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Hyukjin Kwon resolved SPARK-21405. ---------------------------------- Resolution: Incomplete > 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 > Priority: Major > Labels: bulk-closed > > 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 (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org