Figure 5.8: 3D scatter plot of the standardized symmetry, concavity, and perimeter variables. Note that in general we recommend against using 3D visualizations; here we show the data in 3D only to illustrate what higher dimensions and nearest neighbors look like, for learning purposes.
diff --git a/pull574/regression1.html b/pull574/regression1.html index 5a1d14bb2..8663b0755 100644 --- a/pull574/regression1.html +++ b/pull574/regression1.html @@ -1239,7 +1239,7 @@Figure 7.10: K-NN 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.
diff --git a/pull574/regression2.html b/pull574/regression2.html index e892df5e0..9b6f824f6 100644 --- a/pull574/regression2.html +++ b/pull574/regression2.html @@ -934,7 +934,7 @@Figure 8.7: 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.