How do I implement regularization in my machine learning model?

How do I implement regularization in my machine learning model?

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?

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 actually add this to my model?

If you're using libraries like Scikit-learn, you can set the alpha parameter in models like Ridge (L2) or Lasso (L1) to apply regularization.

Can you give me an example code snippet?

Sure! Here's a simple example using Scikit-learn:

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