Menon Aditya Krishna, Jiang Xiaoqian J, Vembu Shankar, Elkan Charles, Ohno-Machado Lucila
University of California, San Diego, 9500 Gilman Drive, La Jolla CA 92093, USA.
University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.
Proc Int Conf Mach Learn. 2012;2012:703-710.
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 , 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.
在机器学习分类器的许多实际应用中，预测一个示例属于特定类别的概率至关重要。本文提出了一种基于优化一个量，然后进行保序回归来预测概率的简单技术。这种半参数技术既具有良好的排序和回归性能，并且能够比诸如逻辑回归等统计学常用方法对更丰富的概率分布进行建模。我们提供的实验结果表明了该技术在概率预测实际应用中的有效性。
