What are the limitations of k-fold cross-validation?

What are the limitations of k-fold cross-validation?

K-fold cross-validation has several limitations, including increased computation time, potential for data leakage, and variability in results depending on how the data is split.

Can you explain how data leakage occurs in k-fold?

Data leakage in k-fold can occur if the same data points are present in both the training and validation sets, leading to overly optimistic performance estimates.

What can be done to mitigate these limitations?

To mitigate these limitations, you can use stratified k-fold to maintain class distribution, ensure proper data splitting, and consider using other validation techniques like leave-one-out.

Are there any alternatives to k-fold cross-validation?

Yes, alternatives include leave-one-out cross-validation, bootstrapping, and using a simple train-test split, depending on the dataset size and problem type.