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https://issues.apache.org/jira/browse/SPARK-3158?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-3158.
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Resolution: Fixed
Fix Version/s: 1.2.0
Issue resolved by pull request 2708
[https://github.com/apache/spark/pull/2708]
> Avoid 1 extra aggregation for DecisionTree training
> ---------------------------------------------------
>
> Key: SPARK-3158
> URL: https://issues.apache.org/jira/browse/SPARK-3158
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Joseph K. Bradley
> Assignee: Qiping Li
> Fix For: 1.2.0
>
>
> Improvement: computation
> Currently, the implementation does one unnecessary aggregation step. The
> aggregation step for level L (to choose splits) gives enough information to
> set the predictions of any leaf nodes at level L+1. We can use that info and
> skip the aggregation step for the last level of the tree (which only has leaf
> nodes).
> This update could be done by:
> * allocating a root node before the loop in the main train() method
> * allocating nodes for level L+1 while choosing splits for level L
> * caching stats in these newly allocated nodes, so that we can calculate
> predictions if we know they will be leaves
> * DecisionTree.findBestSplits can just return doneTraining
> This will let us cache impurity and avoid re-calculating it in
> calculateGainForSplit.
> Some above notes were copied from discussion in
> [https://github.com/apache/spark/pull/2341]
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