Machine Learning in R


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Documentation for package ‘mlr’ version 2.8

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A B C D E F G H I J L M N O P R S T U W Y

-- A --

acc Performance measures.
Aggregation Aggregation object.
aggregations Aggregation methods.
agri.task European Union Agricultural Workforces clustering task.
analyzeFeatSelResult Show and visualize the steps of feature selection.
arsq Performance measures.
asROCRPrediction Converts predictions to a format package ROCR can handle.
auc Performance measures.

-- B --

b632 Aggregation methods.
b632plus Aggregation methods.
bac Performance measures.
bc.task Wisconsin Breast Cancer classification task.
benchmark Benchmark experiment for multiple learners and tasks.
BenchmarkResult BenchmarkResult object.
ber Performance measures.
bh.task Boston Housing regression task.
bootstrapB632 Fit models according to a resampling strategy.
bootstrapB632plus Fit models according to a resampling strategy.
bootstrapOOB Fit models according to a resampling strategy.
brier Performance measures.

-- C --

CalibrationData Generate classifier calibration data.
capLargeValues Convert large/infinite numeric values in a data.frame or task.
cindex Performance measures.
ClassifTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
ClusterTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
configureMlr Configures the behavior of the package.
convertBMRToRankMatrix Convert BenchmarkResult to a rank-matrix.
convertMLBenchObjToTask Convert a machine learning benchmark / demo object from package mlbench to a task.
costiris.task Iris cost-sensitive classification task.
CostSensClassifModel Wraps a classification learner for use in cost-sensitive learning.
CostSensClassifWrapper Wraps a classification learner for use in cost-sensitive learning.
CostSensRegrModel Wraps a regression learner for use in cost-sensitive learning.
CostSensRegrWrapper Wraps a regression learner for use in cost-sensitive learning.
CostSensTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
CostSensWeightedPairsModel Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
CostSensWeightedPairsWrapper Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
createDummyFeatures Generate dummy variables for factor features.
crossover Crossover.
crossval Fit models according to a resampling strategy.
cv10 Create a description object for a resampling strategy.
cv2 Create a description object for a resampling strategy.
cv3 Create a description object for a resampling strategy.
cv5 Create a description object for a resampling strategy.

-- D --

db Performance measures.
downsample Downsample (subsample) a task or a data.frame.
dropFeatures Drop some features of task.
dunn Performance measures.

-- E --

estimateRelativeOverfitting Estimate relative overfitting.
estimateRelativeOverfitting.ResampleDesc Estimate relative overfitting.
estimateResidualVariance Estimate the residual variance.
expvar Performance measures.

-- F --

f1 Performance measures.
FailureModel Failure model.
fdr Performance measures.
featperc Performance measures.
FeatSelControl Create control structures for feature selection.
FeatSelControlExhaustive Create control structures for feature selection.
FeatSelControlGA Create control structures for feature selection.
FeatSelControlRandom Create control structures for feature selection.
FeatSelControlSequential Create control structures for feature selection.
FeatSelResult Result of feature selection.
filterFeatures Filter features by thresholding filter values.
FilterValues Calculates feature filter values.
fn Performance measures.
fnr Performance measures.
fp Performance measures.
fpr Performance measures.
friedmanPostHocTestBMR Perform a posthoc Friedman-Nemenyi test.
friedmanTestBMR Perform overall Friedman test for a BenchmarkResult.

