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

["Till Rohrmann (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-1728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Till Rohrmann closed FLINK-1728.
--------------------------------
    Resolution: Won't Do

{{flink-ml}} will most likely be retired soon.

> 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
>            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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