AFAIK, we can guarantee with/without standardization, the models always
converged to the same solution if there is no regularization. You can refer
the test casts at:
https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala#L
(BTW I think it means "when no standardization is applied", which is how
you interpreted it, yes.) I think it just means that if feature i is
divided by s_i, then its coefficients in the resulting model will end up
larger by a factor of s_i. They have to be divided by s_i to put them back
on the sa
I have a question regarding how the default standardization in the ML
version of the Logistic Regression (Spark 1.6) works.
Specifically about the next comments in the Spark Code:
/**
* Whether to standardize the training features before fitting the model.
* The coefficients of models will be alw
