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Posted to issues@flink.apache.org by "Devang Bacharwar (JIRA)" <ji...@apache.org> on 2015/11/05 20:21:27 UTC
[jira] [Commented] (FLINK-1728) Add random forest ensemble method
to machine learning library
[ https://issues.apache.org/jira/browse/FLINK-1728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14992298#comment-14992298 ]
Devang Bacharwar commented on FLINK-1728:
-----------------------------------------
Hello,
I would like to work on this. Can I please know the current status for this feature and next steps.
Thanks,
Devang
> Add random forest ensemble method to machine learning library
> -------------------------------------------------------------
>
> Key: FLINK-1728
> URL: https://issues.apache.org/jira/browse/FLINK-1728
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Mikio Braun
> Labels: ML
>
> Random forests [2,3] are a well-established mean to mitigate the decision trees' weakness of overfitting. Therefore this would be a valuable contribution to Flink's machine learning library.
> Google [1] describes some of the techniques they used to do ensemble learning of MapReduce. This could be helpful while implementing a distributed random forest.
> Resources:
> [1] [http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/36296.pdf]
> [2] [http://www.stat.berkeley.edu/~breiman/randomforest2001.pdf]
> [3] [http://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf]
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