31. You observe the following while fitting a linear regression to the data: As you increase the amount of training data, the test error decreases and the training error increases. The train error is quite low (almost what you expect it to), while the test error is much higher than the train error. What do you think is the main reason behind this behavior. Choose the most probable option.

- High variance
- High model bias
- High estimation bias
- None of the above

32. A measure of goodness of fit for the estimated regression equation is the

- multiple coefficient of determination
- mean square due to error
- mean square due to regression
- none of the above

33. A nearest neighbor approach is best used

- with large-sized datasets.
- when irrelevant attributes have been removed from the data.
- when a generalized model of the data is desireable.
- when an explanation of what has been found is of primary importance.

34. A regression model in which more than one independent variable is used to predict the dependent variable is called

- a simple linear regression model
- a multiple regression models
- an independent model
- none of the above

35. A term used to describe the case when the independent variables in a multiple regression model are correlated is

- regression
- correlation
- multicollinearity
- none of the above

36. Adding more basis functions in a linear model... (pick the most probably option)

- Decreases model bias
- Decreases estimation bias
- Decreases variance
- Doesnt affect bias and variance

37. Another name for an output attribute.

- predictive variable
- independent variable
- estimated variable
- dependent variable

38. Bootstrapping allows us to

- choose the same training instance several times.
- choose the same test set instance several times.
- build models with alternative subsets of the training data several times.
- test a model with alternative subsets of the test data several times.

39. Choose the options that are correct regarding machine learning (ML) and artificial intelligence (AI),(A) ML is an alternate way of programming intelligent machines.(B) ML and AI have very different goals.(C) ML is a set of techniques that turns a dataset into a software.(D) AI is a software that can emulate the human mind.

- (A), (B), (D)
- (A), (C), (D)
- (B), (C), (D)
- All are correct

40. Classification problems are distinguished from estimation problems in that

- classification problems require the output attribute to be numeric.
- classification problems require the output attribute to be categorical.
- classification problems do not allow an output attribute.
- classification problems are designed to predict future outcome.

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