yahoo_entertainment

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

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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	12730
Attributes	32022
Inputs	32001
Labels	21
Labelsets	337
Single labelsets	151
Max frequency	2769
Cardinality	1.4137
Density	0.0673
Mean IR	64.4169
SCUMBLE	0.0387
TCS	19.2381

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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(31822 more attributes)

Label1 SCUMBLE 0.4431 Label2 SCUMBLE 0.04 Label3 SCUMBLE 0.0421 Label4 SCUMBLE 0.7674 Label5 SCUMBLE 0.1157 Label6 SCUMBLE 0.0426 Label7 SCUMBLE 0.0299 Label8 SCUMBLE 0.0939 Label9 SCUMBLE 0.1197 Label10 SCUMBLE 0.0916 Label11 SCUMBLE 0.0328 Label12 SCUMBLE 0.0694 Label13 SCUMBLE 0.2299 Label14 SCUMBLE 0.103 Label15 SCUMBLE 0.8413 Label16 SCUMBLE 0.1233 Label17 SCUMBLE 0.188 Label18 SCUMBLE 0.762 Label19 SCUMBLE 0.8004 Label20 SCUMBLE 0.8392 Label21 SCUMBLE 0.8312

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