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https://issues.apache.org/jira/browse/SPARK-17055?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15614576#comment-15614576
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Rémi Delassus commented on SPARK-17055:
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I had an issue that could be solved by this kind of technique : Each sample got
a timesamp and the error can only be computed on a full month of data. Thus I
need months of data to be distributed in folds, no each sample individually.
But in my opinion this is not the good way to solve that. Since there is an
infinite number of ways to split the data, I think we should be able to pass
the split method as an argument to the crossvalidator. The method described
here could be implemented, as well as any other.
> 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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