[jira] [Commented] (SPARK-4591) Algorithm/model parity in spark.ml (Scala)

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-4591?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15225018#comment-15225018 ] 

Joseph K. Bradley commented on SPARK-4591:
------------------------------------------

I created subtasks for reviewing the major algorithm classes.  We can later decide how to handle the following items.

User-facing:
* Streaming ML (to be done under structured streaming in the 2.x line)
* evaluation
* fpm
* pmml
* stat

Developer-facing:
* optimization
* random, rdd
* util

Note that linalg is being handled separately: [SPARK-13944]

> Algorithm/model parity in spark.ml (Scala)
> ------------------------------------------
>
>                 Key: SPARK-4591
>                 URL: https://issues.apache.org/jira/browse/SPARK-4591
>             Project: Spark
>          Issue Type: Umbrella
>          Components: ML
>            Reporter: Xiangrui Meng
>            Priority: Critical
>
> This is an umbrella JIRA for porting spark.mllib implementations to use the DataFrame-based API defined under spark.ml.  We want to achieve feature parity for the next release.
> Create or link subtasks for:
> * missing algorithms or models (However, this does NOT include stats or linear algebra; those will be handled separately.)
> * existing algorithms or models which are missing features, params, etc.
> This only covers Scala since we can compare Scala vs. Python in spark.ml itself.
> _Note: Please search JIRA for existing issues to avoid duplicates._



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