What is “early stopping” in machine learning?

Answer:

Explanation:

Early stopping is a regularization technique used to prevent overfitting in machine learning models, especially in neural networks. It works by monitoring the model’s performance on a validation set and stopping the training process when the validation performance starts to deteriorate, indicating that the model is beginning to overfit the training data.

By stopping the training at the right time, early stopping ensures that the model generalizes well to new, unseen data while avoiding the risk of overfitting. This technique is widely used in deep learning, where models can easily overfit if trained for too long.

Early stopping is often used in conjunction with other regularization techniques, such as dropout and weight decay, to build more robust models.