Machine Learning: MCQs Set – 03

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?

  • (A) 0.0368
  • (B) 0.0238
  • (C) 0.0526
  • (D) 0.0473



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