论文标题
X-shap：用于机器学习的乘法解释性
X-SHAP: towards multiplicative explainability of Machine Learning
论文作者
论文摘要
本文介绍了X-Shap，这是一种模型 - 不合Snostic方法，可评估局部和全局预测的变量的乘法贡献。这种方法在理论上和操作上扩展了所谓的添加形状方法。它证明了因素的有用的基础乘法相互作用，通常在传统上使用了普遍的线性模型（例如保险或生物学）的领域中产生。我们在各种数据集上测试了该方法，并根据单个X-shap贡献提出了一组技术，以构建汇总的乘法贡献并捕获乘法特征重要性，我们将其与传统技术相提并论。
This paper introduces X-SHAP, a model-agnostic method that assesses multiplicative contributions of variables for both local and global predictions. This method theoretically and operationally extends the so-called additive SHAP approach. It proves useful underlying multiplicative interactions of factors, typically arising in sectors where Generalized Linear Models are traditionally used, such as in insurance or biology. We test the method on various datasets and propose a set of techniques based on individual X-SHAP contributions to build aggregated multiplicative contributions and to capture multiplicative feature importance, that we compare to traditional techniques.
