Although all the above four machine learning models-RF, XGBoost, GradBoost, and Light-GBM -use ensemble techniques, their approaches to prediction differ. Random Forest is a bagging-based technique that builds multiple decision trees independently, averaging their predictions to reduce variance and improve stability. Gradient Boosting applies a boosting strategy to further train the weak learners one after another in a sequence to minimize the loss function while correcting the mistakes of the preceding one. The other two methods -XGBoost, and LightGBM -are more advanced versions of the Gradient Boosting algorithm.