Analytic Study of Double Descent in Binary Classification: The Impact of Loss
This work provides incremental theoretical insights into double descent phenomena for binary classification, focusing on the impact of loss functions and training set size.
The authors extended the study of double descent in binary linear classification to gradient descent with square loss, showing that the phenomenon persists but with differences in transition thresholds and minima compared to logistic loss, and derived analytic results using Gaussian features to corroborate empirical findings.
Extensive empirical evidence reveals that, for a wide range of different learning methods and datasets, the risk curve exhibits a double-descent (DD) trend as a function of the model size. In a recent paper [Zeyu,Kammoun,Thrampoulidis,2019] the authors studied binary linear classification models and showed that the test error of gradient descent (GD) with logistic loss undergoes a DD. In this paper, we complement these results by extending them to GD with square loss. We show that the DD phenomenon persists, but we also identify several differences compared to logistic loss. This emphasizes that crucial features of DD curves (such as their transition threshold and global minima) depend both on the training data and on the learning algorithm. We further study the dependence of DD curves on the size of the training set. Similar to our earlier work, our results are analytic: we plot the DD curves by first deriving sharp asymptotics for the test error under Gaussian features. Albeit simple, the models permit a principled study of DD features, the outcomes of which theoretically corroborate related empirical findings occurring in more complex learning tasks.