Machine Learning Multiple Choice Questions and Answers

41. This clustering algorithm initially assumes that each data instance represents a single cluster.

1. agglomerative clustering
2. conceptual clustering
3. K-Means clustering
4. expectation maximization

42. This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.

1. agglomerative clustering
2. expectation maximization
3. conceptual clustering
4. K-Means clustering

43. This supervised learning technique can process both numeric and categorical input attributes.

1. linear regression
2. Bayes classifier
3. logistic regression
4. backpropagation learning

44. This technique associates a conditional probability value with each data instance.

1. linear regression
2. logistic regression
3. simple regression
4. multiple linear regression

45. This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.

1. agglomerative clustering
2. conceptual clustering
3. K-Means clustering
4. expectation maximization

46. When doing least-squares regression with regularisation (assuming that the optimisation can be done exactly), increasing the value of the regularisation parameter (Lambda)

1. will never decrease the training error.
2. will never increase the training error.
3. will never decrease the testing error.
4. will never increase the testing error.

47. Which is not true about Gradient of a continuous and differentiable function

1. is zero at a minimum
2. is non-zero at a maximum
3. is zero at a saddle point
4. decreases as you get closer to the minimum

48. Which of the following is a common use of unsupervised clustering?

1. detect outliers
2. determine a best set of input attributes for supervised learning
3. evaluate the likely performance of a supervised learner model
4. determine if meaningful relationships can be found in a dataset

49. Which of the following is not an advantage of Grid search

1. It can be applied to non-differentiable functions.
2. It can be applied to non-continuous functions.
3. It is easy to implement.
4. It runs reasonably fast for multiple linear regression.

50. Which of the following points would Bayesians and frequentists disagree on?

1. The use of a non-Gaussian noise model in probabilistic regression.
2. The use of probabilistic modelling for regression.
3. The use of prior distributions on the parameters in a probabilistic model.
4. The use of class priors in Gaussian Discriminant Analysis