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Apache Spark commented on SPARK-7780: ------------------------------------- User 'holdenk' has created a pull request for this issue: https://github.com/apache/spark/pull/6386 > 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. -- 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