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mlr (version 2.19.1)

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

2.19.1

License

BSD_2_clause + file LICENSE

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Repository

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

Homepage

https://mlr.mlr-org.com

Maintainer

Patrick Schratz

Last Published

September 29th, 2022

Functions in mlr (2.19.1)

Result of feature selection.

Aggregation object.

BenchmarkResult

BenchmarkResult object.

LearnerProperties

Query properties of learners.

makeClassifTask

Create a classification task.

Create control structures for feature selection.

makeClusterTask

Create a cluster task.

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

Prediction object.

Description object for task.

ResampleResult object.

European Union Agricultural Workforces clustering task.

Internal construction / wrapping of learner object.

Create a regression task.

ResamplePrediction

Prediction from resampling.

Boston Housing regression task.

Result of tuning.

TuneMultiCritResult

Result of multi-criteria tuning.

ConfusionMatrix

Confusion matrix

makeCostSensTask

Create a cost-sensitive classification task.

Create a survival task.

MeasureProperties

Query properties of measures.

Get or delete mlr cache directory

Wisconsin Breast Cancer classification task.

asROCRPrediction

Converts predictions to a format package ROCR can handle.

Run machine learning benchmarks as distributed experiments.

Drop some features of task.

Control object for tuning

Returns a list of mlr's options.

getBMRFeatSelResults

Extract the feature selection results from a benchmark result.

estimateResidualVariance

Estimate the residual variance.

makeMultilabelTask

Create a multilabel task.

estimateRelativeOverfitting

Estimate relative overfitting.

TuneMultiCritControl

Create control structures for multi-criteria tuning.

analyzeFeatSelResult

Show and visualize the steps of feature selection.

Benchmark experiment for multiple learners and tasks.

extractFDABsignal

Bspline mlq features

Configures the behavior of the package.

Confusion matrix.

Change Task Data

Exported for internal use only.

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

Compute new measures for existing ResampleResult

generateCritDifferencesData

Generate data for critical-differences plot.

Aggregation methods.

calculateConfusionMatrix

Confusion matrix.

generateFeatureImportanceData

Generate feature importance.

getBMRLearnerIds

Return learner ids used in benchmark.

getBMRLearnerShortNames

Return learner short.names used in benchmark.

checkPredictLearnerOutput

Check output returned by predictLearner.

calculateROCMeasures

Calculate receiver operator measures.

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

convertBMRToRankMatrix

Convert BenchmarkResult to a rank-matrix.

createDummyFeatures

Generate dummy variables for factor features.

extractFDADTWKernel

DTW kernel features

convertMLBenchObjToTask

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

getBMRPerformances

Extract the test performance values from a benchmark result.

createSpatialResamplingPlots

Create (spatial) resampling plot objects.

getFeatureImportance

Calculates feature importance values for trained models.

Get the type of the task.

getRRPredictionList

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

friedmanTestBMR

Perform overall Friedman test for a BenchmarkResult.

getBMRPredictions

Extract the predictions from a benchmark result.

getFailureModelDump

Return the error dump of FailureModel.

extractFDAMultiResFeatures

Multiresolution feature extraction.

Return learners used in benchmark.

getBMRMeasureIds

Return measures IDs used in benchmark.

extractFDATsfeatures

Time-Series Feature Heuristics

getLearnerParamSet

Get the parameter set of the learner.

getDefaultMeasure

Get default measure.

Iris cost-sensitive classification task.

extractFDAWavelets

Discrete Wavelet transform features.

extractFDAFeatures

Extract features from functional data.

extractFDAFourier

Fast Fourier transform features.

Extract functional principal component analysis features.

Is the model a FailureModel?

getFeatSelResult

Returns the selected feature set and optimization path after training.

getFailureModelMsg

Return error message of FailureModel.

generateFilterValuesData

Calculates feature filter values.

generateHyperParsEffectData

Generate hyperparameter effect data.

getLearnerShortName

Get the short name of the learner.

getLearnerPredictType

Get the predict type of the learner.

fuelsubset.task

FuelSubset functional data regression task.

Get the type of the learner.

getFeatureImportanceLearner

Calculates feature importance values for a given learner.

getNestedTuneResultsOptPathDf

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

getNestedTuneResultsX

Get the tuned hyperparameter settings from a nested tuning.

generateCalibrationData

Generate classifier calibration data.

