Assignment 5.1 (60 marks) Given the following employee data represented as a table with the dimensions (i.e., columns) Department, Status, Age and Salary and the measure Count (the number of employees that fall into the corresponding cell): Department Status Age Salary Count Sales senior 31..35 46K..50K 30 Sales junior 26..30 26K..30K 40 Sales junior 31..35 31K..35K 40 Systems junior 21..25 46K..50K 20 Systems senior 31..35 66K..70K 5 Systems junior 26..30 46K..50K 3 Systems senior 41..45 66K..70K 3 Marketing senior 36..40 46K..50K 10 Marketing junior 31..35 41K..45K 4 Secretary senior 46..50 36K..40K 4 Secretary junior 26..30 26K..30K 6 We want to classify employees with respect to their Salary, given the values for the other dimensions Department, Status, and Age, using a Nave Bayes classifier. a) Use the data as training dataset to estimate all parameters (unconditional and conditional probabilities) of the Nave Bayes classifier. Report the values of all these parameters. (30 marks, proportional to the percentage of correct answers) b) Apply your Nave Bayes classifier to predict the class of an unseen employee with Department = "Systems", Status = junior and Age = 26..30. (10 marks) c) Given a test data instance with Department = "Systems", Status = "junior and Age = 26..30, for which classes is the conditional probability (i.e., the probability for this instance to belong to one class) equal to zero? What is the reason for this test data instance to have a zero probability on those classes? Why is this phenomenon not good? (20 marks)