Question: Problem A [5 points]: Derive the bias-variance decomposition for the squared error loss function. That is, show that for a model fS trained on a

Problem A [5 points]: Derive the bias-variance decomposition for the squared

Problem A [5 points]: Derive the bias-variance decomposition for the squared error loss function. That is, show that for a model fS trained on a dataset S to predict a target y(x) for each x, ES[Eout(fS)]=Ex[Bias(x)+Var(x)] given the following definitions: F(x)Eout(fS)Bias(x)Var(x)=ES[fS(x)]=Ex[(fS(x)y(x))2]=(F(x)y(x))2=ES[(fS(x)F(x))2]

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