A B C D E F I M N P R S T U misc
| utiml-package | utiml: Utilities for Multi-Label Learning |
| as.bipartition | Convert a mlresult to a bipartition matrix |
| as.matrix.mlresult | Convert a mlresult to matrix |
| as.mlresult | Convert a matrix prediction in a multi label prediction |
| as.mlresult.default | Convert a matrix prediction in a multi label prediction |
| as.mlresult.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 |
| br | Binary Relevance for multi-label Classification |
| brplus | BR+ or BRplus for multi-label Classification |
| cc | Classifier Chains for multi-label Classification |
| compute_multilabel_predictions | Compute the multi-label ensemble predictions based on some vote schema |
| 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 |
| ctrl | CTRL model for multi-label Classification |
| dbr | Dependent Binary Relevance (DBR) for multi-label Classification |
| ebr | Ensemble of Binary Relevance for multi-label Classification |
| ecc | Ensemble of Classifier Chains for multi-label Classification |
| fill_sparce_mldata | Fill sparce dataset with 0 or " values |
| fixed_threshold | Apply a fixed threshold in the results |
| fixed_threshold.default | Apply a fixed threshold in the results |
| fixed_threshold.mlresult | Apply a fixed threshold in the results |
| is.bipartition | Test if a mlresult contains crisp values as default |
| is.probability | Test if a mlresult contains score values as default |
| mbr | Meta-BR or 2BR for multi-label Classification |
| mcut_threshold | Maximum Cut Thresholding (MCut) |
| mcut_threshold.default | Maximum Cut Thresholding (MCut) |
| mcut_threshold.mlresult | Maximum Cut Thresholding (MCut) |
| merge_mlconfmat | Join a list of multi-label confusion matrix |
| mlpredict | Prediction transformation problems |
| mlpredict.baseKNN | Prediction transformation problems |
| mlpredict.C5.0 | Prediction transformation problems |
| mlpredict.default | Prediction transformation problems |
| mlpredict.J48 | Prediction transformation problems |
| mlpredict.majorityModel | Prediction transformation problems |
| mlpredict.naiveBayes | Prediction transformation problems |
| mlpredict.randomForest | Prediction transformation problems |
| mlpredict.randomModel | Prediction transformation problems |
| mlpredict.rpart | Prediction transformation problems |
| mlpredict.svm | Prediction transformation problems |
| mltrain | Build transformation models |
| mltrain.baseC5.0 | Build transformation models |
| mltrain.baseCART | Build transformation models |
| mltrain.baseJ48 | Build transformation models |
| mltrain.baseKNN | Build transformation models |
| mltrain.baseMAJORITY | Build transformation models |
| mltrain.baseNB | Build transformation models |
| mltrain.baseRANDOM | Build transformation models |
| mltrain.baseRF | Build transformation models |
| mltrain.baseSVM | Build transformation models |
| mltrain.default | Build transformation models |
| multilabel_confusion_matrix | Compute the confusion matrix for a multi-label prediction |
| multilabel_evaluate | Evaluate multi-label predictions |
| multilabel_evaluate.mlconfmat | Evaluate multi-label predictions |
| multilabel_evaluate.mldr | 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) |
| pcut_threshold.default | Proportional Thresholding (PCut) |
| pcut_threshold.mlresult | Proportional Thresholding (PCut) |
| predict.BRmodel | Predict Method for Binary Relevance |
| predict.BRPmodel | Predict Method for BR+ (brplus) |
| predict.CCmodel | Predict Method for Classifier Chains |
| predict.CTRLmodel | Predict Method for CTRL |
| 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.MBRmodel | Predict Method for Meta-BR/2BR |
| predict.NSmodel | Predict Method for Nested Stacking |
| predict.PruDentmodel | Predict Method for PruDent |
| predict.RDBRmodel | Predict Method for RDBR |
| print.BRmodel | Print BR model |
| print.BRPmodel | Print BRP model |
| print.CCmodel | Print CC model |
| print.CTRLmodel | Print CTRL model |
| print.DBRmodel | Print DBR model |
| print.EBRmodel | Print EBR model |
| print.ECCmodel | Print ECC model |
| print.kFoldPartition | Print a kFoldPartition object |
| print.majorityModel | Print Majority model |
| print.MBRmodel | Print MBR model |
| print.mlconfmat | Print a Multi-label Confusion Matrix |
| print.mlresult | Print the mlresult |
| print.NSmodel | Print NS model |
| print.PruDentmodel | Print PruDent model |
| print.randomModel | Print Random model |
| print.RDBRmodel | Print RDBR model |
| prudent | PruDent classifier for multi-label Classification |
| rcut_threshold | Rank Cut (RCut) threshold method |
| rcut_threshold.default | Rank Cut (RCut) threshold method |
