machine learning

Double Descent

Introduction A recent blog article introduced me to the idea of double descent. This is the phenomenon by which the fitting error in the testing set of a (presumably gradient descent-based) machine-learning method goes down as the number of parameters increases, then rises again (a phenomenon known as overfitting), but then goes down again as the number of parameters is increased still further. I do not want to comment on the specifics of this article as they pertain to machine learning, but rather on a sub-aspect of it.