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https://issues.apache.org/jira/browse/SPARK-17055?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15420569#comment-15420569
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Apache Spark commented on SPARK-17055:
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User 'VinceShieh' has created a pull request for this issue:
https://github.com/apache/spark/pull/14640
> add labelKFold to CrossValidator
> --------------------------------
>
> Key: SPARK-17055
> URL: https://issues.apache.org/jira/browse/SPARK-17055
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 2.0.0
> Reporter: Vincent
> Priority: Minor
>
> Current CrossValidator only supports k-fold, which randomly divides all the
> samples in k groups of samples. But in cases when data is gathered from
> different subjects and we want to avoid over-fitting, we want to hold out
> samples with certain labels from training data and put them into validation
> fold, i.e. we want to ensure that the same label is not in both testing and
> training sets.
> Mainstream package like Sklearn already supports such cross validation
> method.
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