When it comes to bagging, it can be applied to both classification and regression. However, there is another technique that is also part of the ensemble family: boosting. However, the underlying principle of these two are quite different. In bagging, each of the models runs independently and then the results are aggregated at the end. This is a parallel operation. Boosting acts in a different way, since it flows sequentially. Each model here runs and passes on the significant features to another model: