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From "Apache Spark (JIRA)" <>
Subject [jira] [Assigned] (SPARK-7780) The intercept in LogisticRegressionWithLBFGS should not be regularized
Date Sun, 24 May 2015 08:07:17 GMT


Apache Spark reassigned SPARK-7780:

    Assignee:     (was: Apache Spark)

> The intercept in LogisticRegressionWithLBFGS should not be regularized
> ----------------------------------------------------------------------
>                 Key: SPARK-7780
>                 URL:
>             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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