Test Bank 4

A

1. What are the two steps of classification? a. learning step and a classification step b. classification step and optimization c. Optimization and regression d. K-means and clustering

A

10. Which one is incorrect about bagging? a. The sampling can't be done with replacement b. It is also known as bootstrap aggregating. c. The sampling is done with replacement d. Some instances may appear several times in the same training set.

D

101. The simplest and most fundamental version of cluster analysis is partitioning, which organizes the objects of a set into several exclusive groups or clusters. a. Predictive b. Normalization c. Both A & B d. None of above

D

102. ______________is a linear signal processing technique that, applied to a data vector X, transforms it to a numerically different vector, X0, of wavelet coefficients. a. The discrete wavelet transform (DWT) b. Linear regression c. Decision tree d. None of above

A

11. Which one is not an attribute selection measure? a. Induction b. Information gain c. Minimum description length d. Multivariate splits

A

12. Which one is incorrect about tree pruning? a. Pruned trees tend to be larger and more complex b. It is a method that addresses the problem of overfitting the data c. Pruned trees are easier to comprehend d. Prepruning and postpruning are two

D

13. A decision tree has which of the following nodes? a. Root node b. Internal nodes c. Leaf/terminal nodes d. All of the above

A

14. A linear SVM is often known as a a. Maximal margin classifier b. Linear Decision Boundary c. Minimal margin classifier d. Structural risk minimization

D

15. Which of the following is a component(s) of Bias-Variance Decomposition? a. Bias b. Variance c. Noise d. All of the above

D

16. Which of the following is an example of ensemble methods a. Bagging b. Boosting c. Random Forests d. All of the above

E

17. Which of the following is a method for constructing the ensemble of classifiers? a. By manipulating the training set b. By manipulating the input features c. By manipulating the class labels d. By manipulating the learning algorithm e. All of the above

C

18. Data classification consists of which of the following step. a. Learning step b. Classification step c. Both A and B d. None of the Above

B

19. The target function is also known informally as a ________ model? a. Descriptive b. Classification c. Target d. Predictive

D

2. Which of the following are not attribution selection measures? a. Information Gain b. Gain Ratio c. Gini Index d. Splitting Index

C

20. The target function is also known informally as a ________ model? a. Descriptive b. Classification c. Target d. Predictive

D

21. Which of these is an attribute type: a. Binary b. Ordinal c. Nominal d. all of the above

A

27. What does each classification technique employ? a. Learning algorithm b. Code c. Calculus d. Loops

A

3. What is a decision tree in the classification process? a. A decision tree is a flowchart-like tree structure. b. It's a training tulip tool that helps analyzing data. c. It is a root node in a flowchart-like tree structure. d. None of the above

B

31. A classification technique that has received considerable attention is: A. Support Virtual Machine B. Support Vector Machine C. Maximum Margin Hyperplanes D. Rationale for Maximum Margin

A

32. Each technique employs a learning algorithm to identify a model that best fits the relationship between the attribute set and; a. class label of the input data b. Class label of the output data c. Class label of the predictable data d. Class label of the model data

D

33. All are examples of Decision Tree nodes except? a. A Root Node b. An internal Node c. Leaf or terminal Node d. Temperature Node

C

34. The ensemble of classifiers can be constructed in many ways EXCEPT a. By manipulating the training set. b. By manipulating the input features c. By manipulating the bias-variance d. By manipulating the class labels e. By manipulating the learning algorithm

A

35. The class can be obtained by taking a majority vote on the individual predictions or by weighting each prediction with the accuracy of the; a. base classifier b. bade method c. base number d. base aggregating

A

36. A classifier is usually trained to minimize its, a. training boundary b. training variability c. training compositions d. training error

D

36. A classifier is usually trained to minimize its, a. training boundary b. training variability c. training compositions d. training error

A

38. Each classification technique employs a ____ to identify a model that best fits the relationship between the attribute set and class label. A. Learning Algorithm B. Regressive model C. Data cleaning technique D. Visualization output

D

39. A decision tree has which type of nodes? A. Root node B. Internal node C. Terminal node D. All of the above

A

4. Bagging, boosting, and random forests are examples of a. Ensemble methods b. Regression c. Association analysis d. Text mining

A

40. A linear sum is a classifier that searches for a hyperplane with the largest margins is also known as A. Maximal Margin Classifier B. Capacity C. Structural Risk Minimization D. Universal Approximators

A

41. _______ and numeric prediction are two major types of prediction problems. A. Classification B. Clustering C. Regression D. Unsupervised Learning

C

42. The _____ of a classifier on a given test is the percentage of test set tuples that are correctly classified by the classifier. A. Supervised learning B. Unsupervised learning C. Accuracy D. Overfit

D

43. What is a classification model used for? a. Descriptive modeling b. Predictive Modeling c. Neither d. Both

A

44. Which set of data is used to build classification model? a. Training set b. Test set c. Neither

B

44. Which set of data is used to build classification model? a. Training set b. Test set c. Neither

D

46. Which of these are examples of Ensemble methods? a. Bagging b. Boosting c. Random Forests d. All of the above

C

47. Which approach does not involve sampling? a. Bootstrapping b. Leave one out c. Thresh hold moving

A

48. What is Boosting used for? a. Boosting is used to change the distribution of training sets b. Boosting is used to change the error rates c. Boosting is used to fix certain models

A

49. ______________ is a classification model can serve as an explanatory tool to distinguish between objects of different classes. a) Descriptive Modeling b) Predictive modeling c) Decision tree d) None of above

D

5. What method uses a nonlinear mapping to transform the original training data into a higher dimension and in this new dimension, it searches for the linear optimal separating hyperplane. a. Bagging b. Boosting c. Random Forest d. SVM

A

50. _______________ is a classification model can also be used to predict the class label of unknown records. a. Predictive Modeling b. Linear regression c. Decision tree d. None of above

A

51. Evaluation of the performance of a classification model is based on the counts of test records correctly and incorrectly predicted by the model. These counts are tabulated in a table known as a______________. a) Confusion matrix b) Normal Metrix c) Simple Metrix d) None of above

A

52. _________ and _______are two examples of ensemble methods that manipulate their training sets. a) Bagging and Boosting b) Linear; decision tree c) Simple regression; multiple regression d) None of above

A

54. The simplest and most fundamental version of cluster analysis is ________, which organizes the objects of a set into several exclusive groups or clusters. a) Partitioning b) Normalization c) Predictive d) None of above

B

55. This algorithm is the basis of many existing decision tree induction algorithms. a. C4.5 b. Hunts c. CART d. K-means

A

56. What kind of decision tree can be used to allow test conditions that involve more than one attribute? a. Oblique b. Skeleton c. Decision boundary d. Gain Ratio

C

57. Another name for a formal explanation relating the margin of linear classifier to its generalization error is: a. SVM b. CART c. SRM d. Slack variable

D

58. This is the basic algorithm for: Create a node N; if tuples in D are all of the same class, C, then... return N as a leaf node labeled with the class C; if attribute list is empty then ... return N as a leaf node labeled with the majority class in D; // majority voting a. K-Means b. Clustering c. C4.5 d. Decision Tree

C

59. Traditionally, learning models assume that data classes are balanced and well distributed, however, in real life, data is class-imbalanced. This is also known as: a. Cost and benefits b. Cross validation c. Class imbalance problem d. boosting

D

6. What type of SVM would you use in order to correctly classify nonlinear data? a. Linear b. Radial c. Polynomial d. B or C

B

60. Another name for showing interesting relationships between attribute-value pairs that occur frequently in a given data set. a. Classification b. Frequent patterns c. Associative classification d. Association rules

D

61. Which is not an algorithm parameter? a. D, the complete set of training tuples and associated class labels b. Attribute list c. Attribution selection method d. Attribution method

