What is the difference between k-fold and stratified k-fold cross-validation?

What is the difference between k-fold and stratified k-fold cross-validation?

K-fold cross-validation divides the dataset into k subsets, while stratified k-fold ensures each fold has the same proportion of classes.

Why is stratified k-fold preferred for imbalanced datasets?

Stratified k-fold is preferred for imbalanced datasets because it maintains the class distribution in each fold, leading to better model evaluation.

Can you give an example of when to use each method?

Use k-fold for balanced datasets and stratified k-fold for imbalanced datasets, like in medical diagnosis or fraud detection.

What are the potential downsides of using stratified k-fold?

The main downside is that it can be computationally more intensive, especially with large datasets, as it requires careful class distribution.