Pre-Requisites
Before diving into nested cross-validation, make sure you have R installed along with the Caret and Tidymodels packages. You can install them using the following commands:
R
# Load a built-in dataset from the mlbench package data (Sonar) |
- caret : caret enables you to train different types of algorithms using a simple train function
- tidymodels : Tidymodels for modeling and statistical analysis that shares the underlying design philosophy, grammar, and data structures of the tidyverse.
- mlbench : mlbench is a collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.
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.