1-Consider the data in the following table:

TID

Home Owner

Marital Status

Annual Income

Defaulted Borrower

1

Yes

Single

[120 - < 150K]

No

2

No

Married

[90 - < 120K]

No

3

No

Single

[60 - < 90K]

No

4

Yes

Married

[120 - < 150K]

No

5

No

Divorced

[90 - < 120K]

Yes

6

No

Married

[60 - < 90K]

No

7

Yes

Divorced

[120 - < 150K]

No

8

No

Single

[90 - < 120K]

Yes

9

No

Married

[60 - < 90K]

No

10

No

Single

[90 - < 120K]

Yes

Let Defaulted Borrower be the class label attribute.

a)Given a data tuple X = (Homeowner= No, Marital Status= Married, Income= $120K). What would a naive Bayesian classification of the Defaulted Borrower for the tuple be?

2-Consider the training example in the following table for a binary classification problem.

Customer ID

Gender

Car Type

Shirt Size

Class

1

M

Family

S

C0

2

M

Sports

M

C0

3

M

Sports

M

C0

4

M

Sports

L

C0

5

M

Sports

XL

C0

6

M

Sports

XL

C0

7

F

Sports

S

C0

8

F

Sports

S

C0

9

F

Sports

M

C0

10

F

Luxury

L

C0

11

M

Family

L

C1

12

M

Family

XL

C1

13

M

Family

M

C1

14

M

Luxury

XL

C1

15

F

Luxury

S

C1

16

F

Luxury

S

C1

17

F

Luxury

M

C1

18

F

Luxury

M

C1

19

F

Luxury

M

C1

20

F

Luxury

L

C1

a)Find the gain for Gender, Car Type, and Shirt Size.

b)Which attribute will be selected as the splitting attribute?