When should I use regularization methods in Python?

When should I use regularization methods in Python?

Regularization methods are used to prevent overfitting in machine learning models. You should consider using them when your model performs significantly better on training data than on validation data.

What are some common regularization techniques?

Common regularization techniques include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net, which combines both L1 and L2.

How do I implement Lasso regularization in Python?

You can implement Lasso regularization using the Lasso class from the sklearn.linear_model module. Here's a simple example: from sklearn.linear_model import Lasso; model = Lasso(alpha=0.1); model.fit(X_train, y_train).

What parameters should I tune for regularization?

The main parameter to tune is the regularization strength, often denoted as alpha. You can use techniques like cross-validation to find the optimal value.