Support Vector Machines (SVMs) QUIZ QUESTIONS

Question: 1

What is the primary goal of a Support Vector Machine (SVM)?

Question: 2

What are support vectors in the context of SVMs?

Question: 3

What is the kernel trick in the context of SVMs?

Question: 4

Which of the following is a common kernel function used in SVMs?

Question: 5

What is the main advantage of using SVMs over other classification algorithms?

Question: 6

Which of the following is a disadvantage of using SVMs?

Question: 7

In the context of SVMs, what is the purpose of the C parameter?

Question: 8

What is the main difference between a hard-margin SVM and a soft-margin SVM?

Question: 9

Which of the following problems can be addressed using SVMs?

Question: 10

Which of the following is a disadvantage of using SVMs for multi-class classification problems?

Question: 11

How can SVMs be extended to handle regression problems?

Question: 12

Which of the following is NOT a valid method for selecting the optimal kernel function for an SVM?

Question: 13

In the context of SVMs, what is the dual problem?

Question: 14

What is the main advantage of using a linear kernel in an SVM?

Question: 15

Which of the following is a disadvantage of using a radial basis function (RBF) kernel in an SVM?

Question: 16

What is the role of the gamma parameter in SVMs with an RBF kernel?

Question: 17

Which of the following techniques can be used to improve the performance of SVMs on imbalanced datasets?

Question: 18

How can the performance of an SVM be evaluated?

Question: 19

Can SVMs be used for multi-label classification problems?

Question: 20

In an SVM, what is the effect of increasing the C parameter?

Question: 21

How do SVMs handle categorical features?

Question: 22

How do SVMs handle missing data?

Question: 23

What is the main difference between a linear SVM and a non-linear SVM?

Question: 24

In the context of SVMs, what is a hinge loss function?

Question: 25

In an SVM, what is the effect of decreasing the gamma parameter in an RBF kernel?

Question: 26

What is the main advantage of using a polynomial kernel in an SVM?

Question: 27

What is one disadvantage of using an SVM for regression problems?

Question: 28

Which of the following techniques can be used to reduce the computational complexity of training an SVM?

Question: 29

How can the performance of an SVM be improved when the data is not linearly separable in the original feature space?

Question: 30

Which of the following is NOT a valid method for selecting the optimal hyperparameters for an SVM?