Hi All, LogisticRegressionWithLBFGS set useFeatureScaling to true default which can improve the convergence during optimization. However, other model training method such as LogisticRegressionWithSGD does not set useFeatureScaling to true by default and the corresponding set function is private in mllib scope which can not be set by users.
The default configuration will cause mismatch training and prediction. Suppose that users prepare input data for training set and predict set with the same format, then run model training with LogisticRegressionWithLBFGS and prediction. But they do not know that it contains feature scaling in training step but w/o it in prediction step. When prediction step, it will apply model on dataset whose extent or scope is not consistent with training step. Should we make setFeatureScaling function to public and change default value to false? I think it is more clear and comprehensive to make feature scale and normalization in preprocessing step of the machine learning pipeline. If this proposal is OK, I will file a JIRA to track.
