Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. Give the correct Answer for following statements.1. It is important to perform feature normalization before using the Gaussian kernel.2. The maximum value of the Gaussian kernel is 1.
a. 1 is true, 2 is false
b. 1 is false, 2 is true
c. 1 is true, 2 is true
d. 1 is false, 2 is false
Q. Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?
a. negative log-likelihood
b. log-liklihood
c. cross entropy
d. residual sum of square
Q. Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that Select one:
a. y is false when x is known to be false.
b. y is true when x is known to be true.
c. x is true when y is known to be true
d. x is false when y is known to be false.
Q. Which of the following statements about Naive Bayes is incorrect?
a. attributes are equally important.
b. attributes are statistically dependent of one another given the class value.
c. attributes are statistically independent of one another given the class value.
d. attributes can be nominal or numeric
Q. How the entries in the full joint probability distribution can be calculated?
a. using variables
b. using information
c. both using variables & information
d. none of the mentioned
Q. The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?
a. the attributes are not linearly related.
b. as the value of one attribute increases the value of the second attribute also increases
c. as the value of one attribute decreases the value of the second attribute increases
d. the attributes show a linear relationship
Q. 8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations:C1: {(2,2), (4,4), (6,6)}C2: {(0,4), (4,0),(2,5)}C3: {(5,5), (9,9)}What will be the cluster centroids if you want to proceed for second iteration?
a. c1: (4,4), c2: (2,2), c3: (7,7)
b. c1: (6,6), c2: (4,4), c3: (9,9)
c. c1: (2,2), c2: (0,0), c3: (5,5)
d. c1: (4,4), c2: (3,3), c3: (7,7)
Q. In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"?
a. p(x/c)
b. p(c/x)
c. p(c)
d. p(x)
Q. Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance2. Generalized models3. Better interpretability
a. 1 and 3
b. 2 and 3
c. 1, 2 and 3
d. 1 and 2
Q. What is back propagation?
a. it is another name given to the curvy function in the perceptron
b. it is the transmission of error back through the network to adjust the inputs
c. it is the transmission of error back through the network to allow weights to be adjusted so that the network can learn
d. none of the mentioned
Q. Which of the following is an application of NN (Neural Network)?
a. sales forecasting
b. data validation
c. risk management
d. all of the mentioned
Q. Neural Networks are complex ______________ with many parameters.
a. linear functions
b. nonlinear functions
c. discrete functions
d. exponential functions