The training of an ensemble of models is understood to be the procedure of training a final set of elementary algorithms, whose results are then combined to form the forecast of an aggregated classifier. The model ensemble's purpose is to improve the accuracy of the prediction of the aggregated classifier, particularly when compared with the accuracy of every single elementary classifier. It is intuitively clear that combining simple classifiers can give a more accurate result than each simple classifier separately. Despite that, simple classifiers can be sufficiently accurate on particular datasets, but at the same time, they can make mistakes on different datasets.
An example of ensembles is Condorcet's jury theorem (1784). A jury must come to a correct or incorrect consensus, and each juror has an independent opinion. If the probability...
