In Platt scaling, you fit a function to the predicted raw scores from a trained model, on a test set, in order to convert future scores into probabilities.
The function is: p(x) = 1 / (1 + exp(a*x + b))
x being a raw score. So, two parameters (a, b) are fit.
What I don't like in this method is that a model about the probabilities is assumed (a logistic curve here).
If Leo Breiman was still with us, what would have he used or invented?
Put another way: are there non parametric alternatives to Platt scaling?
Thanks for your input.
Bibliography:  Platt, J. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3), 61-74.