Overview

Double descent is a phenomenon in which the test error of a model first decreases, then increases, and then decreases again as the model complexity increases. This phenomenon is counterintuitive because it suggests that increasing the complexity of a model can actually improve its performance, even after it has already started to overfit the training data. In this article, we will explore the concept of double descent and discuss some of the factors that can contribute to this phenomenon.