| Filter a Multi-Label Result | [.mlresult |
| Join two multi-label confusion matrix | +.mlconfmat |
| Convert a mlresult to a bipartition matrix | as.bipartition |
| Convert a multi-label Confusion Matrix to matrix | as.matrix.mlconfmat |
| Convert a mlresult to matrix | as.matrix.mlresult |
| Convert a matrix prediction in a multi label prediction | as.mlresult as.mlresult.default as.mlresult.mlresult |
| Convert a mlresult to a probability matrix | as.probability |
| Convert a mlresult to a ranking matrix | as.ranking |
| Baseline reference for multilabel classification | baseline |
| Binary Relevance for multi-label Classification | br |
| BR+ or BRplus for multi-label Classification | brplus |
| Classifier Chains for multi-label Classification | cc |
| Calibrated Label Ranking (CLR) for multi-label Classification | clr |
| Compute the multi-label ensemble predictions based on some vote schema | compute_multilabel_predictions |
| Create a holdout partition based on the specified algorithm | create_holdout_partition |
| Create the k-folds partition based on the specified algorithm | create_kfold_partition |
| Create a random subset of a dataset | create_random_subset |
| Create a subset of a dataset | create_subset |
| Multi-label cross-validation | cv |
| Dependent Binary Relevance (DBR) for multi-label Classification | dbr |
| Ensemble of Binary Relevance for multi-label Classification | ebr |
| Ensemble of Classifier Chains for multi-label Classification | ecc |
| Ensemble of Pruned Set for multi-label Classification | eps |
| Ensemble of Single Label | esl |
| Fill sparse dataset with 0 or '' values | fill_sparse_mldata |
| Apply a fixed threshold in the results | fixed_threshold fixed_threshold.default fixed_threshold.mlresult |
| Foodtruck multi-label dataset. | foodtruck |
| Hierarchy Of Multilabel classifiER (HOMER) | homer |
| Test if a mlresult contains crisp values as default | is.bipartition |
| Test if a mlresult contains score values as default | is.probability |
| Threshold based on cardinality | lcard_threshold lcard_threshold.default lcard_threshold.mlresult |
| LIFT for multi-label Classification | lift |
| Label Powerset for multi-label Classification | lp |
| Meta-BR or 2BR for multi-label Classification | mbr |
| Maximum Cut Thresholding (MCut) | mcut_threshold mcut_threshold.default mcut_threshold.mlresult |
| Join a list of multi-label confusion matrix | merge_mlconfmat |
| Fix the mldr dataset to use factors | mldata |
| Multi-label KNN (ML-KNN) for multi-label Classification | mlknn |
| Prediction transformation problems | mlpredict |
| Build transformation models | mltrain |
| Compute the confusion matrix for a multi-label prediction | multilabel_confusion_matrix |
| Evaluate multi-label predictions | multilabel_evaluate multilabel_evaluate.mlconfmat multilabel_evaluate.mldr |
| Return the name of all measures | multilabel_measures |
| Create a mlresult object | multilabel_prediction |
| Normalize numerical attributes | normalize_mldata |
| Nested Stacking for multi-label Classification | ns |
| Create the multi-label dataset from folds | partition_fold |
| Proportional Thresholding (PCut) | pcut_threshold pcut_threshold.default pcut_threshold.mlresult |
| Pruned Problem Transformation for multi-label Classification | ppt |
| Predict Method for BASELINE | predict.BASELINEmodel |
| Predict Method for Binary Relevance | predict.BRmodel |
| Predict Method for BR+ (brplus) | predict.BRPmodel |
| Predict Method for Classifier Chains | predict.CCmodel |
| Predict Method for CLR | predict.CLRmodel |
| Predict Method for DBR | predict.DBRmodel |
| Predict Method for Ensemble of Binary Relevance | predict.EBRmodel |
| Predict Method for Ensemble of Classifier Chains | predict.ECCmodel |
| Predict Method for Ensemble of Pruned Set Transformation | predict.EPSmodel |
| Predict Method for Ensemble of Single Label | predict.ESLmodel |
| Predict Method for HOMER | predict.HOMERmodel |
| Predict Method for LIFT | predict.LIFTmodel |
| Predict Method for Label Powerset | predict.LPmodel |
| Predict Method for Meta-BR/2BR | predict.MBRmodel |
| Predict Method for ML-KNN | predict.MLKNNmodel |
| Predict Method for Nested Stacking | predict.NSmodel |
| Predict Method for Pruned Problem Transformation | predict.PPTmodel |
| Predict Method for PruDent | predict.PruDentmodel |
| Predict Method for Pruned Set Transformation | predict.PSmodel |
| Predict Method for RAkEL | predict.RAkELmodel |
| Predict Method for RDBR | predict.RDBRmodel |
| Predict Method for RPC | predict.RPCmodel |
| Print BR model | print.BRmodel |
| Print BRP model | print.BRPmodel |
| Print CC model | print.CCmodel |
| Print CLR model | print.CLRmodel |
| Print DBR model | print.DBRmodel |
| Print EBR model | print.EBRmodel |
| Print ECC model | print.ECCmodel |
| Print EPS model | print.EPSmodel |
| Print ESL model | print.ESLmodel |
| Print a kFoldPartition object | print.kFoldPartition |
| Print LIFT model | print.LIFTmodel |
| Print LP model | print.LPmodel |
| Print Majority model | print.majorityModel |
| Print MBR model | print.MBRmodel |
| Print a Multi-label Confusion Matrix | print.mlconfmat |
| Print MLKNN model | print.MLKNNmodel |
| Print the mlresult | print.mlresult |
| Print NS model | print.NSmodel |
| Print PPT model | print.PPTmodel |
| Print PruDent model | print.PruDentmodel |
| Print PS model | print.PSmodel |
| Print RAkEL model | print.RAkELmodel |
| Print Random model | print.randomModel |
| Print RDBR model | print.RDBRmodel |
| Print RPC model | print.RPCmodel |
| PruDent classifier for multi-label Classification | prudent |
| Pruned Set for multi-label Classification | ps |
| Random k-labelsets for multilabel classification | rakel |
| Rank Cut (RCut) threshold method | rcut_threshold rcut_threshold.default rcut_threshold.mlresult |
| Recursive Dependent Binary Relevance (RDBR) for multi-label Classification | rdbr |
| Remove attributes from the dataset | remove_attributes |
| Remove labels from the dataset | remove_labels |
| Remove unusual or very common labels | remove_skewness_labels |
| Remove unique attributes | remove_unique_attributes |
| Remove examples without labels | remove_unlabeled_instances |
| Replace nominal attributes Replace the nominal attributes by binary attributes. | replace_nominal_attributes |
| Ranking by Pairwise Comparison (RPC) for multi-label Classification | rpc |
| SCut Score-based method | scut_threshold scut_threshold.default scut_threshold.mlresult |
| Subset Correction of a predicted result | subset_correction |
| Summary method for mltransformation | summary.mltransformation |
| Toy multi-label dataset. | toyml |
| utiml: Utilities for Multi-Label Learning | utiml |
| Return the name of measures | utiml_measure_names |
