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Not clear why adding additional non-polynomial features fix high bias in Machine learning

It is clear to me that adding polynomial features fixes high bias. So far so good. But there is also the claim that adding more (non polynomial) features fixes high bias. I don't see why. In my humble opinion it will not fix it since linear regression will stay linear regression if a new, non-polynomial feature is added. With one feature we will have a straight line in a plain, and by adding one feature we will have a straight plane in a 3 dimensional space.