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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:12:24 UTC
[jira] [Resolved] (SPARK-8971) Support balanced class labels when
splitting train/cross validation sets
[ https://issues.apache.org/jira/browse/SPARK-8971?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-8971.
---------------------------------
Resolution: Incomplete
> Support balanced class labels when splitting train/cross validation sets
> ------------------------------------------------------------------------
>
> Key: SPARK-8971
> URL: https://issues.apache.org/jira/browse/SPARK-8971
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Feynman Liang
> Assignee: Seth Hendrickson
> Priority: Major
> Labels: bulk-closed
>
> {{CrossValidator}} and the proposed {{TrainValidatorSplit}} (SPARK-8484) are Spark classes which partition data into training and evaluation sets for performing hyperparameter selection via cross validation.
> Both methods currently perform the split by randomly sampling the datasets. However, when class probabilities are highly imbalanced (e.g. detection of extremely low-frequency events), random sampling may result in cross validation sets not representative of actual out-of-training performance (e.g. no positive training examples could be included).
> Mainstream R packages like already [caret|http://topepo.github.io/caret/splitting.html] support splitting the data based upon the class labels.
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