+.mlconfmat             Join two multi-label confusion matrix
[.mlresult              Filter a Multi-Label Result
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.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
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)
merge_mlconfmat         Join a list of multi-label confusion matrix
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)
predict.BRPmodel        Predict Method for BR+ (brplus)
predict.BRmodel         Predict Method for Binary Relevance
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.BRPmodel          Print BRP model
print.BRmodel           Print BR 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.MBRmodel          Print MBR model
print.NSmodel           Print NS model
print.PruDentmodel      Print PruDent model
print.RDBRmodel         Print RDBR model
print.kFoldPartition    Print a kFoldPartition object
print.majorityModel     Print Majority model
print.mlconfmat         Print a Multi-label Confusion Matrix
print.mlresult          Print the mlresult
print.randomModel       Print Random model
prudent                 PruDent classifier for multi-label
                        Classification
rcut_threshold          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
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_IG         Calculate the Information Gain for each pair of
                        labels
utiml_labels_correlation
                        Phi Correlation Coefficient
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_AUC
                        Compute the binary AUC
utiml_measure_binary_accuracy
                        BINARY MEASURES -------------- Compute the
                        binary accuracy
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_AUC
                        Multi-label Macro-AUC Measure
utiml_measure_macro_accuracy
                        Multi-label Macro-Accuracy 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_AUC
                        Multi-label Macro-AUC Measure
utiml_measure_micro_accuracy
                        Multi-label Micro-Accuracy 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_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_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
