In pratice, it is usual that we don't have enough observations to build our desired models. An idea come to my mind is that the population can be factored: in other words, we can simply duplicate every observation, making (say) 5 copies of every observation. However, this is risky because if we oversample both training and testing set, we may overestimate the performance of the trained model.
One way to overcome this situation might be to factor only the training set, build the model on the duplicated training set, and test it on the original testing set. Is this a good idea? I guess that in most of the situations, it could even lead to a worse model. Is there any case in which factoring the training set could make sense?