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From "Meihua Wu (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-7129) Add generic boosting algorithm to spark.ml
Date Tue, 22 Sep 2015 20:26:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-7129?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14903375#comment-14903375
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Meihua Wu commented on SPARK-7129:
----------------------------------

[~sethah] Thank you very much for the write up! That is a very good starting point and I will
get back to you. 

> 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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