Gradient boosting machines (GBM) are a powerful ensemble learning technique used for regression and classification problems. They work by combining multiple weak learners, typically decision trees, to create a strong predictive model that minimizes errors iteratively. This approach adjusts the model based on the errors made by previous trees, allowing for highly accurate predictions and improved performance in predictive analytics and forecasting tasks.
congrats on reading the definition of gradient boosting machines. now let's actually learn it.