[jira] [Commented] (FLINK-1728) Add random forest ensemble method to machine learning library

["Seungchul Lee (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-1728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16780139#comment-16780139 ] 

Seungchul Lee commented on FLINK-1728:
--------------------------------------

[~till.rohrmann]

Hi,

I would like to know the status of this issue. If the status has no progress, can I take this issue?

> 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: Library / Machine Learning Library
>            Reporter: Till Rohrmann
>            Priority: Major
>              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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