What are the best practices for K-Fold cross-validation?

What are the best practices for K-Fold cross-validation?

K-Fold cross-validation is a technique used to assess the performance of a model. Best practices include choosing an appropriate number of folds, ensuring stratification for imbalanced datasets, and using the same random seed for reproducibility.

How do I choose the 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.

What does stratification mean in this context?

Stratification ensures that each fold has a representative distribution of the target variable, which is especially important for imbalanced datasets.

Can K-Fold be used for time series data?

K-Fold is not ideal for time series data due to the temporal dependencies. Instead, consider using Time Series Split.

What are some common pitfalls to avoid?

Common pitfalls include using too few folds, not stratifying data, and leaking information from the test set into the training set.