  I briefly report on some unexpected results that I obtained when optimizing
the model parameters of the Lasso. In simulations with varying
observations-to-variables ratio n=p, I typically observe a strong peak in the
test error curve at the transition point n/p = 1. This peaking phenomenon is
well-documented in scenarios that involve the inversion of the sample
covariance matrix, and as I illustrate in this note, it is also the source of
the peak for the Lasso. The key problem is the parametrization of the Lasso
penalty (as e.g. in the current R package lars) and I present a solution in
terms of a normalized Lasso parameter.
