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https://issues.apache.org/jira/browse/SPARK-3043?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-3043:
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Assignee: Joseph K. Bradley
> DecisionTree aggregation is inefficient
> ---------------------------------------
>
> Key: SPARK-3043
> URL: https://issues.apache.org/jira/browse/SPARK-3043
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.1.0
> Reporter: Joseph K. Bradley
> Assignee: Joseph K. Bradley
>
> 2 major efficiency issues in computation and storage:
> (1) DecisionTree aggregation involves reshaping data unnecessarily.
> E.g., the internal methods extractNodeInfo() and getBinDataForNode() involve
> reshaping the data multiple times without real computation.
> (2) DecisionTree splits and aggregate bins can include many unused
> bins/splits.
> The same number of splits/bins are used for all features. E.g., if there is
> a continuous feature which uses 100 bins, then there will also be 100 bins
> allocated for all binary features, even though only 2 are necessary.
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