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Date: Sun, 24 May 2015 08:07:17 +0000 (UTC)
From: "Apache Spark (JIRA)" <jira@apache.org>
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Message-ID: <JIRA.12831586.1432176345000.18744.1432454837827@Atlassian.JIRA>
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 <JIRA.12831586.1432176345283@arcas>
Subject: [jira] [Assigned] (SPARK-7780) The intercept in
 LogisticRegressionWithLBFGS should not be regularized
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     [ https://issues.apache.org/jira/browse/SPARK-7780?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-7780:
-----------------------------------

    Assignee:     (was: Apache Spark)

> 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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