Machine Learning MCQ Quiz - Multiple Choice Questions & Answers Page 5

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