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Posted to issues@spark.apache.org by "Evan Zamir (JIRA)" <ji...@apache.org> on 2018/12/17 21:45:00 UTC

[jira] [Created] (SPARK-26387) Parallelism seems to cause difference in CrossValidation model metrics

Evan Zamir created SPARK-26387:
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             Summary: Parallelism seems to cause difference in CrossValidation model metrics
                 Key: SPARK-26387
                 URL: https://issues.apache.org/jira/browse/SPARK-26387
             Project: Spark
          Issue Type: Bug
          Components: ML, MLlib
    Affects Versions: 2.3.2, 2.3.1
            Reporter: Evan Zamir


I can only reproduce this issue when running Spark on different Amazon EMR versions, but it seems that between Spark 2.3.1 and 2.3.2 (corresponding to EMR versions 5.17/5.18) the presence of the parallelism parameter was causing AUC metric to increase. Literally, I run the same exact code with and without parallelism and the AUC of my models (logistic regression) are changing significantly. I can't find a previous bug report relating to this, so I'm posting this as new.



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