Question 1

Data mining methods for classifying or estimating an outcome based on a set of input variables is referred to as

Accepted Answer

A) supervised learning. 
B) unsupervised learning. 
C) dimension reduction. 
D) data sampling. 
A) supervised learning. 
B) unsupervised learning. 
C) dimension reduction. 
D) data sampling. A

Question 2

How many Class 1's are correctly classified as Class 1 in the Table below? $$\begin{array} { | l | c | c | } 
\hline  { \text { Confusion Matrix } } \
\hline &  { \text { Predicted Class } } \
\hline \text { Actual Class } & \mathbf { 1 } & \mathbf { 0 } \
\hline \mathbf { 1 } & 221 & 100 \
\hline \mathbf { 0 } & 30 & 3,000 \
\hline
\end{array}$$ ?

Accepted Answer

A) 221 
B) 100 
C) 30 
D) 3,000 
A) 221 
B) 100 
C) 30 
D) 3,000 A

Question 3

__________ is a category of data mining techniques in which an algorithm learns how to classify or estimate an outcome variable of interest.

Accepted Answer

A) Supervised learning 
B) Unsupervised learning 
C) Dimension reduction 
D) Data sampling 
A) Supervised learning 
B) Unsupervised learning 
C) Dimension reduction 
D) Data sampling A

Question 4

How many Class 1's are incorrectly classified as Class 0? $$\begin{array} { | l | c | c | } 
\hline { \text { Confusion Matrix } } \
\hline & { \text { Predicted Class } } \
\hline \text { Actual Class } & \mathbf { 1 } & \mathbf { 0 } \
\hline \mathbf { 1 } & 221 & 100 \
\hline \mathbf { 0 } & 30 & 3,000 \
\hline
\end{array}$$ ?

Accepted Answer

A) 221 
B) 100 
C) 30 
D) 3,000 
A) 221 
B) 100 
C) 30 
D) 3,000

Question 5

__________ is dividing the sample data into three sets for training, validation, and testing of the data mining algorithm performance.

Accepted Answer

A) Data sampling 
B) Data partitioning 
C) Data preparation 
D) Model assessment 
A) Data sampling 
B) Data partitioning 
C) Data preparation 
D) Model assessment

Question 6

__________ is NOT a step of the data mining process.

Accepted Answer

A) Data sampling 
B) Data partitioning 
C) Model construction 
D) Supervised learning 
A) Data sampling 
B) Data partitioning 
C) Model construction 
D) Supervised learning

Question 7

One minus the overall error rate is often referred to as the __________ of the model.

Accepted Answer

A) sensitivity 
B) accuracy 
C) specificity 
D) cutoff value 
A) sensitivity 
B) accuracy 
C) specificity 
D) cutoff value

Question 8

__________ is one minus the Class 0 error rate.

Accepted Answer

A) Sensitivity 
B) Specificity 
C) Accuracy 
D) Cutoff value 
A) Sensitivity 
B) Specificity 
C) Accuracy 
D) Cutoff value

Question 9

The percent of misclassified records out of the total records in the validation data is known as the

Accepted Answer

A) overall error rate. 
B) error. 
C) accuracy. 
D) class. 
A) overall error rate. 
B) error. 
C) accuracy. 
D) class.

Question 10

Determine a freshman's likely first-year grade point average from the student's Scholastic Aptitude Test (SAT) score, high school grade point average, and number of extra-curricular activities. This is an example of

Accepted Answer

A) classification of a categorical outcome. 
B) estimation of a continuous outcome. 
C) prediction of a categorical outcome. 
D) unsupervised learning. 
A) classification of a categorical outcome. 
B) estimation of a continuous outcome. 
C) prediction of a categorical outcome. 
D) unsupervised learning.

Question 11

As we increase the cutoff value, _______ error will decrease and _________ error will rise.

Accepted Answer

A) Class 0, Class 1 
B) Class 1, Class 0 
C) false, true 
D) None of these are correct. 
A) Class 0, Class 1 
B) Class 1, Class 0 
C) false, true 
D) None of these are correct.

Question 12

__________ compares the number of actual Class 1 observations identified if considered in decreasing order of their estimated probability if randomly classified.

Accepted Answer

A) Cumulative lift 
B) Confusion 
C) Decile-wise lift chart 
D) ROC curve 
A) Cumulative lift 
B) Confusion 
C) Decile-wise lift chart 
D) ROC curve

Question 13

A(n) __________ matrix displays a model's correct and incorrect classification.

Accepted Answer

A) cumulative lift 
B) confusion 
C) decile-wise lift chart 
D) ROC curve 
A) cumulative lift 
B) confusion 
C) decile-wise lift chart 
D) ROC curve

Question 14

Applying descriptive statistics and data visualization to the training set to understand the data and assist in the selection of an appropriate technique is a part of

Accepted Answer

A) data exploration. 
B) data partitioning. 
C) data preparation. 
D) model assessment. 
A) data exploration. 
B) data partitioning. 
C) data preparation. 
D) model assessment.

Question 15

Separate error rates with respect to the false negative and false positive cases are computed to take into account the

Accepted Answer

A) asymmetric costs in misclassification. 
B) symmetric weights of these two cases. 
C) distortions due to outliers. 
D) effect of sampling error. 
A) asymmetric costs in misclassification. 
B) symmetric weights of these two cases. 
C) distortions due to outliers. 
D) effect of sampling error.

