Machine Learning MCQ Questions and Answers Quiz

31. The adjusted multiple coefficient of determination accounts for

  1. the number of dependent variables in the model
  2. the number of independent variables in the model
  3. unusually large predictors
  4. none of the above

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

  1. root mean squared error
  2. mean squared error
  3. mean absolute error
  4. mean positive error

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

  1. mean squared error
  2. root mean squared error
  3. mean absolute error
  4. 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?

  1. There is no relationship between salary and years worked.
  2. Individuals that have worked for the company the longest have higher salaries.
  3. Individuals that have worked for the company the longest have lower salaries.
  4. 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?

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

36. The leaf nodes of a model tree are

  1. averages of numeric output attribute values.
  2. nonlinear regression equations.
  3. linear regression equations.
  4. sums of numeric output attribute values.

37. The multiple coefficient of determination is computed by

  1. dividing SSR by SST
  2. dividing SST by SSR
  3. dividing SST by SSE
  4. none of the above

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

  1. Deduction
  2. abduction
  3. induction
  4. conjunction

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

  1. The sample variance divided by the total number of sample instances.
  2. The population variance divided by the total number of sample instances.
  3. The sample variance divided by the sample mean.
  4. The population variance divided by the sample mean.

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

  1. agglomerative clustering
  2. conceptual clustering
  3. K-Means clustering
  4. expectation maximization
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