title: Predicting accurate probabilities with a ranking loss.
abstract: 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 aranking 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.
authors.affiliation: University of California, San Diego, 9500 Gilman Drive, La Jolla CA 92093, USA.
authors.forename: Aditya Krishna
authors.initials: AK
authors.lastname: Menon
authors.affiliation: University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.
authors.forename: Xiaoqian J
authors.initials: XJ
authors.lastname: Jiang
authors.forename: Shankar
authors.initials: S
authors.lastname: Vembu
authors.forename: Charles
authors.initials: C
authors.lastname: Elkan
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado