Question 1

______________ involves descriptive statistics, data visualization, and clustering.

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 A

Question 2

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. A

Question 3

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. A

Question 4

A _____ classifies a categorical outcome variable by splitting observations into groups via a sequence of hierarchical rules.

Accepted Answer

A) regression tree 
B) scatter chart 
C) classification tree 
D) classification confusion matrix 
A) regression tree 
B) scatter chart 
C) classification tree 
D) classification confusion matrix

Question 5

____________ is a category of data-mining techniques in which an algorithm learns how to predict or classify 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

Question 6

____________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 7

Misclassifying an actual ______ observation as a(n) ______ observation is known as a false positive.

Accepted Answer

A) Class 0, Class 1 
B) Class 1, Class 0 
C) error, accuracy 
D) false, true 
A) Class 0, Class 1 
B) Class 1, Class 0 
C) error, accuracy 
D) false, true

Question 8

Test set is the data set used to

Accepted Answer

A) build the data mining model. 
B) estimate accuracy of candidate models on unseen data. 
C) estimate accuracy of final model on unseen data. 
D) show counts of actual versus predicted class values. 
A) build the data mining model. 
B) estimate accuracy of candidate models on unseen data. 
C) estimate accuracy of final model on unseen data. 
D) show counts of actual versus predicted class values.

Question 9

_______compares the number of actual Class 1 observations identified if considered in decreasing order of their estimated probability if randomly selected.

Accepted Answer

A) Cumulative lift 
B) ​Classification confusion 
C) Decile-wise lift chart 
D) ROC curve 
A) Cumulative lift 
B) ​Classification confusion 
C) Decile-wise lift chart 
D) ROC curve

Question 10

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 11

Given the following classification confusion matrix, what is the accuracy?
​ $$\begin{array} { | l | c | c | } 
\hline  { \text { Classification Confusion Matrix } } \
\hline &  { \text { Predicted Class } } \
\hline \text { Actual Class } & 1 & 0 \
\hline 1 & 224 & 85 \
\hline 0 & 28 & 3,258 \
\hline
\end{array}$$ ​
​

Accepted Answer

The answer of Given the following classification confusion matrix, what...

Question 12

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 13

_____ refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data.

Accepted Answer

A) Underfitting 
B) Overfitting 
C) Oversampling 
D) Undersampling 
A) Underfitting 
B) Overfitting 
C) Oversampling 
D) Undersampling

Question 14

Given the following classification confusion matrix, what is the overall error rate? $$\begin{array} { | l | c | c | } 
\hline  { \text { Classification Confusion Matrix } } \
\hline &  { \text { Predicted Class } } \
\hline \text { Actual Class } & 1 & 0 \
\hline 1 & 224 & 85 \
\hline 0 & 28 & 3,258 \
\hline
\end{array}$$ ​
​
​

Accepted Answer

The answer of Given the following classification confusion matrix, what...

Question 15

___________ is a generalization of linear regression for predicting a categorical outcome variable.

Accepted Answer

A) Multiple linear regression 
B) Logistic regression 
C) Discriminant analysis 
D) Cluster analysis 
A) Multiple linear regression 
B) Logistic regression 
C) Discriminant analysis 
D) Cluster analysis

Question 16

______________ is NOT a step of 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 17

A(n)_______________ is often displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.

Accepted Answer

A) record 
B) data point 
C) classification 
D) location 
A) record 
B) data point 
C) classification 
D) location

Question 18

Estimation methods are also referred to as

Accepted Answer

A) prediction methods. 
B) clustering methods. 
C) association methods. 
D) supervised methods. 
A) prediction methods. 
B) clustering methods. 
C) association methods. 
D) supervised methods.

Question 19

Data-mining methods for predicting 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.

Question 20

The X axis of a lift chart shows

Accepted Answer

A) number of actual Class 1 records identified. 
B) ratio of decile mean to overall mean. 
C) the number of actual Class 1 records. 
D) the ratio of the overall mean to the decile mean. 
A) number of actual Class 1 records identified. 
B) ratio of decile mean to overall mean. 
C) the number of actual Class 1 records. 
D) the ratio of the overall mean to the decile mean.

______________ involves descriptive statistics, data visualization, and clustering.

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

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

A _____ classifies a categorical outcome variable by splitting observations into groups via a sequence of hierarchical rules.

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

____________is a method of extracting data relevant to the business problem under consideration. It is the first step in the Data Mining process.

Misclassifying an actual observation as a(n) observation is known as a false positive.

Test set is the data set used to

_______compares the number of actual Class 1 observations identified if considered in decreasing order of their estimated probability if randomly selected.

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

Given the following classification confusion matrix, what is the accuracy? Classification Confusion Matrix Predicted Class Actual Class 1 0 1 224 85 0 28 3,258

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

_____ refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data.

Given the following classification confusion matrix, what is the overall error rate? Classification Confusion Matrix Predicted Class Actual Class 1 0 1 224 85 0 28 3,258

___________ is a generalization of linear regression for predicting a categorical outcome variable.

______________ is NOT a step of Data Mining Process.

A(n)_______________ is often displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.

Estimation methods are also referred to as

Data-mining methods for predicting an outcome based on a set of input variables is referred to as

The X axis of a lift chart shows

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

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Monte Carlo Simulation

Decision Analysis

Filters

Exam 9: Predictive Data Mining

______________ involves descriptive statistics, data visualization, and clustering.

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

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

A _____ classifies a categorical outcome variable by splitting observations into groups via a sequence of hierarchical rules.

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

____________is a method of extracting data relevant to the business problem under consideration. It is the first step in the Data Mining process.

Misclassifying an actual ______ observation as a(n) ______ observation is known as a false positive.

Test set is the data set used to

_______compares the number of actual Class 1 observations identified if considered in decreasing order of their estimated probability if randomly selected.

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

Given the following classification confusion matrix, what is the accuracy? ​ Classification Confusion Matrix Predicted Class Actual Class 1 0 1 224 85 0 28 3,258 ​ ​

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

_____ refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data.

Given the following classification confusion matrix, what is the overall error rate? Classification Confusion Matrix Predicted Class Actual Class 1 0 1 224 85 0 28 3,258 ​ ​ ​

___________ is a generalization of linear regression for predicting a categorical outcome variable.

______________ is NOT a step of Data Mining Process.

A(n)_______________ is often displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.

Estimation methods are also referred to as

Data-mining methods for predicting an outcome based on a set of input variables is referred to as

The X axis of a lift chart shows

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

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Monte Carlo Simulation

Decision Analysis

Filters

Misclassifying an actual observation as a(n) observation is known as a false positive.

Given the following classification confusion matrix, what is the accuracy? Classification Confusion Matrix Predicted Class Actual Class 1 0 1 224 85 0 28 3,258

Given the following classification confusion matrix, what is the overall error rate? Classification Confusion Matrix Predicted Class Actual Class 1 0 1 224 85 0 28 3,258