In the universal bank data in this chapter, only 10 percent of the records represented customers who had taken out a personal loan (the target variable). If we were to score a new customer based upon the attributes we used in algorithm, we would be accurate in the prediction about 90 percent of the time if we always scored the individual as "not accepting a personal loan" because that indeed is what most customers have done in the past. Why not accept being correct 90 percent of the time with this very simple decision rule?