# 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.

• Consider changing the topic of your question to "Why adding polynomial features..." instead of "additional". – user209249 Apr 14 '20 at 21:33

Linear regression with two components: $$LinReg(x_1, x_2) = a_0 + a_1 x_1 + a_2 x_2$$
Polynomial regressions with one component: $$PolyReg(x_1) = a_0 + a_1 x_1 + a_2 x_1^2$$
If you set $$x_2 = x_1^2$$ you end up with the same expression.