Adaptive Boosting

Adaptive boosting (AdaBoost) is an ensemble learning method that combines multiple weak classifiers to create a strong, accurate predictor by iteratively weighting training samples based on their classification difficulty. Current research focuses on enhancing AdaBoost's performance and addressing limitations, including developing variations like CatBoost and XGBoost, improving fairness in classification, and adapting it for dynamic data streams and large language model integration. These advancements are significant for various applications, from poverty prediction and healthcare diagnostics to text classification and improving the interpretability of complex models.