Support Vector Machine Quiz

What is the main objective of a Support Vector Machine (SVM)?

• Minimize the distance between data points

• Maximize the margin between the decision boundary and the nearest data points

• Minimize the number of support vectors

• Maximize the number of support vectors

In SVM, what are support vectors?

• Data points that are farthest from the decision boundary

• Data points that lie closest to the decision boundary

• Data points that are incorrectly classified

• Data points that do not influence the decision boundary

Which of the following kernels is NOT commonly used in SVM?

• Linear Kernel

• Polynomial Kernel

• Radial Basis Function (RBF) Kernel

• Sigmoid Kernel

• Exponential Kernel

What is the kernel trick in SVM used for?

• To map the data into higher-dimensional space

• To reduce the dimensionality of the data

• To compute the margin between support vectors

• To find the decision boundary in linear space

Which of the following is NOT an advantage of using SVM?

• Effective in high-dimensional spaces

• Works well with both linear and non-linear classification

• Computationally expensive for large datasets

• Requires a large amount of memory to train the model

What does the regularization parameter "C" control in SVM?

• The width of the margin

• The complexity of the decision boundary

• The number of support vectors

• The kernel function to be used

In SVM, what is the primary goal of maximizing the margin?

• To minimize the error on the test set

• To minimize the error on the training set

• To make the model less sensitive to overfitting

• To increase the complexity of the decision boundary

Which type of SVM is used for classification tasks?

• Linear Regression

• Support Vector Classification (SVC)

• Support Vector Regression (SVR)

• Naive Bayes

If the data is not linearly separable, which kernel is most commonly used in SVM?

• Polynomial Kernel

• Linear Kernel

• Radial Basis Function (RBF) Kernel

• Sigmoid Kernel

What is the purpose of the slack variable in the soft margin SVM?

• To handle outliers and noise in the dataset

• To reduce the dimensionality of the data

• To increase the number of support vectors

• To improve the speed of the algorithm