Gradient Boosting

Gradient Boosting is a machine learning technique that builds a strong predictive model by combining many simpler ones, called weak learners. It works sequentially, where each new weak learner focuses on correcting the mistakes of the previous ones. This process involves measuring errors, then training the next model to reduce those errors, gradually improving overall accuracy. Think of it as an iterative teaching process, where each step refines the model’s predictions. Gradient Boosting is widely used because it tends to produce very accurate results, especially for tasks like predicting customer behavior or financial outcomes.