Question 1

Another name for an output attribute.

Accepted Answer

A)  predictive variable 
B)  independent variable 
C)  estimated variable 
D)  dependent variable 
A)  predictive variable 
B)  independent variable 
C)  estimated variable 
D)  dependent variable D

Question 2

Given desired class C and population P, lift is defined as

Accepted Answer

A)  the probability of class C given population P divided by the probability of C given a sample taken from the population. 
B)  the probability of population P given a sample taken from P. 
C)  the probability of class C given a sample taken from population P. 
D)  the probability of class C given a sample taken from population P divided by the probability of C within the entire population P. 
A)  the probability of class C given population P divided by the probability of C given a sample taken from the population. 
B)  the probability of population P given a sample taken from P. 
C)  the probability of class C given a sample taken from population P. 
D)  the probability of class C given a sample taken from population P divided by the probability of C within the entire population P. D

Question 3

Classification problems are distinguished from estimation problems in that

Accepted Answer

A)  classification problems require the output attribute to be numeric. 
B)  classification problems require the output attribute to be categorical. 
C)  classification problems do not allow an output attribute. 
D)  classification problems are designed to predict future outcome. 
A)  classification problems require the output attribute to be numeric. 
B)  classification problems require the output attribute to be categorical. 
C)  classification problems do not allow an output attribute. 
D)  classification problems are designed to predict future outcome. B

Question 4

Assume that we have a dataset containing information about 200 individuals. One hundred of these individuals have purchased life insurance. A supervised data mining session has discovered the following rule:
 IF age < 30 & credit card insurance = yes
THEN life insurance = yes
Rule Accuracy: 70%
Rule Coverage: 63%
How many individuals in the class life insurance= no have credit card insurance and are less than 30 years old?

Accepted Answer

A)  63 
B)  70 
C)  30 
D)  27 
A)  63 
B)  70 
C)  30 
D)  27

Question 5

Which statement about outliers is true?

Accepted Answer

A)  Outliers should be identified and removed from a dataset. 
B)  Outliers should be part of the training dataset but should not be present in the test data. 
C)  Outliers should be part of the test dataset but should not be present in the training data. 
D)  The nature of the problem determines how outliers are used. 
E)  More than one of a,b,c or d is true. 
A)  Outliers should be identified and removed from a dataset. 
B)  Outliers should be part of the training dataset but should not be present in the test data. 
C)  Outliers should be part of the test dataset but should not be present in the training data. 
D)  The nature of the problem determines how outliers are used. 
E)  More than one of a,b,c or d is true.

Question 6

Use the three-class confusion matrix below to answer questions 1 through 3.
 $$\begin{array}{l}
\text { Computrd Decision }\
\begin{array} { | c | c | c | c | } 
\hline & \text { Class 1 } & \text { Class } 2 & \text { Class } 3 \
\hline \text { Class 1 } & 10 & 5 & 3 \
\hline \text { Class } 2 & 5 & 15 & 3 \
\hline \text { Class } 3 & 2 & 2 & 5 \
\hline
\end{array}
\end{array}$$
-How many class 2 instances are in the dataset?

Accepted Answer

The answer of Use the three-class confusion matrix below to...

Question 7

Which statement is true about prediction problems?

Accepted Answer

A)  The output attribute must be categorical. 
B)  The output attribute must be numeric. 
C)  The resultant model is designed to determine future outcomes. 
D)  The resultant model is designed to classify current behavior. 
A)  The output attribute must be categorical. 
B)  The output attribute must be numeric. 
C)  The resultant model is designed to determine future outcomes. 
D)  The resultant model is designed to classify current behavior.

Question 8

Use the three-class confusion matrix below to answer questions 1 through 3.
 $$\begin{array}{l}
\text { Computrd Decision }\
\begin{array} { | c | c | c | c | } 
\hline & \text { Class 1 } & \text { Class } 2 & \text { Class } 3 \
\hline \text { Class 1 } & 10 & 5 & 3 \
\hline \text { Class } 2 & 5 & 15 & 3 \
\hline \text { Class } 3 & 2 & 2 & 5 \
\hline
\end{array}
\end{array}$$
-How many instances were incorrectly classified with class 3?

Accepted Answer

The answer of Use the three-class confusion matrix below to...

