What is early stopping in machine learning?

What is early stopping in machine learning?

Early stopping is a technique used to prevent overfitting during the training of machine learning models by halting training when performance on a validation set starts to degrade.

How does early stopping work in practice?

In practice, you monitor the model's performance on a validation set after each epoch. If the performance doesn't improve for a set number of epochs, training is stopped.

What are the benefits of using early stopping?

The benefits include reducing overfitting, saving training time, and improving model generalization on unseen data.

Are there any downsides to early stopping?

Yes, if not configured properly, it can lead to underfitting or stopping too early, resulting in a model that hasn't fully learned the data.