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mlr (version 2.18.0)

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

8,314

Version

2.18.0

License

BSD_2_clause + file LICENSE

Issues

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Repository

https://github.com/mlr-org/mlr

Homepage

https://mlr.mlr-org.com

Maintainer

Patrick Schratz

Last Published

October 5th, 2020

Functions in mlr (2.18.0)

makeClusterTask

Create a cluster task.

BenchmarkResult

BenchmarkResult object.

Result of feature selection.

Aggregation object.

makeClassifTask

Create a classification task.

ConfusionMatrix

Confusion matrix

LearnerProperties

Query properties of learners.

Create control structures for feature selection.

makeCostSensTask

Create a cost-sensitive classification task.

Prediction object.

Internal construction / wrapping of learner object.

Create a regression task.

ResamplePrediction

Prediction from resampling.

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

Description object for task.

MeasureProperties

Query properties of measures.

analyzeFeatSelResult

Show and visualize the steps of feature selection.

asROCRPrediction

Converts predictions to a format package ROCR can handle.

makeMultilabelTask

Create a multilabel task.

European Union Agricultural Workforces clustering task.

TuneMultiCritResult

Result of multi-criteria tuning.

ResampleResult object.

Wisconsin Breast Cancer classification task.

Result of tuning.

Benchmark experiment for multiple learners and tasks.

calculateConfusionMatrix

Confusion matrix.

Run machine learning benchmarks as distributed experiments.

calculateROCMeasures

Calculate receiver operator measures.

TuneMultiCritControl

Create control structures for multi-criteria tuning.

checkPredictLearnerOutput

Check output returned by predictLearner.

Control object for tuning

createDummyFeatures

Generate dummy variables for factor features.

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

Create a survival task.

Compute new measures for existing ResampleResult

Exported for internal use only.

createSpatialResamplingPlots

Create (spatial) resampling plot objects.

Drop some features of task.

extractFDAFeatures

Extract features from functional data.

estimateRelativeOverfitting

Estimate relative overfitting.

Extract functional principal component analysis features.

extractFDADTWKernel

DTW kernel features

Get or delete mlr cache directory

Aggregation methods.

Boston Housing regression task.

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

fuelsubset.task

FuelSubset functional data regression task.

generateFeatureImportanceData

Generate feature importance.

generateCritDifferencesData

Generate data for critical-differences plot.

extractFDAWavelets

Discrete Wavelet transform features.

Filter features by thresholding filter values.

getBMRAggrPerformances

Extract the aggregated performance values from a benchmark result.

generateThreshVsPerfData

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

generateCalibrationData

Generate classifier calibration data.

Return learners used in benchmark.

getBMRLearnerIds

Return learner ids used in benchmark.

extractFDAFourier

Fast Fourier transform features.

getBMRPerformances

Extract the test performance values from a benchmark result.

getBMRLearnerShortNames

Return learner short.names used in benchmark.

getBMRMeasureIds

Return measures IDs used in benchmark.

getBMRPredictions

Extract the predictions from a benchmark result.

convertMLBenchObjToTask

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

Iris cost-sensitive classification task.

generateFilterValuesData

Calculates feature filter values.

extractFDAMultiResFeatures

Multiresolution feature extraction.

extractFDATsfeatures

Time-Series Feature Heuristics

Change Task Data

Confusion matrix.

getBMRTaskDescriptions

Extract all task descriptions from benchmark result (DEPRECATED).

getBMRTaskDescs

Extract all task descriptions from benchmark result.

Get the note for the learner.

getLearnerModel

Get underlying R model of learner integrated into mlr.

generateLearningCurveData

Generates a learning curve.

getOOBPredsLearner

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

Extracts out-of-bag predictions from trained models.

getFeatSelResult

Returns the selected feature set and optimization path after training.

Return measures used in benchmark.

generatePartialDependenceData

Generate partial dependence.

Configures the behavior of the package.

getRRTaskDescription

Get task description from resample results (DEPRECATED).

getResamplingIndices

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

convertBMRToRankMatrix

Convert BenchmarkResult to a rank-matrix.

estimateResidualVariance

Estimate the residual variance.

Extract all models from benchmark result.

Get current parameter settings for a learner.

