Machine Learning Quiz Question with Answer

41. Computational complexity of Gradient descent is

  1. linear in D
  2. linear in N
  3. polynomial in D
  4. dependent on the number of iterations

42. Computers are best at learning

  1. facts.
  2. concepts.
  3. procedures.
  4. principles.

43. Consider a binary classification problem. Suppose I have trained a model on a linearly separable training set, and now I get a new labeled data point which is correctly classified by the model, and far away from the decision boundary. If I now add this new point to my earlier training set and re-train, in which cases is the learnt decision boundary likely to change?

  1. When my model is a perceptron and logistic regression.
  2. When my model is logistic regression and Gaussian discriminant analysis.
  3. When my model is an SVM.
  4. When my model is a perceptron

44. Data used to build a data mining model.

  1. validation data
  2. training data
  3. test data
  4. hidden data

45. Data used to optimize the parameter settings of a supervised learner model.

  1. training
  2. test
  3. verification
  4. validation

46. 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

47. 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

48. K-fold cross-validation is

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

49. 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)

50. 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

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