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https://issues.apache.org/jira/browse/SPARK-1547?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14049659#comment-14049659
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Hector Yee commented on SPARK-1547:
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Just generic log loss with L1 regularization should suffice. Most of the work
is in feature engineering anyway. It is no hurry at all, I already have several
implementations not in MLLib that I am using. It would just be convenient to
have another implementation to compare against.
> Add gradient boosting algorithm to MLlib
> ----------------------------------------
>
> Key: SPARK-1547
> URL: https://issues.apache.org/jira/browse/SPARK-1547
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.0.0
> Reporter: Manish Amde
> Assignee: Manish Amde
>
> This task requires adding the gradient boosting algorithm to Spark MLlib. The
> implementation needs to adapt the gradient boosting algorithm to the scalable
> tree implementation.
> The tasks involves:
> - Comparing the various tradeoffs and finalizing the algorithm before
> implementation
> - Code implementation
> - Unit tests
> - Functional tests
> - Performance tests
> - Documentation
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