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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-1548) Add Partial Random Forest algorithm to MLlib
Date Mon, 02 Mar 2015 23:20:05 GMT

    [ https://issues.apache.org/jira/browse/SPARK-1548?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14343980#comment-14343980
] 

Sean Owen commented on SPARK-1548:
----------------------------------

Same question, curious about the status as there hasn't been activity in a while. It sounds
like this is proposed as a separate algorithm, but isn't this just a change to the bootstrapping
strategy for RDF? In which case, is this subsumed by the general idea of making bootstrapping
available to a bunch of algorithms, as in https://issues.apache.org/jira/browse/SPARK-2516
?

> 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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