  This study assessed the effectiveness of machine learning models in
predicting poverty levels in the Philippines using five boosting algorithms:
Adaptive Boosting (AdaBoost), CatBoosting (CatBoost), Gradient Boosting Machine
(GBM), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient
Boosting (XGBoost). CatBoost emerged as the superior model and achieved the
highest scores across accuracy, precision, recall, and F1-score at 91 percent,
while XGBoost and GBM followed closely with 89 percent and 88 percent
respectively. Additionally, the research examined the computational efficiency
of these models to analyze the balance between training time, testing speed,
and model size factors crucial for real-world applications. Despite its longer
training duration, CatBoost demonstrated high testing efficiency. These results
indicate that machine learning can aid in poverty prediction and in the
development of targeted policy interventions. Future studies should focus on
incorporating a wider variety of data to enhance the predictive accuracy and
policy utility of these models.
