Test error ($k = 3$): $0.02403344$. Higher than the training error but slightly lower than the validation error. According to us it is a pretty good model considering that it correct $\approx 98 \%$ of times.
Test error ($k = 3$): $E_{\text{mis,test}} = 0.02403344$. Higher than the training error but slightly lower than the validation error. According to us it is a pretty good model considering that it correct $\approx 98 \%$ of times.
5. Optimal $k = 6$, when the average cross-entropy loss is the lowest. Average cross-entropy loss takes probabilities in the prediction into account which is a better represntation of a model with multionmial distribution. An important aspect is that we can determina how wrong a classification is, not just wether it is wrong or not.
