[
https://issues.apache.org/jira/browse/SPARK-16098?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sean Owen resolved SPARK-16098.
-------------------------------
Resolution: Won't Fix
> Multiclass SVM Learning
> -----------------------
>
> Key: SPARK-16098
> URL: https://issues.apache.org/jira/browse/SPARK-16098
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 2.0.0
> Environment: Spark MLLib and ML 1.6.1
> Reporter: Hayri Volkan Agun
> Priority: Minor
> Original Estimate: 1,512h
> Remaining Estimate: 1,512h
>
> There exists a OneVsRest classifier for using all binary classification
> classifiers in multi-class classification. However for Linear SVM using
> OneVsRest may create an imbalanced dataset scenarios where SVM of Spark
> certainly fails. I verified this by creating LinearSVM classifier and
> implemented predictRaw method of ClassificationModel class. In all
> experiments the results came very poor in terms of F-Measure. The only
> explanation is SVM is very sensitive to imbalanced dataset, and naturally
> OneVsRest classifier creates an imbalanced dataset.
> For multi-class classification, linear SVM can be optimized by considering
> imbalanced datasets.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
