What are some common mistakes in k-fold cross-validation?

What are some common mistakes in k-fold cross-validation?

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

Why is it important to shuffle the data before k-fold cross-validation?

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

What happens if I use the same data for training and validation?

Using the same data can lead to overfitting, where the model performs well on training data but poorly on unseen data.

How do I choose the right value for k?

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

Can you give an example of a situation where k-fold might not be suitable?

K-fold may not be suitable for time series data, where the order of observations is crucial, as it can disrupt temporal dependencies.