Support Vector Machines - Machine Learning Quiz

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

Quiz 7: Support Vector Machines