What are common mistakes in k-fold validation?

What are common mistakes in k-fold validation?

Some common mistakes include not shuffling the data, using the same data for training and validation, and improper selection of k.

Why is shuffling the data important?

Shuffling ensures that each fold is representative of the overall dataset, reducing bias in the model evaluation.

How do I choose the right value for k?

A common approach is to use k=5 or k=10, but it can depend on the dataset size and the specific problem.

What happens if I use too high a value for k?

Using a high k can lead to longer computation times and may result in overfitting, as the model sees too many variations.

Can you give an example of a situation where k-fold validation might fail?

Sure! If the dataset is very small or imbalanced, k-fold validation might not provide a reliable estimate of model performance.