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Posted to issues@spark.apache.org by "Seth Hendrickson (JIRA)" <ji...@apache.org> on 2016/03/30 00:44:25 UTC
[jira] [Commented] (SPARK-12326) Move GBT implementation from
spark.mllib to spark.ml
[ https://issues.apache.org/jira/browse/SPARK-12326?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15216999#comment-15216999 ]
Seth Hendrickson commented on SPARK-12326:
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[~josephkb] When moving the helper classes to ML, I agree it will be good to make classes private where possible. However, I am not sure what you mean by "change the APIs." Also, could you give an example of what you had in mind as far as eliminating duplicate data stored in the final model? Thanks!
> Move GBT implementation from spark.mllib to spark.ml
> ----------------------------------------------------
>
> Key: SPARK-12326
> URL: https://issues.apache.org/jira/browse/SPARK-12326
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Reporter: Seth Hendrickson
> Assignee: Seth Hendrickson
>
> Several improvements can be made to gradient boosted trees, but are not possible without moving the GBT implementation to spark.ml (e.g. rawPrediction column, feature importance). This Jira is for moving the current GBT implementation to spark.ml, which will have roughly the following steps:
> 1. Copy the implementation to spark.ml and change spark.ml classes to use that implementation. Current tests will ensure that the implementations learn exactly the same models.
> 2. Move the decision tree helper classes over to spark.ml (e.g. Impurity, InformationGainStats, ImpurityStats, DTStatsAggregator, etc...). Since eventually all tree implementations will reside in spark.ml, the helper classes should as well.
> 3. Remove the spark.mllib implementation, and make the spark.mllib APIs wrappers around the spark.ml implementation. The spark.ml tests will again ensure that we do not change any behavior.
> 4. Move the unit tests to spark.ml, and change the spark.mllib unit tests to verify model equivalence.
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