Format

Send to

Choose Destination
Proc Int Conf Mach Learn. 2012;2012:703-710.

Predicting accurate probabilities with a ranking loss.

Author information

1
University of California, San Diego, 9500 Gilman Drive, La Jolla CA 92093, USA.
2
University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.

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 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.

PMID:
25285328
PMCID:
PMC4180410

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center