Deck 8: Introduction to Data Mining

Question 1

Cross-Industry Standard Process for Data Mining (CRISP-DM) consists of six phases. Of the six, which one represents the phase where data wrangling occurs?

A) Deployment
B) Modeling
C) Data understanding
D) Data preparation

Answer 1

Data preparation

Question 2

Using the Manhattan distance between pairwise observations, which pairwise observation is most similar?

A) Observations 1 & 2
B) Observations 2 & 3
C) Observations 1 & 3
D) Both Observations 1 & 2 and 2 & 3

Answer 2

Observations 1 & 2

Question 3

Using the Manhattan distance between pairwise observations, which pairwise observation is most similar?

A) Observations 2 & 3
B) Observations 1 & 3
C) Observations 1 & 2
D) Both Observations 2 & 3 and 1 & 3

Answer 3

Observations 2 & 3

Question 4

Using the Euclidean distance between pairwise observations, which pairwise observation is most dissimilar?

A) Observations 1 & 3
B) Observations 2 & 3 and 1 & 3
C) Observations 2 & 3
D) Observations 1 & 2 and 2 & 3

Question 5

Using the Euclidean distance between pairwise observations, which pairwise observation is most dissimilar?

A) Observations 1 & 3
B) Observations 2 & 3 and 1 & 3
C) Observations 2 & 3
D) Observations 1 & 2 and 2 & 3

Question 6

Consider the partial data set in the table represents online hours spent shopping by age and income. The average and standard deviation for the full data set is $47,701 and $14,362, respectively. Using z-scores to standardize the observations, what is the average standard deviation of Income for the three provided?

A) 0.926
B) 0.3780
C) 0.8640
D) 0.7637

Question 7

Consider the partial data set in the table represents online hours spent shopping by age and income. The average and standard deviation for the full data set is $47,667 and $14,292, respectively. Using z-scores to standardize the observations, what is the average standard deviation of Income for the three provided?

A) 1.003
B) 0.7320
C) 0.2410
D) 0.6997

Question 8

Consider the partial data set in the table represents online hours spent shopping by age and income. Using the min-max transformation to normalize Income, what is the average standard deviation of Income for the chart provided?
Use the min-max transformation to normalize the observations for Income spent online.

A) 0.686
B) 0.6147
C) 0
D) 0.6727

Question 9

Consider the partial data set in the table represents online hours spent shopping by age and income. Using the min-max transformation to normalize Income, what is the average standard deviation of Income for the chart provided? Use the min-max transformation to normalize the observations for Income spent online.

A) 1
B) 6417
C) 0
D) 0.6997

Question 10

The following table is a segment of Loan Data from a bank for car loans. Compute the matching coefficient between Pairs 1 and 4.

A) Matching coefficient is 0.25.
B) Matching coefficient is 0.13.
C) Matching coefficient is 0.88.
D) Matching coefficient is 0.35.

Question 11

The following table is a segment of Loan Data from a bank for car loans. Compute the matching coefficient between Pairs 1 and 4.

A) Matching coefficient is 0.50.
B) Matching coefficient is 0.25.
C) Matching coefficient is 0.75.
D) Matching coefficient is 0.40.

Question 12

Sandra began collecting transaction details to see if the same items were in each sales transaction. Compute the matching coefficient and Jaccard's coefficient for pairwise transaction 1 & 2.

A) Matching Coefficient = 0.60 and Jaccard's = 0.40
B) Matching Coefficient = 0.40 and Jaccard's = 0.50
C) Matching Coefficient = 0.60 and Jaccard's = 0.33
D) Matching Coefficient = 0.40 and Jaccard's = 0.40

Question 13

Sandra began collecting transaction details to see if the same items were in each sales transaction. Compute the matching coefficient and Jaccard's coefficient for pairwise transaction 1 & 2.

A) Matching Coefficient = 0.60 and Jaccard's = 0.40
B) Matching Coefficient = 0.40 and Jaccard's = 0.50
C) Matching Coefficient = 0.60 and Jaccard's = 0.50
D) Matching Coefficient = 0.40 and Jaccard's = 0.40

Question 14

The process of dividing a data set into a training, a validation, and an optimal test data set is called ________.

A) overfitting
B) oversampling
C) optional testing
D) data partitioning

Question 15

Cameron is performing a study on the IQ of groups in various areas. He has calculated that the average IQ of Group A is 108 with a standard deviation of 8. What is the z-score for someone with an IQ of 112?

