What are the best practices for regularization in Python?

What are the best practices for regularization in Python?

Regularization helps prevent overfitting in machine learning models. Common techniques include L1 (Lasso) and L2 (Ridge) regularization. It's best to start with L2 regularization for linear models and use cross-validation to tune the regularization parameter.

How do I implement Lasso and Ridge in Python?

You can use the sklearn library. For Lasso, use from sklearn.linear_model import Lasso, and for Ridge, use from sklearn.linear_model import Ridge. Then, create an instance of the model and fit it to your data.

What are the differences between Lasso and Ridge?

Lasso (L1) regularization can shrink some coefficients to zero, effectively performing feature selection, while Ridge (L2) regularization shrinks coefficients but does not set them to zero. This means Lasso can lead to simpler models.

When should I use regularization?

Use regularization when you have a complex model or a small dataset to prevent overfitting. It's particularly useful when you suspect that some features may not be relevant.