Cross-Validation

A statistical method for evaluating machine learning models by splitting data into multiple subsets (folds), training the model on some folds and testing on the remaining ones, then rotating through all combinations. Stratified cross-validation ensures each fold maintains the same class distribution as the overall dataset, which is particularly important when working with imbalanced data such as accessibility bug reports, which typically make up a small fraction of all reports. It provides a more reliable estimate of model performance than a single train-test split.