What are the different regularization methods in Python and how do they compare?

What are the different regularization methods in Python?

The main regularization methods in Python include L1 (Lasso), L2 (Ridge), and Elastic Net. Each has its own strengths.

How do these methods compare in terms of performance?

L1 can lead to sparse models, L2 tends to perform better with multicollinearity, and Elastic Net combines both benefits.

Can you give an example of when to use each method?

Use L1 when you want feature selection, L2 for generalization in high-dimensional data, and Elastic Net when you have correlated features.

What libraries in Python can I use for these methods?

You can use libraries like Scikit-learn, Statsmodels, and TensorFlow for implementing these regularization techniques.