What are common mistakes in K-Fold cross-validation?

What are common mistakes in K-Fold cross-validation?

Some common mistakes include not shuffling the data, using too few folds, and not stratifying the data.

Why is it important to shuffle the data?

Shuffling helps ensure that each fold is representative of the overall dataset, reducing bias.

What does it mean to stratify the data?

Stratifying means ensuring that each fold has a proportional representation of different classes in the dataset.

How do I choose the right number of folds?

A common choice is 5 or 10 folds, but it can depend on the size of your dataset and the model complexity.

Can K-Fold be used for time series data?

K-Fold is generally not suitable for time series data due to the temporal dependencies; consider using time series specific methods.