The algorithm for Gradient Boosting Regression is a recasted adaptation of AdaBoost that employs boosting methods in regression trees . The general idea is to compute a sequence of simple trees, where each successive tree is constructed for the prediction residuals of the preceding tree . Minimization of the loss (residuals) of the model (or regressor) is pursued by adding weak learners using a gradient descent procedure. Therefore, three elements are considered directly in developing a regressor with gradient boosting: a loss function, a weak learner, and finally an additive model to add weak learners to minimize the loss function. Considering decision trees as the weak learner, they are added one at the time, while the existing trees are kept unchanged. To ensure the simplicity of the learned trees, it is common to assign specific constraints to control the growth of trees, for instance, defining a maximum limit for depth, or the number of leaf nodes.