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

- will never decrease the training error.
- will never increase the training error.
- will never decrease the testing error.
- will never increase the testing error.

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

- is zero at a minimum
- is non-zero at a maximum
- is zero at a saddle point
- decreases as you get closer to the minimum

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

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

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

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

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

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

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

- It relates inputs to outputs.
- It is used for prediction.
- It may be used for interpretation.
- It discovers causal relationships.

27. Which statement about outliers is true?

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

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

- Both models require input attributes to be numeric.
- Both models require numeric attributes to range between 0 and 1.
- The output of both models is a categorical attribute value.
- 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?

- The output attribute must be categorical.
- The output attribute must be numeric.
- The resultant model is designed to determine future outcomes.
- The resultant model is designed to classify current behavior.

30. With Bayes classifier, missing data items are

- treated as equal compares.
- treated as unequal compares.
- replaced with a default value.
- ignored.

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