Filter features by thresholding filter values.

getStackedBaseLearnerPredictions

Returns the predictions for each base learner.

friedmanPostHocTestBMR

Perform a posthoc Friedman-Nemenyi test.

makeFunctionalData

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

Create multiple learners at once.

makeCustomResampledMeasure

Construct your own resampled performance measure.

getRRPredictions

Get predictions from resample results.

getBMRFilteredFeatures

Extract the feature selection results from a benchmark result.

getPredictionTaskDesc

Get summarizing task description from prediction.

Get task description from resample results (DEPRECATED).

plotCritDifferences

Plot critical differences for a selected measure.

Get number of observations in task.

generateLearningCurveData

Generates a learning curve.

generatePartialDependenceData

Generate partial dependence.

getProbabilities

Deprecated, use getPredictionProbabilities instead.

getBMRTaskDescriptions

Extract all task descriptions from benchmark result (DEPRECATED).

mergeBenchmarkResults

Merge different BenchmarkResult objects.

Get the ID of the learner.

Return measures used in benchmark.

getTaskClassLevels

Get the class levels for classification and multilabel tasks.

Extract all models from benchmark result.

getTaskTargetNames

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

getFunctionalFeatures

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

getLearnerParVals

Get the parameter values of the learner.

Return task ids used in benchmark.

getBMRTaskDescs

Extract all task descriptions from benchmark result.

getHomogeneousEnsembleModels

Deprecated, use getLearnerModel instead.

getBMRTuneResults

Extract the tuning results from a benchmark result.

Extracts out-of-bag predictions from trained models.

getOOBPredsLearner

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

generateThreshVsPerfData

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

getFilteredFeatures

Returns the filtered features.

joinClassLevels

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

getLearnerModel

Get underlying R model of learner integrated into mlr.

Get the id of the task.

Get a summarizing task description.

getLearnerPackages

Get the required R packages of the learner.

Returns the optimal hyperparameters and optimization path after training.

getTuneResultOptPath

Get the optimization path of a tuning result.

getCaretParamSet

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

getBMRAggrPerformances

Extract the aggregated performance values from a benchmark result.

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

getClassWeightParam

Get the class weight parameter of a learner.

getTaskDescription

Deprecated, use getTaskDesc instead.

Get current parameter settings for a learner.

List the supported task types in mlr

Return the error dump of ResampleResult.

Get the note for the learner.

getRRTaskDescription

Get task description from resample results (DEPRECATED).

getPredictionDump

Return the error dump of a failed Prediction.

getResamplingIndices

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

helpLearnerParam

Get specific help for a learner's parameters.

getPredictionProbabilities

Get probabilities for some classes.

Get number of features in task.

makeTuneWrapper

Fuse learner with tuning.

makeBaggingWrapper

Fuse learner with the bagging technique.

Extract costs in task.

Extract data in task.

List of supported learning algorithms.

listFilterEnsembleMethods

List ensemble filter methods.

getTaskFeatureNames

Get feature names of task.

Impute and re-impute data

listFilterMethods

List filter methods.

getMultilabelBinaryPerformances

Retrieve binary classification measures for multilabel classification predictions.

NCCTG Lung Cancer survival task.

makeCostSensRegrWrapper

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

makeClassifTaskDesc

Exported for internal use.

Get formula of a task.

Gunpoint functional data classification task.

Iris classification task.

hasFunctionalFeatures

Check whether the object contains functional features.

makeCostSensWeightedPairsWrapper

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

makeImputeMethod

Create a custom imputation method.

listMeasureProperties

List the supported measure properties.

Find matching learning algorithms.

parallelization

Supported parallelization methods

Find matching measures.

makeExtractFDAFeatsWrapper

Fuse learner with an extractFDAFeatures method.

getPredictionResponse

Get response / truth from prediction object.

makeStackedLearner

Create a stacked learner object.

makeFeatSelWrapper

Fuse learner with feature selection.

makeTuneControlGenSA

Create control object for hyperparameter tuning with GenSA.

makeBaseWrapper

Exported for internal use only.

makeFilterWrapper

Fuse learner with a feature filter method.

Built-in imputation methods.

makeFixedHoldoutInstance

Generate a fixed holdout instance for resampling.

makeClassificationViaRegressionWrapper

Classification via regression wrapper.

Feature selection by wrapper approach.

Only exported for internal use.

makeUndersampleWrapper

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

Get target data of task.

Deprecated, use hasLearnerProperties instead.

Create a feature filter.

makeConstantClassWrapper

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

makeOverBaggingWrapper

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

mlr: Machine Learning in R

makeDummyFeaturesWrapper

Fuse learner with dummy feature creator.

makeExtractFDAFeatMethod

Constructor for FDA feature extraction methods.

makeMultilabelNestedStackingWrapper

Use nested stacking method to create a multilabel learner.

makeMultilabelStackingWrapper

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

makeTaskDescInternal

Exported for internal use.

listLearnerProperties

List the supported learner properties

makeFilterEnsemble

Create an ensemble feature filter.

