The two paradigms of ensemble methods are:
Parallel ensemble methods : these methods build the several estimators or models independently and then take average for regression or voting for classification problems. Example are: Bagging methods, Random Forest
Sequential ensemble methods : These fall under the family of Boosting methods where the base estimators are butilt sequentially and then reduces thes bias of the combined estimator. Examples: AdaBoost, Gradient Boost, XGBoost.
