useR! 2024

Statistical learning methods are widely utilised in tackling complex problems due to their flexibility, good predictive performance and ability to capture complex relationships among variables. One of the main drawbacks of statistical learning is the lack of interpretability of the results. Having interpretable statistical learning methods is necessary for obtaining a deeper understanding of these models. Specifically in problems in which spatial information is relevant, combining interpretable methods with spatial data can help to provide a better understanding of the problem and an improved interpretation of the results. This presentation focused on individual conditional expectation plot (ICE-plot), a model-agnostic method for interpreting statistical learning models and combining them with spatial information. An ICE-plot extension is proposed in which spatial information is used as a restriction to define spatial ICE (SpICE) curves (https://github.com/natydasilva/SpICE).