Essence of Boosting Ensembles for Machine Learning
Boosting is a powerful and popular class of ensemble learning techniques.
Historically, boosting algorithms were challenging to implement, and it was not until AdaBoost demonstrated how to implement boosting that the technique could be used effectively. AdaBoost and modern gradient boosting work by sequentially adding models that correct the residual prediction errors of the model. As such, boosting methods are known to be effective, but constructing models can be slow, especially for large datasets.
More recently, extensions designed for computational efficiency have made the methods fast enough for broader adoption. Open-source implementations, such as XGBoost and LightGBM, have meant that boosting algorithms have become the preferred and often top-performing approach in machine learning competitions for classification and regression on