yahoo_science

mldr.datasets::get.mldr("yahoo_science")

Select your download

Partitions: select your desired partitioning strategy, validation and format

Random Stratified Iterative stratified
Hold out MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
2x5-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
10-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr

Summary

Instances 6428
Attributes 37227
Inputs 37187
Labels 40
Labelsets 457
Single labelsets 252
Max frequency 1200
Cardinality 1.4498
Density 0.0362
Mean IR 52.6318
SCUMBLE 0.0575
TCS 20.3373

Citation

Ueda, N.; Saito, K. (2002). Parametric mixture models for multi-labeled text. In Advances in neural information processing systems, 721--728.
@inproceedings{,
  title="Parametric mixture models for multi-labeled text",
author="Ueda, N. and Saito, K.",
booktitle="Advances in neural information processing systems",
pages="721--728",
year="2002"
}

Concurrence plot

In this concurrence plot, sectors represent labels and links between them depict label co-occurrences. SCUMBLE is a measure designed to assess the concurrence among imbalanced labels.