-- G --

G1 Performance measures.
G2 Performance measures.
generateCalibrationData Generate classifier calibration data.
generateCritDifferencesData Generate data for critical-differences plot.
generateFilterValuesData Calculates feature filter values.
generateLearningCurveData Generates a learning curve.
generatePartialPredictionData Generate partial predictions.
generateThreshVsPerfData Generate threshold vs. performance(s) for 2-class classification.
getBMRAggrPerformances Extract the aggregated performance values from a benchmark result.
getBMRFeatSelResults Extract the feature selection results from a benchmark result.
getBMRFilteredFeatures Extract the feature selection results from a benchmark result.
getBMRLearnerIds Return learner ids used in benchmark.
getBMRLearners Return learners used in benchmark.
getBMRLearnerShortNames Return learner short.names used in benchmark.
getBMRMeasureIds Return measures IDs used in benchmark.
getBMRMeasures Return measures used in benchmark.
getBMRModels Extract all models from benchmark result.
getBMRPerformances Extract the test performance values from a benchmark result.
getBMRPredictions Extract the predictions from a benchmark result.
getBMRTaskIds Return task ids used in benchmark.
getBMRTuneResults Extract the tuning results from a benchmark result.
getCaretParamSet Get tuning parameters from a learner of the caret R-package.
getClassWeightParam Get the class weight parameter of a learner.
getConfMatrix Confusion matrix.
getDefaultMeasure Get default measure.
getFailureModelMsg Return error message of FailureModel.
getFeatSelResult Returns the selected feature set and optimization path after training.
getFilteredFeatures Returns the filtered features.
getFilterValues Calculates feature filter values.
getHomogeneousEnsembleModels Deprecated, use 'getLearnerModel' instead.
getHyperPars Get current parameter settings for a learner.
getLearnerModel Get underlying R model of learner integrated into mlr.
getLearnerProperties Query properties of learners.
getMlrOptions Returns a list of mlr's options.
getMultilabelBinaryPerformances Retrieve binary classification measures for multilabel classification predictions.
getNestedTuneResultsOptPathDf Get the 'opt.path's from each tuning step from the outer resampling.
getNestedTuneResultsX Get the tuned hyperparameter settings from a nested tuning.
getParamSet Get a description of all possible parameter settings for a learner.
getPredictionProbabilities Get probabilities for some classes.
getPredictionResponse Get response / truth from prediction object.
getPredictionSE Get response / truth from prediction object.
getPredictionTruth Get response / truth from prediction object.
getProbabilities Deprecated, use 'getPredictionProbabilities' instead.
getRRPredictions Get predictions from resample results.
getStackedBaseLearnerPredictions Returns the predictions for each base learner.
getTaskClassLevels Get the class levels for classification and multilabel tasks.
getTaskCosts Extract costs in task.
getTaskData Extract data in task.
getTaskDescription Get a summarizing task description.
getTaskFeatureNames Get feature names of task.
getTaskFormula Get formula of a task.
getTaskId Get the id of the task.
getTaskNFeats Get number of features in task.
getTaskSize Get number of observations in task.
getTaskTargetNames Get the name(s) of the target column(s).
getTaskTargets Get target data of task.
getTaskType Get the type of the task.
getTuneResult Returns the optimal hyperparameters and optimization path after training.
gmean Performance measures.
gpr Performance measures.

-- H --

hamloss Performance measures.
hasLearnerProperties Query properties of learners.
hasProperties Deprecated, use 'hasLearnerProperties' instead.
holdout Fit models according to a resampling strategy.
hout Create a description object for a resampling strategy.

-- I --

imputations Built-in imputation methods.
impute Impute and re-impute data
imputeConstant Built-in imputation methods.
imputeHist Built-in imputation methods.
imputeLearner Built-in imputation methods.
imputeMax Built-in imputation methods.
imputeMean Built-in imputation methods.
imputeMedian Built-in imputation methods.
imputeMin Built-in imputation methods.
imputeMode Built-in imputation methods.
imputeNormal Built-in imputation methods.
imputeUniform Built-in imputation methods.
iris.task Iris classification task.
isFailureModel Is the model a FailureModel?

-- J --

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

-- L --

Learner Create learner object.
learnerArgsToControl Convert arguments to control structure.
LearnerProperties Query properties of learners.
learners List of supported learning algorithms.
LearningCurveData Generates a learning curve.
listFilterMethods List filter methods.
listLearners Find matching learning algorithms.
listLearners.character Find matching learning algorithms.
listLearners.default Find matching learning algorithms.
listLearners.Task Find matching learning algorithms.
listMeasures Find matching measures.
listMeasures.character Find matching measures.
listMeasures.default Find matching measures.
listMeasures.Task Find matching measures.
lung.task NCCTG Lung Cancer survival task.

