What are the best practices for regularization in machine learning?

What are the best practices for regularization in machine learning?

Regularization helps prevent overfitting. Common practices include L1 and L2 regularization, dropout, and early stopping.

Can you explain L1 and L2 regularization?

L1 regularization adds the absolute value of coefficients to the loss function, promoting sparsity. L2 adds the square of coefficients, which discourages large weights.

When should I use dropout?

Dropout is effective in neural networks, especially when you have a large model and limited data. It randomly drops units during training to prevent co-adaptation.

What is early stopping?

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