Question 16

The set of recorded values of variables associated with a single entity is a(n)

Accepted Answer

A) observation. 
B) data point. 
C) classification. 
D) location. 
A) observation. 
B) data point. 
C) classification. 
D) location.

Question 17

The impurity of a group of observations is based on the variance of the outcome value for the observations in the group for

Accepted Answer

A) regression trees. 
B) time-series plots. 
C) classification trees. 
D) cumulative lift charts. 
A) regression trees. 
B) time-series plots. 
C) classification trees. 
D) cumulative lift charts.

Question 18

An observation classified as part of a group with a characteristic when it actually does not have the characteristic is termed as a(n)

Accepted Answer

A) false negative. 
B) false positive. 
C) residual. 
D) outlier. 
A) false negative. 
B) false positive. 
C) residual. 
D) outlier.

Question 19

__________ is a method of extracting data relevant to the business problem under consideration. It is the first step in the data mining process.

Accepted Answer

A) Data sampling 
B) Data partitioning 
C) Model construction 
D) Model assessment 
A) Data sampling 
B) Data partitioning 
C) Model construction 
D) Model assessment

Question 20

Classifying a record as belonging to one class when it belongs to another class is referred to as a(n)

Accepted Answer

A) overall error rate. 
B) error. 
C) accuracy. 
D) class. 
A) overall error rate. 
B) error. 
C) accuracy. 
D) class.

Data mining methods for classifying or estimating an outcome based on a set of input variables is referred to as

How many Class 1's are correctly classified as Class 1 in the Table below? Confusion Matrix Predicted Class Actual Class 221 100 30 3,000 ?

__________ is a category of data mining techniques in which an algorithm learns how to classify or estimate an outcome variable of interest.

How many Class 1's are incorrectly classified as Class 0? Confusion Matrix Predicted Class Actual Class 221 100 30 3,000 ?

__________ is dividing the sample data into three sets for training, validation, and testing of the data mining algorithm performance.

__________ is NOT a step of the data mining process.

One minus the overall error rate is often referred to as the __________ of the model.

__________ is one minus the Class 0 error rate.

The percent of misclassified records out of the total records in the validation data is known as the

Determine a freshman's likely first-year grade point average from the student's Scholastic Aptitude Test (SAT) score, high school grade point average, and number of extra-curricular activities. This is an example of

As we increase the cutoff value, _ error will decrease and ___ error will rise.

__________ compares the number of actual Class 1 observations identified if considered in decreasing order of their estimated probability if randomly classified.

A(n) __________ matrix displays a model's correct and incorrect classification.

Applying descriptive statistics and data visualization to the training set to understand the data and assist in the selection of an appropriate technique is a part of

Separate error rates with respect to the false negative and false positive cases are computed to take into account the

The set of recorded values of variables associated with a single entity is a(n)

The impurity of a group of observations is based on the variance of the outcome value for the observations in the group for

An observation classified as part of a group with a characteristic when it actually does not have the characteristic is termed as a(n)

__________ is a method of extracting data relevant to the business problem under consideration. It is the first step in the data mining process.

Classifying a record as belonging to one class when it belongs to another class is referred to as a(n)

Introduction

Descriptive Statistics

Data Visualization

Descriptive Data Mining

Probability: an Introduction to Modeling Uncertainty

Statistical Inference

Linear Regression

Time Series Analysis and Forecasting

Spreadsheet Models

Monte Carlo Simulation

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Decision Analysis

Filters

Exam 9: Predictive Data Mining

Data mining methods for classifying or estimating an outcome based on a set of input variables is referred to as

How many Class 1's are correctly classified as Class 1 in the Table below? Confusion Matrix Predicted Class Actual Class 221 100 30 3,000 ?

__________ is a category of data mining techniques in which an algorithm learns how to classify or estimate an outcome variable of interest.

How many Class 1's are incorrectly classified as Class 0? Confusion Matrix Predicted Class Actual Class 221 100 30 3,000 ?

__________ is dividing the sample data into three sets for training, validation, and testing of the data mining algorithm performance.

__________ is NOT a step of the data mining process.

One minus the overall error rate is often referred to as the __________ of the model.

__________ is one minus the Class 0 error rate.

The percent of misclassified records out of the total records in the validation data is known as the

Determine a freshman's likely first-year grade point average from the student's Scholastic Aptitude Test (SAT) score, high school grade point average, and number of extra-curricular activities. This is an example of

As we increase the cutoff value, _______ error will decrease and _________ error will rise.

__________ compares the number of actual Class 1 observations identified if considered in decreasing order of their estimated probability if randomly classified.

A(n) __________ matrix displays a model's correct and incorrect classification.

Applying descriptive statistics and data visualization to the training set to understand the data and assist in the selection of an appropriate technique is a part of

Separate error rates with respect to the false negative and false positive cases are computed to take into account the

The set of recorded values of variables associated with a single entity is a(n)

The impurity of a group of observations is based on the variance of the outcome value for the observations in the group for

An observation classified as part of a group with a characteristic when it actually does not have the characteristic is termed as a(n)

__________ is a method of extracting data relevant to the business problem under consideration. It is the first step in the data mining process.

Classifying a record as belonging to one class when it belongs to another class is referred to as a(n)

Introduction

Descriptive Statistics

Data Visualization

Descriptive Data Mining

Probability: an Introduction to Modeling Uncertainty

Statistical Inference

Linear Regression

Time Series Analysis and Forecasting

Spreadsheet Models

Monte Carlo Simulation

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Decision Analysis

Filters

As we increase the cutoff value, _ error will decrease and ___ error will rise.