What are the advantages of using stratified K-fold cross-validation?

What are the advantages of using stratified K-fold cross-validation?

Stratified K-fold cross-validation ensures that each fold has a representative distribution of classes, which is crucial for imbalanced datasets.

Why is it important to have a representative distribution of classes?

Having a representative distribution helps in evaluating the model's performance more accurately, especially when dealing with imbalanced classes.

Can you give an example of when to use stratified K-fold?

Sure! It's particularly useful in scenarios like medical diagnosis, where one class (e.g., positive cases) may be much rarer than the other.

Are there any downsides to using stratified K-fold?

One downside is that it can be computationally more intensive than regular K-fold, especially with large datasets.