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 always returned on the original scale, * so it will be transparent for users. *Note that with/without standardization,* ** the models should be always converged to the same solution when no regularization* ** is applied.* In R's GLMNET package, the default behavior is true as well. * Default is true. * * @group setParam */ Specifically I am having issues with understanding why the solution should converge to the same weight values with/without standardization ? Thanks ! -- Cesar Flores
