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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