What are some common regularization methods in Python?

What are some common regularization methods used in Python for machine learning?

Common regularization methods include L1 (Lasso), L2 (Ridge), and Elastic Net. They help prevent overfitting.

How do I implement Lasso regularization in Python?

You can use the Lasso class from the sklearn.linear_model module. Here's a simple example:

from sklearn.linear_model import Lasso
model = Lasso(alpha=0.1)
model.fit(X_train, y_train)

What is the difference between Lasso and Ridge regularization?

Lasso uses L1 regularization which can shrink some coefficients to zero, while Ridge uses L2 regularization which shrinks coefficients but never to zero.

Can you show me an example of Elastic Net regularization?

Sure! Here's how to implement Elastic Net:

from sklearn.linear_model import ElasticNet
model = ElasticNet(alpha=0.1, l1_ratio=0.5)
model.fit(X_train, y_train)