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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-6683) Handling feature scaling properly for GLMs
Date Fri, 03 Apr 2015 20:01:53 GMT

     [ https://issues.apache.org/jira/browse/SPARK-6683?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joseph K. Bradley updated SPARK-6683:
-------------------------------------
    Summary: Handling feature scaling properly for GLMs  (was: GLMs with GradientDescent could
scale step size instead of features)

> Handling feature scaling properly for GLMs
> ------------------------------------------
>
>                 Key: SPARK-6683
>                 URL: https://issues.apache.org/jira/browse/SPARK-6683
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 1.3.0
>            Reporter: Joseph K. Bradley
>            Priority: Minor
>
> GeneralizedLinearAlgorithm can scale features.  This improves optimization behavior (and
also affects the optimal solution, as is being discussed and hopefully fixed by [https://github.com/apache/spark/pull/5055]).
> This is a bit inefficient since it requires making a rescaled copy of the data.
> GradientDescent could instead scale the step size separately for each feature (and adjust
regularization as needed; see the PR linked above).  This would require storing a vector of
length numFeatures, rather than making a full copy of the data.
> I haven't thought this through for LBFGS, so I'm not sure if it's generally usable or
would require a specialization for GLMs with GradientDescent.



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