Early stopping is a technique used in Machine Learning to prevent model overfitting during the training process by monitoring the performance of the model on a validation set.
Overfitting occurs when a model makes low errors on the training set, but high errors on the validation set or test set. This arises when the model captures the noise in the training set rather than the underlying patterns. Early stopping is one of the methods used to overcome this issue.
Early stopping involves monitoring the performance of the model at predefined checkpoints during the training process, and stopping the training process if the model’s performance on the validation set does not improve beyond a certain number of epochs (iterations). In other words, if there is no improvement in validation set performance within a certain number of epochs, the training process is stopped automatically.
The main motivation behind this technique is that a model generally tends to perform better on the validation set, which it had not seen during training, when it has not been overfitted to the training data. This is because, as the model overfits, it starts learning the noise in the training data and neglects the underlying patterns which generalize better on new data.
For example, consider a model that is being trained to predict the price of a product based on certain features, such as the product category, availability, reviews, etc. If the model is allowed to train for too long, it will eventually start fitting to the noise or outliers, causing the validation set performance to degrade.
By using early stopping, the training process can be stopped before it overfits on the training data, resulting in a model that performs better on the validation testing. Ultimately, this leads to a model that can generalize well to new data, which is the main objective of Machine Learning.