Gradient Boosting Machines (GBM)

Gradient Boosting Machines (GBMs) are a powerful machine learning technique that builds an accurate predictive model by combining many small, simple models called decision trees. It works sequentially, where each new tree corrects the errors of the previous ones, gradually improving overall accuracy. Think of it as an iterative learning process: each step learns from the mistakes of the last, refining predictions over time. GBMs are widely used for tasks like classification and regression because they effectively handle complex patterns in data, often outperforming other methods when properly tuned.