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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-14478) Should StandardScaler use biased variance to scale?
Date Wed, 20 Apr 2016 06:03:25 GMT

     [ https://issues.apache.org/jira/browse/SPARK-14478?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-14478:
------------------------------------

    Assignee: Joseph K. Bradley  (was: Apache Spark)

> 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
>
> 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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