Feature request: Validation data metrics for model selection

[romanovzky]

Currently, SymbolicRegressor returns a model that better complies with a certain criteria. This, however, is computed on the training set. Machine learning best practices dictate that model selection should be done using a validation set. Currently, this can be "hacked" by selecting the best pareto front individual against a validation metric after the SymbolicRegressor completes its run. However, with callbacks (see https://github.com/heal-research/pyoperon/issues/18) this feature could allow for earlystop criteria using the validation set. This is common in machine learning packages with iterative training (see Keras, Lightning, xGboost, etc for examples).