# NPTEL Introduction To Machine Learning – IITKGP Assignment 4 Answers 2023

NPTEL Introduction To Machine Learning – IITKGP Assignment 4 Answers: In this post, We have provided answers of NPTEL Introduction to Machine Learning – IITKGP Assignment 4 Week 4. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

## About Introduction To Machine Learning – IITKGP

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms. We will discuss hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

## NPTEL Introduction To Machine Learning – IITKGP Week 4 Assignment Answers 2023

Questions 1-4 with the data provided below:
A spam filtering system has a probability of 0.95 to classify correctly a mail as spam and 0.10
probability of giving false positives. It is estimated that 0.5% of the mails are actual spam
mails.
Q1) Suppose that the system is now given a new mail to be classified as spam/ not-spam, what is the probability that the mail will be classified as spam?
a. 0.89575
b. 0.10425
c. 0.00475
d. 0.09950

`Answer:- b`

Q2. Find the probability that, given a mail classified as spam by the system, the mail actually being spam.
a. 0.04556
b. 0.95444
c. 0.00475
d. 0.99525

`Answer:- For Answer Click Here`

Q3. Given that a mail is classified as not spam, the probability of the mail actually being not spam
a. 0.10425
b. 0.89575
c. 0.003
d. 0.997

`Answer:- `

Q4. Find the probability that the mail is misclassified:
a. 0.90025
b. 0.09975
c. 0.8955
d. 0.1045

`Answer:- For Answer Click Here`

Q5. What is the naive assumption in a Naive Bayes Classifier?
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:- `

Q6.

`Answer:- For Answer Click Here`

Q7. Find P (K=0| a=1, b=1).
a. 1/3
b. 2/3
C. 1/9
d. 8/9

`Answer:- `

Q8. What is the joint probability distribution in terms of conditional probabilities?
a. P(D1) * P(D2\D1) * P(S1|D1) * P(S2]D1) * P(S3|D2)
b. P(D1) * P(D2) * P(S1\D1) * P(S2]D1) * P(S3|D1, D2)
c. P(D1) * P(D2) * P(S1 D2) * P(S2]D2) * P(S3|D2)
d. P(D1) * P(D2) * P(S1|D1) * P(S2|D1, D2) * P(S3|D2)

`Answer:- For Answer Click Here`

Q9. Suppose P(D1) = 0.4, P(D2) = 0.7 , P(SID1)=0.3 and P(S1| D1′)= 0.6. Find P(S1)
a. 0.12
b. 0.48
c. 0.36
d. 0.60

`Answer:- `

Q10. What is the Markov blanket of variable, S3
a. D1
b. D2
c. D1 and D2
d. None

`Answer:- For Answer Click Here`

Q11.

`Answer:- `

Q12.

`Answer:- `

Questions 13-14 with the data given below:
In an oral exam you have to solve exactly one problem, which might be one of three types, A. B, or C, which will come up with probabilities 30%, 20%, and 50%, respectively. During your preparation you have solved 9 of 10 problems of type A. 2 of 10 problems of type B, and 6 of 10 problems of type C.

13) What is the probability that you will solve the problem of the exam?
а. 0.61
b. 0.39
c. 0.50
d. 0.20

`Answer:- For Answer Click Here`

Q14. Given you have solved the problem, what is the probability that it was of type A?
а. 0.35
b. 0.50
c. 0.56
d. 0.44

`Answer:- `

Q15. Naive Bayes is a popular classification algorithm in machine learning. Which of the
following statements is/are true about Naive Bayes?
a. Naive Bayes assumes that all features are independent of each other, given the class.
b. It is particularly well-suited for text classification tasks, like spam detection.
c. Naive Bayes can handle missing values in the dataset without any special treatment.
d. It is a complex algorithm that requires a large amount of training data.

`Answer:- `

## 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`

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`

NPTEL Introduction To Machine Learning – IITKGP Assignment 4 Answer: In this post, We have provided answers of NPTEL Introduction to Machine Learning – IITKGP Assignment 4 Week 4. We provided answers here only for reference. Plz, do your assignment at your own knowledge.