@Article{Belkin2019,
author = {Belkin, Mikhail and Hsu, Daniel and Xu, Ji}, 
title = {Two models of double descent for weak features}, 
journal = {}, 
volume = {}, 
number = {}, 
pages = {}, 
year = {2019}, 
abstract = {The \"double descent\" risk curve was recently proposed to qualitatively describe the out-of-sample prediction accuracy of variably-parameterized machine learning models. This article provides a precise mathematical analysis for the shape of this curve in two simple data models with the least squares/least norm predictor. Specifically, it is shown that the risk peaks when the number of features \$p\$ is close to the sample size \$n\$, but also that the risk decreases towards its minimum as \$p\$ increases beyond \$n\$. This behavior is contrasted with that of \"prescient\" models that select features in an a priori optimal order.}, 
location = {}, 
keywords = {}}

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