How do I implement regularization in my machine learning models?

How do I implement regularization in my machine learning models?

Regularization helps prevent overfitting by adding a penalty to the loss function. Are you using L1 or L2 regularization?

I'm not sure. What's the difference between L1 and L2 regularization?

L1 regularization adds the absolute value of the coefficients as a penalty, while L2 adds the square of the coefficients. L1 can lead to sparse models, while L2 tends to distribute the error more evenly.

How do I apply L2 regularization in Python?

You can use libraries like scikit-learn. For example, in a linear regression model, you can set the 'alpha' parameter in Ridge regression to apply L2 regularization.

Can you show me a code snippet for that?

Sure! Here's a simple example:

from sklearn.linear_model import Ridge
model = Ridge(alpha=1.0)
model.fit(X_train, y_train)