There is a classification problem(two classes). We have train data, for which we know class labels and we have test data.Imagine, that you have created model that with good accuracy(~95%) make predictions and we know that we are not overfitted.If we make prediction on test data, extract objects for which we sure in class label(for example, predict_proba higher than 90%) and add this objects to train data.Does this tactic make any sense?
Use test data as train: does it make sense?
data mining;classification;data cleaning
This idea will most likely increase the bias in the model. Let's assume that the model has non-zero bias in the model. In this case, when it assumes its predictions to be true, without confirmation from an Oracle as in active learning, the bias of the model increases. In common terms, if the model has some amount of bias in its predictions, and it uses its predictions to learn on, the bias in the model can only increase. This issue does not arise when there is 0 bias in the model to begin with, however, in that case, there is no need to learn any further!  Note that this is a highly intuitive answer but I cannot think of an argument against the intuition :-) I will appreciate any feedback on this. 