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?


Interpreting distance from hyperplane in SVM

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.

  • $\begingroup$ If you belong less to a class and it is a 2 class model then that means you belong more to the other class.. that was my main point of my question - is this true & how trustworthy it is based on accuracy $\endgroup$ – Christo Oct 27 '17 at 12:46
  • 1
    $\begingroup$ Late, but worthwhile addition: It is always true if your classification construct is uni-dimensional (classify good/bad students using median test score as the only feature), but that hardly requires an SVM. What's to say that there is not a 3rd class that you do not know about, that explains some of the variance of features? I.E., an SVM will happily classify a 5-class set into 2 classes, but the feature weights will not represent the actual feature importance, and the distance is based in a nonsensical class definition. Suggested reading: Factor analysis, PCA, construct validity. $\endgroup$ – TvZ Sep 4 '18 at 10:31

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.