Machine Learning MCQ Quiz - Multiple Choice Questions & Answers Page 2

11. 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.

12. 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.

13. 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

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

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

15. 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.

16. 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

17. This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.

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

18. This supervised learning technique can process both numeric and categorical input attributes.

1. linear regression
2. Bayes classifier
3. logistic regression
4. backpropagation learning

19. This technique associates a conditional probability value with each data instance.

1. linear regression
2. logistic regression
3. simple regression
4. multiple linear regression

20. This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.

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