  This study presents two supervised multiclassification machine learning
models to predict the poverty status of Costa Rican households as a way to
support government and business sectors make decisions in a rapidly changing
social and economic environment. Using the Costa Rican household dataset
collected via the proxy means test conducted by the Inter-American Development
Bank, Random Forest and Gradient Boosted Trees achieved F1 scores of 64.9% and
68.4%, respectively. This study also reveals that education has the greatest
impact on predicting poverty status.
