Suppose that you are working for a phone company that wants to predict churn. Churn is when customers cancel their subscriptions. Let x designate a customer, let actual be a binary random variable meaning that the customer actually churns, and let flag be a binary random variable meaning that a classifier predicts that the customer will churn. Note that P(actual = 1) is the churn base rate, that is the fraction of customers that really do churn. Similarly, P(flag = 1) is the fraction of customers that are predicted to churn. We know that the churn base rate is 5%. Define a = P(actual = 1|flag = 1) and b = P(actual = 0|flag = 0). You have hired Dr. Lin as a data mining consultant. He claims that he can train a classifier that will achieve a = 0.8 and b = 0.9