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Posted to issues@spark.apache.org by "zhengruifeng (JIRA)" <ji...@apache.org> on 2017/09/14 09:44:00 UTC
[jira] [Updated] (SPARK-22009) Using treeAggregate improve some
algs
[ https://issues.apache.org/jira/browse/SPARK-22009?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
zhengruifeng updated SPARK-22009:
---------------------------------
Description:
I test on a dataset of about 13M instances, and found that using `treeAggregate` give a speedup in following algs:
OneHotEncoder ~ 5%
StatFunctions.calculateCov ~ 7%
StatFunctions.multipleApproxQuantiles ~ 9%
RegressionEvaluator ~ 8%
was:
I test on a dataset of about 13M instances, and found that using `treeAggregate` give a speedup in following algs:
OneHotEncoder ~ 5%
StatFunctions.calculateCov ~ 13%
StatFunctions.multipleApproxQuantiles ~ 9%
RegressionEvaluator ~ 8%
> Using treeAggregate improve some algs
> -------------------------------------
>
> Key: SPARK-22009
> URL: https://issues.apache.org/jira/browse/SPARK-22009
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.3.0
> Reporter: zhengruifeng
> Priority: Minor
>
> I test on a dataset of about 13M instances, and found that using `treeAggregate` give a speedup in following algs:
> OneHotEncoder ~ 5%
> StatFunctions.calculateCov ~ 7%
> StatFunctions.multipleApproxQuantiles ~ 9%
> RegressionEvaluator ~ 8%
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