Question 1

For how long do SVM models continue to be accurate and actionable?

Accepted Answer

A)  for as long as the developers stay with the firm 
B)  for as long as management support continues to exist for the project 
C)  for as long as you choose to use them 
D)  for as long as the behavior of the domain stays the same 
A)  for as long as the developers stay with the firm 
B)  for as long as management support continues to exist for the project 
C)  for as long as you choose to use them 
D)  for as long as the behavior of the domain stays the same

Question 2

Backpropagation learning algorithms for neural networks are

Accepted Answer

A)  the least popular algorithm due to their inaccuracy. 
B)  used without hidden layers for effectiveness. 
C)  used without a training set of data. 
D)  required to have error tolerance set in advance. 
A)  the least popular algorithm due to their inaccuracy. 
B)  used without hidden layers for effectiveness. 
C)  used without a training set of data. 
D)  required to have error tolerance set in advance.

Question 3

Define the term sensitivity analysis as it relates to ANNs.

Accepted Answer

Sensitivity analysis is a meth

Question 4

The opening vignette teaches us that ________ medicine is a relatively new term coined in the healthcare arena, where the main idea is to dig deep into past experiences to discover new and useful knowledge to improve medical and managerial procedures in healthcare.

Accepted Answer

The answer of The opening vignette teaches us that ________...

Question 5

Due largely to their better classification results, support vector machines (SVMs) have recently become a popular technique for ________-type problems.

Accepted Answer

The answer of Due largely to their better classification results,...

Question 6

In the opening vignette, predictive modeling is described as

Accepted Answer

A)  estimating the future using the past. 
B)  not yet accepted in the business world. 
C)  the least practiced branch of data mining. 
D)  unable to handle complex predictive problems. 
A)  estimating the future using the past. 
B)  not yet accepted in the business world. 
C)  the least practiced branch of data mining. 
D)  unable to handle complex predictive problems.

Question 7

In the process of image recognition (or categorization), images are first transformed into a multidimensional ________ and then, using machine-learning techniques, are categorized into a finite number of classes.

Accepted Answer

The answer of In the process of image recognition (or...

Question 8

Using support vector machines, you must normalize the data before you numericize it.

Accepted Answer

A) True 
 B)False

Question 9

Why have neural networks shown much promise in many forecasting and business classification applications?

Accepted Answer

Because of their ability to "l

Question 10

The use of hidden layers and new topologies and algorithms renewed waning interest in neural networks.

Accepted Answer

A) True 
 B)False

Question 11

When using support vector machines, in which stage do you select the kernel type (e.g., RBF, Sigmoid)?

Accepted Answer

A)  preprocessing the data 
B)  developing the model 
C)  experimentation 
D)  deploying the model 
A)  preprocessing the data 
B)  developing the model 
C)  experimentation 
D)  deploying the model

Question 12

The k-nearest neighbor algorithm appears well-suited to solving image recognition and categorization problems.

Accepted Answer

A) True 
 B)False

Question 13

What are the five steps in the backpropagation learning algorithm?

Accepted Answer

1.Initialize weights with random values

Question 14

Support vector machines are a popular machine learning technique primarily because of

Accepted Answer

A)  their relative cost and superior predictive power. 
B)  their superior predictive power and their theoretical foundation. 
C)  their relative cost and relative ease of use. 
D)  their high effectiveness in the very few areas where they can be used. 
A)  their relative cost and superior predictive power. 
B)  their superior predictive power and their theoretical foundation. 
C)  their relative cost and relative ease of use. 
D)  their high effectiveness in the very few areas where they can be used.

Question 15

Each ANN is composed of a collection of neurons that are grouped into layers. One of these layers is the hidden layer. Define the hidden layer.

Accepted Answer

A hidden layer is a layer of n

Question 16

In the power generators case study, data mining-driven software tools, including data-driven ________ technologies with historical data, helped an energy company reduce emissions of NOx and CO.

Accepted Answer

The answer of In the power generators case study, data...

Question 17

Neural computing refers to a ________ methodology for machine learning.

Accepted Answer

The answer of Neural computing refers to a ________ methodology...

Question 18

What is a major drawback to the basic majority voting classification in kNN?

Accepted Answer

A)  It requires frequent human subjective input during computation. 
B)  Classes that are more clustered tend to dominate prediction. 
C)  Even the naive version of the algorithm is hard to implement. 
D)  Classes with more frequent examples tend to dominate prediction. 
A)  It requires frequent human subjective input during computation. 
B)  Classes that are more clustered tend to dominate prediction. 
C)  Even the naive version of the algorithm is hard to implement. 
D)  Classes with more frequent examples tend to dominate prediction.

Question 19

Why is sensitivity analysis frequently used for artificial neural networks?

