What are the best practices in cross-validation?

What are the best practices in cross-validation?

Best practices in cross-validation include using k-fold cross-validation, stratifying your folds, and ensuring your data is shuffled.

Why is stratification important in cross-validation?

Stratification ensures that each fold has a representative distribution of the target variable, which helps in reducing bias.

How do I choose the right number of folds?

A common choice is 5 or 10 folds, but it can depend on your dataset size. More folds can lead to better estimates but increase computation time.

Can you explain leave-one-out cross-validation?

Leave-one-out cross-validation (LOOCV) is a special case where each training set is created by leaving out one sample, which is then used for testing.

What are the limitations of cross-validation?

Limitations include increased computation time, potential for overfitting with small datasets, and not always reflecting real-world performance.