Support Vector Machine: Machine Learning Interview Prep 03

Support Vector Machine (SVM) is a powerful tool in machine learning. It works by finding the best possible line or boundary (hyperplane) to separate different classes of data points. This makes SVM useful for classification tasks where we need to categorize data into distinct groups. By maximizing the margin between classes, SVM aims to make accurate predictions even with new, unseen data. Overall, SVM is valued for its versatility and effectiveness in handling both linear and nonlinear classification problems.

Let’s check your knowledge of Support Vector Machine. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

Image Source: A Top Machine Learning Algorithm Explained: Support Vector Machines (SVMs)

Question 1: What’s the objective of the support vector machine algorithm?
(A) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of features.
(B) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of samples.
(C) to find an optimal hyperplane in an N-dimensional space that distinctly classifies the data points where N is the number of target variables.
(D) None of these

Question 2: Support Vector Machine (SVM) can be used for _____.
(A) classification only
(B) regression only
(C) classification and regression both
(D) None of these

Question 3: In SVM, the dimension of the hyperplane depends upon which one?
(A) the number of features
(B) the number of samples
(C) the number of target variables
(D) All of the above

Question 4: In SVM, if the number of input features is 2, then the hyperplane is a _____.
(A) line
(B) circle
(C) plane
(D) None of these

Question 5: In SVM, if the number of input features is 3, then the hyperplane is a _____.
(A) line
(B) circle
(C) plane
(D) None of these

Question 6: In SVM, what is a hyperplane?
(A) decision boundaries
(B) data points
(C) features
(D) None of these

Question 7: For SVM, which options are correct? (Select two)
(A) Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane
(B) Support vectors are data points that are far away from the hyperplane and influence the position and orientation of the hyperplane
(C) Deleting the support vectors will change the position of the hyperplane
(D) Deleting the support vectors won’t change the position of the hyperplane

Question 8: In SVM, we are looking to maximize the margin between the data points and the hyperplane. The loss function that helps maximize the margin is called ______.
(A) hinge loss
(B) categorical cross-entropy loss
(C) binary cross-entropy loss
(D) None of these

Question 9: The distance of the vectors from the hyperplane is called the margin which is a separation of a line to the closest class points. We would like to choose a hyperplane that maximizes the margin between classes. Which options are true for the margin? (Select two)

Image source: A Top Machine Learning Algorithm Explained: Support Vector Machines (SVMs)

(A) Hard margin — if the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as large as possible.
(B) Hard margin — if the training data is linearly separable, we can select two parallel hyperplanes that separate the two classes of data, so that the distance between them is as small as possible.
(C) Soft margin — doesn’t allow some data points to stay on either the incorrect side of the hyperplane or between the margin and correct side of the hyperplane.
(D) Soft margin — allows some data points to stay on either the incorrect side of the hyperplane or between the margin and the correct side of the hyperplane.

Question 10: Which options are true for SVM? (Select two)
(A) The distance of the vectors from the margin is called the hyperplane
(B) The loss function that helps minimize the margin is called hinge loss
(C) SVM can solve the linearly separable data points
(D) SVM can solve the data points that are not linearly separable

The solutions will be published in the next quiz Machine Learning Quiz 04: Logistic Regression.

Logistic Regression: Machine Learning Interview 04

Happy learning. If you like the questions and enjoy taking the test, please subscribe to my email list for the latest ML questions, follow my Medium profile, and leave a clap for me. Feel free to discuss your thoughts on these questions in the comment section. Don’t forget to share the quiz link with your friends or LinkedIn connections. If you want to connect with me on LinkedIn: my LinkedIn profile.

The solution of previous Machine Learning Quiz 02: Ridge, Lasso — 1(D), 2(A, B), 3(A), 4(A, B), 5(A), 6(C), 7(D), 8(D), 9(A, B), 10(D)

Machine Learning Quiz 02: Ridge, Lasso

Photo: Ernst Tinaja, Big Bend National Park, TX, USA Credit: Tasnim and Kawsar

Decision Tree (Part 1): Machine Learning Interview 05

Decision Tree (Part 2): Machine Learning Interview 06