Machine Learning Unit - 3 mcq

1. _____ terms are required for building a bayes model.
(A) 1
(B) 2
(C) 3
(D) 4

Correct option is C

2. Which of the following is the consequence between a node and its predecessors while creating bayesian network?
(A) Conditionally independent
(B) Functionally dependent
(C) Both Conditionally dependant & Dependant
(D) Dependent

Correct option is A

3. Why it is needed to make probabilistic systems feasible in the world?
(A) Feasibility
(B) Reliability
(C) Crucial robustness
(D) None of the above

Correct option is C

4. Bayes rule can be used for:-
(A) Solving queries
(B) Increasing complexity
(C) Answering probabilistic query
(D) Decreasing complexity

Correct option is C

5. _____ provides way and means of weighing up the desirability of goals and the likelihood of achieving them.
(A) Utility theory
(B) Decision theory
(C) Bayesian networks
(D) Probability theory

Correct option is A

6. Which of the following provided by the Bayesian Network?
(A) Complete description of the problem
(B) Partial description of the domain
(C) Complete description of the domain
(D) All of the above

Correct option is C

7. Probability provides a way of summarizing the ______ that comes from our laziness and ignorances.
(A) Belief
(B) Uncertaintity
(C) Joint probability distributions
(D) Randomness

Correct option is B

8. The entries in the full joint probability distribution can be calculated as
(A) Using variables
(B) Both Using variables & information
(C) Using information
(D) All of the above

Correct option is C

9. Causal chain (For example, Smoking cause cancer) gives rise to:-
(A) Conditionally Independence
(B) Conditionally Dependence
(C) Both
(D) None of the above

Correct option is A

10. The bayesian network can be used to answer any query by using:-
(A) Full distribution
(B) Joint distribution
(C) Partial distribution
(D) All of the above

Correct option is B

11. Bayesian networks allow compact specification of:-
(A) Joint probability distributions
(B) Belief
(C) Propositional logic statements
(D) All of the above

Correct option is A

12. The compactness of the bayesian network can be described by
(A) Fully structured
(B) Locally structured
(C) Partially structured
(D) All of the above

Correct option is B

13. The Expectation Maximization Algorithm has been used to identify conserved domains in unaligned proteins only. State True or False.
(A) True
(B) False

Correct option is B

14. Which of the following is correct about the Naive Bayes?
(A) Assumes that all the features in a dataset are independent
(B) Assumes that all the features in a dataset are equally important
(C) Both
(D) All of the above

Correct option is C

15. Which of the following is false regarding EM Algorithm?
(A) The alignment provides an estimate of the base or amino acid composition of each column in the site
(B) The column-by-column composition of the site already available is used to estimate the probability of finding the site at any position in each of the sequences
(C) The row-by-column composition of the site already available is used to estimate the probability
(D) None of the above

Correct option is C

16. Naïve Bayes Algorithm is a ________ learning algorithm.
(A) Supervised
(B) Reinforcement
(C) Unsupervised
(D) None of these

Correct option is A

17. EM algorithm includes two repeated steps, here the step 2 is ______.
(A) The normalization
(B) The maximization step
(C) The minimization step
(D) None of the above

Correct option is C

18. Examples of Naïve Bayes Algorithm is/are
(A) Spam filtration
(B) Sentimental analysis
(C) Classifying articles
(D) All of the above

Correct option is D

19. In the intermediate steps of "EM Algorithm", the number of each base in each column is determined and then converted to fractions.
(A) True
(B) False

Correct option is A

20. Naïve Bayes algorithm is based on _______ and used for solving classification problems.
(A) Bayes Theorem
(B) Candidate elimination algorithm
(C) EM algorithm
(D) None of the above

Correct option is A

21. Types of Naïve Bayes Model:
(A) Gaussian
(B) Multinomial
(C) Bernoulli
(D) All of the above

Correct option is D

22. Disadvantages of Naïve Bayes Classifier:
(A) Naive Bayes assumes that all features are independent or unrelated, so it cannot learn the relationship between features.
(B) It performs well in Multi-class predictions as compared to the other Algorithms.
(C) Naïve Bayes is one of the fast and easy ML algorithms to predict a class of datasets.
(D) It is the most popular choice for text classification problems.

Correct option is A

23. The benefit of Naïve Bayes:-
(A) Naïve Bayes is one of the fast and easy ML algorithms to predict a class of datasets.
(B) It is the most popular choice for text classification problems.
(C) It can be used for Binary as well as Multi-class Classifications.
(D) All of the above

Correct option is D

24. In which of the following types of sampling the information is carried out under the opinion of an expert?
(A) Convenience sampling
(B) Judgement sampling
(C) Quota sampling
(D) Purposive sampling

Correct option is B

25. Full form of MDL.
(A) Minimum Description Length
(B) Maximum Description Length
(C) Minimum Domain Length
(D) None of these

Correct option is A