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Posted to issues@spark.apache.org by "Kai Sasaki (JIRA)" <ji...@apache.org> on 2015/09/19 03:21:04 UTC

[jira] [Commented] (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:comment-tabpanel&focusedCommentId=14876787#comment-14876787 ] 

Kai Sasaki commented on SPARK-10668:
------------------------------------

[~mengxr] Hello, can I work on this JIRA? Please assign it to me. Thank you.

> 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
>            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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