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https://issues.apache.org/jira/browse/SPARK-8971?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Feynman Liang updated SPARK-8971:
---------------------------------
Description:
{{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.
was:
{{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.
> 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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