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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2014/11/01 02:31:33 UTC
[jira] [Resolved] (SPARK-2329) Add multi-label evaluation metrics
[ https://issues.apache.org/jira/browse/SPARK-2329?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng resolved SPARK-2329.
----------------------------------
Resolution: Fixed
Fix Version/s: 1.2.0
Issue resolved by pull request 1270
[https://github.com/apache/spark/pull/1270]
> Add multi-label evaluation metrics
> ----------------------------------
>
> Key: SPARK-2329
> URL: https://issues.apache.org/jira/browse/SPARK-2329
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Alexander Ulanov
> Assignee: Alexander Ulanov
> Fix For: 1.2.0
>
> Original Estimate: 72h
> Remaining Estimate: 72h
>
> There is no class in Spark MLlib for measuring the performance of multi-label classifiers. Multilabel classification is when the document is labeled with several labels (classes).
> This task involves adding the class for multilabel evaluation and unit tests. The following measures are to be implemented: Precision, Recall and F1-measure (1) based on documents averaged by the number of documents; (2) per label; (3) based on labels micro and macro averaged; (4) Hamming loss. Reference: Tsoumakas, Grigorios, Ioannis Katakis, and Ioannis Vlahavas. "Mining multi-label data." Data mining and knowledge discovery handbook. Springer US, 2010. 667-685.
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