Statistical learning methods are widely utilized in tackling complex problems due to their flexibility, good predictive performance and its ability to capture complex relationships among variables. Additionally, recently developed automatic workflows have provided a standardized approach to implementing statistical learning methods across various applications. However these tools highlight a main drawbacks of statistical learning: its lack of interpretation in their results. In the past few years an important amount of research has been focused on methods for interpreting black box models. Having interpretable statistical learning methods is relevant to have a deeper understanding of the model. In problems were spatial information is relevant, combined interpretable methods with spatial data can help to get better understanding of the problem and interpretation of the results. This paper is focused in the individual conditional expectation (ICE-plot), a model agnostic methods for interpreting statistical learning models and combined them with spatial information. ICE-plot extension is proposed where spatial information is used as restriction to define Spatial ICE curves (SpICE). Spatial ICE curves are estimated using real data in the context of an economic problem concerning property valuation in Montevideo, Uruguay. Understanding the key factors that influence property valuation is essential for decision-making, and spatial data plays a relevant role in this regard.

翻译：统计学习方法因其灵活性、良好的预测性能以及捕获变量间复杂关系的能力，被广泛应用于解决复杂问题。此外，近期开发的自动化工作流为跨不同应用场景实施统计学习方法提供了标准化途径。然而，这些工具凸显了统计学习的一个主要缺陷：其结果缺乏可解释性。近年来，大量研究聚焦于黑箱模型的解释方法。具有可解释性的统计学习方法有助于深入理解模型本身。在空间信息具有重要性的问题中，将可解释方法与空间数据相结合，能够促进对问题的理解与结果的解读。本文聚焦于个体条件期望图（ICE-plot）——一种用于解释统计学习模型的模型无关方法，并将其与空间信息相结合。我们提出了一种ICE图的扩展方法，即以空间信息作为约束条件来定义空间ICE曲线（SpICE）。基于乌拉圭蒙得维的亚房产估价的经济问题，利用真实数据对空间ICE曲线进行了估计。理解影响房产估价的关键因素对决策至关重要，而空间数据在此方面发挥着关键作用。