegrWrapper Wraps a regression learner for use in cost-sensitive learning.
makeDownsampleWrapper Fuse learner with simple downsampling (subsampling).
makeCostSensClassifWrapper Wraps a classification learner for use in cost-sensitive learning.
makeDummyFeaturesWrapper Fuse learner with dummy feature creator.
getOOBPredsLearner Provides out-of-bag predictions for a given model and the corresponding learner.
makeFeatSelWrapper Fuse learner with feature selection.
getHyperPars Get current parameter settings for a learner.
makeFilter Create a feature filter.
getProbabilities Deprecated, use getPredictionProbabilities instead.
getLearnerId Get the ID of the learner.
getRRPredictionList Get list of predictions for train and test set of each single resample iteration.
getRRDump Return the error dump of ResampleResult.
makeLearners Create multiple learners at once.
getRRPredictions Get predictions from resample results.
getTaskDesc Get a summarizing task description.
impute Impute and re-impute data
makeMeasure Construct performance measure.
getParamSet Get a description of all possible parameter settings for a learner.
getTaskDescription Deprecated, use getTaskDesc instead.
iris.task Iris classification task.
getTuneResult Returns the optimal hyperparameters and optimization path after training.
makeModelMultiplexer Create model multiplexer for model selection to tune over multiple possible models.
listMeasures Find matching measures.
getTuneResultOptPath Get the optimization path of a tuning result.
listTaskTypes List the supported task types in mlr
makeModelMultiplexerParamSet Creates a parameter set for model multiplexer tuning.
makeChainModel Only exported for internal use.
makeOverBaggingWrapper Fuse learner with the bagging technique and oversampling for imbalancy correction.
makeMultilabelNestedStackingWrapper Use nested stacking method to create a multilabel learner.
makeFunctionalData Create a data.frame containing functional features from a normal data.frame.
makeClassificationViaRegressionWrapper Classification via regression wrapper.
makeImputeMethod Create a custom imputation method.
makeMultilabelStackingWrapper Use stacking method (stacked generalization) to create a multilabel learner.
makePreprocWrapper Fuse learner with preprocessing.
makeMulticlassWrapper Fuse learner with multiclass method.
makeMultilabelClassifierChainsWrapper Use classifier chains method (CC) to create a multilabel learner.
gunpoint.task Gunpoint functional data classification task.
makeRemoveConstantFeaturesWrapper Fuse learner with removal of constant features preprocessing.
makeTuneControlGrid Create control object for hyperparameter tuning with grid search.
makeMultilabelDBRWrapper Use dependent binary relevance method (DBR) to create a multilabel learner.
makeTuneControlIrace Create control object for hyperparameter tuning with Irace.
makeResampleDesc Create a description object for a resampling strategy.
hasFunctionalFeatures Check whether the object conatins functional features.
makeMultilabelBinaryRelevanceWrapper Use binary relevance method to create a multilabel learner.
makeExtractFDAFeatsWrapper Fuse learner with an extractFDAFeatures method.
makeRLearner.classif.fdausc.kernel Learner for kernel classification for functional data.
makeCostSensWeightedPairsWrapper Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
makeTaskDescInternal Exported for internal use.
makeImputeWrapper Fuse learner with an imputation method.
makeRLearner.classif.fdausc.np Learner for nonparametric classification for functional data.
makeTuneControlCMAES Create control object for hyperparameter tuning with CMAES.
makeCustomResampledMeasure Construct your own resampled performance measure.
makeLearner Create learner object.
makeTuneControlDesign Create control object for hyperparameter tuning with predefined design.
makeTuneControlGenSA Create control object for hyperparameter tuning with GenSA.
makePreprocWrapperCaret Fuse learner with preprocessing.
makeExtractFDAFeatMethod Constructor for FDA feature extraction methods.
makeTuneWrapper Fuse learner with tuning.
makeRLearner.classif.fdausc.glm Classification of functional data by Generalized Linear Models.
mergeSmallFactorLevels Merges small levels of factors into new level.
makeResampleInstance Instantiates a resampling strategy object.
mlrFamilies mlr documentation families
makeUndersampleWrapper Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
makeTuneControlMBO Create control object for hyperparameter tuning with MBO.
measures Performance measures.
makeTuneControlRandom Create control object for hyperparameter tuning with random search.
makeSMOTEWrapper Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
makeWrappedModel Induced model of learner.
mtcars.task Motor Trend Car Road Tests clustering task.
