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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-22451) Reduce decision tree aggregate size for unordered features from O(2^numCategories) to O(numCategories)
Date Mon, 06 Nov 2017 10:15:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-22451?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16240101#comment-16240101
] 

Apache Spark commented on SPARK-22451:
--------------------------------------

User 'WeichenXu123' has created a pull request for this issue:
https://github.com/apache/spark/pull/19666

> Reduce decision tree aggregate size for unordered features from O(2^numCategories) to
O(numCategories)
> ------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-22451
>                 URL: https://issues.apache.org/jira/browse/SPARK-22451
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 2.2.0
>            Reporter: Weichen Xu
>   Original Estimate: 24h
>  Remaining Estimate: 24h
>
> Do not need generate all possible splits for unordered features before aggregate,
> in aggregete (executor side):
> 1. Change `mixedBinSeqOp`, for each unordered feature, we do the same stat with ordered
features. so for unordered features, we only need O(numCategories) space for this feature
stat.
> 2. After driver side get the aggregate result, generate all possible split combinations,
and compute the best split.
> This will reduce decision tree aggregate size for each unordered feature from O(2^numCategories)
to O(numCategories),  `numCategories` is the arity of this unordered feature.
> This also reduce the cpu cost in executor side. Reduce time complexity for this unordered
feature from O(numPoints * 2^numCategories) to O(numPoints).
> This won't increase time complexity for unordered features best split computing in driver
side.



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