The correct answer is TRUE.
The decision boundary is the line that separates the two classes in a classification problem. In this case, the two classes are the red points and the blue points. The decision boundary is the line that goes through the red points and separates them from the blue points.
If we remove the non-red circled points from the data, the decision boundary will change. This is because the decision boundary is based on all of the data points, including the non-red circled points. If we remove the non-red circled points, the decision boundary will be based on a different set of data points, and it will therefore be different.
Here is a diagram that shows the decision boundary with and without the non-red circled points:
[Diagram of the decision boundary with and without the non-red circled points]
The decision boundary is shown in black. The red points are on one side of the decision boundary, and the blue points are on the other side of the decision boundary. When the non-red circled points are removed, the decision boundary changes. The new decision boundary is shown in blue. The red points are still on one side of the decision boundary, but the blue points are now on both sides of the decision boundary.
I hope this explanation is helpful. Please let me know if you have any other questions.