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https://issues.apache.org/jira/browse/SPARK-8971?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15976027#comment-15976027
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Tiago Albineli Motta commented on SPARK-8971:
---------------------------------------------
Why not a variation of TrainValidatorSplit to stratify the split of training
and test?
We just need to extract this code in TrainValidationSplit.scala as a new method:
{code}
val Array(trainingDataset, validationDataset) =
dataset.randomSplit(Array($(trainRatio), 1 - $(trainRatio)), $(seed))
trainingDataset.cache()
validationDataset.cache()
{code}
And them create a subclass like TrainValidatorBalancedSplit overriding this
method
> 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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