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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-1548) Add Partial Random Forest algorithm to MLlib
Date Mon, 02 Mar 2015 23:52:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-1548?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14344037#comment-14344037
] 

Joseph K. Bradley commented on SPARK-1548:
------------------------------------------

I think the idea is to train a different tree on each worker, rather than using the distributed
tree learning algorithm.  That could be generalized to any algorithm, but it's a bit different
than the bootstrapping JIRA, which seems to be about a wrapper for algorithms.

> Add Partial Random Forest algorithm to MLlib
> --------------------------------------------
>
>                 Key: SPARK-1548
>                 URL: https://issues.apache.org/jira/browse/SPARK-1548
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>    Affects Versions: 1.0.0
>            Reporter: Manish Amde
>            Assignee: Frank Dai
>
> This task involves creating an alternate approximate random forest implementation where
each tree is constructed per partition.
> The tasks involves:
> - Justifying with theory and experimental results why this algorithm is a good choice.
> - Comparing the various tradeoffs and finalizing the algorithm before implementation
> - Code implementation
> - Unit tests
> - Functional tests
> - Performance tests
> - Documentation



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