Gradient Boosting Machines

boosting-machines">Gradient Boosting Machines (GBMs) are a powerful type of machine learning technique used for predictive modeling. They work by combining the predictions of many simple models, called decision trees, each one correcting the errors of the previous trees. This process builds the model incrementally, enhancing accuracy step by step. Essentially, it focuses on where the previous models went wrong and tries to improve on those mistakes. GBMs are widely used for tasks like classification and regression because they can handle different types of data and often yield high-quality results.