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https://issues.apache.org/jira/browse/SPARK-12326?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15060887#comment-15060887
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Joseph K. Bradley commented on SPARK-12326:
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The plan sounds good. The critical item is #1 of course since that will let us
improve GBTs in spark.ml.
For #2, I'd also recommend we take this opportunity to make some of those
helper classes private when possible (especially if they are only needed during
training) and maybe change the APIs (especially if we can eliminate duplicate
data stored in the final model).
Can you please make 1 subtask for each of these 4 steps? 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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