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 ...
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 ...
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