 purposefully
egregious—we are going to (in the case of the random subsampling) just
fix the train set sample size (<code>n1</code>) to 80 and the test set
sample size (<code>n2</code>) to 20, and assume (using the same data)
for the <span class="math inline">\(k\)</span>-fold cross-validation
correction that the same numbers were obtained on such a method. Again,
the values are not important here, it is the interface for performing
corrections that is the focus.</p>
<p>In the case of repeated <span class