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Hi ,

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?

Thanks!

Comment

**Answer** by srowen
·
Apr 12 at 06:40 PM

That's a very broad question, and I wouldn't say that ML doesn't make assumptions about distributions. For example, you assume you test/train data follow the same distribution. Using regularization means making assumptions about the prior distribution of coefficients. Any regressor minimizing squared error assumes there's a model it can represent such that errors around the prediction are Gaussian.

I suppose I wouldn't agree with this stats vs ML distinction then.

**Answer** by RossyBrown
·
Apr 29 at 08:41 PM

Informative discussion. Really, I am very glad to read this effective information about the difference between liner regression in Machine Learning and statistical model. We all know nowadays, Machine Learning is now become very popular and advance technology. As my profession, I am a professional writer and tech reviewer at https://anyassignment.com/communications/. Writing is one of the great passion in my life but I am also very interested to know about Science and Tech related topic. So, I much appreciate @Arijit Chakraborty to share this knowledgeable article. Thank you.!

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