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