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https://issues.apache.org/jira/browse/FLINK-1728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Till Rohrmann updated FLINK-1728:
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Description:
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]
was:
Random forests 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
> 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
> 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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