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https://issues.apache.org/jira/browse/SPARK-18822?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Felix Cheung updated SPARK-18822:
---------------------------------
Description:
>From Joseph Bradley:
"
Supporting Pipelines and advanced use cases: There really needs to be more
design discussion around SparkR. Felix Cheung would you be interested in
leading some discussion? I'm envisioning something similar to what was done a
while back for Pipelines in Scala/Java/Python, where we consider several use
cases of MLlib: fitting a single model, creating and tuning a complex Pipeline,
and working with multiple languages. That should help inform what APIs should
look like in Spark R.
"
Certain ML model, such as OneVsRest, is harder to represent in a single call R
API. Having advanced API or Pipeline API like this could help to expose that to
our users
was:
>From Joseph Bradley:
"
Supporting Pipelines and advanced use cases: There really needs to be more
design discussion around SparkR. Felix Cheung would you be interested in
leading some discussion? I'm envisioning something similar to what was done a
while back for Pipelines in Scala/Java/Python, where we consider several use
cases of MLlib: fitting a single model, creating and tuning a complex Pipeline,
and working with multiple languages. That should help inform what APIs should
look like in Spark R.
"
> Support ML Pipeline in SparkR
> -----------------------------
>
> Key: SPARK-18822
> URL: https://issues.apache.org/jira/browse/SPARK-18822
> Project: Spark
> Issue Type: New Feature
> Components: ML, SparkR
> Reporter: Felix Cheung
>
> From Joseph Bradley:
> "
> Supporting Pipelines and advanced use cases: There really needs to be more
> design discussion around SparkR. Felix Cheung would you be interested in
> leading some discussion? I'm envisioning something similar to what was done a
> while back for Pipelines in Scala/Java/Python, where we consider several use
> cases of MLlib: fitting a single model, creating and tuning a complex
> Pipeline, and working with multiple languages. That should help inform what
> APIs should look like in Spark R.
> "
> Certain ML model, such as OneVsRest, is harder to represent in a single call
> R API. Having advanced API or Pipeline API like this could help to expose
> that to our users
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