Question 1

What does the SVM algorithm attempt to optimize?

Accepted Answer

A)  Percent of variance explained 
B)  Number of Hyperplanes 
C)  Maximum margins 
D)  Maximum number of support vectors 
E)  Minimum cost 
A)  Percent of variance explained 
B)  Number of Hyperplanes 
C)  Maximum margins 
D)  Maximum number of support vectors 
E)  Minimum cost C

Question 2

Which modeling package centers of creating and using "Learners" and "Tasks"? __________________.

Accepted Answer

mlr3

Question 3

When machine learning models (not just SVM) are implemented using the train command of the caret package, tuning of the model is accomplished on a built-in basis.

Accepted Answer

A) True 
 B)False  True

Question 4

In one sentence, what does R's "caret" package do? Name three of its procedures discussed in Chapter 6.

Accepted Answer

The answer of In one sentence, what does R's "caret"...

Question 5

The support vector machine (SVM) method may be used to address which of the following?

Accepted Answer

A)  Assembly line process problems 
B)  Classification problems 
C)  Regression problems 
D)  Both B and C 
A)  Assembly line process problems 
B)  Classification problems 
C)  Regression problems 
D)  Both B and C

Question 6

Which is NOT true of learning vector quantization (LVQ) models?

Accepted Answer

A)  LVQ is a type of artificial neural network methodology. 
B)  LVQ can handle both classification and regression problems. 
C)  LVQ requires supervised learning. 
D)  The researcher can set the learning rate of the LVQ algorithm. 
A)  LVQ is a type of artificial neural network methodology. 
B)  LVQ can handle both classification and regression problems. 
C)  LVQ requires supervised learning. 
D)  The researcher can set the learning rate of the LVQ algorithm.

Question 7

SVM routinely outperforms OLS regression when the data a nonlinear?

Accepted Answer

A) True 
 B)False

Question 8

The author of the svm command believes SVM models must be tuned to obtain sensible results.

Accepted Answer

A) True 
 B)False

Question 9

What is the default kernel in SVM regression using the svm command, and in most other SVM commands?

Accepted Answer

A)  Linear 
B)  Polynomial 
C)  Sigmoid 
D)  Radial 
A)  Linear 
B)  Polynomial 
C)  Sigmoid 
D)  Radial

Question 10

A "loss function" is a metric to measure model performance. Is the loss function in SVM closer to that in OLS regression or logistic regression? Is it the same as the one you pick (explain briefly)?
Which closer? ______________________
Same?/Explain:

Accepted Answer

The answer of A "loss function" is a metric to...

Question 11

What is the Kappa statistic in the context of caret's machine learning models?

Accepted Answer

A)  Kappa represents is the percent by which the researcher's model is better than random. 
B)  Kappa is one type of kernel for caret models. 
C)  Kappa is a visualization (plotting) parameter under caret. 
D)  Kappa is a tuning parameter under caret. 
A)  Kappa represents is the percent by which the researcher's model is better than random. 
B)  Kappa is one type of kernel for caret models. 
C)  Kappa is a visualization (plotting) parameter under caret. 
D)  Kappa is a tuning parameter under caret.

Question 12

In what package is the svm command found as the default SVM method?_______________________

Accepted Answer

The answer of In what package is the svm command...

Question 13

What is the purpose of "kernels" in SVM?

Accepted Answer

A)  Kernels try to linearize nonlinear problems and make a solution possible. 
B)  Kernels optimize model selection (selection of the best variables) 
C)  Kernels visualize SVM results 
D)  Kernels are the source code underlying SVM 
A)  Kernels try to linearize nonlinear problems and make a solution possible. 
B)  Kernels optimize model selection (selection of the best variables) 
C)  Kernels visualize SVM results 
D)  Kernels are the source code underlying SVM

Question 14

The "mlr3" package is an alternative to which other widely-used R package?

Accepted Answer

A)  rpart 
B)  kknn 
C)  caret 
D)  svm 
A)  rpart 
B)  kknn 
C)  caret 
D)  svm

Question 15

Which is NOT a positive aspect of SVM methods?

Accepted Answer

A)  It handles both classification and regression problems 
B)  It supports traditional parametric significance testing 
C)  Cross-validation is built in. 
D)  It handles nonlinearity and interaction effects autormatically 
A)  It handles both classification and regression problems 
B)  It supports traditional parametric significance testing 
C)  Cross-validation is built in. 
D)  It handles nonlinearity and interaction effects autormatically

Question 16

In SVM, what are gamma, degree, coef0, and nu?

Accepted Answer

A)  Possible tuning parameters which may optimize the SVM model. 
B)  Labels for coefficients output by SVM as part of the solution 
C)  Labels for stages in solving a problem with SVM 
D)  All of the above 
A)  Possible tuning parameters which may optimize the SVM model. 
B)  Labels for coefficients output by SVM as part of the solution 
C)  Labels for stages in solving a problem with SVM 
D)  All of the above

Question 17

For problems where the DV is binary, both OLS regression and SVM are inappropriate and logistic regression should be used instead.

Accepted Answer

A) True 
 B)False

Question 18

Which is NOT true of gradient boosting machine (GBM) models?

