Final answer:
In SVM, the most difficult data points to classify, which lie on the margin boundaries and have a direct influence on the decision boundary, are known as support vectors.
Step-by-step explanation:
In the context of Support Vector Machines (SVM), the data points that are the most difficult to classify are called support vectors. These data points are closest to the decision boundary, or the hyperplane, that SVM creates to separate different classes. They are crucial as they directly influence the position and orientation of the decision boundary. The description provided, concerning measuring the distance to the line of best fit and considering points beyond a certain threshold as outliers, is not directly applicable to SVM. Rather, the SVM finds the optimal hyperplane that maximizes the margin between classes, and the data points that lie on the margin boundaries are the support vectors.