论文标题
通过神经网络检测通用线性模型的相互作用变量
Detection of Interacting Variables for Generalized Linear Models via Neural Networks
论文作者
论文摘要
保险公司经常使用的广义线性模型（GLM）的质量取决于相互作用变量的选择。搜索互动是耗时的，尤其是对于具有大量变量的数据集，很大程度上取决于精算师的专家判断，并且通常依赖于视觉性能指标。因此，我们提出了一种自动化寻找相互作用的过程的方法，该过程应添加到GLM中以提高其预测能力。我们的方法依赖于神经网络和一种特定于模型的相互作用检测方法，该方法在计算上比传统使用的方法更快，例如Friedman H统计或SHAP值。在数值研究中，我们提供了关于人为生成的数据以及开源数据的方法的结果。
The quality of generalized linear models (GLMs), frequently used by insurance companies, depends on the choice of interacting variables. The search for interactions is time-consuming, especially for data sets with a large number of variables, depends much on expert judgement of actuaries, and often relies on visual performance indicators. Therefore, we present an approach to automating the process of finding interactions that should be added to GLMs to improve their predictive power. Our approach relies on neural networks and a model-specific interaction detection method, which is computationally faster than the traditionally used methods like Friedman H-Statistic or SHAP values. In numerical studies, we provide the results of our approach on artificially generated data as well as open-source data.
