rcv1sub3

mldr.datasets::get.mldr("rcv1sub3")

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 47337
Inputs 47236
Labels 101
Labelsets 939
Single labelsets 591
Max frequency 635
Cardinality 2.6142
Density 0.0259
Mean IR 68.3326
SCUMBLE 0.2075
TCS 22.2228

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.