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.

Let status be the class label attribute.
a. 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)?
b. Use your algorithm to construct a decision tree from the given data.
c. Given a data tuple having the values "systems," " $26\dots 30$," and "46-50K" for the attributes department, age, and salary, respectively, what would a naïve Bayesian classification of the status for the tuple be?

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department status age sales sales sales salary count senior 31...35 46K...50K 30 junior 26...30 26K...30K 40 junior 31...35 31K...35K 40 junior 21...25 46K...50K 20 senior 31...35 66K...70K 5 3 systems systems systems junior 26...30 46K...50K 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