[jira] [Created] (SPARK-8971) Support balanced class labels when splitting train/cross validation sets

["Feynman Liang (JIRA)" <ji...@apache.org>]

Feynman Liang created SPARK-8971:
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             Summary: 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


{{CrossValidator}} and the proposed {{TrainValidatorSplit}} 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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