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https://issues.apache.org/jira/browse/SPARK-1547?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14150964#comment-14150964
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Manish Amde commented on SPARK-1547:
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Given the interesting in boosting algorithms for the MLlib 1.2 release, I have
revived the boosting work that Hirakendu Das and I worked on a few months ago
but was stalled due to the decision tree optimization effort.
A basic version of the GBT code with "pluggable" loss functions can be found
here:
https://github.com/manishamde/spark/compare/gbt?diff=unified
I will put it up for a WIP PR in a few days if I don't hear any major concerns.
Here are a few things that are left:
1. Stochastic gradient boosting support -- I am waiting for the [RF
ticket|https://issues.apache.org/jira/browse/SPARK-1545] to be closed so that I
can re-use the BaggedPoint approach.
2. Checkpointing -- This approach will avoid long lineage chains. Need
Hirakendu's inputs on this especially his findings on large-scale experiments.
Also need to conduct experiments of my own.
3. Unit Tests -- I have done some basic test but I need to add unit tests.
4. Classification support -- It should be straightforward to add
5. Create public APIs
6. Tests on multiple cluster sizes and datasets -- require help from the
community on this front.
Feedback will be appreciated.
> 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
> [Ensembles design document (Google doc) |
> https://docs.google.com/document/d/1J0Q6OP2Ggx0SOtlPgRUkwLASrAkUJw6m6EK12jRDSNg/]
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