What are some common regularization methods in Python?

What are some common regularization methods in Python?

Common regularization methods in Python include L1 (Lasso), L2 (Ridge), and Elastic Net. Would you like to know more about any specific method?

Can you explain Lasso regularization?

Lasso regularization adds a penalty equal to the absolute value of the magnitude of coefficients. It can help in feature selection by shrinking some coefficients to zero.

How does Ridge regularization differ from Lasso?

Ridge regularization adds a penalty equal to the square of the magnitude of coefficients, which helps to reduce model complexity but does not perform feature selection like Lasso.

What is Elastic Net?

Elastic Net combines both L1 and L2 regularization, allowing for both feature selection and coefficient shrinkage. It's useful when there are correlations among features.

How do I implement these methods in Python?

You can implement these methods using libraries like Scikit-learn. For example, use Lasso, Ridge, or ElasticNet classes from sklearn.linear_model.