Why Use LASSO?
LASSO is a linear regression technique that adds a penalty term to the linear regression cost function. This penalty encourages the model to shrink some coefficients to exactly zero, effectively performing feature selection. LASSO is valuable when dealing with datasets with many features or when you suspect that some features are irrelevant.
How to do nested cross-validation with LASSO in caret or tidymodels?
Nested cross-validation is a robust technique used for hyperparameter tuning and model selection. When working with complex models like LASSO (Least Absolute Shrinkage and Selection Operator), it becomes essential to understand how to implement nested cross-validation efficiently. In this article, we’ll explore the concept of nested cross-validation and how to implement it with LASSO using popular R packages, Caret and Tidymodels.