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https://issues.apache.org/jira/browse/SPARK-10385?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-10385:
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Description:
Similar to SPARK-10384, it would be nice to have bivariate statistics support
in DataFrames (defined as UDAFs). This JIRA discuss general implementation and
track subtasks. Bivariate statistics include:
* continuous: covariance, Pearson's correlation, and Spearman's correlation
* categorical: ??
If we define them as UDAFs, it would be flexible to use them with DataFrames,
e.g.,
{code}
df.groupBy("key").agg(corr("x", "y"))
{code}
was:
Similar to SPARK-10384, it would be nice to have bivariate statistics defined
as UDAFs. This JIRA discuss general implementation and track subtasks.
Bivariate statistics include:
* continuous: covariance, Pearson's correlation, and Spearman's correlation
* categorical: ??
If we define them as UDAFs, it would be flexible to use them with DataFrames,
e.g.,
{code}
df.groupBy("key").agg(corr("x", "y"))
{code}
> Bivariate statistics in DataFrames
> ----------------------------------
>
> Key: SPARK-10385
> URL: https://issues.apache.org/jira/browse/SPARK-10385
> Project: Spark
> Issue Type: Umbrella
> Components: ML, SQL
> Reporter: Xiangrui Meng
> Assignee: Burak Yavuz
>
> Similar to SPARK-10384, it would be nice to have bivariate statistics support
> in DataFrames (defined as UDAFs). This JIRA discuss general implementation
> and track subtasks. Bivariate statistics include:
> * continuous: covariance, Pearson's correlation, and Spearman's correlation
> * categorical: ??
> If we define them as UDAFs, it would be flexible to use them with DataFrames,
> e.g.,
> {code}
> df.groupBy("key").agg(corr("x", "y"))
> {code}
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