In this article we will analyse how to compute the contribution of each input value to its aggregate output in some nonlinear models. Regression and classification applications, together with related algorithms for deep neural networks are presented. The proposed approach merges two methods currently present in the literature: integrated gradient and deep Taylor decomposition. Compared to DeepLIFT and Deep SHAP, it provides a natural choice of the reference point peculiar to the model at use.
翻译:在本篇文章中,我们将分析如何计算某些非线性模型中每个输入值对其总产出的贡献。 介绍了递减和分类应用,以及深神经网络的相关算法。 提议的方法合并了文献中目前存在的两种方法: 集成梯度和深泰勒分解。 与深LIFT 和深SHAP 相比,它提供了使用中模型特有的参考点的自然选择。