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 that each fold is representative of the overall dataset, and using stratified sampling for imbalanced datasets.

How do I choose the number of folds?

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

What if my dataset is very small?

If your dataset is small, you might consider using Leave-One-Out Cross-Validation (LOOCV), which uses each data point as a separate fold.

Can K-Fold Cross-Validation be used for time series data?

For time series data, traditional K-Fold may not be suitable. Instead, consider using Time Series Split, which respects the temporal order of data.

Are there any pitfalls to avoid?

Yes, avoid data leakage by ensuring that the training and validation sets do not overlap, and be cautious with hyperparameter tuning.