How would you use StratifiedKFold for a classification problem when class distribution is uneven

Question

Can you tell me How would you use StratifiedKFold for a classification problem when class distribution is uneven?

Answer 1

You can use StratifiedKFold in Scikit-learn to maintain the same class distribution across folds when class imbalance exists in a classification problem.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• StratifiedKFold(n_splits=5) ensures each fold has a proportional representation of classes.
• shuffle=True randomizes the data before splitting for more representative folds.
• cross_val_score() evaluates the model using stratified cross-validation.
• mean() computes the average accuracy across folds, measuring consistent performance.

Hence, StratifiedKFold prevents biased evaluation by preserving class distribution, leading to more reliable model validation on imbalanced datasets.