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arXiv · 1206.4661

Predicting accurate probabilities with a ranking loss

Aditya Menon·Xiaoqian Jiang·Shankar Vembu·Charles Elkan·Lucila Ohno-Machado
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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.

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BibTeXRIS

Aditya Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado. 2012-06-18. Predicting accurate probabilities with a ranking loss. https://arxiv.org/abs/1206.4661

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