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https://issues.apache.org/jira/browse/SPARK-20228?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15960471#comment-15960471
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Sean Owen commented on SPARK-20228:
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Without more detail I'm not sure what to make of it. Just giving more memory
shouldn't change anything directly, but it could affect things like caching and
therefore locality, could affect whether your jobs are failing for lack of
memory. I have never encountered this when working with decision forests and
varying memory settings. It's hard to investigate with no reproduction. Can you
suggest what the issue is?
> 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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