Overtraining

Overtraining occurs when the test set error increases while the training set error continues to descend. This indicates that memorization is the predominant learning mode. When a test set error has reached a global minimum and increases indefinitely thereafter, overtraining has occurred. Training a network after the test set error global minimum has been reached can actually hurt the predictive capabilities of the model being developed.