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