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https://issues.apache.org/jira/browse/SPARK-7129?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14903119#comment-14903119
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Joseph K. Bradley commented on SPARK-7129:
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Hi, I'd recommend starting with smaller tasks before tackling a larger one like
this. Here's a link with a lot more info on contributing:
[https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark]
You can also find the VM link from the MOOC here:
[http://mail-archives.us.apache.org/mod_mbox/spark-user/201505.mbox/%3CCAG5NM6SsHmv-vet85=afdyyoafgoluicmka9ck77qv3f0pk...@mail.gmail.com%3E]
But exploring installation issues might be a good way to understand the Spark
build a bit more! : )
> 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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