I know that knn.cv(train=predictors.training, cl=classes.training, k=3, prob=TRUE) works but by doing so I fix the tuning paramer k to be 3. Isn't cross validation a technique to choose the optimal tuning parameter trying a range of different values for the tuning parameter?
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