Stratified cross-validation is a technique used to assess the performance of machine learning models by dividing the dataset into subsets, ensuring that each subset maintains the same proportion of classes as the entire dataset. This method is particularly useful for imbalanced datasets, as it helps prevent bias in model evaluation by ensuring that every fold has a representative distribution of classes. By preserving class distribution, stratified cross-validation provides a more reliable estimate of model performance.
congrats on reading the definition of Stratified Cross-Validation. now let's actually learn it.