In this paper, we suggest an explainable machine learning approach to model the claim frequency of a telematics car dataset. In fact, we use a data-driven method based on tree ensembles, namely, the random forest, to create a claim frequency model. Then, we present a method to build a tree that faithfully synthesizes the predictions of a tree ensemble model such as those derived from the random forest or gradient boosting. This tree serves as a global explanation of the predictions of the black-box. Thanks to this surrogate model, we can extract knowledge from a black-box tree ensemble model. Then, we provide an application to improve the performance of a generalized linear model. Indeed, we integrate this new knowledge into a generalized linear model to increase the predictive power.

在本文中，我们提出了一种可解释的机器学习方法来对远程信息处理汽车数据集的索赔频率进行建模。实际上，我们使用基于树集成的数据驱动方法（即随机森林）来创建索赔频率模型。然后，我们提出了一种构建树的方法，该方法可以忠实地合成树集合模型的预测，例如从随机森林或梯度增强得出的树集合模型的预测。该树用作对黑匣子预测的全局解释。由于有了这种替代模型，我们可以从黑盒树集成模型中提取知识。然后，我们提供了一个用于提高广义线性模型性能的应用程序。实际上，我们将此新知识整合到广义线性模型中以提高预测能力。