Boosting is an ensemble learning technique that improves model accuracy in machine learning by training sequential models to correct errors of previous models, thus reducing bias and variance. It contrasts with bagging, which uses randomized subsets of data. There are various boosting algorithms like AdaBoost, Gradient Boosting, XGBoost, and CatBoost, each catering to different scenarios. Boosting is effective for tasks like classification, regression, content recommendation, and fraud detection, offering advantages such as reduced bias and lower data requirements. However, it faces challenges like longer training times, outlier sensitivity, and a need for extensive hyperparameter tuning.
Boosting Techniques in Machine Learning: Enhancing Accuracy and Reducing Errors