D

62. Which is not a scenario for splitting attribute? a. The splitting attribute is Discrete-valued b. The splitting attribute is Continuous-valued c. The splitting attribute is Discrete-valued and Binary tree d. The splitting attribute is Continuous-valued and Binary tree

D

63. Which is not a method for expressing attributes? a. Binary Attribute b. Nominal Attribute c. Ordinal Attribute d. Discrete Attribute

D

64. Which is not a popular Ensemble method? a. Bagging b. Boosting c. Bumping d. Random Forest

D

65. Which is not a method for partitioning? a. Holdout b. Random sampling c. Cross Validation d. Pruning

D

66. Which is not a type of tree node? a. Root Node b. Internal Node c. Leaf Node d. Stem Node

A

67. what is SVM? a. Support Vector Machines b. Supply Victory Man c. Supplemental Vaccine Master d. Super Victory Machine

A

68. What is C or the optimal separating hyperplane in the topic of Support Vector Machines? a. It is the minimum perpendicular distance between each point and these parating line b. It is the crossing value c. It is the distance between triangle and rectangle d. It is the choosing point

A

69. What is the basic focus of a support vector classifier? a. Separable Hyperplanes b. Hyper x c. sential lining d. Having variables

A

7. Which statement about tuple (x,y) is correct? a. x is the attribute set and y is a special attribute b. x and y are attribute sets c. x and y are special attributes d. x is a special attribute and y is the attribute set

D

70. Common kernel functions DOES NOT include a. Linear b. Polynomial c. Radial Basis d. Visualization tools

A

71. What are Non-Separating Classes? a. for any straight line or plane drawn there will always be at least some points on the wrong side of the line b. for any classes you have, the support vector classifier gets more complex c. for all the points in the map, they are all different classes and have identities d. for every class, there is a point associated with it and they don't separate

A

72. The support vector classifier is fairly easy to think about. However it may not be all that powerful. Why? a. because it only allows for a linear decision boundary b. because it is hard for interpret c. because it is outdated d. because it only allows using certain variables, specific computer applications to run

A

73. What is anti-monotone property? a. Support for an itemset never exceeds the support for its subsets b. A rule against constants c. A constant variable d. A training set

A

74. When is an association rule? a. An implication expression of the form x -> y where x and y are disjoint item sets b. A rule to constrain variables c. A rule to change variables d. A rule to swap variables

A

75. What is clustering? a. Grouping variables b. Separating variables c. Making variables close d. Removing outliers

A

76. What are some of the requirements for cluster analysis? a. Scalability, ability to deal with different types of attributes, ability to deal with noise data b. Outliers, scalability, noise data c. Scalability, extrapolation, noise data d. Noise data, ability to deal with difference

C

77. In what terms is the strength of the association rule measured with? a. Support b. Confidence c. Both A and B d. None of the above

C

78. Which one of these is a strategy to decrease the number of clusters, while trying to minimize the increase in the total sum of the squared error (SSE)? a. Disperse a cluster b. Merge two clusters c. All of the above d. None of the above

A

79. Descriptive Modeling is a classification model that can serve as an explanatory tool to distinguish between objects of different: a. Classes b. Predictive models c. Decision trees d. None of above

A

8. Which one is not an example of classification technique? a. Induction b. Decision tree c. Rule-based classifiers d. Neural networks

A

80. Predictive Modeling is a classification model that can also be used to predict the class label of: a. Unknown records b. Linear regression c. Decision tree d. Classes

A

81. Evaluation of the performance of a ________ ______ is based on the counts of test records correctly and incorrectly predicted by the model. a. Classification Model b. Decision Tree c. Linear Regression d. None of above

A

82. Bagging and Boosting are two examples of ensemble methods that manipulate their _______. a. Training sets b. Decision tree c. Simple regression d. None of above

A

83. The discrete wavelet transform is a linear signal processing technique that, applied to a data vector X, transforms it to a numerically different vector, X0, of _____: a. Wavelet coefficients b. Multiple coefficients c. Decision tree coefficients d. None of above

