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https://issues.apache.org/jira/browse/SPARK-1547?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14049644#comment-14049644
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Manish Amde commented on SPARK-1547:
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Yes, the loss function and the solver should ideally be independent of the tree
algorithm and hence work with sparse data. Any particular non-tree algorithm
you had in mind?
I will definitely keep your suggestion in mind during the implementation
(coming up soon) but might postpone it for a later release if it involves a
lot more work than implementing it for decision trees since the goal is to get
ensembles built on top of decision trees ASAP.
> 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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