Boosting versus Bagging

Boosting and bagging are two classes of machine learning techniques.

• Libraries for Boosting:  catboost, XGBoost (eXtreme Gradient Boosting)
• Libraries for Bagging: scikit-learn (BaggingClassifier, BaggingRegressor), imbalanced-learn (scikit-learn extension for imbalanced data), ML-Ensemble

Boosting is basically stacking mini-models that incrementally improve on previous models. Bagging is using ensemble/averaging techniques i.e. running models in parallel and computing some form of average.