yahoo_recreation

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

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Full dataset

MULAN/MEKA (.arff) MULAN (.xml) LibSVM KEEL mldr

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	12828
Attributes	30346
Inputs	30324
Labels	22
Labelsets	530
Single labelsets	284
Max frequency	1652
Cardinality	1.4289
Density	0.065
Mean IR	12.203
SCUMBLE	0.0301
TCS	19.6836

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.

Attributes

all attributes labels

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(30146 more attributes)

Label1 SCUMBLE 0.0271 Label2 SCUMBLE 0.0571 Label3 SCUMBLE 0.0394 Label4 SCUMBLE 0.032 Label5 SCUMBLE 0.2737 Label6 SCUMBLE 0.018 Label7 SCUMBLE 0.0309 Label8 SCUMBLE 0.5481 Label9 SCUMBLE 0.106 Label10 SCUMBLE 0.7024 Label11 SCUMBLE 0.1132 Label12 SCUMBLE 0.193 Label13 SCUMBLE 0.535 Label14 SCUMBLE 0.046 Label15 SCUMBLE 0.0855 Label16 SCUMBLE 0.0558 Label17 SCUMBLE 0.0594 Label18 SCUMBLE 0.019 Label19 SCUMBLE 0.0821 Label20 SCUMBLE 0.3795 Label21 SCUMBLE 0.2372 Label22 SCUMBLE 0.0353

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