When does more regularization imply fewer degrees of freedom? Sufficient conditions and counterexamples
更强正则化何时意味着更少自由度？充分条件与反例
Regularization aims to improve prediction performance by trading an increase in training error for better agreement between training and prediction errors, which is often captured through decreased degrees of freedom. In this paper we give examples which show that regularization can increase the degrees of freedom in common models, including the lasso and ridge regression. In such situations, both training error and degrees of freedom increase, making the regularization…