(2) The following table consists of training data from an employee database.

(2) The following table consists of training data from an employee database. The data have been generalized. For example, "31... 35" for age represents the age range of 31 to 35. For a given row entry, count represents the number of data tuples having the values for department, status, age, and salary given in that row. salary department status sales sales sales . age 31... 35 senior junior 26. 30 35 junior 31. junior 21. 25 systems 66K... 70K systems senior 31... 35 systems junior 26. 30 46K... 50K systems senior 41. 45 66K... 70K 46K... 50K 10 41K... 45K 4 marketing senior 36. 40 marketing junior 31. 35 secretary senior 46... 50 secretary junior 26... 30 4 36K... 40K 26K... 30K 6 count 30 40 40 46K... 50K 26K... 30K 31K... 35K 46K... 50K 20 5 3 3 Let status be the class label attribute. i. [5 points] How would you modify the basic decision tree algorithm to take into consideration the count of each generalized data tuple (i.e., of each row entry)? ii. [10 points] Use your algorithm to construct a decision tree from the given data. iii. [5 points] Given a data tuple having the values "systems", "26... 30", and "46-50K" for the attributes department, age, and salary, respectively, what would a naive Bayesian classification of the status for the tuple be?