Stratified cross-validation is a variation of cross-validation that ensures each fold of the dataset has the same proportion of different classes as the entire dataset. This method is particularly useful when dealing with imbalanced datasets, as it helps to maintain the distribution of classes and provides a more reliable estimate of the model's performance across all classes.
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