Download DBATU B.Tech 2019 Oct-Nov CSE 1st Sem Machine Learning Question Paper

Download DBATU (Dr. Babasaheb Ambedkar Technological University) B Tech 2019 Oct-Nov (Bachelor of Technology) CSE 1st Sem Machine Learning Question Paper

DR'BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY, LONERE
? . Mid Semester Examination ? September 2019
Course: T.Y. B.Tech (CSE)
Subject Name: Machine Learning
Max Marks: 20 Date:- 25/09/2019
Instructions to the Students:
1. Check that you have received a correct Question paper.
2. Assume suitable data if necessary and mention it clearly
3. Draw NEAT labeled diagrams wherever necessary
Q.1. Attempt any Si; Questions
De?ne Hypothesis Space
?What is information gain?
What is difference 'betWeen supervised and unsupervised learning
? Whatdis iiro?s validafion? ' , ? ' " "
Write Bays theorem.
De?ne confusion matrix with suitable example.
What is SVM?
1
H-P?P?PPNT?
. Q. 2. Attempt any Twoofthe following ,
1. Explain problem of over?tting in ML?
2. Differentiate Linear Vs Logistic regression. Give?suitable example of each.
'Sem: I
Subject Code: BTCOC503
Duration:- 1 Hr.
(1*6=6Marks)
(2*3 =6 Marks)
3. Apply KNN for following dataset and predict class of test example (A1 = 3, A2 = 7). Assume K=3
A1 ? Class
True
- True
' False
True
' False
True
ONM??w??
Aww-b-b-b-xl
.2:
Q.3. Attempt any 9L9. of the following
(1* 8= 8Marks)
1. Explain Bayesian Leamingand Na?ive Bayeeg?assi?er. Solve the, following example, ?
A patient takes a'lab test and the result comesbatk positive. The test returns a correct positive
result in only 98% of the cases in which the disease is actually present, and a correct negative
result in only 97% of the cases in which the disease?is not present. Furthermore, .008 of the entire
population have this cancer. Find P(cancerl +) and P(?cancerl +)

2. Illustrate the operation of ID3 for the following training example. Consider Information Gaifl as ,
Attribute Selection Measure.
PlayTennis: training examples
. Day Outlook Temperature Humidity Wind PlayTeImis
? D1 Sunny Hot ? High Weak No
D2 Sunny Hot High Strong No
D3 Overcast I Hot High Weak, Yes
D4 Rain Mild ? High ? Weak ' Yes
D5 Rain Cool Normal Weak Yes
D6 Rain, Cool Normal Strong 1 No
D7 Overcast C001 Nomal ? Strong Yes
D8 Sunny ? Mild V High ' Weak No
D9 , Sunny (20011 Normal Weak Yes
1310? Rain Mild Noam] , Weak Yes
Dll Sunny . Mild Normal Strong Yea
D12 Overcast 1 ? Mild High Strong Yes} 1
D13 Qvercast ? 'Hot Normal Weak Yes.
D14 Rain Mild High Stfong No