A

84. The simplest and most fundamental version of ______ ______ is partitioning, which organizes the objects of a data set into several exclusive groups or clusters. a. Cluster analysis b. Normalization analysis c. Predictive analysis d. None of above

A

85. ________ is the task of learning a target function / that maps each attribute set x to one of the predefined class Iabels y. a. Classification b. Modelling c. descriptive modelling d. regression

A

86. ________ A classification model can also be used to predict the class label of unknown records. a. Predictive Modeling b. performance metric c. decision tree d. Internal nodes

A

87. ________each of which has exactly one incoming edge and two or more outgoing edges. a. Internal nodes b. Leaf c. root mode d. exterior nodes

A

88. _________ where a classification algorithm builds the classifier by analyzing or "learning from" a training set made up of database tuples and their associated class labels. a. learning step b. training set c. attribute vector d. training tuples

A

89. ________ is constructed to predict class (categorical) labels, such as "safe" or "risky" for the loan application data; "yes" or "no" for the marketing data; or "treatment A," "treatment B," or "treatment C" for the medical data. a. classifier b. predictor c. classification d. learning step

A

9. Which one is a classifier that searches for a hyperplane with the largest margin? a. A linear SVM b. Minimal margin classifier c. A non-linear SVM d. Kernel trick

A

90. _________ is a heuristic for selecting the splitting criterion that "best" separates a given data partition, D, of class-labeled training tuples into individual classes. a. attribute selection measure b. splitting rules c. information gain d. Induction of a decision tree

D

92. The decision tree has three types of nodes. Which of the following is correct? ① root node ② internal node ③ leaf or terminal node a. ① and ② b. ② and ③ c. ① and ③ d. ①, ②, ③

D

93. Which of the following are classification methods? a. Decision tree-based methods b. Rule-based methods c. Logistic regression d. All of the above

D

94. Classification methods can be compared and evaluated according to different criteria. Which of the following is NOT one of the criteria? a. Accuracy b. Speed c. Scalability d. Quality

A

96. Which of the following is NOT the advantage of decision tree-based classification? a. Easy to interpret for big-sized trees b. Extremely fast at classifying unknown records c. Accuracy is comparable to other classification techniques for many simple data sets d. Inexpensive to construct

D

97. ______________ is a classification model can serve as an explanatory tool to distinguish between objects of different classes. a. Linear regression b. Predictive modeling c. Both A & B d. None of above

D

98. _______________ is a classification model can also be used to predict the class label of unknown records. a. Decision tree b. Linear regression c. Both A & B d. None of above

D

99. Evaluation of the performance of a classification model is based on the counts of test records correctly and incorrectly predicted by the model. These counts are tabulated in a table known as a______________. a. Simple Metrix b. Normal Metrix c. Both A & B d. None of above

A

A classification model is useful for lots of purposes. Which of the following is correct? ① Descriptive Modeling ② Predictive Modeling ③ Linear Modeling a. ① and ② b. ② and ③ c. only ① d. only ②

A

Classification is: a. The task of assigning objects to one of several predefined categories b. Identifying trends c. Removing anomalies d. Adding anomalies

C

Support vector machines (SVMs) are a method for classification type? a. Linear data b. Nonlinear data c. All of the above d. None of the above

A

The learning step in data classification is also known as? a. Training phase b. Training set c. Training tuples d. None of the above

A

What are classification techniques most suited for? a. Predicting or describing data sets with binary or nominal categories b. Sorting data c. Finding data d. Extrapolating data

A

What is a class label attribute? a. A discrete-valued and unordered predefined class determined by another database attribute b. A speed up to data analysis c. A fast method for analyzing data d. A quick look at data

A

What is a training tuple? a. Individual tuples making up the training set b. A decision tree-based dataset c. A database with huge datasets d. A decision tree that branches off many nodes

A

What is regression analysis? a. A statistical methodology that is most often used for numeric prediction b. A technique that splits sample size c. A technique that increases sample size d. A technique that decreases sample size

D

Which of these is an example of ensemble method? a. Bagging b. Boosting c. Random forests d. All of the above