How do I tune hyperparameters in Elastic Net?

How do I tune hyperparameters in Elastic Net?

Tuning hyperparameters in Elastic Net involves adjusting parameters like alpha and l1_ratio. Would you like to know how to do this using cross-validation?

Yes, please explain how to use cross-validation for this.

You can use techniques like GridSearchCV from scikit-learn to perform cross-validation. It helps in finding the best combination of alpha and l1_ratio.

What values should I consider for alpha and l1_ratio?

Common practice is to try alpha values in a logarithmic scale, like [0.001, 0.01, 0.1, 1, 10], and l1_ratio values between 0 and 1.

How do I implement this in Python?

You can implement it using the following code snippet:

from sklearn.linear_model import ElasticNetCV
model = ElasticNetCV(alphas=[0.001, 0.01, 0.1, 1, 10], l1_ratio=[0.1, 0.5, 0.9], cv=5)
model.fit(X_train, y_train)