What are the best practices in K-Fold Cross-Validation?

What are the best practices in K-Fold Cross-Validation?

K-Fold Cross-Validation is a technique used to assess the performance of a model. Best practices include ensuring that the data is shuffled before splitting, using stratified sampling for classification tasks, and selecting an appropriate number of folds.

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.

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 is stratified sampling?

Stratified sampling ensures that each fold has the same proportion of classes as the entire dataset, which is especially important for imbalanced datasets.

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

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