Machine Learning MCQ Questions and Answers Quiz

21. Generalization error measures how well an algorithm perform on unseen data. The test error obtained using cross-validation is an estimate of the generalization error. Is this estimate unbiased?

  1. Yes
  2. No

22. Grid search is

  1. Linear in D and Polynomial in D
  2. Polynomial in D
  3. Exponential in D and Linear in N
  4. Polynomial in D and Linear in N

23. K-fold cross-validation is

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

24. 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)

25. 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

26. 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

27. 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.

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

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

29. 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

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

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