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Answer: Stratified K-fold cross-validation ensures that each fold has a balanced representation of the target variable.
Stratified K-fold cross-validation is a variation of K-fold cross-validation that ensures that each fold has a balanced representation of the target variable. This is particularly useful for datasets with imbalanced classes, where a simple K-fold cross-validation might result in folds with an unbalanced distribution of the target variable. By maintaining the proportion of each class in each fold, stratified K-fold cross-validation provides a more reliable estimate of the model's performance. Therefore, the correct statement is that stratified K-fold cross-validation ensures that each fold has a balanced representation of the target variable.
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In a machine learning project, you are using cross-validation to evaluate the performance of your model. You have a dataset with 1000 samples and want to perform stratified K-fold cross-validation. Which of the following statements is true, and why?
A
Stratified K-fold cross-validation ensures that each fold has a balanced representation of the target variable.
B
Stratified K-fold cross-validation ensures that the training set is always larger than the validation set.
C
Stratified K-fold cross-validation is not suitable for time series data.
D
Stratified K-fold cross-validation is not suitable for datasets with imbalanced classes.