utiml0.1.7 package

Convert a mlresult to a bipartition matrix

Convert a multi-label Confusion Matrix to matrix

Convert a mlresult to matrix

Convert a matrix prediction in a multi label prediction

Convert a mlresult to a probability matrix

Convert a mlresult to a ranking matrix

Baseline reference for multilabel classification

Binary Relevance for multi-label Classification

BR+ or BRplus for multi-label Classification

Classifier Chains for multi-label Classification

Calibrated Label Ranking (CLR) for multi-label Classification

Compute the multi-label ensemble predictions based on some vote schema

Create a holdout partition based on the specified algorithm

Create the k-folds partition based on the specified algorithm

Create a random subset of a dataset

Create a subset of a dataset

Multi-label cross-validation

Dependent Binary Relevance (DBR) for multi-label Classification

Ensemble of Binary Relevance for multi-label Classification

Ensemble of Classifier Chains for multi-label Classification

Ensemble of Pruned Set for multi-label Classification

Ensemble of Single Label

Fill sparse dataset with 0 or '' values

Apply a fixed threshold in the results

Hierarchy Of Multilabel classifiER (HOMER)

Test if a mlresult contains crisp values as default

Test if a mlresult contains score values as default

Threshold based on cardinality

LIFT for multi-label Classification

Label Powerset for multi-label Classification

Meta-BR or 2BR for multi-label Classification

Maximum Cut Thresholding (MCut)

Join a list of multi-label confusion matrix

Fix the mldr dataset to use factors

Multi-label KNN (ML-KNN) for multi-label Classification

Prediction transformation problems

Build transformation models

Compute the confusion matrix for a multi-label prediction

Evaluate multi-label predictions

Return the name of all measures

Create a mlresult object

Normalize numerical attributes

Nested Stacking for multi-label Classification

Create the multi-label dataset from folds

Proportional Thresholding (PCut)

Join two multi-label confusion matrix

Pruned Problem Transformation for multi-label Classification

Predict Method for BASELINE

Predict Method for Binary Relevance

Predict Method for BR+ (brplus)

Predict Method for Classifier Chains

Predict Method for CLR

Predict Method for DBR

Predict Method for Ensemble of Binary Relevance

Predict Method for Ensemble of Classifier Chains

Predict Method for Ensemble of Pruned Set Transformation

Predict Method for Ensemble of Single Label

Predict Method for HOMER

Predict Method for LIFT

Predict Method for Label Powerset

Predict Method for Meta-BR/2BR

Predict Method for ML-KNN

Predict Method for Nested Stacking

Predict Method for Pruned Problem Transformation

Predict Method for PruDent

Predict Method for Pruned Set Transformation

Predict Method for RAkEL

Predict Method for RDBR

Predict Method for RPC

Print BR model

Print BRP model

Print CC model

Print CLR model

Print DBR model

Print EBR model

Print ECC model

Print EPS model

Print ESL model

Print a kFoldPartition object

Print LIFT model

Print LP model

Print Majority model

Print MBR model

Print a Multi-label Confusion Matrix

Print MLKNN model

Print the mlresult

Print NS model

Print PPT model

Print PruDent model

Print PS model

Print RAkEL model

Print Random model

Print RDBR model

Print RPC model

PruDent classifier for multi-label Classification

Pruned Set for multi-label Classification

Random k-labelsets for multilabel classification

Rank Cut (RCut) threshold method

Recursive Dependent Binary Relevance (RDBR) for multi-label Classifica...

Remove attributes from the dataset

Remove labels from the dataset

Remove unusual or very common labels

Remove unique attributes

Remove examples without labels

Replace nominal attributes Replace the nominal attributes by binary at...

Ranking by Pairwise Comparison (RPC) for multi-label Classification

SCut Score-based method

Filter a Multi-Label Result

Subset Correction of a predicted result

Summary method for mltransformation

utiml: Utilities for Multi-Label Learning

Return the name of measures