Understanding Nested Cross-Validation
Nested cross-validation is a technique for evaluating and tuning machine learning models that helps prevent overfitting and provides a more realistic estimate of a model’s performance on unseen data. It consists of two levels of cross-validation:
- Outer Loop: This loop divides the dataset into training and testing sets. It helps estimate the model’s performance on independent data splits.
- Inner Loop: Inside each outer fold, another cross-validation loop is used to select the best hyperparameters for the model.
Nested cross-validation is particularly useful when you have a limited dataset or need to optimize hyperparameters to ensure model generalization.
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.