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mlr (version 2.17.0)

Machine Learning in R

Description

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.

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Install

install.packages('mlr')

Monthly Downloads

9,216

Version

2.17.0

License

BSD_2_clause + file LICENSE

Issues

8

Pull Requests

5

Stars

1,644

Forks

405

Maintainer

Patrick Schratz

Last Published

January 10th, 2020

Functions in mlr (2.17.0)

FeatSelResult

Result of feature selection.
makeClassifTask

Create a classification task.
FailureModel

Failure model.
makeCostSensTask

Create a cost-sensitive classification task.
makeClusterTask

Create a cluster task.
Aggregation

Aggregation object.
BenchmarkResult

BenchmarkResult object.
ConfusionMatrix

Confusion matrix
LearnerProperties

Query properties of learners.
FeatSelControl

Create control structures for feature selection.
makeRegrTask

Create a regression task.
MeasureProperties

Query properties of measures.
batchmark

Run machine learning benchmarks as distributed experiments.
TaskDesc

Description object for task.
asROCRPrediction

Converts predictions to a format package ROCR can handle.
Task

Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
addRRMeasure

Compute new measures for existing ResampleResult
aggregations

Aggregation methods.
ResamplePrediction

Prediction from resampling.
makeMultilabelTask

Create a multilabel task.
TuneControl

Control object for tuning
checkLearner

Exported for internal use only.
cache_helpers

Get or delete mlr cache directory
bh.task

Boston Housing regression task.
ResampleResult

ResampleResult object.
createDummyFeatures

Generate dummy variables for factor features.
checkPredictLearnerOutput

Check output returned by predictLearner.
createSpatialResamplingPlots

Create (spatial) resampling plot objects.
bc.task

Wisconsin Breast Cancer classification task.
agri.task

European Union Agricultural Workforces clustering task.
benchmark

Benchmark experiment for multiple learners and tasks.
analyzeFeatSelResult

Show and visualize the steps of feature selection.
makeSurvTask

Create a survival task.
Prediction

Prediction object.
TuneMultiCritControl

Create control structures for multi-criteria tuning.
extractFDAWavelets

Discrete Wavelet transform features.
costiris.task

Iris cost-sensitive classification task.
capLargeValues

Convert large/infinite numeric values in a data.frame or task.
changeData

Change Task Data
dropFeatures

Drop some features of task.
convertMLBenchObjToTask

Convert a machine learning benchmark / demo object from package mlbench to a task.
estimateRelativeOverfitting

Estimate relative overfitting.
configureMlr

Configures the behavior of the package.
extractFDADTWKernel

DTW kernel features
convertBMRToRankMatrix

Convert BenchmarkResult to a rank-matrix.
extractFDAFPCA

Extract functional principal component analysis features.
extractFDAFourier

Fast Fourier transform features.
extractFDAFeatures

Extract features from functional data.
extractFDAMultiResFeatures

Multiresolution feature extraction.
generateCritDifferencesData

Generate data for critical-differences plot.
getBMRLearnerIds

Return learner ids used in benchmark.
extractFDATsfeatures

Time-Series Feature Heuristics
generateFeatureImportanceData

Generate feature importance.
getBMRPerformances

Extract the test performance values from a benchmark result.
getBMRLearnerShortNames

Return learner short.names used in benchmark.
getBMRPredictions

Extract the predictions from a benchmark result.
RLearner

Internal construction / wrapping of learner object.
TuneMultiCritResult

Result of multi-criteria tuning.
TuneResult

Result of tuning.
extractFDABsignal

Bspline mlq features
calculateROCMeasures

Calculate receiver operator measures.
crossover

Crossover.
downsample

Downsample (subsample) a task or a data.frame.
getBMRMeasures

Return measures used in benchmark.
friedmanTestBMR

Perform overall Friedman test for a BenchmarkResult.
estimateResidualVariance

Estimate the residual variance.
calculateConfusionMatrix

Confusion matrix.
friedmanPostHocTestBMR

Perform a posthoc Friedman-Nemenyi test.
generateThreshVsPerfData

Generate threshold vs. performance(s) for 2-class classification.
generateFilterValuesData

