Yanbo Liang created SPARK-20810:
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Summary: ML LinearSVC vs MLlib SVMWithSGD output different solution
Key: SPARK-20810
URL: https://issues.apache.org/jira/browse/SPARK-20810
Project: Spark
Issue Type: Question
Components: ML, MLlib
Affects Versions: 2.2.0
Reporter: Yanbo Liang
Fitting with SVM classification model on the same dataset, ML {{LinearSVC}}
produces different solution compared with MLlib {{SVMWithSGD}}. I understand
they use different optimization solver (OWLQN vs SGD), does it make sense to
converge to different solution?
AFAIK, both of them use Hinge loss which is convex but not differentiable
function. Since the derivative of the hinge loss at certain place is
non-deterministic, should we switch to use squared hinge loss?
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