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https://issues.apache.org/jira/browse/SPARK-15767?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15368594#comment-15368594
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Shivaram Venkataraman commented on SPARK-15767:
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Sorry I missed this thread. I agree with [~mengxr] that we should go with the
`spark.algo` scheme and use the MLlib param names. In the future if we feel
like we have significant overlap we can add a `rpart` wrapper that can mimic
the existing package.
In terms of naming my vote would be to go with something like
`spark.decisiontree` or `spark.randomforest` -- its slightly better to be
explicit is my take.
> Decision Tree Regression wrapper in SparkR
> ------------------------------------------
>
> Key: SPARK-15767
> URL: https://issues.apache.org/jira/browse/SPARK-15767
> Project: Spark
> Issue Type: Sub-task
> Components: ML, SparkR
> Reporter: Kai Jiang
> Assignee: Kai Jiang
>
> Implement a wrapper in SparkR to support decision tree regression. R's naive
> Decision Tree Regression implementation is from package rpart with signature
> rpart(formula, dataframe, method="anova"). I propose we could implement API
> like spark.rpart(dataframe, formula, ...) . After having implemented
> decision tree classification, we could refactor this two into an API more
> like rpart()
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