Consider the problem of predicting whether a person is a good credit risk given the following attributes: hair color, income, weight, time in the current job, marital status, height, age, and birth month. If you had to choose between Ripper and a k-nearest neighbor classifier, which would you prefer? Indicate your choice of classifier and briefly explain why the other one may not work so well?