What are some advanced techniques in cross-validation?

What are some advanced techniques in cross-validation?

Advanced techniques in cross-validation include k-fold, stratified k-fold, leave-one-out, and nested cross-validation.

Can you explain how stratified k-fold works?

Stratified k-fold ensures that each fold has the same proportion of classes as the entire dataset, improving model evaluation.

What is nested cross-validation?

Nested cross-validation is used for hyperparameter tuning, where an inner loop optimizes parameters and an outer loop evaluates the model.

When should I use leave-one-out cross-validation?

Leave-one-out is useful for small datasets, as it maximizes training data by using all but one sample for training.

Are there any drawbacks to these techniques?

Yes, some techniques can be computationally expensive and may lead to overfitting if not used carefully.