In Which Epoch Should I Stop the Training to Avoid Overfitting?

Answer: You should stop the training when the validation loss starts to increase, indicating the onset of overfitting.

Determining the optimal epoch to stop training and avoid overfitting depends on monitoring the model’s performance on a validation dataset. Here’s a detailed explanation:

1. Validation Loss: During training, it’s common to split the dataset into training and validation sets. The training set is used to update the model parameters, while the validation set is used to evaluate the model’s performance on unseen data after each epoch.
2. Early Stopping: One effective strategy to prevent overfitting is early stopping, where training is halted when the validation loss begins to increase or no longer decreases. This indicates that the model’s performance on the validation set is deteriorating, suggesting that it has started to overfit the training data.
3. Monitoring Validation Loss: Throughout the training process, the validation loss is monitored at the end of each epoch. If the validation loss consistently decreases or remains stable for several epochs and then starts to increase, it signifies that the model is beginning to memorize the training data and losing its ability to generalize to new examples.
4. Threshold Criteria: To determine when to stop training, a threshold criterion is often employed. This could involve monitoring the validation loss over a certain number of epochs and stopping training if it increases for a specified number of consecutive epochs (e.g., early stopping patience).
5. Cross-Validation: In some cases, cross-validation techniques may be used to estimate the optimal stopping epoch more robustly. By partitioning the data into multiple subsets and performing training/validation cycles on different splits, cross-validation provides a more reliable estimate of model performance and helps identify the best stopping epoch.

Conclusion:

The epoch at which training should be stopped to avoid overfitting varies depending on the dataset, model architecture, and training procedure. By monitoring the validation loss and employing techniques like early stopping, practitioners can determine an appropriate stopping epoch to prevent overfitting and ensure that the trained model generalizes well to unseen data.