What do we do with the data to get the difference between models in bagging?

In bagging (bootstrap aggregating), the method of bootstrap sampling is used to obtain differences between models: multiple sub-samples are randomly formed with replacement from the original training set. Each model is trained on its own unique sub-sample, leading to model diversity due to differences in data. This reduces overfitting and increases the robustness of the ensemble.