论文标题
使用共形性能预测使学习更加透明
Making learning more transparent using conformalized performance prediction
论文作者
论文摘要
在这项工作中，我们研究了一些新颖的相构推理技术应用，以提供机器学习程序以更透明，准确和实用的性能保证。我们提供了传统的保形预测框架的自然扩展，以使我们可以通过通过一个尚未看到的训练集时对任意学习算法的未来表现做出有效且精心校准的预测性陈述。此外，我们包括一些新生的经验示例，以说明潜在的应用。
In this work, we study some novel applications of conformal inference techniques to the problem of providing machine learning procedures with more transparent, accurate, and practical performance guarantees. We provide a natural extension of the traditional conformal prediction framework, done in such a way that we can make valid and well-calibrated predictive statements about the future performance of arbitrary learning algorithms, when passed an as-yet unseen training set. In addition, we include some nascent empirical examples to illustrate potential applications.
