Edge AI
Machine Learning
A regularization technique that stops training when the model's performance on a validation set starts to degrade.
Detailed Explanation
Early Stopping is a regularization technique in machine learning that halts training when the model's performance on a validation set begins to decline, indicating potential overfitting. By stopping early, it prevents the model from excessively fitting to the training data, thus improving generalization to unseen data and enhancing overall model robustness.
Use Cases
- •Use case: Prevents overfitting by halting training early when validation performance deteriorates, ensuring better generalization on unseen datasets.
Related Terms
Other terms in the Machine Learning category