Get the ID of the learner.

getFeatureImportance

Calculates feature importance values for trained models.

extractFDABsignal

Bspline mlq features

generateHyperParsEffectData

Generate hyperparameter effect data.

getDefaultMeasure

Get default measure.

getLearnerShortName

Get the short name of the learner.

getFunctionalFeatures

Get only functional features from a task or a data.frame.

getHomogeneousEnsembleModels

Deprecated, use getLearnerModel instead.

getCaretParamSet

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

Get the type of the learner.

getNestedTuneResultsOptPathDf

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

getNestedTuneResultsX

Get the tuned hyperparameter settings from a nested tuning.

getTaskClassLevels

Get the class levels for classification and multilabel tasks.

getStackedBaseLearnerPredictions

Returns the predictions for each base learner.

getTaskFeatureNames

Get feature names of task.

Get the id of the task.

getClassWeightParam

Get the class weight parameter of a learner.

Returns a list of mlr's options.

Get number of features in task.

getFailureModelMsg

Return error message of FailureModel.

getFailureModelDump

Return the error dump of FailureModel.

Get target data of task.

friedmanTestBMR

Perform overall Friedman test for a BenchmarkResult.

getBMRFeatSelResults

Extract the feature selection results from a benchmark result.

friedmanPostHocTestBMR

Perform a posthoc Friedman-Nemenyi test.

getBMRFilteredFeatures

Extract the feature selection results from a benchmark result.

Gunpoint functional data classification task.

hasFunctionalFeatures

Check whether the object contains functional features.

Get formula of a task.

helpLearnerParam

Get specific help for a learner's parameters.

Built-in imputation methods.

Get the type of the task.

getPredictionProbabilities

Get probabilities for some classes.

getMultilabelBinaryPerformances

Retrieve binary classification measures for multilabel classification predictions.

Return task ids used in benchmark.

getBMRTuneResults

Extract the tuning results from a benchmark result.

getPredictionResponse

Get response / truth from prediction object.

getRRPredictions

Get predictions from resample results.

getLearnerPackages

Get the required R packages of the learner.

getFeatureImportanceLearner.regr.randomForestSRC

Calculates feature importance values for a given learner.

Returns the optimal hyperparameters and optimization path after training.

getTuneResultOptPath

Get the optimization path of a tuning result.

getLearnerParVals

Get the parameter values of the learner.

listLearnerProperties

List the supported learner properties

makeCostSensWeightedPairsWrapper

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

List the supported task types in mlr

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

makeCostSensRegrWrapper

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

getLearnerParamSet

Get the parameter set of the learner.

NCCTG Lung Cancer survival task.

getLearnerPredictType

Get the predict type of the learner.

getFilteredFeatures

Returns the filtered features.

Find matching learning algorithms.

learnerArgsToControl

Convert arguments to control structure.

List of supported learning algorithms.

makeFeatSelWrapper

Fuse learner with feature selection.

makeExtractFDAFeatsWrapper

Fuse learner with an extractFDAFeatures method.

getPredictionTaskDesc

Get summarizing task description from prediction.

Get task description from resample results (DEPRECATED).

getPredictionDump

Return the error dump of a failed Prediction.

getProbabilities

Deprecated, use getPredictionProbabilities instead.

Return the error dump of ResampleResult.

makeAggregation

Specify your own aggregation of measures.

listMeasureProperties

List the supported measure properties.

getRRPredictionList

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

Get a summarizing task description.

Extract costs in task.

makeBaggingWrapper

Fuse learner with the bagging technique.

makeCostMeasure

Creates a measure for non-standard misclassification costs.

Find matching measures.

makeCostSensClassifWrapper

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

getTaskDescription

Deprecated, use getTaskDesc instead.

Deprecated, use hasLearnerProperties instead.

makeDummyFeaturesWrapper

Fuse learner with dummy feature creator.

makeExtractFDAFeatMethod

Constructor for FDA feature extraction methods.

makeImputeWrapper

Fuse learner with an imputation method.

Get number of observations in task.

Extract data in task.

getTaskTargetNames

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

Iris classification task.

Impute and re-impute data

Create learner object.

makeFilterWrapper

Fuse learner with a feature filter method.

makeFixedHoldoutInstance

Generate a fixed holdout instance for resampling.

makeMultilabelNestedStackingWrapper

Use nested stacking method to create a multilabel learner.

Access help page of learner functions.

Is the model a FailureModel?

listFilterMethods

List filter methods.

makeClassificationViaRegressionWrapper

Classification via regression wrapper.

listFilterEnsembleMethods

List ensemble filter methods.

joinClassLevels

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

makeOverBaggingWrapper

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

makeConstantClassWrapper

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

makePreprocWrapper

Fuse learner with preprocessing.

makeBaseWrapper

Exported for internal use only.

makeResampleInstance

Instantiates a resampling strategy object.

makeCustomResampledMeasure

Construct your own resampled performance measure.

