[jira] [Updated] (SPARK-17241) SparkR spark.glm should have configurable regularization parameter
[ https://issues.apache.org/jira/browse/SPARK-17241?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Shivaram Venkataraman updated SPARK-17241: -- Assignee: Xin Ren > SparkR spark.glm should have configurable regularization parameter > -- > > Key: SPARK-17241 > URL: https://issues.apache.org/jira/browse/SPARK-17241 > Project: Spark > Issue Type: Improvement >Reporter: Junyang Qian >Assignee: Xin Ren > Fix For: 2.1.0 > > > Spark has configurable L2 regularization parameter for generalized linear > regression. It is very important to have them in SparkR so that users can run > ridge regression. -- 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
[jira] [Updated] (SPARK-17241) SparkR spark.glm should have configurable regularization parameter
[ https://issues.apache.org/jira/browse/SPARK-17241?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Junyang Qian updated SPARK-17241: - Summary: SparkR spark.glm should have configurable regularization parameter (was: SparkR spark.glm should have configurable regularization parameter(s)) > SparkR spark.glm should have configurable regularization parameter > -- > > Key: SPARK-17241 > URL: https://issues.apache.org/jira/browse/SPARK-17241 > Project: Spark > Issue Type: Improvement >Reporter: Junyang Qian > > Spark has configurable L2 regularization parameter for linear regression and > an additional elastic-net parameter for generalized linear model. It is very > important to have them in SparkR so that users can run ridge regression and > elastic-net. -- 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
[jira] [Updated] (SPARK-17241) SparkR spark.glm should have configurable regularization parameter
[ https://issues.apache.org/jira/browse/SPARK-17241?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Junyang Qian updated SPARK-17241: - Description: Spark has configurable L2 regularization parameter for generalized linear regression. It is very important to have them in SparkR so that users can run ridge regression. (was: Spark has configurable L2 regularization parameter for linear regression and an additional elastic-net parameter for generalized linear model. It is very important to have them in SparkR so that users can run ridge regression and elastic-net.) > SparkR spark.glm should have configurable regularization parameter > -- > > Key: SPARK-17241 > URL: https://issues.apache.org/jira/browse/SPARK-17241 > Project: Spark > Issue Type: Improvement >Reporter: Junyang Qian > > Spark has configurable L2 regularization parameter for generalized linear > regression. It is very important to have them in SparkR so that users can run > ridge regression. -- 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
