Support Vector Machine (SVM) Part 2: Machine Learning Interview Prep 18
A Support Vector Machine (SVM) is like a super-smart line drawer. It helps us classify things by drawing a line (or a plane) that separates different groups. The cool thing about SVM is that it aims to draw this line in a way that maximizes the space between the groups, making it easier to tell them apart. It’s like finding the best-fit fence between two different yards, making sure there’s as much space between them as possible. So, SVM is great for tasks like predicting if an email is spam or not, or identifying whether a tumor is malignant or benign.
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!
1. What is the name of the line that separates different classes in SVM?
(A) Decision Boundary
(B) Separator Line
(C) Class Divider
(D) Margin Line
2. In SVM, what are Support Vectors?
(A) Data points that are closest to the decision boundary
(B) The most distant data points in each class
(C) All the data points used in training
(D) Data points with the highest classification error
3. The value of the Regularization parameter C is inversely proportional to the strength of the regularization. What is the role of the ‘C’ parameter in SVM?
(A) Determines the kernel function
(B) Controls the trade-off between smooth decision boundary and classifying training points correctly
(C) Sets the number of support vectors
(D) Regulates the dimension of the feature space
4. When the Regularization parameter C is small, the penalty for misclassification is small, and the strength of the regularization is large. So a decision boundary with a large margin will be selected. What does a large value of ‘C’ in SVM imply?
(A) Less regularization
(B) More regularization
(C) Lower margin
(D) Higher bias
5. What is the margin in an SVM context?
(A) Distance between the nearest data point of each class
(B) Distance between the support vectors
(C) Distance from the decision boundary to the nearest data point of any class
(D) Total number of support vectors
6. In SVM, what is the soft margin?
(A) Allowing some misclassifications in the training data to achieve better generalization on unseen data
(B) Using a non-linear kernel
(C) Reducing the dimensionality of the feature space
(D) Increasing the computational complexity
7. What does it mean if an SVM model has a low bias and high variance?
(A) The model is underfitting
(B) The model is likely to overfit
(C) The model is perfectly balanced
(D) The model requires more training data
8. Which of the following statements about SVM is true?
(A) SVMs can only handle binary classification tasks.
(B) SVMs are not sensitive to outliers.
(C) SVMs require feature scaling for better performance as they are sensitive to the scale of the input features.
(D) SVMs inherently handle multi-class classification.
9. Support vector machine (SVM) can only provide linear boundaries by default. For a dataset that is not linearly separable, a support vector machine (SVM) needs to understand the higher dimensional relationship to separate it by a hyperplane. The kernel trick is the trick of describing how the data points relate to each other at the high-dimensional space without actually transforming the data to a higher dimension. This reduces the computation time of the support vector machine (SVM). The kernel function in SVM transforms the data into a higher-dimensional space to make it easier to separate classes. What role does the ‘kernel trick’ play in SVM?
(A) It allows SVM to solve linear problems more efficiently
(B) It enables SVM to form linear decision boundaries in the original feature space
(C) It helps in reducing the dimensionality of the data
(D) It allows SVM to solve non-linear problems without explicitly mapping data to higher dimensions
10. Support vector machines is a supervised machine learning algorithm that works both on classification and regression problems. How does SVM perform classification?
(A) By finding a decision boundary that maximizes the margin between the classes
(B) By calculating the probability of each class
(C) By clustering data points based on their similarity
(D) By reducing the dimensionality of the data
The solutions will be published in the next blog Regression Accuracy Metrics (Part 1): Machine Learning Interview Prep 19.
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The solution of XGBoost (Part 2): Machine Learning Interview Prep 17
- 1(B), 2(B), 3(A), 4(D), 5(C), 6(A), 7(D), 8(D), 9(D), 10(D).
References:
[1] Support Vector Machines Part 1 (of 3): Main Ideas!!!
[2] Support Vector Machines Part 2: The Polynomial Kernel
[3] Support Vector Machines Part 3: The Radial (RBF) Kernel
[4] Support Vector Machines in Python from Start to Finish.
[5] Top 7 Support Vector Machine (SVM) Interview Questions for Data Science and Machine Learning
[6] What is Kernel Trick in SVM? Interview questions related to Kernel Trick
[7] Interview Questions on SVM
[8] The Only SVM Explanation You Need
