What are the best practices for K-Fold validation?

What are the best practices for K-Fold validation?

K-Fold validation is a powerful technique for assessing the performance of a model. Best practices include ensuring that the data is shuffled before splitting, using stratified K-Fold for imbalanced datasets, and keeping the number of folds reasonable to avoid excessive computation.

Why is it important to shuffle the data?

Shuffling the data helps to ensure that each fold is representative of the overall dataset, reducing bias and variance in the model evaluation.

What is stratified K-Fold?

Stratified K-Fold is a variation of K-Fold that ensures each fold has the same proportion of classes as the entire dataset, which is especially useful for imbalanced datasets.

How do I choose the number of folds?

A common choice is 5 or 10 folds, but it can depend on the size of your dataset. More folds can provide a better estimate but increase computation time.

Can K-Fold validation be used for time series data?

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