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https://issues.apache.org/jira/browse/SPARK-14077?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15213481#comment-15213481
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Mohamed Baddar commented on SPARK-14077:
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[~mengxr] [~josephkb] In sktlearn code , they implement the same feature by
scaling the target variable after binarization. Here's the source code link
https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/naive_bayes.py#L507.
I think we can follow sktlearn implementation as a guideline and it will also
help in the unit test. Any thoughts ?
> Support weighted instances in naive Bayes
> -----------------------------------------
>
> Key: SPARK-14077
> URL: https://issues.apache.org/jira/browse/SPARK-14077
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Xiangrui Meng
> Labels: naive-bayes
>
> In naive Bayes, we expect inputs to be individual observations. In practice,
> people may have the frequency table instead. It is useful for us to support
> instance weights to handle this case.
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