Can you explain the difference between K-Fold and Stratified K-Fold?
Sure! K-Fold cross-validation splits the dataset into K equal parts, while Stratified K-Fold ensures each fold has the same proportion of classes as the entire dataset.
Why is Stratified K-Fold preferred in some cases?
Stratified K-Fold is preferred when dealing with imbalanced datasets, as it helps maintain the distribution of classes in each fold.
Can you give an example of when to use each method?
Use K-Fold for balanced datasets and Stratified K-Fold for imbalanced datasets, like when one class is much larger than another.
What are the potential downsides of using K-Fold?
K-Fold can lead to biased estimates if the dataset is imbalanced, as some folds may not represent all classes well.
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