XGBoost and MAPA's Budgetary Practice: Influence on HDI and Agribusiness GDP

This study applies the XGBoost algorithm, with hyperparameter optimization via GridSearchCV, to model two target variables — percentage variation of Agribusiness GDP (%GDP-AGRO) and the Human Development Index (HDI) of Brazilian states — from MAPA's open budgetary data (2018–2022), integrated with variables from the 2017 Agricultural Census (IBGE) and the CEPEA/USP historical series. The dataset comprises 465 observations across federal budget units (MAPA, EMBRAPA, INCRA, CONAB, SFB). Feature selection employed Recursive Feature Elimination (RFE) and multicollinearity analysis. The %GDP-AGRO model achieved R² = 0. 8764 (RMSE = 0. 00956) ; the HDI model achieved R² = 0. 9632 (RMSE = 0. 00712). In both targets, the percentage of rural properties receiving technical guidance emerged as the most important feature (28. 18% and 52. 11%, respectively), followed by investments in Projects/CAPEX (21. 59%) in the GDP model and geographic location — Southeast region (31. 89%) — in the HDI model. The results provide empirical evidence for data-driven public policy formulation in the agricultural sector. This record includes two versions: - English version: artigoₓgboostₘapaₑn. pdf- Portuguese version: artigoₓgboostₘapaₚt. pdf Source code and data: https: //github. com/Jotta-se/MBAUSPAcknowledgements: MBA in Data Science & Analytics, USP/ESALQ, 2024.