Boosting Algorithm

Boosting algorithms combine multiple weak learners—classifiers or regressors with slightly better-than-chance accuracy—to create a strong learner with significantly improved performance. Current research focuses on improving sample efficiency, developing novel algorithms like AdaBoost variants and gradient boosting methods (including those incorporating decision trees), and exploring applications in diverse fields such as cybersecurity, poverty prediction, and image-text matching. These advancements enhance the accuracy, interpretability, and fairness of machine learning models, leading to more robust and reliable solutions across various domains.