-- M --

mae Performance measures.
makeAggregation Specify your own aggregation of measures.
makeBaggingWrapper Fuse learner with the bagging technique.
makeClassifTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
makeClusterTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
makeCostMeasure Creates a measure for non-standard misclassification costs.
makeCostSensClassifWrapper Wraps a classification learner for use in cost-sensitive learning.
makeCostSensRegrWrapper Wraps a regression learner for use in cost-sensitive learning.
makeCostSensTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
makeCostSensWeightedPairsWrapper Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
makeCustomResampledMeasure Construct your own resampled performance measure.
makeDownsampleWrapper Fuse learner with simple downsampling (subsampling).
makeFeatSelControlExhaustive Create control structures for feature selection.
makeFeatSelControlGA Create control structures for feature selection.
makeFeatSelControlRandom Create control structures for feature selection.
makeFeatSelControlSequential Create control structures for feature selection.
makeFeatSelWrapper Fuse learner with feature selection.
makeFilter Create a feature filter.
makeFilterWrapper Fuse learner with a feature filter method.
makeFixedHoldoutInstance Generate a fixed holdout instance for resampling.
makeImputeMethod Create a custom imputation method.
makeImputeWrapper Fuse learner with an imputation method.
makeLearner Create learner object.
makeMeasure Construct performance measure.
makeModelMultiplexer Create model multiplexer for model selection to tune over multiple possible models.
makeModelMultiplexerParamSet Creates a parameter set for model multiplexer tuning.
makeMulticlassWrapper Fuse learner with multiclass method.
makeMultilabelBinaryRelevanceWrapper Use binary relevance method to create a multilabel learner.
makeMultilabelTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
makeOverBaggingWrapper Fuse learner with the bagging technique and oversampling for imbalancy correction.
makeOversampleWrapper Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
makePreprocWrapper Fuse learner with preprocessing.
makePreprocWrapperCaret Fuse learner with preprocessing.
makeRegrTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
makeResampleDesc Create a description object for a resampling strategy.
makeResampleInstance Instantiates a resampling strategy object.
makeRLearner Internal construction / wrapping of learner object.
makeRLearnerClassif Internal construction / wrapping of learner object.
makeRLearnerCluster Internal construction / wrapping of learner object.
makeRLearnerMultilabel Internal construction / wrapping of learner object.
makeRLearnerRegr Internal construction / wrapping of learner object.
makeRLearnerSurv Internal construction / wrapping of learner object.
makeSMOTEWrapper Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
makeStackedLearner Create a stacked learner object.
makeSurvTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
makeTuneControlCMAES Create control structures for tuning.
makeTuneControlDesign Create control structures for tuning.
makeTuneControlGenSA Create control structures for tuning.
makeTuneControlGrid Create control structures for tuning.
makeTuneControlIrace Create control structures for tuning.
makeTuneControlRandom Create control structures for tuning.
makeTuneMultiCritControlGrid Create control structures for multi-criteria tuning.
makeTuneMultiCritControlNSGA2 Create control structures for multi-criteria tuning.
makeTuneMultiCritControlRandom Create control structures for multi-criteria tuning.
makeTuneWrapper Fuse learner with tuning.
makeUndersampleWrapper Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
makeWeightedClassesWrapper Wraps a classifier for weighted fitting where each class receives a weight.
makeWrappedModel Induced model of learner.
mcc Performance measures.
mcp Performance measures.
meancosts Performance measures.
Measure Construct performance measure.
measureACC Performance measures.
measureAUC Performance measures.
measureBAC Performance measures.
measureBrier Performance measures.
measureEXPVAR Performance measures.
measureFDR Performance measures.
measureFN Performance measures.
measureFNR Performance measures.
measureFP Performance measures.
measureFPR Performance measures.
measureGMEAN Performance measures.
measureGPR Performance measures.
measureHAMLOSS Performance measures.
measureMAE Performance measures.
measureMCC Performance measures.
measureMEDAE Performance measures.
measureMEDSE Performance measures.
measureMMCE Performance measures.
measureMSE Performance measures.
measureNPV Performance measures.
measurePPV Performance measures.
measureRMSE Performance measures.
measureRSQ Performance measures.
measures Performance measures.
measureSAE Performance measures.
measureSSE Performance measures.
measureTN Performance measures.
measureTNR Performance measures.
measureTP Performance measures.
measureTPR Performance measures.
medae Performance measures.
medse Performance measures.
mergeBenchmarkResultLearner Merge different learners of BenchmarkResult objects.
mergeBenchmarkResultTask Merge different tasks of BenchmarkResult objects.
mergeSmallFactorLevels Merges small levels of factors into new level.
mmce Performance measures.
ModelMultiplexer Create model multiplexer for model selection to tune over multiple possible models.
mse Performance measures.
mtcars.task Motor Trend Car Road Tests clustering task.
multiclass.auc Performance measures.
MultilabelTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.

-- N --

normalizeFeatures Normalize features.
npv Performance measures.

