Abstract
The presence of Artificial Intelligence (AI) in our society is increasing, which brings with it the need to understand the behaviour of AI mechanisms, including machine learning predictive algorithms fed with tabular data, text, or images, among other types of data. This work focuses on interpretability of predictive models based on functional data. Designing interpretability methods for functional data models implies working with a set of features whose size is infinite. In the context of scalar on function regression, we propose an interpretability method based on the Shapley value for continuous games, a mathematical formulation that allows to fairly distribute a global payoff among a continuous set players. The method is illustrated through a set of experiments with simulated and real data sets. The open source Python package ShapleyFDA is also presented.
Abstract (translated)
人工智能（AI）在我们社会中的存在日益增加，这带来了理解AI机制行为的需求，包括用表格数据、文本或图像等类型的数据驱动的机器学习预测算法。这项工作重点在于基于功能性数据的预测模型的可解释性。为功能性数据模型设计可解释性方法意味着要处理一个无限大小的功能集。在标量对函数回归的背景下，我们提出了一种基于连续博弈中的Shapley值的可解释性方法，这是一种数学公式，允许公平地将全局收益分配给一组连续玩家。该方法通过使用模拟和真实数据集的一系列实验进行了说明。此外，还介绍了开源Python软件包ShapleyFDA。
URL
https://arxiv.org/abs/2411.18575
