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5.5.2 More than two explanatory v as a 3-dimensional scatter with lines from the new observation to its five nearest neighbors.

- +

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.

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- +

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 db81407d4..7cc633dc2 100644 --- a/pull574/regression2.html +++ b/pull574/regression2.html @@ -934,7 +934,7 @@

8.6 Multivariable linear regressi shown in Figure 8.7.

- +

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.

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