Till Rohrmann created FLINK-1728:
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Summary: 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: Improvement
Components: Machine Learning Library
Reporter: Till Rohrmann
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
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