The residual deviance of a Gaussian GLM is its <residual sum of squares>, $154.30$. Thus the empirical training error for squared-error loss is
$$
\frac1{100}\sum_{i,j}(Y_{ij}-\widehat Y_{ij})^2
=\frac{154.30}{100}=1.543.
$$
If training error is defined as the unnormalized total loss, its value is $154.30$.

Solution

Past exam of the mathematics course of the University of Cambridge / 2025 / iii / Paper 218 / 5 / c / Solution

 Solution

ID: past-exam-of-the-mathematics-course-of-the-university-of-cambridge/2025/iii/paper-218/5/c/solution