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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/12/07 20:34:10 UTC
[jira] [Resolved] (SPARK-10232) Decide whether spark.ml Decision
Tree and Random Forest can replace spark.mllib implementation
[ https://issues.apache.org/jira/browse/SPARK-10232?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley resolved SPARK-10232.
---------------------------------------
Resolution: Fixed
Fix Version/s: 1.6.0
Target Version/s: (was: )
I'm closing this since I believe it's safe to replace the old implementation. It has now been 2 releases, so I've looked for regressions twice using spark-perf with various problem sizes. It should be fine to remove the old implementation, except for 1 improvement to the old impl which needs to be ported to the new impl: [SPARK-10064]
> Decide whether spark.ml Decision Tree and Random Forest can replace spark.mllib implementation
> ----------------------------------------------------------------------------------------------
>
> Key: SPARK-10232
> URL: https://issues.apache.org/jira/browse/SPARK-10232
> Project: Spark
> Issue Type: Task
> Components: ML, MLlib
> Reporter: Joseph K. Bradley
> Assignee: Joseph K. Bradley
> Fix For: 1.6.0
>
> Attachments: GBT.png, RandomForest.png
>
>
> This JIRA is for discussing replacing the spark.mllib DecisionTree and RandomForest implementations with the implementation in spark.ml. The new implementation is simply a copy, with slight modifications (removing "bins").
> Pros:
> * Support only 1 implementation.
> * Efficiency gains in spark.ml will benefit both APIs.
> Cons:
> * As spark.ml tree functionality increases, we will need to maintain conversion code for converting spark.ml trees to spark.mllib trees.
> Must:
> * Ensure we do not have significant regressions in the new implementation.
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