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https://issues.apache.org/jira/browse/SPARK-25781?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ruxi zhang updated SPARK-25781:
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Description: There is an R package relaimpo that generates relative
importance for linear regression features. This method utilizes sharply value
regression, which will take a long time to run on big datasets. This method is
quite useful for many use cases such as attribution model in marketing. It
will be great if it is written in Spark with paralleled computing, which would
be producing result within a much short time. (was: There is an R package
relaimpo that generate relative importance for linear regression features.
This method utilizes sharply value regression, which will take a long time to
run on big datasets. This method is quite useful for many use cases such as
attribution model in marketing. It will be great if it is written in Spark
with paralleled computing, which would be producing result within a much short
time.)
> relative importance of linear regression
> ----------------------------------------
>
> Key: SPARK-25781
> URL: https://issues.apache.org/jira/browse/SPARK-25781
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 2.3.2
> Reporter: ruxi zhang
> Priority: Minor
> Labels: features
> Attachments: v17i01.pdf
>
>
> There is an R package relaimpo that generates relative importance for linear
> regression features. This method utilizes sharply value regression, which
> will take a long time to run on big datasets. This method is quite useful
> for many use cases such as attribution model in marketing. It will be great
> if it is written in Spark with paralleled computing, which would be producing
> result within a much short time.
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