fix M->Malignant, B->Benign causing missing rendered numbers

Author: trevorcampbell

source/classification1.Rmd

@@ -1438,8 +1438,8 @@ prediction <- predict(knn_fit, new_observation)
 prediction
 ```
 
-The classifier predicts that the first observation is benign ("B"), while the second is
-malignant ("M"). Figure \@ref(fig:05-workflow-plot-show) visualizes the predictions that this 
+The classifier predicts that the first observation is benign, while the second is
+malignant. Figure \@ref(fig:05-workflow-plot-show) visualizes the predictions that this 
 trained $K$-nearest neighbor model will make on a large range of new observations.
 Although you have seen colored prediction map visualizations like this a few times now,
 we have not included the code to generate them, as it is a little bit complicated.

source/classification2.Rmd

@@ -410,8 +410,8 @@ train_prop <- cancer_train |>
 ```
 
 We can use `group_by` and `summarize` to \index{group\_by}\index{summarize} find the percentage of malignant and benign classes 
-in `cancer_train` and we see about `r round(filter(train_prop, Class == "B")$proportion, 2)*100`% of the training
-data are benign and `r round(filter(train_prop, Class == "M")$proportion, 2)*100`% 
+in `cancer_train` and we see about `r round(filter(train_prop, Class == "Benign")$proportion, 2)*100`% of the training
+data are benign and `r round(filter(train_prop, Class == "Malignant")$proportion, 2)*100`% 
 are malignant, indicating that our class proportions were roughly preserved when we split the data.
 
 ```{r 06-train-proportion}
@@ -591,7 +591,7 @@ cancer_proportions
 
 ```{r 06-proportions-2, echo = FALSE, warning = FALSE}
 cancer_propn_1 <- cancer_proportions |>
-                filter(Class == 'B') |>
+                filter(Class == 'Benign') |>
                 select(percent)
 ```