[jira] [Commented] (SPARK-3156) DecisionTree: Order categorical features adaptively

["Apache Spark (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3156?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14110234#comment-14110234 ] 

Apache Spark commented on SPARK-3156:
-------------------------------------

User 'jkbradley' has created a pull request for this issue:
https://github.com/apache/spark/pull/2125

> DecisionTree: Order categorical features adaptively
> ---------------------------------------------------
>
>                 Key: SPARK-3156
>                 URL: https://issues.apache.org/jira/browse/SPARK-3156
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Joseph K. Bradley
>            Assignee: Joseph K. Bradley
>
> Improvement: accuracy
> Currently, ordered categorical features use a fixed bin ordering chosen before training based on a subsample of the data.  (See the code using centroids in findSplitsBins().)
> Proposal: Choose the ordering adaptively for every split.  This would require a bit more computation on the master, but could improve results by splitting more intelligently.
> Required changes: The result of aggregation is used in findAggForOrderedFeatureClassification() to compute running totals over the pre-set ordering of categorical feature values.  The stats should instead be used to choose a new ordering of categories, before computing running totals.



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