I wouldn't use the term "error" because some people might take that to mean there was a way to avoid these problems.
over-constraining your model space means having too few parameters in your model But fixing your data size, the "power" of your search goes down when you increase the number of parameters.
So it is no so much an issue of avoiding errors, but of choosing the right number of parameters for your model.
I called them errors because that's usually the technical term for them, but the real point, as always, is tradeoffs. The less "finite" a ML setup you want to buy, the less "error".
over-constraining your model space means having too few parameters in your model But fixing your data size, the "power" of your search goes down when you increase the number of parameters.
So it is no so much an issue of avoiding errors, but of choosing the right number of parameters for your model.