Github user tanyinyan commented on the pull request:
https://github.com/apache/spark/pull/5055#issuecomment-88834566
" If featureScaling is false, then we scale features internally but also
adjust regularization. This will improve optimization behavior but will not
change the optimal solution."
@jkbradley , I'm not understanding the meaning of "optimization behavior"
here, does it means convergence rate? If we scale features internally and
also adjust regularization, then we will get the same gradient as not scale
features for every labeled point, so I think if the optimal solution is not
changed , so does the optimization behavior.
Have I understood correctlyï¼
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