Let's say I have a model which separates two classes with an SVM. Let's take a point from each of the classes. If two points have lower distance can this be interpreted as 'The model assumes these points are 'd' similar to each other ( low distance = high similarity )'. If yes, to what degree and how do I make sense of it in terms of my model accuracy?
Points that are further away from the hyperplane belong to their class to a greater degree. Identical distances express an identical degree of belonging (to their respective superclass) for two points, not a degree of similarity between two points.