41. Computational complexity of Gradient descent is

- linear in D
- linear in N
- polynomial in D
- dependent on the number of iterations

42. Computers are best at learning

- facts.
- concepts.
- procedures.
- 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?

- When my model is a perceptron and logistic regression.
- When my model is logistic regression and Gaussian discriminant analysis.
- When my model is an SVM.
- When my model is a perceptron

44. Data used to build a data mining model.

- validation data
- training data
- test data
- hidden data

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

- training
- test
- verification
- 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?

- Yes
- No

47. Grid search is

- Linear in D and Polynomial in D
- Polynomial in D
- Exponential in D and Linear in N
- Polynomial in D and Linear in N

48. K-fold cross-validation is

- linear in K
- quadratic in K
- cubic in K
- 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?

- O(D)
- O(N)
- O(ND)
- O(ND
^{2})

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

- linear, numeric
- linear, binary
- nonlinear, numeric
- nonlinear, binary

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