What are the different regularization methods in Python?

What are the different regularization methods in Python?

In Python, common regularization methods include L1 (Lasso), L2 (Ridge), and Elastic Net. Would you like to know more about each?

Yes, can you explain Lasso and Ridge regularization?

Lasso regularization adds a penalty equal to the absolute value of the magnitude of coefficients, promoting sparsity. Ridge adds a penalty equal to the square of the magnitude of coefficients, which helps in reducing overfitting.

When should I use Lasso over Ridge?

Use Lasso when you want feature selection and a simpler model, while Ridge is better for multicollinearity and when you want to keep all features.

Can you give me an example of how to implement these in Python?

Sure! You can use libraries like scikit-learn. For Lasso, you can use from sklearn.linear_model import Lasso, and for Ridge, from sklearn.linear_model import Ridge. Would you like a code snippet?