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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2015/09/21 17:20:04 UTC
[jira] [Updated] (SPARK-10668) Use WeightedLeastSquares in
LinearRegression with L2 regularization if the number of features is small
[ https://issues.apache.org/jira/browse/SPARK-10668?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng updated SPARK-10668:
----------------------------------
Assignee: Kai Sasaki
> Use WeightedLeastSquares in LinearRegression with L2 regularization if the number of features is small
> ------------------------------------------------------------------------------------------------------
>
> Key: SPARK-10668
> URL: https://issues.apache.org/jira/browse/SPARK-10668
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: Kai Sasaki
> Priority: Critical
>
> If the number of features is small (<=4096) and the regularization is L2, we should use WeightedLeastSquares to solve the problem rather than L-BFGS. The former requires only one pass to the data.
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