How does cross-validation work for Lasso and Ridge regression?

How does cross-validation work for Lasso and Ridge regression?

Cross-validation is a technique used to assess the performance of Lasso and Ridge regression models by partitioning the data into subsets. It helps in selecting the best hyperparameters.

What are the main differences between Lasso and Ridge regression?

Lasso regression adds an L1 penalty, which can shrink some coefficients to zero, effectively performing variable selection. Ridge regression adds an L2 penalty, which shrinks coefficients but does not set them to zero.

How do I choose the right alpha value for these models?

You can use cross-validation to find the optimal alpha value. By testing different alpha values and evaluating the model's performance on validation sets, you can select the one that minimizes prediction error.

Can you explain k-fold cross-validation?

Sure! In k-fold cross-validation, the dataset is divided into k subsets. The model is trained on k-1 subsets and validated on the remaining subset. This process is repeated k times, with each subset used as the validation set once.

What metrics should I use to evaluate the models?

Common metrics include Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE). These metrics help you understand how well your model is performing.