Man pages for rivolli/utiml
Utilities for Multi-Label Learning

as.bipartitionConvert a mlresult to a bipartition matrix
as.matrix.mlresultConvert a mlresult to matrix
as.mlresultConvert a matrix prediction in a multi label prediction
as.probabilityConvert a mlresult to a probability matrix
as.rankingConvert a mlresult to a ranking matrix
baselineBaseline reference for multilabel classification
brBinary Relevance for multi-label Classification
brplusBR+ or BRplus for multi-label Classification
ccClassifier Chains for multi-label Classification
clrCalibrated Label Ranking (CLR) for multi-label Classification
compute_multilabel_predictionsCompute the multi-label ensemble predictions based on some...
create_holdout_partitionCreate a holdout partition based on the specified algorithm
create_kfold_partitionCreate the k-folds partition based on the specified algorithm
create_random_subsetCreate a random subset of a dataset
create_subsetCreate a subset of a dataset
ctrlCTRL model for multi-label Classification
dbrDependent Binary Relevance (DBR) for multi-label...
ebrEnsemble of Binary Relevance for multi-label Classification
eccEnsemble of Classifier Chains for multi-label Classification
epsEnsemble of Pruned Set for multi-label Classification
fill_sparse_mldataFill sparse dataset with 0 or " values
fixed_thresholdApply a fixed threshold in the results
homerHierarchy Of Multilabel classifiER (HOMER)
is.bipartitionTest if a mlresult contains crisp values as default
is.probabilityTest if a mlresult contains score values as default
lcard_thresholdThreshold based on cardinality
liftLIFT for multi-label Classification
lpLabel Powerset for multi-label Classification
mbrMeta-BR or 2BR for multi-label Classification
mcut_thresholdMaximum Cut Thresholding (MCut)
merge_mlconfmatJoin a list of multi-label confusion matrix
mldataFix the mldr dataset to use factors
mlknnMulti-label KNN (ML-KNN) for multi-label Classification
mlpredictPrediction transformation problems
mltrainBuild transformation models
multilabel_confusion_matrixCompute the confusion matrix for a multi-label prediction
multilabel_evaluateEvaluate multi-label predictions
multilabel_measuresReturn the name of all measures
multilabel_predictionCreate a mlresult object
normalize_mldataNormalize numerical attributes
nsNested Stacking for multi-label Classification
partition_foldCreate the multi-label dataset from folds
pcut_thresholdProportional Thresholding (PCut)
plus-.mlconfmatJoin two multi-label confusion matrix
pptPruned Problem Transformation for multi-label Classification
predict.BASELINEmodelPredict Method for BASELINE
predict.BRmodelPredict Method for Binary Relevance
predict.BRPmodelPredict Method for BR+ (brplus)
predict.CCmodelPredict Method for Classifier Chains
predict.CLRmodelPredict Method for CLR
predict.CTRLmodelPredict Method for CTRL
predict.DBRmodelPredict Method for DBR
predict.EBRmodelPredict Method for Ensemble of Binary Relevance
predict.ECCmodelPredict Method for Ensemble of Classifier Chains
predict.EPSmodelPredict Method for Ensemble of Pruned Set Transformation
predict.HOMERmodelPredict Method for HOMER
predict.LIFTmodelPredict Method for LIFT
predict.LPmodelPredict Method for Label Powerset
predict.MBRmodelPredict Method for Meta-BR/2BR
predict.MLKNNmodelPredict Method for ML-KNN
predict.NSmodelPredict Method for Nested Stacking
predict.PPTmodelPredict Method for Pruned Problem Transformation
predict.PruDentmodelPredict Method for PruDent
predict.PSmodelPredict Method for Pruned Set Transformation
predict.RAkELmodelPredict Method for RAkEL
predict.RDBRmodelPredict Method for RDBR
predict.RPCmodelPredict Method for RPC
print.BRmodelPrint BR model
print.BRPmodelPrint BRP model
print.CCmodelPrint CC model
print.CLRmodelPrint CLR model
print.CTRLmodelPrint CTRL model
print.DBRmodelPrint DBR model
print.EBRmodelPrint EBR model
print.ECCmodelPrint ECC model
print.EPSmodelPrint EPS model
print.kFoldPartitionPrint a kFoldPartition object
print.LIFTmodelPrint LIFT model
print.LPmodelPrint LP model
print.majorityModelPrint Majority model
print.MBRmodelPrint MBR model
print.mlconfmatPrint a Multi-label Confusion Matrix
print.MLKNNmodelPrint MLKNN model
print.mlresultPrint the mlresult
print.NSmodelPrint NS model
print.PPTmodelPrint PPT model
print.PruDentmodelPrint PruDent model
print.PSmodelPrint PS model
print.RAkELmodelPrint RAkEL model
print.randomModelPrint Random model
print.RDBRmodelPrint RDBR model
print.RPCmodelPrint RPC model
prudentPruDent classifier for multi-label Classification
psPruned Set for multi-label Classification
rakelRandom k-labelsets for multilabel classification
rcut_thresholdRank Cut (RCut) threshold method
rdbrRecursive Dependent Binary Relevance (RDBR) for multi-label...
