please help answer this 4 short answer.

Chapter 9: Predictive Data Mining, Part 1 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 XL Miner 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 1 2 3 4 5 6 7 8 9 Class 1 0 1 1 0 1 0 1 0 Answer the following questions based on the above information. Question 9 (3 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 10 (3 points) For the new observation, the probability of being in Class 1 equals 0.56 0.44 0.40 0.50 Question 11 (3 points) If the cutoff value is 0.50, the new observation should be classified as Class 1 Cannot tell; more information is needed. Class o Question 12 (3 points) If the cutoff value is 0.60, the new observation should be classified as Class 1 Class o Cannot tell; more information is needed