In the k-Nearest Neighbors method for classification, you set k = 1 to 20 and XLMiner reported the best k = 10. The table below gives the 10 nearest neighbors in the training set for a new observation and the class membership for each neighbor. Neighbor 1 2 3 4 5 6 7 8 9 10 Class 1 0 0 1 0 1 0 1 0 0 Answer the following questions based on the above information. Question 53 (4 points) In the data partitioning procedure, if a rare event is involved in classifying a categorical outcome, then should be used for the training set. standard partitioning overfitting standard sampling oversampling Question 54 (4 points) The best k achieves the smallest on the validation set. class 0 error rate overall error rate root mean squared error class 1 error rate Question 55 (4 points) If the cutoff value is 0.50, the new observation should be classified as Class 1 Cannot be determined; more information is needed. Class o Cannot be determined; more information is needed. Class o Question 56(4 points) For the new observation, the probability of being in Class 1 A Use 2-decimal accuracy for the final . answer, e.g., 0.12, when necessary