What is boosting meaning?

Boosting, in the context of machine learning, refers to a family of algorithms that iteratively combine simpler models, known as weak learners, into a single, more accurate model. The idea is to improve the accuracy of the final model by iteratively adjusting the weights of the training examples to emphasize previously misclassified examples. Boosting has been widely used in various applications, including classification, regression, and ranking. Some popular boosting algorithms include AdaBoost, Gradient Boosting Machines (GBM), and XGBoost. Boosting is generally considered to be a highly effective method for improving the performance of machine learning models, particularly in situations where the data is noisy or when the weak learners used to build the model are relatively simple.