What is Stratified K-Fold?

Can you explain what stratified k-fold is?

Stratified k-fold is a cross-validation technique that ensures each fold has a representative distribution of classes.

Why is stratified k-fold important in machine learning?

It's important because it helps to prevent bias in the model evaluation, especially with imbalanced datasets.

How does it differ from regular k-fold cross-validation?

Unlike regular k-fold, which randomly splits data, stratified k-fold maintains the same class distribution in each fold.

Can you give an example of when to use it?

Use it when you have a dataset with imbalanced classes, like in medical diagnosis, to ensure all classes are represented.