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https://issues.apache.org/jira/browse/SPARK-12326?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15056715#comment-15056715
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Seth Hendrickson commented on SPARK-12326:
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[~josephkb] Could you review the plan above? I couldn't find any other Jira for
moving GBTs to ML and it seems like it would be good to get this done so we can
move on some other improvements that are needed as well. 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
>
> 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.
> Steps 2, 3, and 4 should be in separate Jiras.
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