You used the k-Nearest Neighbors method for classification (e.g., whether a customer should be classified as Default or No Default on a loan). You set k = 1 to 20 and XLMiner reported the best k = 9. The table below gives the 9 nearest neighbors in the training set for a new observation and the class membership for each neighbor. Neighbor Class 67 8 9 0 1 0 12345 1011 01 Answer the following questions based on the above information. Question 17 (4 points) The best k achieves the smallest on the validation set. overall error rate root mean squared error class 1 error rate class 0 error rate Question 18 (4 points) For the new observation, the probability of being in Class 1 equals 0.40 0.50 0.44 0.56 Question 19 (4 points) If the cutoff value is 0.50, the new observation should be classified as cannot tell; more information is needed. Class 1 Class 0 Question 20 (4 points) If the cutoff value is 0.60, the new observation should be classified as Class 0 Class 1 cannot tell; more information is needed.