When you’re building a machine learning model you’re faced with the bias-variance tradeoff, where you have to find the balance between having a model that: Is very expressive and captures the real ...

Regularization aims to improve prediction performance by trading an increase in training error for better agreement between training and prediction errors, which is ...

We consider the generic regularized optimization problem $\hat{\beta}(\lambda)={\rm arg}\ {\rm min}_{\beta}\ L({\rm y},X\beta)+\lambda J(\beta)$. Efron, Hastie ...