uniformize K-NN

Author: trevorcampbell

source/classification2.Rmd

@@ -1249,7 +1249,7 @@ does this by first finding the $K$ points in the training data nearest
 to the new observation, and then returning the majority class vote from those
 training observations. We can tune and evaluate a classifier by splitting the data randomly into a
 training and test data set. The training set is used to build the classifier,
-and we can tune the classifier (e.g., select the number of neighbors in $K$-NN)
+and we can tune the classifier (e.g., select the number of neighbors in K-NN)
 by maximizing estimated accuracy via cross-validation. After we have tuned the
 model we can use the test set to estimate its accuracy.
 The overall process is summarized in Figure \@ref(fig:06-overview).