ABSTRACT

Deep neural networks (DNN) have been demonstrated to achieve unparalleled prediction accuracy in a wide range of applications. Despite its strong performance, in certain areas, the usage of DNN has met resistance because of its black-box nature. In this article, we propose a new method to estimate a mixture of linear models (MLM) for regression or classification that is relatively easy to interpret. We use DNN as a proxy of the optimal prediction function such that MLM can be effectively estimated. We propose visualization methods and quantitative approaches to interpret the predictor by MLM. Experiments show that the new method allows us to tradeoff interpretability and accuracy. The MLM estimated under the guidance of a trained DNN fills the gap between a highly explainable linear statistical model and a highly accurate but difficult to interpret predictor. Supplementary materials for this article are available online.

摘要

深度神经网络 (DNN) 已被证明可以在广泛的应用中实现无与伦比的预测精度。尽管性能强劲，但在某些领域，由于 DNN 的黑盒性质，其使用遇到了阻力。在这篇文章中，我们提出了一种新方法来估计线性模型 (MLM) 的混合，用于回归或分类，这种方法相对容易解释。我们使用 DNN 作为最佳预测函数的代理，以便可以有效地估计 MLM。我们提出可视化方法和定量方法来解释 MLM 的预测器。实验表明，新方法允许我们权衡可解释性和准确性。在经过训练的 DNN 的指导下估计的 MLM 填补了高度可解释的线性统计模型与高度准确但难以解释的预测器之间的差距。本文的补充材料可在线获取。