As a greedy algorithm, gradient boosting has overfitting potential in training data, quickly. Therefore, common techniques such as regularization, assists the performance of prediction by penalizing various parts of the algorithm. The discussed constraints on tree construction is a example of the regularization methods to control the greediness of gradient boosting .