On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization
Holger R. Maier, Feifei Zheng, Hoshin V. Gupta, Junyi Chen, Juliane Mai, Dragan Savić, Ralf Loritz, Wenyan Wu, Danlu Guo, Andrew Bennett, Anthony J. Jakeman, Saman Razavi, Jianshi Zhao
Abstract
Models play a pivotal role in advancing our understanding of Earth's physical nature and environmental systems, aiding in their efficient planning and management. The accuracy and reliability of these models heavily rely on data, which are generally partitioned into subsets for model development and evaluation. Surprisingly, how this partitioning is done is often not justified, even though it determines what model we end up with, how we assess its performance and what decisions we make based on these model outputs. In this study, we shed light on the paramount importance of meticulously considering data partitioning in the model development and evaluation process, and its significant impact on model generalization. We identify flaws in existing data-splitting approaches and propose a forward-looking strategy to effectively confront the “elephant in the room”, leading to improved model generalization capabilities.