Is there any rules of thumb for the relation of number of iterations and training size for lightgbm?
In a similar problem in deep learning with Keras: I do it by using an early stopper and cross validation with train and validation data, and let the model optimize itself using validation data during trainings. 
After each training, I test the model with test data and examine the mean accuracies. In the mean time after each training I save the stopped_epoch from EarlyStopper. If CV scores are satisfying, I take the mean of stopped epochs and do a full training (including all data I have) with the number of mean stopped epochs, and save the model.
When I train a classification model using lightgbm, I usually use validation set and early stopping to determine the number of iterations. 
Now I want to combine training and validation set to train a model (so I have more training examples), and use the model to predict the test data, should I change the number of iterations derived from the validation process? 
Thanks!