English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A regression model with more parameters than data points in the training datais overparametrized and has the capability to interpolate the training data.Based on the classical bias-variance tradeoff expressions, it is commonlyassumed that models which interpolate noisy training data are poor togeneralize. In some cases, this is not true. The best models obtained areoverparametrized and the testing error exhibits the double descent behavior asthe model order increases. In this contribution, we provide some analysis toexplain the double descent phenomenon, first reported in the machine learningliterature. We focus on interpolating models derived from the minimum normsolution to the classical least-squares problem and also briefly discuss modelfitting using ridge regression. We derive a result based on the behavior of thesmallest singular value of the regression matrix that explains the peaklocation and the double descent shape of the testing error as a function ofmodel order.

Analysis of Interpolating Regression Models and the Double Descent Phenomenon