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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2016/06/16 17:39:05 UTC
[jira] [Closed] (SPARK-15986) SVMWithSGD requires LabeledPoint of
Regression
[ https://issues.apache.org/jira/browse/SPARK-15986?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley closed SPARK-15986.
-------------------------------------
Resolution: Duplicate
Thanks for reporting this; it's being worked on!
> SVMWithSGD requires LabeledPoint of Regression
> ----------------------------------------------
>
> Key: SPARK-15986
> URL: https://issues.apache.org/jira/browse/SPARK-15986
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
> Affects Versions: 2.0.0
> Environment: Spark 2.0 Snapshot
> Reporter: Hayri Volkan Agun
> Original Estimate: 504h
> Remaining Estimate: 504h
>
> SVMWithSGD uses the LabelledPoint in mllib-regression however a classification routine that extends ml's Predictor class uses LabelledPoint of ml.feature. So basically any rotutine, for ex: a classifier in ml pipeline cannot uses SVMWithSGD. This consistency problem can be solved by removing LabelledPoint in regression. Two different labelled points (one uses linalg.Vector, and other ml.linalg.Vector) is also confusing.
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