Github user hhbyyh commented on the issue:
https://github.com/apache/spark/pull/17862
On many large dataset, LinearSVC cannot get the similar result with
sklearn. e.g., SKLearn may get coefficients (5, 10, 15, 20), and spark
LinearSVC will get (10, 20, 30, 40). It's different but in most cases it's
proportional scaling. This may partly because the different handling of
intercept scaling in sklearn and spark. But on larger dataset, I found LBFGS
and OWLQN also presents the similar issue.
I wonder if it's normal to get different (but proportional) coefficients
from different optimization method for the same dataset and parameter settings.
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