  In many real-world applications of machine learning classifiers, it is
essential to predict the probability of an example belonging to a particular
class. This paper proposes a simple technique for predicting probabilities
based on optimizing a ranking loss, followed by isotonic regression. This
semi-parametric technique offers both good ranking and regression performance,
and models a richer set of probability distributions than statistical
workhorses such as logistic regression. We provide experimental results that
show the effectiveness of this technique on real-world applications of
probability prediction.
