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https://issues.apache.org/jira/browse/SPARK-14478?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15248825#comment-15248825
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Joseph K. Bradley commented on SPARK-14478:
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Adding a param seems reasonable, though probably pretty low priority. To make
a judgement call...how about we leave it as is for now? I'll send a PR to
document that it's using unbiased variance. If any user ever needs biased,
then we can add the Param (but I've never heard anyone except myself complain).
> 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
>
> Currently, MLlib's StandardScaler scales columns using the unbiased standard
> deviation. 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?
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