You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/04/03 22:01:53 UTC
[jira] [Updated] (SPARK-6683) Handling feature scaling properly for
GLMs
[ 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org