I'm studying SVC and I understood that a decision boundary of SVC only uses subset data of entire data set, which are so called support vectors. However, why do we need test data of a model of SVC?
Let's assume that we split train/test data. Important data, which are support vectors are included in the test set. Then, our decision boundary which is constructed by training data can't seperate data properly. In this case, the model builds wrong decision boundary.
So can you tell me why does SVC need to split entire data to train/test data?