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https://issues.apache.org/jira/browse/SPARK-22009?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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zhengruifeng updated SPARK-22009:
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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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