A Comparative Study of XGBoost and Random Forest for Modern Machine Learning Applications

Selecting the appropriate ensemble method is critical for predictive tasks in modern data science. This paper provides a comparative study between two widely used tree-ensemble algorithms: Random Forest (bagging) and XGBoost (gradient boosting). We analyze algorithmic design, computational characteristics, hyperparameter sensitivity, handling of missing/sparse data, interpretability, and typical application scenarios. Empirical patterns reported in the literature and practical guidelines are synthesized to help practitioners choose between these approaches. The study highlights XGBoost’s superior predictive performance in many benchmark scenarios and Random Forest’s robustness and simplicity for rapid development. [1]