A Hybrid Tree Ensemble Framework: Integrating Adaptive Random Forest and XGBoost for Enhanced Predictive Intelligence

Predictive models must adapt to data changes and concept drift. Traditional tree-based ensembles like Random Forest are robust but lack ongoing adaptation, while gradient boosting methods such as XGBoost excel at nonlinear interactions but are limited to static learning. This study proposes a hybrid framework that merges XGBoost with Adaptive Random Forest (ARF) in a two-stage pipeline. Utilizing a dataset of financial fraud transactions, the framework improves features via engineering and outperforms baseline models (Random Forest, XGBoost, standalone ARF) in accuracy, precision, recall, F1-score, and AUC-ROC. This method balances predictive performance and adaptability, making it effective for dynamic classification tasks, with suggestions for future exploration of imbalance handling and real-time deployments.