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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/01/16 18:11:39 UTC
[jira] [Commented] (SPARK-7780) The intercept in
LogisticRegressionWithLBFGS should not be regularized
[ https://issues.apache.org/jira/browse/SPARK-7780?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15103259#comment-15103259 ]
Apache Spark commented on SPARK-7780:
-------------------------------------
User 'holdenk' has created a pull request for this issue:
https://github.com/apache/spark/pull/10788
> The intercept in LogisticRegressionWithLBFGS should not be regularized
> ----------------------------------------------------------------------
>
> Key: SPARK-7780
> URL: https://issues.apache.org/jira/browse/SPARK-7780
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Reporter: DB Tsai
>
> The intercept in Logistic Regression represents a prior on categories which should not be regularized. In MLlib, the regularization is handled through `Updater`, and the `Updater` penalizes all the components without excluding the intercept which resulting poor training accuracy with regularization.
> The new implementation in ML framework handles this properly, and we should call the implementation in ML from MLlib since majority of users are still using MLlib api.
> Note that both of them are doing feature scalings to improve the convergence, and the only difference is ML version doesn't regularize the intercept. As a result, when lambda is zero, they will converge to the same solution.
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