The fine line dividing ML modelling and statistical modelling

I've been thinking about the difference between ML modelling and statistical modelling.

I would to ask, on a philosophical level, is my thinking correct: modelling is basically a process of fitting a data-generating function to a set of data. Is this the case that in statistical modelling, we are explicitly finding a function that's expressible in parameters (in a manual way), but in ML modelling, we just automate this process, at the expense we can never write down explicitly a formula for the resultant model obtained from ML model?

Topic data-science-model parameter-estimation statistics machine-learning

Category Data Science

1
answered at

I disagree. Deep neural networks, about as “machine learning” as one can get, are just functions of the features. You might have a big (huge) equation with millions or billions or parameters, but it’s just an equation, and a small neural network has a small equation that you can fit on a notebook page.

Frank Harrell’s blog has some interesting posts on statistical modeling vs machine learning.

https://www.fharrell.com/post/stat-ml/

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