How do I choose the value of k in k-fold cross-validation?

What factors should I consider when choosing k for k-fold cross-validation?

When choosing k, consider the size of your dataset, the model complexity, and the computational cost. A common choice is 5 or 10.

Is there a rule of thumb for selecting k?

Yes, a common rule of thumb is to set k to 5 or 10, but it can vary based on your specific dataset and problem.

What happens if k is too small or too large?

If k is too small, the model may not generalize well. If k is too large, it can lead to high variance and increased computation time.

Can I use cross-validation with imbalanced datasets?

Yes, but you may want to use stratified k-fold to ensure that each fold has a representative distribution of classes.