Machine Learning MCQ Questions and Answers Quiz

21. K-fold cross-validation is

  1. linear in K
  2. quadratic in K
  3. cubic in K
  4. exponential in K

22. Let us say that we have computed the gradient of our cost function and stored it in a vector g. What is the cost of one gradient descent update given the gradient?

  1. O(D)
  2. O(N)
  3. O(ND)
  4. O(ND2)

23. Logistic regression is a ........... regression technique that is used to model data having a ........... outcome.

  1. linear, numeric
  2. linear, binary
  3. nonlinear, numeric
  4. nonlinear, binary

24. Machine learning techniques differ from statistical techniques in that machine learning methods

  1. typically assume an underlying distribution for the data.
  2. are better able to deal with missing and noisy data.
  3. are not able to explain their behavior.
  4. have trouble with large-sized datasets

25. Regarding bias and variance, which of the follwing statements are true? (Here high and low are relative to the ideal model)

  1. Models which overfit have a high bias and underfit have a high variance.
  2. Models which overfit have a high bias and underfit have a low variance.
  3. Models which overfit have a low bias and underfit have a high variance.
  4. Models which overfit have a low bias and underfit have a low variance.

26. Regression trees are often used to model ........... data.

  1. linear
  2. nonlinear
  3. categorical
  4. symmetrical

27. Selecting data so as to assure that each class is properly represented in both the training and test set.

  1. cross validation
  2. stratification
  3. verification
  4. bootstrapping

28. Simple regression assumes a ........... relationship between the input attribute and output attribute.

  1. linear
  2. quadratic
  3. reciprocal
  4. inverse

29. Supervised learning and unsupervised clustering both require at least one

  1. hidden attribute.
  2. output attribute.
  3. input attribute.
  4. categorical attribute.

30. Suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting?

  1. Increase the amount of training data.
  2. Improve the optimisation algorithm being used for error minimisation.
  3. Decrease the model complexity.
  4. Reduce the noise in the training data.
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