[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=14904298#comment-14904298 ] Yanbo Liang commented on SPARK-10668: - [~mengxr] If [~lewuathe] is busy with other issues, I can take over this task. > 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. -- 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] [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=14904390#comment-14904390 ] Apache Spark commented on SPARK-10668: -- User 'Lewuathe' has created a pull request for this issue: https://github.com/apache/spark/pull/8884 > 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. -- 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] [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=14904395#comment-14904395 ] Kai Sasaki commented on SPARK-10668: So sorry for being late for submitting patch and thank you for supporting me. [~mengxr] [~yanbo] Could you review current patch? > 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. -- 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] [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=14903935#comment-14903935 ] Xiangrui Meng commented on SPARK-10668: --- [~lewuathe] any progress? > 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. -- 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] [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=14900807#comment-14900807 ] Xiangrui Meng commented on SPARK-10668: --- [~lewuathe] This JIRA blocks several others. Could you try to submit a PR in 2 days? > 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. -- 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] [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=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. -- 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
