When should I use k-fold cross-validation?

What is k-fold cross-validation and when should I use it?

K-fold cross-validation is a technique for assessing how a statistical analysis will generalize to an independent dataset. You should use it when you want to ensure that your model is robust and not overfitting.

How do I choose the number of folds?

The number of folds is typically chosen based on the size of your dataset. A common choice is 5 or 10 folds, but if your dataset is small, you might consider using leave-one-out cross-validation.

Are there any downsides to using k-fold cross-validation?

Yes, k-fold cross-validation can be computationally expensive, especially with large datasets or complex models, as it requires training the model k times.

Can I use k-fold cross-validation for time series data?

K-fold cross-validation is not ideal for time series data due to the temporal dependencies. Instead, consider using time series specific methods like time series split.