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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/06/21 00:42:58 UTC

[jira] [Assigned] (SPARK-15574) Python meta-algorithms in Scala

     [ https://issues.apache.org/jira/browse/SPARK-15574?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-15574:
------------------------------------

    Assignee: Apache Spark

> Python meta-algorithms in Scala
> -------------------------------
>
>                 Key: SPARK-15574
>                 URL: https://issues.apache.org/jira/browse/SPARK-15574
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, PySpark
>            Reporter: Joseph K. Bradley
>            Assignee: Apache Spark
>
> This is an experimental idea for implementing Python ML meta-algorithms (CrossValidator, TrainValidationSplit, Pipeline, OneVsRest, etc.) in Scala.  This would require a Scala wrapper for algorithms implemented in Python, somewhat analogous to Python UDFs.
> The benefit of this change would be that we could avoid currently awkward conversions between Scala/Python meta-algorithms required for persistence.  It would let us have full support for Python persistence and would generally simplify the implementation within MLlib.



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