Your Learning Progress
0/32 topics (0%)
Quiz 7: Support Vector Machines
Test your understanding of Support Vector Machines (SVM) concepts.
Next Quiz
All Quizzes
1. What is the primary goal of Support Vector Machines (SVM)?
To minimize the number of support vectors
To maximize the margin between classes
To reduce the dimensionality of data
To classify data using a decision tree
2. What is the function of the margin in SVM?
To reduce the number of misclassified points
To measure the distance between data points
To separate the closest points from the decision boundary
To maximize robustness to new data points
3. What are support vectors in the context of SVM?
Data points closest to the decision boundary
All data points used for training
The weights of the SVM model
The hyperplane parameters
4. Which real-world application is NOT typically solved using SVM?
Spam detection
Image classification
Dimensionality reduction
Medical diagnosis
5. What happens to the misclassification rate when the margin width increases?
The misclassification rate decreases
The misclassification rate increases
The misclassification rate remains unchanged
The model ignores misclassification
6. Which kernel is commonly used for non-linear classification in SVM?
Linear kernel
Polynomial kernel
Radial Basis Function (RBF) kernel
Gaussian kernel
7. How does SVM handle overlapping data points?
It ignores overlapping points
It minimizes the misclassification rate using soft margins
It adjusts the hyperplane to avoid overlapping
It removes overlapping data points from training
Submit
Next Quiz
All Quizzes