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https://issues.apache.org/jira/browse/SPARK-1548?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14344037#comment-14344037
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Joseph K. Bradley commented on SPARK-1548:
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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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