I am going through the ISLR book (by Hastie, et al.). In the chapter on model validation, the author suggests that we build the final model on the full data instead of the training data only. The way I always understood the process is that we build the model on training set, validate it with test set and finalize the model. If we use the full data to build the model then what is the point of anything that we did before that step? Thank you!
The point of breaking up the data is to characterize model performance and tune hyper parameters. Building the model on all of the data gives you the largest possible sample size for the purpose of parameter estimation. Two different goals in these phases, so two different procedure.