Support Vector Machines (SVMs)

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?