Machine Learning Multiple Choice Questions and Answers

1. A multiple regression model has

1. only one independent variable
2. only one independent variable
3. more than one dependent variable
4. more than one dependent variable
5. more than one independent variable
6. more than one independent variable
7. none of the above
8. none of the above

2. 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.

3. Supervised learning differs from unsupervised clustering in that supervised learning requires

1. at least one input attribute.
2. input attributes to be categorical.
3. at least one output attribute.
4. ouput attriubutes to be categorical.

4. A multiple regression model has

1. only one independent variable
2. only one independent variable
3. more than one dependent variable
4. more than one dependent variable
5. more than one independent variable
6. more than one independent variable
7. none of the above
8. none of the above

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

1. multiple coefficient of determination
2. mean square due to error
3. mean square due to regression
4. none of the above

6. A nearest neighbor approach is best used

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

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

1. a simple linear regression model
2. a multiple regression models
3. an independent model
4. none of the above

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

1. regression
2. correlation
3. multicollinearity
4. none of the above

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

1. Decreases model bias
2. Decreases estimation bias
3. Decreases variance
4. Doesnt affect bias and variance

10. Another name for an output attribute.

1. predictive variable
2. independent variable
3. estimated variable
4. dependent variable