Bagging (from the bootstrap aggregation) is one of the earliest and most straightforward types of ensembles. Bagging is based on the statistical bootstrap method, which aims to obtain the most accurate sample estimates and to extend the results to the entire population. The bootstrap method is as follows.
Suppose there is an X dataset of size M. Evenly take from the dataset N objects and return each object. Before selecting the next one, we can generate N sub-datasets. This procedure means that N times, we select an arbitrary sample object (we assume that each object is picked up with the same probability ), and each time, we choose from all the original M objects.
We can imagine this as a bag ...
