How do I implement L2 regularization in my model?

How do I implement L2 regularization in my model?

To implement L2 regularization, you can add a penalty term to your loss function. This term is the sum of the squares of the weights multiplied by a regularization parameter.

What is the purpose of the regularization parameter?

The regularization parameter controls the strength of the penalty. A higher value means more regularization, which can help prevent overfitting.

Can you show me a code example?

Sure! In Python with scikit-learn, you can use Ridge regression for L2 regularization. Here's a simple example:

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

What are the differences between L1 and L2 regularization?

L1 regularization adds the absolute value of the weights to the loss function, promoting sparsity, while L2 adds the square of the weights, which tends to distribute weight more evenly.