Calculates feature filter values.
getPredictionProbabilities

Get probabilities for some classes.
getLearnerPackages

Get the required R packages of the learner.
generateHyperParsEffectData

Generate hyperparameter effect data.
getPredictionDump

Return the error dump of a failed Prediction.
getFilteredFeatures

Returns the filtered features.
getFeatureImportanceLearner.regr.randomForestSRC

Calculates feature importance values for a given learner.
getLearnerNote

Get the note for the learner.
getRRTaskDesc

Get task description from resample results (DEPRECATED).
getRRTaskDescription

Get task description from resample results (DEPRECATED).
getTaskTargetNames

Get the name(s) of the target column(s).
generateLearningCurveData

Generates a learning curve.
getCaretParamSet

Get tuning parameters from a learner of the caret R-package.
filterFeatures

Filter features by thresholding filter values.
getBMRMeasureIds

Return measures IDs used in benchmark.
getClassWeightParam

Get the class weight parameter of a learner.
generatePartialDependenceData

Generate partial dependence.
getBMRLearners

Return learners used in benchmark.
getHomogeneousEnsembleModels

Deprecated, use getLearnerModel instead.
getHyperPars

Get current parameter settings for a learner.
getBMRModels

Extract all models from benchmark result.
getBMRTaskDescriptions

Extract all task descriptions from benchmark result (DEPRECATED).
getMultilabelBinaryPerformances

Retrieve binary classification measures for multilabel classification predictions.
getTaskClassLevels

Get the class levels for classification and multilabel tasks.
getPredictionResponse

Get response / truth from prediction object.
getPredictionTaskDesc

Get summarizing task description from prediction.
getNestedTuneResultsOptPathDf

Get the opt.paths from each tuning step from the outer resampling.
getDefaultMeasure

Get default measure.
getConfMatrix

Confusion matrix.
getBMRAggrPerformances

Extract the aggregated performance values from a benchmark result.
getTaskCosts

Extract costs in task.
getBMRTaskDescs

Extract all task descriptions from benchmark result.
getTaskDescription

Deprecated, use getTaskDesc instead.
getFailureModelDump

Return the error dump of FailureModel.
getTuneResultOptPath

Get the optimization path of a tuning result.
makeConstantClassWrapper

Wraps a classification learner to support problems where the class label is (almost) constant.
getTaskFeatureNames

Get feature names of task.
listFilterEnsembleMethods

List ensemble filter methods.
learners

List of supported learning algorithms.
gunpoint.task

Gunpoint functional data classification task.
makeCostMeasure

Creates a measure for non-standard misclassification costs.
getFailureModelMsg

Return error message of FailureModel.
getLearnerId

Get the ID of the learner.
hasFunctionalFeatures

Check whether the object conatins functional features.
hasProperties

Deprecated, use hasLearnerProperties instead.
getTaskTargets

Get target data of task.
getLearnerPredictType

Get the predict type of the learner.
getLearnerModel

Get underlying R model of learner integrated into mlr.
makeImputeMethod

Create a custom imputation method.
makeCostSensWeightedPairsWrapper

Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
makeCustomResampledMeasure

Construct your own resampled performance measure.
makeImputeWrapper

Fuse learner with an imputation method.
getLearnerShortName

Get the short name of the learner.
listFilterMethods

List filter methods.
getLearnerParamSet

Get the parameter set of the learner.
getLearnerParVals

Get the parameter values of the learner.
getProbabilities

Deprecated, use getPredictionProbabilities instead.
getBMRFeatSelResults

Extract the feature selection results from a benchmark result.
fuelsubset.task

FuelSubset functional data regression task.
generateCalibrationData

Generate classifier calibration data.
getNestedTuneResultsX

Get the tuned hyperparameter settings from a nested tuning.
getBMRTaskIds

Return task ids used in benchmark.
getBMRFilteredFeatures

Extract the feature selection results from a benchmark result.
getBMRTuneResults

Extract the tuning results from a benchmark result.
makePreprocWrapper

Fuse learner with preprocessing.
makePreprocWrapperCaret

Fuse learner with preprocessing.
makeTuneControlCMAES

Create control object for hyperparameter tuning with CMAES.
getFeatSelResult

Returns the selected feature set and optimization path after training.
getLearnerType

