Boosting is a machine learning technique used to improve the accuracy of predictive models. It works by combining multiple weak learners, which are models that perform slightly better than random guessing, into a single strong learner. Each weak learner is trained sequentially, focusing on the errors made by the previous ones, which helps to reduce bias and improve overall performance. One popular boosting algorithm is AdaBoost, which adjusts the weights of training samples based on their classification accuracy. Other well-known boosting methods include Gradient Boosting and XGBoost. These techniques are widely used in various applications, such as finance, healthcare, and marketing, to enhance decision-making processes.