Publication Results



Code: 36/2020
Title: Generalized Mixed-Effects Random Forest: a flexible approach to predict university student dropout
Date: Sunday 24th May 2020
Author(s) : Pellagatti, M.; Masci, C.; Ieva, F.; Paganoni A.m.
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Abstract: 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, considering both continuous and discrete covariates and without assuming a closed form in the association between the response and the fixed-effects covariates. At the same time GMERF takes into consideration the nested structure of hierarchical data, modelling the latent grouping structure that exists in the higher level of the hierarchy and allowing statistical inference on this structure. In the case study, we apply GMERF to Higher Education data to analyse the university students dropout; in particular, we are interested in predicting students dropout probability given students-level information and considering the degree program they are enrolled in as the grouping factor.

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Published: Pellagatti, M., Masci, C., Ieva, F., & Paganoni, A. M. (2020). Generalized mixed?effects random forest: A flexible approach to predict university student dropout. Statistical Analysis and Data Mining: The ASA Data Science Journal. https://doi.org/10.1002/sam.11505