Machine Learning: MCQs Set – 10

Machine Learning: MCQs Set – 10

Q91: Adding more basis functions in a linear model… (pick the most probably option)

  • (A) Decreases model bias
  • (B) Decreases estimation bias
  • (C) Decreases variance
  • (D) Doesn’t affect bias and variance

Q92: Regarding bias and variance, which of the follwing statements are true?

  • (A) Models which overfit have a high bias and underfit have a high variance
  • (B) Models which overfit have a high bias and underfit have a low variance
  • (C) Models which overfit have a low bias and underfit have a high variance
  • (D) Models which overfit have a low bias and underfit have a low variance

Q93: This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.

  • (A) agglomerative clustering
  • (B) expectation maximization
  • (C) conceptual clustering
  • (D) K-Means clustering

Q94: Different learning methods does not include?

  • (A) Memorization
  • (B) Analogy
  • (C) Deduction
  • (D) Introduction

Q95: In the k-Means Algorithm, which of the following alternatives can be utilized to get global minima? (1). Experiment with different centroid initialization algorithms (2). Change the number of iterations (3). Determine the appropriate number of clusters.

  • (A) 2 and 3
  • (B) 1 and 3
  • (C) 1 and 2
  • (D) All of above

Q96: Knowing the weight and bias of each neuron in a neural network is the most essential stage. You can estimate any function if you can acquire the proper value of weight and bias for each neuron. What is the best method to go about this?

  • (A) Assign random values and pray to God they are correct
  • (B) Search every possible combination of weights and biases till you get the best value
  • (C) Iteratively check that after assigning a value how far you are from the best values, and slightly change the assigned values values to make them better
  • (D) None of these

Q97: Let’s say your model is overfitting. Which of the following is NOT a suitable method for attempting to decrease overfitting?

  • (A) Increase the amount of training data.
  • (B) Improve the optimization algorithm being used for error minimization
  • (C) Decrease the model complexity.
  • (D)Reduce the noise in the training data.

Q98: In the kernelized perceptron algorithm with learning rate  = 1, the coefficient ai corresponding to a training example xi represents the weight for K(xi , x). Suppose we have a two-class classification problem with yi ∈ {1, −1}. If yi = 1, which of the following can be true for ai?

  • (A) ai = −1
  • (B) ai = 1
  • (C) ai = 0
  • (D) ai = 5

Q99: If the correlation coefficient (r) between scores in a math test and amount of physical exercise by a student is 0.86, what percentage of variability in math test is explained by the amount of exercise?

  • (A) 86%
  • (B) 74%
  • (C) 14%
  • (D) 26%

Q100: In a class of 30 students, what is the likelihood that two of them would have their birthdays on the same day (assuming it is not a leap year)? Students having birthdays on January 3rd, 1993 and January 3rd, 1994, for example, would be a positive occasion.

  • (A) 49%
  • (B) 52%
  • (C) 70%
  • (D) 35%