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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/08/11 23:44:46 UTC
[jira] [Assigned] (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 ]
Apache Spark reassigned SPARK-8971:
-----------------------------------
Assignee: Seth Hendrickson (was: Apache Spark)
> 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
>
> {{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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