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https://issues.apache.org/jira/browse/SPARK-21405?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16089386#comment-16089386
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Nick Pentreath commented on SPARK-21405:
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Ok, sounds good to me.
Do you think we would be able to re-use the {{logLikelihood}} in
{{LogisticRegression}} and {{LinearRegression}}? This is sort of the like the
generalized {{LossFunction}} interface I was alluding to in your optimizer
abstraction work.
> 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.
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