Machine Learning Multiple Choice Questions and Answers for Practice

16. 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

17. 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

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

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

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

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

20. 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

21. 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.

22. 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

23. 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

24. 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.

25. 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

26. Which of the following sentence is FALSE regarding regression?

1. It relates inputs to outputs.
2. It is used for prediction.
3. It may be used for interpretation.
4. It discovers causal relationships.

27. Which statement about outliers is true?

1. Outliers should be identified and removed from a dataset.
2. Outliers should be part of the training dataset but should not be present in the test data.
3. Outliers should be part of the test dataset but should not be present in the training data.
4. The nature of the problem determines how outliers are used.

28. Which statement is true about neural network and linear regression models?

1. Both models require input attributes to be numeric.
2. Both models require numeric attributes to range between 0 and 1.
3. The output of both models is a categorical attribute value.
4. Both techniques build models whose output is determined by a linear sum of weighted input attribute values.

29. Which statement is true about prediction problems?

1. The output attribute must be categorical.
2. The output attribute must be numeric.
3. The resultant model is designed to determine future outcomes.
4. The resultant model is designed to classify current behavior.

30. With Bayes classifier, missing data items are

1. treated as equal compares.
2. treated as unequal compares.
3. replaced with a default value.
4. ignored.