What are the best practices for model regularization in machine learning?

What are the best practices for model regularization in machine learning?

Model regularization helps prevent overfitting. Key practices include L1 and L2 regularization, dropout, and early stopping.

Can you explain L1 and L2 regularization?

L1 regularization adds a penalty equal to the absolute value of the coefficients, promoting sparsity. L2 adds a penalty equal to the square of the coefficients, which helps in reducing model complexity.

What is dropout and how does it work?

Dropout randomly sets a fraction of input units to zero during training, which helps prevent overfitting by ensuring that the model does not rely too heavily on any one feature.

When should I use early stopping?

Early stopping is used when you notice that the model's performance on a validation set starts to degrade while training continues to improve, indicating overfitting.