A B C D E F G H I J L M N O P R S T U W Y
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. |
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. |
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. |
db | Performance measures. |
downsample | Downsample (subsample) a task or a data.frame. |
dropFeatures | Drop some features of task. |
dunn | Performance measures. |
estimateRelativeOverfitting | Estimate relative overfitting. |
estimateRelativeOverfitting.ResampleDesc | Estimate relative overfitting. |
estimateResidualVariance | Estimate the residual variance. |
expvar | Performance measures. |
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. |
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. |
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. |
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? |
joinClassLevels | Join some class existing levels to new, larger class levels for classification problems. |
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. |
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. |
normalizeFeatures | Normalize features. |
npv | Performance measures. |
oversample | Over- or undersample binary classification task to handle class imbalancy. |
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. |
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. |
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. |
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. |
undersample | Over- or undersample binary classification task to handle class imbalancy. |
wpbc.task | Wisonsin Prognostic Breast Cancer (WPBC) survival task. |
WrappedModel | Induced model of learner. |
yeast.task | Yeast multilabel classification task. |