Neural networks are powerful predictive models, but they provide little insight into the nature of relationships between predictors and outcomes. Although numerous methods have been proposed to quantify the relative contributions of input features, statistical inference and hypothesis testing of feature associations remain largely unexplored. We propose a permutation-based approach to testing that uses the partial derivatives of the network output with respect to specific inputs to assess both the significance of input features and whether significant features are linearly associated with the network output. These tests, which can be flexibly applied to a variety of network architectures, enhance the explanatory power of neural networks, and combined with powerful predictive capability, extend the applicability of these models.
翻译:神经网络是强大的预测模型,但对于预测数据和结果之间的关系性质却很少有洞察力。虽然提出了许多量化投入特征相对贡献的方法,但是对地物协会的统计推论和假设测试基本上尚未探索。我们建议采用基于变式的测试方法,在具体投入方面使用网络产出的部分衍生物来评估输入特征的重要性和重要特征是否与网络产出有线性联系。这些测试可以灵活地应用于各种网络结构,增强神经网络的解释力,并结合强大的预测能力,扩大这些模型的适用性。