When should you consider stopping the training of a model?

Stopping the training of a model when loss metrics begin to increase is critical to avoid overfitting. During the training process, the model learns from the training data, and ideally, the loss should decrease as the model becomes better at predicting the training data. However, if the loss starts to increase after a period of decrease, this is an indication that the model may be learning noise from the training data rather than the underlying patterns. This phenomenon often means that the model is starting to overfit—performing well on the training data but poorly on unseen validation data.

Monitoring loss metrics offers a direct insight into the model's learning progress and helps identify when to halt training to retain the most generalizable version of the model. This strategy is often implemented in training processes using techniques such as early stopping, where the training is terminated if there’s no improvement in the validation loss for a predetermined number of training epochs.