31. The adjusted multiple coefficient of determination accounts for

- the number of dependent variables in the model
- the number of independent variables in the model
- unusually large predictors
- none of the above

32. The average positive difference between computed and desired outcome values.

- root mean squared error
- mean squared error
- mean absolute error
- mean positive error

33. The average squared difference between classifier predicted output and actual output.

- mean squared error
- root mean squared error
- mean absolute error
- mean relative error

34. The correlation between the number of years an employee has worked for a company and the salary of the employee is 0.75. What can be said about employee salary and years worked?

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

35. The correlation coefficient for two real-valued attributes is 0.85. What does this value tell you?

- The attributes are not linearly related.
- As the value of one attribute increases the value of the second attribute also increases.
- As the value of one attribute decreases the value of the second attribute increases.
- The attributes show a curvilinear relationship.

36. The leaf nodes of a model tree are

- averages of numeric output attribute values.
- nonlinear regression equations.
- linear regression equations.
- sums of numeric output attribute values.

37. The multiple coefficient of determination is computed by

- dividing SSR by SST
- dividing SST by SSR
- dividing SST by SSE
- none of the above

38. The process of forming general concept definitions from examples of concepts to be learned.

- Deduction
- abduction
- induction
- conjunction

39. The standard error is defined as the square root of this computation.

- The sample variance divided by the total number of sample instances.
- The population variance divided by the total number of sample instances.
- The sample variance divided by the sample mean.
- The population variance divided by the sample mean.

40. This clustering algorithm initially assumes that each data instance represents a single cluster.

- agglomerative clustering
- conceptual clustering
- K-Means clustering
- expectation maximization

MCQ Multiple Choice Questions and Answers on Machine Learning

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