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https://issues.apache.org/jira/browse/SPARK-17055?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15432427#comment-15432427
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Sean Owen commented on SPARK-17055:
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>From a comment in the PR, I get it. This is not actually about labels, but
>about some arbitrary attribute or function of each example. The purpose is to
>group examples into train/test such that examples with the same attribute
>value always go into the same data set. So maybe you want all examples for one
>customer ID to go into train, or all into test, but not split across both.
This needs a different name I think because 'label' has a specific and
different meaning, and even scikit says they want to rename it. It's coherent,
but I still don't know how useful it is. It would need to be reconstruted for
Spark ML.
> add labelKFold 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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