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https://issues.apache.org/jira/browse/SPARK-15767?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Kai Jiang updated SPARK-15767:
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Description: 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() (was: 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.decisionTreeRegression(dataframe,
formula, ...) . After having implemented decision tree classification, we
could refactor this two into an API more like rpart())
> Decision Tree Regression wrapper in SparkR
> ------------------------------------------
>
> Key: SPARK-15767
> URL: https://issues.apache.org/jira/browse/SPARK-15767
> Project: Spark
> Issue Type: New Feature
> 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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