KNN to K-NN

Author: trevorcampbell

source/classification2.Rmd

@@ -1154,10 +1154,10 @@ we set the number of neighbors $K$ to 1, 7, 20, and 300.
 
 ### Evaluating on the test set
 
-Now that we have tuned the KNN classifier and set $K =$ `r best_k`,
+Now that we have tuned the K-NN classifier and set $K =$ `r best_k`,
 we are done building the model and it is time to evaluate the quality of its predictions on the held out
 test data, as we did earlier in Section \@ref(eval-performance-cls2).
-We first need to retrain the KNN classifier
+We first need to retrain the K-NN classifier
 on the entire training data set using the selected number of neighbors.
 
 ```{r 06-eval-on-test-set-after-tuning, message = FALSE, warning = FALSE}

source/regression1.Rmd

@@ -513,12 +513,12 @@ Figure \@ref(fig:07-choose-k-knn-plot). What is happening here?
 
 Figure \@ref(fig:07-howK) visualizes the effect of different settings of $K$ on the
 regression model. Each plot shows the predicted values for house sale price from
-our KNN regression model on the training data for 6 different values for $K$: 1, 3, 25, `r kmin`, 250, and 680 (almost the entire training set).
+our K-NN regression model on the training data for 6 different values for $K$: 1, 3, 25, `r kmin`, 250, and 680 (almost the entire training set).
 For each model, we predict prices for the range of possible home sizes we
 observed in the data set (here 500 to 5,000 square feet) and we plot the
 predicted prices as a blue line.
 
-```{r 07-howK, echo = FALSE, warning = FALSE, fig.height = 13, fig.width = 10,fig.cap = "Predicted values for house price (represented as a blue line) from KNN regression models for six different values for $K$."}
+```{r 07-howK, echo = FALSE, warning = FALSE, fig.height = 13, fig.width = 10,fig.cap = "Predicted values for house price (represented as a blue line) from K-NN regression models for six different values for $K$."}
 gridvals <- c(1, 3, 25, kmin, 250, 680)
 
 plots <- list()