MIXED-EFFECTS MODELS WITH GENERALIZED RANDOM FOREST: IMPROVED FOOD INSECURITY ANALYSIS

Abstract

Food insecurity is a complex issue that requires a deep understanding of its influencing factors. Accurate predictions are crucial for effective interventions. Machine learning is well-suited to the large and complex data available in the big data era. However, machine learning generally does not accommodate hierarchical or clustered data structures, making them challenging for machine learning modeling. One model that accommodates hierarchical data structures is the mixed-effects model. This study introduces a novel approach to predict food insecurity by integrating mixed-effects models and a generalized random forest. Mixed-effects models capture variations in hierarchical or clustered data, such as differences between regions, and the generalized random forest, as extended and developed from the traditional random forest, is integrated to model fixed effects and improve prediction accuracy. The empirical data used were the food insecurity data from 2021 in West Java, Indonesia. The results show that mixed-effects models with a generalized random forest significantly improve the prediction accuracy compared to other models. The average performance measure shows GMEGRF is a good model and has a balanced accuracy value of 0.6789709, which is the highest result compared to other methods. This methodological advancement offers a new robust model for understanding and potentially mitigating food insecurity, ultimately informing efforts towards SDG 2 (Zero Hunger).