You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/11/06 10:15:00 UTC
[jira] [Assigned] (SPARK-22451) Reduce decision tree aggregate size
for unordered features from O(2^numCategories) to O(numCategories)
[ https://issues.apache.org/jira/browse/SPARK-22451?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-22451:
------------------------------------
Assignee: (was: Apache Spark)
> 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.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org