In this methodology, bootstrap samples are drawn from training data and, each time, separate models will be fitted (individual models could be decision trees, random forest, and so on) on the drawn sample, and all these results are combined at the end to create an ensemble. This method suits dealing with highly flexible models where variance reduction will still improve performance:
In the following example, AdaBoost is used as a base classifier and the results of individual AdaBoost models are combined using the bagging classifier to generate final outcomes. Nonetheless, each AdaBoost is made up of decision trees with a depth of 1 (decision stumps). Here, we would like to show that classifier inside classifier inside classifier is possible (sounds like the Inception movie though!):
# Ensemble of Ensembles - by applying bagging on simple classifier
>>> from sklearn.tree import DecisionTreeClassifier
...