Four popular subcategories of ensemble modeling are bagging, boosting, a bucket of models, and stacking. Bagging, as we know, is short for “boosted aggregating” and is an example of a homogenous ensemble. This method draws upon randomly drawn datasets and combines predictions to design a unified model based on a voting process among the training data. Expressed in another way, bagging is a special process of model averaging. Random forest, as we know, is a popular example of bagging.
