Xiangrui Meng created SPARK-10385:
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Summary: Bivariate statistics as UDAFs
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 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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