Ivan Palomares Carrascosa disambiguates three terms:
Unity makes strength. This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task. This article describes three common ways to build ensemble models: boosting, bagging, and stacking. Let’s get started!
My explanation, which makes sense for people who grew up during the 1980s: bagging is Voltron, boosting is Rocky, and stacking is three racoons in a trench coat.