17944 Pages: 3
12 a) Discuss the necessity of dimensionality reduction in machine learning. (3)

b) Illustrate the idea of PCA for a two dimensional data using suitable diagrams. (6)
13 a) Let X =R? and C be the set of all possible rectangles in two dimensional plane (6)
which are axis aligned (not rotated). Show that this concept class is PAC

learnable.
b) Describe the applications of machine learning in any three different domains. (3)
PART C
Answer any two full questions, each carries 9 marks.
14 The following table consists of training data from an employee database. Fora (9)

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. Given a data tuple having the values “systems”, “31..35”, and
“4۸6-501” for the attributes department, age, and salary, respectively, what
would a Naive Bayesian classification of the status for the tuple be?

}sales പ

| ‏ععلده‎ |
systems
systems
systems
systems
marketing
marketing
secretary
marketing

15 With the following data set, generate a decision tree and predict the class label (9)
for a data point with values .

Gender | Car Travel cost Income Transport mode
Ow nership level
Male |0 | Cheap Low [Bs | |
Male |1 | Cheap Medium | 8०5 |
0 |
1]

Female 2 Expensive High Car
Female |1 | Cheap Medium | Train |
Male 0 | Standard Medium | Train |

Female |1 | Standard Medium | Train |

16 a) Point out the benefits of pruning in decision tree induction. Explain different (5)

Page 2of 3