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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2016/04/20 20:49:25 UTC
[jira] [Resolved] (SPARK-14478) Should StandardScaler use biased
variance to scale?
[ https://issues.apache.org/jira/browse/SPARK-14478?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley resolved SPARK-14478.
---------------------------------------
Resolution: Fixed
Fix Version/s: 2.0.0
Issue resolved by pull request 12519
[https://github.com/apache/spark/pull/12519]
> Should StandardScaler use biased variance to scale?
> ---------------------------------------------------
>
> Key: SPARK-14478
> URL: https://issues.apache.org/jira/browse/SPARK-14478
> Project: Spark
> Issue Type: Question
> Components: ML, MLlib
> Reporter: Joseph K. Bradley
> Assignee: Joseph K. Bradley
> Fix For: 2.0.0
>
>
> Currently, MLlib's StandardScaler scales columns using the corrected standard deviation (sqrt of unbiased variance). This matches what R's scale package does.
> However, it is a bit odd for 2 reasons:
> * Optimization/ML algorithms which require scaled columns generally assume unit variance (for mathematical convenience). That requires using biased variance.
> * scikit-learn, MLlib's GLMs, and R's glmnet package all use biased variance.
> *Question*: Should we switch to unbiased?
> *Decision*: No. Document what we do, and possibly add support for unbiased later on.
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