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https://issues.apache.org/jira/browse/SPARK-16728?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-16728:
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Labels: bulk-closed (was: )
> migrate internal API for MLlib trees from spark.mllib to spark.ml
> -----------------------------------------------------------------
>
> Key: SPARK-16728
> URL: https://issues.apache.org/jira/browse/SPARK-16728
> Project: Spark
> Issue Type: Sub-task
> Components: MLlib
> Reporter: Vladimir Feinberg
> Priority: Major
> Labels: bulk-closed
>
> Currently, spark.ml trees rely on spark.mllib implementations. There are two
> issues with this:
> 1. Spark.ML's GBT TreeBoost algorithm requires storing additional information
> (the previous ensemble's prediction, for instance) inside the TreePoints
> (this is necessary to have loss-based splits for complex loss functions).
> 2. The old impurity API only lets you use summary statistics up to the 2nd
> order. These are useless for several impurity measures and inadequate for
> others (e.g., absolute loss or huber loss). It needs some renovation.
> 3. We should probably coalesce the ImpurityAggregator, ImpurityCalculator,
> and Impurity into a single class (and use virtual calls rather than case
> statements when toggling over impurity types).
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