Miyato et. al. introduced the idea of virtual adversarial examples to reduce this phenomenon. Violations of the one-in-ten rule leads to higher prevalence of this phenomenon. Michael A. Babyak’s paper (15[1])“What You See May (15[1])Not Be What You Get” is a “nontechnical” introduction to this phenomenon, which unintuitively (-5[1])occurs only at intermediate size in double descent. The 2k (-5[1])term in the Akaike (15[1]-5[1])information criterion adjusts for this phenomenon. (15[1])Elastic (15[1])net reduces this phenomenon by combining the (*) L1 and L2 (10[1])norm (10[1])penalties (10[1]-5[1])from LASSO and ridge regression. (10[1])Early stopping is an example (-5[1])of regularization techniques used to mitigate this phenomenon, (10[1])which is exhibited by models with very low bias and very high variance. For 10 points, name this phenomenon where a model has too many parameters, causing test error to be much larger than training (10[2])error. (10[1])■END■ (10[2])