NPTEL Introduction To Machine Learning IITKGP Assignment 4 Answers:– Hello students in this article we are going to share NPTEL Introduction To Machine Learning – IITKGP assignment week 2 answers. All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.
Below you can find NPTEL INTRODUCTION TO MACHINE LEARNING IIT KGP Assignment 4 Answers
| Assignment No. | Answers |
|---|---|
| Introduction To Machine Learning – IITKGP Assignment 1 | Click Here |
| Introduction To Machine Learning – IITKGP Assignment 2 | Click Here |
| Introduction To Machine Learning – IITKGP Assignment 3 | Click Here |
| Introduction To Machine Learning – IITKGP Assignment 4 | Click Here |
| Introduction To Machine Learning – IITKGP Assignment 5 | Click Here |
| Introduction To Machine Learning – IITKGP Assignment 6 | Click Here |
| Introduction To Machine Learning – IITKGP Assignment 7 | Click Here |
| Introduction To Machine Learning – IITKGP Assignment 8 | Click Here |
NPTEL Introduction To Machine Learning IITKGP Assignment 4 Answers 2022 :-
1. A man is known to speak the truth 2 out of 3 times. He throws a die and reports that the number obtained is 4. Find the probability that the number obtained is actually 4:
a. 2/3
b. 3/4
c. 5/22
d. 2/7 .
Answer:- d
2. Consider the following graphical model, mark which of the following pair of random variables are independent given no evidence?
a. a,b
b. c,d
c. e,d
d. C,e
Answer:- a
3. Two cards are drawn at random from a deck of 52 cards without replacement. What is the probability of drawing a 2 and an Ace in that order?
a. 4/51
b. 1/13
c. 4/256
d. 4/663
Answer:- d
4. Consider the following Bayesian network. The random variables given in the model are modeled as discrete variables (Rain = R, Sprinkler = S and Wet Grass = W) and the corresponding probablity values are given below.
Calculate P(S |W, R).
a. 1
b. 0.5
c. 0.22
c. 0.78
Answer:- c
Next Week Assignment Answers
5. What is the naive assumption in a Nave Bayes Classitier?
A. All the classes are independent of each other
B. All the features of a class are independent of each other
C. The most probable feature for a class is the most important feature to be considered for classification
D. All the features of a class are conditionally dependent on each other.
Answer:- b
6. A drug test (random variable 1) has 1% false positives (1.e., 1% of those not taking drugs show positive in the test). and 5% false negatives (i.e., 5% of those taking drugs test negative). Suppose that 2% of those tested are taking drugs. Determine the probability that somebody who tests positive is actually taking drugs (random variable D).
A. 0.66
B. 0.34
C. 0.50
D. 0.91
Answer:- a
7. It is given that P(A]B) = 2/3 and P(A|B) = 1/4. Compute the value of P (B|A).
A. 1/2
B. 2/3
C. 3/4
D. Not enough information.
Answer:- a
8. What is the joint probability distribution in terms of conditional probabilities?
A. P(D1) P(D2|D1)* P(S1|D1) * P($2|D1) * P(S3|D2)
B. P(D1) * P(D2) * P(S1|D1) * P($2|D1) * P($3|D1, D2)
C. P(D1) P(D2) * P(S1|D2) * P(S2|D2) * P($3|D2)
D. P(D1) * P(D2) * P(S1|D1) * P($2|D1, D2) * P($3|D2)
Answer:- d
9. Suppose P(DI) = 0.5, P(D2)=0.6, P(S1D1)=0.4 and P(S1| DI’)=0.6. Find P(S1)
A. 0.14
B. 0.36
C. 0.50
D. 0.66
Answer:- b
10. In a Bayesian network a node with only Outgoing edge(s) represents
A. a variable conditionally independent of the other variables.
B. a variable dependent on its silings.
C. a variable whose dependency is uncertain.
D. None of the above.
Answer:- a
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