Support Vector Machine - SVM ( Machine Learning)

Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.

If you remove the following any one red points from the data. Does the decision boundary will change?

• True

• False

What do you mean by generalization error in terms of the SVM?

• How far the hyperplane is from the support vectors

• How accurately the SVM can predict outcomes for unseen data

• The threshold amount of error in an SVM

When the C parameter is set to infinite, which of the following holds true?

• The optimal hyperplane if exists, will be the one that completely separates the data

• The soft-margin classifier will separate the data

• None of the above

What do you mean by a hard margin?

• The SVM allows very low error in classification

• The SVM allows high amount of error in classification

• None of the above

The effectiveness of an SVM depends upon

• Selection of Kernel

• Kernel Parameters

• Soft Margin Parameter C

• All of the above

Support vectors are the data points that lie closest to the decision surface.

• True

• False

The SVM’s are less effective when:

• The data is linearly separable

• The data is clean and ready to use

• The data is noisy and contains overlapping points

What would happen when you use very small C (C~0)?

• Misclassification would happen

• Data will be correctly classified

• Can’t say

• None of these

Suppose you are dealing with 4 class classification problem and you want to train a SVM model on the data for that you are using One-vs-all method.

How many times we need to train our SVM model in such case?

• 1

• 2

• 3

• 4

What is/are true about kernel in SVM?

1. Kernel function map low dimensional data to high dimensional space

2. It’s a similarity function

• 1

• 2

• 1 and 2

• None of these