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, with each one focusing on the errors made by the previous models, thereby enhancing overall performance. One popular boosting algorithm is AdaBoost, which adjusts the weights of incorrectly classified instances to give them more importance in subsequent models. Another well-known method is Gradient Boosting, which optimizes the model by minimizing the loss function through gradient descent. Both methods are widely used in various applications, including classification and regression tasks.