The Gradient Boosted Trees (GBT) algorithm is a powerful machine-learning technique for regression and classification problems. It builds an ensemble model using weak prediction models like decision trees. The core idea behind the Gradient Boosted Trees (GBT) algorithm is to sequentially add these trees to the ensemble, where each new tree helps to correct errors made by the previously added trees. This is achieved through gradient boosting, which optimizes a loss function, essentially steering the model’s learning in the direction that minimizes prediction errors. Each tree is fitted to the negative gradient of the loss function. Hence, the term ‘gradient boosting.’ One of the critical strengths of Gradient Boosted Trees is their ability to automatically handle missing data and incorporate complex nonlinear relationships between features and the target variable. Despite being computationally intensive, GBT algorithms, like XGBoost and LightGBM, have gained popularity for their effectiveness in producing highly accurate models, which makes them a go-to method in a wide range of applications, including but not limited to ranking, classification, and regression tasks.