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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:...

LASSO Regression

A regularisation method is lasso regression. For a more accurate forecast, it is preferred over regression techniques. Shrinkage is used in this model. When data values shrink towards the mean, this is referred to as shrinkage. Models with fewer parameters are encouraged by the lasso technique since they are straightforward and sparse. When a model exhibits a high degree of multicollinearity or when you wish to automate some steps in the model selection process, such as variable selection and parameter removal, this specific sort of regression is ideally suited....

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....

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:...

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Load the dataset

R # Define your control parameters for outer CV ctrl <- trainControl(   method = "cv",   number = 5,   summaryFunction = twoClassSummary,   classProbs = TRUE,   search = "grid" )   # Define a hyperparameter grid for LASSO (aplha = 1) grid <- expand.grid(   alpha = 1,   lambda = seq(0.001, 1, length = 10) )   # Perform nested cross-validation set.seed(123) model <- train(   Class ~ .,   data = Sonar,   method = "glmnet",   trControl = ctrl,   tuneGrid = grid )   # Print the best hyperparameters print(model$bestTune)...

Implementing Nested Cross-Validation with Caret

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Nested Cross-Validation with Tidymodels on mtcars Dataset

R data(mtcars)...

Conclusion

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