What are some other regularization techniques in machine learning?

What are some other regularization techniques in machine learning?

In addition to L1 and L2 regularization, techniques like dropout, early stopping, and data augmentation are commonly used.

Can you explain how dropout works?

Dropout randomly sets a fraction of input units to zero during training, which helps prevent overfitting.

What is early stopping?

Early stopping involves monitoring the model's performance on a validation set and halting training when performance starts to degrade.

How does data augmentation help in regularization?

Data augmentation increases the diversity of training data by applying transformations, which helps the model generalize better.

Are there any other techniques I should know about?

Yes, techniques like batch normalization and ensemble methods can also help improve model performance and reduce overfitting.