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Joseph K. Bradley commented on SPARK-3728: ------------------------------------------ I was actually thinking of closing this issue. I originally made it since Sequoia Forests support this feature, but I have not heard of real use cases for it. If you have use cases, it'd be good to hear about. Otherwise, I think we should focus on improvements to in-memory use cases. > 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 > > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org