Final answer:
Lasso regression is a technique used for variable selection and regularization in linear regression. Cross-validation can be used to find the optimal lambda parameter. Cross-validation involves splitting the data into subsets and evaluating the model's performance on each subset.
Step-by-step explanation:
Lasso regression is a technique used for variable selection and regularization in linear regression. It involves adding a penalty term to the cost function that forces some of the regression coefficients to be exactly zero, effectively performing feature selection. To find the optimal lambda parameter for Lasso regression, you can use cross-validation.
Cross-validation is a technique used to assess the performance of a model and tune its hyperparameters. In the context of Lasso regression, you can perform k-fold cross-validation by splitting your data into k subsets (folds). You then train the model on k-1 folds and evaluate its performance on the remaining fold. This process is repeated for each fold, and the performance metrics are averaged across all folds.
The optimal lambda parameter is the one that results in the best performance metric in cross-validation. This metric could be the mean squared error (MSE) or the coefficient of determination (R-squared), for example. By systematically testing different values of lambda and evaluating the model's performance, you can determine the lambda parameter that produces the best results.