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https://issues.apache.org/jira/browse/SPARK-20228?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15957530#comment-15957530
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Sean Owen commented on SPARK-20228:
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This alone wouldn't matter directly, but could certainly matter indirectly.
More resource might mean you can run faster, and build better trees in the same
time. More memory might mean you run fewer executors and the way the trees are
built might actually benefit from that. There is a maxMemoryMB parameter you
might be increasing which allows more splits to be evaluated. This isn't enough
info and isn't evidence of a problem.
> Random Forest instable results depending on spark.executor.memory
> -----------------------------------------------------------------
>
> Key: SPARK-20228
> URL: https://issues.apache.org/jira/browse/SPARK-20228
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.1.0
> Reporter: Ansgar Schulze
>
> If I deploy a random forrest modeling with example
> spark.executor.memory 20480M
> I got another result as if i depoy the modeling with
> spark.executor.memory 6000M
> I excpected the same results but different runtimes.
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