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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-2329) Add multi-label evaluation metrics
Date Sat, 01 Nov 2014 01:31:33 GMT

     [ 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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