**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 |
---|---|

I Assignment 1ntroduction To Machine Learning – IITKGP | Click Here |

Introduction To Machine Learning – IITKGP Assignment 2 | Click Here |

I Assignment 3ntroduction To Machine Learning – IITKGP | Click Here |

I Assignment 4ntroduction To Machine Learning – IITKGP | Click Here |

Introduction To Machine Learning – IITKGP Assignment 5 | Click Here |

IAssignment 6ntroduction To Machine Learning – IITKGP | Click Here |

Introduction To Machine Learning – IITKGPAssignment 7 | Click Here |

Introduction To Machine Learning – IITKGPAssignment 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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