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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2016/03/14 19:18:33 UTC
[jira] [Closed] (SPARK-13712) Add OneVsOne to ML
[ https://issues.apache.org/jira/browse/SPARK-13712?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley closed SPARK-13712.
-------------------------------------
Resolution: Won't Fix
> Add OneVsOne to ML
> ------------------
>
> Key: SPARK-13712
> URL: https://issues.apache.org/jira/browse/SPARK-13712
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: zhengruifeng
> Priority: Minor
>
> Another Meta method for multi-class classification.
> Most classification algorithms were designed for balanced data.
> The OneVsRest method will generate K models on imbalanced data.
> The OneVsOne will train K*(K-1)/2 models on balanced data.
> OneVsOne is less sensitive to the problems of imbalanced datasets, and can usually result in higher precision.
> But it is much more computationally expensive, although each model are trained on a much smaller dataset. (2/K of total)
> The OneVsOne is implemented in the way OneVsRest did:
> val classifier = new LogisticRegression()
> val ovo = new OneVsOne()
> ovo.setClassifier(classifier)
> val ovoModel = ovo.fit(data)
> val predictions = ovoModel.transform(data)
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