-- O --

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

-- P --

PartialPredictionData Generate partial predictions.
performance Measure performance of prediction.
pid.task PimaIndiansDiabetes classification task.
plotBMRBoxplots Create box or violin plots for a BenchmarkResult.
plotBMRRanksAsBarChart Create a bar chart for ranks in a BenchmarkResult.
plotBMRSummary Plot a benchmark summary.
plotCalibration Plot calibration data using ggplot2.
plotCritDifferences Plot critical differences for a selected measure.
plotFilterValues Plot filter values using ggplot2.
plotFilterValuesGGVIS Plot filter values using ggvis.
plotLearnerPrediction Visualizes a learning algorithm on a 1D or 2D data set.
plotLearningCurve Plot learning curve data using ggplot2.
plotLearningCurveGGVIS Plot learning curve data using ggvis.
plotPartialPrediction Plot a partial prediction with ggplot2.
plotPartialPredictionGGVIS Plot a partial prediction using ggvis.
plotROCCurves Plots a ROC curve using ggplot2.
plotThreshVsPerf Plot threshold vs. performance(s) for 2-class classification using ggplot2.
plotThreshVsPerfGGVIS Plot threshold vs. performance(s) for 2-class classification using ggvis.
plotTuneMultiCritResult Plots multi-criteria results after tuning using ggplot2.
plotTuneMultiCritResultGGVIS Plots multi-criteria results after tuning using ggvis.
plotViperCharts Visualize binary classification predictions via ViperCharts system.
ppv Performance measures.
predict.WrappedModel Predict new data.
Prediction Prediction object.
predictLearner Predict new data with an R learner.

-- R --

regr.randomForest regression using randomForest.
RegrTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
reimpute Re-impute a data set
removeConstantFeatures Remove constant features from a data set.
removeHyperPars Remove hyperparameters settings of a learner.
repcv Fit models according to a resampling strategy.
resample Fit models according to a resampling strategy.
ResampleDesc Create a description object for a resampling strategy.
ResampleInstance Instantiates a resampling strategy object.
ResamplePrediction Prediction from resampling.
ResampleResult ResampleResult object.
RLearner Internal construction / wrapping of learner object.
RLearnerClassif Internal construction / wrapping of learner object.
RLearnerCluster Internal construction / wrapping of learner object.
RLearnerMultilabel Internal construction / wrapping of learner object.
RLearnerRegr Internal construction / wrapping of learner object.
RLearnerSurv Internal construction / wrapping of learner object.
rmse Performance measures.
rsq Performance measures.

-- S --

sae Performance measures.
selectFeatures Feature selection by wrapper approach.
setAggregation Set aggregation function of measure.
setHyperPars Set the hyperparameters of a learner object.
setHyperPars2 Only exported for internal use.
setId Set the id of a learner object.
setPredictThreshold Set the probability threshold the learner should use.
setPredictType Set the type of predictions the learner should return.
setThreshold Set threshold of prediction object.
silhouette Performance measures.
smote Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
sonar.task Sonar classification task.
sse Performance measures.
subsample Fit models according to a resampling strategy.
subsetTask Subset data in task.
summarizeColumns Summarize columns of data.frame or task.
summarizeLevels Summarizes factors of a data.frame by tabling them.
SurvTask Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.

-- T --

Task Create a classification, regression, survival, cluster, cost-sensitive classification or multilabel task.
TaskDesc Description object for task.
test.join Aggregation methods.
test.max Aggregation methods.
test.mean Aggregation methods.
test.median Aggregation methods.
test.min Aggregation methods.
test.range Aggregation methods.
test.rmse Aggregation methods.
test.sd Aggregation methods.
test.sum Aggregation methods.
testgroup.mean Aggregation methods.
ThreshVsPerfData Generate threshold vs. performance(s) for 2-class classification.
timeboth Performance measures.
timepredict Performance measures.
timetrain Performance measures.
tn Performance measures.
tnr Performance measures.
tp Performance measures.
tpr Performance measures.
train Train a learning algorithm.
train.max Aggregation methods.
train.mean Aggregation methods.
train.median Aggregation methods.
train.min Aggregation methods.
train.range Aggregation methods.
train.rmse Aggregation methods.
train.sd Aggregation methods.
train.sum Aggregation methods.
trainLearner Train an R learner.
TuneControl Create control structures for tuning.
TuneControlCMAES Create control structures for tuning.
TuneControlGenSA Create control structures for tuning.
TuneControlGrid Create control structures for tuning.
TuneControlIrace Create control structures for tuning.
TuneControlRandom Create control structures for tuning.
TuneMultiCritControl Create control structures for multi-criteria tuning.
TuneMultiCritControlGrid Create control structures for multi-criteria tuning.
TuneMultiCritControlNSGA2 Create control structures for multi-criteria tuning.
TuneMultiCritControlRandom Create control structures for multi-criteria tuning.
TuneMultiCritResult Result of multi-criteria tuning.
tuneParams Hyperparameter tuning.
tuneParamsMultiCrit Hyperparameter tuning for multiple measures at once.
TuneResult Result of tuning.
tuneThreshold Tune prediction threshold.

-- U --

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

-- W --

wpbc.task Wisonsin Prognostic Breast Cancer (WPBC) survival task.
WrappedModel Induced model of learner.

-- Y --

yeast.task Yeast multilabel classification task.