Training data: $\mathcal {T} =\{(2,1),(3,2),(4,6),(0,0),(1,1)\}$

you already computed a predictor for the output using linear regression by least squares, where you used the first 3 samples as training samples:

$f(X) = -4.5  +  2.5X$

Approximate the generalization error using the validation set approach, i.e. on the remaining validation set.

How I started:

$\text{Error = Irreducible Error + Bias$^2$ + Variance .}$

$\text{$EGE(f, x_0) $=$σ^2_ε$ + $[E_T (f_T (x_0)) − f_{exact}(x_0)]^2$ + $E_T(f_T (x_0) − E_T (f_T (x_0)))^2$ }$

How to compute the term $E_T (f_T (x_0))$ and $E_T(f_T (x_0) − E_T (f_T (x_0)))^2$


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