Performance measures.

makeMultilabelClassifierChainsWrapper

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

Set aggregation function of measure.

makeModelMultiplexer

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

makeModelMultiplexerParamSet

Creates a parameter set for model multiplexer tuning.

Access help page of learner functions.

makeMulticlassWrapper

Fuse learner with multiclass method.

makePreprocWrapper

Fuse learner with preprocessing.

makeTuneControlGrid

Create control object for hyperparameter tuning with grid search.

makeAggregation

Specify your own aggregation of measures.

makeTuneControlCMAES

Create control object for hyperparameter tuning with CMAES.

makeMultilabelBinaryRelevanceWrapper

Use binary relevance method to create a multilabel learner.

makePreprocWrapperCaret

Fuse learner with preprocessing.

makeImputeWrapper

Fuse learner with an imputation method.

makeTuneControlIrace

Create control object for hyperparameter tuning with Irace.

makeRLearner.classif.fdausc.glm

Classification of functional data by Generalized Linear Models.

plotHyperParsEffect

Plot the hyperparameter effects data

mergeSmallFactorLevels

Merges small levels of factors into new level.

learnerArgsToControl

Convert arguments to control structure.

plotLearnerPrediction

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

removeHyperPars

Remove hyperparameters settings of a learner.

makeRLearner.classif.fdausc.kernel

Learner for kernel classification for functional data.

makeCostMeasure

Creates a measure for non-standard misclassification costs.

makeCostSensClassifWrapper

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

makeDownsampleWrapper

Fuse learner with simple downsampling (subsampling).

Create learner object.

makeWeightedClassesWrapper

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

makeTuneControlDesign

Create control object for hyperparameter tuning with predefined design.

Set threshold of prediction object.

makeRLearner.classif.fdausc.np

Learner for nonparametric classification for functional data.

makeMultilabelDBRWrapper

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

makeResampleInstance

Instantiates a resampling strategy object.

Subset data in task.

mlr documentation families

makeTuneControlMBO

Create control object for hyperparameter tuning with MBO.

Set the type of predictions the learner should return.

makeTuneControlRandom

Create control object for hyperparameter tuning with random search.

makeSMOTEWrapper

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

Construct performance measure.

plotLearningCurve

Plot learning curve data using ggplot2.

plotPartialDependence

Plot a partial dependence with ggplot2.

plotTuneMultiCritResult

Plots multi-criteria results after tuning using ggplot2.

PimaIndiansDiabetes classification task.

Motor Trend Car Road Tests clustering task.

Plot a benchmark summary.

Re-impute a data set

normalizeFeatures

Normalize features.

plotCalibration

Plot calibration data using ggplot2.

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

removeConstantFeatures

Remove constant features from a data set.

Yeast multilabel classification task.

plotBMRBoxplots

Create box or violin plots for a BenchmarkResult.

predict.WrappedModel

Predict new data.

Sonar classification task.

Spam classification task.

Predict new data with an R learner.

Measure performance of prediction.

makeRemoveConstantFeaturesWrapper

Fuse learner with removal of constant features preprocessing.

makeWrappedModel

Induced model of learner.

Phoneme functional data multilabel classification task.

plotBMRRanksAsBarChart

Create a bar chart for ranks in a BenchmarkResult.

makeResampleDesc

Create a description object for a resampling strategy.

summarizeColumns

Summarize columns of data.frame or task.

Set the id of a learner object.

Plots a ROC curve using ggplot2.

summarizeLevels

Summarizes factors of a data.frame by tabling them.

reextractFDAFeatures

Re-extract features from a data set

Set the ID of a learner object.

Set parameters of performance measures

Train a learning algorithm.

Create residual plots for prediction objects or benchmark results.

setPredictThreshold

Set the probability threshold the learner should use.

reduceBatchmarkResults

Reduce results of a batch-distributed benchmark.

plotThreshVsPerf

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

Tune prediction threshold.

Fit models according to a resampling strategy.

Only exported for internal use.

J. Muenchow's Ecuador landslide data set

Hyperparameter tuning.

Wisonsin Prognostic Breast Cancer (WPBC) survival task.

plotFilterValues

Plot filter values using ggplot2.

tuneParamsMultiCrit

Hyperparameter tuning for multiple measures at once.

Set the hyperparameters of a learner object.

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

simplifyMeasureNames

Simplify measure names.

Train an R learner.