Bayes Theorem, Naive Bayes Algorithm, KNN Algorithm MCQs
1.
Formula for Bayes theorem is _______
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Ans: A
2. 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
Ans: B
3. 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) None of the above
Ans: C
4. Naïve Bayes Algorithm is a
________ learning algorithm.
(A) Supervised
(B) Reinforcement
(C) Unsupervised
(D) None of these
Ans: A
5. Examples of Naïve Bayes Algorithm is/are
(A) Spam filtration
(B) Sentimental analysis
(C) Classifying articles
(D) All of the above
Ans: D
6. 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
Ans: A
7. 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.
Ans: A
8. 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
Ans: D
9. Identify the distance
matrix used in KNN Algorithm___________
A. Euclidean Distance.
B. Cosine Similarity.
C. Manhattan Distance.
D. All.
Ans: D
10. ______in KNN is a
parameter that refers to the number of nearest neighbors to include in the
majority of the voting process.
A. K.
B. First N.
C. Second N.
D. All of the above.
Ans: A
