# How do you use in sample error estimator in regression?

In their book The Elements of Statistical Learning, Friedman and coll. talk about the in sample error $Err_{in}$ (p. 229): $$Err_{in} = \frac{1}{n}\sum_{i=1}^n\mathbb{E}\left[\left(Y_i-\hat{f}\left( x_i\right)\right)^2\right]$$ there exists an unbiased estimator of this error in case of linear regression which is $$Err_{in}=\text{training_error} + 2\frac{d}{n}\hat{\sigma_{\epsilon}}^2$$ with $d$ the dimension, $n$ the number of observations and $\hat{\sigma_{\epsilon}}^2$ the unbiased estimator of the residuals.

My question is how do you use this criterion ?

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