Q21: Which of the following statements regarding the prediction are correct??
(A) The output attribute must be categorical
(B) The output attribute must be numerical
(C) The resultant model is designed to determine future outcomes
(D) The resultant model is designed to classify current behavior
Q22: Data used to build a data mining model
(A) validation data
(B) training data
(C) test data
(D) hidden data
Q23: The association between the number of years an employee has been with a firm and the person’s pay is 0.75. What can be stated regarding employee pay and years of service?
(A) There is no relationship between salary and years worked
(B) Individuals that have worked for the company the longest have higher salaries
(C) Individuals that have worked for the company the longest have lower salaries
(D) The majority of employees have been with the company a long time
Q24: Which of the following points would Bayesians and frequentists disagree on?
(A) The use of a non-Gaussian noise model in probabilistic regression
(B) The use of probabilistic modelling for regression
(C) The use of prior distributions on the parameters in a probabilistic model
(D) The use of class priors in Gaussian Discriminant Analysis
Q25: Introducing a non-essential variable into a linear regression model may result in : (1).Increase in R-square, (2).Decrease in R-square
(A) Only 1 is correct
(B) Only 2 is correct
(C) Either 1 or 2
(D) None of these
Q26: For a classification task, instead of random weight initializations in a neural network, we set all the weights to zero. Which of the following statements is true?
(A) There will not be any problem and the neural network will train properly
(B) The neural network will train but all the neurons will end up recognizing the same thing
(C) The neural network will not train as there is no net gradient change
(D) None of these
Q27: The kernel trick
(A) can be applied to every classification algorithm
(B) is commonly used for dimensionality reduction
(C) changes ridge regression so we solve a d × d linear system instead of an n × n system, given n sample points with d feature
(D) exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points
Q28: The line described by the linear regression equation (OLS) attempts to ____?
(A) Pass through as many points as possible.
(B) Pass through as few points as possible
(C) Minimize the number of points it touches
(D) Minimize the squared distance from the points
Q29: Ritesh has two children, one of them is a girl. What is the likelihood that the second kid is likewise a girl? You can suppose that the globe has an equal number of males and females.
(A) 0.5
(B) 0.25
(C) 0.333
(D) 0.75
Q30: A roulette wheel has 38 slots, 18 are red, 18 are black, and 2 are green. You play five games and always bet on red. What is the probability that you win all the 5 games?
