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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:15:36 UTC

[jira] [Resolved] (SPARK-20729) Reduce boilerplate in Spark ML models

     [ https://issues.apache.org/jira/browse/SPARK-20729?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-20729.
----------------------------------
    Resolution: Incomplete

> Reduce boilerplate in Spark ML models
> -------------------------------------
>
>                 Key: SPARK-20729
>                 URL: https://issues.apache.org/jira/browse/SPARK-20729
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, SparkR
>    Affects Versions: 2.2.0
>            Reporter: Maciej Szymkiewicz
>            Priority: Major
>              Labels: bulk-closed
>
> Currently we implement both {{predict}} and {{write.ml}} for ML wrappers, although R code is virtually identical and all the model specific logic is handled by Scala wrappers.
> Since we use S4 classes we can extract these functionalities into separate traits. 



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