A) -0.50
B) 0.50
C) -0.30
D) 1.30

Question 16

Cameron is performing a study on the IQ of groups in various areas. He has calculated that the average IQ of Group A is 105 with a standard deviation of 10. What is the z-score for someone with an IQ of 98?

A) 0.7
B) -0.7
C) 0.9
D) 0.1

Question 17

Cameron is performing a study on the IQ of groups in various areas. He has calculated that the average IQ of Group B is 148 with a standard deviation of 10. What is the z-score for someone with an IQ of 155?

A) 1.10
B) -0.70
C) 0.22
D) 0.70

Question 18

Cameron is performing a study on the IQ of groups in various areas. He has calculated that the average IQ of Group B is 118 with a standard deviation of 12. What is the z-score for someone with an IQ of 125?

A) 0.98
B) -0.58
C) 0.10
D) 0.58

Question 19

When a predictive model is made overly complex to fit in the quirks of given sample data, it is called ______.

A) oversampling
B) overfitting
C) partitioning
D) distribution

Question 20

Molly e-mailed her clients offering a free 30-minute massage for referrals. In the following validation set of 100, Class 1 reflects the clients predicted to provide referrals and Class 0 reflects the clients predicted to not provide referrals.

-Based on the confusion matrix, what was the True Positive (TP) of current clients who provided referrals for a free massage?

A) 36
B) 10
C) 26
D) 54

Question 21

Molly e-mailed her clients offering a free 30-minute massage for referrals. In the following validation set of 100, Class 1 reflects the clients predicted to provide referrals and Class 0 reflects the clients predicted to not provide referrals.

-Based on the confusion matrix, what was the True Positive (TP) of current clients who provided referrals for a free massage?

A) 29
B) 11
C) 18
D) 60

Question 22

Molly e-mailed her clients offering a free 30-minute massage for referrals. In the following validation set of 100, Class 1 reflects the clients predicted to provide referrals and Class 0 reflects the clients predicted to not provide referrals.

-Based on the confusion matrix, what was the False Negative (FN) of current clients who provided referrals for a free message?

A) 29
B) 18
C) 11
D) 60

Question 23

The sensitivity, also called recall, is computed using which equation?

A) Sensitivity = TP ÷ (TP + FN).
B) Sensitivity = TN ÷ (TP + FN).
C) Sensitivity = (TP + TN) ÷ (TP + TN + FP + FN).
D) Sensitivity = TP ÷ (TP + FP).

Question 24

The precision, also called positive predictive value, is computed using which equation?

A) Precision = TP ÷ (TP + FN).
B) Precision = TN ÷ (TP + FN).
C) Precision = (TP + TN) ÷ (TP + TN + FP + FN).
D) Precision = TP ÷ (TP + FP).

Question 25

Based on the following confusion matrix with a validation set of 100, Class 1 reflects the members targeted who purchased services and Class 0 reflects the non-targeted respondents who did not purchase services. Calculate the specificity rate.

A) 72%
B) 88%
C) 84%
D) 84.4%

Question 26

Based on the following confusion matrix with a validation set of 100, class 1 reflects the members targeted who purchased services and class 0 reflects the non-targeted respondents who did not purchase services. Calculate the specificity rate.

A) 62%
B) 78%
C) 84%
D) 84.5%

Question 27

Based on the following confusion matrix with a validation set of 100, Class 1 reflects the members targeted who purchased services and Class 0 reflects the non-targeted respondents who did not purchase services. Calculate the sensitivity rate.

A) 86%
B) 85%
C) 70%
D) 40%

Question 28

Based on the following confusion matrix with a validation set of 100, Class 1 reflects the members targeted who purchased services and Class 0 reflects the non-targeted respondents who did not purchase services. Calculate the sensitivity rate.

A) 78%
B) 84%
C) 62%
D) 32%

Question 29

The calculated average error measuring the average magnitude of errors in predictive performance measures is called _______.

A) mean percentage error
B) root mean square error
C) mean error
D) mean absolute deviation

Question 30

The _______ error is the average absolute percentage error, shown as a percentage of the actual value, displaying the magnitude of the errors in performance measures.

A) RMSE
B) MAD
C) MAPE
D) ME

Question 31

Which is the best-fit definition for the use of Principal Component Analysis (PCA)?

