An extremal type of cross$-$validation is n$-$fold cross$-$validation on a training set of size n$.$ If we want to estimate the error of k$-$NN$,$ this amounts to classifying each training point by running k$-$NN on the remaining n $1$ points, and then looking at the fraction of mistakes made. It is commonly called leave$-$one$-$out cross$-$validation $($LOOCV$).$

Consider the following simple data set of just four points: What is the LOOCV error for $1-$NN$?$ For $3-$NN$?$