Imagine that you are given the following set of training examples. each feature can take on one of three nominal values: a, b, or c. test example: f1 = a, f2 = c, f3 = b f1 f2 f3 category a c a + c a c + a a c - b c a - c c b -
1. using the bayes rules, which class has highest posterior probability given the test example?
2. plot and show how a 3-nearest-neighbor algorithm would classify the test example given above.