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https://issues.apache.org/jira/browse/SPARK-20228?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15958755#comment-15958755
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Sean Owen commented on SPARK-20228:
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I don't think there's a problem here. I'm saying that if you vary these
parameters you might legitimately get better or worse results. I'm also asking
if you are varying these other things. Also is this consistent or just on one
run?
> 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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