Only exported for internal use.

Create a feature filter.

makeDownsampleWrapper

Fuse learner with simple downsampling (subsampling).

makeMultilabelStackingWrapper

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

makeTaskDescInternal

Exported for internal use.

makeFilterEnsemble

Create an ensemble feature filter.

makeSMOTEWrapper

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

makeRLearner.classif.fdausc.glm

Classification of functional data by Generalized Linear Models.

makePreprocWrapperCaret

Fuse learner with preprocessing.

Create multiple learners at once.

Construct performance measure.

makeRLearner.classif.fdausc.kernel

Learner for kernel classification for functional data.

makeTuneControlCMAES

Create control object for hyperparameter tuning with CMAES.

makeWeightedClassesWrapper

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

makeMultilabelClassifierChainsWrapper

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

makeWrappedModel

Induced model of learner.

Phoneme functional data multilabel classification task.

makeTuneControlGrid

Create control object for hyperparameter tuning with grid search.

makeTuneControlIrace

Create control object for hyperparameter tuning with Irace.

makeRLearner.classif.fdausc.np

Learner for nonparametric classification for functional data.

makeTuneControlDesign

Create control object for hyperparameter tuning with predefined design.

normalizeFeatures

Normalize features.

Plot a benchmark summary.

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

PimaIndiansDiabetes classification task.

reduceBatchmarkResults

Reduce results of a batch-distributed benchmark.

reextractFDAFeatures

Re-extract features from a data set

makeMultilabelDBRWrapper

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

parallelization

Supported parallelization methods

makeStackedLearner

Create a stacked learner object.

makeClassifTaskDesc

Exported for internal use.

removeConstantFeatures

Remove constant features from a data set.

Re-impute a data set

Measure performance of prediction.

Subset data in task.

plotCalibration

Plot calibration data using ggplot2.

makeTuneControlGenSA

Create control object for hyperparameter tuning with GenSA.

J. Muenchow's Ecuador landslide data set

makeFunctionalData

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

Performance measures.

makeImputeMethod

Create a custom imputation method.

Set the hyperparameters of a learner object.

Only exported for internal use.

mergeSmallFactorLevels

Merges small levels of factors into new level.

makeTuneControlRandom

Create control object for hyperparameter tuning with random search.

makeTuneControlMBO

Create control object for hyperparameter tuning with MBO.

Set parameters of performance measures

plotBMRBoxplots

Create box or violin plots for a BenchmarkResult.

mlr: Machine Learning in R

mergeBenchmarkResults

Merge different BenchmarkResult objects.

Yeast multilabel classification task.

Sonar classification task.

plotTuneMultiCritResult

Plots multi-criteria results after tuning using ggplot2.

plotThreshVsPerf

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

Set the ID of a learner object.

Set the id of a learner object.

plotCritDifferences

Plot critical differences for a selected measure.

Spam classification task.

plotBMRRanksAsBarChart

Create a bar chart for ranks in a BenchmarkResult.

summarizeColumns

Summarize columns of data.frame or task.

plotFilterValues

Plot filter values using ggplot2.

setPredictThreshold

Set the probability threshold the learner should use.

summarizeLevels

Summarizes factors of a data.frame by tabling them.

predict.WrappedModel

Predict new data.

Predict new data with an R learner.

Train an R learner.

Train a learning algorithm.

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.

makeMultilabelBinaryRelevanceWrapper

Use binary relevance method to create a multilabel learner.

plotLearningCurve

Plot learning curve data using ggplot2.

makeRemoveConstantFeaturesWrapper

Fuse learner with removal of constant features preprocessing.

makeResampleDesc

Create a description object for a resampling strategy.

makeTuneWrapper

Fuse learner with tuning.

plotPartialDependence

Plot a partial dependence with ggplot2.

Feature selection by wrapper approach.

simplifyMeasureNames

Simplify measure names.

Hyperparameter tuning.

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

Set aggregation function of measure.

tuneParamsMultiCrit

Hyperparameter tuning for multiple measures at once.

makeUndersampleWrapper

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

mlr documentation families

plotHyperParsEffect

Plot the hyperparameter effects data

Motor Trend Car Road Tests clustering task.

plotLearnerPrediction

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

Plots a ROC curve using ggplot2.

Create residual plots for prediction objects or benchmark results.

removeHyperPars

Remove hyperparameters settings of a learner.

Fit models according to a resampling strategy.

Set threshold of prediction object.

Tune prediction threshold.

Set the type of predictions the learner should return.

Wisonsin Prognostic Breast Cancer (WPBC) survival task.