I understand how the bias-variance decomposition was done, but I'm not sure what the author means when he says "Unless the nearest neighbor is at 0, $\hat{y}_{o}$ will be smaller than f(0) in this example, and so the average estimate will be biased downward." I understand that since any other x point will return a $\hat{y}_{o}$ value lower than f(0) that the average estimate will always be less, but how was that conclusion come about from the bias-variance decomposition?
I understand how the bias-variance decomposition was done, but I'm not sure what the author means when he says "Unless the nearest neighbor is at 0, $\hat{y}_{o}$ will be smaller than f(0) in this example, and so the average estimate will be biased downward." I understand that since any other x point will return a $\hat{y}_{o}$ value lower than f(0), but how was that conclusion come about from the bias-variance decomposition?
I understand how the bias-variance decomposition was done, but I'm not sure what the author means when he says "Unless the nearest neighbor is at 0, $\hat{y}_{o}$ will be smaller than f(0) in this example, and so the average estimate will be biased downward." I understand that since any other x point will return a $\hat{y}_{o}$ value lower than f(0) that the average estimate will always be less, but how was that conclusion come about from the bias-variance decomposition?
I understand how the bias-variance decomposition was done, but I'm not sure what the author means when he says "Unless the nearest neighbor is at 0, $\hat{y}_{o}$ will be smaller than f(0) in this example, and so the average estimate will be biased downward." I understand that since any other x point will return a $\hat{y}_{o}$ value lower than f(0), but how was that conclusion come about from the bias-variance decomposition?
