I had the understanding that the major difference between machine learning and statistical model is, the later "assumes" certain type of distribution of data & based on that different model paradigm as well as statistical results we obtain (e.g. p-values, F-statistics, t-stat, etc.). But in case of machine learning, we don't bother about distribution of data and more interested in prediction.
When I was going through Mllib doc, I found for linear regression we are specifying a distribution. But Mllib is a machine learning package. So, I've the following questions:
1) Is my understanding between ML & statistical method is wrong?
2) Is spark is using statistical modeling for linear regression and GLMs?
Note: There are lot of wonderful post regarding the difference between machine learning and statistical method. But this more related to spark MLLIB.