I have read about the Platt scaling approach to compute posterior probabilities for the SVM classifier $P(y=1|x)$. In Scikit-learn's SVC (SVM) implementation this is the approach used to produce probabilites. My question is what are the classifier scores $f(x)$?
To make this a bit more confusing the Scikit-learn's SVC has a score function which returns the mean accuracy on the given test data and labels. I'd expect the score $f(x)$ they refer to in the Platt page to be the distance between the classified data point and the SVM decision boundary i.e. how deep in the specific class area that data point is ... or am I missing anything?