Accepted Answer

A)  GBM can be implemented under the "caret" package. 
B)  Decision trees and random forests can be base learning methods under GBM. 
C)  OLS regression can be a base learner model under GBM. 
D)  The GBM solution is attained in an iterative process weighting cases differentially, with gradual shifts between solutions. 
E)  All of the above are true. 
A)  GBM can be implemented under the "caret" package. 
B)  Decision trees and random forests can be base learning methods under GBM. 
C)  OLS regression can be a base learner model under GBM. 
D)  The GBM solution is attained in an iterative process weighting cases differentially, with gradual shifts between solutions. 
E)  All of the above are true.

Question 19

Which is NOT a negative aspect of SVM methods?

Accepted Answer

A)  SVM has "black box" aspects which make it less transparent than OLS regression 
B)  Trial and error methods may be needed to optimize the model 
C)  In spite of cross-validation it is still possible to overfit SVM models to noise in the data 
D)  All of the above. 
A)  SVM has "black box" aspects which make it less transparent than OLS regression 
B)  Trial and error methods may be needed to optimize the model 
C)  In spite of cross-validation it is still possible to overfit SVM models to noise in the data 
D)  All of the above.

Question 20

What is true of SVM in relation to understanding the relative importance of predictor variables?

Accepted Answer

A)  SVM is a "black box" methodology which hides the relative roles of predictor variables. 
B)  Listing of variables by importance is a menu choice in RStudio. 
C)  Variable importance may be assessed through a leave-one-out method in which one predictor at a time is dropped from the model. 
D)  As with OLS regression, beta weights indicate relative predictive importance in SVM 
A)  SVM is a "black box" methodology which hides the relative roles of predictor variables. 
B)  Listing of variables by importance is a menu choice in RStudio. 
C)  Variable importance may be assessed through a leave-one-out method in which one predictor at a time is dropped from the model. 
D)  As with OLS regression, beta weights indicate relative predictive importance in SVM

What does the SVM algorithm attempt to optimize?

Which modeling package centers of creating and using "Learners" and "Tasks"? __________________.

When machine learning models (not just SVM) are implemented using the train command of the caret package, tuning of the model is accomplished on a built-in basis.

In one sentence, what does R's "caret" package do? Name three of its procedures discussed in Chapter 6.

The support vector machine (SVM) method may be used to address which of the following?

Which is NOT true of learning vector quantization (LVQ) models?

SVM routinely outperforms OLS regression when the data a nonlinear?

The author of the svm command believes SVM models must be tuned to obtain sensible results.

What is the default kernel in SVM regression using the svm command, and in most other SVM commands?

A "loss function" is a metric to measure model performance. Is the loss function in SVM closer to that in OLS regression or logistic regression? Is it the same as the one you pick (explain briefly)? Which closer? ______________________ Same?/Explain:

What is the Kappa statistic in the context of caret's machine learning models?

In what package is the svm command found as the default SVM method?_______________________

What is the purpose of "kernels" in SVM?

The "mlr3" package is an alternative to which other widely-used R package?

Which is NOT a positive aspect of SVM methods?

In SVM, what are gamma, degree, coef0, and nu?

For problems where the DV is binary, both OLS regression and SVM are inappropriate and logistic regression should be used instead.

Which is NOT true of gradient boosting machine (GBM) models?

Which is NOT a negative aspect of SVM methods?

What is true of SVM in relation to understanding the relative importance of predictor variables?

Using and Abusing Data Analytics in Social Science

Statistical Analytics With R Part 1

Statistical Analytics With R Part 2

Classification and Regression Trees in R

Random Forests

Neural Network Models and Deep Learning

Network Analysis

Text Analytics

Filters

Exam 6: Modeling and Machine Learning

What does the SVM algorithm attempt to optimize?

Which modeling package centers of creating and using "Learners" and "Tasks"? __________________.

When machine learning models (not just SVM) are implemented using the train command of the caret package, tuning of the model is accomplished on a built-in basis.

In one sentence, what does R's "caret" package do? Name three of its procedures discussed in Chapter 6.

The support vector machine (SVM) method may be used to address which of the following?

Which is NOT true of learning vector quantization (LVQ) models?

SVM routinely outperforms OLS regression when the data a nonlinear?

The author of the svm command believes SVM models must be tuned to obtain sensible results.

What is the default kernel in SVM regression using the svm command, and in most other SVM commands?

A "loss function" is a metric to measure model performance. Is the loss function in SVM closer to that in OLS regression or logistic regression? Is it the same as the one you pick (explain briefly)? Which closer? ______________________ Same?/Explain:

What is the Kappa statistic in the context of caret's machine learning models?

In what package is the svm command found as the default SVM method?_______________________

What is the purpose of "kernels" in SVM?

The "mlr3" package is an alternative to which other widely-used R package?

Which is NOT a positive aspect of SVM methods?

In SVM, what are gamma, degree, coef0, and nu?

For problems where the DV is binary, both OLS regression and SVM are inappropriate and logistic regression should be used instead.

Which is NOT true of gradient boosting machine (GBM) models?

Which is NOT a negative aspect of SVM methods?

What is true of SVM in relation to understanding the relative importance of predictor variables?

Using and Abusing Data Analytics in Social Science

Statistical Analytics With R Part 1

Statistical Analytics With R Part 2

Classification and Regression Trees in R

Random Forests

Neural Network Models and Deep Learning

Network Analysis

Text Analytics

Filters