Get the type of the learner.
getFeatureImportance

Calculates feature importance values for trained models.
getRRDump

Return the error dump of ResampleResult.
getOOBPreds

Extracts out-of-bag predictions from trained models.
listLearnerProperties

List the supported learner properties
lung.task

NCCTG Lung Cancer survival task.
makeAggregation

Specify your own aggregation of measures.
makeTuneControlDesign

Create control object for hyperparameter tuning with predefined design.
getOOBPredsLearner

Provides out-of-bag predictions for a given model and the corresponding learner.
getMlrOptions

Returns a list of mlr's options.
getTaskData

Extract data in task.
getRRPredictions

Get predictions from resample results.
getRRPredictionList

Get list of predictions for train and test set of each single resample iteration.
getParamSet

Get a description of all possible parameter settings for a learner.
getResamplingIndices

Get the resampling indices from a tuning or feature selection wrapper..
getTaskFormula

Get formula of a task.
getTaskDesc

Get a summarizing task description.
getTaskId

Get the id of the task.
makeDummyFeaturesWrapper

Fuse learner with dummy feature creator.
makeDownsampleWrapper

Fuse learner with simple downsampling (subsampling).
makeMeasure

Construct performance measure.
getStackedBaseLearnerPredictions

Returns the predictions for each base learner.
helpLearner

Access help page of learner functions.
makeTuneControlIrace

Create control object for hyperparameter tuning with Irace.
makeTuneControlMBO

Create control object for hyperparameter tuning with MBO.
mlrFamilies

mlr documentation families
helpLearnerParam

Get specific help for a learner's parameters.
mlr-package

mlr: Machine Learning in R
makeRLearner.classif.fdausc.glm

Classification of functional data by Generalized Linear Models.
makeModelMultiplexer

Create model multiplexer for model selection to tune over multiple possible models.
plotFilterValues

Plot filter values using ggplot2.
plotHyperParsEffect

Plot the hyperparameter effects data
listMeasureProperties

List the supported measure properties.
listLearners

Find matching learning algorithms.
makeCostSensClassifWrapper

Wraps a classification learner for use in cost-sensitive learning.
getTaskNFeats

Get number of features in task.
makeCostSensRegrWrapper

Wraps a regression learner for use in cost-sensitive learning.
makeExtractFDAFeatsWrapper

Fuse learner with an extractFDAFeatures method.
makeExtractFDAFeatMethod

Constructor for FDA feature extraction methods.
makeLearner

Create learner object.
makeLearners

Create multiple learners at once.
makeMultilabelDBRWrapper

Use dependent binary relevance method (DBR) to create a multilabel learner.
imputations

Built-in imputation methods.
getTuneResult

Returns the optimal hyperparameters and optimization path after training.
getTaskType

Get the type of the task.
makeRLearner.classif.fdausc.kernel

Learner for kernel classification for functional data.
getTaskSize

Get number of observations in task.
makeSMOTEWrapper

Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
makeMultilabelNestedStackingWrapper

Use nested stacking method to create a multilabel learner.
makeStackedLearner

Create a stacked learner object.
plotPartialDependence

Plot a partial dependence with ggplot2.
plotROCCurves

Plots a ROC curve using ggplot2.
setMeasurePars

Set parameters of performance measures
setLearnerId

Set the ID of a learner object.
makeTuneControlRandom

Create control object for hyperparameter tuning with random search.
iris.task

Iris classification task.
setThreshold

Set threshold of prediction object.
oversample

Over- or undersample binary classification task to handle class imbalancy.
makeTuneWrapper

Fuse learner with tuning.
impute

Impute and re-impute data
listTaskTypes

List the supported task types in mlr
listMeasures

Find matching measures.
simplifyMeasureNames

Simplify measure names.
makeBaggingWrapper

Fuse learner with the bagging technique.
makeTaskDescInternal

Exported for internal use.
makeClassifTaskDesc

Exported for internal use.
makeBaseWrapper

Exported for internal use only.
makeFilterWrapper

Fuse learner with a feature filter method.
makeFilterEnsemble

Create an ensemble feature filter.
makeModelMultiplexerParamSet

Creates a parameter set for model multiplexer tuning.
makeWeightedClassesWrapper

Wraps a classifier for weighted fitting where each class receives a weight.
summarizeLevels

