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Shivaram Venkataraman commented on SPARK-15767: ----------------------------------------------- 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() -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org