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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2015/08/03 02:31:04 UTC
[jira] [Resolved] (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 resolved SPARK-6683.
--------------------------------------
Resolution: Duplicate
Fix Version/s: 1.5.0
> 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
> Assignee: DB Tsai
> Fix For: 1.5.0
>
>
> GeneralizedLinearAlgorithm can scale features. This has 2 effects:
> * improves optimization behavior (essentially always improves behavior in practice)
> * changes the optimal solution (often for the better in terms of standardizing feature importance)
> Current problems:
> * Inefficient implementation: We make a rescaled copy of the data.
> * Surprising API: For algorithms which use feature scaling, users may get different solutions than with R or other libraries. (Note: Feature scaling could be handled without changing the solution.)
> * Inconsistent API: Not all algorithms have the same default for feature scaling, and not all expose the option.
> This is a proposal discussed with [~mengxr] for an "ideal" solution. This solution will require some breaking API changes, but I'd argue these are necessary for the long-term since it's the best API we have thought of.
> Proposal:
> * Implementation: Change to avoid making a rescaled copy of the data (described below). No API issues here.
> * API:
> ** Hide featureScaling from API. (breaking change)
> ** Internally, handle feature scaling to improve optimization, but modify it so it does not change the optimal solution. (breaking change, in terms of algorithm behavior)
> ** Externally, users who want to rescale feature (to change the solution) should do that scaling as a preprocessing step.
> Details on implementation:
> * 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, but I hope [~dbtsai] can weigh in here.
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