Gradient boosting is a powerful technique that builds models sequentially, each one correcting the errors of its predecessor. The first model serves as a baseline average, while subsequent models are decision trees that refine predictions. This method stands apart from traditional bagging and boosting techniques, offering a unique approach to enhancing accuracy through focused improvements.