Question 9

Use the confusion matrix for Model X and confusion matrix for Model Y to answer questions 4 through 6.
 $$\begin{array} { | c | c | c | c | c | c | c | } 
\hline \begin{array} { c } 
\text { Model } \
\mathrm { X }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Accept }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Reject }
\end{array} & \begin{array} { c } 
\text { Model } \
\text { Y }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Accept }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Reject }
\end{array} \
\hline \text { Accept } & 10 & 5 &  \text { Accept  } & 6 & 9 \
\hline \text { Reject } & \mathbf { 2 5 } & 60 &  \text { Reject } & 15 & 70 \
\hline
\end{array}$$
-Compute the lift for Model Y.

Accepted Answer

The answer of Use the confusion matrix for Model X...

Question 10

Unlike traditional production rules, association rules

Accepted Answer

A)  allow the same variable to be an input attribute in one rule and an output attribute in another rule. 
B)  allow more than one input attribute in a single rule. 
C)  require input attributes to take on numeric values. 
D)  require each rule to have exactly one categorical output attribute. 
A)  allow the same variable to be an input attribute in one rule and an output attribute in another rule. 
B)  allow more than one input attribute in a single rule. 
C)  require input attributes to take on numeric values. 
D)  require each rule to have exactly one categorical output attribute.

Question 11

Use the three-class confusion matrix below to answer questions 1 through 3.
 $$\begin{array}{l}
\text { Computrd Decision }\
\begin{array} { | c | c | c | c | } 
\hline & \text { Class 1 } & \text { Class } 2 & \text { Class } 3 \
\hline \text { Class 1 } & 10 & 5 & 3 \
\hline \text { Class } 2 & 5 & 15 & 3 \
\hline \text { Class } 3 & 2 & 2 & 5 \
\hline
\end{array}
\end{array}$$
-What percent of the instances were correctly classified?

Accepted Answer

The answer of Use the three-class confusion matrix below to...

Question 12

Use the confusion matrix for Model X and confusion matrix for Model Y to answer questions 4 through 6.
 $$\begin{array} { | c | c | c | c | c | c | c | } 
\hline \begin{array} { c } 
\text { Model } \
\mathrm { X }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Accept }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Reject }
\end{array} & \begin{array} { c } 
\text { Model } \
\text { Y }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Accept }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Reject }
\end{array} \
\hline \text { Accept } & 10 & 5 &  \text { Accept  } & 6 & 9 \
\hline \text { Reject } & \mathbf { 2 5 } & 60 &  \text { Reject } & 15 & 70 \
\hline
\end{array}$$
-How many instances were classified as an accept by Model X?

Accepted Answer

The answer of Use the confusion matrix for Model X...

Question 13

Use the confusion matrix for Model X and confusion matrix for Model Y to answer questions 4 through 6.
 $$\begin{array} { | c | c | c | c | c | c | c | } 
\hline \begin{array} { c } 
\text { Model } \
\mathrm { X }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Accept }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Reject }
\end{array} & \begin{array} { c } 
\text { Model } \
\text { Y }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Accept }
\end{array} & \begin{array} { c } 
\text { Computed } \
\text { Reject }
\end{array} \
\hline \text { Accept } & 10 & 5 &  \text { Accept  } & 6 & 9 \
\hline \text { Reject } & \mathbf { 2 5 } & 60 &  \text { Reject } & 15 & 70 \
\hline
\end{array}$$
-You will notice that the lift for both models is the same. Assume that the cost of a false reject is significantly higher than the cost of a false accept. Which model is the better choice?

Accepted Answer

The answer of Use the confusion matrix for Model X...

Question 14

Which of the following is a common use of unsupervised clustering?

Accepted Answer

A)  detect outliers 
B)  determine a best set of input attributes for supervised learning 
C)  evaluate the likely performance of a supervised learner model 
D)  determine if meaningful relationships can be found in a dataset 
E)  All of a,b,c, and d are common uses of unsupervised clustering. 
A)  detect outliers 
B)  determine a best set of input attributes for supervised learning 
C)  evaluate the likely performance of a supervised learner model 
D)  determine if meaningful relationships can be found in a dataset 
E)  All of a,b,c, and d are common uses of unsupervised clustering.

Question 15

The average positive difference between computed and desired outcome values.

