[kafkaesk]

I have no professional experience with ML, so I might be missing something obvius here that's part of the industry paradigm.

But the article gives two points why you shouldn't use coefficient values to determine feature importance, which I think are only valid to some extent.

>a) changing the scale of the variable changes the absolute value of the coefficient

and

>(b) if features are multi-collinear, coefficients can shift from one feature to others.

Regarding a), well, that's what standardized coefficients are for.

b) is a bit trickier, but most regression models are based on the assumption of non-collinearity. This is of course a problem with real-world data, because you will quite often find some level of collinearity. That's when you (1) test for this issue and (2) look towards multilevel models.