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. |