[
https://issues.apache.org/jira/browse/SPARK-21405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16087418#comment-16087418
]
Seth Hendrickson commented on SPARK-21405:
------------------------------------------
Good point, Nick. Though conveniently the machinery to deal with this is
already in place: https://github.com/apache/spark/pull/15930
> Add LBFGS solver for GeneralizedLinearRegression
> ------------------------------------------------
>
> Key: SPARK-21405
> URL: https://issues.apache.org/jira/browse/SPARK-21405
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.3.0
> Reporter: Seth Hendrickson
>
> GeneralizedLinearRegression in Spark ML currently only allows 4096 features
> because it uses IRLS, and hence WLS, as an optimizer which relies on
> collecting the covariance matrix to the driver. GLMs can also be fit by
> simple gradient based methods like LBFGS.
> The new API from
> [SPARK-19762|https://issues.apache.org/jira/browse/SPARK-19762] makes this
> easy to add. I've already prototyped it, and it works pretty well. This
> change would allow an arbitrary number of features (up to what can fit on a
> single node) as in Linear/Logistic regression.
> For reference, other GLM packages also support this - e.g. statsmodels, H2O.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
