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https://issues.apache.org/jira/browse/SPARK-6683?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-6683:
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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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