[jira] [Commented] (SPARK-1547) Add gradient boosting algorithm to MLlib

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

    [ https://issues.apache.org/jira/browse/SPARK-1547?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14149918#comment-14149918 ] 

Joseph K. Bradley commented on SPARK-1547:
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[~hector.yee] I strongly agree about keeping ensembles general enough to work with any weak learning algorithm.  This is difficult now because of the lack of a general class hierarchy, but that will be easier after the [current API redesign|https://issues.apache.org/jira/browse/SPARK-1856].  Starting with trees, and later generalizing once the new API is available, will be great.

> Add gradient boosting algorithm to MLlib
> ----------------------------------------
>
>                 Key: SPARK-1547
>                 URL: https://issues.apache.org/jira/browse/SPARK-1547
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>    Affects Versions: 1.0.0
>            Reporter: Manish Amde
>            Assignee: Manish Amde
>
> This task requires adding the gradient boosting algorithm to Spark MLlib. The implementation needs to adapt the gradient boosting algorithm to the scalable tree implementation.
> The tasks involves:
> - Comparing the various tradeoffs and finalizing the algorithm before implementation
> - Code implementation
> - Unit tests
> - Functional tests
> - Performance tests
> - Documentation
> [Ensembles design document (Google doc) | https://docs.google.com/document/d/1J0Q6OP2Ggx0SOtlPgRUkwLASrAkUJw6m6EK12jRDSNg/]



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