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https://issues.apache.org/jira/browse/SPARK-1548?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15940130#comment-15940130
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Mohamed Baddar commented on SPARK-1548:
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[~manishamde] [~sowen] [~josephkb]
I have small experience in contributions on starter tasks in spark, and found
this issue interesting. I was investigating regarding the partial
implementation of RF, and found these resources:
https://mahout.apache.org/users/classification/partial-implementation.html
https://github.com/apache/mahout/blob/b5fe4aab22e7867ae057a6cdb1610cfa17555311/mr/src/main/java/org/apache/mahout/classifier/df/mapreduce/partial/package-info.java
I thinks analyzing mahout implementation provides a good basis to start
analyzing RF partial implementation in theory and practically. If this issue is
still important to Spark, It would be great if I can start on it. I can start
with creating analysis document for current mahout implementation to assess its
performance
> Add Partial Random Forest algorithm to MLlib
> --------------------------------------------
>
> Key: SPARK-1548
> URL: https://issues.apache.org/jira/browse/SPARK-1548
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.0.0
> Reporter: Manish Amde
>
> This task involves creating an alternate approximate random forest
> implementation where each tree is constructed per partition.
> The tasks involves:
> - Justifying with theory and experimental results why this algorithm is a
> good choice.
> - Comparing the various tradeoffs and finalizing the algorithm before
> implementation
> - Code implementation
> - Unit tests
> - Functional tests
> - Performance tests
> - Documentation
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