remove_attributesRemove attributes from the dataset
remove_labelsRemove labels from the dataset
remove_skewness_labelsRemove unusual or very common labels
remove_unique_attributesRemove unique attributes
remove_unlabeled_instancesRemove examples without labels
replace_nominal_attributesReplace nominal attributes Replace the nominal attributes by...
rpcRanking by Pairwise Comparison (RPC) for multi-label...
scut_thresholdSCut Score-based method
sub-.mlresultFilter a Multi-Label Result
subset_correctionSubset Correction of a predicted result
summary.mltransformationSummary method for mltransformation
toymlToy multi-label dataset.
utimlutiml: Utilities for Multi-Label Learning
utiml_all_measures_namesMEASURES METHODS ————– Return the tree with the...
utiml_compute_ensembleCompute binary predictions
utiml_ensemble_averageAverage vote combination for a single-label prediction
utiml_ensemble_check_voteschemaVerify if a schema vote name is valid
utiml_ensemble_majorityMajority vote combination for single-label prediction
utiml_ensemble_maximumMaximum vote combination for single-label prediction
utiml_ensemble_methodDefine the method name related with the vote schema
utiml_ensemble_minimumMinimum vote combination for single-label prediction
utiml_ifelseConditional value selection
utiml_is_equal_setsDefine if two sets are equals independently of the order of...
utiml_iterative_splitInternal Iterative Stratification
utiml_labels_correlationPhi Correlation Coefficient
utiml_labels_IGCalculate the Information Gain for each pair of labels
utiml_lapplySelect the suitable method lapply or mclaplly
utiml_measure_accuracyMULTILABEL MEASURES ————- Multi-label Accuracy...
utiml_measure_average_precisionMulti-label Average Precision Measure
utiml_measure_binary_accuracyBINARY MEASURES ————– Compute the binary accuracy
utiml_measure_binary_AUCCompute the binary AUC
utiml_measure_binary_balaccCompute the binary balanced accuracy
utiml_measure_binary_f1Compute the binary F1 measure
utiml_measure_binary_precisionCompute the binary precision
utiml_measure_binary_recallCompute the binary recall
utiml_measure_coverageMulti-label Coverage Measure
utiml_measure_f1Multi-label F1 Measure
utiml_measure_hamming_lossMulti-label Hamming Loss Measure
utiml_measure_is_errorMulti-label Is Error Measure
utiml_measure_macro_accuracyMulti-label Macro-Accuracy Measure
utiml_measure_macro_AUCMulti-label Macro-AUC Measure
utiml_measure_macro_f1Multi-label Macro-F1 Measure
utiml_measure_macro_precisionMulti-label Macro-Precision Measure
utiml_measure_macro_recallMulti-label Macro-Recall Measure
utiml_measure_margin_lossMulti-label Margin Loss Measure
utiml_measure_micro_accuracyMulti-label Micro-Accuracy Measure
utiml_measure_micro_AUCMulti-label Macro-AUC Measure
utiml_measure_micro_f1Multi-label Micro-F1 Measure
utiml_measure_micro_precisionMulti-label Micro-Precision Measure
utiml_measure_micro_recallMulti-label Micro-Recall Measure
utiml_measure_namesReturn the name of measures
utiml_measure_one_errorMulti-label One Error Measure
utiml_measure_precisionMulti-label Precision Measure
utiml_measure_ranking_errorMulti-label Ranking Error Measure
utiml_measure_ranking_lossMulti-label Hamming Loss Measure
utiml_measure_recallMulti-label Recall Measure
utiml_measure_subset_accuracyMulti-label Subset Accuracy Measure
utiml_newdataReturn the newdata to a data.frame or matrix
utiml_normalizeInternal normalize data function
utiml_predict_binary_ensemblePredict binary predictions
utiml_preserve_seedPreserve current seed
utiml_random_splitRandom split of a dataset
utiml_renameRename the list using the names values or its own content
utiml_restore_seedRestore the current seed
utiml_stratified_splitLabelsets Stratification Create the indexes using the...
utiml_validate_splitmethodReturn the name of split method and validate if it is valid
rivolli/utiml documentation built on Nov. 30, 2017, 12:29 p.m.