Generalized mixed-effects random forest: A flexible approach to predict university student dropout

We propose a new statistical method, called generalized mixed-effects random forest (GMERF), that extends the use of random forest to the analysis of hierarchical data, for any type of response variable in the exponential family. The method maintains the flexibility and the ability of modeling complex patterns within the data, typical of tree-based ensemble methods, and it can handle both continuous and discrete covariates. At the same time, GMERF takes into account the nested structure of hierarchical data, modeling the dependence structure that exists at the highest level of the hierarchy and allowing statistical inference on this structure. In the case study, we apply GMERF to Higher Education data to analyze the university student dropout phenomenon. We predict engineering student dropout probability by means of student-level information and considering the degree program students are enrolled in as grouping factor.