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?

The correct answer is A. Yes.

A support vector is a data point that is on the margin of the decision boundary. The decision boundary is the line that separates the two classes of data. If you remove a support vector, the decision boundary will change because the margin will no longer be the same.

The following is a diagram that shows the decision boundary with and without a support vector:

[Diagram of a decision boundary with and without a support vector]

The red points are the support vectors. The blue line is the decision boundary. The green line is the decision boundary without the support vector.

As you can see, the decision boundary changes when the support vector is removed. This is because the margin is no longer the same. The margin is the distance between the decision boundary and the closest data points. When the support vector is removed, the margin becomes smaller. This causes the decision boundary to move closer to the data points.

In conclusion, the decision boundary will change if you remove a support vector. This is because the margin will no longer be the same. The margin is the distance between the decision boundary and the closest data points. When the support vector is removed, the margin becomes smaller. This causes the decision boundary to move closer to the data points.