Summarizes factors of a data.frame by tabling them.
makeUndersampleWrapper

Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
train

Train a learning algorithm.
isFailureModel

Is the model a FailureModel?
mtcars.task

Motor Trend Car Road Tests clustering task.
makeMulticlassWrapper

Fuse learner with multiclass method.
learnerArgsToControl

Convert arguments to control structure.
makeClassificationViaRegressionWrapper

Classification via regression wrapper.
joinClassLevels

Join some class existing levels to new, larger class levels for classification problems.
makeChainModel

Only exported for internal use.
pid.task

PimaIndiansDiabetes classification task.
parallelization

Supported parallelization methods
makeMultilabelStackingWrapper

Use stacking method (stacked generalization) to create a multilabel learner.
makeResampleDesc

Create a description object for a resampling strategy.
plotBMRRanksAsBarChart

Create a bar chart for ranks in a BenchmarkResult.
makeOverBaggingWrapper

Fuse learner with the bagging technique and oversampling for imbalancy correction.
makeResampleInstance

Instantiates a resampling strategy object.
makeTuneControlGrid

Create control object for hyperparameter tuning with grid search.
mergeBenchmarkResults

Merge different BenchmarkResult objects.
makeTuneControlGenSA

Create control object for hyperparameter tuning with GenSA.
mergeSmallFactorLevels

Merges small levels of factors into new level.
plotBMRSummary

Plot a benchmark summary.
reduceBatchmarkResults

Reduce results of a batch-distributed benchmark.
setHyperPars

Set the hyperparameters of a learner object.
setAggregation

Set aggregation function of measure.
predictLearner

Predict new data with an R learner.
trainLearner

Train an R learner.
spam.task

Spam classification task.
spatial.task

J. Muenchow's Ecuador landslide data set
plotBMRBoxplots

Create box or violin plots for a BenchmarkResult.
tuneParams

Hyperparameter tuning.
normalizeFeatures

Normalize features.
makeFeatSelWrapper

Fuse learner with feature selection.
plotLearnerPrediction

Visualizes a learning algorithm on a 1D or 2D data set.
makeFilter

Create a feature filter.
makeFunctionalData

Create a data.frame containing functional features from a normal data.frame.
makeMultilabelClassifierChainsWrapper

Use classifier chains method (CC) to create a multilabel learner.
makeMultilabelBinaryRelevanceWrapper

Use binary relevance method to create a multilabel learner.
makeWrappedModel

Induced model of learner.
makeFixedHoldoutInstance

Generate a fixed holdout instance for resampling.
makeRLearner.classif.fdausc.np

Learner for nonparametric classification for functional data.
makeRemoveConstantFeaturesWrapper

Fuse learner with removal of constant features preprocessing.
plotResiduals

Create residual plots for prediction objects or benchmark results.
measures

Performance measures.
plotLearningCurve

Plot learning curve data using ggplot2.
reextractFDAFeatures

Re-extract features from a data set
performance

Measure performance of prediction.
smote

Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
reimpute

Re-impute a data set
sonar.task

Sonar classification task.
removeConstantFeatures

Remove constant features from a data set.
removeHyperPars

Remove hyperparameters settings of a learner.
plotThreshVsPerf

Plot threshold vs. performance(s) for 2-class classification using ggplot2.
setId

Set the id of a learner object.
setHyperPars2

Only exported for internal use.
predict.WrappedModel

Predict new data.
selectFeatures

Feature selection by wrapper approach.
plotCalibration

Plot calibration data using ggplot2.
plotTuneMultiCritResult

Plots multi-criteria results after tuning using ggplot2.
phoneme.task

Phoneme functional data multilabel classification task.
plotCritDifferences

Plot critical differences for a selected measure.
resample

Fit models according to a resampling strategy.
summarizeColumns

Summarize columns of data.frame or task.
tuneParamsMultiCrit

Hyperparameter tuning for multiple measures at once.
subsetTask

Subset data in task.
setPredictThreshold

Set the probability threshold the learner should use.
tuneThreshold

Tune prediction threshold.
setPredictType

Set the type of predictions the learner should return.
wpbc.task

Wisonsin Prognostic Breast Cancer (WPBC) survival task.
yeast.task

Yeast multilabel classification task.