论文标题
随机森林的态和认识不确定性
Aleatoric and Epistemic Uncertainty with Random Forests
论文作者
论文摘要
由于机器学习与实际应用的相关性稳步增加，其中许多是安全要求所带来的，因此不确定性的概念在过去几年中引起了机器学习研究的越来越多的关注。特别是，在监督学习的环境中，研究了两种重要类型的不确定性类型的想法，通常是辩护的，通常被视为态度和认知。在本文中，我们建议用随机森林量化这些不确定性。更具体地说，我们展示了如何在预测中测量学习者的核心和认知不确定性的两种一般方法，可以用决策树和随机森林作为分类环境中的学习算法实例化。在这方面，我们还将随机森林与深层神经网络进行了比较，这些森林已用于类似目的。
Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties with random forests. More specifically, we show how two general approaches for measuring the learner's aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose.
