What are the different cross-validation methods in machine learning?
There are several cross-validation methods, including k-fold, stratified k-fold, leave-one-out, and more. Would you like a detailed comparison?
Yes, please explain k-fold and stratified k-fold.
K-fold cross-validation splits the dataset into k subsets. The model is trained on k-1 subsets and tested on the remaining one. Stratified k-fold ensures that each fold has the same proportion of classes as the entire dataset, which is especially useful for imbalanced datasets.
What are the advantages of using stratified k-fold over regular k-fold?
Stratified k-fold provides a better representation of the dataset's class distribution in each fold, leading to more reliable performance estimates, especially in cases of class imbalance.
Are there any situations where I should avoid using cross-validation?
Yes, if your dataset is very small, cross-validation might lead to high variance in performance estimates. In such cases, a simple train-test split might be more appropriate.
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