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https://issues.apache.org/jira/browse/SPARK-7129?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15229285#comment-15229285
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Seth Hendrickson commented on SPARK-7129:
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I have amended [~meihuawu]'s design document with some updates and more
specifics
[here|https://docs.google.com/document/d/1ukJH9qWAKj_9OAqg4lhBPRuUvP7zfbvjUU1ZrOfU-Xs/edit?usp=sharing].
It is still a work in progress.
I realize this is not a priority for Spark 2.0, but I am wondering if there is
still interest in this Jira at all? I would like to work continue working on it
but would like to get a better picture of the interest before I go on. I
actually have a rough prototype of Adaboost with generic weak learners.
Hopefully we can get some feedback and discussion going on the Jira again.
cc [~mlnick] [~josephkb]
> Add generic boosting algorithm to spark.ml
> ------------------------------------------
>
> Key: SPARK-7129
> URL: https://issues.apache.org/jira/browse/SPARK-7129
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Joseph K. Bradley
>
> The Pipelines API will make it easier to create a generic Boosting algorithm
> which can work with any Classifier or Regressor. Creating this feature will
> require researching the possible variants and extensions of boosting which we
> may want to support now and/or in the future, and planning an API which will
> be properly extensible.
> In particular, it will be important to think about supporting:
> * multiple loss functions (for AdaBoost, LogitBoost, gradient boosting, etc.)
> * multiclass variants
> * multilabel variants (which will probably be in a separate class and JIRA)
> * For more esoteric variants, we should consider them but not design too much
> around them: totally corrective boosting, cascaded models
> Note: This may interact some with the existing tree ensemble methods, but it
> should be largely separate since the tree ensemble APIs and implementations
> are specialized for trees.
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