Question

(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. 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 systems junior 26. 30 46K... 50K systems senior 41. 45 66K... 70K marketing senior 36. . . 40 46K... 50K marketing junior 31. . . 35 41K... 45K secretary senior 46... 5036K... 40K 4 secretary junior 26... 30 26K... 30K 6 COCOT 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?

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.

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

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 naıve Bayesian classification of the status for the tuple be?

Answer 1

@ Answer: - Given Table consists of training data from an employee database. The data have been generalized. Now = 6 How would you modify the basic decision tree algorithm to take into coneideration the count of each generalized data tuple (ie, of each row entry) :- NOW, + The basic decision tree algorithim should be modified as follows: - * Here, to take into consideration the count of each generalized data tuple. → ie., The Count of each teple must be integrated into the calculation of the attribute Selection measure such as information gain And take the count into confideration to determine the most common class among the tuplex.

→ → ore your algorithm to construct a decision tree from the given date : - To construct a decision tree from the given data i.e. The resulting tree it Salary - 266 ... 306: junior junior - 30K ... 35K = 36 ... 40K : Senior : 416 -. 45k : Junior =46K - SOK --- (department = = Secretary ; Junior - Sales ; Senior · Systems; junior : marketing; senior). = = 6GK ... Fok; senior Given a data tuple having the values "Systems","36... 30" and 46-sok for the attributes department, age, and salary respectively

To find what would a naive Bayesian classification of the states for the tople be:- The status for the tuple be, Ple/senior) =0; PO) junior) = 0.018; Hence, A Naive Bayesran classification predicts ! junpor!