[jira] [Resolved] (SPARK-3158) Avoid 1 extra aggregation for DecisionTree training

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-3158?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng resolved SPARK-3158.
----------------------------------
       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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