[jira] [Resolved] (SPARK-10385) Bivariate statistics in DataFrames

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-10385?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-10385.
----------------------------------
    Resolution: Incomplete

> 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
>            Priority: Major
>              Labels: bulk-closed
>
> 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 (SPARK-9297), Pearson's correlation (SPARK-9298), and Spearman's correlation (SPARK-10645)
> * 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}



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org