Accepted Answer

A)  root mean squared error 
B)  mean squared error 
C)  mean absolute error 
D)  mean positive error 
A)  root mean squared error 
B)  mean squared error 
C)  mean absolute error 
D)  mean positive error

Question 16

Which statement is true about neural network and linear regression models?

Accepted Answer

A)  Both models require input attributes to be numeric. 
B)  Both models require numeric attributes to range between 0 and 1. 
C)  The output of both models is a categorical attribute value. 
D)  Both techniques build models whose output is determined by a linear sum of weighted input attribute values. 
E)  More than one of a,b,c or d is true. 
A)  Both models require input attributes to be numeric. 
B)  Both models require numeric attributes to range between 0 and 1. 
C)  The output of both models is a categorical attribute value. 
D)  Both techniques build models whose output is determined by a linear sum of weighted input attribute values. 
E)  More than one of a,b,c or d is true.

Another name for an output attribute.

Given desired class C and population P, lift is defined as

Classification problems are distinguished from estimation problems in that

Which statement about outliers is true?

Use the three-class confusion matrix below to answer questions 1 through 3. Computrd Decision Class 1 Class 2 Class 3 Class 1 10 5 3 Class 2 5 15 3 Class 3 2 2 5 -How many class 2 instances are in the dataset?

Which statement is true about prediction problems?

Use the three-class confusion matrix below to answer questions 1 through 3. Computrd Decision Class 1 Class 2 Class 3 Class 1 10 5 3 Class 2 5 15 3 Class 3 2 2 5 -How many instances were incorrectly classified with class 3?

Use the confusion matrix for Model X and confusion matrix for Model Y to answer questions 4 through 6. Model Computed Accept Computed Reject Model Y Computed Accept Computed Reject Accept 10 5 Accept 6 9 Reject 60 Reject 15 70 -Compute the lift for Model Y.

Unlike traditional production rules, association rules

Use the three-class confusion matrix below to answer questions 1 through 3. Computrd Decision Class 1 Class 2 Class 3 Class 1 10 5 3 Class 2 5 15 3 Class 3 2 2 5 -What percent of the instances were correctly classified?

Use the confusion matrix for Model X and confusion matrix for Model Y to answer questions 4 through 6. Model Computed Accept Computed Reject Model Y Computed Accept Computed Reject Accept 10 5 Accept 6 9 Reject 60 Reject 15 70 -How many instances were classified as an accept by Model X?

Which of the following is a common use of unsupervised clustering?

The average positive difference between computed and desired outcome values.

Which statement is true about neural network and linear regression models?

Data Mining: a First View

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Statistical Techniques

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters

Exam 2: Data Mining: a Closer Look

Another name for an output attribute.

Given desired class C and population P, lift is defined as

Classification problems are distinguished from estimation problems in that

Which statement about outliers is true?

Use the three-class confusion matrix below to answer questions 1 through 3. Computrd Decision Class 1 Class 2 Class 3 Class 1 10 5 3 Class 2 5 15 3 Class 3 2 2 5 -How many class 2 instances are in the dataset?

Which statement is true about prediction problems?

Use the three-class confusion matrix below to answer questions 1 through 3. Computrd Decision Class 1 Class 2 Class 3 Class 1 10 5 3 Class 2 5 15 3 Class 3 2 2 5 -How many instances were incorrectly classified with class 3?

Use the confusion matrix for Model X and confusion matrix for Model Y to answer questions 4 through 6. Model Computed Accept Computed Reject Model Y Computed Accept Computed Reject Accept 10 5 Accept 6 9 Reject 60 Reject 15 70 -Compute the lift for Model Y.

Unlike traditional production rules, association rules

Use the three-class confusion matrix below to answer questions 1 through 3. Computrd Decision Class 1 Class 2 Class 3 Class 1 10 5 3 Class 2 5 15 3 Class 3 2 2 5 -What percent of the instances were correctly classified?

Use the confusion matrix for Model X and confusion matrix for Model Y to answer questions 4 through 6. Model Computed Accept Computed Reject Model Y Computed Accept Computed Reject Accept 10 5 Accept 6 9 Reject 60 Reject 15 70 -How many instances were classified as an accept by Model X?

Which of the following is a common use of unsupervised clustering?

The average positive difference between computed and desired outcome values.

Which statement is true about neural network and linear regression models?

Data Mining: a First View

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Statistical Techniques

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters