eXtreme Gradient Boosting (XGB): XGB is an ensembling method that is based on the concept of gradient boosting. It builds the base learners sequentially, where each learner is built to reduce a loss function whose terms include the gradients (residuals) of previously built learners. As a result, each consecutively built learner aims to correct the mistakes of past learners using the gradients. We use the XGBoost Python package implementation. The XGB hyperparameters involve varying the maximum depth, learning rate, and number of estimators (Tables and).