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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2016/03/15 19:06:35 UTC
[jira] [Updated] (SPARK-13448) Document MLlib behavior changes in
Spark 2.0
[ https://issues.apache.org/jira/browse/SPARK-13448?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley 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.
* SPARK-12363: Bug fix for PowerIterationClustering which will likely change results
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: 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.
> * SPARK-12363: Bug fix for PowerIterationClustering which will likely change results
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