[jira] [Commented] (SPARK-3728) RandomForest: Learn models too large to store in memory

["Yan Facai (颜发才 JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15936208#comment-15936208 ] 

Yan Facai (颜发才) commented on SPARK-3728:
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RandomForest already use a stack to save node, as [~jgfidelis] said before. However, all trees are still kept in memory, see `topNodes`.  

Perhaps, writing trees to disk is still needed if too many trees trained.

> RandomForest: Learn models too large to store in memory
> -------------------------------------------------------
>
>                 Key: SPARK-3728
>                 URL: https://issues.apache.org/jira/browse/SPARK-3728
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Joseph K. Bradley
>            Priority: Minor
>
> Proposal: Write trees to disk as they are learned.
> RandomForest currently uses a FIFO queue, which means training all trees at once via breadth-first search.  Using a FILO queue would encourage the code to finish one tree before moving on to new ones.  This would allow the code to write trees to disk as they are learned.
> Note: It would also be possible to write nodes to disk as they are learned using a FIFO queue, once the example--node mapping is cached [JIRA].  The [Sequoia Forest package]() does this.  However, it could be useful to learn trees progressively, so that future functionality such as early stopping (training fewer trees than expected) could be supported.



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