How do I tune the alpha parameter in ridge regression?

How do I tune the alpha parameter in ridge regression?

Tuning the alpha parameter in ridge regression involves using techniques like cross-validation to find the optimal value.

What is the effect of alpha on the model's performance?

A higher alpha value increases regularization, which can reduce overfitting but may also lead to underfitting.

Can you suggest a method for cross-validation?

You can use k-fold cross-validation, where the dataset is split into k subsets, and the model is trained k times.

How do I implement this in Python?

You can use libraries like scikit-learn, which provide functions to perform ridge regression and cross-validation easily.