What are some examples of regularization methods in Python?

What are some examples of regularization methods in Python?

Regularization methods help prevent overfitting in machine learning models. Common examples include L1 (Lasso) and L2 (Ridge) regularization.

Can you show me how to implement Lasso regularization in Python?

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

from sklearn.linear_model import Lasso

# Sample data
X = [[0, 0], [1, 1], [2, 2]]
y = [0, 1, 2]

# Create a Lasso regressor
lasso = Lasso(alpha=0.1)
lasso.fit(X, y)

# Coefficients
print(lasso.coef_)

What about Ridge regularization? How is it different?

Ridge regularization also helps prevent overfitting but uses L2 penalty instead of L1. It tends to keep all features but reduces their impact.

Can you provide a code example for Ridge regularization?

Absolutely! Here's how you can implement Ridge regularization in Python:

from sklearn.linear_model import Ridge

# Sample data
X = [[0, 0], [1, 1], [2, 2]]
y = [0, 1, 2]

# Create a Ridge regressor
ridge = Ridge(alpha=1.0)
ridge.fit(X, y)

# Coefficients
print(ridge.coef_)