Hi Sophia, Linear separators are classified by single line decision boundary in the space. A data set that can be successfully separated by a linear classifier is called linearly separable. Binary classification uses linear separator to separate classes in feature space. The support vector machines are supervised learning models with associated learning algorithms that analyze data nad recognize patterns and are used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of the two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other. A SVM constructs a hyperplane or a set of hyperplanes in a high or infinite dimensional space, which can be used for classification or regression or other tasks. A good separation is achieved by a hyperplane that has the largest distance to the nearest training data point of any class. This is because the larger the margin the lower the generalization error of the classifier. So in case of two dimensional analysis in SVMs linear separator is used.