mergeBenchmarkResults Merge different BenchmarkResult objects.
makeStackedLearner Create a stacked learner object.
performance Measure performance of prediction.
plotBMRRanksAsBarChart Create a bar chart for ranks in a BenchmarkResult.
normalizeFeatures Normalize features.
phoneme.task Phoneme functional data multilabel classification task.
makeClassifTaskDesc Exported for internal use.
plotResiduals Create residual plots for prediction objects or benchmark results.
plotBMRSummary Plot a benchmark summary.
plotHyperParsEffect Plot the hyperparameter effects data
makeWeightedClassesWrapper Wraps a classifier for weighted fitting where each class receives a weight.
plotThreshVsPerf Plot threshold vs. performance(s) for 2-class classification using ggplot2.
plotPartialDependence Plot a partial dependence with ggplot2.
plotLearnerPrediction Visualizes a learning algorithm on a 1D or 2D data set.
pid.task PimaIndiansDiabetes classification task.
removeHyperPars Remove hyperparameters settings of a learner.
plotROCCurves Plots a ROC curve using ggplot2.
plotLearningCurve Plot learning curve data using ggplot2.
plotBMRBoxplots Create box or violin plots for a BenchmarkResult.
setPredictType Set the type of predictions the learner should return.
resample Fit models according to a resampling strategy.
setLearnerId Set the ID of a learner object.
selectFeatures Feature selection by wrapper approach.
predict.WrappedModel Predict new data.
oversample Over- or undersample binary classification task to handle class imbalancy.
setHyperPars2 Only exported for internal use.
setMeasurePars Set parameters of performance measures
regr.randomForest RandomForest regression learner.
parallelization Supported parallelization methods
setId Set the id of a learner object.
simplifyMeasureNames Simplify measure names.
reduceBatchmarkResults Reduce results of a batch-distributed benchmark.
reextractFDAFeatures Re-extract features from a data set
plotFilterValues Plot filter values using ggplot2.
setPredictThreshold Set the probability threshold the learner should use.
spatial.task J. Muenchow's Ecuador landslide data set
plotTuneMultiCritResult Plots multi-criteria results after tuning using ggplot2.
regr.featureless Featureless regression learner.
summarizeLevels Summarizes factors of a data.frame by tabling them.
trainLearner Train an R learner.
removeConstantFeatures Remove constant features from a data set.
plotCalibration Plot calibration data using ggplot2.
train Train a learning algorithm.
tuneParams Hyperparameter tuning.
spam.task Spam classification task.
subsetTask Subset data in task.
plotCritDifferences Plot critical differences for a selected measure.
summarizeColumns Summarize columns of data.frame or task.
yeast.task Yeast multilabel classification task.
setAggregation Set aggregation function of measure.
tuneParamsMultiCrit Hyperparameter tuning for multiple measures at once.
predictLearner Predict new data with an R learner.
setHyperPars Set the hyperparameters of a learner object.
tuneThreshold Tune prediction threshold.
setThreshold Set threshold of prediction object.
smote Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
sonar.task Sonar classification task.
wpbc.task Wisonsin Prognostic Breast Cancer (WPBC) survival task.
reimpute Re-impute a data set
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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 6.0.1
NeedsCompilation yes
Packaged 2018-03-29 09:28:32 UTC; ripley
Repository CRAN
Date/Publication 2018-03-29 10:02:44 UTC
suggests ada , adabag , bartMachine , batchtools , brnn , bst , C50 , care , caret (>= 6.0-57) , class , clue , cluster , clusterSim (>= 0.44-5) , clValid , cmaes , CoxBoost , crs , Cubist , deepnet , DiceKriging , DiceOptim , DiscriMiner , e1071 , earth , elasticnet , elmNN , emoa , evtree , extraTrees , fda.usc , FDboost , fields , flare , FNN , fpc , frbs , FSelector , gbm , GenSA , glmnet , GPfit , h2o (>= 3.6.0.8) , Hmisc , ipred , irace (>= 2.0) , kernlab , kknn , klaR , knitr , laGP , LiblineaR , lintr (>= 1.0.0.9001) , lqa , 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) , randomForest , randomForestSRC (>= 2.2.0) , ranger (>= 0.8.0) , refund , rex , rFerns , rknn , rmarkdown , robustbase , ROCR , rotationForest , rpart , RRF , rrlda , rsm , RSNNS , RWeka , sda , shiny , 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

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