| rcut_threshold.mlresult | Rank Cut (RCut) threshold method |
| rdbr | Recursive Dependent Binary Relevance (RDBR) for multi-label Classification |
| 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 binary attributes. |
| scut_threshold | SCut Score-based method |
| scut_threshold.default | SCut Score-based method |
| scut_threshold.mlresult | SCut Score-based method |
| 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_all_measures_names | MEASURES METHODS --- Return the tree with the measure names |
| utiml_binary_prediction | Create a binary.prediction object |
| utiml_compute_ensemble | Compute binary predictions |
| utiml_create_binary_data | Create a data.frame from original mldr data for a single label |
| utiml_create_model | Create Dynamically the model for Binary Relevance Methods |
| utiml_ensemble_average | Average vote combination for a single-label prediction |
| utiml_ensemble_check_voteschema | Verify if a schema vote name is valid |
| utiml_ensemble_majority | Majority vote combination for single-label prediction |
| utiml_ensemble_maximum | Maximum vote combination for single-label prediction |
| utiml_ensemble_method | Define the method name related with the vote schema |
| utiml_ensemble_minimum | Minimum vote combination for single-label prediction |
| utiml_ifelse | Conditional value selection |
| utiml_is_equal_sets | Define if two sets are equals independently of the order of the elements |
| utiml_iterative_split | Internal Iterative Stratification |
| utiml_labels_correlation | Phi Correlation Coefficient |
| utiml_labels_IG | Calculate the Information Gain for each pair of labels |
| utiml_lapply | Select the suitable method lapply or mclaplly |
| utiml_measure_accuracy | MULTILABEL MEASURES --- Multi-label Accuracy Measure |
| utiml_measure_average_precision | Multi-label Average Precision Measure |
| utiml_measure_binary_accuracy | BINARY MEASURES --- Compute the binary accuracy |
| utiml_measure_binary_AUC | Compute the binary AUC |
| utiml_measure_binary_f1 | Compute the binary F1 measure |
| utiml_measure_binary_precision | Compute the binary precision |
| utiml_measure_binary_recall | Compute the binary recall |
| utiml_measure_coverage | Multi-label Coverage Measure |
| utiml_measure_f1 | Multi-label F1 Measure |
| utiml_measure_hamming_loss | Multi-label Hamming Loss Measure |
| utiml_measure_is_error | Multi-label Is Error Measure |
| utiml_measure_macro_accuracy | Multi-label Macro-Accuracy Measure |
| utiml_measure_macro_AUC | Multi-label Macro-AUC Measure |
| utiml_measure_macro_f1 | Multi-label Macro-F1 Measure |
| utiml_measure_macro_precision | Multi-label Macro-Precision Measure |
| utiml_measure_macro_recall | Multi-label Macro-Recall Measure |
| utiml_measure_margin_loss | Multi-label Margin Loss Measure |
| utiml_measure_micro_accuracy | Multi-label Micro-Accuracy Measure |
| utiml_measure_micro_AUC | Multi-label Macro-AUC Measure |
| utiml_measure_micro_f1 | Multi-label Micro-F1 Measure |
| utiml_measure_micro_precision | Multi-label Micro-Precision Measure |
| utiml_measure_micro_recall | Multi-label Micro-Recall Measure |
| utiml_measure_names | Return the name of measures |
| utiml_measure_one_error | Multi-label One Error Measure |
| utiml_measure_precision | Multi-label Precision Measure |
| utiml_measure_ranking_error | Multi-label Ranking Error Measure |
| utiml_measure_ranking_loss | Multi-label Hamming Loss Measure |
| utiml_measure_recall | Multi-label Recall Measure |
| utiml_measure_subset_accuracy | Multi-label Subset Accuracy Measure |
| utiml_newdata | Return the newdata to a data.frame or matrix |
| utiml_newdata.default | Return the newdata to a data.frame or matrix |
| utiml_newdata.mldr | Return the newdata to a data.frame or matrix |
| utiml_normalize | Internal normalize data function |
| utiml_predict | Create a predictive multi-label result |
| utiml_predict_binary_ensemble | Predict binary predictions |
| utiml_predict_binary_model | Dinamically call the prediction function |
| utiml_predict_ensemble | Compute the multi-label ensemble predictions based on some vote schema |
| utiml_prepare_data | Prepare a Transformed Multi-Label Data to be processed |
| utiml_preserve_seed | Preserve current seed |
| utiml_random_split | Random split of a dataset |
| utiml_rename | Rename the list using the names values or its own content |
| utiml_restore_seed | Restore the current seed |
| utiml_stratified_split | Labelsets Stratification Create the indexes using the Labelsets Stratification approach. |
| utiml_validate_splitmethod | Return the name of split method and validate if it is valid |
| +.mlconfmat | Join two multi-label confusion matrix |
| [.mlresult | Filter a Multi-Label Result |