Dropout Regularization
Machine Learning
A regularization technique that randomly deactivates neurons during training to prevent overfitting.
Detailed Explanation
Dropout is a regularization method used in machine learning to reduce overfitting by randomly deactivating a subset of neurons during each training iteration. This prevents neurons from becoming overly reliant on specific features, promoting more robust and generalized learning. During testing, all neurons are active, ensuring the model's predictions are based on a diverse set of features.
Use Cases
- •Use case: Improves neural network generalization by preventing over-reliance on specific neurons during training, leading to better performance on unseen data.
Related Terms
Other terms in the Machine Learning category