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From "Yanbo Liang (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-13448) Document MLlib behavior changes in Spark 2.0
Date Mon, 29 Feb 2016 06:48:18 GMT

     [ https://issues.apache.org/jira/browse/SPARK-13448?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Yanbo Liang updated SPARK-13448:
--------------------------------
    Description: 
This JIRA keeps a list of MLlib behavior changes in Spark 2.0. So we can remember to add them
to the migration guide.

* SPARK-13429: change convergenceTol in LogisticRegressionWithLBFGS from 1e-4 to 1e-6.
* SPARK-7780: LogisticRegressionWithLBFGS intercept will not be regularized. 
Meanwhile if users train binary classification model with L1/L2 Updater, it calls ML LogisiticRegresson
implementation.
When without regularization, training with or without feature scaling will return the same
solution by the same convergence rate(because they run the same code route).

  was:
This JIRA keeps a list of MLlib behavior changes in Spark 2.0. So we can remember to add them
to the migration guide.

* SPARK-13429: change convergenceTol in LogisticRegressionWithLBFGS from 1e-4 to 1e-6.
* SPARK-7780: LogisticRegressionWithLBFGS intercept will not be regularized. Meanwhile if
without regularization, training with or without feature scaling will return the same solution
by the same convergence rate(because they run the same code route).


> Document MLlib behavior changes in Spark 2.0
> --------------------------------------------
>
>                 Key: SPARK-13448
>                 URL: https://issues.apache.org/jira/browse/SPARK-13448
>             Project: Spark
>          Issue Type: Documentation
>          Components: ML, MLlib
>            Reporter: Xiangrui Meng
>            Assignee: Xiangrui Meng
>
> This JIRA keeps a list of MLlib behavior changes in Spark 2.0. So we can remember to
add them to the migration guide.
> * SPARK-13429: change convergenceTol in LogisticRegressionWithLBFGS from 1e-4 to 1e-6.
> * SPARK-7780: LogisticRegressionWithLBFGS intercept will not be regularized. 
> Meanwhile if users train binary classification model with L1/L2 Updater, it calls ML
LogisiticRegresson implementation.
> When without regularization, training with or without feature scaling will return the
same solution by the same convergence rate(because they run the same code route).



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