What are regularization techniques in machine learning?

What are regularization techniques in machine learning?

Regularization techniques are methods used to prevent overfitting in machine learning models by adding a penalty to the loss function.

Can you explain L1 and L2 regularization?

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

When should I use L1 over L2 regularization?

Use L1 regularization when you want feature selection, as it can shrink some coefficients to zero. L2 is better for cases where you want to keep all features but reduce their impact.

Are there any other regularization techniques?

Yes, other techniques include Elastic Net, which combines L1 and L2, and Dropout, commonly used in neural networks to prevent overfitting.