Boosting is an ensemble learning technique that aims to improve the accuracy of machine learning models by combining multiple weak learners to create a strong learner. It works iteratively, where each new model is trained to correct the errors made by the previous ones. This process enhances the model’s performance by focusing on the instances that were misclassified. Boosting algorithms, such as AdaBoost and Gradient Boosting, have gained popularity due to their effectiveness in various applications, including classification and regression tasks. They are particularly known for achieving state-of-the-art results in competitive machine learning environments.