arXiv:1903.07571cs.LG
Two models of double descent for weak features
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Abstract
The "double descent" risk curve was 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.
Most of the DL models we use are already above the "interpolation threshold" because they use millions of parameters which implies that they are already being trained in the "double descent" regime. Those papers and more [0] argue to explain why using millions of parameters…
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