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Generalized mixed‐effects random forest: A flexible approach to predict university student dropout
Author(s) -
Pellagatti Massimo,
Masci Chiara,
Ieva Francesca,
Pagai Anna M.
Publication year - 2021
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11505
Subject(s) - dropout (neural networks) , random forest , covariate , computer science , hierarchy , statistical inference , flexibility (engineering) , hierarchical database model , data mining , inference , tree structure , statistics , machine learning , mathematics , artificial intelligence , algorithm , binary tree , economics , market economy
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.