What is GBMs?

GBMs (Gradient Boosting Machines) are a machine learning technique used for regression and classification tasks. They work by combining multiple weak learners (typically decision trees) sequentially, where each new tree corrects the errors made by the previous ones. The 'gradient' in the name refers to the gradient descent algorithm used to minimize the loss function during the boosting process. GBMs are commonly used in various applications, including fraud detection, ranking, and prediction.