What is boosting and how does it improve weak learners?

Boosting sequentially trains weak models, correcting errors from previous models.

Boosting builds models iteratively where each new model focuses on correcting errors of previous ones. It assigns higher weights to misclassified samples. Popular algorithms include AdaBoost, Gradient Boosting, XGBoost, and LightGBM, which optimize loss functions efficiently.