rcv1sub5

mldr.datasets::get.mldr("rcv1sub5")

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 6000
Attributes 47336
Inputs 47235
Labels 101
Labelsets 946
Single labelsets 586
Max frequency 526
Cardinality 2.6415
Density 0.0262
Mean IR 69.6815
SCUMBLE 0.2381
TCS 22.2303

Citation

Lewis, D. D.; Yang, Y.; Rose, T. G.; Li, F. (2004). RCV1: A new benchmark collection for text categorization research. In The Journal of Machine Learning Research, 5(), 361--397.
@article{,
  title="RCV1: A new benchmark collection for text categorization research",
  author="Lewis, D. D. and Yang, Y. and Rose, T. G. and Li, F.",
  journal="The Journal of Machine Learning Research",
  volume="5",
  pages="361--397",
  year="2004"
}

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.