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 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.
Cite
Text
Menon et al. "Predicting Accurate Probabilities with a Ranking Loss." International Conference on Machine Learning, 2012.Markdown
[Menon et al. "Predicting Accurate Probabilities with a Ranking Loss." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/menon2012icml-predicting/)BibTeX
@inproceedings{menon2012icml-predicting,
title = {{Predicting Accurate Probabilities with a Ranking Loss}},
author = {Menon, Aditya Krishna and Jiang, Xiaoqian and Vembu, Shankar and Elkan, Charles and Ohno-Machado, Lucila},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/menon2012icml-predicting/}
}