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https://issues.apache.org/jira/browse/SPARK-9478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15861714#comment-15861714
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Joseph K. Bradley commented on SPARK-9478:
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[~sethah] Thanks for researching this! +1 for not using weights during bagging
and using importance weights to compensate. Intuitively, that seems like it
should give better estimators for class conditional probabilities than the
other option.
If you're splitting this into trees and forests, could you please target your
PR against a subtask for trees?
> Add sample weights to Random Forest
> -----------------------------------
>
> Key: SPARK-9478
> URL: https://issues.apache.org/jira/browse/SPARK-9478
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 1.4.1
> Reporter: Patrick Crenshaw
>
> Currently, this implementation of random forest does not support class
> weights. Class weights are important when there is imbalanced training data
> or the evaluation metric of a classifier is imbalanced (e.g. true positive
> rate at some false positive threshold).
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