What is the importance of stratified K-fold cross-validation?

What is the importance of 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 better than regular K-fold cross-validation?

Regular K-fold may lead to folds that do not represent the overall class distribution, potentially skewing model evaluation.

Can you give an example of when to use it?

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

How do I implement stratified K-fold in Python?

You can use StratifiedKFold from sklearn.model_selection. It allows you to specify the number of splits and the random state.