A) The elimination of the number of components in a data set to identify errors.
B) The transformation of a small set of correlated variables into larger uncorrelated subsets.
C) The transformation of a large number of correlated variables into a smaller number of uncorrelated variables.
D) The driving of pattern recognition in a supervised data mining set used to visualize data methods.

Question 32

The following table displays the weights for computing the principal components and the data for two Observations. The mean and standard deviation for x ₁ are 3.60 and 1.70, respectively. What is the z-score of x ₁ for Observation 1?

A) 0.42
B) 0.41
C) 0.97
D) 0.45

Question 33

The following table displays the weights for computing the principal components and the data for two Observations. The mean and standard deviation for x₁ are 4.2 and 1.4, respectively. What is the z-score of x ₁ for Observation 1?

A) 0.18
B) 0.17
C) 0.73
D) 0.21

Question 34

The following table displays the weights for computing the principal components and the data for two Observations. The mean and standard deviation for x ₁ are 4.2 and 1.9, respectively. The mean and standard deviation for x ₂ are 6.2 and 4.8, respectively. Compute the first principal component score for Observation 1.

A) -0.533
B) -0.084
C) 0.740
D) 0.403

Question 35

The following table displays the weights for computing the principal components and the data for two Observations. The mean and standard deviation for x ₁ are 4.2 and 1.4, respectively. The mean and standard deviation for x ₂ are 6.8 and 4.8, respectively. Compute the first principal component score for Observation 1.

A) -0.293
B) -0.742
C) 0.949
D) 0.612

Question 36

Of the following selections, which is not a descriptor of principal component analysis?

A) The first principal account is not suitable for analysis.
B) Principal components are uncorrelated variables.
C) The first principal accounts for most of the variability.
D) Principal component variables are weighted linear combinations of the original variables.

Question 37

Calculate the misclassification rate for the following confusion matrix.

A) 0.68
B) 0.18
C) 0.74
D) 0.24

Question 38

Calculate the misclassification rate for the following confusion matrix.

A) 0.72
B) 0.22
C) 0.78
D) 0.28

Question 39

Calculate the accuracy rate for the following confusion matrix.

A) 0.78
B) 0.28
C) 0.72
D) 0.34

Question 40

Calculate the accuracy rate for the following confusion matrix.

A) 0.72
B) 0.22
C) 0.78
D) 0.28

Question 41

Tess is tasked with analyzing a data set with multiple variables with various scales. To reduce the difference in scale in variables, she is following a common process called ______ to make the observations unit-free.

A) data mining
B) Euclidean distance
C) Manhattan distance
D) standardizing

Question 42

Which chart allows for a visual representation to determine a point where a model's predictions become less useful?

A) ROC Curve
B) Cumulative Lift Chart
C) Decile-wise Lift Chart
D) Sensitivity Measure

Question 43

Which chart is a bar chart displayed in 10 equal-sized intervals, or every 10% of the observations?

A) ROC Curve
B) Cumulative Lift Chart
C) Decile-wise Lift Chart
D) Sensitivity Measure

Question 44

When using PCA, all the following are disadvantages except

A) PCA results are difficult to interpret clearly.
B) components are weighted linear combinations and abstract.
C) PCA only works with numerical data.
D) PCA significantly increases the dimension of the data.

Question 45

The process of applying a set of analytical techniques for the development of machine learning and artificial intelligence is called data mining.

Question 46

The key distinction between supervised and unsupervised data mining is that the identification of the target variable is identified in supervised data mining.

Question 47

Common applications of unsupervised learning include dimension reduction and prediction model.

Question 48

Normalization is the process that makes the numerical data independent of scale.

Question 49

The Jaccard's coefficient is appropriate when it is more informative to match negative outcomes between observations.

Question 50

Oversampling involves intentionally selecting more samples from one class than from other classes to adjust the class distribution of a data set.

Question 51

A diagram that represents the information in equal-sized intervals, deciles, is called a cumulative lift chart.

Question 52

In real-world situations, data sets contain many variables. If some variables are eliminated, valuable information may be lost.

Question 53

The principal component analysis (PCA) is a dimension reduction technique used to reduce variables without removing variables.

Question 54

In Excel, Analytic Solver only provides the covariance matrix for performing principal component analysis (PCA).