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

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NPTEL Introduction To Machine Learning – IITKGP Week 4 Assignment Answer 2023

NPTEL Introduction To Machine Learning - IITKGP Assignment Answer 2023

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:- 
NPTEL Introduction To Machine Learning - IITKGP Week 4 Assignment Answers 2023

Q6.

NPTEL Introduction To Machine Learning - IITKGP Week 4 Assignment Answers 2023
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:- 
NPTEL Introduction To Machine Learning - IITKGP Week 4 Assignment Answers 2023

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.

NPTEL Introduction To Machine Learning - IITKGP Week 4 Assignment Answers 2023
Answer:- 

Q12.

NPTEL Introduction To Machine Learning - IITKGP Week 4 Assignment Answers 2023
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:- 

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