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https://issues.apache.org/jira/browse/SPARK-13448?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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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: Intercept will not be regularized if users train binary
classification model with L1/L2 Updater by LogisticRegressionWithLBFGS, because
it calls ML LogisticRegresson implementation. Meanwhile if users set 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),
this behavior is different from the old API.
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 if
users train binary classification model with L1/L2 Updater by
LogisticRegressionWithLBFGS, because it calls ML LogisticRegresson
implementation. Meanwhile if users set 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), this behavior is different from the
old API.
> 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: Intercept will not be regularized if users train binary
> classification model with L1/L2 Updater by LogisticRegressionWithLBFGS,
> because it calls ML LogisticRegresson implementation. Meanwhile if users set
> 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), this behavior is different from the old API.
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