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 $E_T(f_T (x_0) − E_T (f_T (x_0)))^2$
