Consider using the 3-nearest-neighbor regression to estimate the true curve. Figure 1.2 presents the true curve f and the resulting KNN estimate f. 0.2- sample Response y 0.1 - 0.0 10 20 30 Predictor x Figure 1.2: The true curve f (black line) and the estimated curve f as a result of using 3-nearest-neighbor regression. [ 2 PTS ] Do you think the value of the tuning parameter (K = 3) is the optimal value for estimating the true regression curve? Justify your answer (you need to refer to the bias and variance of f). [ 2 PTS ] Given the predictor value X0. explain how the corresponding response value Yo is obtained using the 3- nearest-neighbor regression.[ 4 PTS ] Suppose that we would like to study the behavior of the training MSE and the test MSE over several values of the tuning parameter K of the KNN regression, which is feasible because we are working with a simulation study. Therefore, suppose that we have M training datasets {(x,, y;) ]training data(1) to ((x , y()]training data(M) which we use to obtain the curve estimates f(), ..., f() using the KNN regression. Then we will use these estimates on other M test datasets {(x;, y; )}test data(1) to {(x;, y; )}test data(0) to predict the response at the given predictor values. With this setting, for each K value, we are able to draw the training MSE averaged over all training datasets and the test MSE averaged over all test datasets. Briefly sketch the shape of the training MSE and test MSE. training MSE test MSE 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Tuning Parameter K Tuning Parameter K