yahoo_business

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

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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	11214
Attributes	21954
Inputs	21924
Labels	30
Labelsets	233
Single labelsets	115
Max frequency	5992
Cardinality	1.599
Density	0.0533
Mean IR	880.1777
SCUMBLE	0.1252
TCS	18.8476

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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(21754 more attributes)

Label1 SCUMBLE 0.1261 Label2 SCUMBLE 0.2721 Label3 SCUMBLE 0.4389 Label4 SCUMBLE 0.2095 Label5 SCUMBLE 0.5173 Label6 SCUMBLE 0.3685 Label7 SCUMBLE 0.3376 Label8 SCUMBLE 0.6591 Label9 SCUMBLE 0.3405 Label10 SCUMBLE 0.3855 Label11 SCUMBLE 0.3867 Label12 SCUMBLE 0.2094 Label13 SCUMBLE 0.7525 Label14 SCUMBLE 0.3341 Label15 SCUMBLE 0.1995 Label16 SCUMBLE 0.681 Label17 SCUMBLE 0.7659 Label18 SCUMBLE 0.229 Label19 SCUMBLE 0.2316 Label20 SCUMBLE 0.1705 Label21 SCUMBLE 0.4798 Label22 SCUMBLE 0.414 Label23 SCUMBLE 0.8899 Label24 SCUMBLE 0.3085 Label25 SCUMBLE 0 Label26 SCUMBLE 0.5215 Label27 SCUMBLE 0.8464 Label28 SCUMBLE 0.9727 Label29 SCUMBLE 0.2601 Label30 SCUMBLE 0.37

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