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https://issues.apache.org/jira/browse/SPARK-7780?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DB Tsai updated SPARK-7780:
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Assignee: holdenk
> 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
> Assignee: holdenk
> Fix For: 2.0.0
>
>
> 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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