What are the limitations of cross-validation?

What are the limitations of cross-validation?

Cross-validation is a powerful technique, but it has some limitations. For instance, it can be computationally expensive, especially with large datasets. Additionally, it may not always provide a reliable estimate of model performance if the data is not representative.

Can you explain why it's computationally expensive?

Sure! Cross-validation involves training the model multiple times on different subsets of the data. For example, in k-fold cross-validation, the model is trained k times, which can significantly increase the computation time, especially with complex models.

Are there any scenarios where cross-validation might not be suitable?

Yes, cross-validation may not be suitable in cases of time series data, where the order of data matters. In such cases, using techniques like time series split is more appropriate.

What are some alternatives to cross-validation?

Alternatives include using a simple train-test split, bootstrapping methods, or employing techniques like leave-one-out cross-validation, depending on the dataset and the problem at hand.