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https://issues.apache.org/jira/browse/SPARK-17055?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15625378#comment-15625378
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Sean Owen commented on SPARK-17055:
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My general opinion is that k-fold isn't that useful in the context of big data,
compared to simple test/train split. You can construct any test/train split you
like with Spark APIs, and an additional special API doesn't help. And there are
indeed lots of criteria you could imagine. Hence I also don't see particular
value in implementing this specially as a different type of k-fold cross
validation.
> add groupKFold to CrossValidator
> --------------------------------
>
> Key: SPARK-17055
> URL: https://issues.apache.org/jira/browse/SPARK-17055
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> 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 packages like Sklearn already supports such cross validation
> method.
> (http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.LabelKFold.html#sklearn.cross_validation.LabelKFold)
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