What is boosting in ensemble learning?

Boosting in ensemble learning is a technique that combines multiple weak learners to create a strong predictive model. Unlike traditional ensemble methods where models are trained independently and their predictions are combined through averaging or voting, boosting builds models sequentially. Each new weak learner is trained to correct the errors made by the previous models, focusing more on instances that were misclassified. By iteratively adding new models and adjusting their weights based on the performance of the ensemble, boosting aims to improve the overall predictive power of the model.

Popular boosting algorithms include AdaBoost, Gradient Boosting Machines (GBM), XGBoost, and LightGBM. These algorithms differ in their approaches to updating model weights and combining weak learners, but they all share the goal of sequentially improving the ensemble’s predictive performance by focusing on difficult-to-classify instances. Boosting algorithms are widely used in machine learning due to their ability to effectively improve model accuracy and generalization, making them a powerful tool in predictive modeling tasks.