Accepted Answer

A)  because it is required by all major artificial neural networks 
B)  because some consequences of mistakes by the network might be fatal, so justification may matter 
C)  because it is generally informative, although it cannot help to identify cause-and-effect relationships among variables 
D)  because it provides a complete description of the inner workings of the artificial neural network 
A)  because it is required by all major artificial neural networks 
B)  because some consequences of mistakes by the network might be fatal, so justification may matter 
C)  because it is generally informative, although it cannot help to identify cause-and-effect relationships among variables 
D)  because it provides a complete description of the inner workings of the artificial neural network

Question 20

All of the following are disadvantages/limitations of the SVM technique EXCEPT

Accepted Answer

A)  model building involves complex and time-demanding calculations. 
B)  selection of the kernel type and kernel function parameters is difficult. 
C)  they have high algorithmic complexity and extensive memory requirements for complex tasks. 
D)  their accuracy is poor in many domains compared to neural networks. 
A)  model building involves complex and time-demanding calculations. 
B)  selection of the kernel type and kernel function parameters is difficult. 
C)  they have high algorithmic complexity and extensive memory requirements for complex tasks. 
D)  their accuracy is poor in many domains compared to neural networks.

For how long do SVM models continue to be accurate and actionable?

Backpropagation learning algorithms for neural networks are

Define the term sensitivity analysis as it relates to ANNs.

The opening vignette teaches us that ________ medicine is a relatively new term coined in the healthcare arena, where the main idea is to dig deep into past experiences to discover new and useful knowledge to improve medical and managerial procedures in healthcare.

Due largely to their better classification results, support vector machines (SVMs) have recently become a popular technique for ________-type problems.

In the opening vignette, predictive modeling is described as

In the process of image recognition (or categorization), images are first transformed into a multidimensional ________ and then, using machine-learning techniques, are categorized into a finite number of classes.

Using support vector machines, you must normalize the data before you numericize it.

Why have neural networks shown much promise in many forecasting and business classification applications?

The use of hidden layers and new topologies and algorithms renewed waning interest in neural networks.

When using support vector machines, in which stage do you select the kernel type (e.g., RBF, Sigmoid)?

The k-nearest neighbor algorithm appears well-suited to solving image recognition and categorization problems.

What are the five steps in the backpropagation learning algorithm?

Support vector machines are a popular machine learning technique primarily because of

Each ANN is composed of a collection of neurons that are grouped into layers. One of these layers is the hidden layer. Define the hidden layer.

In the power generators case study, data mining-driven software tools, including data-driven ________ technologies with historical data, helped an energy company reduce emissions of NOx and CO.

Neural computing refers to a ________ methodology for machine learning.

What is a major drawback to the basic majority voting classification in kNN?

Why is sensitivity analysis frequently used for artificial neural networks?

All of the following are disadvantages/limitations of the SVM technique EXCEPT

An Overview of Business Intelligence, Analytics, and Decision Support

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Business Reporting, Visual Analytics, and Business Performance Management

Data Mining

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Exam 6: Techniques for Predictive Modeling

For how long do SVM models continue to be accurate and actionable?

Backpropagation learning algorithms for neural networks are

Define the term sensitivity analysis as it relates to ANNs.

The opening vignette teaches us that ________ medicine is a relatively new term coined in the healthcare arena, where the main idea is to dig deep into past experiences to discover new and useful knowledge to improve medical and managerial procedures in healthcare.

Due largely to their better classification results, support vector machines (SVMs) have recently become a popular technique for ________-type problems.

In the opening vignette, predictive modeling is described as

In the process of image recognition (or categorization), images are first transformed into a multidimensional ________ and then, using machine-learning techniques, are categorized into a finite number of classes.

Using support vector machines, you must normalize the data before you numericize it.

Why have neural networks shown much promise in many forecasting and business classification applications?

The use of hidden layers and new topologies and algorithms renewed waning interest in neural networks.

When using support vector machines, in which stage do you select the kernel type (e.g., RBF, Sigmoid)?

The k-nearest neighbor algorithm appears well-suited to solving image recognition and categorization problems.

What are the five steps in the backpropagation learning algorithm?

Support vector machines are a popular machine learning technique primarily because of

Each ANN is composed of a collection of neurons that are grouped into layers. One of these layers is the hidden layer. Define the hidden layer.

In the power generators case study, data mining-driven software tools, including data-driven ________ technologies with historical data, helped an energy company reduce emissions of NOx and CO.

Neural computing refers to a ________ methodology for machine learning.

What is a major drawback to the basic majority voting classification in kNN?

Why is sensitivity analysis frequently used for artificial neural networks?

All of the following are disadvantages/limitations of the SVM technique EXCEPT

An Overview of Business Intelligence, Analytics, and Decision Support

Foundations and Technologies for Decision Making

Data Warehousing

Business Reporting, Visual Analytics, and Business Performance Management

Data Mining

Text Analytics, Text Mining, and Sentiment Analysis

Web Analytics, Web Mining, and Social Analytics

Model-Based Decision Making: Optimization and Multi-Criteria Systems

Modeling and Analysis: Heuristic Search Methods and Simulation

Automated Decision Systems and Expert Systems

Knowledge Management and Collaborative Systems

Big Data and Analytics

Business Analytics: Emerging Trends and Future Impacts

Filters