You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/05/12 23:48:04 UTC
[jira] [Assigned] (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 ]
Apache Spark reassigned SPARK-20729:
------------------------------------
Assignee: (was: Apache Spark)
> 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
>
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org