#### Q21: Which of the following statements regarding the prediction are correct??

- (A) The output attribute must be categorical
- (B) The output attribute must be numerical
- (C) The resultant model is designed to determine future outcomes
- (D) The resultant model is designed to classify current behavior

#### Q22: Data used to build a data mining model

- (A) validation data
- (B) training data
- (C) test data
- (D) hidden data

#### Q23: The association between the number of years an employee has been with a firm and the person’s pay is 0.75. What can be stated regarding employee pay and years of service?

- (A) There is no relationship between salary and years worked
- (B) Individuals that have worked for the company the longest have higher salaries
- (C) Individuals that have worked for the company the longest have lower salaries
- (D) The majority of employees have been with the company a long time

#### Q24: Which of the following points would Bayesians and frequentists disagree on?

- (A) The use of a non-Gaussian noise model in probabilistic regression
- (B) The use of probabilistic modelling for regression
- (C) The use of prior distributions on the parameters in a probabilistic model
- (D) The use of class priors in Gaussian Discriminant Analysis

#### Q25: Introducing a non-essential variable into a linear regression model may result in : (1).Increase in R-square, (2).Decrease in R-square

- (A) Only 1 is correct
- (B) Only 2 is correct
- (C) Either 1 or 2
- (D) None of these

#### Q26: For a classification task, instead of random weight initializations in a neural network, we set all the weights to zero. Which of the following statements is true?

- (A) There will not be any problem and the neural network will train properly
- (B) The neural network will train but all the neurons will end up recognizing the same thing
- (C) The neural network will not train as there is no net gradient change
- (D) None of these

#### Q27: The kernel trick

- (A) can be applied to every classification algorithm
- (B) is commonly used for dimensionality reduction
- (C) changes ridge regression so we solve a d × d linear system instead of an n × n system, given n sample points with d feature
- (D) exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points

#### Q28: The line described by the linear regression equation (OLS) attempts to ____?

- (A) Pass through as many points as possible.
- (B) Pass through as few points as possible
- (C) Minimize the number of points it touches
- (D) Minimize the squared distance from the points

#### Q29: Ritesh has two children, one of them is a girl. What is the likelihood that the second kid is likewise a girl? You can suppose that the globe has an equal number of males and females.

- (A) 0.5
- (B) 0.25
- (C) 0.333
- (D) 0.75

#### Q30: A roulette wheel has 38 slots, 18 are red, 18 are black, and 2 are green. You play five games and always bet on red. What is the probability that you win all the 5 games?

- (A) 0.0368
- (B) 0.0238
- (C) 0.0526
- (D) 0.0473

## Answers:

Question | Q21 | Q22 | Q23 | Q24 | Q25 | Q26 | Q27 | Q28 | Q29 | Q30 |

Answer | D | B | B | C | A | B | D | D | C | B |