added note about 3d plots to regression 3d plots

Author: trevorcampbell

regression1.Rmd

@@ -750,10 +750,10 @@ This time when we performed KNN regression on the same data set, but also
 included number of bedrooms as a predictor, we obtained a RMSPE test error 
 of `r format(round(knn_mult_mets |> pull(.estimate)), big.mark=",", nsmall=0, scientific=FALSE)`.
 Figure \@ref(fig:07-knn-mult-viz) visualizes the model's predictions overlaid on top of the data. This 
-time the predictions are a surface in 3-D space, instead of a line in 2-D space, as we have 2
+time the predictions are a surface in 3D space, instead of a line in 2D space, as we have 2
 predictors instead of 1.  
 
-```{r 07-knn-mult-viz, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "KNN regression model’s predictions represented as a surface in 3-D space overlaid on top of the data using three predictors (price, house size, and the number of bedrooms).", out.width="80%"}
+```{r 07-knn-mult-viz, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "KNN regression model’s predictions represented as a surface in 3D space overlaid on top of the data using three predictors (price, house size, and the number of bedrooms). Note that in general we recommend against using 3D visualizations; here we use a 3D visualization only to illustrate what the surface of predictions looks like for learning purposes.", out.width="80%"}
 xvals <- seq(from = min(sacramento_train$sqft), 
              to = max(sacramento_train$sqft), 
              length = 50)

regression2.Rmd

@@ -453,7 +453,7 @@ is `r format(round(lm_mult_test_results %>% filter(.metric == 'rmse') %>% pull(.
 In the case of two predictors, we can plot the predictions made by our linear regression creates a *plane* of best fit, as
 shown in Figure \@ref(fig:08-3DlinReg).
 
-```{r 08-3DlinReg, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "Linear regression plane of best fit overlaid on top of the data (using price, house size, and number of bedrooms as predictors).", out.width="80%"}
+```{r 08-3DlinReg, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "Linear regression plane of best fit overlaid on top of the data (using price, house size, and number of bedrooms as predictors). Note that in general we recommend against using 3D visualizations; here we use a 3D visualization only to illustrate what the regression plane looks like for learning purposes.", out.width="80%"}
 xvals <- seq(from = min(sacramento_train$sqft), 
              to = max(sacramento_train$sqft), 
              length = 50)