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https://issues.apache.org/jira/browse/SPARK-15767?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15368547#comment-15368547
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Xiangrui Meng commented on SPARK-15767:
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This was discussed in SPARK-14831. We should call it `spark.algo(data, formula,
method, required params, [optional params])` and use the same param names as in
MLlib. But I'm not sure what method name to use here. We should think about
method names for all tree methods together. cc [~josephkb]
> 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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