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Joseph K. Bradley commented on SPARK-12566: ------------------------------------------- Here's my preferred design. I prefer to abstract the implementation (solver) from the API (model) as much as possible. * R glm calls Scala GLM, using solver = auto by default * Scala GLM has solver = auto by default. Auto should mean "best effort" ** With few features (< 4K or so), *** For family = gaussian and link = identity, use normal equations. *** For others, use IRLS. ** With many features, use LBFGS if possible (for family, link). Otherwise, throw an exception. * Scala LinearRegression, LogisticRegression call GLM. I.e., they uses normal equations, IRLS when possible. What do yall think? > GLM model family, link function support in SparkR:::glm > ------------------------------------------------------- > > Key: SPARK-12566 > URL: https://issues.apache.org/jira/browse/SPARK-12566 > Project: Spark > Issue Type: New Feature > Components: ML, SparkR > Reporter: Joseph K. Bradley > Assignee: yuhao yang > Priority: Critical > > This JIRA is for extending the support of MLlib's Generalized Linear Models > (GLMs) to more model families and link functions in SparkR. After > SPARK-12811, we should be able to wrap GeneralizedLinearRegression in SparkR > with support of popular families and link functions. -- 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