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 to implement them?

Yes, can you show me how to implement Lasso regularization?

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

from sklearn.linear_model import Lasso

# Create a Lasso regression model
model = Lasso(alpha=0.1)

# Fit the model to your data
model.fit(X_train, y_train)

Would you like to see more details or examples?

What about Ridge regression? How is it different?

Ridge regression uses L2 regularization, which penalizes the sum of the squares of the coefficients. It's less sensitive to outliers compared to Lasso. Here's how you can implement it:

from sklearn.linear_model import Ridge

# Create a Ridge regression model
model = Ridge(alpha=1.0)

# Fit the model to your data
model.fit(X_train, y_train)

Would you like to explore Elastic Net next?