I have a binary classification model which I have trained on a training set. On the validation set its accuracy is ~85%. I set up early stopping which ended training when validation loss increased. Let's call this final model
Because of the nature of the task I can generate as much training data as I want. I have a huge dataset (say, reddit comments) and am generating positive and negative examples in a way that there are an astronomical number of combinations. So I'm not worried that the model is "memorizing" my training data.
So I generated another dataset and fine-tuned
modelA until the early stopping trigger was fired. The validation accuracy of this model,
modelB, was ~87%.
I repeated the process once more, getting a final validation accuracy of ~89% for
My question is, will this cause overfitting? Is this iterative fine-tuning a common industry practice?