I'm confused. I agree that overfitting can lead to very bad models.
But, what I don't understand is that I thought that "linear" in ML contexts was normally used in the sense of 'linear transformations', which is a sense of linear that 'line-fit' from excel isn't -- it's affine.
Is a linear model with thousands/millions of weights/parameters (like deep learning models) really substantially simpler to understand? Can it do anything useful?