Implementing Nested Cross-Validation with Caret
Here’s how you can perform nested cross-validation with LASSO using Caret:
R
summary(mtcars) |
Output:
alpha lambda
1 1 0.001
- Loading Data: Initially, you load the “Sonar” dataset sourced from the “mlbench” package. This dataset serves as the foundation for a classification task.
- Control Parameters for Outer CV: You establish control parameters for the outer cross-validation loop, specifying the methodology as “cv,” denoting 5-fold cross-validation rounds. The chosen twoClassSummary function computes performance metrics for binary classification, including class probabilities. Additionally, you opt for grid search as the search method.
- Hyperparameter Grid for LASSO: You define a hyperparameter grid to fine-tune the LASSO model. The alpha value is set to 1, signifying pure LASSO regularization, while the lambda range spans from 0.001 to 1 in increments, regulating the degree of coefficient shrinkage.
- Perform Nested Cross-Validation: Employing the “caret” package’s train function, you execute nested cross-validation. Your classification task, denoted as “Class ~ .,” employs all available variables as features. The “glmnet” method, representing LASSO logistic regression, is employed. You supply the pre-specified control parameters (trControl) for cross-validation and the hyperparameter grid (tuneGrid) for fine-tuning.
- Printing the Best Hyperparameters: Concluding the process, you display the optimal hyperparameters derived from the nested cross-validation procedure, encompassing the most favorable alpha and lambda values for optimizing model performance.
This code essentially performs nested cross-validation to find the best hyperparameters for a LASSO logistic regression model, using predefined control parameters and a hyperparameter grid. The goal is to identify the hyperparameters that yield the best classification performance.
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.