Enhancing Multilevel Models Through Supervised Machine Learning

Clustered data are common in various fields, such as social sciences (multiple individual measurements) and machine learning (city-wise weather forecasts, regional house price predictions). In such data, observations within clusters exhibit dependencies, violating assumptions of independence and identical distribution. To address this, multilevel random effects are used instead of fixed effects. Starting from linear approaches, typical multilevel frameworks extend to non-linear approaches by direct specifications (e.g., products of variables), guided by theory or trial and error in model comparisons. However, when the primary aim is to find the best prediction model in a multilevel context, this approach can be cumbersome, and non-linear models may lack flexibility. Here, we introduce mixed-effects machine learning (mixedML), incorporating multilevel effects into supervised regression machine learning models. This framework enhances flexibility in prediction model functional forms. We discuss its applicability in multilevel modeling, following its publication and presentation at IMPS 2023. By popular request after the presentation, we explain how to apply mixedML from a traditional multilevel modeling perspective. For detailed technical information, please refer to the original publication.