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

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

Some best practices include ensuring that the folds are stratified, using a sufficient number of folds, and avoiding data leakage.

Why is stratification important in K-Fold?

Stratification ensures that each fold has a representative distribution of the target variable, which helps in better model evaluation.

How many folds should I use?

Typically, 5 to 10 folds are recommended, but it can depend on the size of your dataset and the complexity of your model.

What should I do if my dataset is very small?

If your dataset is small, consider using Leave-One-Out Cross-Validation (LOOCV) or increasing the number of folds to maximize training data.

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.