  A regression model with more parameters than data points in the training data
is overparametrized and has the capability to interpolate the training data.
Based on the classical bias-variance tradeoff expressions, it is commonly
assumed that models which interpolate noisy training data are poor to
generalize. In some cases, this is not true. The best models obtained are
overparametrized and the testing error exhibits the double descent behavior as
the model order increases. In this contribution, we provide some analysis to
explain the double descent phenomenon, first reported in the machine learning
literature. We focus on interpolating models derived from the minimum norm
solution to the classical least-squares problem and also briefly discuss model
fitting using ridge regression. We derive a result based on the behavior of the
smallest singular value of the regression matrix that explains the peak
location and the double descent shape of the testing error as a function of
model order.
