Stratified K-Fold Cross-Validation preserves the class distribution within each fold to ensure consistent representation of different classes.Review LaterTrueFalse

Stratification is a technique used to address class imbalance in cross-validation.Review LaterTrueFalse

K-Fold Cross-Validation splits the data into K equal-sized folds and trains the model K timesReview LaterTrueFalse

What is the main characteristic of Shuffle Split Cross-Validation?Review LaterIt preserves the class distribution within each foldIt uses historical data for training and recent data for validationIt creates random train/validation splits with controlled proportionsIt ensures that samples belonging to the same group are kept together

Group K-Fold Cross-Validation is beneficial when dealing with:Review LaterClass imbalanceTime series dataCorrelated or dependent data pointsLarge datasets

The selected features were assessed across five diverse ML classifiers: Logistic Regression, Support Vector Machines, Naïve Bayes, Random Forest and XGBoost. Within the training dataset, a fivefold cross-validation using stratified k-fold was performed for each of the five classifiers.