论文标题
石灰分析文本数据
An Analysis of LIME for Text Data
论文作者
论文摘要
通过机器学习算法，越来越多地以自动化方式处理文本数据。但是，由于它们的复杂性，处理这些数据的模型并不总是被理解的，并且越来越多地称为“黑盒”。可解释性方法旨在解释这些模型的运作方式。其中，石灰已成为近年来最受欢迎的石灰之一。但是，它没有理论上的保证：即使对于简单的模型，我们也不确定石灰行为准确。在本文中，我们为文本数据提供了第一个关于石灰的理论分析。由于我们的理论发现，我们表明石灰确实为简单模型（即决策树和线性模型）提供了有意义的解释。
Text data are increasingly handled in an automated fashion by machine learning algorithms. But the models handling these data are not always well-understood due to their complexity and are more and more often referred to as "black-boxes." Interpretability methods aim to explain how these models operate. Among them, LIME has become one of the most popular in recent years. However, it comes without theoretical guarantees: even for simple models, we are not sure that LIME behaves accurately. In this paper, we provide a first theoretical analysis of LIME for text data. As a consequence of our theoretical findings, we show that LIME indeed provides meaningful explanations for simple models, namely decision trees and linear models.
