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 how they differ?

Yes, how do Lasso and Ridge regularization differ?

Lasso (L1) adds a penalty equal to the absolute value of the coefficients, which can lead to sparse models. Ridge (L2) adds a penalty equal to the square of the coefficients, which tends to shrink coefficients but not eliminate them.

When should I use Lasso over Ridge?

Use Lasso when you suspect that many features are irrelevant and want to perform feature selection. Ridge is better when you believe all features are relevant but want to prevent overfitting.

Can you show me a code example for both?

Sure! Here's a simple example using scikit-learn:

from sklearn.linear_model import Lasso, Ridge

# Lasso
lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)

# Ridge
ridge = Ridge(alpha=0.1)
ridge.fit(X_train, y_train)